Reviewer #2 (Public review):

This manuscript from Zuniga-Pflucker laboratory describes that thymic macrophages are heterogeneous in flow cytometric and transcriptomic profiles, containing two major populations characterized by TIMD4 and CX3CR1 expression. These macrophage populations are both parenchymal in the thymus but are unequal in developmental ontogeny, Flt3 expression history, and CCR2 dependency. The manuscript further reports the interesting findings that the depletion of thymic macrophages impairs thymocyte development at the DN3 beta-selection checkpoint. These results provide an important advance for further understanding of thymus biology, especially in view of the contribution of heterogenous thymic macrophage subpopulations.

Reviewer #2 (Public review):

Summary:

In this study, Zhou et al investigated the expression and function of AIRE in B cells in peripheral lymphoid tissues. First, they found the expression of AIRE protein in mature B cells in the follicles in human tonsils and spleens from healthy donors. Flow cytometry analyses using human samples as well as Aire-reporter mice demonstrated AIRE expression in germinal center B cells. The expression of Aire in B cells was induced by CD40 signals. Then, to investigate the impact of AIRE deficiency on B cell function, the authors used a method of transplanting bone marrow cells from Aire-KO and WT mice into B-cell-deficient mice, comparing B cell development and function reconstituted in the recipient mice. Their results showed that Aire-deficient B cells strongly responded to immunization with antigens, exhibiting enhanced class switching and somatic hypermutation of antibodies compared with WT B cells. The same phenomena were observed in CRISPRed B cell lines lacking Aire. The authors successfully utilized the Aire-deficient B cell line to demonstrate that Aire suppresses antibody class switching and somatic hypermutation via its interaction with AID. Finally, using B cell transfer into B cell-deficient mice demonstrated that mice harboring Aire-deficient B cells produced high levels of autoantibodies against Th17 cytokines and exhibited reduced resistance to Candida infection. This mirrors characteristic symptoms in AIRE-deficient patients. The findings of this study not only reveal an unexpected function of AIRE in B cells but also have the potential to contribute to understanding the pathogenesis of APECED and offering a new direction for developing therapies.

Strengths:

The strength of this study lies in demonstrating the expression of function of AIRE in B cells in both mice and humans. It also revealed the direct interaction between AIRE and AID, along with its binding mode (requiring CARD and NLS domains of AIRE), and showed that this interaction is crucial for AIRE function in B cells. It is also significant that the study demonstrated how B cell-intrinsic dysfunction of AIRE leads to autoantibody production against cytokines.

Comments on revised version.

My previous concerns have been properly addressed.

Reviewer #2 (Public review):

Summary:

The authors apply a hydrogel nanoliter-well in situ microRNA assay to tissue sections from a mouse model of BRCA1-related triple-negative breast cancer, in which tumors had acquired resistance to a PARP inhibitor, and test two drug combinations. They develop a spatial analysis that groups wells into microRNA "topics" and relates these topics to treatment sensitivity and to immune infiltration, aiming to show that the spatial arrangement of microRNAs can report on drug efficacy and stratify tumors by their eventual sensitivity or resistance.

Strengths:

(1) The in vivo combination-therapy experiments are technically careful, and the finding that adding Poly(I:C) to olaparib improves antitumor activity is new.

(2) The data and the analysis code are openly deposited on Zenodo.

(3) Whether the spatial organization of microRNAs carries treatment-relevant information remains a worthwhile question.

Weaknesses:

(1) It is not clear what the spatial measurement adds. The discrimination of sensitive vs resistant tumors was already reported in the authors' prior work, and in this dataset the separation reduces to the relative amount of two microRNAs, let-7a and miR-21, that the bulk analysis had already nominated.

(2) The platform is on the well-level rather than single-cell, and the effective spatial resolution (well size and the number of cells per well) is not stated, so the meaning of "spatial" is unclear, and the sensitivity and specificity of the assay are not established in this work.

(3) The sensitive vs resistant separation is an in-sample description of labels that were fixed in advance, on roughly 21 tumors with about three per treatment arm and nothing held out; the model is refit for each analysis rather than frozen, so it cannot be evaluated as a classifier.

(4) The headline association of a let-7a topic with resistance is correlative, unvalidated, and runs opposite to the canonical roles of let-7 as a tumor suppressor and miR-21 as an oncomiR, and it may reflect the abundance ratio of the two dominant probes.

(5) The topic model is simplistic, collapses to two informative topics, and does not use the existing morphology or H&E information already available on the same sections; the co-localization analysis relies on an image-quality metric that is not appropriate for this purpose and lacks a null.

(6) The conclusions are dependent on a single mouse model at a single timepoint with no human data, but the framing attempts to extend to patients.

Case#: Female, age 44 years old

DiseaseAssertion: Discordant STGD phenotype

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

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

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

CaseNotHPOs: N/a

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

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

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

PreviouslyPublished: N/a

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

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

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

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

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

Reviewer #2 (Public review):

This is a very interesting study from Vandendoren and colleagues examining the role of PVN oxytocin neurons during thermoregulatory behaviors, in particular during thermoregulatory huddling. The findings are important and have implications for the thermoregulation field as well as the social/naturalistic behavior field. The findings are compelling and use a combination of state-of-the-art tools (photometry, optogenetics, automated behavior tracking, thermal imaging, and core body temperature measurement), often in combination with each other, to produce a rigorous and high-dimensional dataset.

Comments on revised version.

I appreciate the effort the authors have put into addressing all of my questions, and I have no remaining concerns.

Reviewer #2 (Public review):

The manuscript by Zhu et al. describes MAIT cell activation by riboflavin metabolites presented by MR1. The authors provide solid evidence for this activation and anti-cancer functional consequence using an array of selected cell lines, primary ex vivo and engineered xenograft models. Broadly, the results are thorough and well controlled, and provide a highly informative insight into the metabolite-MAIT-cancer cell interactions. However, the majority of this work is undertaken using models that preferentially express key targets, and whilst still useful, the (current) broader implications of this research are overstated. Additionally, the suggested MAIT modulation of the tumor microenvironment requires clarification.

Major Comments:

(1) In Figures 2b-d, the authors suggest microbial metabolite stimulation of PBMC cultures increased MAIT cell frequency up to 60%. Whilst their flow data is compelling, the frequency of one population can be influenced by changes in other populations. A form of absolute or relative-to-total count should be used.

(2) The statements regarding cytokine induction in Figure 4e are too strong; many of those inflammatory cytokines are not automatically and consistently tumour-suppressive. The line 299 '...were not induced' may just reflect death of tumor cells. It would be useful to include tumour cell-only controls in Figure 4.

(3) Figure 7 is interesting, but the authors' conclusion that MAIT+5-OP-RU controls the tumor microenvironment is not robustly supported by their evidence.

a) It is not clear how CD14+ cells established a sustained suppressive environment.

b) It is not clear how the peritoneal addition of microbial metabolites 'significantly enhanced MAIT-mediated tumor control'. The authors show that the addition of 5-OP-RU reduced the number of GFP-expressing tumour cells present in peritoneal lavage fluid. There is limited evidence to suggest this occurs through MAIT cells or MR1 in this figure.

c) It is difficult to draw conclusions from peritoneal lavage flow when some experimental groups received cells IP, but then all groups were equally assessed for key populations, and all data are presented as frequencies. The authors should use absolute counts (or similar) to appropriately show changes in cell populations to account for varying total/live/cd45+ cell compartments.

d) It would be necessary at a minimum to include 5-OP-RU-only controls, and ideally include MR1 blocking or the cancer line with MR1 removed. Alongside this, the authors should substantially reduce the strength of their statements on microbial metabolite-MAIT suppression of the tumor microenvironment.

Reviewer #2 (Public review):

Summary:

This is an elegant, rigorous, and thought-provoking study that examines how different neural circuits are altered in response to loss of a common sensory input. To study this question, the authors use the mouse retina as a model system to investigate how downstream retinal circuits undergo modifications following a well-controlled partial loss of cone photoreceptors.

Strengths:

The experiments were conducted with a high degree of rigor, and the authors carefully considered and implemented appropriate controls throughout the study. Multiple parameters were tested, including pharmacological approaches to assess responses from different ganglion cell types. In addition, the authors complemented their functional data with confocal imaging to further support their findings. Overall, this is a well-written paper that provides a thorough analysis demonstrating how two similar ganglion cell types undergo distinct adaptations (i.e., compensation versus remodeling) in response to the loss of the same sensory input.

Weaknesses:

No additional experiments are needed. However, the authors may wish to consider the following points:

(1) Do the differences in compensation versus remodeling observed in ganglion cells reflect changes in the OPL? Different bipolar types may remodel their dendrites and form aberrant contacts with rods in the absence of cones. However, this would be challenging to test because there are currently no good markers for different bipolar types.

(2) It would be interesting to determine whether these functional changes can be detected at the transcriptomic level or whether they are mediated primarily through post-translational modifications.

Reviewer #2 (Public review):

Summary:

In this review article, the authors discuss the whole brain activity changes induced by brain stimulation. They review the literature on how these activity changes depend on the cognitive state of the brain and divide the results by the scale of the change being induced, from microscale changes across small groups of neurons, up to macroscale changes across the entire brain. Finally, they describe attempts to model these changes using computational models.

Strengths:

The review provides an overview of the results within this sub-field of neuroscience, and the authors are able to discuss a lot of prior results. The framing of the changes in neuronal activity in terms of computational changes is also a helpful approach.

We thank the authors for the updates that they have made in response to our original comments. Their attempts to address many of the comments that we raised have greatly improved the paper. We believe that there are two major points that still require some additional changes:

(1) We raised the concern that the results within each of the three spatial scales did not join together into a cohesive single framework. The authors responded by updating the conclusion section to provide a more conceptual picture linking the different spatial scales. This is much appreciated. However, by placing this framework at the end of the paper, it prevents the reader from using this understanding to building a conceptual model as they progress through the paper. We would ask that the authors intersperse this conceptual picture within the main text, and to then re-emphasize it in the conclusions. This would frame each section in terms of the findings that led directly to it and, therefore, allow the reader to build a conceptual understanding within each section. As one example, we note that the authors have made no changes to the mesoscale processing section. Therefore, when reading that section, it is completely unclear how any of the results seen in the microscale may relate to the changes observed at the mesoscale.

(2) The authors have greatly improved their explanation of the complexity metrics. However, the paper still lacks a conceptual understanding for why "perturbation-based complexity metrics" are a reasonable way to study the state-dependent dynamics? What does studying perturbations provide that studying the spontaneous activity in different states alone, would not provide? Why is this the preferred way to study such dynamical systems? Such a justification would strongly support the analyses reviewed in the paper and would increase the reader's understanding of the methodology.

Reviewer #2 (Public review):

Summary:

Abbasi et al. examine how signaling through the major G-protein pathways (Gs, Gq, and Gi) influences tumor necrosis factor expression in astrocytes and microglia. Using a combination of pharmacological receptor activation, chemogenetic manipulation, primary rodent glial cultures, human induced pluripotent stem cell-derived astrocytes, and an in vivo astrocyte-targeted Gi manipulation, the authors report a broadly consistent pattern in which Gs- and Gq-associated signaling reduces tumor necrosis factor expression, whereas Gi signaling increases it. The study's cross-species and cross-preparation design, spanning astrocytes and microglia as well as in vitro and in vivo systems, provides a potentially valuable framework for understanding how neuromodulatory pathways may regulate glial inflammatory signaling.

Strengths:

A major strength of the study is the breadth of experimental systems used, which includes primary rat glia, human induced pluripotent stem cell-derived astrocytes, and an in vivo manipulation, allowing for comparison across species and levels of biological complexity. The use of chemogenetic receptors in astrocytes provides relatively direct control over Gq and Gi signaling, and these experiments yield consistent effects on both tumor necrosis factor messenger RNA and protein, strengthening the internal validity of the astrocyte findings. The observation that similar directional effects are seen in human-derived astrocytes and in microglial cultures further supports the idea that aspects of this regulatory relationship may be conserved across glial cell types. More broadly, the study addresses an important and timely question about how neuromodulatory signaling pathways interface with glial inflammatory outputs, and it generates a coherent set of observations that could serve as a foundation for more mechanistic work.

Weaknesses:

The central claim that Gs, Gq, and Gi signaling broadly and directly constitute a general regulatory code for tumor necrosis factor expression is more expansive than the current evidence fully supports. In particular, the evidence for Gs-dependent effects is indirect, relying on beta-adrenergic receptor activation and forskolin-mediated adenylyl cyclase stimulation rather than direct manipulation of Gs itself, leaving uncertainty about pathway specificity. More generally, the use of different endogenous receptors to represent each G-protein class in microglia complicates interpretation, since individual receptors may engage additional signaling pathways beyond their canonical G-protein coupling, limiting the extent to which the results can be attributed to G-protein class alone.

The in vivo experiment also does not definitively establish the cellular source of the observed increase in tumor necrosis factor, as measurements are taken from bulk cortical tissue following astrocyte-targeted Gi activation. This leaves open the possibility that the observed changes arise indirectly from other cell types, particularly microglia, which are shown elsewhere in the study to be strongly responsive to Gi-related manipulations. In addition, the specificity of chemogenetic expression in vivo is not quantitatively demonstrated, further limiting cell-type attribution.

There are also important issues related to experimental design and statistical interpretation. Across several experiments, it is unclear whether reported sample sizes reflect independent biological replicates, technical replicates, or imaging fields, which is especially consequential for the human induced pluripotent stem cell-derived astrocyte experiments where donor-level independence is not clearly established. The in vivo design also appears to treat hemispheres as independent observations despite their paired nature, which may inflate statistical independence given the small sample size.

Finally, several conclusions would benefit from more cautious framing. The data support differential regulation of tumor necrosis factor relative to interleukin-1 rather than strict cytokine specificity, and measurements based solely on messenger RNA should not be interpreted as direct evidence of cytokine production. The comparison between glial signaling effects and neuronal excitation or inhibition also juxtaposes fundamentally different biological readouts and should not be interpreted as a direct functional opposition. Overall, while the study provides interesting and potentially important observations, the broader pathway-level and cell-type-specific conclusions are not yet fully established by the current experimental evidence.

Reviewer #2 (Public review):

Summary:

Although our visual system is continuously analyzing the current visual scene, its processing proceeds in discrete episodes separated by brief eye movements (saccades). It has generally been assumed that the analysis of the next visual snapshot begins in earnest when the eyes land on a new fixated location just after a saccade, but there have been various studies indicating that at least some amount of processing occurs earlier, as the system anticipates the impending eye movement. Here, the authors use magentoencephalography (MEG) measurements to record visually-driven responses and determine at what point exactly the processing of a new visual snapshot begins.

Strengths:

(1) The work is concise and to the point, and the techniques used are a good way to answer the underlying question about visual processing, since they reflect widespread activity in the brain (rather than activity at a particular location or structure).

(2) The use of natural images and extensive data collection from 5 participants is a nice feature of the experimental design which permits characterization of the common effects and of variance across individuals.

(3) The data are analyzed rigorously, but the results are also understood intuitively; for instance, by visual comparison of responses aligned on fixation onset versus saccade onset.

(4) The results provide a clean characterization of when visual analysis begins relative to saccade onset under natural viewing conditions.

Weaknesses:

(1) There were questions about how the scene-onset condition was established, and how data were selected for it.

(2) The significance of the results is slightly overstated; the text would benefit if some of the claims were phrased with a bit more carefully.

(3) In particular, the issue of how motor-related processes (versus stimulus-related content) may determine the processing of the next visual snapshot should be discussed with a bit more nuance.

These are minor weaknesses. Overall, I found the work to be novel and instructive, as it bridges neurophysiological and psychophysical findings in a satisfactory way.

Reviewer #2 (Public review):

Summary:

The authors extend their previous population-based Ct-value framework for inferring community epidemic trajectories from human infections to vector infections, using mosquitoes as vectors for West Nile virus. They use agent-based modelling to distinguish virus-positive detections arising from non-active infection states from those reflecting active infections, and then apply this framework to mosquito surveillance data from Colorado and Texas.

Overall, this is a well-designed and carefully evaluated study. The manuscript proposes a feasible and potentially valuable framework for vector infection surveillance. The findings are supported by both mechanistic agent-based simulations and applications to real-world mosquito surveillance data, which strengthens the biological plausibility and practical relevance of the proposed approach.

Strengths:

A major strength of the study is its clear methodological extension from human infection surveillance to vector infection surveillance. The agent-based modelling framework provides a useful basis for distinguishing active infections from virus-positive detections that may reflect non-active infection states. The application to surveillance data from two different geographic settings further supports the feasibility of the framework. Overall, the study is carefully designed, and the model schematic and main analyses are generally clear.

Reviewer #2 (Public review):

Summary:

Tagoe and colleagues present a thorough analysis of the calcium (Ca2+) binding capacity of calreticulin (CRT), an endoplasmic reticulum (ER) Ca2+-buffer protein, using a mutant version (CRT del52) found in myeloproliferative neoplasms (MPNs). The authors use purified human CRT protein variants, CRT-KO cell lines, and an MPN cell line to elucidate the differing Ca2+ dynamics, both on the level of the protein and on cell-wide Ca2+-governed processes. In sum, the authors provide new insights into CRT that can be applied to both normal and malignant cell biology.

First the authors purify CRT protein and perform isothermal titration calorimetry to quantify the Ca2+ binding capacity of CRT. They use full-length human CRT, CRT del52, and two truncations of CRT (1-339 and 1-351, the former of which should lead to the entire loss of low affinity Ca2+ binding). While CRT del52 has previously been shown to lead to a decrease in Ca2+ binding affinity in other models, the ITC data shows that this is retained in CRT del52.

Next, the authors utilize a CRT-KO cell line with subsequent addition of CRT protein variants to validate these findings with flow cytometric analysis. Cells were transfected with a ratiometric ER Ca2+ probe, and fluorescence indicates that CRT del52 is unable to restore basal ER Ca2+ levels to the same extent as CRT wild-type. To translate these findings to MPNs, the authors perform CRT-KO in a megakaryocytic cell line, where reconstitution with either CRT variant did not cause a difference in cytosolic calcium levels. The authors further test store-operated calcium entry (SOCE), an important process to maintaining ER Ca2+ levels, in these cells, and find that CRT-KO cells have lower SOCE activity, and that this can be slightly recovered with CRT addition.

Finally, the authors ask whether other effects of CRT-KO/reconstitution can affect cellular Ca2+ signaling pathway and levels. RNASeq analysis revealed showed that CRT-KO lead to an increase in various chaperone protein expressions, and that reconstitution with CRT del52 is unable to reduce expression to the same extent as reconstitution with CRT wildtype.

Comments on revised version.

The authors have sufficiently addressed my concerns from the first review.

Reviewer #2 (Public review):

Summary:

The authors studied cognitive control and attention in response to mnemonic prediction errors (MPEs): situations in which the external reality violates internal memory-based predictions. The behavioral task first established strong versus weak predictions, and then either confirmed or violated these predictions. The authors examined markers of cognitive control (frontal theta) and attention (posterior alpha suppression, pupil response) while strong and weak predictions were confirmed or violated. They found increased cognitive control (frontal theta) for strong MPEs, which correlated with subsequent memory. Markers of attention (alpha suppression, pupil response) also accompanied strong MPEs but did not correlate with subsequent memory. Pupil response was investigated using an interesting approach that decomposes the response into different components, finding that different components respond earlier or later and show different correlations with MPEs and their strength. The authors also investigated how EEG, reaction time, and pupil responses correlated with one another, providing further insight into the mechanism underlying the response to MPEs. Together, the study points toward multiple control and attention mechanisms involved in MPE response and memory.

Strengths:

The study has a clear behavioral paradigm with multiple measures - behavioral, EEG, and pupillometry that offer an investigation into different aspects of MPE response and memory.

The study is also very comprehensive in looking at multiple phases in processing MPEs: the prediction phase (prior to the violation), the response to MPEs, and subsequent memory of MPEs, all within one study. Specifically, the link between neural mechanisms and subsequent memory is a major advancement, as most prior studies did not include this component. Mechanisms underlying subsequent memory of MPEs are theoretically important, as a primary function of MPEs is to promote learning and memory. As the authors mention, the different neural and pupillary signals are not robustly correlated, suggesting multiple mechanisms underlying MPE detections, which is interesting, offers avenues for future research, and can facilitate a better theory of how MPEs are processed in the brain. Finally, the decomposition of pupil response into different components and their correlation with behavior (RT during match/MPE detection) is interesting.

Reviewer #2 (Public review):

Summary:

This manuscript addresses an important and timely question in the molecular simulation of biomolecular condensates. Most residue-level coarse-grained models used for IDP phase separation employ implicit solvent and represent effective interactions through relatively simple pairwise potentials. While these models have been very useful, they usually do not explicitly distinguish direct contacts from solvent-separated interactions, nor do they include an energetic barrier associated with water removal. This manuscript attempts to address that limitation by introducing desolvation-inspired terms into coarse-grained models and examining their consequences for phase behavior, chain conformations, dense-phase packing, and dynamics.

The central idea is physically well motivated. Using a simple homopolymer model, the authors show that increasing the desolvation barrier suppresses phase separation, whereas stabilizing solvent-separated contacts enhances phase separation. They further show that solvent-separated interactions can reduce dense-phase over-compaction, which is a meaningful result given the known challenges in obtaining both accurate single-chain dimensions and realistic dense-phase properties from the same coarse-grained model. The finding that desolvation-like terms can reshape dense-phase packing without simply rescaling the overall interaction strength is interesting and could be useful for future model development. I also found the attempt to connect conformational changes across dilute and dense phases with thermal distance from the critical point to be intriguing. The dynamic analysis, including the FRAP-like simulations and the discussion of kinetic arrest during coarsening, adds another useful dimension to the work.

Overall, I think this is a useful and potentially important contribution.

Comments on revised version.

The authors have addressed my earlier comment regarding conformational changes between the dilute and condensed phases. One small additional suggestion is that they may find two related studies useful in this context: Devarajan et al., Nature Communications (2024), on relationships between dilute-phase conformations and condensate material properties, and Wang et al., Chemical Science (2024), which examines sequence-dependent conformational changes during condensation for both model polyampholyte sequences and naturally occurring IDPs. These studies may provide some complementary context for the discussion. This is simply a literature suggestion and does not affect my overall assessment of the revised manuscript.

Reviewer #2 (Public review):

Summary:

Grichine et al. investigate platelet-mediated fibrin compaction using human donor platelets and propose a novel mechanistic model in which platelets generate contractile forces and wind fibrin fibres into compact, coiled structures. Using a combination of 2D spreading assays, 3D clot imaging via expansion microscopy, live-cell imaging, and computational modelling, the authors present evidence of cage-like fibrin architectures, coiled fibre morphologies, and platelet-centred "rosette" structures that are present during fibre compaction. They suggest the involvement of actomyosin in fibre compaction and, overall, the study addresses an important and longstanding question in thrombosis and haemostasis while offering a conceptually novel perspective on clot compaction.

Strengths:

The integration of multiple imaging modalities is a notable strength. In particular, the 2D fibre-retraction assay provides a useful model for understanding the spatiotemporal dynamics of platelet-mediated fibrin compaction, which could be applied to other systems and may yield detailed mechanistic insights into biological processes. The live-imaging approaches are particularly well executed and provide valuable dynamic insights into fibre accumulation and compaction.

Weaknesses:

The primary weakness of the paper is the absence of direct evidence demonstrating the mechanism of fibre compaction via cytoskeletal swirling. Consequently, the relationship between platelet dynamics and fibrin organisation, including coordinated measurements of platelet motion and fibre rearrangement, is not directly assessed (perhaps due to technical barriers). However, the paper does provide solid evidence through myosin inhibition and computational modelling, demonstrating how platelets might mediate fibre compaction.

Comments on revised version.

Overall, the study addresses an important question in thrombosis and haemostasis and introduces a potentially impactful conceptual framework for understanding clot compaction. The imaging approaches and datasets presented will be valuable to the community, particularly to researchers interested in platelet mechanics and fibrin organisation. The possibility that fibres can be compacted extracellularly through cytoskeletal swirling represents a compelling and relatively unexplored mechanism. Therefore, this paper does a good job of establishing a thought-provoking mechanism with solid supporting evidence, although a direct demonstration of the underlying molecular mechanism requires further investigation.

Reviewer #2 (Public review):

The authors have substantially revised the paper in response to the original review, which is greatly appreciated. It is clear that it will eventually make a nice contribution to the literature. This being said, the following points are somewhere between major and minor in term of their implications for interpretation of the study results. If they were to be addressed, the paper would be again improved.

There are a few remnants of the past language that are not helpful re interpretation of the study results: 1) "Specifically, the observed population gradients could emerge either from the pooled activity of frequency-selective neurons that respond to individual tones or from neuronal subpopulations that integrate information across tones to encode their learned threat-value."; and 2) "Together, these findings suggest that the PL integrates sensory similarity with learned threat value to generate stable representations that support adaptive generalization and discrimination." Neither of these statement follows what has been shown in the study, even with inclusion of the results from the GLM analysis (see point 4 below).

(1) This paragraph in the Discussion is difficult to follow: "Generalization has traditionally been explained by perceptual similarity (Shepard, 1987), whereby stimuli resembling a conditioned cue recruit overlapping sensory representations and evoke similar behavioral responses (Corches et al., 2019; Grosso et al., 2018). Although perceptual similarity clearly influences the extent of generalization, accumulating evidence indicates that it cannot fully account for generalized responding (Verra et al., 2026). More recent frameworks propose that associative learning assigns learned value to novel stimuli by integrating their sensory similarity with previous experience, allowing behavior to scale according to predicted biological significance (Verra et al., 2026; Zaman et al., 2023). Our findings provide a neural framework consistent with these ideas. Sensory similarity promoted consistent neuronal population responses across tones, whereas associative learning organized these responses into graded representations that tracked learned threat value across the stimulus continuum. Thus, sensory similarity appears to define the neuronal substrate upon which associative learning constructs value-based representations that support graded behavioral generalization."

While the revisions have removed the many unnecessary references to inference and integration, this paragraph seems like it is adhering to the original idea of how the authors wished to present their work. If the authors wished to talk about something more than perceptual similarity in the context of generalization, they should have used a task that lends itself to a more-than-perceptual-similarity explanation. Again, the inclusion of the GLM analysis is suggestive for some of what the authors wish to say, but doesn't justify the statements that: "Sensory similarity promoted consistent neuronal population responses across tones, whereas associative learning organized these responses into graded representations that tracked learned threat value across the stimulus continuum." In short, the analysis does not substitute for the design that could have and should have been used to assess learned threat value independently of sensory similarity.

(2) The next paragraph in the Discussion is also confusing. "Such reorganization has been proposed to provide flexibility by allowing new information to be incorporated into existing cortical representations while preserving stable behavioral performance (Mau et al., 2020; Zaki & Cai, 2024). Several mechanisms could contribute to this turnover, including systems consolidation, retrieval-induced reconsolidation or memory updating, and repeated nonreinforced stimulus exposure (Lacagnina et al., 2019; Mau et al., 2020; Sangha, 2015; Zaki & Cai, 2024). Although our experiments cannot distinguish between the first two possibilities, the behavioral data argue against extinction as the primary explanation. Extinction is generally associated with the formation of new CS+-safety associations (Bouton et al., 2021), whereas discrimination ratios increased across retrieval sessions, indicating that animals progressively improved their discrimination between threat-associated and safe stimuli rather than acquiring generalized safety responses. This pattern is consistent with previous work showing that discrimination learning sharpens stimulus representations and narrows behavioral generalization gradients (Dunsmoor & LaBar, 2013; Herzog et al., 2021; Jenkins & Harrison, 1960; Lommen et al., 2017). Importantly, turnover was not uniform across the population. Graded neurons retained remarkably consistent response profiles across retrieval sessions, and their activity remained more strongly associated with learned threat value than with freezing behavior. These observations indicate that stable components of the population code can coexist with extensive reorganization of surrounding neuronal ensembles."

The issue with repeated testing is *not* caused by extinction per se. The issue is that non-reinforcement across the repeated testing should differentially affect the CS+ and CS-. Specifically, it should extinguish responding to the CS- stimulus at a rate that matches its distance from the CS+, thereby sharpening the CS+ versus CS- discrimination in precisely the ways that have been observed. Ergo, the repeated testing *is* a problem for inferences that might be drawn about the way that generalization gradients change with time; and *is* a problem for statements regarding "dynamic reorganization of cortical activity patterns over time." There is nothing in the study that allows one to comment on the reorganization of cortical activity patterns over time. The reorganization can and should be attributed to the repeated testing, which is confounded with time. Nonetheless, the reorganization must be due to the repeated testing and NOT time as the present findings are inconsistent with the well-documented broadening of generalization gradients with time.

(3) In the next paragraph, the authors state: "At the same time, narrower generalization gradients and improved discrimination across retrieval sessions suggests ongoing memory updating. These observations are consistent with contemporary theories proposing that systems consolidation and retrieval-dependent updating are complementary processes through which memories continue to evolve after learning (Mau et al., 2020; Tome et al., 2024; Zaki & Cai, 2024)."

In general, I'm not sure why one would invoke systems consolidation or retrieval-induced reconsolidation as an explanation for any of the present findings: they are not explanations of much at all. In this specific text, the authors seem to be implying an updating process that occurs independently of what is learned across the repeated sessions of testing. Why? The changes that occur in the behaviour and neuronal representations are perfectly explicable in terms of additional learning that occurs - of the sort that I hope to have made clear in my previous comment. Why invoke more than what is needed to explain the observed pattern of results?

(4) Re the GLM analysis - The authors write that: "the fact that the GLM analysis indicates that these neurons reflect learned threat value more than freezing behavior, suggests that they encode an abstract property of the learned stimulus rather than simply mirroring behavioral output."

This is fine if freezing fully indexes the state of conditioned fear and there are no other behaviours in which animals express their fear. If, however, fear is expressed in a range of other behaviours that are likely coordinated by the PL (e.g., startle, vigilance, scanning, orienting to source of danger), this interpretation of the GLM analysis is unwarranted. This is an important point and would be worth noting somewhere in the paragraph where the statement appears.

Reviewer #2 (Public review):

Summary:

This manuscript examines decision-making in a context where the information for the decision is not continuous, but separated by a short temporal gap. The authors use a standard motion direction discrimination task over two discrete dot motion pulses (but unlike previous experiments, fill the gaps in evidence with 0-coherence random dot motion of differently coloured dots). Previous studies using this task (Kiani et al., 2013; Tohidi-Moghaddam et al., 2019; Azizi et al., 2021; 2023) or other discrete sample stimuli (Cheadle et al., 2014; Wyart et al., 2015; Golmohamadian et al., 2025) have shown decision-makers to integrate evidence from multiple samples (although with some flexible weighting on each sample). In this experiment, decision-makers tended not to use the second motion pulse for their decision. This allows the separation of neural signatures of momentary decision-evidence samples from the accumulated decision-evidence. In this context, classic electroencephalography signatures of accumulated decision-evidence (central-parietal positivity) are shown to reflect the momentary decision-evidence samples.

Strengths:

The authors present an excellent analysis of the data in support of their findings. In terms of proportion correct, participants show poorer performance than predicted if assuming both evidence samples were integrated perfectly. A regression analysis suggested a weaker weight on the second pulse, and in line with this, the authors show an effect of the order of pulse strength that is reversed compared to previous studies: A stronger second pulse resulted in worse performance than a stronger first pulse (this is in line with the visual condition reported in Golmohamadian et al., 2025). The authors also show smaller changes in electrophysiological signatures of decision-making (central parietal positivity, and lateralised motor beta power) in response to the second pulse. The authors describe these findings with a computational model which allows for early decision-commitment, meaning the second pulse is ignored on the majority of trials. The model-predicted electrophysiological components describe the data well. Some flexible weighting of the second pulse also described the data well (in line with previous studies), but this explanation suffers from additional model complexity. In particular, this analysis of model-predicted electrophysiology is impressive in providing simple and clear predictions for understanding the data.

Weaknesses:

Behaviour in this experiment is different from previous experiments which use very similar designs (Kiani et al., 2013; Tohidi-Moghaddam et al., 2019; Azizi et al., 2021; 2023). The authors provide some possible explanations for this in the discussion. Overall performance in this experiment was much worse than previous experiments: Participants achieved ~85% correct following 400 ms of 33 - 45% coherent motion. In previous work, performance was ~90% correct following 240ms of 12.8% coherent motion. A second weakness is that, while bounded model can describe the data in this manuscript, it cannot explain the data from previous experiments showing a stronger weight on the second pulse.

Reviewer #2 (Public review):

This study by Anttonen, Christensen-Dalsgaard and Elemans describes the development of hearing thresholds in an altricial songbird species, the zebra finch. The results are very clear and along what might have been expected for altricial birds: at hatch (2 days post-hatch), the chicks are functionally deaf. Auditory evoked activity in the form of auditory brainstem responses (ABR) can start to be detected at 4 days post-hatch but only at very loud sound levels. The study also shows that ABR response matures rapidly and reaches adult like properties around 25 days post-hatch. The functional development of the auditory system is also frequency dependent with a low to high frequency time course. All experiments are very well performed. The careful study throughout development and with the use of multiple time-points early in development is important to further ensure that the negative results found right after hatching are not the result of the experimental manipulation. The results themselves could be classified as somewhat descriptive but, as the authors point out, they are particularly relevant and timely. Since 2016, there has been a series of studies published in high profile journals that have presumably showed the importance of prenatal acoustic communication in altricial birds, mostly in zebra finches. This early acoustic communication would serve various adaptive functions. Although acoustic communication between embryos in the egg and parents has been shown in precocial birds (and crocodiles), finding an important function for pre-natal communication in altricial birds came as a surprise. Unfortunately, none of those studies performed a careful assessment of the chicks' hearing abilities. This is done here, and the results are clear: zebra finches at 2 and 6 days post hatch are functionally deaf. Since, it is highly improbably that the hearing in the egg is more developed than at birth, one can only conclude that zebra finches in the egg (or at birth) cannot hear the heat whistles. The paper also ruled out the detection on egg vibrations as an alternative path. The prior literature will have to be corrected, or further studies conducted to solve the discrepancies. For this purpose, the "companion" paper on bioRxiv that studies the bioacoustical properties of heat calls from the same group will be particularly useful. Researchers from different groups will be able to precisely compare their stimuli.

Beyond the quality of the experiments, I also found that the paper was very well written. The introduction was particularly clear and complete (yet concise).

Weaknesses:

My only minor criticism is that you don't discuss potential differences between behavioral audiograms and ABRs. Optimally, one would need to repeat the work of Okanoya and Dooling with your set up and using the same calibration. The ~20dB difference might be real or it might be due to SPL measured with different instruments, at different distances, etc. Either way, you could add a sentence in the discussion that states that even with the 20 dB difference in audiogram heat whistles would not be detected during the early days post-hatch, but that adding a (novel) behavioral assay in young birds could further resolve the issue.

More Minor Points.

(1) As mentioned in the main text, the duration of pips (form pips to bursts) affects the effective bandwidth of the stimulus. I believe that you could give an estimate of this effective bandwidth given what is know from bird auditory filters. I think that this estimate could be useful to compare to the effective bandwidth of the heat-call which you can now also estimate.

(2) Fig 5b. label the green and pink areas as song and heat-call spectrum. Also note that in the legend you say: "Green and red areas display the frequency windows related to the best hearing sensitivity of zebra finches and to heat calls, respectively". I don't think this is what you meant. I agree that 1-4 kHz is the best frequency sensitivity of zebra finches but you probably meant green == "song frequency spectrum" and pink == "heat call spectrum". In either case the figure and the legend need clarification.

(3) Fig 5c. Here also, I would change the song and heat-call labels to "song spectrum", "heat call spectrum". You don't want readers to think you used song and heat calls in these experiments (maybe next time?). For the same reason, maybe in 5a you could add a cartoon of the oscillogram of a frequency sweep next to your speaker.

(4) Methods. In your description of the stimulus, you describe "5ms long tone bursts" but these are the tone pips in the main part of the manuscript. Use the same terms.

Comments on revisions:

In the latest version of this manuscript, Anntonen et al have diligently addressed all the issues raised by the reviewers. As I mentioned in our discussions among reviewers, it is impossible to "prove" a null hypothesis and both methods and experimental design could always be improved. At this stage however, they provide convincing evidence that the sound intensity of heat calls are below the hearing thresholds of zebra finch chicks.

Reviewer #2 (Public review):

Summary:

The authors studied aggregation, buckling, and particle collection by the filamentous cyanobacterium Fluctiforma draycotensis, as well as by the filamentous Pseudanabaena sp. (order Pseudoanabenales). They performed a range of experiments, from imaging individual gliding filaments to multiple-day experiments showing the formation of large aggregates around a particle formed from a precipitate. They also developed a model of buckling filaments to argue that the ability of elastic filaments to collect particles and form macrostructures is confined to a part of the filament phase space in terms of length and flexibility, meaning that gliding combined with certain filament length and flexibility naturally reproduces the observations.

Strengths:

This is an impressive study that uses multiple tools to connect macrostructure formation with filaments' gliding motility and buckling. It adds an important perspective on the biological and physical factors at play in the emergence of aggregates.

Weaknesses:

The authors ignore the possibility that filament behavior plays an important role in the emergence of the observed patterns. Cyanobacteria have been shown to control their gliding motility (Pfreundt et al Science 2023; Kurjahn et al Nature Comm 2024), and their molecular motors are known to be regulated by chemotaxis-like signaling pathways (Risser ARM 2025). As far as I know, how the coordination between the pulling agents along an individual filament works is actively debated, but there seems to be little doubt that it exists. 

To illustrate this point better, note that the aggregation observed by the authors is consistent with the length-dependent ability of filaments to coordinate gliding (I'm not saying this is how it works in Fluctiforma draycotensis; I'm saying it's consistent). Suppose the coordination requires sufficiently long filaments, which could be the case when signaling molecules travel along the filament, propagating information about when individual pulling agents should reverse. In such a model, short filaments act randomly because they fail to coordinate gliding by the time they glide off nascent aggregates, whereas longer filaments can perform informed reversals because they have more time for coordination. Such behavior then explains the lack of aggregation in Pseudanabaena sp. (via behavior, not lack of stiffness). Note that Trichodesmium is stiff; its filaments do not buckle, yet Trichodesmium forms organized aggregates via tightly controlled motility. Note also that, as the authors report, since Pseudanabaena sp. is both shorter and faster, its filaments have relatively (to the time needed to glide the filaments' length) little time to coordinate reversals. In my opinion, whether the observed patterns passively emerge from gliding and buckling or result from active behavior remains an open question.

I also have a small suggestion regarding this statement on model novelty:

The essential novelty of this model is that the filament itself is active and out of equilibrium, and additionally, the forces and torques are applied locally along its centreline, and not at its extremities as in previous steady-state mechanical studies of elastic, twistable filaments such as DNA [31-33] (see Methods and SI).

This statement needs to be revised as it ignores a substantial body of work on self-organization of active filaments: (R. E. Isele-Holder, J. Elgeti, G. Gompper, Soft Matter 2015; Pfreudnt et al, Science 2023; Faluweki et al PRL 2023; Kurjahn et al Nature Comm 2024).

Last point: the authors often say that their observations are reproducible ('...reproducibly forms macroscopic granules...'). What is meant? Different experiments on different days, different aliquots?

Reviewer #2 (Public review):

Summary:

In this study, the authors used genetic, transcriptomic, imaging, and electrophysiological approaches to investigate the role of P2X7R in retinal remodeling in rd1 mice. The authors propose that RA signaling upregulates P2X7R, leading to altered Ca²⁺ signaling, HCN1 expression, and spontaneous RGC hyperactivity.

Strengths:

Multiple lines of experiments were conducted, focusing on an important question in the field.

Weaknesses:

However, some aspects of the experimental design, statistical analysis, and interpretation require clarification. In particular, the genetic controls and causal evidence should be strengthened before the proposed RA-P2X7R-Ca²⁺-HCN1 pathway can be fully supported.

Some major issues:

(1) The Abstract states that P2X7R deletion prevents the upregulation of RA-responsive genes, whereas the Results state that RAR-dependent genes were not consistently changed by P2rx7 deletion. This central statement should be corrected and clarified.

(2) The P2rx7 knockout model requires further discussion. JAX strain 005576 targets exon 13 and has previously been reported to retain truncated P2X7 transcripts with residual activity (Masin et al., 2012). The authors should avoid describing this allele as complete loss of the entire P2rx7 gene unless additional isoform-specific validation is provided.

(3) The evidence that RAR directly regulates P2rx7 transcription remains incomplete. Predicted promoter motifs and reduced P2X7R protein after BMS-493 treatment are supportive, but they do not establish direct transcriptional regulation. Measurement of P2rx7 mRNA, RAR promoter occupancy, or promoter mutagenesis would strengthen this conclusion.

(4) Several statistical results require verification. In Figure 2H, four paired eyes are analyzed using an unpaired Mann-Whitney test, and the reported p<0.001 is difficult to reconcile with n=4. Similarly, the significance levels in Figure 5A-D are not compatible with a two-sided Wilcoxon rank-sum test using n=3 mice per group. The statistical unit, exact P values, and number of biological replicates should be rechecked.

(5) The overall causal pathway remains partially inferential. The study does not directly show that increased Ca²⁺ causes HCN1 upregulation or that HCN1 is required for RGC hyperactivity. These steps should either be experimentally tested or presented as a proposed model rather than an established mechanism.

Reviewer #2 (Public review):

Summary:

The study's aim was to establish whether stability in thought patterns relates to the particular thought pattern, the task context, or their interaction. And further, whether stable thought patterns could be linked with distinct brain patterns.

Strengths:

The core reliability framing is novel, and the trait/state/interaction decomposition is a fruitful way to pose the question, leading to the finding that stability is neither a pure trait nor a pure task property, but emerges from their interaction, which is a solid contribution.

The aim to characterise aspects of stability across individuals and across tasks was achieved and is well supported.

Weaknesses:

While the paper makes excellent use of existing data sets, the independent samples, i.e., one study sample for the cognitive/thought-sampling data, and several different study samples to generate the brain maps, do limit the brain-behaviour conclusions that can be drawn.

Reviewer #2 (Public review):

Summary:

In this paper, the authors use electrophysiological recordings from the olfactory tubercle (OTu) and ventral tegmental area (VTA) of mice learning an olfactory Pavlovian conditioning task to demonstrate the consistency of changes in OTu odour responses with gradient descent updates minimising reward prediction error (RPE). The paper is clearly written and the work well motivated. The authors address a gap in the literature on animal reinforcement learning by providing neural evidence for gradient-based representation learning, something that had been proposed but not yet tested. The results are convincing and the limitations comprehensively addressed. Of particular interest is the proposal that OTu SPNs could solve the weight transport problem through knowledge of the sign of their downstream connections from their expression of either D1/D2 receptors. This makes a concrete experimental prediction that future research could test.

Strengths:

The paper provides one of the first demonstrations backed by neural recordings that representation learning in the brain is consistent with gradient descent. It shows how, although weight transport may be biologically implausible, the brain appears to find other ways to compute a gradient in multilayered networks for efficient learning. The study builds nicely on recent work in systems neuroscience and provides evidence for a concrete implementation in the OTu-VTA circuitry of mice.

The paper demonstrates that changes in OTu striatal projection neuron (SPN) activity over trials are proportional not only to the RPE relayed by VTA dopamine neurons, but also account for the influence of each particular SPN on the RPE. If an SPN decreases the RPE when active, its activity will increase on the next trial after a positive RPE. The activity will instead decrease for an SPN that increases the RPE. This relation is encapsulated in the update rule of Equation 5.

Weaknesses:

The main weakness of the paper is that it provides only indirect evidence for the update mechanism by inferring synaptic weights based on the (justified) assumption of VTA dopamine neurons encoding RPE. This is still a substantial contribution to understanding representation learning in the brain, though I do think that the authors could provide some additional evidence to further convince readers. One idea could be to analyse the distribution of inferred weights and validate whether it agrees with known statistics of connectivity between OTu SPNs and their downstream projections (e.g. fraction of D1/D2 SPNs).

Further, as dopamine neurons are known to have asymmetrical responses for positive and negative RPEs (with the dips in activity related to negative RPEs being generally smaller) I'd expect an improvement in the correlation of the learning updates particularly after the reversal if the authors account for this in the model.

Reviewer #2 (Public review):

Summary:

In this study, Benamar et al. investigate whether DNA double-strand breaks induce extensive abnormal DNA synthesis in cancer cells and whether this response contributes to the cytotoxic effects of ionizing radiation and other DNA-damaging treatments. The authors refer to this process as double-strand-break-induced genomic amplification (DIGA). Mechanistically, they propose that insufficient protection of broken DNA ends permits excessive resection, followed by RAD51-dependent strand invasion and RAD52-POLD3-POLD4-dependent DNA synthesis resembling break-induced replication.

Overall, the study addresses an interesting and potentially important question. Break-induced replication was originally defined as a pathway that repairs one-ended DNA breaks through extended synthesis from an invaded homologous template. Studies in yeast established that this process involves a migrating DNA-synthesis bubble, conservative inheritance of newly synthesized DNA, frequent template switching, and high mutagenicity. Related forms of DNA synthesis have subsequently been described in mammalian cells at collapsed replication forks, telomeres, under-replicated mitotic regions, and some transcription-associated DNA breaks.

The important advance of this study is the proposal that double-strand-break-associated DNA synthesis, with several features of break-induced replication, can become sufficiently extensive to cause a measurable increase in total cellular DNA content and that this synthesis correlates with radiation-induced cell death. However, the physical structure, genomic distribution, and extent of the additional DNA have not yet been directly established.

Strengths:

Radiation-induced breaks are generally considered in relation to DNA repair, chromosome rearrangements, checkpoint activation, mitotic failure, senescence, and cell death. The possibility that broken DNA ends can also initiate extensive DNA synthesis provides a potentially important additional link between defective break repair, genome amplification, and treatment-induced cytotoxicity.

The authors examine this phenomenon using several complementary approaches and comparisons across multiple cancer cell lines. The finding that several double-strand-break-inducing treatments produce a similar response suggests that the phenotype is not restricted to ionizing radiation or to a single cellular background.

The authors also make efforts to distinguish DIGA from canonical origin-dependent re-replication, such as that caused by CDT1 stabilization and inappropriate origin relicensing. They find that, following irradiation, CDT1 is degraded rather than stabilized and that CDT1 depletion does not suppress DIGA. Perturbation of ORC1 or ORC2 also has little effect on the phenotype. In addition, depletion of SET8 or loss of SUV4-20H1 increases, rather than decreases, DIGA.

The authors carry out a systematic analysis of DNA-end protection and processing. Loss of ATM, RNF8, RNF168, 53BP1, RIF1, or Shieldin components enhances DIGA, whereas depletion or inhibition of CtIP, MRE11, EXO1, RAD51, RAD52, POLD3, or POLD4 suppresses it. These experiments support a model in which insufficient end protection permits excessive DNA-end resection, followed by strand invasion and recombination-associated DNA synthesis involving factors linked to break-induced replication.

Overall, the findings place DIGA within a growing body of evidence that recombination-associated DNA synthesis can extend well beyond short repair patches. The study attempts to connect the processing of double-strand breaks with abnormal DNA synthesis, increased cellular DNA content, and the cytotoxic response to radiation.

Major weaknesses and suggested experiments:

(1) The main evidence for DIGA is the appearance of cells with greater-than-G2/M DNA content by flow cytometry. BrdU incorporation shows that active DNA synthesis occurs within this population, and the density-gradient experiments provide further evidence for newly synthesized DNA. However, these methods do not reveal the physical nature of the proposed genomic amplification-for example, which genomic regions are copied or how long the synthesis tracts are.

This is important for the central conclusion of the study because mammalian break-induced replication can produce genomic duplications, but the extent of synthesis depends strongly on the type of DNA lesion. Previous work has shown that repair of damaged replication forks can produce segmental genomic duplications (Costantino et al., 2014; PMID: 24310611). By contrast, recent measurements at defined two-ended double-strand breaks suggest that mammalian break-induced-replication tracts can be relatively restricted (Li et al., 2021; PMID: 33470420; Shah et al., 2024; PMID: 39368985).

Thus, the large increase in total DNA content detected by flow cytometry in this study would require many simultaneous synthesis events, very long synthesis tracts, or an alternative process such as whole-genome duplication. The authors should therefore characterize the additional DNA directly. Whole-genome sequencing of sorted cells with greater-than-G2/M DNA content could be particularly informative.

(2) The genetic dependencies are consistent with synthesis initiated from resected DNA ends, but they do not directly demonstrate that the new DNA synthesis begins at double-strand breaks. DNA damage could indirectly alter replication-origin activity, cell-cycle progression, or DNA synthesis at genomic regions distant from the original lesions. Increased DNA content alone therefore does not establish amplification initiated directly at DNA breaks.

(3) MLN4924-induced re-replication is used throughout the study, but it is unclear whether the authors have combined MLN4924 with ionizing radiation and measured the resulting DNA synthesis. This experiment could clarify whether conventional re-replication and DIGA are independent, overlapping, or mechanistically connected.

(4) The results involving canonical non-homologous end joining are complex. Loss or inhibition of DNA-PKcs increases DIGA, whereas loss of XRCC4 or XLF strongly suppresses it, and loss or inhibition of LIG4 has a weaker suppressive effect. The authors suggest that XRCC4 and XLF stabilize broken DNA ends and thereby permit the synthesis reaction independently of final ligation. This is an interesting model, but the current evidence does not yet establish it. XRCC4 and XLF can affect end synapsis, break persistence, chromosome fusion, resection, and cell-cycle progression. Their loss could therefore reduce DIGA through several indirect mechanisms. The authors should measure DNA-end resection, RAD51 loading, persistence of double-strand breaks, and recruitment of RAD52 or POLD3 in XRCC4-, XLF-, and LIG4-deficient cells. Complementation with separation-of-function mutants that differentially affect XRCC4-XLF end bridging and LIG4 recruitment could test whether end stabilization, rather than ligation, is important. Without this, the opposing effects of upstream and downstream non-homologous end-joining components remain difficult to understand.

(5) The correlation between the amount of DIGA and radiation sensitivity across cancer cell lines is interesting. However, cell lines differ in many factors that influence radiation responses, including p53 status, apoptosis, checkpoint activity, ploidy, homologous recombination capacity, and proliferation rate. Matched models would provide stronger evidence. Conversely, restoring DNA-end protection in a DIGA-prone cancer cell line could test whether this reduces susceptibility. These experiments would help determine whether DIGA is a general property of cancer cells or a feature of particular repair-defective genetic backgrounds.

Reviewer #2 (Public review):

Summary:

This study by Dong et al characterizes the roles of highly-expressed Rab GTPases Rab5, Rab7 and Rab11 in the development and wiring of olfactory projection neurons in Drosophila. This convincing descriptive study provides complementary approaches of Rab expression and localization profiling, conventional dominant negative mutants, and clonal loss of function mutants to address the roles of different endosomal trafficking pathways across circuit development. They show distinct distributions and phenotypes for different Rabs. Overall, the study sets the stage for future mechanistic studies in this well-defined central neuron.

Strengths:

Beautiful imaging in central neurons demonstrates differential roles of 3 key Rab proteins in neuronal morphogenesis as well as interesting patterns of subcellular endosome distribution. These descriptions will be critical for future mechanistic studies. The manuscript is well-written and explanatory, very accessible to a wide audience without sacrificing technical accuracy.

Comments on revised version.

The paper is greatly improved with new data and better-nuanced interpretation. The clarity of explanations for a non-Drosophila reader have been redone particularly well.

Reviewer #2 (Public review):

Summary:

By analyzing cells' frequency-dependent viscoelastic properties and intracellular activity through microrheology, Münker et al simplify the complex active mechanical state into six key parameters that constitute the mechanical fingerprint. They apply this concept to cells treated with cytoskeleton-inhibiting drugs. Additionally, a comprehensive statistical analysis across various cell types shows how cells coordinate their mechanical properties within a defined phase-space marked by activity, mechanical resistance, and fluidity.

Strengths:

(1) The distribution of the six parameters: they have been well characterized based on established theories, and they can be used to understand cell-type-specific biomechanical differences. The examples of muscle cells and immune cells were profound and informative.<br /> (2) Efforts to perform dimension reduction of parameter space into activity (E), fluidity (C1) and resistance (A) are insightful and will be helpful for future characterization of cell mechanics.

Comments on revised version.

In the original submission, cytochalasin B alone showed little effect on viscoelastic and active energy parameters, and it was unclear whether this reflected a true absence of actin's role or an artifact of the perturbation method used. In the revised manuscript, the authors addressed this by repeating the cytochalasin B measurements with larger sample sizes and adding latrunculin A, a mechanistically distinct and more potent actin-depolymerizing drug, together with immunostaining to confirm cytoskeletal disruption. This convincingly shows that actin depolymerization does affect the solid-like prefactor and fluidity, resolving the original concern.

Nocodazole-induced microtubule depolymerization previously did not appear to reduce the solid-like property A, which was unexplained. The revised manuscript removes the speculative compensation-mechanism explanation, adds a discussion comparing the results to prior AFM literature (explaining the discrepancy as reflecting different mechanical compartments probed - cortex vs. intracellular), and the new data now show a significant reduction of A with nocodazole treatment as well. This weakness is resolved.

Reviewer #2 (Public Review):

Summary:

In this study, Styer et al. impose artificial selection on root-associated microbiomes to increase drought tolerance in rice plants using different soils as starting microbiomes. Using NDVI and biomass as a proxy for plant health, they find that iterative passaging of the microbiomes of the best-performing plants increased plant resilience to drought stress in a soil-dependent manner. The study makes use of numerous controls. The authors survey the microbiota of the plants across generations, using an array of interesting analyses to characterize their observations. Firstly, the authors find that the acquired microbiomes are divergent towards the beginning of the selection experiment, but nearly converge later suggesting that the selected communities become more similar over time. One reason is that the diversity of the microbiomes severely decreases after only one or two generations of selection AND that microbes from each inoculation source appear to easily disperse across the experiment, leading to microbiome homogeneity. The authors then present an analysis to correlate ASVs with the NDVI and Biomass over the course of the experiment (using the rice soil selection lines) to develop hypotheses about which ASVs may impact plant traits.

Strengths:

The authors set out to refine the understanding of microbiome artificial selection, a topic of recent interest to the plant microbiome field. The authors use an established approach (Mueller et al), expanding upon it by including multiple starting soil inocula to ask whether the strength of selection varies by input microbiome. This is an important and novel question. Using drought resilience as measured by NDVI and plant biomass to select upon was a wise choice for this type of study, given their relative ease and quickness to assess. The inclusion of several types of controls, multiple selection lines, and several starting soil inocula showed a thoughtful experimental design. The analyses were diverse, non-standard, and attempted to address microbiome dynamics on multiple fronts. I am not necessarily convinced by some of the conclusions (see below), however, I think this study examines an important and exciting topic in the area of plant microbiomes. I predict the findings of the experiments will inform a wide audience of researchers attempting similar studies and be helpful in their designs.

Reviewer #2 (Public Review):

The revised manuscript has substantially improved, and the authors have satisfactorily addressed most of the concerns raised in my original review. Overall, the experimental evidence and its relationship to the computational model are now presented more clearly and rigorously, substantially strengthening the manuscript.

My main reservation in the original review concerned the role of the initial topographic connectivity bias in the computational model. The revised manuscript provides a clearer interpretation of this aspect. The initial bias represents a coarse activity-independent scaffold, while the final organization of the maps depends on its interaction with the structure and degree of correlated spontaneous activity. Importantly, the simulations show that the presence of the initial bias alone does not determine the final connectivity pattern. I therefore consider the computational results substantially better supported and interpreted in the revised manuscript. Nevertheless, as the model starts from a predefined coarse topographic organization of the projections from the primary sensory cortices to RL, in my opinion, the results demonstrate how structured spontaneous activity can refine and align an initially organized connectivity scaffold, rather than showing that spontaneous activity itself establishes this topographic organization.

This distinction is relevant when interpreting the central mechanistic conclusion of the study. The work provides convincing support for the idea that correlated spontaneous activity can contribute to the refinement and alignment of multisensory cortical maps, conditional on the existence of an initial coarse topographic organization. Establishing experimentally how this initial connectivity is organized during the relevant developmental period, and how spontaneous activity modifies it, remains an important question for future work.

Overall, I consider the revised manuscript considerably stronger than the original submission. Most of my previous concerns have been adequately resolved, and the study provides valuable experimental and computational insight into how spontaneous activity may contribute to the development of aligned multisensory representations.

Reviewer #2 (Public review):

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

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

Comments on revised version:

I appreciate the consideration of my comments and the authors' responses. The additional data on PN synapse number is intriguing (and welcome) as is the text discussing potential postsynaptic compensatory mechanisms. I respect the authors' decision in retaining the connectivity analysis, but despite the textual changes, I still feel that it is peripherally related to the main thesis of the work and would best be omitted from the paper and included in a separate, more relevant study. Ultimately, though, that is their choice.

Reviewer #2 (Public review):

Summary:

This manuscript examines how GATA6-dependent programming of resident peritoneal macrophages regulates their lipidome and, in turn, eosinophil homeostasis, combining lipid imaging, mass spectrometry, transcriptional analysis and in vivo pharmacology. BODIPY/CARS microscopy with targeted lipidomics convincingly shows substantial lipid changes following myeloid GATA6 deficiency, particularly in sphingolipids, with Smpd1 and Gba2 manipulations providing mechanistic support.

Strengths:

The authors also connect these changes to eosinophil biology, confirming increased peritoneal eosinophils in Gata6-deficient mice with reduced apoptosis (via two methods) rather than increased production. Testing of alternative explanations (chemokines, IL-5, prostaglandins, 12/15-LOX products) strengthens the argument by narrowing candidate mechanisms. The identification of increased LTE4 is notable as it correlates with eosinophil abundance, and zileuton reduces LTE4, eosinophil numbers, and increases apoptosis. This supports a role for 5-LOX/cysteinyl-leukotriene signalling.

Weaknesses:

The principal weakness is specificity: zileuton affects the broader leukotriene pathway, not LTE4 alone, so correlation with LTE4 doesn't establish causality. This matters more given the LTE4 receptor remains unidentified (to the best of my knowledge). Similarly, the proposed transcellular biosynthesis mechanism (ImmGen data suggesting complementary enzyme expression across cell types converting LTC4 to LTE4) is inferential; direct evidence of cellular source and transfer is lacking.

Design limitations include reliance on pooled animals in some lipidomic measurements, small replicate numbers, and a stronger eosinophil phenotype in females that shifts subsequent analysis toward females. Indeed, this sex dependence deserves more discussion given it limits generalisability.

Overall, this is a technically strong, conceptually interesting study. The core conclusions, that GATA6-dependent regulation of the macrophage lipidome and a role for cystl Lts in eosinophil survival, are well supported. The more specific claim that LTE4 is the causal factor via a defined transcellular pathway is plausible but not yet firmly established. Experimental strengthening or moderated claims would improve the study.

Reviewer #2 (Public review):

Summary:

Koh and colleagues investigate the broader sensory role of LITE-1, a gustatory receptor previously linked to UV light detection in C. elegans. Their study explores whether LITE-1 also mediates avoidance of specific chemical stimuli-namely, high concentrations of diacetyl and 2,3-pentanedione. They show that LITE-1 is required in the ADL and ASK neurons for calcium responses to diacetyl, and that its expression in body-wall muscles is sufficient to trigger hypercontraction upon odorant exposure. Molecular docking suggests both odorants may directly bind to LITE-1 with micromolar affinity. These findings suggest LITE-1 may act as a multimodal receptor for both light and chemical stimuli.

Strengths:<br /> • Methodological Precision: The study is technically strong, with well-executed calcium imaging and quantitative behavioral assays that clearly show neural and muscular responses to chemical stimuli.<br /> • Novelty and Scope: The work presents a compelling case for LITE-1 functioning as a multimodal sensor, which is an intriguing expansion of its known role.<br /> • Potential Impact: If validated, the findings could significantly advance the understanding of sensory integration in C. elegans, and the tools developed may be broadly useful to the research community.<br /> • Relevance to the Field: The study adds to evidence that C. elegans uses non-canonical sensory pathways and may inspire further exploration of multimodal receptor functions in other systems.

Weaknesses:<br /> • Lack of Rescue Experiments: The absence of rescue experiments makes it difficult to definitively link the observed phenotypes to loss of lite-1.<br /> • Single Loss-of-Function Approach: The reliance on a single genetic mutant limits interpretability. Additional strategies such as RNAi (e.g., neuron-specific knockdown) would provide stronger evidence.<br /> • Unclear Neuronal Contribution: While calcium responses in ADL and ASK are reduced, it's unclear which neuron(s) are necessary for behavioral avoidance. Cell-specific rescue or knockdown experiments are needed.<br /> • Unvalidated Docking Data: The molecular docking predictions lack experimental validation. Site-directed mutagenesis would be needed to support claims of direct interaction.<br /> • Limited Odorant Specificity Testing: Docking analysis does not include non-binding odorants, making it difficult to assess binding specificity.<br /> • Incomplete Quantification: Some calcium imaging results (e.g., in AWA neurons of unc-13 mutants) lack statistical comparisons, which limits their interpretive value.

Comments on revisions:

I thank the authors for their thorough revision. The manuscript is substantially improved, and most of the concerns raised in my original review have been addressed.

The strongest improvement is the addition of new genetic evidence supporting a role for LITE-1 in high-concentration diacetyl avoidance. The use of multiple independent lite-1 alleles strengthens the conclusion that the phenotype is specifically due to loss of lite-1 function, and the ADL-specific rescue experiment is an important addition. While the rescue is not complete, it is convincing and supports the idea that LITE-1 activity in ADL contributes to the avoidance response.

The neuronal analysis is also stronger. The new statistical analysis across the sensory neurons addresses my previous concerns regarding quantification, and the revised interpretation of the calcium imaging data is more balanced. The revised title of this section is also more consistent with the data and appropriately focuses on ADL and ASK rather than ASH.

I also appreciate the additional controls addressing possible effects of ambient light. The light versus dark experiments make it unlikely that the observed behavioral phenotypes are secondary to unintended activation of LITE-1 by environmental illumination.

The expanded odorant analysis and inclusion of docking predictions for additional compounds are useful additions. Together with the ectopic expression experiments in body-wall muscles, these data strengthen the argument that LITE-1 can respond to diacetyl and related compounds.

My main remaining concern is the same one raised in the initial review: the docking results remain largely computational predictions and have not been tested experimentally through binding-site mutagenesis or other functional validation. As a result, the manuscript still does not demonstrate direct ligand binding to LITE-1. However, I think the authors have strengthened the indirect evidence considerably, and the conclusions are now generally written in an appropriately cautious manner. I would encourage the authors to continue framing the docking results as supportive of a direct interaction rather than definitive proof of one.

Overall, I believe the manuscript has been significantly strengthened by the revision. The remaining limitation is largely mechanistic and does not, in my opinion, undermine the central conclusions of the study.

Reviewer #2 (Public review):

The manuscript by Hajimohammadi, Mohr, O'Connell and Kelly is intended to demonstrate that participants integrate evidence over time to make a decision, even in a noise-free, static decision context. This is validated by the observation that 1) participant accuracy improves with increased exposure to the stimulus; and 2) there is a correlation between participant accuracy and a neural index of evidence accumulation, as measured by centro-parietal positivity (CPP).

Strengths:

(1) Joint modelling of accuracy and CPP dynamics is a significant achievement, as behaviour alone often cannot distinguish between competing theories of decision-making. In the case of protracted sampling in particular, the absence of reaction times (RT) due to the delayed nature of the response makes this method highly appealing.

(2) The experimental manipulations and the method used to extract the different neural indices are well chosen, enabling the mapping of putative cognitive processes such as evidence accumulation and motor preparation onto the recorded EEG with clarity.

(3) The in-depth discussion of the results clearly articulates those reported by the authors and in previous works.

Weaknesses:

(1) Regarding the first point I raised in the first version of the manuscript, I'm satisfied with the author's response.

(2) For the second point, however, I'm still unconvinced, noting that my understanding of the fitting routine is relatively limited. To me, the comparison of the behavioral and the joint-modelling is relatively weak in terms of evidence. Despite the revision, it is still unclear whether the behavioral models are well conditioned given the very weak constraint exerted by (aggregated, see below) binary decision data only. To my previous comment, the authors reply, "We can instead address identifiability somewhat indirectly through parameter estimate consistency across the 10 fits we conducted with different instantiations of noise". I'm worried that the different instantiations of noise (limited to 10), generated for the parameter search, have any impact at all. A better quantification of the uncertainty in parameter estimates, as well as the AIC, is needed, for example through a cross-validation scheme, or bootstrapping/jack-knifing at the level of the participants. Without this, I remain unconvinced that the behavioral models are suited for the comparison with the joint-neural models:

(3) Relatedly, regarding the response of the authors to my minor comment 4, after the revision of the manuscript, it is clearer that the behavioral models were fitted only on 6 data points (accuracies averaged over participants for each condition) for models with up to 3 parameters. I understand that the authors average behavior to make a comparison with the joint neural model, whose neural signals are noisy at the participant level. However, if the neural model, or the chosen linking function, needs this aggregation to outperform the behavioral model, that questions a bit whether the joint model is really useful in practice.

While I see these two last points as serious for the comparison between behavioural and joint-neural models, this does not change the main addition of the paper: that is, the joint model and the evidence for protracted sampling.

Reviewer #2 (Public review):

Summary:

The authors aimed to identify the specific neurons, neurotransmitters, and neuropeptides that mediate the longevity effects of the hypoxic response in C. elegans. By genetically dissecting the pathway downstream of HIF-1, they define a neural circuit involving ADF serotonergic neurons, the SER-7 receptor in the RIS interneuron, tyraminergic signaling from RIM, and neuropeptide NLP-17, ultimately linking neuronal hypoxic sensing to pro-longevity signaling in the intestine.

Strengths:

The study employs a diverse genetic toolkit, including neuron-specific transgenes, tissue-specific knockouts and rescues, RNAi knockdowns, allowing the authors to pinpoint causality, sufficiency, and necessity with high resolution. The comprehensive mapping of cell-nonautonomous signaling adds depth to our understanding of how HIF and serotonin signaling interface with aging pathways. The conclusions are supported by consistent survival assays and fmo-2 gene expression analyses.

Reviewer #4 (Public review):

Summary of General Strengths & Weaknesses:

The studies here are highly informative for anatomical tracing and sympathetic nerve function in the liver in relation to glucose levels, but because they are conducted in a single species, it is challenging to translate them to humans or determine whether these neural circuits are evolutionarily conserved. Dual-labeling anatomical studies are elegant, and the addition of chemogenetic and optogenetic studies provides mechanistically informative. Denervation studies lack proper controls, and sensory innervation in the liver is overlooked.

Specific Weaknesses - Major:

(1) The species name should be included in the title.

(2) Tyrosine hydroxylase was used to mark sympathetic fibers in the liver, but this marker also labels a portion of sensory fibers that need to be ruled out in whole-mount imaging data.

(3) Chemogenetic and optogenetic data demonstrating hyperglycemia should be described in the context of prior work demonstrating liver nerve involvement in these processes. The Discussion currently mentions this only briefly, but comparing methods and observations would be helpful.

(4) Sympathetic denervation with 6-OHDA can drive compensatory increases in tissue sensory innervation, and this should be measured in the liver denervation studies to implicate potential crosstalk, especially given the increase in LPGi cFOS that may be due to afferent nerve activity. Compensatory sympathetic drive may not be the only culprit, though that is clearly assumed. The sensory or parasympathetic/vagal innervation of the liver is altogether ignored in this paper and could be better described in general.

Comments on the revised version.

Across all reviewer comments, the revised resubmission has adequately addressed all concerns.

Reviewer #2 (Public review):

Summary:

Zhang et al. investigate how blood feeding and dietary protein influence sleep in the mosquito Aedes aegypti. The authors first establish a behavioural definition of sleep using postural analysis and arousal threshold measurements, then demonstrate that both blood meals and a bovine serum albumin (BSA)-based protein diet increase sleep for several days. They further show that RNAi-mediated knockdown of the leucokinin receptor (Lkr) enhances sleep, implicating neuropeptide signalling in the regulation of postprandial sleep.

Strengths:

The central question is well-motivated, and the experimental approach is systematic. The use of multiple independent methods to characterise sleep - postural analysis, infrared activity monitoring, videography, and arousal threshold - provides converging evidence. The 10-minute immobility criterion is grounded in the arousal threshold data, bouts exceeding 10 minutes corresponding to the first bin at which a significant effect emerges. The demonstration that the sleep increase is already detectable before oviposition establishes that the phenotype begins with feeding rather than with the completion of the reproductive cycle. The BSA feeding experiment is a particularly effective demonstration that dietary protein, rather than other blood components, is a key regulator of the sleep increase. The conservation of leucokinin signalling in sleep regulation between Drosophila and Ae. aegypti is a noteworthy finding that adds comparative depth. The "opportunistic versus determined" host-seeking distinction is appropriately framed as a hypothesis for future testing rather than as a conclusion drawn from the present data, and the limits of the design with respect to reproductive physiology are stated explicitly.

Weaknesses:

(1) Confound of reproduction and sleep. Blood and BSA both support egg development, so neither condition isolates nutrient sensing from reproductive physiology. The relative contributions of diet, egg development and post-reproductive recovery remain undetermined.

(2) Sleep versus reduced locomotion. The pDoze and pWake measures are defined here as proportions of time above or below a velocity threshold, rather than as the per-minute transition probabilities of the established definition (Wiggin et al. 2020, PNAS). So defined, they are equivalent to percent sleep and percent wake and cannot distinguish a sleep-like state from the mechanical consequences of engorgement.

(3) Data availability. Raw data are stated to be available on request rather than deposited in a public repository, which makes independent reanalysis less straightforward than it need be.

Reviewer #2 (Public review):

Summary:

The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.

BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.

Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the "late epoch" of taste processing (i.e., 500ms post taste delivery) correlated more strongly with the individualized rat's BAT preference rather than with a canonical preference ranking. Importantly, this correlation was stronger for the last BAT session compared to the first. Finally, the authors show that the correlation disappeared in a second, consecutive recording session, indicating that exposure to tastants reconfigure preferences.

Strengths:

(1) The experimental design allows for an unprecedented look at the relationship between individual variability in taste preferences and neural processing.

(2) The study demonstrates that taste preference variability is not mere experimental noise but reflects the dynamic nature of taste. A key strength is the clear evidence that behavioral variability is reflected in neural activity patterns, establishing a strong correlation between brain and behavior.

(3) The evidence that simple exposure to familiar tastes can reconfigure preferences and taste representations is interesting.

Weaknesses:

The authors appropriately addressed the weaknesses in the revision process.

Reviewer #2 (Public review):

Summary

Antimalarial combination therapy is the standard of care for malaria, a disease that impacts hundreds of millions of people annually. Combination therapy is crucial for effectively treating the disease and delaying the emergence of drug resistance. Despite the importance of choosing appropriate partner antimalarials for combination therapy, drug interactions are typically evaluated late in the course of drug development. Standard in vitro assays that determine synergistic, antagonistic or additive interactions between drug combinations rely on measuring inhibition of parasite proliferation, which is inadequate for translation to pharmacodynamic models for parasite clearance in the patient. Direct measurement of parasite viability under drug treatment has previously relied on methods that are labor and resource intensive, limiting applications to single compounds and single concentrations. Here, Hellingman et al make use of an inducible chemiluminescence reporter to measure cell viability and apply this novel approach to quantify drug interactions. The methodology is a significant improvement upon prior methods, requiring significantly fewer resources, half the time, and substantially less handling than the standard PRRv2 assay, whilst maintaining high resolution and sensitivity.

They assess the limit of detection for the improved method and cross-reference their results for single drugs at a single concentration with the currently standard PRRv2 assay. The authors next established analytical methods to characterize the impact of drug combinations on parasite viability using the GDPI pharmacodynamic model and compare their MULT-i2 assay to the prior cPRR approach. Their refined workflow allowed them to comprehensively evaluate the known synergistic combination between atovaquone and proguanil with greater resolution than the comparable cPRR assay and identified additional interaction parameters between the fast-acting antimalarials piperaquine and pyrimethamine. Overall, the authors demonstrate that their inducible lacZ system provides significant advantages compared with prior approaches to determine parasite viability. They convincingly demonstrate the strengths of their approach by characterizing two antimalarial combinations at much greater resolution than previously possible with prior methods. The system and methods established here will be particularly useful for evaluating novel antimalarial combinations with chemical series in preclinical evaluation and to optimize future antimalarial therapies.

Strengths:

The streamlined approach relies on induction of the lacZ enzyme only after drug washout. As opposed to when stably expressed, this allows the authors to estimate parasite viability without undergoing serial dilutions to estimate viable parasite titers. This innovation vastly reduced resource and time intensity, enabling greater throughput for parasite viability estimation. The established methodology and analysis pipeline enabled the testing of 49 drug combinations for parasite viability in the MULT-i2 assay compared to only 9 in the conventional cPRR assay. This provided improved resolution in the ability to estimate drug combination parameters in a pharmacodynamic model. The ability to comprehensively characterize combination pharmacodynamic properties in vitro will have important implications for downstream modelling of in vivo combinations, and for optimizing future antimalarial combination therapies.

The authors made good use of modelling and AICc for parametric estimation and model evaluation to demonstrate the advantages of the richer dataset afforded by the MULT-i2 assay.

Weaknesses:

The authors correctly identified a range of confounding effects that lead to artefacts in their assay results when compared to the cPRR assay. For instance, the authors observed reduced signal at high parasite density during recovery due to overgrowth and likely enzyme degradation, and suggested residual signal may remain from non-proliferating sexual stage parasites surviving drug treatment that would not be detected in the cPRR assay.<br /> Measurement of parasite viability in the MULT-i2 assay was achieved by extrapolating chemoluminescence signal to that of a serial dilution of parasites made at the initiation of drug treatment. How did the authors account for differing levels of enzyme expression at early (eg ring) vs late stage parasites (trophozoite or schizonts)? Were cultures synchronized prior to initiation of assays? Could differences in life-cycle progression following drug treatment be an additional confounding factor that may account for differences with the PRRv2 assay?

The addition of an inducible element is an improvement of their earlier lacZ/β-galSENSOR (PMID: 41575867), however, the authors fail to explain why this is an improvement and how this adds additional merit over the initial system. While the authors compare their new assay to the PRRv2, they fail to compare it to their own non-inducible lacZ/β-galSENSOR system. Their non-inducible system already showed superiority to the cPRR assays and it would be good to show how they compare and what the advantages of the new system are over the old. Eg how is the signal to noise improved? How does the sensitivity compare? How quickly does the can the signal be detected after induction? They show signal after 48h but it would be very useful to the community to look at earlier timepoints as well and compare it to the uninduced line and a line that has been induced 48h earlier to match the expression patterns throughout the lifecycle (something like 2h,4h,6h, 12h and 24h).

Is the chemiluminescence signal for the i-lacZ induced parasites comparable to the stably expressed lacZ parasites previously characterized by the group? If so, do the authors consider this inducible iteration a complete replacement for PRR assays?

Comments on revised version:

The authors adequately addressed our comments and the resulting manuscript describes a specialized resource for antimalarial drug development.

Reviewer #2 (Public review):

Summary:

Vig's lab delineates a critical role for STX11 in CRAC channel function, particularly in the context of the fatal immune disorder familial hemophagocytic lymphohistiocytosis type 4 (FHL4). They demonstrate that Syntaxin 11 directly binds and regulates Orai1, and that STX11 depletion abolishes CRAC currents and downstream signaling. Loss of STX11 reduces IL2 gene expression and impairs degranulation, both of which are rescued by the constitutively active Orai1 mutant H134S, whereas a gain‑of‑function mutant targeting the C‑terminus fails to restore these defects. The authors conclude that STX11 primes Orai1 for optimal local assembly that is independent of STIM1 yet required for CRAC channel gating.

Strengths:

This study is firmly grounded in disease biology and demonstrates that STX11 downregulation leads to profound functional defects. Using a comprehensive suite of methods and analyses, the authors interrogate the co-regulation of STX11 and Orai1 and present a near-complete view of STX11's modulatory role in CRAC channel function and downstream signaling pathways. The figures are clear, and the statistical analyses are rigorous and convincing.

Weaknesses:

The authors conclude that Syntaxin 11 directly binds Orai1. This conclusion is well supported by a multifaceted approach-including co-immunoprecipitation (co-IP), molecular dynamics simulations, co-localization/FRET assays, and targeted mutational analysis-all of which are thoroughly executed. While the interaction appears reasonably strong in co-IP experiments, the STX11-Orai1 interaction is comparatively weaker in pull-down assays, which the authors attribute to instability of the purified His-STX11 protein. A remaining gap is direct evidence of interaction in live cells; this is understandably challenging given that fluorescent tagging of STX11 is not feasible. Fully resolving this question lies beyond the scope of the present study and will require more advanced approaches to capture STX11 binding dynamics.

Comments on revised version.

The authors have addressed all my comments highly satisfactorily!

Reviewer #2 (Public review):

Summary:

In this study, Vialat and collaborators study the role of steroid hormone signalling on the development of prostate cancer (patients) and of accessory gland tumours in Drosophila, a tissue functionally equivalent to the prostate. Mining publicly available prostate cancer expression data and using gene expression signatures, they uncover that androgen signalling is actually down-regulated in castration resistant prostate cancers (CRPC) compared to "primary" cancers, leading the authors to wonder whether down-regulation of canonical androgen signalling could represent an important event increasing tumour aggressiveness. They then take advantage of their recently published tumour model in the accessory gland of Drosophila adult males, in which cells are primed for tumorigenesis by the constitutive activation of the EGFR receptor, to test directly this hypothesis. They show that the genetic invalidation of ecdysone reception and signalling increases the aggressiveness of the "pre-cancerous" lesions, and that ecdysone-insensitive tumours present higher proliferation and initiate basal extrusion.

Strengths:

The authors bring original observations on the role of ecdysone signalling to prevent male accessory gland tumour development in Drosophila

Weaknesses:

(1) The link between the human data mining and Drosophila model is not straightforward.<br /> (2) Important information, in particular clinical information, is missing in the presentation of the cancer patients' data, making it complicated to grasp the solidity of the claims.<br /> (3) Data-mining insights should be validated by orthogonal approaches.<br /> (4) Ecdysone signalling activity should be monitored.

While the two parts of the study both investigate the role of steroid signalling on tumour growth, the link remains slightly artificial. I think starting with Drosophila and then opening with some patient data would be better suited to the level of proof reached here, implying that the anti-tumoral role of steroids observed experimentally in the fly might be conserved based on data mining in patients, rather than trying to prove in the fly the hints gained from public data mining. Indeed, there are many important differences between the mammalian prostate and the fly accessory gland, as well as between sex hormone androgen signalling and developmental timing ecdysone signalling.

The prostate cancer data mining and re-evaluation brings some interesting observations that appear to challenge the androgen driver, contrary to the vast amount of literature. Indeed, the authors observe an apparent decrease in androgen signalling in the more advanced states of the disease, in particular CRPC. In order to better evaluate its clinical relevance, more background on the tumours analysed should be provided.

What treatments were received by the patients? Hormonotherapy? LH/RH analogues? +/- anti-androgens? Are these treatments still given when CRPC emerge and tissues were banked? Metastatic disease? Are these only primary tumours in situ? Are there metastases included in the analyses?

Frequently, castration resistance is associated with alternatively spliced variants of the AR (AR-V7) that become constitutive and could bind to new AR-sensitive enhancers, even in the absence of androgen. Is the splice variant status of patients known, or could it be inferred from the expression data? Would there be different responses according to AR-V7 status?

Regarding the signature used. Why not monitor PSMA, one of the major prostate cancer markers, which is regulated by AR?

Finally, to consolidate the surprising observation that AR signalling is repressed in CRPCs, the authors should back these in silico predictions with orthogonal approaches such as histochemistry on patients' TMA or tissues from mouse models, monitoring AR activity.

Regarding the fly experiments, the observation that ecdysone signalling depletion cooperates with EGFR-lambda activation to generate big overgrowths that delaminate basally without passing through the muscular sheet is interesting. However, several important controls need to be provided in order to support the claims:<br /> a) The authors should use an ecdysone reporter (ERE-LacZ, ERE-GFP...) to monitor and show that Ecdysone signalling is indeed lower in the tumours after genetic manipulations, or that it is higher in EGFR-lambda small clones.<br /> b) EcR is normally a repressor, which is turned into an activator in the presence of 20-hydroxyecdysone. The removal of EcR could lead to de-repression of genes and thus slightly activate the pathway. Monitoring ecdysone signalling activity is thus critical.<br /> c) The authors should also monitor the expression of Phantom, Shadow, Shade, and EcR in the different accessory glands (wild-type, EGFR-lambda, EGFR-lambda & EcR-RNAi). It is extremely surprising that systemic ecdysone has so little role since Phantom, Shadow, or Shade RNAi appear as potent as EcR-RNAi. This quantification has actually been performed for Sad in Figure S5, which is not even mentioned in the text. It should be done for Phtm.

A UAS-yellow-RNAi (or similarly irrelevant RNAi) rather than UAS-GFP should be used as a control for the EcR, Phtm, Sad, Shd, and Tub RNAi. Indeed, loading the RNAi machinery could have some unexpected effects not controlled by the UAS-GFP.

The authors should not use the term "sex steroid" when referring to ecdysone. It is a steroid hormone important for developmental timing and rate of growth, but is not a sex hormone, as sex is cell autonomously genetically determined in the fly.

Reviewer #2 (Public review):

Summary:

In this manuscript, Fukui et al. re-examined the ATP hydrolysis mechanism in GHKL ATPases, revealing a cooperative role of two conserved acidic residues rather than one. The authors have used a range of biochemical and structural techniques on various mutants from different members of the GHKL ATPase family to test and validate their proposed mechanism.

Through a detailed re-analysis of their previously published structure of the aqMutL NTD (ATPase domain) in complex with AMPPCP, they identified Glu29 and Glu32 as interacting with nucleophilic water for the catalysis. The authors carefully dissected the respective roles of these two acidic residues with a series of site-directed mutations. Mutations at Glu29 impaired ATPase activity without affecting protein secondary structure or ATP binding in the case of the E29Q mutant. Moreover, mutations at Glu32 did not affect secondary structure (except for E32G) but reduce ATPase activity. Activity was abolished when both residues (E29Q/E32Q) are mutated.

The authors extended their study to another GHKL ATPase, aqGyrB. Their findings further supported the cooperative function of the corresponding acidic residues in aqGyrB (Glu48 and Asp51) during ATP hydrolysis. Mutation of these residues partially impaired ATP hydrolysis without affecting protein secondary structure. ATPase activity was completely lost in the double mutant E48Q/D51M. While the E48Q mutant retained the ability to bind ATP, the E48A mutant did not. High-resolution structures of the WT and E48A, E48Q, D51A and D51N mutants of the aqGyrB NTD demonstrated that nucleophilic water positioning depended on these residues. E48 played a dominant role in water positioning and is critical for stabilising ATP lid formation and associated conformational changes, whereas D51 contributed cooperatively to catalysis.

The authors investigated the functional impact of mutating the corresponding residues in the human MutL homologs PMS2 and MLH1. Clinical variants consistently exhibited reduced or abolished ATPase activity, providing a potential molecular basis for Lynch syndrome, through impaired DNA mismatch repair.

Lastly, through evolutionary analysis, the authors inferred that the second acidic residue was likely present in the common ancestor of MutL, GyrB, and MORC proteins, but was lost in the case of Hsp90.

Strengths:

(1) This study contains a detailed structural and biochemical analysis of a biologically important set of GHKL ATPases. The authors identify a second acidic residue that is conserved and contributes to catalysis in a large subset of GHKL ATPases. An updated and extended mechanistic model of ATP hydrolysis by this class of enzymes is proposed, which involves cooperative and partially overlapping roles for the catalytic residue pair. This revised mechanistic model is invaluable for the interpretation of clinical variants of GHKL ATPases such as PMS2 and MLH1.

(2) The work described was performed to an excellent and rigorous technical standard. The structural and biochemical data are sound. The evidence supporting the claims is compelling.

Weaknesses:

(1) The identification in this study of a second acidic residue contributing to catalysis but not absolutely essential for catalysis is a useful finding. However, given that many structures of GHLK ATPases have been determined with different nucleotide analogs bound and that the essential role of the first acidic residue is well established, the importance and scope of the advances described here remain focused within the field of study of GHKL ATPases.

(2) The authors assessed the consequences of variants in the human MutL homologs PMS2 and MLH1, but various other human GHKL ATPases contain clinically relevant variants, some of which have stronger disease associations than the mutations examined in this study. A broader analysis of any effect of disease-linked mutations in GHKL ATPases would have strengthened this study.

(3) The effect of other aqMutL NTD E32 mutants, particularly, the E32K mutant on ATP binding remains unclear, although experimental assessment of nucleotide binding would be challenging due to the high protein concentrations required for the equilibrium dialysis assay.

Reviewer #2 (Public review):

In this study, the authors aimed to elucidate the precise molecular details underlying the BAX-VDAC2 interaction and subsequent BAX activation. To achieve this, they successfully combined AlphaFold3 structural modeling, cross-linking mass spectrometry, site-directed mutational screening, biochemical assays, and functional electrophysiology experiments.

The authors demonstrate that the direct interaction between BAX and VDAC2 is fully autonomous, occurring independently of any additional mitochondrial or cellular proteins. Their findings suggest that BAX exists on the outer mitochondrial membrane in two distinct populations: loosely membrane-associated, and tightly stabilized via its specific interaction with VDAC2. Crucially, the data overturn historical assumptions by demonstrating that BAX does not insert into the internal VDAC2 channel pore. Instead, the BAX α9 helix docks onto the lipid-facing outer surface of the VDAC2 β-barrel.

Interestingly, while the anchor is external, the soluble domain of BAX physically blocks the pore opening, leading to the observed occlusions of the VDAC channel. Following this docking event, the N-terminal 6A7 epitope of BAX becomes exposed, signaling a conformationally active state. However, the authors show that this structural activation does not trigger an immediate release from VDAC2 or prompt immediate oligomerization. Rather, BAX is maintained in a pre-oligomeric, primed intermediate state while bound to VDAC2. What ultimately regulates the release of this primed intermediate from VDAC2 to allow full oligomerization and pore formation remains an open question.

Altogether, this study provides pivotal mechanistic insights, clearly defining VDAC2 as a key checkpoint regulator of mitochondrial apoptosis.

Reviewer #2 (Public review):

Summary:

Using antibody treatments, genetic models and in vitro studies, the authors convincingly show that E-Selectin is a driver of VIPN.

Strengths:

In vivo studies are robust. Antibody studies as well as the inflammasome-related work are very well done.

Weaknesses:

(1) The spatial transcriptomics data need to be improved in terms of visualization.

The authors should plot genes that define the cell types in the sequencing dataset, and also show the cellular map on the cut, not only UMAPs. Can the authors not use the n=3 samples per condition to perform some statistical analyses? While the CellChat analyses are informative, they are difficult to read. Fold change over control when comparing conditions and pathways may be a better way of visualization.

(2) In Figure 1B, the % DAB is not easy to understand. It would be better to do the staining also via IF, and then maybe to count nuclei. The corresponding figures shown in the supplemental figure are not convincing. If F4/80 is not working well, Iba1 could be an alternative.

(3) The authors should define the background of all the mice used. Have they been back-crossed to B6j mice? One cannot compare C57BL6J mice with full knockouts if they are not littermate controls. Especially immunological responses are completely dependent on the background of mice. See e.g. PMID: 40568896.

(4) Could the authors comment on the role of ICAM2? Why was this not tested as well?

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

DiseaseAssertion: IRD

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

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

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

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

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

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

PreviouslyPublished: n/a

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

ClinVar: 99332, 99340

CAID: n/a

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

Reviewer #2 (Public review):

Summary:

Many ion channels/transporters in endosomes and lysosomes remain poorly understood. Even though the functional importance of endo-lysosomes in astrocytes has been recently recognized in many neurological diseases including lysosomal storage disorders and neurodegenerative diseases, the basic biology has not been much studied. In this manuscript, Spivey et al. showed how one of the key ion channels i.e., TRPML1, regulates endosomes and lysosomes (LELs) in astrocytes -which are also not much investigated in the field, compared to neurons- and its activity may further affect the structure of astrocytes and synaptic activity of neighboring neurons. The authors elegantly use multiple controls from agonists, antagonists, and TRPML1 knockdown to corroborate the results. The data are very strong and well supported. I believe that revising a few parts of the manuscript will greatly augment the significance of this work.

Strength:

The rigor of the study and data using various controls. The quality of the data is also very impressive.

Weakness:

A limitation of the study is that the mechanistic experiments rely primarily on overexpression or genetic knockdown of TRPML1 approaches, both of which alter TRPML1 abundance on endolysosomal membranes and potentially introduce artifacts related to protein level, affecting luminal ion homeostasis. While the overall conclusions are convincing, validation in a genetic MCOLN1 knockout mouse model would provide more definite evidence for the proposed mechanism.

Reviewer #2 (Public review):

Summary:

The authors developed a dataset of protein conformations by running molecular dynamics simulations starting from both native and decoy conformations for a large number of proteins. These conformations were put together as a dataset for querying and downloading, along with their energies under different force fields. The authors suggest that such conformations represent the proteins' conformational landscape, so that they will be useful for evaluating methods generating multiple conformations of proteins.

Strengths:

The dataset is online and working. It has good documentation for others to use.

Weaknesses:

The biggest weakness is that the collected conformations very likely do not represent the true conformational landscape. To represent the conformational landscape, the structures need to be sampled based on the Boltzmann distribution. However, in this study, conformations are generated by running very short (125ps to 375ps) MD simulations starting from near-native conformations and decoys. Such short simulations will produce small fluctuations around the starting conformations, so the distribution of conformations is largely dominated by the distribution of the initial conformations, which by one means are Boltzmann distributed. A conformation might be physically plausible, but it might have very small weight in the Boltzmann distribution. On the other hand, conformations with large weights might not be in the dataset.

Reviewer #2 (Public review):

This manuscript presents a comparative MRI dataset from 16 avian species and uses MRI and tractography to examine variation in brain organization across birds. The authors argue that their analyses support mosaic brain evolution and provide a framework for comparative neuroanatomy. Although the dataset represents a useful resource, particularly given the inclusion of understudied species like penguins, toucans, and hornbills, I have substantial concerns regarding the novelty of the study, the anatomical interpretation of the results, and the validity of the tractography analyses. In its current form, I do not believe the manuscript provides sufficient new biological insight to support many of its conclusions.

Major Concerns

(1) The authors repeatedly state that comparative neuroanatomical studies in birds have largely been unable to examine internal brain organization or "internal parcellation". This claim is inaccurate and reflects limited engagement with a substantial body of literature. For decades, comparative studies have examined variation in the size of major avian brain subdivisions as well as specific sensory, motor, and associative nuclei. For example, the extensive work of Andrew Iwaniuk and colleagues has documented variation in numerous brain regions across birds and related these differences to ecology, behavior, and sensory specialization (e.g., Gutierrez-Ibanez et al., 2009; Iwaniuk et al., 2006, 2008, 2010; Corfield et al., 2015). Other authors have also made important contributions in this area (e.g., Boire and Baron, 1994; Burish et al., 2004; Moore and DeVoogd, 2011, 2017). Importantly, previous work has already examined variation in major subdivisions of the avian brain using relatively standardized datasets (e.g., Iwaniuk et al., 2004; Iwaniuk and Hurd, 2005), including datasets that contain more species and greater taxonomic diversity than the current study. In other words, these studies have already provided detailed analyses of internal brain organization across broad taxonomic samples.

The manuscript should therefore be reframed as providing a new MRI-based resource rather than introducing the first comparative framework for studying internal avian brain organization. The current framing significantly overstates the novelty of the work.

(2) A second significant concern is the lack of anatomical specificity in the tractography analyses. The authors repeatedly refer to regions such as "anterior cortex," "dorsal cortex," and "temporal cortex." These terms are not standard anatomical designations in avian neuroanatomy and provide little information about the actual structures being analyzed.

For example, the "temporal cortex" could potentially include portions of the nidopallium (including the caudolateral nidopallium, NCL), mesopallium, and arcopallium. Similarly, the "anterior cortex" could correspond to the somatosensory or visual Wulst, the anterior nidopallium, or several other structures. The designation "dorsal cortex" is similarly difficult to interpret. Because these seed regions may encompass multiple functionally distinct systems, it is impossible to evaluate the biological significance of the reported connectivity patterns.

I strongly encourage the authors to define their seed regions using accepted avian neuroanatomical terminology and to provide detailed anatomical maps. More informative analyses would focus on well-defined structures with known connectivity, such as the Wulst, arcopallium, entopallium, or NCL. As currently presented, the tractography results are too coarse to support meaningful biological conclusions.

(3) I am not an MRI specialist, but I have concerns regarding the interpretation of the tractography results. Bird brains are small, and diffusion MRI tractography is already known to be challenging even in substantially larger brains. The manuscript provides limited information regarding image resolution, diffusion sampling, and the expected accuracy of tract reconstruction in these specimens. More importantly, there is little validation of the tractography results. Diffusion tractography is prone to both false positives and false negatives, and reconstructed pathways cannot be assumed to represent true anatomical connections.

The authors should provide evidence that their tractography pipeline can accurately recover known pathways. For example, they could compare reconstructed tracts with well-established anatomical pathways such as the anterior commissure or major visual pathways, which would substantially strengthen confidence in the results. Without such validation, it is difficult to determine whether the observed species differences reflect biological variation or methodological artifacts.

(4) I also have some methodological concerns regarding the comparisons of anterior commissure (AC) size and cerebellar foliation. First, the authors measure the AC in a coronal section. I would recommend measuring the AC area in a midsagittal section instead. Furthermore, the authors use the cross-sectional area of the same coronal section as the scaling variable. This seems problematic because the area of any given section will depend on the angle of sectioning and other technical factors. If the objective is to compare the relative size of the AC, then total brain volume or telencephalon volume would be more appropriate scaling variables.

With respect to cerebellar foliation, the authors developed their own metric. I would encourage them to use methods already established in the literature, such as the foliation index described by Iwaniuk et al. (2006). Their approach may yield similar results, but using the foliation index would facilitate direct comparisons with existing datasets and would allow incorporation of additional published data (e.g., Cunha et al., 2021, which includes foliation index measurements for 54 bird species). The authors should also be aware that the foliation index scales with body size. Consequently, the high degree of foliation observed in penguins may not necessarily indicate cerebellar expansion or increased demands for sensorimotor integration associated with their specialized locomotion. I therefore believe that the conclusions regarding variation in AC size and cerebellar foliation should be re-evaluated after more appropriate analyses are performed.

Reviewer #2 (Public review):

Summary:

This study presents a fundamental new finding - the identification of a sensory-neuron itch receptor, MRGPRX4, as an unexpected melanoma oncogene through a mechanism of lineage-inappropriate expression rather than mutation. The evidence supporting the core observation (tumor-specific upregulation, restriction to invasive transcriptional states, and sufficiency to drive fully penetrant metastatic melanoma in vivo) is compelling, drawing on convergent human genomic datasets and a well-controlled genetic mouse model. However, several of the mechanistic and translational claims - particularly regarding causal drivers of invasion, the immunosuppressive tumor microenvironment, and in vivo pharmacological efficacy - remain incomplete, relying on correlative evidence.

Strengths

The authors propose that MRGPRX4, normally restricted to a subset of peripheral sensory neurons, is aberrantly re-expressed in melanoma rather than through mutational mechanisms, and that this re-expression is sufficient to drive tumorigenesis through basal, ligand-independent GPCR signaling. This is a genuinely novel model for oncogenesis, and the manuscript deploys an impressive range of approaches - bulk and single-cell transcriptomics, spatial transcriptomics, proteomics, phosphoproteomics, and pharmacology - to support it.

Strengths:

The claim that MRGPRX4 is selectively upregulated in melanoma and confined to neural-crest-like/invasive transcriptional states is well supported, with consistent results across multiple independent human scRNA-seq datasets. The claim that ectopic MRGPRX4 is sufficient to drive melanoma is convincingly demonstrated by the fully penetrant, metastatic phenotype in the Tyr-CreER;MRGPRX4-LSL model, with appropriate specificity controls showing that MRGPRX1, MRGPRX2, and MRGPRX3 do not phenocopy this effect.

The claim that MRGPRX4 signals through basal, ligand-independent activity is reasonably well supported by bile-acid quantification showing endogenous ligand concentrations well below the EC50 required for activation.

Weaknesses:

The claim that MRGPRX4 remodels the tumor microenvironment toward an immunosuppressive state rests on flow cytometric frequency data (altered neutrophil/eosinophil ratios, increased PD-L1+ myeloid populations) but lacks any functional immune assay to demonstrate that this remodeling actually impairs anti-tumor immune responses.

The claim that the two MRGPRX4-enriched tumor subpopulations (ECM-rich and NC-like/invasive) underlie the observed invasive and metastatic phenotype is not directly tested; the authors appropriately acknowledge this as an open question, but it is worth noting explicitly that this leaves the mechanistic link between the identified cell states and the functional phenotype (proliferation, invasion, metastasis shown in Figure 4) unresolved.

Finally, the comparison with BRAF- and NRAS-driven GEMMs (Figure 3K-L) establishes overlap in transcriptional cell states but does not report whether these canonical models themselves upregulate endogenous Mrgprx4. This omission leaves unclear whether MRGPRX4 acts as a convergent node downstream of canonical oncogenic signaling, or represents an independent, parallel route to a similar phenotypic endpoint - a distinction that matters considerably for how broadly the finding should be interpreted.

Overall assessment:

The manuscript's central, most novel claim - that lineage-inappropriate expression of a sensory GPCR is sufficient to drive melanoma - is compellingly supported. The secondary mechanistic and translational claims built around this finding are convincing and consistent with the broader literature but are currently supported by correlative rather than causal or functional evidence.

Reviewer #2 (Public review):

This work reveals a role for PA lactylation in influenza virus replication, and the proposed involvement of ATAT1 and SIRT1 is certainly intriguing. These observations open up new avenues for understanding how host metabolism may influence viral infection. Nevertheless, a few mechanistic issues remain to be clarified. Notably, the direct evidence for ATAT1 and SIRT1 acting as the writer and eraser of this modification is still incomplete, and the functional relevance of the identified sites could be further substantiated.

Major Comments:

(1) The direct evidence supporting ATAT1 as a PA lactyltransferase and SIRT1 as a PA delactylase is still lacking. It remains possible that these two molecules affect PA lactylation indirectly. Therefore, in vitro lactylation/delactylation assays to clarify whether ATAT1 and SIRT1 act directly on PA should be performed.

(2) Although Figure 4A shows that K605A/K609A mutations reduce PA lactylation, the use of lactylation-mimetic mutants in functional complementation assays could further strengthen the conclusion.

(3) ATAT1 and SIRT1 are known to regulate multiple substrates. Therefore, whether the viral phenotypes resulting from ATAT1/SIRT1 manipulation truly operate via PA K605/K609 remains to be demonstrated. Complementation experiments would help address this issue.

(4) The mechanistic analysis currently focuses on polymerase dimerization. IP-MS assays comparing the host protein interaction profiles of PA WT versus K605/K609 mutants could reveal whether additional host factors are involved.

(5) The downstream consequences of PA lactylation have not been explored in the context of host antiviral immunity, particularly type I interferon (IFN-I) signaling. We would suggest examining the expression of IFN-β, ISG56, and other ISGs upon infection with WT versus PA K605/K609 mutant viruses.

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Reviewer #2 (Public review):

Summary:

Chao et al. study whether multi-species self-supervised pretraining learns sequence representations that can be transferred to improve downstream expression prediction in budding yeast. The rationale is that the budding yeast genome is relatively small and compact, which may not provide enough sequence variation for standard reference-genome-based supervised learning.

This paper has two stages. They first train U-Net-style DNA language models on collections of fungal genomes with increasing phylogenetic breadth using a BERT-style masked language modeling (MLM) objective. The model with the best sequence reconstruction (i.e., lowest perplexity) on held-out budding yeast sequences is called Shorkie LM. They then use Shorkie LM's weights to initialize Shorkie, a supervised model for predicting functional genomic tracks (RNA-seq, ChIP-exo, ChIP-MNase) in budding yeast. Compared with a randomly initialized model with the same architecture, Shorkie clearly performs better on held-out expression prediction and outperforms the randomly initialized model on all three variant effect benchmarks. This is good evidence that the pretraining is beneficial. For Shorkie LM and Shorkie, the authors also use interpretation methods to analyze motifs captured across a range of loci, and the results seem to be consistent with known yeast biology.

Overall, the experiments are well thought out and controlled, and most claims are supported with sufficient evidence. While the idea that pretraining can be beneficial for budding yeast has been reported previously (for scalar expression prediction) [1], this paper's exploration of optimal evolutionary scope and genomic language model pretraining in general still holds practical value for the field. The Shorkie model itself is also a useful resource and ranks first on many tasks in a recent public benchmark for fungal sequence-to-expression models [2]. I only have some minor concerns/suggestions.

Strengths:

(1) The authors put considerable effort into evaluating their modeling choices. For the pretraining corpora, they compare four phylogenetic scopes (a species, strain, order, kingdom). Each corpus is evaluated across multiple model architectures/capacities. Extra care is taken with homology filtering between training and held-out data. For supervised transfer, several candidate language models are carried forward to test whether their ranking by language model selection criterion (perplexity) predicts their ranking on the downstream task. In addition, the same-architecture randomly initialized baseline for Shorkie is itself optimized for learning rate, with additional randomly initialized CNN and U-Net models included as additional controls, giving some strong nulls to compare against.

(2) The experimental dataset generated in this study is also a valuable resource. Using the ministat array, the authors generated ~3,000 time-resolved RNA-seq profiles following transcriptional regulator inductions. Such data are important for understanding transient regulatory events and how they shape the transcriptome over time.

(3) Several figures include schematic panels that make the whole workflow easy to understand.

Weaknesses:

(1) As acknowledged by the authors, the current evolutionary "sweet spot" at 165 Saccharomycetales genomes is potentially confounded by factors like corpus size, annotation quality, and optimization difficulties. I agree that it would be hard to disentangle these factors, but a relatively simple control appears to be missing. Although unlikely, it is still possible that the "sweet spot" is driven partly by the amount of data rather than the phylogenetic scope. One simple control would be to sample 165 genomes from the broader fungal corpus and match the total number of training windows to the Saccharomycetales dataset.

(2) L148, "These results demonstrate that Shorkie LM captures conserved regulatory grammar." The evidence presented in this section is mainly about recovery of known fungal/yeast sequence motifs, which are more like words than grammar. The latter is usually understood as relationships between motifs such as their spacing, multiplicity, and arrangement. So I suggest the authors either tone down the claim or provide additional analyses that directly test this. One possibility would be to generate nucleotide dependency maps [3] for a handful of loci with well-characterized regulatory syntax.

(3) Figure 2E. The interpretation of t-SNE clusters might be confounded by the length of genomic elements. The five classes of elements shown in the plot have very different length distributions, but they are extracted and zero-padded to the same input length, and their embeddings are mean-pooled across the full padded sequence. As a result, the fraction of real sequence entering the mean embedding differs substantially across genomic element classes, which could contribute to observed separation independently of learned regulatory features.

(4) Lines 216-218, "transfer learning from pan-fungal self-supervised pretraining yields generalizable representations of exon-intron structure and regulatory grammar, substantially improving in expression prediction." Similar to point 2, I think this statement is somewhat stronger than what the current evidence supports. The results clearly show that pretraining improves downstream expression prediction, but it is less clear that this improvement can be specifically attributed to the transfer of regulatory grammar and gene structure, rather than motif representations or more general learned inductive biases acquired during pretraining. This is itself an interesting question. It might be possible to get at it by selectively reinitializing the convolution layers vs the transformer blocks while keeping the rest of the Shorkie LM weights, although this could still be hard to interpret if the relevant information is distributed across the model.

(5) The ISM for Figures 4 and 5 seems to correspond to the summed predicted coverage across all output bins (Methods, Equation 17). I was unclear about the intended goal here. For example, when mutating the ATG42 promoter in Figure 5, is the goal to quantify the predicted effect on ATG42 expression specifically? If so, should the ISM instead be computed by summing only the bins covering the target gene? Otherwise, predicted coverage from neighboring genes could also contribute to the ISM score and affect the comparison across induction time points.

References:

[1] Wang Y, Cai Z, Zeng Q, Gao Y, Ouyang J, Xu Y, et al. Genomic Touchstone: benchmarking genomic language models in the context of the central dogma. bioRxiv. Preprint posted online June 30, 2025. doi:10.1101/2025.06.25.661622

[2] Schneider T. ybench: a benchmark for fungal sequence-to-expression models. GitHub. Accessed August 18, 2026. https://github.com/Tom-Ellis-Lab/yeast-seq2expression-benchmark

[3] Tomaz da Silva P, Karollus A, Hingerl J, et al. Nucleotide dependency analysis of genomic language models detects functional elements. Nat Genet. 2025;57(10):2589-2602. doi:10.1038/s41588-025-02347-3

Reviewer #2 (Public review):

Summary:

The authors generated two novel aphid-symbiont associations and examined the impact of these new symbiotic associations on plant-insect-symbiont interactions. The authors notably provide detailed phenotypic assessments of the insect hosts and host plants. They show that one introduced symbiont, Rickettsiella, increases aphid-induced damage to host plants, while the other, Regiella, ameliorates aphid damage. The authors suggest that such novel insect-symbiont pairings may be used as tools to mitigate crop damage in the future.

Strengths:

Although a few experiments seem to have limited sample sizes and limited statistical power, these are often complemented with highly replicated smaller-scale experiments. The combination of larger mesocosm and population-level experiments along with assessments of individual insects generally provides a comprehensive depiction of the effects of these symbionts on their hosts. The opposing impacts of Regiella and Rickettsiella infection on the aphid host plant are of broad interest. It is also surprising that the host plants did not exhibit strong differences in canonical defensive signalling, despite these differences.

Weaknesses:

One thing that I struggled a little with was the rapid spread of Regiella in the shared plant experiments. Possibly this could be attributed to an increased reproductive output (due to faster developmental time, and/or an increase in fecundity) or efficient horizontal transmission. However, the other experiments performed indicate a slight negative impact (Figure 4a) or no influence (Figure 4C, 4D, Figure S6) of Regiella infection on host fitness. Given these other results, it seems that Regiella must spread fairly efficiently between hosts, which comes as a surprise, and there are very few examples of horizontal transmission of Regiella like this in the literature. The manuscript would benefit from a clear and direct demonstration of horizontal transmission, rather than it being inferred indirectly. The similar spread observed in the Rickettsiella mixed cages is less surprising, because there are several examples where this has been demonstrated.

It is also a little surprising that mesocosm-dispersal experiments were not also conducted using Regiella-infected lines. At several points throughout the manuscript, the idea of using symbiont transfections to reduce plant harm is raised. I can understand that these experiments are likely time-, space-, and resource-intensive, but that seems like these would have been relevant experiments, especially in the context of controlling damage to plants.

Reviewer #2 (Public review):

Summary:

The authors have carried out extensive transcriptomic, phenotypic and modelling-based analyses to provide novel insights into the interaction of the type I and II interferon programs in the determination of macrophage activation status and resistance to Mtb infection and infection-mediated damage. They demonstrate how an antagonistic effect between the two programs goes beyond classical downstream immune signalling pathways to lipid peroxidation maintained in a sustained autocrine manner and generation of a persistent pathological activation state (pPAS). Based on these analyses, the authors propose a therapeutic strategy of boosting specific pathways that increase oxidative stress resilience to reduce inflammatory pathology without suppressing host defenses for bacterial control and resisting pPAS. The conceptual framework may prove applicable to interferonopathies and to other bacterial and viral infections, though this remains to be tested.

Strengths:

(1) The study uses macrophages from a disease-relevant genetic murine model in which the sst1 locus drives the formation of necrotic pulmonary granulomas resembling human TB lesions- pathology not seen in standard C57BL/6 mice. This provides a genetically defined comparison between susceptible and resistant macrophages on an otherwise identical background, allowing the authors to attribute differences in activation state to a single locus rather than to strain-level variation.

(2) The experimental design isolates the phenomenon of interest: the TNF withdrawal and restimulation scheme allows the authors to establish that the aberrant activation state persists after removal of the initiating stimulus, rather than simply reflecting ongoing stimulation. The timed IFNAR blockade at 2, 12 and 24 h similarly separates initiation of the state from its maintenance.

(3) Lipid peroxidation is assessed through two orthogonal readouts: 4-HNE immunostaining for accumulated adducts and linoleamide alkyne click chemistry for ongoing synthesis. These, coupled with ROS and labile iron pool measurements, isotype antibody controls, parallel B6 and B6.Sst1S comparisons, and an anti-TNFR control, help in establishing that the phenotype is independent of continued TNF signalling. The convergence of these independent measures gives confidence in the peroxidation phenotype itself.

(4) The cSTAR analysis is applied here using regression rather than classification, generating a continuous DPD_TB score that correlates with measured Mtb fold change and thus provides a quantitative transcriptomic metric of macrophage priming state. Critically, the pathway predictions arising from this analysis (CDK4/6 inhibition and RAR activation) were tested and confirmed experimentally. The inferred network topology further predicted synergy between these two interventions, which bore out experimentally as an approximately ten-fold reduction in the effective dose of each agent in controlling Mtb during infection.

Weaknesses:

(1) Figure 2C is difficult to interpret as presented. The row labels ("No TNF"/"TNF") use different terminology from the corresponding conditions in panel A ("TNF withdrawal"/"TNF restimulated"), and "TNF" appears on both axes referring to different phases of the experiment; no timepoint is given on the panel itself, unlike neighbouring panels. Harmonising the labels with panel A and stating the harvest timepoint would help the reader. More substantively, the remaining lipid peroxidation readouts in this figure (panels D-G) are all at early timepoints of TNF stimulation (2-24 h) rather than during withdrawal, which limits what they can say about sustained autocrine signalling. Extending these assays to the later timepoints used in Figure 1 would considerably strengthen the claim that IFN-I maintains, rather than only initiates, the pathological state. The same applies to Figure 2G, where the contribution of itaconate to 4-HNE accumulation over time is not yet resolved.

(2) Several of the pathways implicated here are reported to behave differently between murine and human macrophages, and between macrophage subsets (alveolar versus monocyte-derived macrophages), during Mtb infection. This does not diminish the findings in this model, but it does bear on how broadly they can be generalised.

a) Type I interferon responses differ by species and by macrophage subset across multiple reports. Since the proposed model depends on autocrine IFN-I signalling reaching a threshold sufficient to sustain the pathological state, these differences in IFN-I output are worth keeping in mind when interpreting the wider significance of the findings.

b) Similarly, itaconate production in murine BMDMs is 20-fold higher than in LPS-activated human monocyte-derived macrophages and 50-fold higher than in LPS-activated alveolar macrophage-like cells, and Mtb infection of these human macrophages very weakly induces ACOD1 with almost no detectable itaconate (PMID 41797714). This is relevant to the Acod1/4-OI arm of the mechanism.

c) In a cross-species comparison of Mtb-infected macrophages, cholesterol homeostasis genes (including HMGCS1, IDI1, LSS) were significantly upregulated in human alveolar macrophages but downregulated in subcutaneous BCG-exposed murine alveolar macrophages (PMID 41208107)- the opposite direction to the lipid biosynthesis suppression treated here as a defining pPAS feature. The same group reports that murine AMs lack c-Maf and IL-10 whereas murine BMDMs express both (PMID 40073087), indicating that the autocrine anti-inflammatory brake on IFN-I responses is itself subset-dependent.

d) Finally, the cSTAR network predictions were inferred from human THP-1 perturbation data, but tested only in murine BMDMs. Establishing how this circuit operates in human macrophages, and during Mtb infection rather than TNF stimulation alone, would be a valuable extension of the work.

(3) The causal relationships linking IFN-I, lipid peroxidation, ROS and loss of IFNγ responsiveness could be drawn together more clearly. These elements are each established, but the connections between them are not always demonstrated directly. IFN-I appears to promote peroxidation through Acod1/itaconate and suppression of lipid biosynthesis rather than through iron, since neither IFNβ nor IFNAR blockade alters the labile iron pool. This would suggest two separable inputs to 4-HNE rather than a single pathway. This raises a further question about the persistent state itself: the labile iron pool rise appears to be TNF-driven, yet all labile iron measurements are made at 24 h in the continued presence of TNF and none under the withdrawal condition, so it is unclear whether elevated catalytic iron is sustained once the initiating stimulus is removed. Similarly, while IFNAR blockade reduces peroxidation, the reciprocal arm is not tested. An antioxidant or iron chelator could be used to ask whether peroxidation in turn drives Ifnb1 super-induction. In the absence of this information, the proposed feedback loop remains partly inferred. It would considerably strengthen the manuscript if the authors could clarify, through additional experiments or in the text, how the labile iron pool and lipid peroxidation relate to one another and what sustains each of them after TNF withdrawal.

(4) Reading across the manuscript, the labile iron pool emerges as the variable most consistently associated with the phenotype. Every protective intervention tested converges on it. By contrast, the alternative candidate mechanisms do not track with outcome. Lipid biosynthesis genes are suppressed by IFNγ yet induced by both trilaciclib and ATRA, all three of which are protective. GPX4 is unchanged under IFNγ and trilaciclib. The Acod1/itaconate axis cannot account for it since IFNγ priming blocks 4-HNE accumulation induced by exogenous itaconate. Yet, a direct causal role for iron is never tested. Additionally, IFNβ induces 4-HNE with the labile iron pool entirely unchanged, indicating at least one route to lipid peroxidation that bypasses catalytic iron. Focusing on iron handling would make the manuscript's message more coherent and its therapeutic argument more compelling.

Reviewer #2 (Public review):

Summary:

This study evaluates whether peripheral blood transcriptomic profiles can be used to classify cattle infected with Mycobacterium bovis using a range of machine-learning approaches. By integrating RNA-seq datasets from naturally and experimentally infected animals, the authors develop and test predictive models capable of distinguishing infected from uninfected cattle and assess their ability to differentiate bovine tuberculosis from other infectious diseases. The study addresses an important challenge in bovine tuberculosis control and presents evidence that host transcriptional signatures may have utility as an adjunct diagnostic approach.

Strengths:

- The study combines data from multiple independent cohorts, including both naturally and experimentally infected cattle, which increases the biological relevance of the findings.

- The analytical workflow is comprehensive, scientifically sound and clearly described. Multiple machine-learning approaches are evaluated and compared rather than relying on a single modelling strategy.

- The inclusion of a held-out test set, especially because such data is limited, provides a useful assessment of model performance beyond cross-validation alone.<br /> - Thorough evaluation against datasets from cattle infected with MAP, BoHV-1 and BRSV is a valuable addition and provides useful information regarding the specificity of the identified transcriptional signatures.

- The authors acknowledge important limitations, including batch effects and the need for additional validation.

- All underlying data and code are made publicly available

Weaknesses:

- My main concern relates to generalisability. Although a separate testing dataset was used, the training and testing datasets were generated through random partitioning of samples from the same underlying studies. As a result, classifier performance in a completely independent external cohort remains unclear. Discussion of this limitation, and whether alternative validation strategies such as leave-one-study-out analyses were considered, would strengthen the manuscript.

- The authors identify substantial study-specific batch effects following dataset integration and appropriately account for these in the modelling framework. However, given the magnitude of the reported batch structure, additional discussion regarding the potential influence of residual between-study variation on classifier performance would be helpful.

- The manuscript is framed in the context of global bovine tuberculosis control, yet the practical implementation of a transcriptomic diagnostic approach is not discussed in great detail. Since bovine tuberculosis remains a significant challenge in many low- and middle-income settings, further consideration of the feasibility, cost, infrastructure requirements, and potential translation of these signatures into more deployable diagnostic platforms would improve the broader relevance of the study.

- The datasets used for classifier development are derived primarily from Ireland, the UK and the United States. It would be useful to discuss whether differences in circulating M. bovis lineages, cattle populations, management systems, or co-infection pressures could influence host transcriptional responses and therefore the performance of the proposed classifiers in other epidemiological settings. This ties to the previous comment, since epidemiological settings in LMIC countries with a high burden of M.bovis disease would be vastly different from where the data was sourced.

Reviewer #2 (Public review):

Summary

Boolean network modeling is more commonly used in cancer and developmental biology than in vaccine research. Deman et al. apply this framework to a practical vaccinology problem: they aimed to build a mechanistic, executable computational framework capable of both explaining and predicting how the early innate immune response to the MVA vaccine changes when specific viral genes are altered, with the longer-term goal of using that framework to guide the rational design of improved MVA-based vaccines. The authors aimed to: (i) construct and calibrate a Boolean network model of the MVA-induced immune response against real longitudinal non-human primate (NHP) data; (ii) test whether the calibrated model, without being fit to this new data, could reproduce the outcomes of several previously published MVA gene-deletion mutants; and (iii) compare this MVA model to an analogous model of the well-established YF-17D yellow fever vaccine, in the hope of identifying specific molecular targets that could reorient the MVA response toward the durable, single-dose protection YF-17D is known to provide.

In pursuit of these aims, the authors construct an executable Boolean network of the innate immune response to the MVA vaccine by merging three KEGG pathways (cytosolic DNA-sensing, apoptosis, NF-κB signaling) with cell-population data, and calibrate it against a small NHP dataset (n=3 macaques, 7 time points; Rosenbaum et al., ref. 47). They show the calibrated network reproduces 86-87% of the observed binarized states, and that forcing the network to mimic known MVA deletion mutants (e.g., the triple mutant ΔC6L/ΔK7R/ΔA46R) reproduces qualitative features (e.g., early IFN-β, TNF-α, and IL-6 upregulation) reported in independent published mouse and cell-line studies. They then build a second, six-pathway "consensus" network shared between MVA and YF-17D, compare the two networks' topology and dynamics, and use this comparison, together with a graph-theoretic search for "highly effective" signaling paths, to propose two previously untested MVA deletion mutants (ΔK7R and ΔF17R) predicted to shift the MVA response toward more YF-17D-like features.

Strengths

The overall workflow (Figure 1) is clearly described. The calibration approach-binarizing longitudinal cellular and transcriptomic data and fitting network trajectories with the ZhegAlCal algorithm (a Zhegalkin-polynomial/SAT-solving-based method for fitting Boolean trajectories to binarized time-series data)-appears to be a defensible, well-reasoned way to translate a literature-derived network into real kinetic data.

The retrospective validation against independent published MVA mutants (deletions in C6L, K7R, A46R, and N2L, and separately an A21L point-mutant with three alanine substitutions rather than a deletion) is a genuine strength: the model's qualitative behavior (upregulation of IFN-β, TNF-α, IL-6; limited change in RIG-I) aligns with what those studies reported. We also appreciated that the authors report instances of partial disagreement alongside their successes (e.g., CCL5/RANTES) - that kind of candor about where the model doesn't quite line up is exactly what gives the parts that do line up more credibility.

The static topological analysis - hub identification, "determinative power" and "vertex betweenness" (two complementary measures of how much a node's state constrains, or lies on paths between, the rest of the network), and "effective graphs" (a measure of how deterministically an edge's regulator sets its target's state) - is a thoughtful use of graph theory to complement the dynamic simulations.

The authors also clearly discuss the Boolean formalism's core approximation, i.e., that binary on/off states can dilute real but subtle quantitative differences (lines 679-688), and that this work was based on modeling blood-only responses rather than those responses that occur at the vaccination site or draining lymph nodes (lines 709-714).

Weaknesses

A few things gave us pause as we read, which we raise here in the spirit of strengthening what already strikes us as a promising framework.

The MVA/YF-17D comparison starts from two vaccines that are already known to differ substantially. The manuscript uses the divergence between the MVA and YF-17D Boolean networks as an entry point for identifying "MVA optimization" opportunities, but MVA and YF-17D are, on their face, very different vaccine platforms. MVA is a non/limited-replicating DNA poxvirus vector, sensed mainly through cytosolic DNA/cGAS-STING pathways, dosed intradermally, and typically requiring two doses for optimal protection. YF-17D, by contrast, is a live, replicating, attenuated RNA flavivirus, sensed through multiple TLR/RIG-I pathways, and given as a single subcutaneous dose that confers durable, often lifelong, protection (see refs 21, 31, 38-44 in the manuscript). Given this, it's not surprising that the authors themselves report "almost opposite behaviors" for several core cell populations - classical monocytes, B cells, NK cells, and CD4/CD8 T cells - between the two calibrated networks (lines 647-657).

This stated motivation made us question how much of that divergence reflects a real, actionable difference in vaccine-induced immune programming (the paper's implicit premise) versus differences in virus biology, dosing route, or study/technical design (different sampling schedules, microarray vs. RNA-seq, n=3 vs. n=12 animals) that a Boolean network comparison can't easily tease apart. To their credit, the authors' Discussion is candid on this point-stating directly that "no clear optimization strategies for the MVA viral vector came to mind from the comparison" (lines 664-666)-a useful signal that the comparison's direct yield was limited. The two candidate mutants that emerged instead came from intersecting the model's high-impact nodes with a pre-existing, literature-curated list of MVA immunomodulatory genes (yielding five candidate deletions), which were then individually simulated and narrowed down to the two, ΔK7R and ΔF17R, that produced notable changes - not from the MVA/YF-17D comparison alone.

The motivation for choosing Boolean modeling over ODE/PDE approaches could be clearer. The choice is motivated mainly by precedent - the authors note it "has rarely been used to model vaccine-induced immune responses, in contrast to statistical modeling or ordinary differential equation (ODE)-based modeling" (lines 100-105) - and by practical considerations, such as not requiring kinetic parameters and being tractable at the scale of networks with hundreds of nodes. What we found ourselves wanting was a more explicit account of the trade-off: ODE and PDE models already simplify the true, spatially resolved, continuously varying underlying biology, and Boolean modeling is a further simplification on top of that. A clearer statement of why this additional simplification is acceptable, or even preferable, for this application (for example: the scale of the curated network, the absence of measured rate constants for most edges, or the interpretability of discrete states) would greatly improve this work.

We found the manuscript dense and long relative to the size of its central, generalizable findings. This is a presentation issue rather than an evidentiary one. The Results section narrates GO-enrichment interpretation update-by-update for four separate network trajectories (unperturbed MVA, perturbed MVA mutants, the MVA arm of the consensus network, and YF-17D), much of which restates what the (extensive) supplementary figures already show.

The paper's only prospective predictions are experimentally untested. The two new mutants proposed at the end of the paper (ΔK7R, ΔF17R) are in silico predictions only; they haven't been constructed or tested experimentally in this study. We read the title's claim of a "predictive" framework and the paper's "rational design" framing as best describing a hypothesis-generation tool validated by retrodiction of previously published phenotypes, rather than a demonstration that these two newly proposed mutants will behave as predicted in vivo.

Did the authors achieve their aims, and do the results support their conclusions?

Looking at each aim in turn, the picture that emerges is mixed. The first aim - building and calibrating a Boolean network model of the MVA-induced immune response - is convincingly achieved: the calibrated network fits the underlying data well (86-98% of binarized states correctly reproduced, depending on which of the two networks is considered), and its successive states correspond to biologically sensible processes (early chemotaxis, then T-cell activation, then antiviral/ROS-related signatures) that track what is independently known about the innate response to poxvirus vaccination. The second aim-showing the calibrated model can reproduce, without being fit to it, the outcomes of previously published MVA mutants-is also substantially achieved, with the caveat noted above that agreement is qualitative and directional rather than exact, and not uniform across every marker tested.

The third aim is where the results, in our reading, support the paper's conclusions least well. The stated purpose of comparing the MVA and YF-17D networks was to identify actionable strategies for reorienting MVA's response, but the authors themselves report that the comparison alone yielded no clear optimization strategy (lines 664-666); as noted above, the two candidates that are ultimately proposed came instead from that separate gene-list intersection and simulation step - not from the YF-17D comparison in the more direct way the framing implies. Given that, the paper's headline conclusion - that this framework "enables rational optimization of MVA-based vaccines" - reads to us as only partially supported by what is actually shown: the framework is well demonstrated as a tool for capturing and reproducing known immune biology, but its capacity to prospectively guide the design of a better vaccine remains, at this point, an untested hypothesis rather than a demonstrated result.

Likely impact and utility to the community:

The most durable contribution of this paper, regardless of how the two specific candidate mutants eventually fare in the laboratory, strikes us as methodological: the explicit workflow for merging curated signaling pathways into a large executable Boolean network and calibrating it against longitudinal experimental data (building on the authors' own previously published calibration method, reference 75) is clearly described and, together with the deposition of the calibrated networks on the public CellCollective platform, should be usable by other groups working on other vaccines or viral vectors. That reusability is a genuine and useful contribution to the systems-vaccinology toolkit, independent of whether MVA specifically benefits from it.

Its more immediate, practical utility is harder to gauge from the paper alone. For vaccine developers specifically interested in MVA, the value of this work currently lies in the two testable hypotheses it generates (ΔK7R, ΔF17R) rather than in validated design guidance, since neither candidate has been built or tested here. It's also worth flagging that the framework's generalizability beyond MVA and poxviruses is untested within this paper - the approach is demonstrated for one vector and one comparator vaccine, so readers working on other vaccine platforms may want to treat it as a promising template to adapt and validate for their own systems, rather than as a result that has already been shown to transfer.

Reviewer #2 (Public review):

Summary:

The study uncovers novel factors driving proliferation in the greater epithelial ridge (GER), a proliferative tissue in the neonatal cochlea that may hold important clues on the quest for hair cell regeneration via proliferative means in the adult cochlea.

Strengths:

The strengths include the use of both cochlear organoids and in vivo mouse models combined with pharmacological and genetic approaches to inhibit or overexpress proliferative targets identified in the RNA-Seq analysis from the FACS-sorted GER cells. The genetic and pharmacologic manipulation experiments are very strong and convincingly demonstrate that galectins 1 and 4 and Myc are necessary (and in some cases sufficient) to drive cell proliferation in cochlear tissue. However, the real treasure trove is the carefully generated RNA-Seq dataset itself, which offers a wealth of additional differentially-expressed genes that likely contribute to cochlear cell proliferation.

Weaknesses:

The primary weakness is that the initial genes studied here, galectins 1 and 3 and Myc, are all associated with tumor formation or cancer progression, so targeting these genes raises concerns about tumor formation in the cochlea.

Reviewer #2 (Public review):

Summary:

Despite their common co-occurrence, depression and anxiety are known to alter mood fluctuations in opposite ways. Here, the authors aimed at distinguishing depression-specific from anxiety-specific from psychopathology-general effects of reward processing on mood fluctuations, focusing on reward prediction errors (RPE) which are known to be linked to mood fluctuations. This mechanistic study aims at uncovering the process through which these psychopathologies are associated with mood modulations. The authors were able to appropriately test their hypothesis and obtained results corroborating their conclusions.

This work provides a convincing demonstration of the relevance of computational psychiatry (Huys et al, 2016) and the use of decision neuroscience to shed light on the interplay of anxiety and depression and mood.

Comments on revised version.

(1) Methodological & Theoretical Framework: The authors used a tripartite model to effectively distinguish depression vs anxiety dimensions from broad psychopathology/distress.

(2) Possible theoretical confounds: This manuscript addressed adequately the concerns one would have regarding risk-attitudes.

(3) Computational Rigor: The computational model elegantly separates reward expectations (EV in the model) from outcome processing through RPE, which are two sequential cognitive processes, providing a fine-grained mechanistic account of mood fluctuations.

(4) Clear & Logical Results Structure: In response to feedback provided during the previous round of review (previously cited as a recommendation for authors), the authors re-organized the Results section into three distinct, easy-to-navigate subsections (Depression, Anxiety, and Depression vs. Anxiety), which substantially improves readability and clarity.

(5) Neurobiological Context: The Discussion has been enriched with a well-integrated overview of the neural circuits (striatal-midbrain dopaminergic, vmPFC, OFC, and anterior insula) likely underpinning RPE-driven mood updates, which is sure to improve the translational interest of this work.

(6) Transparent Reporting: The authors had already provided a trustworthy writing approach when referring to trending statistical results. In this revised manuscript, they have been exceptionally transparent regarding study limitations, data collection timelines (addressing potential AI-related artifacts), and statistical power constraints.

Status of Previous Weaknesses & Suggested Revisions

(1) Clinical Sample Size and Anxiety-Specific Effects

Previous Concern: The sample size of the clinical sample (N=116) may not be sufficient to detect anxiety-specific effects due to the high rate of comorbid anxious depression. It would be beneficial to include the number of MDD vs GAD vs anxious depression diagnoses in the clinical population as this would be likely to shine light on the power limitations.

• Author Revision: The authors have fully addressed this point by adding a diagnostic breakdown in Table S8 which details the diagnosis, illness duration, and medication status. They also included details of the power analysis (Discussion, pages 17-18) putting into perspective their results and provided two possible literature-informed interpretations of their findings. This is also reflected in their updated Abstract.

(2) Re-organization of Results

Previous Suggestion: The results sections 2 (depression) and 3 (anxiety) could be improved by reducing the back and forth between factors throughout the results. It may be useful to split them into 3 sections: depression only, anxiety only, depression vs anxiety.

• Author Revision: The Results section was restructured as suggested, cleanly isolating depression-specific associations, anxiety-specific associations, and direct statistical comparisons between the two ("differential associations" in the manuscript).

(3) Neurocircuitry Discussion

Previous Suggestion: In the discussion, authors could have mentioned the brain areas most likely to be involved in these processes, both cognitive and psychopathological, as previous studies (such as Cecchi et al, 2022) have aimed at identifying regions involved in RPE processing while modulating mood in health. A short section on this would be useful to the neuropsychiatric community.

• Author Revision: A concise section was added to the Discussion mapping computational parameters onto striatal, prefrontal, and insular circuitry (see Strengths #5).

Conclusion:

The authors have satisfactorily resolved all minor-to-moderate issues raised in the previous review.

Reviewer #2 (Public review):

This manuscript investigates the role of SOX17 in the formation and function of the Sertoli valve (SV) at the interface between seminiferous tubules and the rete testis (RT). Building on previous work showing that rete testis-specific deletion of Sox17 disrupts SV formation, leading to defective spermiogenesis and male infertility, the authors explore how SOX17 overexpression in Sertoli cells regulate SV of rodent testes.

Using transgenic mouse models with ectopic Sox17 expression in Sertoli cells, the study demonstrates that SOX17 is not only required but can also modulate SV formation. Ectopic expression in Sertoli cells induces expansion of the SV structure and partially rescues SV defects and spermatogenesis in RT-specific Sox17 conditional knockout animals. The data support a model in which SOX17 acts through paracrine signaling to regulate SV formation, although the precise mechanisms remain to be clarified.

Overall, this is a well-executed study with novel and significant findings. The ability to experimentally manipulate SV size is particularly compelling and provides a valuable framework to study fluid dynamics and epithelial interactions in the testis. This work will be of broad interest to the reproductive biology and developmental biology communities.

What does each stakeholder want museum exhibitions to achieve? (table) Why might government agencies place greater emphasis on public access?

How many exhibitions each type of funder sponsored per year? (graph)

Individual sponsors dropped just as government sponsors skyrocketed. How does the graph demonstrate the shift from individual patronage to institutional funding?

Which type of funder became more influential over time?

Why is the data presented as an average per museum rather than as a total?

How many exhibitions were held and how many received funding? (graph)

What does the number of funded exhibitions suggest about museums’ dependence on outside sponsors?

What does the graph reveal about the proportion of funded and unfunded exhibitions?

What tension exists within government arts funding?

How do wealthy individual patrons differ from institutional funders?

What is the central problem created by new funding sources?

What kinds of exhibitions do corporations tend to support?

What kinds of exhibitions do corporations tend to support?

What is the main motivation behind corporate arts funding?

What major change occurred in corporate arts sponsorship?

What were the main purposes of the NEA and NEH?

What was the main role of wealthy individual philanthropists in early museums?

Why did federal arts funding become important?

How did early American museums differ from European museums?

What historical shift changed museum patronage in the United States?

Reviewer #2 (Public review):

This study by Masser et al. analyzes global replication timing and gene expression in rif-1 null zebrafish. This work is an extension of their previous report of the normal replication timing pattern during wild-type zebrafish development. The major valuable finding here is that Rif1 is not essential for viability in zebrafish, and - counter to expectation from studies in cultured cells and other species - late replication does not strongly depend on Rif1. Instead, the data suggest that Rif1 subtly sharpens replication timing pattern during normal development rather than function generally to delay replication timing. In the absence of Rif1, the normal pattern establishment is somewhat delayed. The authors also document some changes in expression during development with more genes being repressed by Rif1 than activated at some early stages.

The study and analysis are generally rigorous, and the conclusions are supported by convincing data. Given the strong link between replication timing and cell type/development, studying timing in a whole developing organism is important. The experimental approach is technically challenging, particularly the bioinformatic analysis. The scientific advance here is largely confined to documenting the timing of Rif1-affected transcription, the unanticipated effect of the rif1 deletion on replication timing and on sex determination, though the latter is not explored. The difference in timing of the transcription phenotypes and replication phenotypes suggests they may be very distinct Rif1 roles. The overall study a useful set of findings and detailed data for future work.

Loss of Rif1 did not affect viability, but it did strongly influence sex determination, resulting in a lower population of females. This effect is the strongest organismal phenotype, but the study provides no mechanistic explanation for the loss of females from the data gathered here.

Comments on revised version:

We are generally satisfied with the revised version of this manuscript.

Reviewer #2 (Public review):

Summary:

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

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

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

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

Strengths:

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

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

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

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

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

Weaknesses:

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

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

Comments on revision:

The authors addressed the concern related to the use of antibodies and secondary antibodies by performing DNA-PAINT experiment, which revealed highly similar results as obtained with conventional antibodies.

Reviewer #2 (Public review):

Summary:

This manuscript presents a sophisticated investigation into the mechanisms by which different inhibitor classes affect the SARS-CoV-2 main protease (Mpro), a pivotal antiviral drug target. This study reveals that effective inhibition can be achieved by modulating the stabilization of the essential dimeric state. It also indicates the dimer interface could be a druggable allosteric site, which may offer a strategy for developing broad-spectrum anticoronaviral agents.

Strengths:

The identification of dimer interface stabilization/destabilization as distinct inhibitory mechanisms and the discovery of C300 as a potential allosteric site for ebselen are important contributions to the field. The experimental approach is modern, multi-faceted, and generally well-executed.

Comments on revised version:

The authors have very nicely addressed most of the previous comments raised. But one comment remains to be clarified relating to original point 5 and the authors' response:

"We agree with the reviewer about the need for quantitative rigor in reporting HDX changes. We have calculated the fractional deuterium uptake difference for each peptide fragment discussed in the text between the inhibitor-bound and unbound states. These values, along with their statistical significance (p-values from a two-tailed t-test), have been provided in the revised manuscript (Legends for Figures 3 and 4). Although the HDX change of residues 296-306 is relatively small (<5%), this region showed a reproducible difference with low experimental variability and statistical significance (p < 0.05). Given its location within the C-terminal dimerization interface and its consistency with native MS, we interpret this change as a subtle local conformational perturbation."

Two questions remain for the statements in line 376-380. First, while it is stated "residues 296-304 in the C-terminal region of Mpro were more flexible upon ebselen binding", the segment of 296-306 is shown Figure 4c. Second, the HDX change for this segment upon ebselen binding is very subtle in the figure (in contrast to the significant HDX change of the same segment in the protein upon PF-07321332 binding), thus making the strong conclusion that "This suggests that ebselen targeting C300 may induce structural changes in the C-terminal helical segment, weakening key hydrogen bonds at the dimer interface and ultimately inhibiting activity" not convincing. The reviewer would suggest the authors either delete this conclusion or largely tone it down.

Reviewer #2 (Public review):

Summary:

Cerebrospinal fluid contacting neurons (CSF-cNs) are GABAergic cells surrounding the spinal cord central canal (CC). In mammals, their soma lies sub-ependymally, with a dendritic-like apical extension (AP) terminating as a bulb inside the CC.

How this anatomy-soma and AP in distinct extracellular environments-relates to their multimodal CSF-sensing function remains unclear.

The authors confirm in the GATA3:GFP mice where these cells are labeled that CSFcNs exhibit prominent spontaneous electrical activity mediated by PKD2L1 (TRPP2) channels, non-selective cation channels with ~200 pS conductance modulated by protons and mechanical forces.

They investigated PKD2L1 pH sensitivity and its effects on CSFcN excitability. They uncovered that PKD2L1 generates both phasic and tonic currents, bidirectionally modulated by pH with high sensitivity near physiological values.

Combining electrophysiology (intact and isolated AP recordings) with elegant laser-photolysis, they show functional PKD2L1 channels localize specifically to the apical extension (AP).

This spatial segregation, coupled with PKD2L1's biophysical properties (high conductance, pH sensitivity) and the AP's unique features (very high input resistance), renders CSFcN excitability highly sensitive to PKD2L1 modulation. Their findings reveal how the AP's properties are optimised for its sensory role.

Strengths:

This is a very convincing demonstration using elegant and challenging approaches (uncaging, outside out patch of the AP) together to form a complete understanding on how these sensory cells can detect so finely the changes of pH in the CSF.

Reviewer #3 (Public review):

In this study, Jones et al. examine how neural activity in auditory regions (the auditory pallium) of singing male songbirds is modulated by the presence or absence of an audience (a female conspecific). They test whether activity in auditory pallium differs between conditions in which the male is singing to a female (directed song) or alone (undirected song) and whether response to distortions of auditory feedback (DAF) differ between these conditions. Previous work has shown that in other parts of the songbird brain, sensory-motor activity can differ between directed and undirected song, and that responses to DAF are attenuated when males sing directed song versus undirected song. These prior results raise the interesting question of the extent to which such modulations of activity by the presence of an audience are already present in primarily auditory areas within the pallium. This possibility is also motivated by prior work that has shown that activity in the auditory pallium is not exclusively explained by auditory input, but can also be modulated by the bird's state - whether it is singing or not.

Against this background, the questions asked here are of interest for two inter-related reasons:

(1) The authors address whether the presence of an audience (a female conspecific) alters activity in an auditory region during singing. Primary songbird auditory areas such as Field L, and analogous mammalian thalamo-recipient cortical regions such as A1, are often thought of as responding very specifically to the features of sensory stimuli, but are also understood to be modulated by a variety of factors including the attentional and behavioral state of the animal. For audition, such modulation includes whether or not animals are vocalizing and listening to themselves or listening to playback of their own vocalizations. Cited works from Keller (2009) as well as Eliades and Wang (2008) have indicated that the act of vocalizing can modulate auditory responses to self-generated feedback in primary auditory areas relative to those arising from playback of the same sounds. Here, the question is whether responses to self-generated feedback differ between conditions of singing alone versus singing to a female audience. A demonstration that the presence of an audience matters to responses in auditory pallium would add to a general understanding of how it is that non-auditory factors can modulate activity within regions that are considered primarily sensory.

(2) The authors address the possible source of an audience-dependent modulation of responses to feedback perturbation in the VTA previously reported by Goldberg and colleagues (2023). In the VTA, responses to perturbations during singing are consistently attenuated when males are singing to females versus when they are singing alone, but the underlying mechanisms of this modulation are unknown. Here, the authors test the possibility that such modulation by an audience is already present at the level of auditory pallium. The previously reported attenuation in VTA is a nice example of how neural processing can differ with varying behavioral priorities. Understanding whether this modulation of responses to DAF arises already in auditory areas would further a mechanistic understanding of an intriguing example of state-dependent modulation of sensory processing and behavior and lend broad insight into related phenomena.

The authors report 1) that activity in the auditory pallium differs between directed and undirected singing at many individual recording sites, but that these changes are heterogeneous, with both increases and decreases in activity, so that there is no consistent change across the population and 2) that modulation of activity by DAF can differ between directed and undirected song, but that there is no consistent attenuation of response (as observed in the VTA) and instead heterogeneous increases and decreases in response to DAF so that there is no net change at the population level.

These findings are important and of general interest; while they do not readily explain the source of the audience-dependent attenuation of auditory responses to DAF in the VTA, the demonstration of audience-dependent modulation of self-generated feedback and its disruption in the auditory pallium provides an opportunity for further investigation of how changes in social context influence brain and behavior.

Additional comments and suggestions:

The authors have done a good job of addressing many of the issues that were raised in the initial round of reviews. There is additional analysis that strengthens the study, including 1) applying a stability criterion to assess the quality of unit isolation, 2) shifting away from a categorical identification of units as "retuning" or not, to an analysis that presents a continuum of changes to neural firing between conditions, 3) use of non-parametric statistics for the assessment of significance of differences in response measures between conditions and 4) exclusion from analysis data from motifs during which females were observed to be vocalizing.

The authors also have added to the text several important clarifications, and modified language in several ways that improve the presentation and interpretation of results. This includes 1) noting that differences between neural activity during singing with and without DAF does not necessarily reflect "error detection" but could instead reflect how neurons with fixed auditory receptive fields might respond differently to the distinct auditory inputs present between these conditions, 2) clarifying that the recordings were not specifically restricted to Field L, but were distributed more broadly across the auditory pallium, and 3) discussing some of the mechanisms whereby tuning might change due to various sensory, motor and internal factors associated with differences between singing alone and singing to a female.

I only have a couple of areas of remaining concern that I think could be addressed with further analysis, or some additional discussion, according to the authors' preferences.

(1) Stationarity of neural response

My main residual concern relates to the issue raised in the previous round of review of how much of the observed difference in activity between morning sessions when the male is alone and later sessions when the male is singing to a female could reflect changes in neural response properties (non-stationarity) due to the passage of time (sometimes at least several hours) rather than specifically due to the presence or absence of an audience.<br /> The authors restriction of data to recordings that passed a stability criterion for unit waveforms is helpful in addressing whether the same units are 'held' over the course of the experiment. However, even with well isolated units, the response properties or tuning of units can change over time due to a variety of factors that include changes in internal state, circuit excitability, up and down states, neural plasticity, etc.

The previous review noted several examples of data from the manuscript that illustrated this concern - instances where response properties of neurons appeared to change over time within a given condition. Any such changes in response properties that occur in the absence of a change in audience would tend to contribute to the reported "retuning" of responses.

One thing that the authors could do to address this issue would be to discuss potential contributions of non-stationarity of responses over time as a potential confounding variable and then editorialize about why they think this seems unlikely to explain many cases in which response properties change between conditions. See comments to authors for one specific suggestions along these lines.

Alternatively, the authors could carry out additional analyses to evaluate this issue more quantitatively. For example, by measuring the magnitude of "spontaneous" changes in responsiveness observed within conditions (such as by comparing the motif aligned activity for the first n examples within a condition against the activity during the last n examples) and comparing that with the magnitude of changes observed across conditions.

Another approach would be to carry out some sort of "change point analysis" on the motif-related activity for each experiment in order to establish how often the most abrupt changes in activity occur at the transition between conditions versus spontaneously at other times.

Lastly, while the experimental design didn't specifically include interleaved blocks of undirected (alone) singing and female directed singing, the methods indicate that the female directed singing data were collected by repeatedly introducing females for 10 minutes at a time. If there are even a couple of cases where the males produced song alone in the periods between female presentation, it would be worth testing whether modulation of neural firing tracked these interleaved conditions.

Previously published work such as the interleaved recordings of Hessler and Doupe indicate a close and reversible tracking between modulation of neural activity in sensorimotor song system nuclei and switches between singing alone and singing to a female. With respect to the possibility raised in the rebuttal of whether males continue to sing 'female directed song' even after the removal of a female, these and other published data suggest that this is not likely to be the case. But if this were a concern in interpreting any data, the authors could directly assess the male's song for previously described changes in acoustic variability that also track changes in the presence of an audience.

(2) Further discussion of how an audience might influence responses.

With respect to the mechanisms whereby an audience might modulate neural responses, a somewhat expanded discussion of possibilities with reference to relevant literature would be helpful. This could include reference to evidence for various neuromodulatory systems participating in modulating singing related activity in song system nuclei based on presence or absence of a female - do these neuromodulatory systems project to the relevant regions of the auditory pallium or its lower-level inputs within the ascending auditory pathway such that they could concurrently act on auditory circuitry?

In addition to possibility that the presence or absence of an audience affects auditory circuitry via a change in attention, alertness, or motivation, might efference signals associated with singing or locomotion/dancing reach and influence auditory pathways? Given that premotor activity and acoustic structure of song differ between conditions, might any singing-related efference copy activity that reached auditory regions also differ between conditions? A related interesting possibility that could be worth noting is that the presence of a female generally elicits increased locomotion and dancing on the part of the male that accompanies female directed song. Several studies have noted that general forms of locomotion can also result in efference copy signals reaching and influencing auditory regions (e.g. see Schneider and Mooney, Annual Review, 2018; Han et al. "Locomotion-induced neural activity independent of auditory feedback in the mouse inferior colliculus" iScience 2026 - the latter reference is interesting in that it appears to indicate bi-directional modulation of neural activity as observed across units in the current study).

Minor:

(1) The authors describe some units as showing "Activation by the absence of DAF." Because the birds in the study have extensive experience with DAF on a subset of trials, it is possible that the increased responses in the absence of DAF reflect a positive deviation from expectation of distortion (as seems to be the case for VTA neurons in previous work). But it is also possible that the broadband DAF stimulus drives inhibition of auditory responses in some cases, and the greater responses in the interleaved trials with normal feedback simply reflect the absence of that inhibition (rather than a positive deviation from a learned expectation). In keeping with the authors shift away from the use of "error detection" elsewhere in the manuscript, it might also be good to use less interpretive language here instead of "activation by absence of DAF".

(2) At the authors discretion, it would be interesting to know if there is any relationship between the way in which changes in audience affect activity with normal auditory feedback versus with DAF. For example, if normal singing responses are attenuated in the female directed condition, are the responses to DAF also attenuated?

(3) In figure 2, the vertical dashed lines associated with the rasters indicate the onset and offset of motifs. For several of the figure panels, the spectrograms show motifs that are not aligned with these onsets and offsets. Please clarify or modify (are the rasters from time-warped data but the spectrograms are not -time warped?).

(4) The authors equate peaks in activity before the onsets of motifs with premotor activity: ["A previous study recording from Field L in zebra finches reported neural activations prior to the onset of singing, consistent with premotor signaling (Keller and Hahnloser, 2009). We tested for context dependent changes in premotor activity by examining peaks in neural activity aligned to motif onsets. Across the population neurons did not exhibit significant changes in the timing of motif onset-aligned activity (Figure S4)."]<br /> However, the spectrograms as shown in Figures 1 and 2 indicate that each motif is often preceded immediately by other song syllables such as introductory notes or syllables from the end of the preceding motif. Further analysis would be required in the current study to demonstrate that the activity present before motif onsets reflects premotor activity rather than auditory responses to the proceeding syllables. Please soften the claim that this reflects premotor activity or provide additional analysis or argument.

Reviewer #2 (Public review):

Summary:

The fascinating topic of the host range of arthropods, including insects, and the detoxification of host secondary metabolites has been elucidated through studies of the host specificity of two closely related species. The discovery that key genes were acquired from fungi through horizontal gene transfer (HGT) is particularly significant.

Strengths:

(1) The discovery that the TkDOG15 enzyme, acquired through HGT from fungi, plays a key role in the detoxification of green tea catechins in the Kanzawa mite, revealing a new mechanism of plant-herbivore interactions, is highly encouraging.

(2) The verification of this finding through various experiments, including behavioral, toxicological, transcriptomic, and proteomic analyses, RNAi-based gene function analysis, and recombinant enzyme activity assays, is also highly commendable.

(3) By proposing a two-step model in which amino acid substitutions and expression regulation of a specific enzyme gene (TkDOG15) enable host adaptive evolution, this study contributes significantly to our understanding of the evolutionary mechanisms of speciation and plant defense overcoming.

Comments on revised version.

I believe the manuscript has been significantly refined since the initial draft was submitted.

Reviewer #2 (Public review):

Summary:

In this study, Yang et al. develop a real-time system for automatic face detection and identification of multiple unrestrained common marmosets in a home cage setting.

Strengths:

The study aims to address an unmet need in behavioral neuroscience: the ability to non-invasively identify animals is crucial to the automated and rigorous study of neural behaviors; this is especially true for common marmosets, which are rapidly becoming a model system of choice for the study of complex social cognition. By using a YOLOv8 backbone, the study achieves human level performance, both in terms of precision and recall of the trained models.

Weaknesses:

The robustness of the system is not clear from the limited datasets presented.

Comments on revised version.

The authors have adequately addressed my comments from the previous round, and I have no further comments

Reviewer #2 (Public review):

Summary:

In this paper, Xu and co-workers unveil two distinct modes of neutralisation by gp41-targeted broadly neutralizing antibodies on HIV-1 Env. So far, it was unclear as to how the mechanism of neutralisation occurred for this subset of neutralising antibodies (that can target the fusion peptide or the membrane proximal external region of the gp41 subunit). Thanks to single-molecule FRET, the authors show that the majority of broadly neutralizing antibodies stabilize the closed Env conformation (named State 1 since the original work by Munro and colleagues PMID: 25298114). Interestingly, the bivalent 10E8.4/iMab stabilized in turn a CD4-bound open state of Env. The two modes of neutralization described for these antibodies show previously unknown allosteric mechanisms that stabilize closed and open Env conformation, stressing the importance of Env conformational dynamics and its efficiency during the process of fusion.

Strengths:

The article is well-written, and the figures fully depict the data in a convincing way. The authors have used smFRET, which is now established in the field as a good tool to assess Env dynamics.

Comments on revised version:

I am very happy with the comments, answers and the way the new manuscript is shaped after revision. I have no further questions or concerns.

Reviewer #3 (Public review):

Summary:

In this manuscript, Fujita/Jo/Stewart/Osorno et al., investigate the contribution of Nav1.7 in regulating the excitability and firing properties of human dorsal root ganglion (hDRG) neurons in vitro. The authors characterize the effects of a previously reported Nav1.7-selective blocker AM-2099 in recombinant human Nav1.7 channels and in cultured hDRG neurons from postmortem organ donors. The authors observed modest changes in many of the properties expected by inhibiting Nav channels, including decreased action potential upstroke rate and amplitude, while increasing the voltage and current thresholds for spike generation. However, AM-2099 did not change the maximum number of APs in response to suprathreshold stimulation, leading the authors to conclude that Nav1.7 inhibition alone has limited efficacy in reducing the firing properties of hDRG neurons at the soma, and discuss that the effects of Nav inhibition may be different at distal axons.

Strengths:

Experiments are well-designed and executed, and the results presented are convincing. The focus on voltage-gated sodium channels in native human DRG neurons is highly relevant to recent efforts to develop safer analgesic options for chronic pain in people.

Comments on revised version.

The authors have done an excellent job addressing my prior critiques.

Reviewer #2 (Public review):

Summary:

The authors address habituation and potentiation in the single-celled organism Stentor in a large data set by systematically varying stimulus frequency and recovery duration. They analyze habituation dynamics on the level of single cells within a Bayesian inference framework to map out how the response probability of individual cells decays during training. Mapping out the progression of habituation quantified by learning rate versus decaying response probability, they observe different dynamics for different stimulus frequencies and recovery durations, which they reconcile with multiple time-scales governing the memory of prior training.

Strengths:

The authors accumulate a systematic, broad data set of Stentor habituation and potentiation, which, in combination with the Bayesian framework they developed, unfolds its power to probe underlying habituation dynamics and challenge theoretical frameworks.

Weaknesses:

The interlacing of theoretical framework, existing concepts and expectation, and experimental data in their narrative may challenge readers. The Bayesian inference of habituations is very successful in concluding that their variation with stimulus frequency and recovery duration points to multiple time scales of memory are involved. However, the authors' comprehensive analysis of potentiation may need more guidance to follow the authors' conclusions.

The combination of a dynamical systems-driven hypothesis, experimental data, and statistical analysis, as put forward in this work, is immensely powerful for uncovering the mechanisms that facilitate learning, such as habituation and potentiation, in single-celled organisms.

Reviewer #2 (Public Review):

Summary:

The manuscript emphasizes a phylogenetic conservation of the hippocampal region and primary sensory cortical regions in mammalian species. The authors then propose that the evident species-specific differences in behavior and memory-related functions may be due to differences in type and amount of cortico-hippocampal connectivity.

Strengths:

The authors are well-established researchers with a long history of excellent results and publications. The question (co-influence of cortical and hippocampal connections) is potentially interesting.

Weaknesses:

The treatment is very broad and macro scale, ignoring the likelihood that hippocampal-cortical connectivity and behavioral outcomes result from multiple differences at a more micro-scale. The designated "mammalian" sample is also broad. Thus, it can appear incomplete as a sample, and incompletely discussed.

Reviewer #2 (Public review):

Summary:

The findings are conceptually useful - a sequential alpha-then-theta architecture for proactive gating and reactive distractor suppression would be a compelling contribution to the attention control literature - but the evidence is incomplete at best. The central dissociation rests on an inadequate proxy for perceptual dominance, the key alpha-behavior effect is small (d = 0.199) and confined to a single unprotected data quadrant, and the GLMM uses an inadequate random effects structure that inflates false-positive risk.

Strengths:

The SSVEP frequency-tagging + binocular rivalry combination is genuinely inventive for isolating sensory gain signals from the two competing stimuli simultaneously. The finding that distractor cueing enhances sensory processing of the distractor yet fails to impair behavior is a clean result that directly addresses a behavioral paradox in the attentional suppression literature. The non-phase-locked TF analysis and the use of RESS for SSVER extraction are methodologically sound.

Weaknesses:

The most consequential flaw in the paper is the operationalization of "perceptual dominance." The authors explicitly acknowledge in a footnote that trial categorization as "target-dominant" or "distractor-dominant" is based on which eye received the stimulus, not on participants' actual perceptual reports. Because participants were never asked to report which stimulus was dominant (only to reproduce the target's color), the assignment is an anatomical proxy, not a perceptual measure. This matters enormously for the paper's central claims. Specifically: (a) The entire two-mechanism dissociation (theta for target-dominant trials, alpha for distractor-dominant trials) is built on a trial-type categorization that may not reflect subjective perceptual experience on a given trial, and (b) Dominant-eye stimuli do typically win initial rivalry dominance, but dominance alternates, and in a 2-second window (the stimulus duration used), perceptual states likely fluctuate in many trials. The lack of button-press perceptual tracking (e.g., continuous dominance reports) means the authors cannot verify that their neural effects actually correspond to the perceptual states they claim. This is a major structural limitation of the design that can't be retroactively corrected, and it significantly weakens the consciousness/awareness framing of the findings.

Another significant issue is that the parietal alpha effect on behavior is confined to a very specific quadrant of the data: distractor-dominant trials where both target and distractor SSVERs are weak simultaneously. The authors present this as an elegant result - "alpha helps most under high perceptual uncertainty" - but it could equally reflect insufficient statistical power for effects in the other three SSVER-strength cells (target strong/distractor weak; target weak/distractor strong; both strong). The Cohen's d for the alpha effect on target reporting probability is only d = 0.199, which is a very small effect. With N=36 and no correction for the multiple SSVER-strength subgroupings tested, there is a real risk that this specific cell-finding is a false positive, while the null in adjacent cells reflects inadequate power rather than a genuine boundary condition.

A third major limitation is that with a design that includes 6 fixed effects and all their interactions, the random effects structure should include random slopes for at least the key predictors (cueing condition, dominance). Fitting maximal random effects models or justified reduced structures (Barr et al., 2013) is standard in within-subjects EEG research. Using only random intercepts risks inflating Type I error rates for the interaction terms that form the core of the paper's claims. The authors provide a supplementary table (Table S1) but do not describe whether model convergence was verified or alternative random effects structures were tested.

Fourth, the paper's title and central claim are that alpha and theta dynamics operate sequentially. However, the temporal ordering (preparatory alpha -> rivalry-phase theta) is primarily shown by examining each oscillation in its respective analysis window, not by a single analysis testing whether the sequence itself predicts behavior better than either mechanism alone. A path analysis or cross-lagged model linking trial-level alpha to subsequent theta, and both to behavior, would directly substantiate the "relay" framing. Without this, the sequential architecture is more of an interpretation than a demonstrated property.

Finally, the frontal theta cluster identified by permutation testing spans 3 to 16 Hz - a range that extends well into the alpha band. Calling this a "theta" effect while simultaneously discussing alpha as a separate mechanism is difficult to reconcile. At minimum, this frequency boundary issue warrants explicit discussion.

Reviewer #2 (Public review):

Summary:

In this work, Lohse and colleagues develop a system for doing targeted photostimulation in mouse cortex. The system uses a camera image to target laser stimulation to stereotactically defined locations in mouse dorsal cortex.

Strengths:

The hardware is well designed, and the software is well documented and supported. The build guide and well-documented software package should allow for simple implementation of the technology. Without a doubt, this is a valuable community resource for the circuit neuroscience field.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

Prior work identified TMEM30B (knockout mice) as well as ATP8B1 (human genetics and mouse model), ATP8A2 (knockout mice), and ATP811A (human genetics) as relevant for hearing. The authors also reasoned that given the recent discovery of TMC1 and TMC2's dual function as mechanotransduction channels of the inner ear and as lipid scramblases, a counterpart flippase should be in the sensory hair-cell stereocilia bundle where mechanotransduction happens. They use CRISPR/CAS to modify the endogenous mouse genes and add an HA tag at the N-terminus of the ATP8B1, ATP8A1, ATP8A2, and ATP11A proteins. Their experiments with these mice unambiguously localized ATP8B1 at the base of outer hair cell stereocilia bundles. Knockout of ATP8B1 results in loss of outer hair cells, deficient auditory function (ABR), and degeneration of outer hair cell stereocilia bundles. Similarly, hair cells from genetically modified mice with endogenous HA-tagged TMEM30B proteins show localization of this protein to outer hair cell stereocilia bundles. TMEM30B knock out mice phenocopy the ATP8B1 knock out model. Interestingly, the authors show that annexing V staining precedes hair cell loss in ATP8B1 and TMEM30B knockout mice and that proper localization of these proteins is lost in mice that lack CIB2, a protein essential for hair cell mechanotransduction.

Strengths:

(1) Use of knock-in HA-tagged proteins to unambiguously localize ATP8B1 and TMEM30B

(2) Systematic characterization of auditory function (ABR), hair cell loss, and hair-cell stereocilia bundle morphology.

(3) Advances our understanding of the role played by lipid homeostasis in auditory function.

(4) Reports on mouse models that will be helpful to further understand the mechanistic role played by ATP8B1 and TMEM30B in normal hearing and hereditary deafness.

Weaknesses:

(1) Are the HA tags causing any functional issues? Function and localization of tagged proteins can sometimes be compromised. This is checked for TMEM30B and ATP8B1, but not for ATP8A1, ATP8A2, and ATP11A.

(2) Following on the point above, is it possible that ATP8B1-HA is well localized, but localization for the other three flippases (ATP8A1-HA, ATP8A2-HA, and ATP11A-HA) is compromised by the tag? Is this potential miss-localization causing any functional phenotypes? I find surprising that there are flippases only in outer hair cells and only formed by ATP8B1. A possible explanation is that the tag is interfering with trafficking. If so, there should be a phenotype (ABRs), although this might be masked by redundancy among these flippases or caused by systemic issues (admittedly difficult to sort out).

Reviewer #2 (Public review):

Summary:

This paper asks an important question that has not been discussed much in the extensive literature on the High Frequency Oscillations (HFOs) that have been extensively studied in patients with epilepsy and experimental models of epilepsy. The question is whether the Fast Ripples (FRs), the HFOs in the 250-500 Hz frequency band, represent a pathological phenomenon or represent a physiological phenomenon that occurs in the healthy brain but happens to be more frequent in epileptic tissue. It is an important question that has not been systematically addressed until now. The authors conclude, from very extensive simulations, from extensive experimental animal studies (the systemic kianate model of epilepsy in rats), and from a modest amount of human data, that FRs occur in healthy brains as a result of the chance occurrence of bursts of action potentials, and that in epileptic tissue, their frequency of occurrence is approximately 30% higher than what is expected by chance. They conclude that FRs are not a separate phenomenon of epileptic tissue. This finding is reinforced by the recent findings of FRs in experimental models of Alzheimer's disease.

Strengths:

This is a valuable study because it asks an important and original question and because it evaluates it from several angles (simulation, tissue culture, experimental animals, and human patients). The simulations and the analyses of real data are performed very carefully and with original and solidly documented approaches, using extensive simulations and extensive data sets in the cultured cell data and in the in vivo experiments. The paper is clearly written and well-illustrated.

Comments on revised version.

The authors have appropriately addressed the questions I raised in the first review.

Reviewer #2 (Public review):

Ageing poses a significant challenge to the regenerative capacity of oligodendrocyte precursor cells (OPCs). Myelin abnormalities accumulate with age, while the ability of OPCs to differentiate into myelinating oligodendrocytes progressively declines. This likely contributes to inefficient replacement of damaged myelin and oligodendrocytes, impaired remyelination following injury, and reduced adaptive myelination. Identifying the molecular changes associated with this decline is therefore important for understanding and potentially treating age-related deterioration of CNS white matter.

This study sought to identify transcriptional regulators involved in oligodendrocyte-lineage progression whose expression is altered in aged OPCs. The authors developed gSWITCH, a computational tool that identifies genes showing defined dynamic expression patterns across ordered biological states. By combining this analysis with comparisons of young and aged OPC transcriptomes and transcription-factor-binding-site enrichment, they identified Bcl11a as a candidate regulator. Bcl11a transcripts are abundant in young OPCs, decline during oligodendrocyte differentiation, and are markedly reduced in aged OPCs.

A major strength of the study is its combination of computational candidate identification with functional experiments. Bcl11a knockdown substantially impaired the differentiation of young OPCs without measurably affecting their proliferation. Conversely, transient Bcl11a overexpression increased the differentiation of aged OPCs in vitro. Oligodendrocyte-lineage-specific expression of Bcl11a in aged mice also increased the generation of PLP1-positive oligodendrocytes following focal demyelinating injury. Together, these complementary loss- and gain-of-function experiments support the conclusion that Bcl11a expression is functionally important for OPC differentiation and that restoring its expression can improve the differentiation competence of aged OPCs.

While the transcription-factor-binding-site enrichment analysis predicts a Bcl11a-regulated network, the current study does not establish direct binding or identify the downstream genes responsible for its effect on OPC differentiation. Similarly, the upstream mechanisms responsible for the age-associated reduction in Bcl11a expression were not investigated. Further work may help establish a more complete mechanistic framework explaining how restoration of Bcl11a expression improves OPC differentiation.

Overall, this study offers valuable insights into the age-related loss of regenerative capacity in the central nervous system and introduces a computational framework that may be broadly useful for investigating dynamic gene regulation in other biological contexts.

Comments on revised version.

The authors have addressed my previous comments, and the revised manuscript has been substantially strengthened by the inclusion of additional supporting data and an expanded discussion.

Reviewer #2 (Public review):

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

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

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

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

Overall, I find this to be an interesting study that employs state of the art methods of behavioural analyses and whole-brain cFos analyses. The revision has clarified the take-home message considerably: the abstract is now more careful about which behavioural groups are memory-associated, and the causal language in the conclusions has been appropriately softened. Two of my three original main concerns have been addressed. The first is not and having looked at the data again I can now be more specific about what concerns me.

Comments on revised version.

(1) My first concern related to the claim that fear memory behaviour falls into four distinct groups, and specifically to the role of evasiveness in defining them. The authors give three reasons for retaining it, but I remain unconvinced.

The first is that variable evasion in response to alarm substance is a long-standing observation (von Frisch; Suboski et al.), and that dissecting this individual variation is the purpose of the paper. I agree with the motivation, and it is a good reason to measure evasion. But it does not establish that evasion on memory day reflects fear memory, and memory day is the only day used for the clustering and neural activity mapping. The manuscript's own results point the other way: relative to pre-exposure, no strain or sex increased evasion on memory day, and relative to vehicle only female TUs did. The temporal profiles show evasion on memory day to be largely similar between vehicle and CAS-treated fish. Historical observations of variable evasion during CAS exposure do not carry over to the memory phase.

The second is that the clustering itself reveals two kinds of freezing fish - one freezing between bouts of normal swimming, the other between bouts of evasion - demonstrating that a subset of fish increase evasion. In absolute terms, this does not match the data. In Figure 4B, evading freezers are below the population mean for absolute evasion, as are freezers. The text describes evading freezers as "high in freezing and evasive behaviors," and I do not think Figure 4B supports this.

What actually separates the two freezing groups is the third measure, evasion as a percentage of active time. And this is where I have difficulty, because that measure is not an independent behavioural readout. The classifier assigns every window to normal, evasive or freezing, and active time is simply non-freezing time, so evasion-as-percent-of-active is fully determined once the other two are known.

This matters for the clustering specifically. Distance-based methods weight each input dimension equally, so a variable that carries no information beyond the other two nonetheless contributes a full third of the distance between any two fish - and it contributes it in a way that counts freezing twice, once directly and once through the denominator of the derived measure. The space is nonetheless described as three-dimensional throughout, including in the Methods and the Figure 4 legend, when there are only two independent behaviours in it.

The consequences fall hardest on exactly the animals at issue. Both freezing groups sit at 65-70% freezing, so there is very little active time to divide by, and small absolute differences in evasion - together with any noise in estimating them from a couple of minutes of non-frozen behaviour - are inflated into large differences on the rescaled measure. In terms of what the fish actually did, the two groups differ by a few percent of trial time. That is the boundary on which much of the rest of the paper rests.

I recognise that evasion as a proportion of active time is in some respects the more biologically meaningful quantity, and the authors are right that a fish freezing 70% of the time has limited opportunity to do anything else. But that is an argument for reporting it as a descriptive measure, not for entering it into the clustering alongside the two variables from which it is computed.

This impression is reinforced by Figure 4A itself. While the freezer group occupies a reasonably distinct region, the non-reactive, evader and evading freezer groups appear as a single continuous distribution with cluster boundaries drawn through it rather than around visible gaps. I appreciate that UMAP is a projection and that visual separation is not required for genuine structure, but this is the figure by which most readers will judge whether four discrete types exist, and it does not obviously support that reading - particularly given that the embedding is built from the same variables, including the rescaled measure, that most favour the separation.

I would suggest that the authors re-run the clustering using only the two directly measured behaviours, percent freezing and percent evasion of total time, and report whether four groups still emerge and, in particular, whether the evading freezer / freezer split survives.

The third is that the two groups have distinct functional networks despite equally high freezing, so the behavioural difference is real and is manifesting in the brain. This is the strongest of the three arguments, and I accept part of it: something about how a frozen fish spends its remaining active time does appear to be neurally meaningful, which is interesting in its own right. But it does not establish that these are two distinct types, nor that the difference has anything to do with the conditioning. Fish taken from either side of a cut through a continuous distribution will differ neurally if that continuum tracks brain state, so the network result is equally compatible with graded variation. More importantly, Figure 5A shows that a substantial proportion of fish are classified as evaders in the vehicle condition and at pre-exposure, before any CAS has been given. This suggests a pre-existing individual tendency toward evasive behaviour that is independent of the alarm substance, and one would expect such a tendency to persist into the memory trial. If so, the distinction the network analysis is drawing between freezers and evading freezers may simply reflect that baseline trait, and its neural correlates would be correlates of the trait rather than of fear memory. I am therefore not convinced that this distinction is related to CAS or to memory.

(2) This concern is fully resolved. I had misread the CAS preparation: it was pooled from eight donors spanning all four strains and both sexes, so every fish received identical material and the strain and sex differences cannot be attributed to donor variability. The clarification now added to the Results will prevent other readers making the same error. The addition of FDR correction to the Figure 2 comparisons also addresses my related concern about multiple testing.

(3) Somewhat resolved. The conclusion no longer states that behavioural variation is "driven by" activity in particular regions, and the added caveat that neural activity was not directly manipulated sets the right expectation for a mapping study. The scatterplots in Figure S6-2 are a useful addition and give a much better intuition for what the PLS contrasts represent. My remaining reservation is the one above: a great deal of the neural story rests on the evading freezer / freezer contrast, and I am not persuaded that this contrast marks a boundary relevant to fear memory.

Reviewer #2 (Public review):

McGilvary et al. evaluate the recently developed auxin-based gene expression system (AGES) for use in aging studies of Drosophila melanogaster. This system is based on the widely used Gal4/UAS system that enables cell-specific expression of UAS-transgenes under Gal4 activator control. AGES uses an auxin-inducible degron-tagged Gal80 repressor that should prevent Gal4-dependent activation unless flies are fed auxin, providing a useful approach for temporal control of transgene induction - something that would be highly useful for aging studies. The authors perform a comprehensive analysis of AGES-dependent transgene induction in male and female flies at different ages with multiple controls, demonstrating some moderate induction in female flies only - albeit with some substantial background induction even in the absence of auxin.

Overall, transgene induction appears to be both much lower with the AGES system compared to Gal4 driver controls and very leaky, with some tissue-specific differences in induction observed as well. Combined with their observations that auxin feeding has impacts on body mass, triacylglycerol and protein levels, and lifespan, these data raise some concerns regarding the interpretation of data obtained using the AGES system for aging or longevity studies in flies. This study provides well-needed validation for the recently developed AGES system and highlights critical caveats that will support future studies.

Most conclusions of the paper are well supported by data, but additional controls and textual edits would strengthen and clarify the findings. In addition, the abstract and conclusions of this study should more accurately reflect the limitations of transgene induction using this AGES system in adult flies.

Reviewer #2 (Public review):

Summary:

The manuscript "Selective loss of Nkx2.1-lineage neurons in the lateral septum alters the balance between novelty seeking and threat avoidance" is an interesting study by Miguel Turrero García and colleagues. Here, the authors report a novel mouse model allowing complete ablation of neurons pertaining to the Nkx2.1 lineage by conditionally ablating the transcriptional regulator Prdm16 from the Nkx2.1 lineage. The authors combined single-nucleus RNA sequencing, histological and electrophysiological approaches, as well as behavioral analyses to demonstrate that a large portion of LS neurons are profoundly altered by Prdm16 deletion from the Nkx2.1 lineage. This manipulation preferentially impacts Crhr2-expressing neurons, leading to electrophysiological defects. At the behavioral level, this cell population is preferentially recruited in a stressful situation, and ablation of Prdm16 from this lineage leads to enhanced exploratory behavior even in the presence of a perceived threat.

Strengths:

The strengths of this manuscript are (i) leveraging a transcriptional regulator within a specific cell lineage and restricted to an early stage of ontogeny (ii) disrupting the developmental trajectory of a discrete neuronal population identified with an elegant snRNAseq approach and (iii) without obvious compensation (iv) and its impact on behavior in adult mice, with a special emphasis on exploratory drive in the presence of an acute stressor. The manuscript is well written; the experiments are well conducted, organized, and presented in a logical framework. Statistical analyses are well described. Each experimental group includes a sufficient number of subjects, allowing robust statistical comparisons.

Overall, I very much enjoyed this manuscript and the elegant mouse model bridging developmental biology with systems neuroscience. Insights generated from this line of work could illuminate how discrete perturbations in gene expression programs at early stages of ontogeny could have a profound impact on the development, organization, and function of select neural circuits and how they may impinge on behavior at later stages of ontogeny.

Weaknesses:

Some comments and suggestions:

(1) General-

Photoinhibition of LS Crhr2-expressing neurons has no effect on anxiety-like behaviors in the absence of a stressor (Anthony et al., Cell, 2014). It would thus be interesting to reappraise the behavioral experiments performed with cKO mice in response to an acute stressor. The authors duly acknowledge this important point in the discussion section.

(2) Specific-

(a) Figure 2J: Was the increase in Crhr2 expression observed in tdTom-cells from cKO mice in the snRNAseq as well? If so, was Crhr2 expression enhanced in a specific cluster that did not belong to the Nkx2.1 lineage, or was it randomly enhanced across distributed clusters?

(b) Figure 3 and S3: Does the lack of UCN3+/ENK+ terminals reflect a downregulation of UCN3 and ENK, or does it reflect the absence of innervation? Restricting a retrograde viral vector in iLS that expresses a fluorophore to illuminate the UCN3+ cell bodies (and lack thereof) in PefAH of cKO mice could address this question.

(c) Figure 3: Does immunostaining for UCN3 in the PefAH area reveal cell bodies in cKO mice? In other words, is the loss of UCN3 terminal-specific or does it reflect a general downregulation of UCN3 in the PefAH?

(d) Figure 4: The remaining tdTomato+ neurons are more excitable in cKO mice. To what extent can alterations in the electrophysiological properties of tdTomato+ neurons lacking Prdm16 be related to their survival? Is it a general response to Prdm16 deletion that is unrelated to survival? Is it a compensation mechanism in surviving cells? Or, alternatively, is it a unique property of these specific cells that favored their survival despite Prdm16 deletion?

(e) Figure 5 and S5: Really nice figures. Great use of MoSeq with the predator odor test.

(f) Figure 6: Interesting that the decrease in cFos induction in NeuN+ cells of cKO mice is more prominently observed in LSd when tdTomato+ cells are prominently found in the LSi/LSv (Figure S2B). Could this be related to intra-septal connectivity?

(g) Figure 6: If tdTomato+ cells consist of 10-30% of neurons, and these tdTomato+ cells are preferentially found in the LSi/LSv, shouldn't we expect a decrease in c-Fos+NeuN+ density in the LSi/LSv (since there are generally fewer neurons in cKO mice)? If I am not mistaken, this could suggest that another unrelated LS population that is tdTom- displays an increase in cFos expression in cKO mice compared to WT mice. Could be interesting to see if the Crhr2+ neurons that are tdTom- are preferentially recruited in cKO mice as a compensation mechanism in LSi/LSv.

Reviewer #2 (Public review):

The authors aim to understand how inhibitory circuitry within the medial prefrontal cortex regulates the selection of sociosexual behaviour. Rather than studying social interaction in isolation, they develop an elegant behavioural paradigm in which female mice repeatedly choose between interacting with a male and obtaining an appetitive non-social reward. This task allows the authors to examine behavioural choice under conditions that more closely resemble natural decision-making. They combine optogenetic inhibition of oxytocin receptor-expressing interneurons, large-scale calcium imaging of pyramidal neurons, slice electrophysiology, and computational modelling to investigate how inhibition shapes cortical representations that ultimately bias behavioural choice.

The study has several notable strengths. The behavioural paradigm is novel and well-designed, allowing repeated choice measurements while controlling for general social motivation by including both male and juvenile female stimuli. The integration of multiple experimental approaches is particularly impressive. The behavioural effects of optogenetic inhibition are complemented by population imaging demonstrating elevated pyramidal activity, electrophysiological recordings confirming monosynaptic regulation of pyramidal neurons, and a computational model that provides a mechanistic interpretation of the observed circuit dynamics. The work therefore spans multiple levels of analysis, from synaptic interactions to behaviour, and the individual datasets are generally of high technical quality.

The imaging analyses identifying a putative "MALE" ensemble are particularly interesting. The observation that a relatively small subset of pyramidal neurons preferentially represents the male option before behavioural commitment provides an attractive framework for understanding how inhibition can stabilise specific behavioural representations. The temporal analysis suggesting that disruption of this representation precedes impaired behavioural choice is especially compelling, as it moves beyond simple correlations between neural activity and behaviour.

Several aspects of the mechanistic interpretation remain somewhat speculative. The central conclusion relies heavily on the computational competition model, which assumes an asymmetric competition between a relatively small male-selective ensemble and a much larger default pyramidal population. While the model successfully reproduces several experimental observations, many of its architectural assumptions are inferred rather than experimentally demonstrated. In particular, the designation of the remaining pyramidal neurons as a functional "OTHER" population representing the non-social alternative is not directly established experimentally. Alternative circuit architectures may be capable of producing similar behavioural and population-level effects, and the current data do not fully distinguish among these possibilities.

Similarly, although the identification of MALE cells is thoughtfully performed, the classification depends on an operational threshold derived from ROC analysis and correlated activity. It remains uncertain whether these neurons constitute a stable functional ensemble across sessions or merely reflect one end of a continuous representational spectrum. Longitudinal analyses examining the stability of these ensembles across days or across changes in behavioural state would strengthen the claim that they represent a dedicated neuronal population.

An additional limitation concerns the specificity of the behavioural interpretation. The reduction in male choice is interpreted primarily as impaired sociosexual decision-making. While the inclusion of juvenile female stimuli substantially improves the experimental design, it remains difficult to completely separate altered sociosexual motivation from broader changes in motivational salience, valuation, or action selection. The observed changes could reflect alterations in multiple components of the decision-making process, and this distinction deserves a somewhat more balanced discussion.

The interaction with the oestrous state is a very interesting aspect of the work and is consistent with previous studies of oxytocin-dependent sociosexual behaviour. However, this analysis is based on relatively modest numbers of animals and sessions, making it difficult to judge the robustness of these effects. The conclusions regarding hormonal modulation would therefore benefit from a more cautious interpretation.

Overall, the authors achieve their primary objective of demonstrating that oxytocin receptor-expressing interneuron-mediated inhibition contributes to the selection of sociosexual behaviour while regulating pyramidal population dynamics in the medial prefrontal cortex. The behavioural, imaging, and electrophysiological datasets provide convincing evidence that inhibition shapes cortical activity during decision-making. The computational model offers a plausible mechanistic framework linking these observations, although some aspects of this framework remain hypothetical and await further experimental testing.

The work is likely to have a significant impact on the fields of cortical circuit function, social neuroscience, and decision-making. Beyond its specific findings, the study introduces a behavioural paradigm that should prove broadly useful for investigating how competing behavioural options are represented within prefrontal circuits. The combination of behavioural neuroscience, population imaging, and computational modelling represents a valuable resource for the community and provides an important foundation for future studies examining how excitation-inhibition balance shapes flexible social behaviour.

Reviewer #2 (Public review):

Summary:

This is an interesting study that uses drawings to evaluate the extent to which visual representations of letter- and graph-like figures (preferentially) include topological features, like junctions and holes.

The main claim is based on the observation that when participants are asked to draw presented figures from memory, they tend to (1) regularise angles towards 90deg and lengths towards the average length of the lines in the figure, while (2) preserving topological features like T-junctions more assiduously than non-topological features like L-junctions. A third experiment with 'serial reproductions' in which participants copy drawings made by other participants (like a visual version of the 'broken telephone' game) reproduce these patterns in exaggerated form. These findings were also reproduced in children (Experiment 4).

These findings are consistent with the idea that memory representations are low-bandwidth or noisy approximations to the original figure. I would suggest that when participants are asked to reproduce the figure, it is if they combine the noisy stored representation, with generic priors about angles and the average line length. The preferential preservation of T- over L-junctions indicates that they are somehow more salient or memorable. This is not inconsistent with the authors' preferred interpretation of an explicit representation of topological structure. However, it is also not inconsistent with the idea that in order to compress the visual signals for storage, high-information (complex) components of the source are given preferential treatment. This would be compatible with optimal use of limited resources when compressing the information. Additional comparisons and control conditions would help tease these alternatives apart.

Strengths:

+ Innovative use of drawing methods to probe internal visual representations<br /> + Experiments spanning both adults and children

Weaknesses:

- Failure to consider alternative hypotheses that are consistent with the findings

Reviewer #2 (Public review):

I appreciate the thorough responses from the authors, which address my concerns. The expansion of Appendix B as well as the addition of text discussing how the REPOP method interacts with data collection efforts are very useful. These new sections show that relative error decreases with increasing samples, as expected, yet these error metrics, including KL divergence, describing the fit of the full distributions not just the modes, drop off fairly quickly with increasing number of samples showing that REPOP likely minimizes discrepancies between estimated and true distributions even at lower sampling efforts.

Additionally, the extension of the REPOP method to the quantification of multiple bacterial species or phenotypes shows the potential utility of the method in contexts beyond basic plate counts. Between this example and the additional information on how to implement REPOP, I believe this workflow will be attractive and accessible to the audience.

Reviewer #2 (Public review):

Summary:

The following points are those that occurred to me across readings of the paper. They are listed in what I take to be the order of their significance. Many of the points relate to the loose use of language and invocation of concepts that are not warranted, given the study design and results obtained.

Major Comments:

(1) The concept of ensemble turnover is interesting - the way it is introduced and discussed implies some type of spontaneous change in the neural underpinnings of fear discrimination and generalization in the PL. But, of course, every trial involves an opportunity to learn about the threat CS or the generalization test stimuli, and I am troubled by the thought that stability in the neural underpinnings of fear discrimination and generalization will actually reflect the level of defensive behaviours evoked on different trial types and/or the discrepancy between those behaviours and the outcome of a given trial in the generalization test. That is, stability in the neural underpinnings may be related to an animal's certainty or uncertainty in the contingency between a stimulus and danger; or, put another way, an animal's confidence that danger will or won't occur given the presence of some stimulus. This is not uninteresting. It is, however, not considered anywhere in the paper, which is overloaded with references to inferred threat values and integration of information across different types of stimuli. The protocol is not one that requires inference about anything or integration across anything.

(2) I appreciate the link to Gu and Johansen in paragraph 3 of the Introduction, but the type of generalization under investigation here is not the same as the type of 'generalization' studied by Gu and Johansen [who used a sensory preconditioning protocol]. Nonetheless, the authors have forced the language used by Gu and Johansen into their paper, and this has created tension [at least for this reader] as the concepts introduced by Gu and Johansen [inference, integration] are simply not relevant given the generalization protocol used here. Here are a few examples of points where the tension might interfere with a reader's understanding:

a. 'We hypothesized that generalization to novel stimuli depends on stable subnetwork organization that enables comparisons between learned and inferred valence, as well as population-level features that reduce variability across related representations.'

I understand the words in the hypothesis, but can't form a representation of what is being said because of the reference to terms that stand in need of clarification [inferred valence, variability across related representations], but, ultimately, won't be clarified. This needs to be re-expressed so that the reader can appreciate what is being said.

b. 'Our results show that stable cortical subnetworks integrate the emotional "gist" of memory and inferred valence for novel cues over time, despite ongoing ensemble reorganization, and that population-level firing rate similarity across stimulus presentations determines threat generalization.'

Again, what does this mean? How is the gist of a memory integrated with inferred valence for novel cues over time? The statement simply doesn't make sense. This needs to be rewritten for clarity.

c. 'In CS⁺15 mice, positively modulated sound-responsive neurons exhibited graded tone activity reflecting the contingency learned valence as well as the inferred valence of novel tones across testing days...'.

Can this be rewritten as 'In CS⁺15 mice, positively modulated sound-responsive neurons exhibited graded activity to the tone CS and its variants that were used to assess generalization.'? The overloading of the text with references to 'contingency learned valence' and 'inferred valence' is unnecessary and makes it much harder to understand what has been shown in the results.

(3) Re the same passage of text as in 2c:

Is it the case that these neurons are simply tracking the expression of freezing to the various tones? The same question applies to the results obtained for the CS+3 mice. If this is the case, then why should the results be taken to support the banner statement that 'Sound-modulated PL population responses encode learned and inferred valence' - these analyses do not support that statement. And, as indicated, I don't believe that the language of learned and inferred valence is appropriate to such statements, given the nature of the protocol used and results obtained. It is a study looking at how populations of neurons in the PL respond during presentations of auditory stimuli that were subject to discriminative conditioning, and during tests of generalized freezing to other [intermediate] auditory stimuli.

(4) It is stated that:

'In no-shock controls, although both positive and negative responses were present, population activity was not modulated by tone frequency or valence'.

What does this mean? I can understand that population activity was not modulated by tone frequency. But what does it mean to say that it was not modulated by valence? Why should it have been when none of the tones were conditioned in this group and, hence, mice were responding to all the tones equally? And given that this is true, I don't understand the use of 'valence' here, or the subsequent statements in this paragraph that 'graded responses require associative learning' and that 'PL population responses encode graded sound-valence associations that reflect both learning and inference, closely matching behavioral generalization.' The latter statement is particularly unwarranted and, again, highlights a major issue with the paper. It could and should be rewritten as 'PL population responses reflect behavioral generalization.' There is nothing in the additional language that adds to the reader's understanding of what has been shown. The reference to 'graded sound-valence associations that reflect both learning and inference' is completely unwarranted, given the nature of this study. It is anathema to the vast literature on stimulus generalization. If the authors wished to make statements of this sort, they should have taken a different approach, perhaps using protocols like those featured in Gu and Johansen.

(5) The section titled, 'Consistently active neurons preserve valence representations as newly recruited neurons sharpen remote memory traces' ends with the following summary:

'Together, these results indicate that consistently active neurons maintain stable representations of learned and inferred sound associations across time, whereas neurons recruited after conditioning progressively acquire graded tuning at later retrieval stages. This dynamic refinement suggests that cortical memory representations become increasingly selective during systems consolidation, while a stable neuronal subpopulation preserves the core emotional content of the memory.'

Once again, the summary is not in keeping with the results obtained. The 'dynamic refinement' of representations is far more likely to reflect the repeated testing across days 1, 15, and 30 rather than anything to do with systems consolidation - at the very least, it is the simplest interpretation of the results. The impact of repeated testing is evident in the sharpening of generalization gradients over time, which is contrary to what is otherwise observed in the literature - the incredibly well -documented broadening of generalization gradients with time. Given this impact of repeated testing, surely the changes in the neuronal population that underlie performance are more likely to reflect the learning that occurs on days 1, 15, and 30, which is reflected in reduced freezing to the non-conditioned tones. If this is a reasonable take on the results, then I don't see the basis for invoking systems consolidation at all, and I don't see the basis for inferring a stable neuronal subpopulation that preserves the emotional content of the memory. Rather, non-reinforced presentations of 'never-reinforced' tones result in recruitment of additional neurons that result in suppression of freezing responses to those stimuli.

(6) In the section titled, 'Population vector similarity at stimulus onset determines degree of generalization', it is stated that:

'Because population similarity peaked shortly after stimulus onset, we quantified similarity during the first 5 s after tone onset relative to the CS⁺. In CS⁺15 mice, population similarity was highest for 15/15 and 15/11 tone pairs with no differences between them.'

Isn't this consistent with the view that the population response in the PL simply reflects the level of freezing? Freezing to the 15-15 and 15-11 tones is most likely to be similar on their first presentation prior to the effects of extinction on the 11 Hz tone; hence the results obtained. That is, these results appear to clearly indicate that neuronal responses in the PL reflect the degree of stimulus generalization, as evidenced in freezing behavior. Given all that we know about the involvement of the PL in expressing fear responses, it is not appropriate to claim that 'population vector similarity at stimulus onset *determines* the degree of generalization. The PL responses simply reflect the varying levels of performance displayed to the different types of tones. What have I missed that could be taken to support additional statements?

Later in the same section, it is stated that 'population-level similarity at stimulus onset scales with behavioral threat generalization and is maximal for tones associated with robust threat responses.' For simplicity and, therefore, clarity, this should be rewritten as 'population-level similarity at stimulus onset reflects behavioral threat generalization.'

(7) In the section titled, 'Different subnetworks encode acoustic versus learned properties of sound association', it is stated that:

'Our previous analyses show that learned and inferred associations are represented at the population level. However, these results do not resolve whether graded responses arise from pooled activity of frequency-selective neurons or from subnetworks encoding integrated learned valence across tones.'

What does it mean to say 'integrated learned valence across tones'? As it presently stands, the meaning of the phrase is unclear. It only makes sense if one supposes that generalized freezing responses to the 11 and 7 kHZ tones reflect separate associations between those tones and the aversive foot shock US. This supposition is inconsistent with the rich literature on generalization of Pavlovian conditioned fear responses. Specifically, it is inconsistent with the many theories of fear generalization, which attribute the reduction in fear as one moves away from the specific conditioned stimulus to a decrement in the ability of the test stimulus to activate the trained CS-US association. My strong impression is that the authors would do well to ground their findings in theories of stimulus/fear generalization, of which there are many. This would better serve the results obtained [and the reader's appreciation of them] - at present, the unnecessary invocation of concepts does very little to enhance the reader's appreciation or understanding of what has been found in the study.

(8) Another example of what has been a common theme in this review :

'...we hypothesized that the PL active ensemble segregates into functionally distinct subnetworks: one encoding tone-specific sensory features with dynamic characteristics, and another responding to all frequencies encoding stable core memory content and inferred emotional valence.'

What does it mean to say 'all frequencies encoding stable core memory content and inferred emotional valence'? Do the authors mean to say '...and another that tracks freezing/defensive responses regardless of whether they were elicited by the trained CS or one of the generalization test stimuli'?

(9) It is stated that - 'Graded clusters encode emotional valence but constitute only a fraction of the active population; yet valence coding at the population level remains accurate and precise. This indicates that neurons newly recruited into the population-likely frequency-selective and organized within learning-independent clusters-can be shaped by associative processes through modulation of firing activity.'

What does this mean? Are the authors trying to say that - 'Some clusters of PL neurons track freezing responses. In spite of the fact that these are only a fraction of the total active neuronal population, the population-level response of PL neurons also tracks the levels of fear to the trained tone and its variants used in the test for generalization.' If this is what one wants to say, then the final statement in the reproduced section does not follow. That is, there is no indication that 'neurons newly recruited into the population-likely frequency-selective and organized within learning-independent clusters-can be shaped by associative processes through modulation of firing activity.' As noted, the characteristics of other ensembles that become active across the repeated tests on days 1, 15, and 30 are more likely to reflect learning from non-reinforcement that occurs within and across those sessions. Perhaps this is what is meant by the phrase, 'shaped by associative processes'? If so, it should be stated explicitly instead of left to the reader to work out.

(10) The following points all relate to the Discussion and reiterate many of the points above.

a. 'A subset of neurons remains consistently active across sessions, preserving core components of the memory trace and supporting inference of emotional valence for novel sounds, while neurons recruited after conditioning progressively acquire valence selectivity at remote time points.'

'Inference of emotional valence' is unclear and unwarranted for all of the reasons provided above regarding the use of language.

b. '...Our data reconcile these views by demonstrating that cortical representations of emotional valence emerge rapidly after learning and persist within stable subnetworks, even as the broader population undergoes substantial turnover. This architecture preserves core mnemonic content while allowing flexibility in the surrounding ensemble.'

These statements assume that the PL neuronal responses reflect something more than the levels of freezing behavior to the different stimuli; what are the grounds for this assumption?

c. 'Importantly, these subnetworks encode both learned contingencies and the inferred valence of novel stimuli along a graded representational axis, suggesting that strong recurrent connectivity provides a stable scaffold for emotional memory representations.'

What is a graded representational axis, and what part of the first statement suggests that 'strong recurrent connectivity provides a stable scaffold for emotional memory representations'? If the authors' goal was to make statements about emotional memory representations vis-à-vis emotional memory content, they should have used protocols that allowed them to probe such content. The auditory fear conditioning protocol used here [followed by tests for generalization to other auditory stimuli that differ in frequency from the conditioned tone] is not one that lends itself to analysis of emotional memory representations or content.

d. 'Dynamic tone-selective responsive neurons emerge independently of learning, as they are present in both control and experimental mice, reflecting pre-existing PL sensory-driven properties (Hockley & Malmierca, 2024; Zikopoulos & Barbas, 2006).'

Maybe. They are also likely to have developed as a consequence of the repeated testing on days 1, 15, and 30, which involved intermixed exposures to the tones of different frequencies. That is, rather than 'pre-existing PL sensory-driven properties', the responses of these neurons might reflect the emergence of discrimination between the various tones across testing, and greater suppression of freezing to the non-trained tones compared to the trained tone across the various test intervals.

Reviewer #2 (Public Review):

Summary:

Overall, this study provides a thorough description of the formation of syncytia following wounding of the proliferation-competent diploid epithelium of the pupal notum. While this phenomenon has already been described briefly for this particular tissue by the Galko lab in Wang et al 2015, the authors provide a much more detailed description and characterisation of the process providing some novel insights (radial versus tangential border breakdown, cell shrinkage, timings, syncytia outcompeting mononucleated cells, etc.).

Strengths:

This paper provides an elegant, thorough, descriptive characterisation of syncytia-driven wound closure using state-of-the-art confocal live imaging of the pupal notum. The authors show that laser-induced wounding of this diploid, proliferation-competent epithelium results in the formation of syncytia of various sizes in the first few cell rows around the wound edge, which progressively become bigger as healing proceeds. This results in ~50% of cells becoming part of these syncytia. The cell fusion events were convincingly demonstrated by showing the disappearance of p120ctnRFP and E-Cadherin-GFP from cell-cell borders as well as cytoplasmic GFP mixing of GFP-positive cells with a GFP-negative cell.

Apart from cell-cell fusion by border breakdown that mostly happens in the first 2h following wounding, the authors also found that at later stages of wound healing cell shrinkage following cytoplasmic mixing contributed to syncytia formation.

Next, the authors provided some convincing evidence that syncytia outcompete mononuclear cells for being positioned in the first cell row around the wound.

The authors then show that radial border breakdown occurs much less frequently than tangential border breakdown. They suggest that radial border breakdown reduces the requirement for cell-cell intercalations. They also hypothesise that tangential border breakdown might allow fused cells to share resources and provide more resources to be used near the wound edge, e.g. for actomyosin cable formation. To test this, the authors generate single-cell clones that overexpress Actin-GFP. They then show convincingly how a single Actin-GFP-positive cell in the second cell row fuses with one GFP-negative cell in the first cell row. The Actin-GFP signal then spreads in the fused cell and labels some previously unlabelled actin-rich structure near the wound edge which most likely is the actomyosin cable. This provides some evidence for resource sharing by cytoplasmic mixing following fusion.

Reviewer #2 (Public review):

Summary:

The authors compare "Bully" lines, selected for male aggression, to Canton-S controls and find that Bully males have lower mating success, shorter mating durations, and remate sooner. Chemical analyses show Bully males have distinct cuticular hydrocarbon (CHC) signatures and transfer markedly less cVA to females, offering a plausible mechanistic link to weaker mate-guarding. Paradoxically, Bully males live longer and remain fertile at older ages when Cs males no longer mate, indicating a shift in the reproduction-survival trade-off in aggression-selected populations. Importantly, the work sheds light on proximate mechanisms, demonstrating that shifts in CHCs and pheromone transfer co-occur with changes in fitness traits.

Strengths:

The manuscript's strengths lie in its comprehensive and integrative approach framed within an evolutionary context. By combining behavioral assays, chemical profiling, and lifespan measurements, the authors reveal a coherent pattern linking aggression selection to life-history trade-offs. The direct quantification of cVA in the female reproductive tract after mating provides a particularly compelling mechanistic correlate, strengthening the link between behavior and chemical signaling. Findings on altered 5-T and 5-P levels further highlight how chemical communication shapes mating and mate-guarding strategies. Analytical approaches are largely rigorous, and the results provide valuable insights into the pleiotropic effects of selection on socially relevant traits.

The revision responds directly to the main concerns raised previously. The addition of a third, independently selected line (Bully C), together with the Bully × Bully data, considerably reduces the concern that the behavioral phenotypes reflect line-specific drift or founder effects rather than a correlated response to selection. The reorganized survival figure (Figure 5) is a clear improvement over the previous version, with isolated and group-housed males in separate panels and a heterozygous Bully condition added, so the longevity claim can be evaluated more directly. The isolated-male data are especially useful here, since those flies never mate, and a longevity difference under that condition argues that the effect is not simply a consequence of Bully males mating less often. The behavioral schematic, corrected symbols, and reported sample sizes also help, as does the reinterpretation of the post-mating courtship data in terms of courtship motivation rather than a refractory-period effect once no latency difference was found.

Weaknesses:

Most of the remaining weaknesses are ones I raised in the first round, and the revision has narrowed them. The links between the altered CHC profiles, the reduced cVA transfer, and the behavioral outcomes remain correlative. The causal experiments that would establish them (for example, perfuming or cVA-equalization) are acknowledged by the authors as future directions, which is reasonable, but it means the mechanistic claims should be read as candidate explanations rather than demonstrated ones. It is also worth noting that the CHC differences and the behavioral differences may both be downstream of a common selection target (for instance, genes affecting oenocyte function or CHC biosynthesis) rather than one causing the other; the Discussion would be more balanced if this alternative were stated explicitly.

My main remaining concern is with the lifespan data. The behavioral phenotypes are replicated across Bully A, B, and C, but the survival assays were done on Bully A only, so a line-specific contribution to the longevity result, including drift, cannot be excluded, even though this has been addressed for the behavioral traits. This matters because the title and abstract present the survival-reproduction trade-off as a general consequence of selection for aggression, whereas the survival evidence rests on a single line. The authors can either run the lifespan assays on a second line, or calibrate the text, title, and abstract so that the strength of the survival claim matches the single-line evidence behind it, with second-line lifespan data noted as a future step.

The Bully C line is currently underused. Its intermediate aggression, together with the absence of a significant reduction in mating duration, points to a graded rather than binary relationship between aggression intensity and mating duration. This is one of the more interesting features of the expanded dataset, and it deserves more than its present role as a justification for focusing on Bully A.

The authors have appropriately softened causal language in the title, subheadings, and much of the Discussion. A few residual passages still imply causation or directional transfer and would benefit from the same treatment.

Reviewer #2 (Public review):

Summary:

This work identifies a previously unknown way that red light can slow ageing. The authors show that red light lowers the level of a protein called SIRT4 in skin cells. Reducing SIRT4 boosts fatty acid use and increases a type of histone modification that keeps genes active. These changes help cells clear away signs of ageing, reduce inflammation, and restore normal metabolism. The findings open the possibility of developing new treatments that target SIRT4 to reverse age‑related decline.

Strengths:

The evidence is solid because the authors use several complementary methods. They test red light in both cultured cells and naturally aged mice, and they confirm the key role of SIRT4 by silencing its gene. Measurements of metabolism, protein changes, and ageing markers all point in the same direction. However, the exact way red light lowers SIRT4 levels is not fully explained, which leaves a minor gap. Overall, the conclusions are well supported and convincing.

Weaknesses:

The paper does not evolve to use the mechanistic discoveries of the manuscript to help our community to identify the mechanism of photobiomodulation, which is not known so far.

I would like to draw your attention to a recently published paper by Herrera et al. (FEBS Letters 2025, doi:10.1002/1873-3468.70195), which shows that red light (660 nm) stimulates mitochondrial fatty acid oxidation in keratinocytes via AMPK‑dependent phosphorylation of ACC, without altering expression of electron transport chain complexes. I believe this paper is highly complementary to current study.

Herrera et al. demonstrate that red light increases basal, ATP‑linked, and maximal oxygen consumption rates in keratinocytes specifically through enhanced fatty acid oxidation (inhibited by etomoxir). This independently validates the central finding of the current manuscript ,i.e., red light boosts lipid metabolism, strengthening the robustness of this concept.

While the current manuscript focusses on the SIRT4‑MCD axis, Herrera et al. identify AMPK phosphorylation and ACC inhibition as key effectors. Authors can integrate and expand their discussion, since SIRT4 downregulation may converge on AMPK activation, or they may represent parallel, reinforcing mechanisms. This would enrich the mechanistic model and open new hypotheses.

The mechanism of photobiomodulation: Herrera et al. explicitly challenge the prevailing paradigm that red light acts solely via cytochrome c oxidase (by showing long‑lasting effects, unchanged OXPHOS protein levels, and no difference in permeabilized cells). The current finding (red light acts through SIRT4 downregulation, i.e., not direct enzymatic activation, aligns perfectly with Herrera´s critique.

Long‑term metabolic effects - Herrera et al. show that a single red light exposure elevates oxygen consumption for up to 2 days. The current study focuses on changes at 12‑24 h. Their data extend the time window and suggest that the metabolic reprogramming you describe may persist longer than currently discussed, which is clinically relevant.

Discussing Herrera et al. results would not only acknowledge independent, corroborating evidence but also allow the authors to position your SIRT4‑centric mechanism within a broader, emerging understanding of red‑light photobiomodulation.

Comments on the latest version:

The authors have made a terrific work in answering the reviewers and modifying the manuscript.

Reviewer #2 (Public review):

Summary:

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

Strengths:

(1) Experiment design is one of the strengths of this study. The experiments are thorough and cover the length and breadth of the core findings of the study. Although a lot of work has already been done in studying the role of dopamine in memory and locomotion, the dissection of the functions of distinct DANs in larvae has been done meticulously with well-structured experiments.

(2) This study fits quite nicely into the puzzle of memory, especially in the context of Dopamine. Previous studies in *Drosophila* adults have shown the opposing roles of DANs in locomotion depending on the context of DAN activation. This study drives that point home for larvae, providing conclusive evidence in that regard.

(3) The use of clear figures and simple language is one of the strengths of this paper. The figures are comprehensive, complete and manage to narrate the story by themselves. The flow of information is smooth. The simple and effective language used maintains scientific rigor while remaining accessible to those new to the field. A pleasant read.

Weaknesses:

(1) The authors have done a great job at structuring the figures. But some main figures would benefit from including the controls instead of placing them in supplementary.

(2) The paper would benefit from a deeper discussion regarding molecular mechanisms underlying their results. It would be interesting to see what the authors think about different Dopamine receptors and how they relate to the findings of this paper.

(3) Throughout the paper, the authors have been clear and comprehensive, but in some cases, further explanation of their choices were missing. For example, the choice to compare bending and tail velocity over other parameters within the same clusters is unclear.

Comments on revised version.

Most of the comments have been addressed.

Reviewer #2 (Public review):

Summary:

In this manuscript, Fukui et al. re-examined the ATP hydrolysis mechanism in GHKL ATPases, revealing a cooperative role of two conserved acidic residues rather than one. The authors have used a range of biochemical and structural techniques on various mutants from different members of the GHKL ATPase family to test and validate their proposed mechanism.

Through a detailed re-analysis of their previously published structure of the aqMutL NTD (ATPase domain) in complex with AMPPCP, they identified Glu29 and Glu32 as interacting with nucleophilic water for the catalysis. The authors carefully dissected the respective roles of these two acidic residues with a series of site-directed mutations. Mutations at Glu29 impaired ATPase activity without affecting protein secondary structure or ATP binding in the case of the E29Q mutant. Moreover, mutations at Glu32 did not affect secondary structure (except for E32G) but reduce ATPase activity. Activity was abolished when both residues (E29Q/E32Q) are mutated.

The authors extended their study to another GHKL ATPase, aqGyrB. Their findings further supported the cooperative function of the corresponding acidic residues in aqGyrB (Glu48 and Asp51) during ATP hydrolysis. Mutation of these residues partially impaired ATP hydrolysis without affecting protein secondary structure. ATPase activity was completely lost in the double mutant E48Q/D51M. While the E48Q mutant retained the ability to bind ATP, the E48A mutant did not. High-resolution structures of the WT and E48A, E48Q, D51A and D51N mutants of the aqGyrB NTD demonstrated that nucleophilic water positioning depended on these residues. E48 played a dominant role in water positioning and is critical for stabilising ATP lid formation and associated conformational changes, whereas D51 contributed cooperatively to catalysis.

The authors investigated the functional impact of mutating the corresponding residues in the human MutL homologs PMS2 and MLH1. Clinical variants consistently exhibited reduced or abolished ATPase activity, providing a potential molecular basis for Lynch syndrome, through impaired DNA mismatch repair.

Lastly, through evolutionary analysis, the authors inferred that the second acidic residue was likely present in the common ancestor of MutL, GyrB, and MORC proteins, but was lost in the case of Hsp90.

Strengths:

(1) This study contains a detailed structural and biochemical analysis of a biologically important set of GHKL ATPases. The authors identify a second acidic residue that is conserved and contributes to catalysis in a large subset of GHKL ATPases. An updated and extended mechanistic model of ATP hydrolysis by this class of enzymes is proposed, which involves cooperative and partially overlapping roles for the catalytic residue pair. This revised mechanistic model is invaluable for the interpretation of clinical variants of GHKL ATPases such as PMS2 and MLH1.

(2) The work described was performed to an excellent and rigorous technical standard. The structural and biochemical data are sound. The evidence supporting the claims is compelling.

Weaknesses:

(1) The identification in this study of a second acidic residue contributing to catalysis but not absolutely essential for catalysis is a useful finding. However, given that many structures of GHLK ATPases have been determined with different nucleotide analogs bound and that the essential role of the first acidic residue is well established, the importance and scope of the advances described here remain focused within the field of study of GHKL ATPases.

(2) The authors assessed the consequences of variants in the human MutL homologs PMS2 and MLH1, but various other human GHKL ATPases contain clinically relevant variants, some of which have stronger disease associations than the mutations examined in this study. A broader analysis of any effect of disease-linked mutations in GHKL ATPases would have strengthened this study.

(3) The effect of other aqMutL NTD E32 mutants, particularly, the E32K mutant on ATP binding remains unclear, although experimental assessment of nucleotide binding would be challenging due to the high protein concentrations required for the equilibrium dialysis assay.

Reviewer #2 (Public review):

Manini and colleagues present an interesting study on the consequences of early deafness on the organization of temporal regions chiefly engaged in audition in hearing people. Mainly relying on representational similarity analyses, they show that the auditory cortex in deaf individuals represents information about task, sensory modality, and somatosensory frequency. Critically, task and modality representations were also found in the auditory cortex of hearing individuals. There were significant differences between groups, implying that these representations are enhanced as a consequence of deafness.

Overall, I feel that the paper could gain in clarity and impact if the hypothesis space tested in the introduction and discussion was made clearer, if some new analyses were provided to support some claims, and if the authors better matched their conclusions to the observed results.

Reviewer #2 (Public review):

In the manuscript "Supervised domain adaptation mitigates cross-ethnicity prediction errors in neuroimaging-based cognitive prediction", the authors investigated the efficacy of data adaptation techniques to reduce ethnicity-related prediction bias in neuroimaging-based cognitive prediction. They found that data adaptation algorithms, particularly balanced weighting, contributed to mitigating ethnicity-related performance disparities. Furthermore, these bias mitigations could be achieved without requiring a large set of data from the underrepresented ethnic group. This study addressed an important concern in the field of neuroimaging-based behaviour prediction, providing many intriguing results. Nevertheless, the manuscript also suffers from a lack of coherent methods design, the unorganised presentation of information, and the lack of in-depth discussion of results.

The conclusions claimed by the authors are sometimes over-generalised and not fully supported by the study outcomes. Overall, this study demonstrated strong technical designs and convincing statistical analysis for the main outcomes, although clearer presentation would be needed to convey the messages in the manuscript.

The central investigation of this study is whether domain adaptation techniques improve ethnicity-related performance disparities. However, these improvements were only measured against a very weak baseline model, where a small set of African American (AA) subjects were added to the training sample consisting purely of White American (WA) subjects. While the authors recognised that balancing the training sample could already mitigate the ethnicity-related disparities, they considered that such approaches are unfeasible in their experimental scenario, where only a small amount of AA data were available. However, as Li et al. (2022) showed, a balanced sample of around 90-150 AA subjects could already reduce the ethnicity-related bias. Even from a practical standpoint, this balanced sample approach would be a more valid baseline for domain adaptation models to compare against.

The authors made two main conclusions: that domain adaptation methods reduced ethnicity-related bias, and that balanced weighting performed the best and the most stably. Both claims were over-generalised to some extent. First, the adaptation benefit claimed in the first conclusion is not seen in the functional connectivity (FC) modality, which is the most popular modality for neuroimaging-based prediction of behaviour. This difference in adaptation benefit across modalities is an important finding that is meaningful for future studies, the omission of which also removes interesting insights that the audience could take away from this article.

Second, the judgement of prediction performance is based on the area under the improvement curve (AUIC) metric, which summarises a model's performance across different availability of labelled AA data. As a result, the analysis of prediction performance naturally favours algorithms that could perform well with a small amount of added AA data. On the one hand, this provides an easy decision point for users to pick an algorithm to use without being concerned about data availability. On the other hand, important insights could be overlooked with the oversimplified recommendation of balanced weighting. As the authors have also observed, in some cases, domain adaptation strategies do not improve ethnicity-related bias more than the non-adaptation baseline. If the message is to recommend simple, low-cost strategies to reduce ethnicity-related prediction bias, it would be misleading not to note that the simplest and lowest-cost strategy could also be non-adaptation methods sometimes.

Regardless, for the general audience, the underlying assumptions when interpreting the AUIC metric are not immediately clear, which could cause the conclusions to be misleading. Apart from aggregating over different amounts of available AA data, the statistical comparison of AUIC gain across data adaptation algorithms also did not account for the impact of brain phenotype modalities. Even though the upstream analyses have confirmed that adaptation benefits vary greatly across brain modalities, this major observation was not followed in the final analysis where conclusions were made about which algorithm performed the best. Based on visual inspection of Figure 3b, it may be suspected that PRED performed better than or comparably to balanced weighting when task contrasts based on the Destrieux atlas were used.

Finally, the findings from this study align with the common hypothesis that ethnicity-related prediction bias originates from disparities already manifested during data collection and preprocessing. As the authors have noted, the modalities with the most tendency for ethnicity-related bias are the anatomical ones, including all three volume-based modalities (cortical volume, T1 and T2 subcortical volume) in the top ten phenotypes with the largest performance gap. Most prominently, brain features in the occipital pole, frontal pole, and a range of subcortical areas were found to contribute highly to adaptation gain. Subcortical areas are often reported to show noisier measurements compared to cortical areas, whereas the poles of the brain are likely more strongly warped/distorted during alignment to a standard template. From a data quality perspective, these results support the interpretation that ethnicity-related prediction bias may stem from loss of data quality during data collection or preprocessing. In the prediction models based on anatomical brain features, data adaptation methods may have helped to address these disparities in the data, without the more resource-intensive need to improve the bias in preprocessing pipelines.

Li, J., Bzdok, D., Chen, J., ... Genon, S. (2022). Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Science Advances, 8(11), eabj1812.

Reviewer #2 (Public review):

Summary:

Using a public dataset of retinotopic mapping and resting-state data, the authors find that the default mode network has voxels that respond (positively or negatively) to visual stimulation at specific retinotopic positions, and that resting-state activity in these voxels is correlated with activity in more traditional sensory voxels with the same visual-location preference. The retinotopic specificity is bidirectional, such that high activity in default mode voxels drives activity only in voxels with matching receptive fields in sensory cortex, and vice versa. These findings are at odds with traditional views of the default mode network as having abstract (non-retinotopic) representations and competing (rather than cooperating) with external sensory representations.

Strengths:

This study continues an intriguing line of research about how default mode regions interact with sensory cortex. Demonstrating that there are structured interactions between these regions at rest, and that these interactions are in fact organized according to retinotopic location (as opposed to traditional views of representational format in the default mode network), provides a new framework for thinking about large-scale internal and external brain networks. The authors make use of a well-powered public dataset that allows for precise estimates of pRFs and individual-specific resting-state networks and develop a number of interesting analyses that characterize the relationships between DN and dATN voxels. The findings are exciting and could have a major impact on future studies in cognitive neuroimaging.

The authors mention that these findings could shed light on internal/external interactions such as "anticipatory saccades or memory-guided attention," which is true, though I would argue that constructing DN representations of external stimuli is in fact even more fundamental than these specific cases (e.g. see Barnett and Bellana, 2025, "Situation models and the default mode network"). The "highways" identified in this study could play a vital role in real-world perceptual processes that are constantly translating external input into internal mental models.

Weaknesses:

(1) The criterion used for defining voxels as retinotopic seems very liberal. The authors show that only 5% of voxels have R^2>0.14 in a null analysis and therefore define voxels with R^2>0.14 as retinotopic. Although all the networks in Fig 1C show voxel distributions that differ from the null, the number of false positives above R^2>0.14 seems problematic, especially for the DN positive pRFs (red distribution) and to a lesser extent the DN negative pRFs (blue distribution). From visual inspection of the plot, the false discovery rate (fraction of voxels labeled as retinotopic that are false positives) looks like it would be greater than 50% for the DN positive pRFs. The authors do show that the positive pRF voxels have above-chance consistency across runs and also show in a supplementary analysis (Fig S5) that applying a stricter R^2 criterion yields similar results. These help to mitigate this concern, providing evidence that there are true positive voxels in this set which are driving the effects.

(2) The claim that "voxel-level visual response profiles shape DN-dATN coupling during spontaneous resting-state activity" is well-supported for specific sub-groups of DN voxels, though it is unclear whether the overall DN-dATN correlation at rest is primarily driven by the pRF-tuned voxels investigated in this study.

(3) The event-triggered analysis is effective at testing the bidirectional relationship between DN and dATN, with high activity in either network triggering a response in the other network. However, it would be helpful to show more validation that these "events" are meaningful windows of time to study, and that 13 TRs a typical length of time that activity is elevated during one of these events.

(4) The framing of this paper relative to the authors past work, such as Steel et al. 2024 ("A retinotopic code structures the interaction between perception and memory systems") could be improved. The primary novelty here is that this paper examines resting-state data and individually defined whole-brain networks, showing that there are widespread spontaneous interactions between broad internal and external networks, but this distinction is not made explicit in the Introduction.

Reviewer #2 (Public review):

The manuscript by Forbes, Skafida, Karapidaki et al. concerns the in-silico identification of cis-regulatory elements (CREs) in large genomes using chromatin accessibility (ATAC-seq) and sequence conservation (genomic DNA sequencing) data. They exemplify this method by applying it to identify novel CREs in Parhyale hawaiensis, which they validated using reporter constructs.

The results are convincing and are well supported by the data and validations. Identified CREs are valuable for researchers interested in the regulation of the expression of genes they control.

The methodology on the whole is also valid, as suggested by the results and previous publications on various taxa. Sequence conservation, as stated by the authors, was long used as a method to identify regions of non-coding DNA with functional and evolutionary constraints. The same applies to ATAC-seq data, which has also been used as a proxy for functional regions in different animals such as sea urchins and amphioxus. The methodology proposed is likely to be successfully used by researchers working on a variety of experimental organisms.

The authors do not use existing genome assemblies and use short-read sequencing to identify conserved regions, and while it is not conceptually novel, such an approach is becoming more and more viable and useful considering the recent advances in next generation sequencing technology and the decrease in price of short-read sequencing.

The authors have addressed and discussed the limitations and weaknesses of the approach as well as explicitly indicated the advantages.

All in all, the authors provide a valid method to strengthen CRE identification via sequence conservation without the need of multiple complete close species genome assemblies, making it a compelling option for non-model organism research.

Reviewer #2 (Public review):

Summary:

Mubeen and colleagues study the cellular basis of tooth regeneration in cichlid fish. Using an elegant tooth plunking strategy followed by single nucleus RNA-sequencing, the authors were hoping to achieve an atlas of cellular and transcriptional changes that occur within and between cells during whole tooth replacement.

Strengths:

The major strengths of the methods and results are high novelty in the approach in a vertebrate with continuous tooth replacement, the temporal analysis of analyzing at plucking and three later time points, the thorough and sophisticated analysis of the snRNA-seq data including the inferring of trajectories and signaling events, and the robust signal of transcriptional differences induced by tooth plucking.

Weaknesses:

The major weaknesses of the methods and results are no validation of any of the inferred cell types, no functional tests of whether any of the changes in signaling pathways affect the plucking-induced tooth replacement process, and perhaps no clear take-away message for biologists not necessarily interested in tooth replacement.

Conclusions:

The authors achieved their aims of identifying the changes in gene expression and cellular composition that occur during whole tooth replacement accelerated by plucking. Overall, the results support their conclusions, although some slight semantic qualifiers should probably be added (e.g. referring to "cell types" as "putative cell types").

The work should have high impact in the field of tooth and organ regeneration, and the novel methodological paradigm established here of accelerating tooth replacement three-fold by plucking has great promise for future follow up studies to further study this process. The work also could have strong impact by the computational methods used here to infer trajectories and signaling interactions. Specific pathways, genes, and cell types could be tested in other fish such as zebrafish to test function during tooth replacement.

The work is unique and interdisciplinary and also has significance by establishing that robust phenotypically plastic accelerations in regeneration rates occur upon tooth removal. There are very few studies like this one that combine genetic x environmental studies of regeneration. The result that three different species of cichlid fish that normally have very different tooth patterns all accelerate tooth replacement threefold upon tooth plucking also has significance in revealing a highly conserved plucking response.

Reviewer #2 (Public review):

Summary:

The authors examine how hilar mossy cells (MCs) influence adult-born dentate granule cell (abDGC) maturation and dentate gyrus (DG) structural integrity. Using both MC ablation and chronic functional silencing, they find that lacking MC inputs accelerates early abDGC maturation without altering mature cellular or intrinsic properties. MC silencing specifically decreased inner molecular layer (IML) spine density, whereas MC ablation led to IML collapse and an increased E/I ratio. However, neither intervention altered overall network excitability (measured via c-Fos and seizure induction) or seizure thresholds. These results advance our understanding of DG circuit plasticity during neurodegeneration.

Strengths:

(1) The side-by-side comparison of ablation vs. silencing provides a clear distinction between structural synapse loss and functional inactivation.

(2) The multi-level analysis spanning structural anatomy, single-cell physiology, and network-level assays yields a rich, comprehensive dataset.

Weaknesses:

(1) Measuring composite E/I ratios without parsing isolated EPSCs and IPSCs limits direct evaluation of MC-driven excitatory inputs. Furthermore, electrical stimulation in the IML likely recruits local interneuron axons directly alongside MC fibers, complicating the attribution of these responses solely to feed-forward MC circuits.

(2) The dramatic structural reorganization and IML collapse observed following MC ablation make it difficult to attribute changes in the E/I ratio purely to functional synaptic remodeling rather than physical circuit distortion.

(3) Layer boundary shifts following MC ablation complicate the interpretation of site-specific spine density (Figure 4); without accounting for IML collapse, classifying spine loss purely by traditional layer boundaries rather than proximal vs. distal dendrites may obscure local structural changes.

(4) The convulsive dosing protocol used for the seizure threshold test lacks the sensitivity required to reveal subtle changes in excitability.

Reviewer #2 (Public review):

Summary:

The paper proposes a network model that explains how birdsong learning can be guided by reinforcement signals.

Strengths:

It is well known that self-generated motor actions typically suppress their associated sensory input (for example, in the mammalian auditory cortex; see Eliades & Wang, 2003). This study presents a mechanism that effectively reverses this process. The theory posits that, initially, the motor signal generated in HVC, although not yet sufficient to produce an accurate song, nevertheless sends an efference copy to auditory areas, where it acts to cancel external auditory input from the tutor. This establishes a "scaffold," such that only an accurate replica of the tutor song can successfully suppress the corresponding auditory activity.

During learning, poorly generated plastic songs produce residual auditory activity that cannot be fully suppressed. This remaining activity then serves as an error signal that guides the refinement of motor output. The idea is elegant and is supported by experimental evidence.

Weaknesses:

The authors compare several possible sites of synaptic plasticity within the auditory network and conclude that the E-to-I-to-E model provides the best fit to the existing data. In this model, the auditory network consists of recurrent excitatory (E) and inhibitory (I) neurons, and Hebbian plasticity at E-to-I and I-to-E synapses is required to establish the cancellation pattern necessary to reproduce the tutor song.

However, the manuscript's presentation of the underlying plasticity mechanisms is somewhat puzzling. The authors repeatedly emphasize anti-Hebbian learning, even though their most successful model fundamentally relies on Hebbian plasticity. Although the resulting functional relationship may be described as anti-Hebbian, the biological learning mechanism implemented in the model is Hebbian. The repeated emphasis on anti-Hebbian learning therefore distracts from the central message and may confuse readers about the actual mechanism responsible for learning.

This emphasis may reflect an effort to distinguish the present work from previous anti-Hebbian models, but I suggest restructuring the manuscript. The authors should first present the optimal E-to-I-to-E model in detail, clearly explaining its mechanism and biological interpretation. Subsequent sections could then compare this model with the less successful alternative architectures. Such a reorganization would substantially improve the clarity and overall structure of the manuscript.

Finally, the abstract presents self-guided reinforcement learning as a novel concept, although this general idea has been described in previous work (e.g., Fiete et al., 2007). The abstract should therefore be revised to more precisely identify the specific novelty and contribution of the present study, rather than attributing novelty to the broader concept of self-guided reinforcement learning.

Reviewer #2 (Public review):

Summary

The authors ask what recurrent connectivity supports many distinct task-related manifolds when the associated dynamics interfere, how a circuit engages one task while suppressing others, and what produces high-dimensional activity. Extending previous theoretical studies on low-dimensional dynamics in large networks, they use a solvable model whose weight matrix is a weighted sum of many low-rank, task-specific components and develop a dynamical mean-field theory that relates connectivity, dynamics, and measurable population signatures of multi-tasking.

Strengths

(1) The question is timely. Low-rank networks are a leading model for low-dimensional latent dynamics, and the composition of dynamical systems has been proposed as a mechanism allowing for rapid, flexible learning; the paper connects these two ideas under a single theoretical framework.

(2) The proposal that sequential transitions between low-dimensional, low-rank dynamics can account for the _apparent growth of dimensionality with recording time_ is novel and is the paper's most valuable conceptual contribution.

(3) The mathematical analysis is rigorous, and the spontaneous-state theory is convincingly validated against simulation.

(4) The model produces concrete, falsifiable predictions - heterogeneous, syllable-dependent single-neuron tuning, low within-state dimensionality despite single-neuron variability, and distinct dimensionality-versus-recording-time signatures for the spontaneous versus task-switching accounts.

Weaknesses - whether the claims are supported by the data

(1) Chaos is named but not demonstrated._ The large-P and intermediate task-selected regimes are labeled "chaotic," but the manuscript does not establish chaos. In a homogeneous network, it is known that once the fixed point loses stability, the surviving solution is chaotic (Sompolinsky, Crisanti & Sommers 1988); that guarantee does not transfer here. The DMFT noise term is not computed analytically, and the single-neuron correlation functions (Fig. 5) show disorder, not a demonstrated decay of the fluctuation autocorrelation to zero, nor a positive largest Lyapunov exponent. The concern is sharpened by the possibility of _transient_ chaos: orthogonal to a dominant limit cycle, fluctuations may be locally unstable only at certain amplitudes or phases, so the global attractor could remain a stable cycle visited with chaotic excursions. As it stands, the claim of chaos in the intermediate regime is unsupported; it may well hold for some range of the selected-task strength, but this is neither shown numerically nor proven.

(2) "Analytical theory" overstates what is solved in closed form._ For the task-selected state, the kernels are non-stationary: The DMFT is entrained to the dominant task's dynamics, with an O(1) time-dependent quantity inside the nonlinearity. To my knowledge, there is no closed-form DMFT solution under these conditions. The Methods section supports this, explaining that the general scheme is solved by iterative numerical self-consistency (and described there as prohibitively expensive), and tractability is recovered only in a special block-Haar ensemble with Gaussian currents. This is entirely reasonable, but the main text presents it as an analytical theory; the reliance on numerical solutions of the self-consistency equations should be stated plainly.

(3) The spontaneous-state transition is the classical critical-gain transition, only reparametrized._ The onset of the no-task-dominant state is governed by $g_{eff}^2 = \alpha R\langle D^2\rangle$. It appears to depend on the number of tasks only because per-task strength D is held fixed as tasks accumulate; under a normalization that holds g_eff fixed, the transition reduces to a critical-gain point independent of P, as in extensive random networks. Relatedly, the result that chaos "arises solely from learning many tasks" is, mechanistically, random-network chaos: the random task components raise the weight variance and play the role of effective disorder. This is a legitimate and appealing reframing, but it is not a new transition, and the manuscript should make the relationship to the standard criterion explicit.

(4) Significance of the selection mechanism._ That boosting a task's gain selects it is intuitive, and the authors note the extreme (one $D^\mu$ dominating) is trivial. The non-trivial and genuinely useful contribution is quantitative - that only a small, O(1/P) modulation near criticality is required. This deserves to be foregrounded rather than left to the Discussion.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors reported Microscopic PhotoSelection (MiPS), a closed-loop automated robotic platform designed to link time-resolved imaging with physical sample recovery in mother machine microfluidic devices. By pairing a standard mother machine layout with a custom DMD optical path, an LED array, and an optimized DeLTA deep-learning model, the system tracks dynamic single-cell phenotypes and isolates specific cells via automated, targeted phototoxicity, i.e. selection by elimination. This is a novel technical development that addresses a clear limitation of snapshot sorting methods like FACS or MACS when screening for time-resolved, lineage-dependent traits. However, several methodological limitations and presentation errors must be addressed before publication.

Major Comments:

(1) Definition of 'Optimal' Dose (Figure 2D): The authors identify 8.0 W*cm-2 UV light for 300s as the optimal condition. However, this data point lies at the absolute boundary of the tested parameter space. In classical dose-response characterization, an optimum is defined by a local peak or a plateau followed by a decline in performance (typically due to rising off-target toxicity or scatter). Because the performance curve has not rolled over, this represents a boundary condition rather than a demonstrated mathematical optimum. The authors should either extend the parameter sweep to locate the true peak or soften their language to reflect that this is simply the highest performing condition tested.

(2) UV Exposure Time Gap: The exposure time sweep skips directly from 60s to 300s. While the closely spaced early timepoints are appropriate for capturing initial cell-death kinetics, the large gap to 300s leaves a significant engineering blind spot. Figure 3D demonstrates that off-target scattering damage scales linearly with cumulative light energy. If complete target cell arrest can be achieved at an intermediate exposure (e.g., 120s, 180s or 240s), operating the system at 300s unnecessarily subjects neighboring "surviving" cells to secondary global UV stress via device-wide scattering. An intermediate temporal sweep is recommended to optimize the selection window and properly balance target lethality with background library viability.

(3) Baseline Chemical Toxicity of Methylene Blue (MB): The photosensitizer workflow shows a clear improvement in contrast at lower power densities and exposure times. However, lines 151-153 note that the addition of 2 uM MB alone, even without light activation, stunts the baseline bacterial growth rate by ~40%. This is a major biological confounder. For applications like directed evolution or dynamic physiological screening, introducing a chemical stressor that nearly halves fitness imposes an unintended selective pressure. This baseline stress may activate pathways that mask or alter the phenotypes of interest. The authors must expand their discussion on how this baseline toxicity impacts multi-round iterative selections, and should ideally evaluate lower concentrations (e.g., 0.5uM or 1uM) or alternative photosensitizers to identify a more viable operational window.

(4) Negative Selection Framework and Search Space Scale: The MiPS platform relies entirely on negative selection by destroying unwanted variants. While effective for the demonstrated 1:1 binary proof-of-concept mixture, negative selection scales poorly when screening for rare variants within large libraries. For instance, isolating a single high performer from a library of 105 cells requires the system to successfully target and kill 99,999 individual cells; any statistical leak or failure in killing efficiency directly leads to heavy contamination of the recovered sample. The Discussion section requires a quantitative evaluation of these search space constraints, outlining how they limit the system's utility compared to positive selection mechanisms (such as optical tweezers or droplet sorters) when scaling to rare mutations (<1 in 104).

Significance:

This study presents a significant methodological advance in single-cell analysis and microfluidics by integrating long-term live-cell imaging, automated image analysis, and phenotype-guided cell recovery into a closed-loop platform. Existing approaches such as FACS and MACS are largely limited to endpoint or snapshot measurements, whereas MiPS enables selection based on dynamic and lineage-dependent cellular behaviors, thereby addressing an important gap in current single-cell screening technologies.

A key strength is the effective integration of mother machine microfluidics, custom optics, and deep-learning-based tracking into an automated and functional system. While the individual components are established, their combination into a phenotype-driven selection platform is innovative and expands the utility of live-cell microscopy from passive observation to active cell selection. The advance is therefore primarily methodological and technological, with potential to enable future conceptual discoveries in cellular heterogeneity and lineage dynamics.

However, limitations remain regarding scalability, robustness, selection accuracy, and generalizability across biological systems. Additional benchmarking and validation would strengthen the work further.

Overall, the study will be of interest to researchers in microfluidics, single-cell biology, microbial systems biology, bioengineering, quantitative imaging, and synthetic biology.

My expertise is in microfluidics, cell sorting and disease mechanobiology.

Reviewer #2 (Public review):

Summary:

The authors provide a comprehensive description of the neurosecretory network in the adult Drosophila brain. They assigned and verified the types of neurosecretory cells (NSCs) found in three publicly available drosophila brain connectomes. They then describe the organization of synaptic inputs and outputs for across NSC types. They show that NSCs are regulated by multiple sensory modalities, including enteric neurons. The authors then focus on a concise pathway from corazonin-expressing NSCs to a set of descending neurons, DNg27 and demonstrate that this pathway has the capacity to regulate egg-laying in female flies. Leveraging existing transcriptomic data, they also describe the hormone and receptor expressions in the NSCs and show putative paracrine signaling between NSCs. Taken together, this study provides a framework for future functional experiments, which may demonstrate whether and how NSCs, and the circuits to which they belong, shape physiological function and behavior.

Strengths:

This study uses three Drosophila brain connectomes to assign cell types to ten classes of neurosecretory cells (NSCs), based on clustering of synaptic connectivity and morphological features. The authors then verify type assignments for selected populations by matching cluster sizes to anatomical localization and cell counts using immunohistochemistry of neuropeptide expression and markers with known co-expression.

The authors compare their findings to previous work describing the synaptic connectivity of the neurosecretory network in larval Drosophila (Huckesfeld et al., 2021), finding that there are some differences between these developmental stages. Direct comparisons between adult and larvae are made possible through direct comparison in Table 1, as well as the authors' choice to adopt similar (or equivalent) analyses and data visualizations in the present paper's figures.

The authors extract core themes in NSC synaptic connectivity and generate predictions regarding sensory inputs and downstream physiological and behavioral functions. They test one newly identified NSC-premotor pathway, from corazonin-expressing NSCs to the descending neuron DNg27, with loss-of-function experiments and demonstrate that this pathway has the capacity to regulate female egg-laying.

The authors illustrate expression patterns of neuropeptides and receptors across NSC cell types from existing transcriptomic data and present a putative paracrine signaling network among NSCs. The authors also catalog hormone receptor expression across tissues.

Taken together, this study provides a comprehensive account of the neurosecretory system of the adult fly.

Weaknesses:

In Figure 6 authors use a linear dynamical modeling approach (described in Bates et al. 2026) to quantify the influence of different sensory source neuron types on the different NSC classes. The authors should discuss the two main assumptions baked into this approach: 1) all path segments (connections) from sources to targets are given the same sign and therefore result in activation, despite likely biological variation in their synaptic valences. 2) Each connection is given the same time constant for the response kinetics. Therefore, the model assumes uniform intrinsic "biophysical" properties.

Although the actual intrinsic properties (e.g. complements of voltage-gated ion channels) of the intermediate and target neurons are unknown, they are likely heterogenous. Such heterogeneity would have consequences on the steady-state responses. Thus, the response magnitudes measured in this model are unlikely to provide an accurate representation of feedforward "influences" in this circuit.

Although the intrinsic properties of all nodes in these paths will remain unknown in the absence of electrophysiological recordings, one could still consider the signs of connections using neurotransmitter predictions in the connectome (Eckstein et al. 2024). It would then be useful to compare the relative influences calculated with the Bates et al. approach to 1) simple weight propagation methods which are agnostic to time (as in Hoeller et al. 2026; doi: https://doi.org/10.64898/2025.12.22.696097) and 2) this Bates et al. approach and weight propagation methods that conserve the signs of the connections.

In Figure 8 and associated supplements, the authors probe the function of CRZ-expressing NSCs > DNg27 pathways in female and male flies. Although the authors test the effects of silencing both CRZ-expressing cells and DNg27 on feeding, egg-laying, and flight behaviors in females. They recapitulate a previous finding that CRZ-expressing cells regulate feeding behavior and then identify potential regulatory roles for this pathway in egg-laying. However, the authors did not test this full palette of behaviors in males. The authors do not test feeding or flight behaviors in males. They do, however, confirm previously reported activation phenotypes (copulation-like behaviors), via optogenetic activation of CRZ-expressing cells in males. These experiments would be more ethological if executed in freely walking male flies, rather than males that were glued, on their backs. It is unclear why the authors did not also test for activation or loss-of-function phenotypes for DNg27 in males. Taken together: the authors show compelling loss-of-function phenotypes for feeding and egg-laying for the CRZ-expressing NSC > DNg27 pathway in females, but evaluation in males remains incomplete.

Reviewer #2 (Public review):

The results in Ke et al., build on 15 years of work focused on dissecting the pairing properties of the Drosophila Homie insulator. Here, the authors use similar methods to those shown in Fujioka et al., 2016, Ke et al., 2024, and Fujioka et al., 2025, but with a focus on nHomie pairing and the role of Su(Hw) in both Homie and nHomie long-range interactions. The main question the authors hope to address is what the mechanisms are behind the physical interactions involved in boundary:boundary pairing. They attempt to answer this question through mutating the Su(Hw) binding sites located within the nHomie and Homie transgenic sequences and observing how pairing is altered.

The work presented is thorough and thought out; however, some of the conclusions that the authors focus on are not what makes the work interesting and could be reprioritized. For example, the authors spend several paragraphs in the discussion (lines 531-595) addressing how the data presented does not support an argument for cohesion-mediated loop extrusion. While the interactions shown throughout the manuscript do not support cohesion-mediated loop extrusion occurring at the Homie locus, the authors have already made this point in both Bing et al., 2024 and Ke et al., 2024 and thus do not need to expound on this point.

Instead, the authors have a more compelling story in their specificity vs promiscuity arguments. Homie is a unique insulator in Drosophila and even when located 142kb away will still find its unique pairing partners (itself and nHomie). The authors have shown this several times prior, yet here they show that some level of this long-distance homing interaction is dependent upon the Su(Hw) binding site. Additionally, the authors show in this study that addition of gypsy sequence, in a less demanding assay, is sufficient for transvection pairing with Homie. This transvection result is a novel finding, as gypsy was previously shown to be insufficient for long-distance pairing with Homie based on the authors' prior studies. It is likely different architectural proteins that bind within the Homie sequence and allow it to pair specifically with itself, regardless of assay type, and these elements are likely absent from the gypsy sequence, leading to pairing that is more situational (see point 8 in recommendations).

Finally, to no fault of the authors, the art of visualizing complex 3D pairing configurations is difficult. Unfortunately, that can at times mask the ultimate points that the authors are trying to make about pairing early in the manuscript.

Overall, the work mainly supports the authors' claims, and the findings are a useful addition to the insulator and Drosophila 3D genome organization field.

Reviewer #2 (Public review):

Summary:

The authors showed that the high susceptibility to CLP sepsis of Kit-mutant mice is not due to mast cell deficiency, but to dysbiosis.

Recommendations:

(1) The authors showed that E. coli increases in the cecum of Kit-mutant mice, which causes high CLP susceptibility. However, they did not provide any evidence E. coli is responsible for the high susceptibility. In the Figure 3 experiments, the authors administered the same number of cecal bacteria and did not show the number of E. coli after the administration. The authors should provide evidence showing that depletion of E. coli decreases susceptibility.

(2) The author should provide direct evidence of dysbiosis by, for example, shotgun sequencing of cecal and fecal contents.

(3) In case the authors find dysbiosis, they should analyze the mechanisms by which Kit mutation causes dysbiosis.

Comments on revised version.

The revised manuscript focuses on refuting the notion that mast cells play important roles in sepsis. The reviewer agrees with this claim.

Reviewer #3 (Public review):

Summary:

Recently, the off-target activity of antibiotics on human mitoribosome has been paid more attention in the mitochondrial field. Hafner et al applied mitoribosome profiling to study the effect of antibiotics on protein translation in mitochondria as there are similarities between bacterial ribosome and mitoribosome. The authors conclude that some antibiotics act on mitochondrial translation initiation by the same mechanism as in bacteria. On the other hand, the authors showed that chloramphenicol, linezolid and telithromycin trap mitochondrial translation in a context-dependent manner. More interesting, during deep analysis of 5' end of ORF, the authors reported the alternative start codon for ND1 and ND5 proteins instead of previously known one. This is a novel finding in the field and it also provide another application of the technique to further study on mitochondrial translation.

Strengths:

This is the first study which applied mitoribosome profiling method to analyze multiple antibiotics treatment cells. The mitoribosome profiling method had been optimized carefully and has been suggested to be a novel method to study translation events in mitochondria. The manuscript is constructive and well-written.

Comments on revisions:

The authors added a discussion to the revised manuscript, and also carefully investigate structural data from others. I have no more comment. Congratulations to the team for a good manuscript!