Reviewer #1 (Public review):

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

Summary:

D. Fuller et al. set out to study the molecular partners that cooperate with ATG2A, a lipid transfer protein essential for phagophore elongation, during the process of autophagy. Through a series of experiments combining microscopy and biochemistry, the authors identify ARFGAP1 and Rab1A as components of early autophagic membranes, which accumulate at the periphery of aberrant pre-autophagosomal structures induced by loss of ATG2. While ARFGAP1 has no apparent function in autophagy, the authors show that RAB1A is implicated in autophagy, although the precise mechanisms are not explored in the manuscript.

Strengths:

The work presented by Fuller et al. provides new insights into the composition of early autophagic membranes. The authors provide a series of MS experiments identifying proteins in close proximity to ATG2A, which is a valuable dataset for the field. Furthermore, they show for the first time the interaction between ATG2A and RAB1A both in fed and starved conditions, which extends the characterisation of the pre-autophagosomal structures observed in ATG2 DKO cells.

Reviewer #2 (Public review):

The mechanisms governing autophagic membrane expansion remain incompletely understood. ATG2 is known to function as a lipid transfer protein critical for this process; however, how ATG2 is coordinated with the broader autophagic machinery and endomembrane systems has remained elusive. In this study, the authors employ an elegant proximity labeling approach and identify two ER-Golgi intermediate compartment (ERGIC)-localized proteins-Rab1 and ARFGAP1-as novel regulators of ATG2 during autophagic membrane expansion.

Their findings support a model in which autophagosome formation occurs within a specialized subdomain of the ER that is enriched in both ER exit sites (ERES) and ERGIC, providing valuable mechanistic insight. The overall study is well executed and offers an important contribution to our understanding of autophagy. I support its publication in eLife and offer the following minor comments for clarification and improvement.

Reviewer #3 (Public review):

The manuscript by Fuller et al describes a crosstalk between ARTG2A with components of the early secretory pathway, namely RAB1A and ARFGAP1. They show that ATG2A is recruited to membranes positive for RAB1A, which they also show to interact with ATG2A. In agreement with earlier findings by other groups, silencing RAB1A negatively affects autophagy. While ARFGAP1 was also found on ATG2A positive membranes, silencing ARFGAP1 had no impact autophagy. Notably, these ARFGAP1 positive membranes are not Golgi membranes.

The findings are interesting and the data are in general of good quality.

Comments on the previous version:

The revisions carried out by the authors are fine. The new data on ArfGAP1 and about the indirectness of the ATG2A and Rab1A interaction improve both clarity and strength of the manuscript. I have no further comments.

Reviewer #1 (Public review):

Summary:

King and colleagues generated a mouse with a point mutation in IL21R and investigate the influence on IL-21-mediated T and B cell activation and differentiation. They find that mutant mice show a reduced T and B cell response with CD4 T cell differentiation into T follicular helper cells being primarily affected.

Strengths:

The authors combine in vitro and in vivo analysis, including bone-marrow chimeric mice.

Weaknesses:

The effect of the IL21R EINS mutant does not specifically affect STAT1, as clearly shown in Figure 1 H, I. Particularly at lower doses of IL21 which may be more relevant in vivo, the effects are very similar. A second key weakness is the very small Tfh response, a not very clear PD-1 and CXCR5 staining to identify Tfh and a lack of a steady-state (prior to immunisation) comparison of Tfh numbers in the different mouse strains. The latter makes it impossible to know what fraction of the response is antigen-specific.

Comments on revised version.

Thank you for responding to some of my suggestions/comments.

Unfortunately, these responses do not address the concerns raised by the other reviewer and me (see 'weaknesses'). The argument that statistical analysis shows that there is no difference in p-STAT3/5 does not make any sense - it simply reflects the absence of sufficient statistical power.

No further experiments have been performed, and the authors didn't appropriately adjust the conclusions to reflect the limitations of the study.

To provide an example, the authors decided to keep the title 'An IL-21R hypomorph circumvents functional redundancy to define STAT1 signalling in germinal center responses' although both reviewers clearly highlighted that no conclusion about STAT1 can be made and the authors responded that 'We agree that further experiments are needed to definitively show that the effect is attributable to reduced STAT1 activation alone. Rescue experiments will be a focus of future experiments.'

These rescue experiments (and additional analysis of the Tfh response) are essential to allow firms conclusions to be made.

In its current form, the manuscript is unfortunately misleading.

Reviewer #2 (Public review):

Summary:

In the manuscript, "An IL-21R hypomorph circumvents functional redundancy to define STAT1 signaling in germinal center responses," Cecile King and colleagues identify a cytoplasmic site of the IL-21 receptor that differentially regulates STAT1 and STAT3 activation upon IL-21 stimulation. They further examine the immunological consequences of this site-specific alteration on Tfh differentiation and Tfh-dependent humoral immunity, raising important questions about how gene-knockout models may obscure nuanced functional roles of signaling molecules.

Strengths:

The study convincingly highlights a non-redundant role for STAT1 downstream of IL-21-IL-21R signaling in the Tfh differentiation pathway. This conclusion is supported by in vitro analyses of STAT1 and STAT3 activation in CD4 T cells stimulated with IL-21 or IL-6; by in vivo assessments of Tfh and germinal center B cell responses in WT and IL21R-EINS mutant mice, including bone-marrow chimera systems; and by investigating the expression of Tfh-related molecules in WT versus IL21R-EINS CD4 T cells.

Weaknesses:

Although the experiments were carefully executed with appropriate controls, a key question remains unresolved: whether the Tfh differentiation defect in IL21R-EINS mice is directly attributable to reduced STAT1 activation. Rescue experiments that restore STAT1 signaling in IL21R-EINS TCR-transgenic CD4 T cells would provide strong evidence linking the mutation to impaired STAT1 activation and, consequently, defective Tfh differentiation. Without such evidence, it remains formally possible that additional, uncharacterized mutations introduced during ENU mutagenesis contribute to the phenotypes observed, particularly given the discrepancies between IL21R knockout and IL21R-EINS mutant mice.

Comments on revised version.

The revised manuscript failed to address the key question, whether the Tfh differentiation defect in IL21R-EINS mice results from the reduced STAT1 activation in CD4 T cells.

Reviewer #1 (Public review):

Summary:

T cells that recognize lipids-CD1c are frequent in circulation; however, their role in infection is unclear. This study aims to understand how Mtb infection can shape the responses of CD1c-specific T cells. CD1c is expressed in MTB granuloma, but in lower amount than in nearby inflamed tissue. Mtb infection downregulates the expression of CD1c on monocyte derived DCs. Single cell RNA sequencing revealed the cytotoxic program inherent to the lipids-CD1c-specific T cells. Using an in vitro APC system where CD1c expression remains intact upon Mtb infection, the authors suggest that these T cells react better to Mtb-infected than uninfected Cd1c-expressing APC and reduce Mtb burden in infected cells. Therefore, Cd1c downregulation could be an immune evasion strategy used by Mtb.

Strengths:

This study asks an important question. The single cell transcription analysis suggests the inherent cytotoxic program of lipid-CD1c cells and provides insights into their phenotypic and potential functional profiles. Function experiments suggest that these autoreactive T cells can react to Mtb infection, adding to the paradigm of infection control by these non-conventional T cell population. In summary, this study reports potential role of lipid-CD1c specific T cells in Mtb infection., and the evidence overall supports the conclusions.

Weaknesses:

(1) The study engineered THP-1 cells that lacked endogenous beta2 microglobulin (beta2m) and stably expressed chimeric CD1c-beta2m (CD1c-THP-1 cells). T cells isolated from healthy donors showed stimulation when mixed with CD1c-THP-1, suggesting the presence of CD1c reactive T cells in healthy individuals (Fig. 1). It appears that antibody staining profiles for different markers are not from the same experiment (Fig. 1). It is better to show all the representative antibody staining from a single experiment. And a quantification of MFI values from at least three independent experiments should be shown with statistics. Further, in each of the representative antibody profile, unstained/isotype control should be incorporated. The characterization of beta2m KO cells - using DNA sequencing and western blotting - should be shown in supplementary.

(2) Next, using immunohistochemistry, the study suggests expression of CD1c in lung biopsy from TB patients, and that CD1c expression is reduced on primary MoDCs upon Mtb infection (Fig. 2).

(3) To assess CD1c-reactive T cell cytotoxicity in Mtb infection, the authors generated CD1c-T cell lines (from T cells from healthy individuals) using two different approaches. In one, CD1c-specific T cells were expanded upon mixing with CD1c-THP-1 cells, were sorted using CD1c-endo tetramer, and expanded. In another approach, CD1c-specifc T cells were sorted (using CD1c-endo dextramer) directly from blood-derived T cells and were expanded. The line obtained using one of the approaches to assess cytotoxicity towards CD1c-THP-1 cells, uninfected or infected with Mtb. It is seen that CD1c-T cells exert cytotoxicity towards infected, and not towards uninfected, CD1c-THP-1 cells (Figs. 3,4).<br /> The authors should clearly indicate the T line, obtained from which one of the two approaches, was used for the cytotoxicity assay, and what was the result with the line obtained from other approach. Fig. S4 shows the sorting strategy and final profile of the T line obtained after CD1c-endo tetramer-based sorting and expansion. This figure is mistakenly referred as supplementary fig. 3 in the caption of Fig. 3; should be corrected. Further, to complete the Fig. S4, the intermediate step of sorting scheme, which shows negative and positive fraction as defined by streptamer binding, should be included. And Y-axis of left-most lower-most panel (no tetramer) should be labelled. The Profile of the T line shows three distinct population: tetramer-low, tetramer-intermediate, and tetramer-high. The authors should comment on this, particularly on the tetramerlow population that can be CD1c-negative cells that have non-specific weak binding to the tetramer.<br /> The profile of the T line obtained from the other approach should also be shown, and the experiments that used those should be clearly indicated.

(4) To assess whether the elevated response of CD1c-T cells is mediated by the T cell receptor (TCR) that they harbour, the authors performed single T cell sequencing for TCR. They could identify 11 αβ pairs, two of them (named as EM1 and EM2) constituted 10 out of the 11 αβ pairs. It is surprising that such a shallow sampling (despite of the filtering mentioned in Fig. S9) such a high degree of clonal expansion and yielded two context-relevant TCR sequences. These TCRs were indeed DC1c specific, as indicated by their ability to upregulate CD69 upon engagement with CD1c (Fig. 5C). However, they respond very weakly to CD1c-THP-1 cells - the difference (THP-1 KO versus CD1c-THP-1 cells) is small for EM1, and the upregulation of CD69 per se is very weak (within the noise range) for EM2 (Fig. 5D). Nonetheless, the difference shown is statistically significant for both EM1 and EM2.

(5) The study further indicate that the CD1c-specific T cells are enriched for the marker associated with their cytotoxic functions, and they perform slightly better in controlling Mtb growth in culture.

The authors are suggested to carefully go through the manuscript a couple of times so that supplementary figures are referred appropriately and accurately.

Reviewer #2 (Public review):

Summary:

The study by Milton et al titled "Human CD1c-autoreactive T cells recognise Mycobacterium tuberculosis-infected antigen-presenting cells and display cytotoxic effector programmes" characterises CD1c-restricted autoreactive T cells and their potential role in controlling Mtb infection. The authors develop a well-controlled system to assay for the functioning/activation of autoreactive T cells. They report the presence of CD1c-restricted autoreactive T cells in the circulating blood of healthy donors. They show that these T cells respond to CD1c and get activated even in the absence of any exogenous antigen. They next show that CD1c, along with CD1a and b, are typically downregulated on APCs during Mtb infection. These autoreactive T cells are cytotoxic, indicating they respond to Mtb treatment and/or to changes in the T cell ratio. The autoreactive T cells could effectively lyse Mtb-infected or PAMP-stimulated CD1c+APCs. Next, using TCR sequencing, they show that T cell responses were mediated by specific TCR clones with common sequence features. They show that these autoreactive T cells could curtail Mtb growth as measured by luminescence. Finally, using scRNAseq, they selectively identify the CD1c-reactive T cell pool and detect enrichment of typical effector memory CD4 and CD8 cells expressing cytolytic markers such as Granzyme, granulolysin, etc. The lung biopsy staining, along with the other data presented here, suggests that while CD1c-restricted T cells could have potential anti-bacterial roles, Mtb downregulation effectively shuts down this mechanism for TB control.

Strengths:

The study is designed well and has developed many exciting tools to generate specific information.

Weaknesses:

The revised manuscript addresses many concerns, but one section remains weak. The efficiency of these CD1c-restricted T cells in controlling TB remains very limited. The only result that addresses the bacterial control through this mechanism is Fig. 6C, which shows a very modest impact. Even THP1-KO cells show a decline in CFU when cultured with autoreactive CD1c-autoreactive T cells, and the further dip in THP1 CD1c cells is very minimal.

Another issue left unaddressed is the cytolytic response on Mtb-infected cells. How efficient are lytic responses in controlling Mtb infection? Usually, bacteria can emerge from lysed cells and divide extracellularly. How would one show this mechanism in vivo?

Reviewer #3 (Public review):

The authors have addressed most concerns from the initial review, significantly enhancing the manuscript. The expanded characterisation of the engineered THP1-CD1c system provides strong evidence that the observed T-cell responses are unlikely to result from residual conventional MHC recognition. The specificity of the CD1c-autoreactive T-cell lines is further confirmed by multimer staining, their inactivity against THP1-KO cells, and TCR-transfer experiments.

The revised data support the main conclusion that human CD1c-autoreactive T-cells recognise Mtb-infected cells that express Cd1c and exhibit cytotoxic effector functions. The difference between TCR-dependent recognition, shown by EM1 and EM2, and cytotoxicity, demonstrated by the original T-cell lines, is now more clearly presented.

Several limitations remain. The specific CD1c-associated lipid signalling molecule that enhances recognition of Mtb-infected cells has yet to be identified. Additionally, the bacterial luminescence assay lacks validation against CFU counts and cannot differentiate between intracellular and extracellular bacteria. The single-cell RNA sequencing was conducted with only two donors, and the lung immunohistochemistry remains qualitative without comparison to healthy or non-TB inflammatory tissues. These constraints limit detailed mechanistic insights and broader applicability, but they are appropriately reflected in the manuscript.

Overall, this is an important study that advances understanding of human CD1c-autoreactive T-cells in the context of mycobacterial infection. The evidence supporting the principal conclusions is solid, provided that altered CD1c-associated lipid presentation remains as a mechanistic hypothesis and the reduction in Mtb luminescence is not taken as direct evidence of selective intracellular bacterial killing.

Reviewer #1 (Public review):

Summary:

This study asks whether synapses formed by the same broad neuronal class (excitatory pyramidal neurons, PN) adapt their presynaptic organization in a cortex-specific manner, comparing prefrontal cortex (PFC) with primary somatosensory cortex (S1). The authors combine sophisticated electrophysiology (paired recordings and extracellular minimal stimulation), pharmacological perturbations of presynaptic Ca²⁺-secretion coupling, bouton Ca²⁺ imaging, and mechanistic modeling. Across two prominent excitatory connections (Layer 5 (L5) PN-L5PN and L2/3-L5PN), they provide convergent evidence that mature PFC synapses operate with looser Ca²⁺ channel-release sensor coupling than their S1 counterparts.

Overall, the study provides an appealing mechanistic link between synaptic nano/micro-architecture and cortical-area specialization. The idea that PFC synapses retain a more "plasticity-favoring" presynaptic state, while primary sensory cortex emphasizes reliability and timing precision, is potentially impactful for how we think about circuit computation and plasticity across cortical hierarchies.

Strengths:

A major strength is the multi-pronged experimental strategy. The paper first establishes robust, area-dependent differences in synaptic efficacy, reliability, timing, and short-term plasticity (facilitation prevailing in PFC versus depression in S1), using both paired recordings and minimal extracellular stimulation paradigms. The coupling interpretation is then directly supported by differential sensitivity to EGTA (and appropriate positive-control effects of fast chelators). Finally, volume averaged calcium signals are reported to be similar across areas, arguing against trivial explanations based on gross differences in calcium influx, and the modeling provides a quantitative framework for interpreting the observed chelator effects.

Weaknesses:

Limitations are minor and concern interpretation/clarity rather than core results. Some key inferences rely on indirect readouts (chelator sensitivity, fluctuation analysis-derived parameters, bouton-averaged calcium signals), each of which carries assumptions and potential confounds that should be discussed more explicitly. In particular, the, repatching paradigm for the paired-recording EGTA experiment, though very impressive, and the limited number of extracellular calcium conditions used for fluctuation analysis (three concentrations) can influence quantitative estimates and the confidence intervals around them.

Reviewer #2 (Public review):

Schwarze et al. investigated whether synaptic efficacy is brain-region specific. To this end, they compared synaptic connections established by layer 5 (L5) neocortical pyramidal cells and between L5 and L2/3 pyramidal cells. In order to identify the mechanism of this brain region specificity, the authors employed several experimental approaches, including paired electrophysiological recordings, extracellular stimulation, low- and high-affinity intracellular calcium chelators (EGTA and BAPTA), multiple probability fluctuation analysis (MPFA), and intracellular measurements of calcium transients as well as computational modelling. The findings of the present study indicate that synaptic connections in the primary somatosensory cortex (S1) are significantly stronger and more reliable than those in the prefrontal cortex (PFC).

The study is timely and the topic is of significant interest to the neuroscience community. Despite the extensive research that has been carried out on the neuroanatomy and receptor distribution of different brain regions, comparatively little attention has been paid to differences in synaptic physiology. The authors' approach is characterised by its elegance and comprehensive nature, and the conclusions drawn are compelling.

Comments on revised manuscript:

I have no further issues with the present version of the manuscript. All my concerns and/or recommendations were satisfactorily addressed.

Reviewer #3 (Public review):

Summary:

In this manuscript, Max Schwarze and colleagues examined the coupling distance between presynaptic Ca²⁺ channels and the vesicular release sensor at neocortical synapses in mouse. They propose that Ca²⁺ channel-release sensor coupling differs across cortical areas, with relatively loose (microdomain) coupling in prefrontal cortex (PFC) and tighter (nanodomain) coupling in primary somatosensory cortex (S1) for comparable pyramidal-neuron synapse types. To test this, they combine paired recordings and minimal stimulation with chelator manipulations (EGTA/BAPTA), mean-variance/MPFA-style analyses, presynaptic Ca²⁺ imaging, and computational modeling. They conclude that presynaptic coupling organization is area-specific in the mature cortex and contributes to regional differences in synaptic timing, reliability, and short-term plasticity.

Strengths:

This study tackles an important question and is strengthened by a cohesive body of evidence assembled from multiple complementary approaches. A major asset is the inclusion of high-value datasets, particularly the paired recordings between L5 pyramidal neurons and the systematic assessment of EGTA sensitivity, which provide a solid functional foundation for the authors' central claims. The work is further distinguished by its genuinely multimodal design: combining electrophysiology with presynaptic calcium imaging (and integrating these observations with quantitative analyses and modeling) offers a more mechanistic view of neurotransmitter release than any single method could provide. Overall, the direct, within-framework comparison of presynaptic release-control mechanisms across cortical areas for comparable synapse types is compelling and gives the conclusions a level of robustness and interpretability that is often difficult to achieve in studies of cortical synaptic diversity.

Weaknesses:

The principal limitation is incomplete cellular and synaptic specificity in parts of the study. The L2/3-L5PN experiments rely on minimal extracellular stimulation and therefore do not unambiguously identify the presynaptic neuron, its subtype or the number of recruited axons. Similarly, calcium imaging was performed at boutons on L5PN axon collaterals without identifying their postsynaptic targets. The imaging measurements could therefore combine boutons contacting pyramidal neurons and interneurons, potentially obscuring target-dependent differences in presynaptic calcium regulation. Recent connectomic studies demonstrate that local L5 pyramidal-cell axons can distribute substantial fractions of their output to inhibitory neurons, although the exact proportions depend strongly on pyramidal-cell subtype and distance along the axon.

The quantitative coupling-distance estimate is also model-dependent. The approximately 50-nm estimate for PFC synapses follows from a particular ring-like VGCC geometry and release-sensor model. The simulations demonstrate that this configuration is compatible with the data, but they do not uniquely identify the underlying molecular architecture.

Overall, the experiments support the narrower conclusion that the examined PFC and S1 synapses differ in functional Ca²⁺-channel-release-sensor coupling. The associated differences in synaptic timing, efficacy and plasticity are compelling, although coupling distance is not isolated causally from other regional differences in release-site number and quantal properties. The proposal that loose coupling is a general correlate of higher-order cortical function remains an interesting but currently speculative interpretation. Further comparisons across additional cortical regions and genetically or projection-defined synapse types will be particularly helpful in establishing the broader generality of this concept

Comments on revised version.

The authors have addressed most of my comments in the revised manuscript. I have only one remaining, relatively minor suggestion concerning point 5. I appreciate that the authors now acknowledge the possibility that the imaged boutons may contact different postsynaptic targets. However, the argument that interneuron-targeting boutons are likely to make only a minor contribution, based on the overall proportions of excitatory neurons or inhibitory synapses in the cortex, may not fully resolve this concern. Excitatory pyramidal neurons can distribute their outputs non-randomly across excitatory and inhibitory targets, and this distribution may depend on pyramidal-cell subtype and axonal distance. For example, a recent MICrONS/Allen Institute connectomic analysis of L5 extratelencephalic neurons in mouse visual cortex found that approximately two-thirds of their proximal synaptic outputs contacted inhibitory neurons. The proportion was close to 80% near the soma and decreased progressively with distance along the axon.

These findings concern a specific L5 pyramidal-cell subtype in visual cortex and therefore cannot be transferred directly to the PFC and S1 preparations examined here. Nevertheless, they illustrate that the postsynaptic target distribution of L5 pyramidal-neuron boutons cannot necessarily be inferred from the overall abundance of excitatory and inhibitory neurons or synapses.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

[Editors' note: The Reviewing Editor has assessed the revised article without further input from the original reviewers. The authors have made considerable efforts to address the methodological and reporting concerns raised by the initial peer review, resulting in a substantially revised manuscript.]

Reviewer #1 (Public review):

Summary:

This work explores the relationship between sexual conflict and species diversification in a clade of small water striders. They use multiple phylogenetic methods to establish a potential phylogenetic tree and explore incomplete lineage sorting and introgression. They then compare their calibrated species tree with phenotypic measures of many species to try to infer the relationship between species diversity and sexual conflict. They show evidence for both ILS and introgression within the broader clade. They also find evidence that male leg diversity is associated with speciation, potentially due to sexual conflict, and that male genital grasping structures as well as female anti-grasping traits have less evidence for an association with speciation. This paper seems like a generally rigorous and interesting addition to the literature on sexual conflict and speciation, and I believe the conclusions are well supported with only a few minor concerns with methodology.

Strengths:

This paper verifies results using multiple methodologies to ensure robustness to changes in software use, and presents convincing evidence for the hypotheses tested.

Weaknesses:

(1) Lines 299-304: For the categorization of sexual conflict traits, were categories chosen by a blinded participant or by a researcher who knew the species they were observing? If any of these traits are subtle, this could add bias to the categorisation of traits.

(2) Lines 325-327: It is unclear to me if you control for phylogenetic relationships in this model. Diversification rate could be lineage-specific regardless of sexual conflict, so it seems like potentially including phylogenetic relationships in a model would control for that. And similarly, lines 331-333, I may be mistaken, but it sounds like you are using lm() to model a binary outcome (presence/absence), but lm() doesn't do logistic regression as far as I know, so it may be better to model this as a logistic regression (controlled for phylogeny) using glm().

(3) There are a few areas where the reporting of inference or statistics could be improved, and throughout the manuscript there are often mentions of 'significant' without any measure of uncertainty such as confidence intervals (example on lines 449-451). In lines 385-390, because the intervals are so wide, I would suggest saying these clades diverged between X-mya and Y-mya, rather than giving an actual estimate. It seems to me like giving a specific date is a bit overconfident when the authors have such wide intervals. On line 436, I think the authors should report confidence intervals for their 5mya estimate. In lines 455-456, what is this correlation and what are the authors' uncertainties around the estimate?

(4) Lines 549-545: When the two possible explanations for this result are reported, it seems like the authors are saying that because they can't think of how to test this possibility, the other possibility is more likely. But I don't think that is an argument against the first possibility.

(5) Lines 591-602: This paragraph confuses me. I thought that much of the introduction and Figure 1 seemed to be putting forth that the terminal segment complexity was a conflict structure that we were interested in. However, here it is stated that it does not strongly influence mating success, so is it necessarily a conflict trait? Is it demonstrated that the leg structures influence mating success?

Reviewer #2 (Public review):

Summary:

This study uses comparative phylogenetic methods to examine the evolution of male and female antagonistic traits in a group of small water striders. Water striders have long been a model system for studies into the sexual conflict that arises through anisogamy, the differential investment in gametes by males and females. Here, the authors aimed to reveal the evolutionary rates and trajectories of male grasping and female anti-grasping traits across species of the minute water-strider subgenus Pseudovelia. This was done by combining multiple genomic techniques to generate phylogenies to test trait evolution, quantify rates of evolution, and identify instances of incomplete lineage sorting (a result of rapid diversification) and introgression (the result of interbreeding between genetically different populations/species).

Strengths:

The strengths of this study lie in its comparative macroevolutionary framework, in particular the generation of multiple phylogenetic hypotheses using different methods (mitochondrial genes, USCOs, and SNPs), and contrasting these to glean insights into evolutionary patterns across species.

Weaknesses:

The main weakness of the study is the lack of underlying experimental evidence to explicitly show the grasping and anti-grasping functions of the various male and female traits, relying instead on studies of similar structures in more distantly related taxa. Without explicitly showing the functional mechanisms and reproductive costs of these traits, the resulting interpretations are wholly speculative. However, I would argue that such macroevolutionary studies are still very useful, and provide the groundwork for future studies untangling the relative roles of sexual conflict, cryptic female choice, sperm competition and reproductive interference in trait evolution and ultimately in speciation.

Reviewer #2 (Public review):

The authors initial goal was to demonstrate loss of PG during the slow sporulation process of Myxococcus xanthus, with examination of the PG degradation products in order to implicate possible enzymes involved. Upon finding a predominance of LTG products, they examined sporulation in strains lacking each of the 14 candidate LTGs encoded in the genome, leading to the identification of two sporulation-linked LTGs. An extensive characterization of the roles played by these LTGs. One LTG is responsible for the slow sporulation PG degradation, while another is required for the rapid sporulation process. Interestingly, the "slow" LTG seems to provide an important regulatory brake on the rapid enzyme. Single molecule fluorescent tracking of these enzymes was used to develop a model for their interaction with PG that mimics their observed activity. The rate of PG synthesis activity was also shown to impact the rate of PG degradation, suggesting potential interplay between the synthetic and degradative enzymes.

Strengths:

The genetic analysis to identify sporulation-linked LTGs and their effects on growth sporulation, and spore properties was well done and productive. The fluorescence microscopy to track LTG mobility, presumably tied to activity, produced a convincing argument about the mechanism of regulation of one LTG by another. The authors have responded well to most points of the previous review.

Weaknesses:

While the impact of LTGs on sporulation was clearly demonstrated, the PG analysis that resulted in the study of LTGs raised some important unanswered questions. The analyses suggest that the PG is degraded to quite small fragments, which would normally be lost during the purification of PG. The conclusions concerning the PG degradation during sporulation needs to be clarified, as described below. The authors suggest a "new mechanism of sporulation" when they have actually simply identified an important factor (PG degradation by LTGs) within a complex "process of sporulation". This needs to be reflected also in title of the paper.

Reviewer #1 (Public review):

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

Meiotic recombination at chromosome ends can be deleterious, and its initiation-the programmed formation of DSBs-has long been known to be suppressed. However, the underlying mechanisms of this suppression remained unclear. A bottleneck has been the repetitive sequences embedded within chromosome ends, which make them challenging to analyze using genomic approaches. The authors addressed this issue by developing a new computational pipeline that reliably maps ChIP-seq reads and other genomic data, enabling exploration of previously inaccessible yet biologically important regions of the genome.

In budding yeast, chromosome ends (~20 kb) show depletion of axis proteins (Red1 and Hop1) important for recruiting DSB-forming proteins. Using their newly developed pipeline, the authors reanalyzed previously published datasets and data generated in this study, revealing here-to-fore-unseen details at chromosome ends. While axis proteins are depleted at chromosome ends, the meiotic cohesin component Rec8 is not. Y' elements play a crucial role in this suppression. The suppression does not depend on the physical chromosome ends but on cis-acting elements. Dot1 suppresses Red1 recruitment at chromosome ends but promotes it in interior regions. Sir complex renders subtelomeric chromatin inaccessible to the DSB-forming machinery.

The high-quality data and extensive analyses provide important insights into the mechanisms that suppress meiotic DSB formation at chromosome ends.

Comments on latest version:

I have checked the authors' responses and the revised analyses. I think they have adequately addressed my main concerns, particularly regarding the quantitative analyses of the chromosome fusion and SK1/S288c comparisons. I have no further comments and am content for you to proceed.

Reviewer #2 (Public review):

Summary:

In this manuscript, Raghavan and his colleagues sought to identify cis-acting elements and/or protein factors that limit meiotic crossover at chromosome ends. This limitation is important for avoiding chromosome rearrangements and preventing chromosome mis-segregation.

By comparing protein axis recruitment in SK1 and S288C background, which differ in their number and distribution of Y' elements, the authors show that Y' element have a limited impact on axis protein enrichment. Genetic analyses coupled with ChIP experiments revealed that the differential binding of the Red1 protein in subtelomeric regions requires the methyltransferase Dot1. Interestingly, the lack of Red1 depletion in subtelomeric regions in this mutant does not impact DSB formation. Another surprising finding is that deleting DOT1 has no effect on Red1 loading in the absence of the silencing factor Sir3. Unlike Dot1, Sir3 directly impacts DSB formation, probably by limiting promoter access to Spo11. As now clearly stated in the abstract and the discussion, this explains only a small part of the low levels of DSBs forming in subtelomeric regions and the main mechanisms suppressing crossover close to the ends of chromosomes remain to be deciphered.

Strengths:

This work provides intriguing observations, such as the impact of Dot1 and Sir3 on Red1 loading and the uncoupling of Red1 loading and DSB induction in subtelomeric regions.

The separation of axis protein deposition and DSB induction observed in the absence of Dot1 is interesting because it rules out the possibility that the binding pattern of these proteins is sufficient to explain the low level of DSB in subtelomeric regions.

The demonstration that Sir3 suppresses the induction of DSBs by limiting the openness of promoters in subtelomeric regions is convincing.

Weaknesses:

Sir3's impact on DSB induction is compelling, yet it only accounts for a small proportion of DSB depletion in subtelomeric regions. Thus, the main mechanisms suppressing crossover close to the ends of chromosomes remain to be deciphered. [Update: these limitations have been added to the text.]

Reviewer #1 (Public review):

Summary:

The Voltage-Dependent Anion Channel 1 (VDAC1) is the most abundant β-barrel protein in the outer mitochondrial membrane and the main conduit for metabolite and ion exchange between the cytosol and mitochondria. Its oligomerization has been proposed to control mitochondrion-mediated apoptosis, making it a prime target for therapeutic intervention in diseases associated with excessive cell death, such as neurodegenerative disorders and autoimmunity. VBIT-4 is a small molecule developed to inhibit VDAC oligomerization and has shown therapeutic potential in various preclinical models. Despite its widespread use, the mechanism of action of VBIT-4 has not yet been fully elucidated. In this paper, Ravishankar et al. combine a suite of biophysical approaches with computer simulations to demonstrate that VBIT-4 forms water-permeable defects in membrane bilayers without any detectable effects on VDAC1 channel properties or oligomerization. Furthermore, cytotoxicity assays revealed identical VBIT-4 IC50 values in wild-type and VDAC1-KO cells, indicating that its activity does not depend on VDAC1. Collectively, these findings cast significant doubt on the widely held assumption that VBIT-4 is a specific inhibitor of VDAC1 oligomerization. Instead, it appears that VBIT-4 functions as a membrane-active compound.

Strengths:

This is a carefully conducted and well-written study that highlights potential side effects of VBIT-4, a compound that has been used to study the role of VDAC1 in a range of physiological and pathological conditions. The work is of interest to a broad readership by showcasing the importance of a systematic assessment of drug-membrane interactions to identify potential off-target membrane-driven effects of small molecules that may be mistakenly attributed to the inhibition of specific proteins. Its strength lies in the variety of complementary approaches the authors used to rigorously challenge the effect of VBIT-4 on VDAC1 organization and function. Overall, the experimental data are compelling and of high quality.

Weaknesses:

The authors used high-speed atomic force microscopy (HS-AFM) to study the impact of VBIT-4 on VDAC1 oligomerization in real time at nanoscale resolution. Toward this end, they adsorbed POPC:POPE:cholesterol membranes reconstituted with or without VDAC1 on mica. This revealed that addition of VBIT-4 produced small perforations in the bilayer that were independent of VDAC1. In the absence of VBIT-4, VDAC1 showed the characteristic honeycomb topography that the authors described in a previous study (Ref. 17). To quantitatively assess whether VBIT-4 affects VDAC1 organization, they analyzed protein compaction within clusters using inter-protein distance measurements. This analysis revealed no significant difference in VDAC1 organization between control conditions, 1 uM and 10 uM VBIT-4, supporting a model in which VBIT-4 primarily perturbs the lipid matrix rather than VDAC1 assemblies. This conclusion is based on the assumption that VDAC channels retain some lateral mobility in bilayers adsorbed onto mica, for which the manuscript does not provide direct evidence. In their rebuttal, the authors cite previous studies indicating that VDAC channels and beta-barrel proteins with larger extracellular domains exhibit measurable lateral diffusion in supported lipid bilayers formed on mica. Based on this, they conclude that their methodology does not constitute a limiting factor for lateral diffusion. It would be appropriate to cover this point and cite the corresponding references in the manuscript.

Reviewer #2 (Public review):

Summary

This manuscript re-evaluates the mechanism of action of VBIT-4, a compound widely used as a putative inhibitor of VDAC1 oligomerization. The authors test whether VBIT-4 acts directly on VDAC1 assemblies or instead perturbs lipid membranes more generally. Using high-speed atomic force microscopy, electrophysiology, liposome leakage assays, Laurdan fluorescence, microscale thermophoresis, coarse-grained molecular dynamics simulations, and cell-based assays in wild-type and VDAC1-knockout HeLa cells, they show that VBIT-4 partitions into lipid bilayers, induces membrane defects and leakage, and causes VDAC1-independent cytotoxicity at concentrations commonly used in the literature to infer VDAC1-specific effects.

Strengths

The main strength of the study is the convergence of multiple independent approaches on the same central conclusion. Atomic force microscopy directly visualizes VBIT-4-induced defects in lipid regions while VDAC1 assemblies remain apparently intact. Electrophysiology separates VDAC1 channel behavior from background membrane conductance and shows that VBIT-4 does not measurably alter VDAC1 conductance or voltage gating, while increasing nonspecific membrane permeability. Lipid-only membranes, lipid nanodiscs lacking VDAC1, and VDAC1-knockout cells provide important controls supporting a VDAC1-independent mechanism.

The wild-type versus VDAC1-knockout cytotoxicity comparison is a particularly strong test of VDAC1 independence at concentrations above 10 µM. The manuscript also usefully emphasizes that VBIT-4 is poorly soluble, aggregation-prone, pH-dependent, membrane-partitioning, and storage-sensitive. These properties are important for interpreting variability across previous studies using this compound.

The manuscript is careful in defining the scope of its conclusions. It distinguishes AFM- and simulation-based measurements of VDAC1 cluster organization from cross-linking-defined proximity, which is important because these are related but non-equivalent readouts of VDAC1 organization. It also explicitly discusses how VBIT-4 solubility, aggregation, protonation, membrane partitioning, and storage sensitivity complicate comparisons based on nominal compound concentration. These points help readers interpret both the current data and the broader literature using VBIT-4.

Limitations

The cellular data strongly support VDAC1-independent cytotoxicity above 10 µM, but the lower-dose mitochondrial functional phenotypes, including effects on respiration, mitochondrial calcium, and mitochondrial membrane potential, were not directly compared between wild-type and VDAC1-knockout backgrounds. The manuscript appropriately avoids overinterpreting these mitochondrial effects as directly VDAC1-independent, but readers should note that VDAC1 independence is more firmly established for cytotoxicity than for the lower-dose mitochondrial phenotypes.

The coarse-grained simulations provide useful mechanistic support for membrane partitioning, aggregation, and defect formation. However, the partitioning validation relies on the neutral VBIT-4 species and comparison with empirical partition-coefficient predictors rather than a matched all-atom octanol-water transfer calculation using the same atomistic model. This is a reasonable modeling choice, but it does not eliminate the likely importance of atomistic-level details for accurately describing pore formation. This is especially relevant for a compound with pH-dependent protonation, aggregation, and interfacial membrane localization. The simulation-derived partitioning and pore-formation results should therefore be interpreted as strong qualitative and mechanistic support rather than as a definitive quantitative description of VBIT-4 behavior across all protonation states, concentrations, and membrane environments.

Overall assessment

Overall, this is an important and timely study that provides a strong reassessment of VBIT-4 as a tool compound. The evidence that VBIT-4 perturbs lipid membranes independently of VDAC1 is compelling and should be useful for researchers interpreting past and future studies that use VBIT-4 as a probe of VDAC1 function.

Reviewer #1 (Public review):

Summary:

In this study, Sidwell and Rothenberg report a genetically rigorous study whose central finding - reduction of Bcl11b at the positive selection stage reroutes CD8 T cells to the T cell virtual memory (TVM) fate - is well supported by the convergence of elegant mouse models (WT, Bcl11bΔEnh, Bcl11b+/-, Bcl11bR3S). In wildtype mice, Bcl11b acts as a transcriptional repressor that limits the reprogramming of CD8 T cells to the TVM cell type. Reducing Bcl11b dosage partially relieves this repression, resulting in increased development toward the TVM fate. Their analysis reveals several interesting aspects: 1)<br /> Strengths:

Factors that drive CD8 TVM fate are important and relatively poorly understood. This study makes the unexpected and interesting observation that Bcl11b gene dosage impacts TVM during T cell selection in the thymus. The conclusions are strongly supported by multiple independent lines of evidence.

Weaknesses:

The direct genomic targets impacted by Bcl11b heterozygosity were not identified.

Reviewer #2 (Public review):

This manuscript by Sidwell and Rothenberg demonstrates that commitment of CD8 T cells to the virtual memory TVM cell lineage is fine-tuned in a dose-dependent manner by the transcription factor Bcl11b during intrathymic positive selection. Using multiple mouse models, the authors show that a subtle, less than two-fold reduction in Bcl11b expression or disruption of its corepressor-recruitment domain biases developing CD8 single-positive thymocytes toward a TVM cell fate without requiring peripheral activation, lymphopenia, or external cytokine signaling. Mechanistically, this modest decrease in Bcl11b does not alter global chromatin accessibility but instead enhances downstream T-cell receptor (TCR) signal responsiveness, effectively mimicking a high-affinity selection response to divert late-cycling CD8SP thymocytes into the TVM pathway. These data suggest that Bcl11b essentially serves to attenuate the interpretation of TCR (and cytokine) mediated signals to prevent the excessive differentiation characterised by virtual memory T cells and the CD44int naïve T cells. This is distinct from alternative pathways of Tvm development that are driven predominantly by exposure to cytokines, namely IL-4, in the thymus, and serves to reinforce our understanding that Tvm cells are an alternate lineage of T cells that arise during development, in part as a consequence of strong TCR signalling. There are some issues arising, not least of which is why the attenuated Bcl11b expression is insufficient to drive negative selection rather than Tvm formation.

This paper was an absolute pleasure to read given its engaging narrative style. However, in some parts it was a bit long-winded and took a while to get to the destination. Some effort should go into making the narrative more concise, while retaining the thoroughly clear explanation and interpretation of the data.

Reviewer #3 (Public review):

Summary:

The authors explore the impact of a modest (<2-fold) reduction in the expression of Bcl11b on the differentiation of CD8+ T cells, with a special focus on the generation of memory-like cells (sometimes called "virtual" memory cells - or TVM). The manuscript covers a lot of ground, but highlights are using diverse models to show that reduced Bcl11b expression during thymic development (but not in naïve CD8+ T cells that have accessed the periphery) leads to enhanced generation of cells with phenotypic, transcriptional, epigenetic and functional characteristics of TVM; that this is a cell-intrinsic effect, but not apparently driven by enhanced responsiveness to cytokines (which promote TVM in some models); decreased Bcl11b improves T cell sensitivity at the mature (and likely in immature thymocytes). Myriad approaches and controls are used, providing a very thoroughly explored model.

This is a tour-de-force in applying the geneticists tool-box for investigating how a tantalizingly modest decrease in Bcl11b expression impacts the generation of TVM-like cells during thymic development. It is unreasonable to request additional data, but clarification of some key conclusions is needed.

Reviewer #1 (Public review):

Summary:

This work builds a theory to implement planning trajectories towards a goal in a known environment, inspired by analyses of prefrontal neural recordings. Unlike standard neural architectures for this task, such as value-based learning and successor representations, their proposed theory is able to adapt to novel goal locations within-trial. The key to the theory is that future times and locations are represented by disjoint groups of neurons. The recurrent connectivity between groups of neurons selective to specific future time and locations reflects the learned knowledge of the task. Finally, the authors show that standard networks trained on the task approximate their proposed theory.

Strengths:

The structure of the work is clear, and the article is very well written, which is particularly noticeable given the consequential amount of results presented. The authors are able to link their theory to experimental findings in neural recordings. The reverse-engineering of trained recurrent neural networks is very thorough, by analyzing both dynamics and connectivity. The assumptions and predictions of their model are clearly stated.

Weaknesses:

I believe the article shows no major weaknesses. There are important points that are beyond the scope of this study, that may limit its impact. For instance, while very little previous literature has linked planning to RNNs, their proposed theory of "space-time attractors" in RNNs is about input-driven stable attractors. The authors clarify this in the text, highlighting differences between classic attractor networks and the mechanism studied here. Related to this, an assumption of this mechanistic theory is that rewards are flexibly rerouted to the corresponding neural populations after every action, which seems difficult to implement biologically. Both aspects are discussed in the current manuscript.

Reviewer #2 (Public review):

This well-written manuscript proposes to use attractors in space and time (STA) as a mechanistic explanation for planning in the prefrontal cortex. The main conceptual hypothesis is that planning is implemented as attractor dynamics in a representation that encodes states at each time step jointly. Depending on inputs the network relaxes to a trajectory that already contains future states that will be visited at each time step, rather than computing a scalar value at each point in time and space like other classical approaches from RL. The authors compare this approach to implementations such as TD learning and successor representation, and further show that trained recurrent neural networks on specific tasks involving planning develop structured subspaces resembling the ones postulated in STA.

The idea of treating attracting trajectories unfolding in time as the computational substrate for planning is very interesting and potentially important. The explicit construction of a state x time representational space and its implementation via recurrent dynamics are appealing and convincing in the idealized tasks considered. I found the ms to be refreshingly explicit regarding several of the assumptions and limitations of the models, for example the fact that certain advantages can be viewed as properties of the state space itself and not necessarily of a fundamentally new planning mechanism.

I thank the authors for their reply and their thorough rebuttal. It answered most of my previous questions and greatly enhanced the understanding of the paper.

I have just two remaining concerns:

(1) The ms shows attractor dynamics in the trained RNN during planning, but it is less clear how these relate to the execution phase. It would be helpful to clarify whether the network state during execution is expected to effectively be close to a FP or at a FP for each input, or whether the RNN implements transient dynamics shaped by the underlying attractor landscape.

(2) Regarding the previously raised point of calling their result a "Mechanistic theory of planning", I did not mean to suggest that a theory cannot be mechanistic, or that "mechanistic theory" is not a valid term, especially in the context of this paper (although I believe this topic would deserve an entire separate discussion in the neuroscience field).

My point was about whether STA should primarily be interpreted as a mechanistic theory of planning, or as a candidate neural mechanism for implementing the planning as inference theory. I am aware that mechanistic theory and mechanistic models are often used interchangeably in neuroscience, and I certainly do not claim that my interpretation is the only valid one. My opinion is that the manuscript presents a convincing and interesting candidate neural mechanism for planning, which can be strongly related to planning as inference. The reason why I am not fully convinced about the framing as a mechanistic theory of planning is mainly that the adjacency-based connectivity isn't emerging or derived, but is instead introduced based on practical and empirical considerations. It's not a major issue, but I would personally frame it as a mechanistic account or model of planning (and/or planning-as-inference), rather than a theory, mechanistic or not.

Reviewer #1 (Public review):

Summary:

The immune system represents a source for melanocyte-extrinsic determinants of melanoma. Multiple immune cell types, including natural killer cells and CD8+ cytotoxic T lymphocytes, destroy cancer cells directly, and this anti-tumor activity is widely thought to eliminate many nascent tumors before they become clinically detectable. Regulatory T (Treg) cells, which are defined by expression of the transcription factor Foxp3, function as critical suppressors of lymphocyte activation in both homeostatic and disease contexts. Intratumoral Treg cell accumulation has been associated with disease progression in the clinic, and Treg cell depletion inhibits melanoma outgrowth in transplantable models of the disease.

However, the role of Treg cells during the early, premalignant stage of melanocyte expansion has not been examined. To study the interplay between incipient melanoma and cutaneous inflammation, the authors subjected an autochthonous murine model of melanoma [LSL-BrafV600E;Ptenfl/fl;Tyr::CreERT2 (BPT)mice] to three distinct inflammatory immune perturbations. Each of these perturbations accelerated premalignant melanocyte outgrowth, which was unexpected given that both Treg cell depletion and DNFB treatment markedly enhanced conventional T (Tconv) cell infiltration into the skin. Detailed analysis of each inflammatory response revealed a shared cellular and molecular signature comprising myeloid infiltration, characteristic cytokines and tissue remodeling factors, and vascular permeability. Altogether, the results support the hypothesis that oncogenic mutations in melanocytes along with altered immune response and inflammation synergistically drive melanomagenesis.

Strengths:

(1) The use of the three distinct inflammatory immune perturbations, such as transient Treg cell depletion, acute UV- B irradiation, and 2,4-dinitrofluorobenzene (DNFB)-induced contact hypersensitivity.

(2) The re-examination of the role of Treg cells in transplantable models of tumor growth by Subcutaneous (s.c.) implantation of syngeneic B16F10 melanoma cells as widely used to study anti-tumor immunity and to interrogate the effects of Treg cells on tumor suppression.

(3) The use of the LSL-TdTomato mice for measuring the TdTomato fluorescence in each immune cell type to assess uptake of melanocyte antigen.

(4) scRNA-seq data document a broad and rapid myeloid inflammatory response in Treg cell-deficient skin.

Weaknesses:

(1) The expression of inflammatory mediators Il1b, Il6, and TNFa, and angiogenic mediators Hif1a and Ang2 in all of the three models of immune perturbation has been verified at the transcript level by qRT-PCR, and it remains to be determined whether it correlates with the same at the protein level.

(2) scRNA-seq data to profile the diversity of immune cells (CD45+) in the ear skin of the DNFB-treated contact hypersensitivity model are currently missing.

(3) The authors indicate that at least 2 prior studies directly implicated inflammatory macrophages in the melanocyte proliferation response. However, no attempts were made for the identification of the UVB-driven factors underlying myeloid recruitment by which these cells activate melanocytes.

(4) It is very surprising to observe that altered immune responses, such as enhanced Tconv cell priming and activation in Treg cell-deficient skin, failed to antagonize mutant melanocyte outgrowth in the BPT model. While UVB-induced skin inflammation differs somewhat from the response to Treg cell depletion, both feature the infiltration of tissue remodeling macrophages and vascular instability. The findings that contact hypersensitivity can promote the expansion of non-malignant BRAF (V600E)Pten- Het melanocytes have interesting implications for benign hyperpigmentation conditions, such as post-inflammatory hyperpigmentation, Riehl's melanosis, and melasma.

Reviewer #2 (Public review):

Summary:

Tran and colleagues investigate how inflammation alters the earliest stages of melanoma tumorigenesis in mice carrying LSL-BrafV600E, Ptenfl/fl, and Tyr-CreERT2 alleles. They compare transient regulatory T cell depletion, acute UVB irradiation, and DNFB-induced contact hypersensitivity. Each perturbation increases ear pigmentation and Tyrp1 expression after oncogene induction. The inflammatory settings also share recruitment of monocytes and macrophages, expression of inflammatory and tissue-remodeling programs, and increased vascular permeability. Dexamethasone attenuates the DNFB-associated phenotype. A secondary finding of particular interest is that regulatory T cell depletion accelerates the premalignant BPT phenotype but inhibits B16F10 tumor growth, suggesting that regulatory T cells can have different effects during tumor initiation and established transplantable disease.

The study addresses an important question that is difficult to approach using transplantable tumor models. The data convincingly show that each perturbation produces substantial inflammation in the skin and that vascular leakage accompanies the response. At present, though, the central biological endpoint is not sufficiently separated from melanogenesis. Darkening of the ear and increased Tyrp1 RNA can reflect more pigment or altered differentiation within the existing oncogene-carrying melanocytes rather than an increase in their number, particularly given that pigment content is itself variable in transformed melanocytes, which range from heavily pigmented to nearly amelanotic. This issue is especially important in the UVB and DNFB experiments, where inflammatory signals can alter pigmentation directly.

Strengths:

The autochthonous BPT model is a major strength. It preserves the native relationship between melanocytes and the surrounding stromal and immune compartments during lesion initiation. Including three distinct inflammatory perturbations makes the recurring association with melanocyte-associated readouts more persuasive than any single model would be. The paired-ear DNFB design is efficient and controls for inter-animal variability. The combination of flow cytometry, single-cell RNA sequencing, intravital imaging, and Evans Blue assays provides useful complementary evidence that the inflammatory interventions remodel the local tissue environment. The B16F10 experiments help establish that the unexpected effect of regulatory T cell depletion is specific to the early autochthonous setting rather than a general failure of the depletion model. The BT-Het experiment is also thoughtful in asking whether inflammation can enhance the phenotype of oncogene-carrying melanocytes in a nevus-stage context that does not proceed to full malignant progression after oncogene induction alone.

Weaknesses:

The strongest caveat concerns the central claim. The outgrowth readouts are ear darkening and bulk Tyrp1 expression, but both may report pigment or differentiation state rather than the number of oncogene-carrying melanocytes. Pigment content is not a reliable proxy for cell number here, since the same population can darken or lighten without any change in cell number. No direct count or lineage-reporter measurement is provided for the regulatory T cell, UVB, or DNFB comparisons. Until that gap is filled, the data support increased pigmentation of oncogene-carrying melanocytes more firmly than the premalignant expansion named in the title, and this concern is most pronounced in the UVB and DNFB settings, where inflammation can change pigmentation on its own.

Secondly, the proposed shared mechanism is largely associative. Dexamethasone appropriately shows that inflammation as a whole is required for the DNFB phenotype, but as a broad anti-inflammatory it cannot isolate any single component. The manuscript singles out blood vessel remodeling as particularly important, and that specific attribution exceeds what a non-selective drug can show, especially as no individual pathway is selectively blocked in a tumor-initiation experiment and Il6 is reduced only modestly. The authors acknowledge that the precise chain of causation is unresolved, so the vascular claim should be softened to match or tested directly.

Also, several of the mechanistic conclusions rest on thin or single cohorts and on single-cell data whose replication is not fully reported, making them less convincing than the inflammatory phenotypes themselves. The systemic regulatory T cell model shows the consequences of body-wide depletion rather than a skin-specific regulatory T cell function, and the inferred monocyte-to-macrophage trajectory reflects transcriptional similarity rather than a demonstrated lineage path. The interpretation of dendritic-cell TdTomato uptake as evidence of antigen presentation or T cell priming is not supported by a direct measure of reactivity.

Finally, the nevus-stage framing should be corrected. The manuscript frames the BT-Het experiment as testing non-oncogenic conditions, but those melanocytes carry BrafV600E, so it is better read as inflammation-enhanced behavior of oncogene-carrying melanocytes at the nevus stage.

Reviewer #3 (Public review):

Summary:

Tran et. al. investigate how inflammation in the skin influences the early stages of melanomagenesis. They use an autochthonous, tamoxifen-inducible mouse melanoma model (LSL-BrafV600E;Ptenfl/fl;Tyr::CreERT2, "BPT") to examine three inflammatory perturbations: transient depletion of regulatory T cells, acute ultraviolet-B irradiation, and contact hypersensitivity induced by 2,4-dinitrofluorobenzene (DNFB). They report that each perturbation promotes the recruitment of immune cells, especially inflammatory monocytes and macrophages, increased expression of inflammatory and tissue-remodeling factors, and enhanced vascular permeability, which ultimately increases the outgrowth of premalignant melanocytes measured by local pigmentation and expression of the melanocyte-associated gene Tyrp1. In the DNFB model, the authors showed that treatment with dexamethasone reduces the effects of contact hypersensitivity on pigmentation, inflammatory gene expression, and vascular leakage, potentially providing a translational angle.

Strengths:

An interesting observation is that transient Treg depletion promotes premalignant melanocyte outgrowth in the autochthonous BPT model while inhibiting the growth of transplantable B16 F10 tumors. This contrast is consistent with a role for Tregs in limiting inflammatory disruption of the skin during early tumorigenesis and highlights the value of autochthonous models. These findings may also have broader implications for understanding the stage- and context-dependent functions of Tregs in cancer.

Another strength of this manuscript is the comparison of three mechanistically distinct inflammatory perturbations. Treg depletion, UVB irradiation, and DNFB-induced contact hypersensitivity engage different inflammatory pathways but converge on myeloid-cell recruitment, inflammatory gene expression, and increased vascular permeability. This convergence strengthens the conclusion that an acute inflammatory microenvironment is associated with enhanced melanocyte outgrowth during the early premalignant phase.

Weaknesses:

The paper convincingly establishes a correlation between the inflammatory signature and melanocyte outgrowth across three distinct perturbations. However, the mechanistic claim that myeloid cells and/or vascular remodeling drive melanocyte expansion rests primarily on the dexamethasone experiments in the DNFB model. Because dexamethasone broadly affects immune, stromal, endothelial, and melanocytic compartments, these experiments do not establish that inflammatory monocytes/macrophages or vascular destabilization are specifically required for the melanocyte response.

A related limitation is that the proposed monocytic origin of the inflammatory macrophage population following perturbation remains inferred. Although the scRNA-seq data and pseudotime analysis in Figure 4 - Supplement 2 are consistent with a trajectory from monocytes to macrophages, they do not exclude local reprogramming of resident macrophages into an inflammatory state. This alternative is particularly relevant because resident macrophage populations have been implicated in vascular remodeling and tumor outgrowth (PMIDs: 36493773 and 40216154).

Finally, the assessment of melanocyte outgrowth is largely through increased pigmentation and whole-ear Tyrp1 expression. Although these measurements may reflect increased melanocyte abundance, they may also be influenced by melanogenic activity or increased Tyrp1 expression per cell. More direct evidence of melanocyte proliferation, such as Ki67 or EdU/BrdU staining specifically within TdTomato-positive melanocytes, would support the use of "expansion" and "proliferation" throughout the manuscript. Histopathological characterization of lesion architecture, atypia, proliferation, and invasion would also help establish the premalignant nature of the lesions.

Reviewer #1 (Public review):

Summary:

This study described the genomic features of K. pneumoniae in tissue samples from children in Zambia who had died from pneumonia, where K. pneumoniae was believed to be the causative organism. This was a substudy of a previous study which described more broadly the aetiologies of community-acquired pneumonia in this population, using minimally invasive tissue sampling.

In this study, the authors used culture-independent molecular tools to characterise the K. pneumoniae genomes from post-mortem tissue samples from 7 children who had died of pneumonia. They described a diversity of strain lineages in the 7 children, with different capsular types and virulence profiles. Notably, all strains had a wide array of antimicrobial resistance genes. This is probably not surprising given the propensity of this organism to acquire AMR genes, but it is concerning in isolates from community-acquired infections.

The study also highlights the value of using advanced culture-independent molecular techniques, and the scope of data that can be obtained. In settings where culture is not always available, storage and subsequent remote analysis of these samples is an option to better understand the microbiology (although expense is still a barrier, and culture-based microbiology should not be completely neglected).

Strengths:

Ascribing aetiologies in respiratory infections is not (yet) an exact science, and there is likely to always be some doubt about whether an organism (whether identified by culture or nucleic acid detection) is a true pathogen. However, the authors have used a robust combination of histology, microbiology, radiology, clinical assessment and verbal autopsy, and this is probably as good as we can get it at present.

The molecular techniques employed and the analysis were robust and technically sound. There are some areas where there is inconsistency between the results from two samples from the same patient, and this is likely due to the lower number of reads - important information for future similar studies. It highlights the potential limitations of this technique.

The data obtained (albeit from a small sample set) are consistent with the other data from Africa, which provides support for the value and reliability of the technique itself.

Weaknesses:

The major weakness is the small sample size. The 7 patients analysed in this study are a subset of the children in the larger study who had been identified as having died from K. pneumoniae respiratory infection. So while the findings of this study highlight the potential role of this organism as a respiratory pathogen and provide some insight into the distribution of lineages in the community, the results don't really allow for major changes to clinical or diagnostic practice as yet. The data highlight a potentially under-recognised problem and would be useful to inform future studies.

A minor weakness is more related to the journal layout, where methods are presented last. The results are not as easy to follow without reviewing the methods - in particular the description of histopathological findings and results of PCR on the biopsies. Until one realises that 6 biopsies had been taken from each of the deceased children, and a subset of these biopsies used for the study, the results seemed confusing.

Reviewer #2 (Public review):

Summary:

This manuscript applies a culture-independent hybridization-capture metagenomic sequencing approach to characterize Klebsiella pneumoniae detected in post-mortem lung tissue from fatal pediatric pneumonia cases in Lusaka, Zambia. The study addresses an important challenge in retrospective genomic investigations where cultured isolates are unavailable and demonstrates the potential of targeted sequencing to recover clinically relevant genomic information directly from archived tissue specimens. The authors report sequence types, capsular loci, antimicrobial resistance determinants, virulence-associated genes, and evidence of closely related isolates in two cases. The work is valuable as a proof-of-concept application of targeted sequencing in challenging post-mortem specimens and provides useful descriptive genomic data from a setting where such information remains limited. However, several epidemiological and public health interpretations extend beyond what can be supported by the available data. The study includes only seven successfully sequenced children from a single setting and was not designed to determine the source of acquisition, transmission pathways, or population-level distributions of antimicrobial resistance or capsular types. The manuscript would therefore be strengthened by more consistently framing the findings as a descriptive genomic investigation of K. pneumoniae detected in children who died outside hospital settings, rather than as evidence of community-acquired infection or broader epidemiological shifts.

Strengths:

The principal strength of the manuscript is its methodological contribution. The authors demonstrate that hybridization-capture metagenomic sequencing can recover informative genomic data from post-mortem lung tissue in cases where conventional culture-based sequencing is not available. This is an important technical advance for retrospective studies, minimally invasive tissue sampling platforms, and settings where sample degradation, prior antibiotic exposure, or lack of routine culture limits genomic surveillance.

The study also addresses an important public health problem. K. pneumoniae is a major cause of severe infection and antimicrobial resistance globally, yet its role in fatal pediatric pneumonia outside hospital settings remains difficult to define. The generation of sequence type, capsular locus, antimicrobial resistance, and virulence-associated gene data from post-mortem specimens is therefore useful and may inform future study designs. The identification of closely related isolates in two infants is also potentially important and raises hypotheses about shared sources or transmission that could be explored in larger studies.

Another strength is that the authors appropriately acknowledge several technical challenges, including low numbers of K. pneumoniae-assigned reads in some specimens and unresolved or discordant capsular locus calls. These issues are important for readers considering the utility of this approach in low-input or mixed-specimen contexts.

Weaknesses:

The main weakness is that the epidemiological framing is stronger than the data allow. The manuscript repeatedly refers to community-acquired K. pneumoniae pneumonia and broader community epidemiology. However, the available data do not establish community acquisition, community transmission, or an epidemiological shift from nosocomial to community disease. Several children appear to have had prior healthcare contact or other potential healthcare-associated exposures, and the study design cannot determine where acquisition occurred. The findings would be more accurately framed as K. pneumoniae detected in post-mortem lung tissue from children who died outside hospital settings.

Causal attribution also requires more careful wording. Detection of K. pneumoniae in post-mortem lung tissue, together with histopathology and DeCoDe findings, provides important supportive evidence that the organism may have been in the causal chain leading to death. However, this does not necessarily establish that K. pneumoniae was the sole or direct cause of fatal pneumonia, particularly where multiple pathogens were detected.

The small sample size and case selection strategy limit the generalizability of the findings. Only seven children were successfully sequenced, and specimens appear to have been selected partly based on molecular signal. This is technically understandable, but it may introduce selection bias by enriching for cases with higher bacterial burden, better DNA preservation, or other specimen characteristics. As a result, the observed lineage diversity, resistance gene profiles, virulence-associated loci, and capsular locus distribution should not be interpreted as representative of community-acquired infections or broader population epidemiology.

The validation of the hybridization-capture approach also requires strengthening. Comparing outputs from different genomic analysis tools applied to the same sequencing data may assess bioinformatic concordance, but it does not independently validate the method. Ideally, the approach should be benchmarked against clinical K. pneumoniae isolates or matched specimens with conventional whole-genome sequencing data. Without this, it is difficult to assess the accuracy of sequence type, capsular locus, antimicrobial resistance determinant, virulence locus, and plasmid marker recovery, especially in low-read or mixed-specimen contexts.

Species-level attribution of antimicrobial resistance, virulence-associated genes, and plasmid replicons is another important limitation. In a culture-independent metagenomic study, these features cannot automatically be assigned to the identified K. pneumoniae lineage because many such elements are shared across Enterobacterales and may originate from co-detected organisms. This affects interpretation of antimicrobial resistance, hypervirulence, and MDR-hypervirulence convergence.

Overall, the authors achieved their methodological aim of demonstrating that targeted sequencing can recover useful genomic information from challenging post-mortem specimens. However, the epidemiological, transmission, antimicrobial resistance, and vaccine-related conclusions should be tempered.

Reviewer #1 (Public review):

Summary:

Overall, this is an interesting and well-written manuscript on a fascinating question in a "charismatic" model system.

Strengths:

(1) The Introduction is concise, though it might be helpful to the non-specialist reader to learn a bit more about what is known about the social control of somatic growth across diverse species (including humans), which would help to make this work more generally interesting.

(2) The experiment is well designed.

(3) The data collected are comprehensive.

(4) The complementary analysis of both feeding and aggression/submission data with and without known social roles is a neat idea and compelling!

Weaknesses:

The authors have addressed my concerns quite well. They now discuss the HPA/stress axis in some detail and also examined the extent to which growth, food intake, agonistic behavior, and/or gene expression patterns are coordinated across P1 vs P2 pairs. While still not ideal, they now provide a reasonable rationale for using whole bodies for the transcriptome analysis. Finally, the Discussion has been streamlined and is easy to follow.

Reviewer #2 (Public review):

In this manuscript, the authors test growth, behavior, and gene expression in pairs of clownfish as they establish social dominance hierarchies, examining patterns of gene expression in these pairs after dominance has been established. The authors show solid evidence that emerging dominant clownfish show increased growth, aggression, and food consumption compared to their submissive or solitary counterparts, eventually adopting distinct gene expression profiles.

Major Comments:

(1) The Introduction is comprehensive, but it could be condensed. Likewise, the discussion could be condensed. There is considerable redundancy between the methods, the results, and the legend in Figure 1. The authors should consolidate and remove the redundancy.

(2) For Figure 3, the authors are showing PC2 and PC3; why is PC1 not shown? There is so much overlap between the three groups in PC2 vs PC3; it seems unlikely that researchers could conclusively identify any individual as belonging to a group based on the expression profile. The ovals shown do not capture all the points within each of the groups, and particularly the grey S oval seems misaligned with the datapoints shown.

(3) The authors indicate that the 15 replicates exhibiting the greatest size difference between P1 and P2 were selected for gene profiling. Does this mean that each of the P1 and P2 were pairs with each other? Have the authors tried examining the gene expression patterns in a paired manner? E.g., for the pairs that showed the greatest size differences, do they also show the greatest differences in gene expression? Do the P1s show the most extreme differences from P2s that also show the most extreme P2 differences? Perhaps lines on Figure 3A connecting datapoints from the P1 and P2 pairs would be informative.

(4) For the specific target pathways that are up- and downregulated in the different backgrounds, I recommend that the authors include boxplots (or heatmaps) showing the actual expression values for these targets. Figure 6 shows a heatmap for appetite-related genes, and it would be great to see a similar graph for the metabolism and glycolysis genes; it would also be informative to see similar graphs for hormonal and sexual maturation pathways as well.

(5) Particularly given that there is a relatively small number of genes enriched in the different rank conditions, I did not understand the need to do the WGCNA module analysis. I thought that an analysis of GO terms across the dataset would have been more meaningful than the GO term analysis shown in Figure 4, which considers only genes assigned to the "brown WGCNA module". This should be simplified or clarified.

(6) The authors say that they have identified coordinated changes in behaviors and the "underlying gene expression, leading to the emergence" of social roles. This is a little bit misleading, since the gene expression analysis occurred well after the behavioral and phenotypic differences emerged. Presumably, the hormonal and genetic shifts that actually caused the behavioral and phenotypic difference occurred during the weeks during which the experiment was underway, and earlier capture of the transcriptome would presumably reveal different patterns, and ones that would be considered more causative. The authors acknowledge this in 434-435, but it could be emphasized further.

(7) The authors have measured a number of differences between the different dominance classes of fish. All these differences were measured relative to the other classes, but in my view, the Solitary group was the closest to a baseline control. So, I'm not sure that it is fair to say that "P2 and S individuals showed consistent downregulation of these genes and pathways" (line 401). I encourage the authors to emphasize the differences in gene expression from the "perspective" of the P1 individuals compared to the baseline of P2 and S individuals. Line 474 says that "P2 fish showed significant upregulation" of a number of pathways. It should be very clear what that is compared to (compared to P1, presumably?)

(8) Along the same lines, the authors say in line 514 that subordinates and solitaries strategically downregulate their growth. I'm not convinced that this is the case: I would consider this growth trajectory to be the default and the baseline. I would interpret that under certain social conditions, a P1 dominant pattern of growth, behavior, and gene expression is allowed to emerge.

Comments on revised version:

The manuscript has been carefully revised. The authors have also responded adequately to all of my previous comments.

Reviewer #3 (Public review):

Summary:

The authors tested the hypothesis that interactions among size- and age-matched rivals will lead to the emergence of social roles, accompanied by divergence in four aspects of individual phenotypes: growth, feeding behavior, fighting behaviors, and gene expression in clownfish.

Strengths:

The data of growth, feeding rate, and fighting behaviors support the authors claim.

Weaknesses:

The results obtained solely from the whole-body transcriptome are limited in supporting the authors' research question. However, the revised manuscript explicitly states this as a limitation.

Reviewer #1 (Public review):

Summary:

This article purports to show that ML-SA8, a synthetic activator of the lysosomal TRPML1 channel, results in AMPK activation and glucose uptake in hepatocytes, and that this action has therapeutic potential for metabolic disease. The final figure shows that glucose levels are improved in db/db mice, although it is not entirely clear whether this is due to an effect on the liver, on other tissues, or on glucose production or uptake. The earlier figures try to make the case that SA8 causes activation and GLUT4 translocation and glucose uptake in liver cells; however, these data are not convincing. GLUT4 is expressed at such low levels in liver that it is likely not physiologically important. The authors use a fluorescent glucose analog to measure glucose uptake, and this molecule has been shown to enter cells largely by fluid phase endocytosis. Overall, this reviewer finds the premise misguided and the data unconvincing.

Strengths and Weaknesses:

The initial figures show phosphorylation of AMPK on Thr172, but no downstream effects are shown. Usually, to convincingly show that AMPK activity is increased, it would be appropriate to immunoblot phospho-ACC or some other substrate. This is minor.

Lines 135-148: GLUT4 is not expressed at levels that are significant for physiology in liver cells, and its function in liver is not particularly relevant. The authors cite references 38-40 to support that it may be expressed at low levels in liver, but no knockout studies have been done to show that this expression is physiologically important.

Figure 1e is not convincing. No controls are included to show the specificity of the antibody for immunofluorescent staining. No intracellular GLUT4 is visible in the unstimulated samples.

In Figure 1f, again, the data are not convincing. The bands seem too sharp for GLUT4, which has 12 membrane-spanning domains as well as an N-linked glycosylation, so that it usually runs as a smear.

Figure 1h. Data are not convincing. 2-NBDG is not a valid approach to measure glucose uptake. 2-NBDG enters cells largely via fluid phase endocytosis, and its accumulation is independent of known GLUT inhibitors such as cytochalasin B (Yazdani et al., MBoC 2022; PMID: 35921166; see also PMID: 42287154). The idea that such a bulky derivative of glucose could enter the transporter channel is not compatible with known structural data.

Supplementary Figure 5 uses 2-NBDG glucose uptake again. This reviewer is not convinced that the data reflect transporter-mediated glucose uptake, as suggested by the authors. As well, although palmitate treatment of cells can cause an insulin-resistant-like phenotype in some cell types, this is not characterized in the present work. Finally, as noted, one would not expect hepatocytes to exhibit insulin-responsive glucose transport. Glycogen synthesis is the main insulin-regulated step that might be affected.

The data in Figures 2b,c,f,g,k,l are not convincing. Again, 2-NBDG is used.

For the glucose consumption measurements in other panels of Figure 2, the methods section states that cells were cultured in 10 mM glucose. What volume was used? It is difficult to believe that a monolayer of cells would consume very much of the glucose that is present in the culture medium. Data are shown as a percent of controls, and look reasonable, but it would be helpful to include absolute as well as relative units.

In Figure 2, in experiments using the TRPML1 KO cells, no panel is shown to demonstrate knockout. The authors cite a previous paper for the construction of these cells, but the control immunoblot should still be shown here.

In Figure 3, controls are missing in the BAPTA experiment in Figure 3a (only SA8-treated cells were treated with BAPTA and with EGTA). Again, it would be helpful to have p-ACC or some other readout of AMPK activity, and not just AMPK phosphorylation. 2NBDG is again used in this figure.

Line 212-213 the text states "considering our finding that TRPML1-mediated Ca2+ release is essential for AMPK activation." This has not been shown. The work uses chelators and does not necessarily indicate a role for TRPML1. The drug may be specific, as suggested by the authors, but the way this phrase is worded is too strong. As well, AMPK was shown to be phosphorylated, but full activation towards its various substrates has not been shown.

Figure 4cd suggests that GLUT4 expression is increased by 2 or 3-fold in the liver of DB+SA8-treated mice, compared to controls. This may be the case, but its abundance is still likely ~1000-fold less in liver compared to skeletal muscle or adipose tissue. This reviewer is still not convinced that this is physiologically relevant. The images in Supplementary Figure 8 suggest a larger increase, but it remains uncertain whether the staining really represents GLUT4.

Data showing that blood glucose and HbA1c are reduced in SA8-treated mice are reasonable, and GTTs and ITTs are shown. Unfortunately, there are no insulin concentrations, and it remains uncertain whether glucose production is reduced or uptake is increased (or if both effects are present).

In the discussion, the authors again state that GLUT4 is present in the liver and that it regulates hepatic glucose homeostasis, and they cite reference 63. This review article does not argue that GLUT4 acts in the liver to regulate hepatic glucose homeostasis, but that its actions in muscle and fat have secondary effects on the liver.

Reviewer #2 (Public review):

Summary:

The manuscript contains interesting studies suggesting that pharmacological activation of TRPML1 could be useful to treat T2D by increasing glucose uptake via activation of AMPK. Preclinical studies suggest the inhibitor improved blood glucose in Db/Db mice. Ex vivo studies in cell lines examine both pharmacologic and genetic manipulations, both to activate and to inactivate TRPML1, and the results consistently suggest that TRPML1 activates AMPK and increases glucose uptake.

Strengths:

The manuscript is well written, and the studies are carefully performed.

Weaknesses:

All mechanistic studies were performed in transformed cell lines; conclusions would be stronger if performed in primary cells. The in vivo studies were only performed in male mice. Performing metabolic studies in both sexes is standard practice now. Whether the findings would extend to females was not tested and remains uncertain. Some controls are missing, such as plasma membrane loading controls for fractionation studies. The GLUT4 staining was performed after fixation and permeabilization, yet control cells appear to be devoid of intracellular (and all) staining, a confusing result that doesn't reflect the expected biology.

Reviewer #3 (Public review):

Summary:

Zhu et al. present a proof-of-concept for targeting the lysosomal calcium channel MCOLN1/TRPML1endolysosomal ion channels to restore type 2 diabetes mellitus (T2DM). Using synthetic TRPML1 agonists (ML-SA8) and genetic manipulation, the authors demonstrate that TRPML1 stimulation triggers localized lysosomal calcium release. This calcium efflux sequentially activates CaMKKβ and phosphorylates AMPK at Thr172 in various cell models, including palmitic acid-induced insulin-resistant HepG2 cells. This signaling pathway promotes GLUT4 translocation to the plasma membrane and increases intracellular glucose uptake. When administered daily to diabetic db/db mice over six weeks, ML-SA8 lowers fasting and random blood glucose, improves oral glucose and insulin tolerance tests, reduces hepatic steatosis, and lowers serum ALT and AST levels.

Strengths:

Based on the TFEB-independent pathway activated by TRPML1 and the experimental approaches described by Medina's group (PMID: 31822666), the authors use a combination of pharmacological and genetic tools to dissect such an intracellular signaling pathway. Additionally, the animal experiments show consistent phenotypic improvements across independent metabolic parameters. The ability of ML-SA8 to restore glycogen deposition and clear hepatic lipid accumulation in db/db mice without causing weight loss or overt toxicity provides a strong rationale for exploring lysosomal targets in metabolic disease.

Weaknesses:

(1) The authors focus almost exclusively on hepatic GLUT4 to explain the observed glucose disposal. However, other glucose transporter isoforms such as GLUT2 dominate basal glucose transport. While the authors show increased AMPK phosphorylation in skeletal muscle and adipose tissue, they do not measure GLUT4 translocation or glucose uptake in these primary disposal organs. As a result, attributing systemic glycemic recovery primarily to hepatic GLUT4 translocation overlooks the major physiological roles of peripheral tissues.

(2) In both HepG2 cells and mouse liver tissues, ML-SA8 treatment increases total GLUT4 protein expression in addition to plasma membrane localization. Because total protein pools expand, the enrichment of GLUT4 in plasma membrane fractions cannot be cleanly attributed to acute vesicular translocation alone. The manuscript does not explain the timescale or mechanism behind this rapid total protein upregulation, leaving a mechanistic gap between acute ion channel gating and protein expression.

(3) While the in vitro specificity of ML-SA8 is well-controlled, the systemic animal experiments lack a specific rescue or knockout control. Small-molecule agonists administered intraperitoneally over six weeks can exert off-target effects. Without demonstrating that co-administering the TRPML1 inhibitor ML-SI5 blunts the therapeutic effect in vivo, or showing that ML-SA8 lacks efficacy in TRPML1-null mice, the definitive link between in vivo glycemic recovery and TRPML1 activation remains incomplete.

Reviewer #1 (Public review):

Summary:

This work by Beaudet and colleagues aims at exploring the effect of phosphorylation on the formation of tau envelopes and consequently on axonal transport both in vitro on reconstituted microtubules and in human excitatory neurons derived from IPSCs.

The authors found that a relatively widely used construct in which 14 serine or threonine residues often hyperphosphorylated in Alzheimer's disease are mutated to alanines (phosphodeficient) increases the density of tau envelopes compared to wildtype tau whereas a phosphomimetic (same residues mutated to glutamic acid) reduces envelopes density both in vitro and in human excitatory neurons derived from IPSCs.

By analysing the trafficking of different kinesins (KIF1a and KIF5C), they observed different effects of tau phosphorylation status on the movement of these two motors.

They then analyse transport of lysosomes by employing live imaging of lysotracker in human excitatory neurons derived from IPSCs transfected with wildtype, phosphodeficient or phosphomimetic tau observing that phosphodeficient tau seems to reduce transport of lysosomes while phosphomimetic increases transport compared to wildtype tau.

Strengths:

(1) The work aims to study a novel and underexplored topic in the tau field, tau envelopes, and investigate their relevance to Alzheimer's disease pathology.

(2) Experiments are well conducted and of high quality.

Weaknesses:

Relying only on in vitro reconstituted microtubules and human neurons derived from IPSCs leaves some doubts about the relevance of these results for Alzheimer's disease considering the embryonic state of IPSCs-derived neurons, but the authors clearly discuss this point.

Reviewer #2 (Public review):

This manuscript examines how disease-associated hyperphosphorylation disrupts tau's role as a cooperative microtubule-binding regulator of intracellular transport. Using in vitro reconstitution assays and live-cell imaging in iPSC-derived neurons, the authors employ phosphomutant tau constructs (E14 to mimic hyperphosphorylation, AP to prevent phosphorylation) at 14 disease-associated residues to isolate phosphorylation effects independent of expression system-dependent PTM heterogeneity. The results show that hyperphosphorylated tau fails to form cooperative envelope-like structures on microtubules, instead binding diffusely and dissociating rapidly. In contrast, wild-type and phospho-resistant tau form cohesive envelopes that regulate motor protein access. At the single-molecule level, hyperphosphorylation reduces KIF5C inhibition while maintaining or enhancing KIF1A inhibition through altered processivity and detachment rates. In live neurons, hyperphosphorylated tau phenocopies tau knockout conditions, weakening tau-mediated inhibition of lysosome transport and increasing processive motility. The authors quantify tau binding using Gaussian mixture model-based image analysis and measure tau kinetics via FRAP, demonstrating that hyperphosphorylation-induced loss of cooperative binding correlates with dysregulated organelle transport. These findings establish a mechanism by which phosphorylation-driven disruption of tau's gatekeeper function on microtubules compromises axonal transport prior to aggregation in tauopathies.

Comments on revised version.

The authors did a good job responding to my comments and I support publication of the revised manuscript.

Reviewer #1 (Public review):

Summary:

This work presents a flexible spike-sorting framework that allows users to run, swap, and benchmark individual modules commonly used in spike sorting. The paper argues that "opening the black box" is essential for understanding which components drive performance differences and for making progress toward more accurate and transparent spike sorting.

Using this modular benchmarking pipeline, the work identifies electrode drift as a primary bottleneck for accurate sorting, and introduces an end-to-end sorter ("Lupin") that combines the best-performing modules and is reported to be on par or maybe even outperform existing spike-sorting packages on their benchmark. While the modules forming Lupin were chosen based on a single benchmark, end-to-end evaluation of different sorters now use multiple benchmarks, including different available datasets.

Overall, this is a strong tool/resource contribution with clear potential to accelerate spike-sorting development and enable more rigorous comparisons. While not the main point of the paper and therefore less important, remaining claims regarding Lupin outperforming other sorters are not well supported. Lupin does not necessarily outperform all other sorters in real data if you consider 'finding most good units' more important than decreasing false positives - something that quality metrics post-sorting can take care off.

Strengths:

This work has high community value and practical utility. The effort to make benchmarking and spike sorting modules accessible and standardized is substantial, and likely to be broadly useful.

Treating spike sorting as a set of interchangeable modules is a useful approach to some extent, and it enables targeted improvements rather than 'new sorters' popping up which are difficult to fully understand.

Implementing this resource within SpikeInterface, an already widely used tool, will facility uptake and community contributions.<br /> Overall, I am positive about this manuscript as a resource paper. The core framework is compelling and timely.

Weaknesses:

(1) I appreciate the use of automatic curation tools to define the quality of units obtained from different spike sorters. However, a discussion on what is considered a good trade-off is missing. For example, one could argue that as long as you apply these curation tools post-sorting, finding more good-quality units is more important than keeping the number of false positives low, as these can be filtered out at a later stage by these curation methods. The manuscript currently seems to argue the balance between high number of good and low number of false positives is more important. This needs to be discussed and rationalized more explicitly, rather than using terms like 'clearly outperforms' and 'good trade-off'.

(2) Although I agree that overfitting to specific data is a general problem with spike sorting (as you can also change parameters of individual sorters), and perhaps this is even required for the best results, I still miss an explicit discussion of this.

(3) While the end-to-end evaluation is a very good addition, defining the best strategy for each module (which in turn led to choices what to use for Lupin) is still based on one benchmark only. This remains a weakness.

Cmments on revised version:

Previously identified weaknesses in limited support for Lupin being superior to other spike sorters, especially using only a single benchmark, have been largely addressed by 1) making superiority of Lupin less of a point in the manuscript and 2) adding more simulations and real datasets. Also, clarification on serial versus iterative spike sorting has been added to the discussion.

Reviewer #2 (Public review):

Summary

Spike sorting, that is, assigning events detected in extracellular electrophysiology data to firing of individual neurons, is an inherently difficult computational problem involving multiple steps. The difficulty arises from low signal to noise, instability in signal due to relative motion of the tissue and recording sites, and large volumes of data. Experimental ground truth data - where the correct assignment of spikes in known - is not available in large enough quantities to test algorithms. This paper describes a tool for creating fully synthetic ground truth data and benchmarking the individual steps of spike sorting to dissect the impact of signal to noise, firing rate, and motion correction on each step. This information is used to construct an optimized algorithm for sorting these ground truth data. One result of particular interest is the dominant role of motion correction in degrading accuracy. Another important technical result is that motion correction via interpolation of the voltages traces yields similar accuracy to interpolation of the spike templates.

Strengths

The paper shows that useful insight can be gained through analyzing process step by step. While this analysis has also been done in papers presenting spike sorters (for example, Pachitariu (2024)) the tools presented here allow users and developers to do similar studies for their own work. This toolset will be useful to many labs, especially those working in less studied brain areas or model systems, cases where the tuning of standard spike sorting tools is not a good match to the data.

Weaknesses/Limitations:

The model ground truth data used in testing spike sorting and its components does not need to be a perfect match to experimental data to provide useful benchmarking. However, as with all measurements of spike sorting accuracy, extrapolation to experimental data can be complicated. Therefore, the insights gained concerning optimization of the individual steps should be interpreted as "correct for that model data. The comparison of the paper's new sorter to standard sorters on experimental recordings suggests that the benchmarking data is reasonable. Nevertheless, users of these tools will need to assess how well the simulated data matches their recordings.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors describe two additions to an existing toolbox (SpikeInterface, Buccino et al., 2020, eLife). The first addition is an empirical simulator for extracellular recordings, in which spikes from predefined templates are added up with Gaussian noise. The second addition involves granting user-level access to intermediate processing steps along spike sorting algorithms. The authors demonstrate the toolbox by evaluating functions (e.g., event detection) or sets of functions (e.g., feature extraction + clustering) on their simulated data and suggest that a specific combination of function implementations provides performance improvement relative to kilosort4 (Pachitariu et al., 2024, Nature Methods).

The validity of the work is poor. In particular, the simulator is unrealistic and the ground truth dataset is too short. Several spike sorters are used as straw men, and most sorters compared have never been described in peer-reviewed literature or in sufficient detail. The purely feedforward architecture of the modules is very limiting and irrelevant for modern sorters. Finally, the reporting of results is sporadic and does not follow scientific reporting standards.

General comments:

(1) Abstract, lines 14-16: "We then leverage these results to create a modular component-based spike sorter that can outperform Kilosort 4 on dense and large simulated recordings and produce similar quantitative results on real data." However, the artificial data are not "large" - they are very short. And "similar quantitative results" cannot be assessed on real data because those data do not have any ground truth. Because this is a revision and the authors have already received similar feedback from this Reviewer, I am not sure what to recommend.

(2) In a previous comment, I indicated that the simulator itself is overly simplistic, and indicated that as far as I am concerned, the authors must improve it in one of two ways: (1) use a set of biophysical equations, with multi-compartmental modeling of currents and return currents; (2) use noised data from extracellular recordings; or (3) some combination thereof. The authors explained in their answer - but not in the MS - some of the shortcomings of biophysical simulators, but chose to do neither. My comment therefore remains unaddressed in the MS.

(3) In a previous comment, I indicated that the duration of 10 minutes is too short. The authors chose to extend the duration to 30 minutes, which is insufficient for units that have low firing rates. Units that fire e.g., 0.1 spikes/s would have fewer than 200 spikes. If this MS is to be taken seriously, the simulation should be done on durations that (1) are similar to the potential applications - which may be many hours or even days; (2) allow identification of low-firing neurons. Therefore, about 3 hours is the bare minimum. Therefore, at present, my comment remains unaddressed.

(4) In a previous comment, I indicated that some sorters have never been described in peer reviewed papers and therefore, they must either be removed from the present comparisons or be described in full. The authors chose to persist in relying on un-reviewed online documentation. Thus, my comment remains unaddressed, and the comparisons that involve those sorters (e.g., TDC, TDC2, SpyKING Circus 2) are simply invalid.

(5) In a previous comment, I indicated that some sorters (SpyKING Circus 2 and TDC2) are effectively straw men and suggested to reorganize the MS to demonstrate its main goal. Specifically, I suggested to reorganize the manuscript so that after every module is evaluated separately based on a limited ground truth dataset, a single "best" sorter would be constructed, and then tested extensively (and compared to the de facto state of the art). Such reorganization would both demonstrate the utility of a modular approach and clarify the general usefulness of the outcome. The authors agreed that these are straw men and gave a historical account of why these were included. However, they did not explain these considerations in the MS itself, and chose not to reorganize the MS. Therefore, my comment remains unaddressed.

(6) In a previous comment, I commented about the presentation, description, and interpretation of the results, and indicated that the choice to report point estimates makes any conclusions based on those results invalid. The authors did not make any changes to the reporting. Therefore, none of the results reported in the MS can be taken as an outcome of scientific inquiry. In other words, the MS does not deliver any solid findings - neither scientific nor methodological.

Reviewer #1 (Public review):

Summary:

The authors utilize both human iPSC-derived RPE and human fetal RPE cultures to interrogate the effect of various types of commonly used cell culture media on several key biological- and disease-relevant RPE properties. These include a comparison of RPE morphology, polarity, transepithelial electrical potential, lipid metabolism, autophagy, and targeted metabolomic profiles across 6 different media compositions.

Strengths:

This manuscript is very well written, and data are presented in a well-organized manner. The authors address media composition as a fundamental variable that will influence the interpretation of assays performed in RPE cell cultures, particularly metabolic studies. Figure 6 provides a useful summary of the study's findings across commonly used media types, and the manuscript's discussion offers insight into which media may be best suited to address specific experimental questions. Overall, this manuscript will not only serve as an important resource for vision scientists utilizing RPE culture models, but it also serves to remind the broader cell biology community of the importance of considering the potential (confounding) experimental effect(s) of various culture media and to consider tailoring the selection of culture media types to the specific experimental question.

Weaknesses:

(1) While the authors report that iPSCs were obtained from several healthy patients and that at least two clones were generated from each individual, it is not clear to this reviewer whether the experiments with each culture media type were performed on the same set of iPSC-RPE in each case. The authors mentioned iPSC differentiation variability as a limitation, but it would be helpful to understand (and quantify) the experimental variability that may exist with the same culture media using iPSCs from different patients and/or separate iPSC clones from the same patient.

(2) Since a major purpose of the manuscript is to highlight how cell culture conditions influence RPE biology and metabolism, it would be helpful to also report whether Mycoplasma testing was performed and confirmed to be negative across all cell lines.

(3) The effect of culture media on mean RPE area and hexagonality was compared in this study. Interestingly, Figure 1E demonstrates higher mean RPE cell area but also substantial variability in cell area for media 2 (MEM-alpha and B27) and media 4 (HPLM and B27). It would be helpful to include a discussion of the potential biological implications of variable cell area across these 2 media types.

(4) The authors speculate that FBS-containing media may encourage a more mesenchymal or de-differentiated state. This could be experimentally determined by interrogating mesenchymal markers (alpha-SMA, fibronectin, etc) by immunoblot and/or immunofluorescence microscopy, similar to how the RPE markers were evaluated in Figure 1.

(5) It would be helpful for the discussion section to include a comparison of key differences (where they exist) between iPSC-RPE and fetal RPE across culture media types.

Reviewer #2 (Public review):

Summary:

In this study, Lim et al. provide a comprehensive analysis of the metabolic and physiologic effects of different media compositions on iPSC-RPE. This analysis includes commonly used iPSC-RPE media bases (MEMα, DMEM-HG/F12 basal media) as well as human plasma-like medium (HPLM) in attempts to establish a more physiologically relevant culture environment.

Strengths:

The analyses in this study provide a very thorough survey of metabolic function as well as an RPE-relevant physiologic characterization. This will be a great resource for optimizing assay conditions for disease-based studies using iPSC-RPE.

Weaknesses:

In the Seahorse studies provided in Figure 3. basal readings for OCR are abnormally low compared to Oligomycin treatment and background, suggesting difficulties with the assay. Findings should be taken with caution.

Reviewer #3 (Public review):

Summary:

The authors systematically compare six culture-media formulations using induced pluripotent stem cell-derived retinal pigment epithelium and fetal retinal pigment epithelium. They examine cell morphology, marker expression, barrier function, polarized secretion, lipid accumulation, ultrastructure, mitochondrial respiration, glycolytic function, and intracellular and extracellular metabolites. The results demonstrate that culture-medium composition and the choice of serum or B27 supplementation substantially influence retinal pigment epithelium phenotype and metabolism. Rather than identifying a single optimal medium, the study provides a comparative framework to guide medium selection according to the biological question being investigated.

Strengths:

The head-to-head comparison of six media under otherwise similar culture conditions addresses an important source of variability in retinal pigment epithelium research. The study uses a broad range of complementary approaches, including imaging, transepithelial resistance, electron microscopy, extracellular flux analysis, and targeted metabolomics. The inclusion of both induced pluripotent stem cell-derived and fetal retinal pigment epithelium increases the potential relevance of the findings across different cell sources. The matched comparisons of serum and B27 supplementation within MEMα and human plasma-like medium are particularly informative because they help distinguish supplement-associated effects from those caused by the basal medium. Overall, the dataset has the potential to serve as a valuable resource for selecting culture conditions and interpreting findings across retinal pigment epithelium studies.

Weaknesses:

The most important limitation is that the experimental unit and degree of biological replication are not clearly defined. It is unclear whether individual observations represent independent donors, clones, differentiated lines, culture preparations, wells, images, or sections. This makes it difficult to determine the independence, robustness, and generalizability of several comparisons.

The metabolic analyses also require additional methodological clarification. For intracellular metabolomics, the culture format, cellular biomass, extraction volume, pooling strategy, and normalization method are not reported sufficiently. Normalization of extracellular measurements to unspent medium accounts for differences in starting metabolite abundance but not for differences in cell number or biomass. Similarly, normalization of intracellular signals to medium 1 does not correct for differences in the amount of cellular material extracted.

For the Seahorse experiments, the main figures present unnormalized values even though the media produce differences in cell number, size, and protein content. These raw measurements represent total metabolic activity per well and may not reflect activity per cell. It is also unclear how normalization was performed because the Methods describe cell-count and protein measurements from two wells, whereas the stress tests included five to six wells per condition. In addition, measurements obtained after transfer into a common assay medium reflect metabolic adaptations retained from the preceding culture conditions rather than real-time metabolism within the original media.

Other limitations include insufficient information about the biological replication underlying the sub-RPE deposit analysis and the inability to fully interpret the effects of X-VIVO 10 because its composition is proprietary. Finally, public availability of the underlying metabolomics data would be important for a study intended to serve as a community resource.

Reviewer #1 (Public review):

Summary:

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

In addition, the authors describe a novel substrate channel within the TDM complex that connects the N-terminal Zn²⁺-dependent TMAO demethylation domain with the C-terminal tetrahydrofolate (THF)-binding domain. This continuous intramolecular tunnel appears highly optimized for shuttling formaldehyde (HCHO), based on its negative electrostatic properties and restricted width. The authors propose the hypothesis that this channel facilitates the safe transfer of HCHO, enabling its efficient conversion to methylenetetrahydrofolate (MTHF) at the C-terminal domain as a microbial detoxification strategy. Experimental data that shows an involvement of TDM in the reaction of HCHO with THF is less convincing.

Strengths:

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

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

Weaknesses:

Although the idea of an enclosed channel for HCHO is compelling, the experimental evidence supporting enzymatic assistance in the reaction of HCHO with THF is less convincing. The linear regression analysis shown in Figure 1C demonstrates a THF concentration-dependent decrease in HCHO; however, it is well established that HCHO and THF can react spontaneously in a non-enzymatic manner, raising the possibility that the observed effect does not require enzymatic involvement. I appreciate the authors' clarification that the data in Figure 1 were not intended to demonstrate enzymatic channeling or catalytic involvement in the HCHO-THF reaction, and that the assay does not distinguish between changes in HCHO production and downstream consumption. The authors' revised statement "Overall, these findings suggest that TDM-mediated TMAO demethylation generates HCHO, which can subsequently react with THF, potentially linking TMAO breakdown to one-carbon metabolism." is appropriately cautious and leaves open the mechanism underlying this process, which is consistent with the evidence presented.

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

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

Comments on revised version.

It is unfortunate that no additional experimental evidence supporting the proposed channeling mechanism could be provided. In the absence of such evidence, I remain somewhat hesitant to regard the evidence as "solid" rather than "incomplete," particularly given that the authors themselves acknowledge that the current experiments do not establish enzymatic channeling. However, the rest of the manuscript is stronger despite this limitation.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers and the authors have satisfactorily answered the previous reviewer's comments.]

Summary:

They use cultures of insulinoma MIN6 cells that form spheroids in a micro-patterned PEG-hydrogel to measure Ca2+ oscillations in multiple cells simultaneously.

Strengths:

They demonstrate that insulinoma spheroids are formed in multi-well plates and that Ca2+ imaging can be performed on them.

Reviewer #2 (Public review):

Summary:

The study by Robben et al., show 3D beta-cell spheroid platform, a valuable tool allowing high-throughput monitoring of cytoplasmic Ca concentrations and insulin secretion, with Ca signals comparable to those recorded in primary islets. The authors demonstrate a solid method to culturing MIN6 cells in a 3D culture system, recording Ca signals in a high-throughput format and characterizing these Ca signals using pharmacological tools, including TRPM3 channel and K-ATP channel modulators. This highlights the utility of the 3D beta-cell spheroid for screening new ion channel modulators in beta-cells of the pancreas.

Strengths:

- The study shows that the MIN-6-based 3D beta-cell model is better to study Ca-signaling and insulin secretion compared to 2D culture of single MIN-6 cells.<br /> - The method allows imaging of Ca signaling in many spheroids in parallel followed by collecting medium to measure insulin release and correlate both effects.<br /> - The authors demonstrate that this system is suitable for screening new pharmacological modulators and used as an agonist of the ATP-sensitive potassium channel (diazoxide) and the agonist and antagonist of the TRPM3 channel.

Reviewer #3 (Public review):

Summary:

The primary objective of this study is to develop high-throughput screening assays utilizing homogeneous 3D cell cultures that more accurately replicate the intricate architecture and cellular communication found in tissues. The authors have chosen pancreatic islet β-cells as a model system to evaluate agents that modulate insulin release, which is particularly relevant given the increasing prevalence of diabetes mellitus-a significant global health concern. Moreover, the incorporation of human-based 3D spheroids, organoids, or organ-on-chip technologies into drug discovery protocols is essential for enhancing clinical translation, as candidate compounds identified using animal models have often demonstrated limited success in clinical settings.

Strengths:

This study was thoughtfully planned and skillfully carried out. The use of micropatterned hydrogels to observe 19 spheroids at once is an ingenious aspect, which has been effectively validated with Ca microfluorography. Overall, I found this investigation to be exceptionally well-executed and free from notable flaws, as the results clearly back up the conclusions. Additionally, the developed method achieved the proposed aims, providing a high-throughput format with 3D cultures. I believe this study deserves publication.

Reviewer #1 (Public review):

Short overview:

This work demonstrates that exposure to the odor diacetyl in C. elegans first induces the expression of genes that act in the DHAP (dihydroxyacetone phosphate) - glycerol shunt, followed by induction of the flavin-containing monooxygenase fmo-2. This diacetyl exposure also increases the survival of starved animals. However, the mechanism through which diacetyl is sensed by the animal does not involve any of the known receptors of diacetyl, and it is unknown whether any sensory neurons are involved in these responses.

Summary:

This manuscript shows that diacetyl exposure induces metabolic remodeling in C. elegans, where genes in the DHAP (dihydroxyacetone phosphate) - glycerol shunt pathway are activated. This leads to a secondary activation of the flavin-containing monooxygenase fmo-2, which is a longevity-promoting gene upon dietary restriction. The activation of fmo-2 is consistent with the authors' observations that diacetyl exposure increases the survival of starved animals, but not of fed animals. The authors also show that diacetyl exposure improves the animals' resistance to hyperosmotic stress, which is consistent with an increase in glycerol and glycerolipids in these animals. Together, the authors show that a volatile odor or odors can induce gene expression changes that promote metabolic changes and survival under certain environmental conditions.

Strengths:

Through transcriptomics, metabolomics and lipidomics of wild type, with or without diacetyl, and phenotypic analyses of mutant or RNAi-treated animals, the authors delineate a mechanism through which an odor or odors remodel metabolism and increase survival. They show that genes of the DHAP-glycerol shunt are activated during diacetyl exposure, and that these in turn activate fmo-2, which is known to promote longevity under different stressors, like food deprivation. The authors also show that an upstream transcription factor, the co-activator MDT-15, is required for the diacetyl-mediated initial activation of the DHAP-glycerol shunt, and thereby of fmo-2. The involvement of MDT-15 and, to a lesser extent, one of its partner nuclear hormone receptors (NHRs), NHR-49, might also explain the lipidomic changes that accompany diacetyl exposure.

Weaknesses:

The authors tested the involvement of the ODR-10 receptor, which is the known receptor for attractive concentrations of diacetyl, the same concentrations used in this study. Surprisingly, ODR-10 is not required for any of the DHAP-glycerol shunt or fmo-2 induction. In addition, they found that loss of sensory cilia (in the background mutation of the NHR daf-12) enhances diacetyl-mediated fmo-2 induction, which suggests that wild-type sensory cilia inhibit fmo-2 and likely synergizes with the DAF-12 receptor. Considering that chemosensory receptors, like ODR-10, are normally localized to the ciliary endings, their observations suggest a different mechanism through which the animals process this odor. There are at least two possibilities.

One, diacetyl might affect the animals by permeating their cuticles. However, the authors did not test if any neurons are involved in their phenotypes. The AWA neuron, where ODR-10 is expressed, is required to sense attractive concentrations of diacetyl. Thus, what happens when the AWA neurons are ablated or lost?

Two, diacetyl is both light- and temperature-sensitive and can break down into two other odors, acetoin and 2, 3-butanediol. Acetoin is sensed by the chemoattractive AWA neurons (Siddiqui et al, eLife 2024, 101936.1), but not by ODR-10 (Zhang et al., PNAS 1997, vol 94, pp 12162-12167). Thus, this odor would have a different receptor. Although C. elegans chemosensory receptors have been found within the cilia, there is a formal possibility that some of the sensory receptors will also be found on other parts of some sensory neurons, like the dendrites, as in Drosophila (Joseph and Carlson, Trends Genet 2016, vol 31, pp 683-695). Regarding the other diacetyl derivative, 2, 3-butanediol, it has not been shown to elicit any chemosensory responses in C. elegans (Siddiqui et al. eLife 2024, vol 13, RP101936), but 2, 3-butanediol has been linked to the microbiome of fmo knockout mice (Said et al., Metabolomics 2025, vol 21, 170). Thus, it is possible that it is the breakdown products of diacetyl that elicit the responses the authors see.

Finally, the authors' data contradict the observations made by Park et al (Aging Cell 2021, vol 20, e13300). Park et al previously showed that diacetyl exposure reduces the survival of food-deprived animals, although this phenotype is also independent of ODR-10 or of the SRI-14 receptor for aversive concentrations of diacetyl.

Reviewer #2 (Public review):

Short overview:

This study presents potentially important findings showing that DHAP-glycerol shunt involved in energy balance is regulated by food availability in a widely used C. elegans model. The genetic evidence supporting this conclusion is solid and is based on an extensive set of experiments; however, key metabolic measurements and comprehensive metabolic profiling are not provided, limiting the strength of the conclusions about the underlying metabolic and redox changes.

Comments:

Giorda and colleagues report interesting findings demonstrating that the DHAP-Gro3P shuttle is modulated by food availability in C. elegans. Although the authors provide multiple interesting observations in worms, supported by an extensive number of experiments, the metabolic aspect of the study requires additional development. It appears that targeted lipidomics and metabolomics analyses were performed, but the corresponding datasets are largely absent from the manuscript. Only a very limited subset of lipid species is presented in Fig. 2D. What about triglycerides? It would be highly informative to include comprehensive lipidomic profiles covering major lipid classes. A similar concern applies to the metabolomics data. Where are the measurements of Gro3P, DHAP, and glycerol? The authors state that their LC-MS method was unsuccessful and that glycerol levels were ultimately measured using a commercial kit. Given that glycerol production and excretion appear to be major output across many of the experiments presented, this approach is not entirely satisfactory. Reliable GC-MS based methods are available for the quantification of all major components of this pathway, including Gro3P, DHAP, and glycerol (derivatization helps to preserve these species, especially glycerol).

Furthermore, comprehensive LC-MS/GC-MS-based metabolic profiling should be included. Metabolites reported and organized by pathway (e.g., glycolysis, TCA cycle, pentose phosphate pathway) would provide a broader understanding of the metabolic consequences of DHAP-glycerol shunt activation.

Finally, because the DHAP-glycerol shunt is closely linked to cellular redox homeostasis, it would be important to determine how its activation affects intracellular pyridine nucleotide pools, and measurements of NAD+, NADH, NADPH, NADP+ would substantially strengthen the mechanistic conclusions and provide direct evidence for alterations in cellular redox state.

Reviewer #3 (Public review):

Short overview:

This study asks whether the perception of a volatile and attractive cue, diacetyl, leads to changes in metabolic nutrient-responsive programs in C. elegans. Using multi-omic and genetic evidence, they connect the transcriptional response to diacetyl to early activation of the DHAP-glycerol shunt, suggesting worms may activate this metabolic pathway in preparation for food intake. This work also identifies transcription factors involved, and while it does not test whether this response is diacetyl-specific, could identify a conserved pathway of food intake preparation.

Summary:

This study asks whether C. elegans can use a volatile food cue alone, in the absence of ingestion, to anticipate nutrient availability. Using the attractive odorant diacetyl, the authors show that fasting worms rapidly induce the DHAP-Glycerol shunt, a metabolic pathway normally associated with glucotoxicity and hyperosmotic stress, and that this response depends on the transcription factor MDT-15. This drives measurable metabolic rewiring (glycerol and phosphatidylglycerol accumulation) and confers protection against subsequent hyperosmotic stress. Prolonged diacetyl exposure further triggers a second, HLH-30-dependent wave of fmo-2 expression linked to depleted NTP levels, resulting in enhanced heat tolerance and food-seeking behavior upon refeeding. Together, the authors build a testable model for a pathway linking sensory cue detection to gene expression, metabolism, and physiological changes.

Strengths:

The central finding that smell alone without ingestion activates a metabolic-stress adaptation pathway is highly interesting and supported by a convergence of methods including RNA-seq, transcriptional reporters, metabolomics, and functional behavior assays. The transcriptomic time course distinguishes two temporally separable gene expression waves (the shunt at 30 min and fmo-2 at 90 min), and epistasis experiments comparing food status and osmotic stress (Fig. 1i-j) convincingly argue diacetyl acts as a food-predictive cue rather than mimicking osmotic stress. A particular strength is the test of the relationship between the two pathways identified in the study. RNAi knockdown of DHAP-Glycerol shunt enzymes block fmo-2 induction, and pretreatment with salt then switching to food deprivation shows that it is the shunt's activation state that drives fmo-2 expression. This is further reinforced by depletion of energetic mechanism (NTP/ATP). Figure 5 extends this upstream to MDT-15 validated through gene expression, metabolomics, and functional readouts. Throughout the study, transcriptional and metabolic findings are paired with functional outcomes such as hyperosmotic protection, heat stress resistance, or survival, strengthening the paper's model. Together, the authors largely achieve their aim of establishing that a food-related olfactory cue is sufficient to trigger an anticipatory metabolic and transcriptional program. The core claim that diacetyl sensing activates the shunt and subsequent fmo-2 induction and physiological protection is well supported by convergent genetic and biochemical experiments.

Weaknesses:

The authors show that diacetyl-induced fmo-2 induction does not require canonical olfactory sensing since neither diacetyl receptor mutants (odr-10, sri-14) nor a cilia-deficient strain (daf-19; daf-12) blocked the response. However, the pathway characterized here is defined almost entirely through diacetyl, and it remains unclear whether the anticipatory response reflects general food-predictive olfaction or a diacetyl-specific effect. This distinction is important because the authors' central hypothesis is whether volatile food cues in general can be used to anticipate nutrient availability. Testing a limited number of additional attractive odorants, ideally sensed through distinct chemosensory receptors, for a limited number of phenotypes, would establish whether this pathway is generalized to food-predictive smells or only diacetyl. This would increase the impact of the work. Identifying the mechanism(s) for diacetyl perception would as well, but is much more challenging and less likely to be feasible in this work.

The RNA-seq and reporter data disagree on when fmo-2 induction begins, and thus do not fully support that there are two temporally distinct waves. RNA-seq (sampled at 5, 15, 30, and 90 min) shows fmo-2 is not a significantly differentially expressed gene at 30 min and only reaches significance at 90 min, while the fmo-2 transcriptional reporter (Fig.S3a) shows detectable induction as early as 30 minutes. Other readouts in the paper use the reporter for fmo-2 but qPCR for shunt genes, further muddling the timing since mRNA should precede reporter visualization. Since reporter signal generally lags transcript level detection, this discrepancy could be further clarified through a time-course qPCR (like what was tested for the shunt genes) between 30 and 90 minutes. This would help establish whether the two waves are separated by time or whether this reflects a difference in detection method.

Minor weaknesses:

In Figure 1i, gpdh-1 induction is compared between control and 200 mM NaCl (3 hr exposure), with food present or absent. While this directly tests food status, a 3-hour exposure approaches the ~5-6-hour window previously shown to be sufficient for salt-food associative learning to form (worms move toward the high-salt side of a gradient plate if pretreated with high salt and food). This raises the possibility that, in the food-present condition, part of the measured gpdh-1 response could reflect an emerging learned association between salt and food forming during the assay itself, rather than purely reflecting an interaction between nutrient status and osmotic stress signaling. Testing a shorter exposure window (e.g., under 1 hour, as used for the diacetyl exposure in panel j) would help clarify this.

Thrashing is used throughout the paper as the primary readout for hyperosmotic stress resistance, including the central result that diacetyl protects against subsequent hyperosmotic stress (Fig. 2g). However, the authors don't explain why this was chosen as the main stress-resistance readout.

nhr-49 knockdown reduces gpdh-1 induction in diacetyl-mediated hyperosmotic protection but has no effect on development or survival on sustained hyperosmotic stress, raising the question of how the role of nhr-49 may be distinct from mdt-15 in this context.

Reviewer #1 (Public review):

Summary:

This work investigated the associations between abstraction and metacognition in the context of reward-guided learning and transdiagnostic symptom dimensions in a sample of N = 249 participants. Participants completed a reward-guided learning task and confidence judgements. Transdiagnostic dimensions used to examine associations with abstraction and metacognition were based on a large existing dataset. Findings showed that in the examined sample, a Compulsive Hypersensitivity dimension was negatively associated with abstraction and metacognitive sensitivity, while a Social Withdrawal dimension was positively associated with metacognitive sensitivity. These data add to the existing literature on associations between metacognition and transdiagnostic symptom dimensions and extend previous work on the association between abstraction and these dimensions.

Strengths:

(1) The study addresses an interesting research question and uses a transdiagnostic approach. While part of the research question is a replication (metacognition), the additional inclusion of an abstraction parameter is highly valuable.

(2) Methodologically, the study is strong. Specifically, the implementation of an experimental task to estimate parameters, the hierarchical Bayesian modelling, the parameter recovery analyses, the bootstrap regression (including corrections for multiple comparisons) and the control of relevant covariates and response tendencies are quite impressive.

(3) The Open Science approach is laudable in general. The study was preregistered and provides open data and open code. Deviations from the preregistration are transparently reported (e.g., bootstrap regression, exclusion criteria). In this vein, the high number of robustness analyses provided in the supplements is very much appreciated.

(4) More generally, the extensiveness of the supplements is particularly valuable.

(5) Finally, it is very useful that the discussion takes into account potential alternative explanations of the findings.

Weaknesses:

(1) High number of exclusions:

As the authors mention (also in their limitations section), more than half of the participants had to be excluded (only 249 out of 512 participants remained), which is substantial. Specifically, 203 participants were excluded because they failed attention checks, 58 failed comprehension questions on the confidence scale, 25 had a reading time of the instruction page below 5 seconds, and 68 showed a performance that was too poor (the sum is probably higher than 263 because these overlap). This high number of excluded cases resulted in a substantially smaller analysed sample than planned (corresponding to approximately 77% power instead of 90%) and potentially limited both statistical power and generalisability. It might even be the case that highly impulsive individuals were excluded systematically. That is, the exclusion criteria may themselves be associated with psychiatric symptoms, so the analysed sample may no longer (fully) represent the target population. It would be interesting to see whether the results also hold when including the excluded participants (because excluded and included participants differ in attentional and impulsivity-related symptoms). A sensitivity analysis would be beneficial.

(2) Deviations from preregistration:

While the preregistration of the study is positive in general, some aspects raise doubts here. First, there have been quite a few deviations from the preregistration. For instance, the primary outcome variable for abstraction has been changed (initially: proportion of blocks in which the Abstract RL model had a better fit than the Feature RL model; instead: mean posterior responsibility of the Abstract RL model across blocks), and this appears to have influenced the findings (e.g., Supplementary Figures: 8 vs. 9). Generally, these deviations introduce additional researcher degrees of freedom and therefore warrant a more detailed justification (or replication in future studies). Second, it appears that the preregistration was uploaded only to an OSF folder and not formally registered with a timestamp. However, while an update to the document has been made according to the metadata, the content still seems to be the same as the original one (created on Jun 11, 2025).

(3) Combined measurement of choices and metacognition:

Choice behaviour and metacognition were not measured independently (i.e., they were measured using a single slider). This challenges the interpretation of results concerning metacognitive sensitivity, as the authors note themselves in the limitations section (even if the effect of starting position was small). It should be clarified whether responses exactly at the centre of the slider were possible or not (and what range the slider had).

(4) Generalisability of findings:

It is questionable whether transdiagnostic factors derived from a Japanese population can be transferred to the sample in this work, especially because it seems to be composed of a heterogeneous international sample comprising participants from multiple countries (South Africa, United States, United Kingdom, Poland, others). While it is appreciated that the authors validate the transdiagnostic factors through a new exploratory factor analysis and by correlating item loadings across samples, the correlations are only moderate and point at least to some degree of variation. In addition, for factor 2 (social withdrawal?), the correlation seems to be mainly existent due to two clusters of items, challenging the validation approach in itself. Also, apart from the correlations themselves, the absolute values of the item loadings appear to vary largely. Considering this, the conclusion that "transdiagnostic symptom dimensions appear broadly consistent across samples (even across countries)" (p. 9) definitely goes too far.

(5) Parameter recovery analysis: The parameter recovery analysis is appreciated; however, the recovery of the learning rate in particular seems to be rather low (r=.40). This raises concerns regarding the validity of the parameter estimates.

(6) Effect sizes: The reported regression coefficients appear relatively small in magnitude (i.e., betas of the associations between metacognition/abstraction and transdiagnostic dimensions appear to be in the range around .02-.04). If these are standardised estimates, the corresponding effect sizes are relatively small. If not, I recommend reporting standardised effect sizes in addition.

Overall, the study provides relevant replications and new insights into the association between reward-guided learning behaviour (abstraction, metacognition) and transdiagnostic symptom dimensions. It should be noted that effect sizes appear to be rather low, however. The analytic approach is generally strong. At the same time, the high number of exclusions, the limitations regarding the validation of the transferred transdiagnostic factor structure, and the limited recovery of the learning rate parameter clearly raise important questions regarding the robustness and generalizability of findings. Given this, any interpretations regarding potential therapeutic implications (e.g., p. 10) should be made with caution.

Reviewer #2 (Public review):

Summary:

In this study, Oka and colleagues recruited an online sample to complete a previously validated abstraction task (Cortese et al., 2021) alongside confidence ratings and a large psychiatric questionnaire battery, which included a variety of methods to screen out inattentive or otherwise biased responders. Questionnaire item scores were combined with factor weights from a large dataset to estimate transdiagnostic factor scores. A computational model was then fit in a hierarchical manner to the abstraction task data, with individuals' fit to an "Abstract RL" model used as a metric of individual-level abstraction ability, and metacognitive bias and sensitivity were estimated from the confidence ratings. Associations between these task-derived measures and both dimensional and symptom-level measures of psychopathology were then estimated using multiple regression. The key findings were that, while metacognitive sensitivity and abstraction ability were associated with symptom-level scores, the associations with transdiagnostic dimensions - higher compulsivity associated with lower abstraction ability and metacognitive sensitivity; higher social withdrawal was associated with higher metacognitive sensitivity - were interpreted by the authors as more coherent.

Strengths:

(1) Robust screening for inattentive responders through catch questions (Zorowitz et al., 2023), as well as incorporating recent recommendations regarding response bias (Sarna et al., 2026).

(2) Assessed the cross-cultural generalisability of the imported factor weights by comparing item loadings from a large external sample against a de novo exploratory factor analysis in their sample.

(3) Directly compares a theory-driven model-defined abstraction metric to metacognition in relation to dimensional and symptom-level measures of psychopathology.

(4) Pre-registered analyses, with deviations from pre-registration clearly stated.

Weaknesses:

(1) The abstraction metric (mean posterior responsibility of the Abstract RL model) differed from the pre-registered metric and has not been validated here for reliability (e.g., split-half across the blocks or similar).

(2) Model recovery is not shown, so it's not clear whether the Abstract RL and Feature RL models are fully dissociable in this task design.

(3) Metacognitive measures are behaviourally defined (AUROC2 for sensitivity and mean confidence for bias), but models do not correct for task accuracy, which may be related to both.

(4) Dimensional and symptom regressions differ: the dimensions are entered in one model, but the symptom measures are entered into separate regressions and the marginal effects corrected for multiple comparisons. If I've understood this correctly, this means that the dimensional coefficients are partial associations adjusting for the other two factors, whereas the symptom-level coefficients are marginal and FDR-corrected, making it difficult to directly compare them.

Additional questions and context:

(1) Could split-half reliability (e.g. odd vs even blocks) be reported for the abstraction measure? Relatedly, a model recovery/confusion analysis for the two abstraction models, and/or posterior predictive checks showing that the two models generate behaviour resembling that of participants would help establish that the responsibility metric is able to dissociate the different abstraction strategies.

(2) Supplementary Table 2 shows the results for the pre-registered discrete proportion metric - here, there is limited evidence (p=0.220) of an association between abstraction and compulsivity, so saying they are "almost consistent" is perhaps a little overstated. Though the argument for using the alternative continuous metric is justified in the text, it's not quite clear whether the difference is due to the inference method or the abstraction metric itself - the bootstrapped analysis of the pre-registered metric is not reported, nor is the analysis without bootstrapping of the continuous metric (I think this may have been what Supplementary Table 1 was meant to report, but currently it's identical to Supplementary Table 5). In addition, it might be helpful if the correlation between the two metrics were presented graphically.

(3) In the Methods and Supplement, the authors mention that they had pre-registered running a sensitivity analysis including excluded participants. This might be interesting given the high exclusion rate, and given that most exclusions were not based on task behaviour (chance-level choosing). If there is concern about shifting group-level parameter distributions, then this could be explicitly included in the model by including an offset on group-level parameters (i.e., interaction term) on excluded participants, which would allow them to systematically differ in model parameters. Alternatively, one could at least estimate the abstraction and metacognition metrics in the excluded sample (perhaps restricted to those excluded on questionnaire-based criteria rather than task performance) to see whether they do indeed differ.

(4) How do factor scores relate to task accuracy - do those with higher compulsivity perform worse, and is this plausibly related to less abstraction?

(5) How do the factors extracted here compare to those in other studies, such as those from Gillan et al. (2016, eLife)? In particular, it'd be interesting to know what questionnaires/symptoms in the "Compulsive hypersensitivity" factor in the present study overlap with the Compulsive behaviour/intrusive thoughts factor from that earlier work, as the latter has been strongly associated with metacognitive measures - higher metacognitive efficiency for anxious/depression, lower metacognitive efficiency for compulsive behaviour - in previous work (Rouault et al., 2018). That three-factor structure also included a factor they labelled "Social withdrawal" - is it similar to the one presented here, or is the one here (including distress) more like their anxious depressive factor?

(6) In the Discussion, the authors state "Our findings are also consistent with previous converging evidence linking compulsive tendencies to less efficient computation and a preference for familiar over goal-directed action". That the Feature RL model might fairly be called less efficient is reasonable, but I'm not sure how the reduction of features in the Abstract RL model relates to goal-directed action (they're both model-free RL algorithms).

(7) The Discussion also mentions "models that integrate abstract and metacognitive representations" - was there a reason these could not be applied in the present study?

Reviewer #3 (Public review):

Summary:

This is an interesting study investigating the relationship between transdiagnostic symptom profiles and computational parameters of abstraction and metacognition. The authors found that a transdiagnostic dimension they term compulsive hypersensitivity was negatively related to abstraction ability and metacognitive sensitivity, and a dimension termed social withdrawal was positively related to metacognitive sensitivity. Overall, the question of whether and how higher-level cognitive processes relate to transdiagnostic psychiatric factors is interesting and addresses a relevant gap in the literature.

Strengths:

This is an overall well-designed study, and the authors have clearly given considerable thought to data quality and validation during the study design. This is evident, for instance, in the use of multiple approaches to assess inattentive responding and acquiescence tendencies. In addition, the use of advanced modelling approaches for the task data represents a strength and allows the authors to capture individual differences in task behaviour in a more nuanced and mechanistic manner in comparison to what would have been possible with model-agnostic analyses.

Weaknesses:

Nevertheless, I would like to raise several concerns, detailed below.

(1) My most pressing concern regards the exclusion rate of roughly half the sample. Of the 512 participants who were tested, only 249 (48.6%) were included in the final analyses, meaning that more than half of the recruited participants were excluded. Although the authors state that these exclusions were based on preregistered criteria, this high exclusion rate still warrants further investigation. Pre-registering exclusion criteria does not eliminate the potential for selection bias or establish that the resulting sample is representative of the recruited sample. In fact, the authors even report that the excluded participants differed significantly on several psychiatric scores. I believe that a detailed account of the exclusion, including how included and excluded participants differed on relevant demographic or study variables, would be beneficial.

Additionally, the authors state that a sensitivity analysis including participants excluded from the primary analysis was preregistered but was not conducted because the excluded and included participants differed significantly. This does not seem a sufficient justification for omitting a preregistered sensitivity analysis; in fact, systematic differences between included and excluded participants warrant this kind of sensitivity analysis. I agree with the authors that this may lead to changes in task parameter estimates. However, I believe this change would be an informative result of the sensitivity analyses rather than a methodological problem to avoid.

Finally, I would like the authors to clarify some inconsistencies in the preregistered exclusion criteria. In the pre-registration, they first state that all participants who fail any infrequency question will be excluded. Later in the pre-registration, they state that participants with two or more failed questions will be excluded and that a sensitivity analysis will be done, in which participants with only one failed question are retained. Neither of these criteria matches what is reported in the manuscript ("Attention check + infrequency item mistake more than 2").

(2) The current sample size of n = 249 participants is not sufficient for a factor analysis with 176 questionnaire items, and I believe that the solution of using factor weights from a previous larger sample is generally sensible. I also commend the authors for wanting to assess the validity of this approach. However, both the methods and results reported for the comparison between the factor analysis based on the current, smaller sample and the large, previous sample are insufficient and warrant substantially more detail. Firstly, it is unclear to me whether the three-factor solution in the factor analysis with the current, smaller sample was empirically grounded or whether three factors were extracted to match the three-factor solution from the larger sample. For both factor analyses, I would recommend reporting the factor extraction methods, the criterion used to determine the number of factors, and whether alternative factor solutions were considered. Secondly, it is unclear what exactly was correlated across the two factor analyses (e.g., factor scores, item loadings, factor weights?). Finally, I do not believe that the result of significant correlation is sufficient to conclude that the dimensions replicate between samples, particularly given they indicate only moderate correspondence (r = 0.46, for instance, corresponds to only 21% shared variance), thereby providing very limited evidence for replication of the factor structure.

These concerns are particularly pressing given that the authors state the consistency of transdiagnostic symptom dimensions across samples and countries as a primary result in the discussion.

(3) It is also unclear to me whether any exclusion was applied based on the first part of the Deary-Liewald reaction time task. The supplement implies that it was ("This task result was used to [...] exclude participants who exhibit problematic behaviours"), but I could not find a corresponding report in the manuscript.

(4) The manuscript refers to an attentional check questionnaire used to identify and exclude inattentive participants, but does not provide a reference for this measure or describe the questions included. Given the substantial overall exclusion rate, it is particularly important that all exclusion procedures and criteria are described in sufficient detail to allow readers to assess their appropriateness and reproducibility. The authors should therefore provide the relevant reference and/or report the specific questions and criteria used to determine inattention.

Relatedly, when describing the control-neutral items in the Supplementary Materials, the authors state that further details are provided in the Supplementary Materials. However, I cannot find another, separate Supplementary Material that may contain this information

Reviewer #1 (Public review):

Howell et al investigate the functional capacities of CD16dim CD56dim NK cells, including their activity against HIV-1-infected T cells. The authors provide an extensive characterization of the functional activity of different NK cell subsets derived from peripheral blood. CD16 is an important receptor expressed on NK cells, and previous studies have demonstrated that CD16 expression changes depending on the activation status of NK cells - one important regulator of CD16 expression is proteolytic shedding/cleavage of CD16 on activated NK cells by the metalloprotease ADAM17. In vitro activation of NK cells, for example in response to K562 cells or other target cells, results in a rapid downregulation of the expression of CD16 on the surface of NK cells, unless an ADAM17 inhibitor is added. This is an important point to consider in the interpretation of the presented results. Overall, the manuscript includes many data in nine figures plus supplemental figures, and would benefit from some focusing of the results.

(1) Figure 1<br /> The observation that CD16dim NK cells responded more strongly by degranulation to K562 cells and HIV-1-infected cells could be due to the shedding of CD16 following activation. In other words, more strongly activated NK cells express higher levels of CD107 but also shed CD16, resulting in higher CD107 expression in CD16low NK cells. The authors should investigate this, for example by performing the degranulation assays shown in Figure 1 in the presence and absence of an ADAM17 inhibitor.

(2) Figure 2<br /> The authors sorted CD16dim and bright NK cells for these experiments and observed higher lysis of HIV-1-infected CD4+ T cells. Important controls should be included in these experiments - how strong was the lysis of HIV-1-uninfected CD4+ T cells by these different NK cell subsets? It also appears that the results shown were derived using NK cells from one donor, and "representative of two independent sort experiments performed with separate donors, each yielding similar results". Why are the authors now showing the respective data? One or two experiments appear too few to come to these conclusions. To support the broad conclusions drawn by the reviewers, the experiments should be performed in a larger number of individuals.

(3) Figure 3<br /> It appears that experiments were performed again using bulk NK cell populations, and superior degranulation and killing frequencies by CD16dim NK cells might reflect different levels of activation again, as described above for Figure 1. The same applies to Figure 4 - lower degranulation events in CD16bright NK cells are consistent with lower activation of these cells, resulting in less CD16 downregulation. Also, it is not clear to the reviewer why CD107a expression and killing frequencies decrease with higher effector-to-target ratios (Figure 3).

(4) Pages 19-25<br /> It would be helpful if the authors could provide some conclusions regarding their findings - it is very difficult for the reader to follow the many reported frequencies and p-values. What does this actually mean? Overall, the results appear to follow prior observations that licensed (KIR3DL+) NK cells respond more strongly than unlicensed (KIR3DL1neg) NK cells. The consistent observation within these different subanalyses that CD16dim NK cells degranulate more than CD16bright NK cells is probably the result of activation-induced CD16 downregulation in these assays, as mentioned above. Providing two-way ANOVA analysis results for these very many observations would furthermore require, in the opinion of the reviewer, adjustments for multiple comparisons.

(5) Figures 5 and 6<br /> These figures demonstrate that NK cell-mediated activation by HIV-1-infected cells depends on NKG2D ligands and can be inhibited by blocking this interaction - this is consistent with data presented by the Barker group and others previously, and does not provide new information.

(6) Figure 7<br /> The authors extended their functional analyses of NK cells to ADCC function. It is very well established that CD16 is downregulated in the context of ADCC following activation of NK cells. Consistent with this, higher degranulation is observed by CD16dim NK cells.

(7) The data using ADAM17 inhibition in the final figures of the manuscript<br /> These data are of interest, but should be presented in a more structured way. First of all, does the addition of ADAM17 inhibitors change the overall proportion of CD16bright and dim NK cells following activation, independent of whether these cells degranulate or not? Overall, the proportion of CD16dim NK cells that degranulate appears to be reduced in the presence of the ADAM inhibitor, which is consistent with reduced CD16 shedding and maintenance of CD16 expression on activated NK cells - and this is supported by the increase in CD107a-positive NK cells that express CD16 (Figure 8a). Overall, the differences between CD16bright and dim NK cells in their level of activation appear to disappear in the presence of an ADAM17 inhibitor, based on the data shown in Figure 8b, suggesting that CD16 downregulation is occurring in response to activation of NK cells as a consequence of CD16 shedding, and can be inhibited by an ADAM17 inhibitor.

Taken together, many of the data presented in the manuscript are consistent with the very well-established downregulation of CD16 expression on activated NK cells, suggesting that the observed association between reduced CD16 expression on CD56dim NK cells and enhanced effector functions is a consequence of higher activation of these NK cells.

Reviewer #2 (Public review):

Summary:

This study investigates the cytotoxic activity of human NK-cell subsets against autologous HIV-1-infected CD4 T cells and identifies CD56dimCD16dim NK cells as the dominant effector population. The authors propose that this subset possesses superior cytotoxic activity compared with CD56dimCD16bright NK cells and could therefore represent an attractive target for HIV cure strategies. While the study addresses an important and clinically relevant question, several of its major conclusions rely on assumptions that are not adequately supported by the experimental design. In particular, CD16 is treated as a stable phenotypic marker throughout most of the study despite its well-established and rapid downregulation following NK cell activation.

Strengths:

(1) The study addresses an important and clinically relevant question regarding which NK cell subset is responsible for the elimination of autologous HIV-1-infected cells. To the best of my knowledge, this is the first study directly comparing the anti-HIV functional activities of CD56dimCD16dim vs CD56dimCD16bright NK cells.

(2) The experiments performed with purified NK cell subset (Figure 2) provide some evidence that CD56dimCD16dim NK cells possess enhanced cytotoxic activity relative to CD56dimCD16bright NK cells. This experimental approach is considerably more convincing than the analyses performed on mixed NK cell populations and should be expanded throughout the study.

Weaknesses:

(1) The central conclusion is weakened by the use of CD16 as a stable phenotypic marker. CD16 is well established to be rapidly downregulated following NK-cell activation and target cell (K562 or infected cells) engagement through ADAM17-mediated shedding. NK cell shedding regulates NK cell effector functions by promoting target cell detachment, boosting serial killing capacity, and preventing overstimulation. Therefore, NK cells displaying a CD56dimCD16dim phenotype after co-culture cannot be assumed to represent a pre-existing subset with intrinsically superior cytotoxic activity, but may instead correspond to activated CD56dimCD16bright NK cells that have downregulated CD16 during the assay. Because the vast majority of the functional experiments classified NK cell subsets based on post-assay CD16 expression, it is difficult to distinguish intrinsic functional differences between NK cell subsets from activation-induced phenotypic conversion. This limitation affects the interpretation of most of the study's principal findings.

(2) The "killing frequency" analysis presented in Figure 3 is based on a mathematical estimate rather than a direct experimental measurement. Since total target cell killing is measured in mixed NK cell populations, it cannot be attributed to individual NK cell subsets. This experiment must be repeated using purified NK cell subsets.

(3) The serial degranulation assay presented in Figure 4 does not directly measure serial target cell killing and therefore does not support the conclusion that CD56dimCD16dim NK cells possess superior serial killing capacity. Furthermore, the increased serial degranulation observed in the CD16dim population could simply reflect activation-induced CD16 downregulation rather than an intrinsic property of this subset. This experiment should therefore be repeated using purified NK cell subsets.

(4) The finding that CD56dimCD16dim NK cells exhibit greater ADCC activity is somewhat counterintuitive given the central role of CD16 in mediating ADCC. Moreover, these experiments are likely confounded by activation-induced CD16 downregulation, which is expected to be even more pronounced during ADCC. Thus, the apparent superiority of the CD56dimCD16dim subset may simply reflect the conversion of activated CD56dimCD16bright NK cells into the CD16dim gate rather than intrinsically greater ADCC activity. To directly compare the intrinsic ADCC capacity of each subset, these experiments should be repeated using purified NK cell populations prior to target-cell stimulation.

Reviewer #1 (Public review):

This work identifies two lectin receptor-like proteins as cell wall -plasma membrane anchors that contribute to persistent attachment sites maintained during plasmolysis. The authors propose that these attachment sites are important for plant resistance to hyperosmotic stress. Although the existence of persistent attachment sites between the cell wall and the plasma membrane, particularly evident in plasmolysed cells, was recognised long ago, the molecular components tethering the two cellular components remain largely unknown. Therefore, the findings presented by Arico et al. address an important question in plant cell biology.

Through a screening of potential anchors, they found that the overexpression of fluorescently tagged LecRK-I.9, LecTM, AGP18 and AT14A in Nicotiana benthamiana increased the density of Hechtian strands in plasmolysed cotyledon epidermis cells. They show that for LecRK-I.9* (* indicates kinase-dead version) and LecTM, this effect depends on the Lectin domain. Focusing on LecRK-I.9*, the overexpressed Lectin domain localized to cell walls, accumulating in certain foci. The authors interpret this as a possible preference for certain cell wall composition. I find these results convincing and the methodology robust.

The second part of the manuscript, however, relies on interpretations that, in my opinion, are not fully supported by the presented evidence. The authors move to Arabidopsis thaliana and show that overexpression of both LecRK-I.9* and LecRK-I.9*ΔLec fluorescent reporters also localizes to the plasma membrane and Hechtian strands in plasmolysed cells, although the density of Hechtian strands is not quantified. Thus, it is unclear whether LecRK-I.9* promotes Lectin domain-dependent strong cell wall-plasma membrane attachment sites in Arabidopsis.

The authors focus on the formation of big signal clusters at the plasma membrane in response to hyperosmotic treatment. They studied the dynamics of the clusters upon treatment application and observed increased mobility of LecRK-I.9* compared to LecRK-I.9*ΔLec and a plasma membrane marker. They then analysed the abundance and size (not the mobility) of these clusters on different plasma membranes facing cell walls that have or are predicted to have different mechanical and chemical properties, identifying differences between LecRK-I.9* and LecRK-I.9*ΔLec. Although the reduced mobility of LecRK-I.9 relative to LecRK-I.9ΔLec is consistent with an interaction between the lectin domain and the cell wall, it does not by itself demonstrate that the observed clusters correspond to CW attachment sites. Moreover, the relation between the clusters and Hechtian strands (bona fide cell wall-plasma membrane attachments) is not explored. Likewise, the differential clustering observed on different cell faces is intriguing but could have different interpretations.

Finally, the physiological relevance of the proposed anchoring mechanism is supported by osmotic stress assays, but the scoring method relies on manual classification of resistant seedlings and could benefit from a more objective quantitative readout.

In summary, although I find all these results valuable, I find that the methodology is not completely adequate and that several aspects of the data interpretation require additional support or clarification before the central conclusions can be fully justified.

Reviewer #2 (Public review):

Summary:

The manuscript submitted by Arico and co-workers describes the impact of two lectin-domain-containing proteins (LecRK-I.9* and LecTM) on the formation and persistence of Hechtian strands. Based on a survey of selected candidates, overexpression of these two proteins resulted in an increase in Hechtian strand formation. Removal of the lectin domains and expression of this variant did not alter HS formation compared to the WT. In addition, the existence of the lectin domain reduced protein mobility, probably due to interactions with the wall. Last but not least, overexpression of LecRK-I.9 increased the resistance of plants towards water loss conditions.

Strengths:

The study seems well conducted, but may require some small additions. While the results themselves seem not surprising, I think that this is a valuable demonstration of the cell wall binding ability of lectin proteins and its physiological and microscopical consequences.

Weaknesses:

At this stage, some of the study would benefit from some additional quantification.

Reviewer #3 (Public review):

Summary:

The cell wall and plasma membrane are tightly associated in plant cells, but even after plasmolysis, sites of strong contact remain between the plasma membrane and cell wall. Several hypotheses have been introduced about the molecular makeup of these sites of PM-CW adhesion (e.g., Rui et al 2026 Cell; Qin et al 2026 Current Biol; Pérez-Sancho et al 2025 Cell). Here, the authors implicate two transmembrane lectin proteins in PM-CW adhesion via overexpression in Nicotiana benthamiana and via Arabidopsis knockout phenotypes for one of these candidates, the lectin receptor kinase LecRK-1.9. They further show that the PM-CW adhesion function of LecRK-1.9 requires the extracellular domain, suggesting that this lectin-like domain may interact with the cell wall. Interestingly, LecRK-1.9 has also been implicated in extracellular ATP binding in the context of biotic and abiotic stress responses (e.g., Choi et al 2014 Science).

Strengths:

Overall, the work is carefully conducted with high-quality imaging and quantitative image analysis. The results present an interesting candidate for future studies of plasma membrane-to-cell-wall attachment.

Weaknesses:

There are two major caveats to this work. First, all work was conducted with the kinase-dead version of LecRK-1.9, which eliminates a significant biological function of this protein, as evidenced by the major differences in expression pattern of wild-type vs kinase-dead LecRK-1.9. Second, controls are essential to document the expression levels of different protein variants and controls, since LecRK-1.9 expression is correlated with Hechtian strand density.

Reviewer #1 (Public review):

Summary:

In this manuscript, Flamholz and colleagues use metagenomic sequencing to profile the microbiome of individuals with sickle cell disease (SCD), the most common genetic blood disorder in the world. To build on previous studies that found dysbiosis in SCD, this manuscript aims to examine whether changes in either bacterial species or bacteriophages correlate with inflammatory hallmarks of the disease. The authors claim that sickle cell dysbiosis does not correlate with inflammatory hallmarks of the disease including aged neutrophil numbers, a cell type previously highlighted in preclinical sickle cell microbiome work.

Strengths:

The primary strength of this paper is the investigation into disease associated changes in bacteriophages. This is an entirely novel idea in the sickle cell field, and based on the current results, may be an important, under-recognized disease hallmark. It is unclear, however, if phages are "the chicken or the egg" in terms of sickle cell inflammatory profiles; do these increases in phage number simply result from other disease process or are they in anyway contributing to disease pathophysiology?

Weaknesses:

The authors addressed many of the initial manuscript weaknesses in their revision. In particular, they have softened language regarding sickle cell dysbiosis and its causative role in disease pathology. This is particularly appropriate given the lack of correlation between dysbiosis and immune factors in this single-center study.

Reviewer #2 (Public review):

Summary:

The study analyzes stool metagenomes from 98 SCD patients and 46 controls, with SCD and control groups matched on age, race, sex, and ethnicity. The authors report lower Shannon diversity, lower Firmicutes/Bacteroidetes ratio, loss of health-associated taxa, increased disease-associated indicators, altered butyrate/fatty-acid metabolism pathways, and enrichment of provirus/prophage fractions in SCD. They further correlate aged-like neutrophils and prophage fractions with inflammatory cytokines. The main strength is that this is not just another 16S comparison. The use of whole-community metagenomics, immune profiling, neutrophil assays, and clinical metadata makes the study more biologically interesting than prior small SCD microbiome papers. The main weakness is that the causal and mechanistic interpretation is too strong. The data support an association between SCD status and microbiome/virome features, but they do not yet establish a clear "axis of pathophysiology." The provirus findings are intriguing, but require stronger statistical control, better validation, and more cautious interpretation.

Strengths:

The major strengths of the study include the clinically relevant disease setting, the use of whole-community sequencing, the integration of microbial, immune-cell, cytokine, and clinical measurements, and the novel attention to bacterial virus-related features. A particularly interesting aspect of the work is the analysis of virus-like elements integrated into bacterial genomes. The authors report that these elements are enriched in the gut microbial communities of patients with sickle cell disease and are associated with several inflammatory signals in blood. This observation is potentially important because it suggests that the microbial contribution to inflammation in sickle cell disease may involve not only bacteria but also bacterial virus-related genetic elements.

Reviewer #3 (Public review):

Summary:

In this manuscript, Flamholz et al. sought to determine whether consistent and significant interactions exist between the gut microbiome and disease pathology in sickle cell disease (SCD). By sequencing and analysing metagenomes from faecal samples collected from 98 SCD patients and 46 control subjects, they identified community-level shifts in both the bacterial and proviral gut microbiome of SCD patients. They further reported correlations between the proviral microbiome and multiple blood cytokines, whereas similar associations were not observed for the bacterial microbiome.

Strengths:

This work includes the largest SCD cohort analysed to date, enabling analysis with relatively strong statistical power. In addition to profiling the bacterial microbiome, the study also examines the gut proviral microbiome, thereby providing a more comprehensive investigation of the topic. The newly generated metagenomic dataset will also be valuable for further meta-analysis by the wider community. Overall, the authors have largely achieved their aims.

Weaknesses:

This study represents a single-centre cross-sectional investigation, and most findings remain correlative in nature. Additional mechanistic and/or longitudinal evidence would be required to unravel causality and the underlying mechanism.

Reviewer #4 (Public review):

Summary:

The current study tested the effects of repeated sessions of tDCS targeting the DLPFC on procrastination behavior. The main outcome is that anodal versus sham DLPFC tDCS reduces procrastination behavior on both a short-term and a long-term scale up to six months after the stimulation sessions.

Strengths:

The current study tests competing models of procrastination with state-of-the-art high-definition transcranial electric stimulation. The study assesses stimulation effects on procrastination on both a short-term and a long-term scale, suggesting that repeated stimulation of the prefrontal cortex reduces procrastination on a time scale of up to six months.

Comments on revised version.

The manuscript has already been reviewed and revised before, and it seems that the quality of the manuscript has substantially improved as a result of this revision process. I agree with the other reviewers that one must be cautious with drawing conclusions regarding the cognitive mechanisms underlying this effect, as many different cognitive functions are implemented by the DLPFC. The effect sizes are surprisingly large, but I am satisfied with the reasons provided by the authors for the large effect sizes.

The authors successfully addressed my previous concerns on the manuscript.

Reviewer #1 (Public review):

Summary:

This interesting paper demonstrates that transgenic over-expression of sphingosine 1-phosphate receptor 1 (S1PR1) on neutrophils alters their phenotype, resulting in (1) accumulation of neutrophils in blood, spleen, lung, and liver; (2) a shift in homing receptor expression with reduced CXCR2 and elevated CXCR4; (3) altered transcriptional profile with an increase in "G5c" neutrophils and reduced "module scores" for apoptosis and inflammatory response; (4) reduced ROS production upon fLMP stimulation; and (5) altered responses to bacterial and viral infections of the lung. It raises many interesting questions about how S1P signaling regulates neutrophil biology, and hence will be the basis of future studies. These include: (1) What is the physiological role of S1PR1 signaling in neutrophils? Although there is no dramatic effect on numbers upon S1PR1 loss, is there an effect on any of the other parameters measured? (2) What is unique about the lung that S1PR1 over-expression is particularly impactful there? (3) What distinguishes the bacterial context in which S1PR1 over-expression is maladaptive from the viral context in which S1PR1 over-expression is protective? and (4) Can treatment with an S1PR1 agonist mimic S1PR1 over-expression? As a possibly related question, when in neutrophil development does S1PR1 signaling function to shift the phenotype?

Strengths:

(1) A comprehensive characterization of S1PR1-transgenic neutrophils.

(2) Opens many interesting areas of investigation.

Weaknesses:

Although some characterization of the neutrophil-specific Mrp8-Cre is done, most of the experiments use the more widely expressed LysM-Cre. The redistribution phenotype is much stronger with LysM-Cre than with Mrp8-Cre, so it is unclear what effects are attributable to a cell-intrinsic role of S1PR1, even in studies of neutrophils analyzed ex vivo.

Reviewer #2 (Public review):

The authors have utilised two main models to assess the function of S1PR1 in neutrophils in mice. The knockout of this receptor shows no conclusive effect on neutrophil numbers or functions; it was only the overexpression that resulted in significant alterations. Therefore, often the conclusions do not describe normal or disease physiology but could be useful in a bioengineering context.

Strengths:

From a bioengineering standpoint, this seems like an important study - showing enforced expression of S1PR1 in neutrophils has improved outcomes for influenza infection (Figures 6 and 7).

Weaknesses:

Although the strength is the influenza model, genetic modification of human neutrophils cannot be a strategy, and therefore, is there any way to increase this receptor for mouse, or more importantly, human neutrophils? This study only looks at mice with a non-physiological model of overexpression. It does not offer a real therapeutic option, which drastically hinders the importance of the study. I have other concerns with the data analysis and interpretation, which I detail on a figure-by-figure basis (and how it relates to conclusions) below:

Main specific issues:

(1) Figure 2A+B: This is unconvincing; in the surface staining there seem to be real cells positive for the receptor (high staining in the histogram), but none of the transgenic protein is getting there? This undermines the idea that the effects of the transgene are related to S1P signalling. In the 'Total S1PR1' this is both underwhelming and misleading, as an isotype control (or better S1PR1 knockout) is missing, which would give a better representation of actual expression (flow cytometry autofluorescence famously increases in the red laser channels with fix/perm). The Imagestream chosen images are showing best-case scenarios - and aren't representative. What does the isotype/ KO look like here? All in all, the conclusion on receptor internalization is not well supported, especially when theoretically the TG overexpression should overload S1P availability. This also highlights the lack of another control - does overexpression of another random/non-functional protein have the same effect? To play devil's advocate, perhaps overloading of the ubiquitin-proteasome system is responsible?

(2) Figures 2E-H: In the text, the authors should fix the statement 'Additionally, surface CXCR2 was downregulated and CXCR4 upregulated in LysM-S1pr1 TG neutrophils across bone marrow, spleen, and blood (Fig. 2, E and F)' to better reflect that there is no significant difference in the bone marrow regarding CXCR4. Of note, the total MFI from this data would also be informative, another noticeable absence being the gating strategies for much of the data. Also, alter the statement: 'CD62L expression was largely preserved across compartments, with only a modest reduction in bone marrow neutrophils (Fig. 2G)'. A 50% reduction in CD62L is not modest.

(3) Supplemental Figure 3. A common theme: the wrong statistics have been used here, which has led to a false conclusion. Megakaryocyte/erythrocyte progenitors (MEPs) were only elevated in 2/3 TG mice, and the numbers are so small that this is not significant by any measure of the word. This is certainly not statistically significant if the correct test of (log-normalized) two-way ANOVA is performed (with Sidak's post hoc test). Another acceptable test would be Kruskal-Wallis with Dunn's post-test just for MEPs.

(4) Starting at Figure 3, the authors refer to 'S1PR1hi neutrophil accumulation'. Crucially, the authors must here and throughout be explicitly clear in which cells they are referring to, as this can be misleading - particularly as there are real S1PR1-high cells identified in Figure 2A surface staining. It is my understanding that the authors here mean the transgenic artificially high mice - a very large distinction.

(5) Figure 3A: It is difficult to interpret the figure with the necessary details about the experiment. For instance, there is no mention that this is sterile inflammation or what caused it.

(6) Figure 3B and C: It should be made clear whether these splenic neutrophils are related to the time course of peritoneal inflammation in 3A. Why are there so many apoptotic neutrophils in the spleen? The low numbers here suggest a processing issue rather than real death in vivo (which usually is absent).

(7) Figure 3D: This can also be misleading - the wrong statistics are again used. This should be a log-transformed two-way ANOVA. Regardless of this, the data is not strong enough to be conclusive, a minor effect at best that could also just be related to the type of cell tracker used.

(8) Figure 5E: It is stated that 'LysM-S1pr1 TG mice exhibited a higher bacterial burden in the lungs than controls (Fig. 5E).' Again, misleading results, first the wrong statistical test was used (correct = log norm one-way ANOVA with Tukey's or Kruskal Wallis with Dunn's), secondly the only significance is between S1PR1(fsf) and the Mrp8-S1PR1, not with the LysM TG. 5F is also not strong, with only 2/7 values appearing outside the range of the control - P values can be misleading when poor statistics are used.

(9) Figure 6G: Some discussion should be given for why Neutrophils are lower in BALF in the IAV model - even though higher in the lung in the non-IAC mice in Figure 1. In general, rather than focusing on the non-physiological differences, the discussion could better reflect the inconsistencies and more fully address the difference between the TG and KO and what this means going forward.

Reviewer #3 (Public review):

Summary:

Using mice that overexpress S1PR1 in myeloid cells or specifically in neutrophils, the authors show that increased S1PR1 promotes neutrophil release from the bone marrow and accumulation in blood and peripheral tissues without causing baseline tissue injury. These cells acquire a CXCR4-high, CXCR2-low, CD101-low phenotype, survive longer, and display enhanced mitochondrial metabolism and mTOR signaling, together with reduced apoptotic, inflammatory, and ROS-related programs. Although phagocytosis is preserved, ROS production is markedly reduced. This is associated with impaired bacterial clearance in the lung but improved outcomes during influenza infection, including better survival, less weight loss, improved oxygenation, lower viral burden, and reduced lung inflammation. In contrast, myeloid S1PR1 deletion produces little detectable phenotype. The authors therefore propose that S1PR1 separates neutrophil persistence from inflammatory function, improving tolerance to viral lung injury at the expense of antibacterial defense.

Strengths:

This is a technically solid paper using novel mouse models to overexpress S1PR1 specifically in myeloid cells as well as neutrophils. The data are striking with respect to neutrophil expansion. The diverse roles of neutrophils and their population heterogeneity are an important scientific area that has led to many recent breakthroughs - PMC11785525; PMC12823425, thus this is a timely study.

Weaknesses:

The study mainly demonstrates what S1PR1 overexpression is sufficient to do, rather than establishing the physiological role of endogenous S1PR1. The conclusions should therefore be narrowed unless the authors provide stronger loss-of-function and physiological validation. As written, the abstract ("S1PR1 promotes mitochondrial fitness, enhances survival, and reduces inflammatory output") and the conclusion ("S1PR1 serves as a key regulatory axis") are sufficiency claims but should not be promoted as necessity claims. The honest sentence is: "Thus, a better conclusion would be that enforced S1PR1 expression is sufficient to reprogram neutrophils".

The authors do not confirm efficient S1pr1 deletion in neutrophils. Furthermore, the knockout is examined only under steady-state conditions and limited in vitro stimulation, but not in the bacterial or influenza models where the transgenic phenotype is observed. Without these experiments, the study cannot establish whether endogenous S1PR1 is necessary for the reported functions.

The degree of S1PR1 overexpression is not quantified relative to normal physiological levels. The authors should determine whether naturally occurring S1PR1-high neutrophils display the same survival, metabolic, trafficking, and inflammatory features observed in the transgenic cells.

Analysis of relevant human or mouse datasets, including sepsis, ARDS, viral infection, cancer, or aging, would also help establish whether this neutrophil state exists physiologically.

Surface S1PR1 expression appears similar between control and transgenic neutrophils, whereas total intracellular receptor is increased. This suggests that the phenotype may depend on receptor internalization or endosomal signaling. An internalization-deficient S1PR1 model, such as S1P1-S5A, would help distinguish sustained surface signaling from internalization-dependent signaling. The authors should also determine whether the phenotype requires ligand binding, Gi signaling, and mTOR activity.

The reduction in CXCR2 and decreased neutrophil accumulation in the airways could alone explain the protection from influenza-induced lung injury. The current experiments do not clearly distinguish neutrophil reprogramming from defective migration into the alveolar space.

Although this may be outside the scope of the current study, the authors should directly test whether CXCR2 inhibition reproduces the phenotype.

The reported reduction in viral load should also be confirmed using plaque assay or TCID50, and the possible contribution of NET formation should be examined.

Reviewer #1 (Public review):

This is an interesting paper, with the primary finding being that localizing MreB or PBP2 to the cell poles in E. coli is primarily demonstrated via an aggregate formed by expressing M. xanthus MreB.

I have 2 main concerns:

(1) First, the authors should clarify and adjust their interpretation of FDAA incorporation: As written, the authors interpret FDAA incorporation as being caused by incorporation during PG polymerization. However, in E. coli, FDAA incorporation does not result from the elongation of PG strands or their initial 4-3 crosslinking by DD transpeptidases following polymerization, but rather by the remodeling of L,D-transpeptidases.

Thus, it is not accurate to refer to the FDAA incorporation as PG elongation, but rather the modification of crosslinks from 4-3 to 3-3 crosslinks at that location. To claim a link to PG polymerization, other experiments or substantial explanations are needed.

(E. coli Cells Incorporate FDAAs by L,D-TPases in a Growth Independent Manner) - https://doi.org/10.1021/acschembio.

(2) Second, the evidence provided that this is polar elongation is not sufficient to prove elongation. In all images claiming polar growth, the FDAA focus appears as a single spot, which corresponds to the MreB aggregate visible in bright-field. That might indicate incorporation, but it does not demonstrate polar elongation. To prove this, the authors should do different-length pulses of FDAAs and demonstrate an increasing length of labeled PG along the cell. Cells with one focus should show increasing length; polar foci should elongate from both ends. I find the current 2-color labeling insufficient, as BADA labels the entire cell.

Small points:

(3) Lines 350- 302: "In this case, as the nonpolar region is no longer the growth zone, the established cylindrical PG structure is sufficient to maintain cell width, where MreB filaments become nonessential." Does polar elongation give robustness to rod shape? The authors should include an analysis of cell width and its variation within a cell and between cells.

(4) In the abstract: "This reprogrammed growth mode bypasses the requirement for MreB filaments, highlighting a plasticity of the Rod system that suggests polar elongation may have emerged through the evolutionary loss of MreB." This argument should not be made without evolutionary analysis or reference to such work indicating this is the case.

(5) 365-367 "PG-depleted spheroplasts can spontaneously regenerate rod shape through curvature-dependent localization of MreB filaments [21, 57]". This should be amended. The Billing paper did indeed study spheroplasts, but the Hussain paper used teichoic acid-depleted cells that still had a cell wall.

Reviewer #2 (Public review):

Summary:

Based on observations of localisation of MreBEc at the poles within an aggregate-like structure, upon heterologous expression of MreBMx, the authors set out to investigate how this non-canonical localisation of MreBs leads to a reprogramming of peptidoglycan synthesis to the poles. This is analogous to the polar growth observed in phyla which are not dependent on dispersed growth of PG, but only at the poles, and are MreB independent.

The authors proceed to establish that PG synthesis is MreB-dependent, Rod enzyme-dependent, and requires the prior establishment of a pole.

Strengths:

(1) It is a very interesting idea to design experiments to demonstrate reprogramming of non-polar to polar growth based on the observation of localisation of a heterologously expressed MreB.

(2) The experiments to demonstrate the factors that determine polar growth and the observation of the PG in each of these experimental situations are convincing.

(3) I find the observation of an extra layer of PG in the heterologously expressed system very intriguing. It will be interesting to see if this layer merges with the other PG layer at some stage or branches from the non-polar growth near the poles.

Weaknesses:

(1) It is not clear what exactly the identity of the polar aggregates is and how much of this activity is an artefact of partially functional MreBs.

(2) I find it intriguing that the localisation and growth are predominantly at one pole only. It is unclear to me how this can be reconciled with growth and shape maintenance, and an increase in length and width. Is the increase in length and width a consequence of misshapen cells that are bulged in the absence of a normal PG layer?

(3) The authors do not follow up on the observations in the first figure on the length and width changes and the extra peptidoglycan layer (which I feel are the most interesting aspects), and how this can be connected to the polar growth observed in the later sections of the manuscript.

(4) The claim that this could be a precursor of an MreB-independent polar growth mechanism appears to be a bit far-fetched, because the system is still dependent on having an established pole for PG synthesis to occur in the new place.

Reviewer #3 (Public review):

Summary:

Most rod-shaped bacteria grow by one of two mechanisms: growth from the pole or growth from the midcell. It is rare for a single species to utilize both modes of growth, although a few examples do exist. Here, the authors have artificially induced E. coli cells to grow from the poles, either by expressing mreB from Myxoccocus xanthus in E. coli, leading to the mislocalization of MreB to the poles in large aggregates, or by forcing the localization of major cell wall synthesis proteins to the cell pole. The fact that cells switched modes of growth suggests an evolutionary pathway from midcell to polar growing cells as well as suggests that there might be unknown conditions in nature when cells may switch growth modes.

Strengths:

(1) The authors use a strain that has replaced mreB with a functional fluorescent version at the native site. This eliminates any effects of having two copies of mreB. Because MreB is fluorescently tagged, they can monitor its localization when mreB from M. xanthus is expressed in E. coli. They notice that MreBec now forms bright polar foci and that there appear to be changes to the cell wall at the pole.

(2) D-amino acids are specific to the cell wall, and fluorescent versions (FDAA) have been used to mark sites of new cell wall insertion. The authors use these FDAAs to determine how cell wall synthesis correlates to MreB and if that changes when MreBmx is expressed. Again, there is pretty clear evidence that cell wall synthesis follows MreB localization to the pole.

(3) MreB itself does not synthesize the cell wall, but localizes the proteins, such as PBP2, that do. Using a published method to force proteins to the pole, the authors show that when they target PBP2 to the pole, they can phenocopy the polar growth seen when MreB is polar. Interestingly, these cells become resistant to A22, a drug that targets MreB, suggesting that localized growth at the pole does not require MreB and is sufficient to maintain rod shape.

Weaknesses:

(1) The authors do not show what the poles of control cells look like, making it difficult to determine if there is a change when MreBmx is expressed. However, the localization of both MreBec and MreBmx clearly forms bright foci at the pole.

(2) While more quantification is needed, the authors show some evidence that RodZ, an MreB interaction partner, is needed for this polar growth, as cells lacking rodZ still form foci at pole-like regions when MreBmx is in the cell; however, these cells remain spherical and do not elongate from these foci.

When MreB is deleted, and cells become spherical, the authors were unable to cause the polar growth mode. They suggest that this is due to the lack of a preexisting pole; however, experimental evidence to test this is missing.

Conclusion:

Overall, the authors do a good job of showing that E. coli can grow with a polar method rather than a midcell method of cell wall insertion. It is unclear why MreBec forms at poles when MreBmx is present and even if this MreB is functional. The foci look similar to inclusion bodies, which are normally aggregates of misfolded proteins that migrate to the poles. Past work has shown that when MreB is more polarly localized, branches form, which is not seen here. Importantly, the authors also show that there is feedback between the localization of MreB and PBP2 as both appear to regulate the localization of the other.

Reviewer #1 (Public review):

Summary:

In this manuscript, Green et al. attempt to use large-scale protein structure analysis to find signals of selection and clustering related to antibiotic resistance. This was applied to the whole proteome of Mycobacterium tuberculosis, with a specific focus on the smaller set of known antibiotic-resistance-related proteins.

Strengths:

The use of geospatial analysis to detect signals of selection and clustering on the structural level is really intriguing. This could have a wider use beyond the AMR-focussed work here and could be applied to a more general evolutionary analysis context. Much of the strength of this work lies in breaking ground into this structural evolution space, something rarely seen in such pathogen data. Additional further research can be done to build on this foundation, and the work presented here will be important for the field.

The size of the dataset and use of protein structure prediction via AlphaFold, giving such a consistent signal within the dataset, is also of great interest and shows the power of these approaches to allow us to integrate protein structure more confidently into evolution and selection analyses.

Comments on revised version.

All my comments from the previous round of reviews have been addressed.

Reviewer #1 (Public review):

The authors sought to devise a model of the song system that captures the essential features of that neural circuitry and couple it to a behavioral model that captures the challenges of motor learning while remaining tractable. They seek to use this model to explain known features of song learning and relate them to the general problems associated with learning via gradient ascent. Their syrinx model uses two control parameters-air sac pressure and syringeal labial tension-to generate birdsong-like spectrograms. Normalized spectrograms generated by the syrinx model are compared to a target spectrogram by computing Pearson's correlation coefficient. This correlation coefficient quantifies the performance of the model, and the goal of learning is to maximize it. The syringeal model, while simplified, is complex enough to generate multiple local maxima in the correlation coefficient within the 2D control space with wide variation in the magnitude of the maxima, making it challenging to find a good optimum via gradient ascent. They also use a more abstract motor model where local maxima are generated by randomly placing Gaussians within the 2D control space. These two approaches to modeling motor space are a major strength of this work.

The neural model is extremely generic and consists of units (each representing a population of excitatory and inhibitory neurons) with continuous-valued outputs ("firing rate") ranging from -1 to +1 with sigmoidal activation functions. Premotor HVC simply generates a fixed temporal sequence that drives activity in RA (analogous to the primary motor cortex) that constitutes commands to the motor controller that translates RA output into 2D control signals for the syrinx. A second, indirect, pathway from HVC to RA is represented by a single node ("BG"); in songbirds this pathway consists of 3 structures (one of them quite complex and heterogeneous) with recurrent connections (i.e., from LMAN back to Area X). Learning is primarily driven by performance-modulated Hebbian plasticity in HVC-BG connection weights. There is also Hebbian plasticity in HVC-RA weights that are in effect trained by the RA activity patterns driven by BG-RA connections.

I worry that this model is too simplified to capture essential features of the song system (and cortico-basal ganglia circuits more generally). That problem is most acute in the way the authors model (or fail to model) the anterior forebrain pathway (AFP) through X, DLM, and LMAN; their model in effect reduces the AFP to just LMAN. I do not think that invalidates this study, but there is a significant danger that this model will end up missing the mark in some important way relative to a more realistic model. However, there is another flaw that comes close to doing that-the connection weights between the units are allowed to vary from -1 to +1. That means, for example, that the connections between HVC and RA units can be inhibitory and can flip between excitation and inhibition during learning. I can imagine some hand-wavy justifications for this (e.g., the connection becomes "inhibitory" because excitation to inhibitory neurons becomes stronger than that to excitatory neurons within the unit), but I can't easily imagine one that I would find persuasive.

A key feature of this model is "synaptic volatility" in the connections between HVC and BG. In songbirds, performance often improves steadily throughout the day, then deteriorates after sleep, a feature which the authors suggest is an important method for avoiding getting stuck on relatively low-performing local optima. I find this suggestion to be intriguing and reasonably persuasive. To implement this in their model, after a simulated "day" of practice, the HVC-BG connection weights are partially randomized (synaptic volatility). There is nothing intrinsically wrong with this idea, but the authors imply that there is experimental support for this phenomenon, which they relate to "continuous remodeling of the cortico-striatal synapses with volatility over hours to days." None of the papers cited really support this kind of synaptic randomization. However, given the simplicity of the authors' neural model, the HVC-BG weights are probably the only place this randomization can be implemented. The authors' implementation does not just add noise to HVC-BG weights "overnight"; it is also scaled inversely by the magnitude of accumulated weight change through the day. The authors do not appear to provide a justification for this aspect of their synaptic volatility.

If we accept the authors' model as detailed and accurate enough for their purposes, it does explain several features of song learning and relates them to solving general problems of learning through gradient ascent. This is particularly true of the daily deterioration of performance and how that relates to escaping local maxima. They show that their model reproduces some of the known effects of lesioning the motor (HVC-RA) and anterior forebrain (HVC-BG-RA) pathways and how those effects depend on the current stage of song learning. They also explore the implications of delayed maturation of the HVC-RA pathway, exemplified by a gradual increase in HVC-RA weights. However, it is not clear that this actually happens; the one paper they cite in support of this contention (Mooney and Rao, 1994) shows no such thing (it does show that HVC axons enter RA later in development than LMAN axons do). Moreover, it's not clear how essential this could be given that some songbird species continue to show profound vocal plasticity throughout their lives and presumably long after their HVC-RA pathway has fully matured. The authors compare their dual pathway model to a single pathway model (an AFP-only model, in effect); a very illuminating comparison that demonstrates the advantage of the dual pathway. They close by showing that dual pathway performance is robust under variation of key parameters.

Reviewer #2 (Public review):

Summary:

The authors describe a computational model for the acquisition of sensorimotor skills and explore these dynamics using vocal learning in the zebra finch, a system rich in experimental data, to describe the developmental trajectory of vocal imitation by trial and error. They set up their model as a dual-pathway system, with a cortical pathway that drives the vocal effector and a basal ganglia (BG) pathway that uses dopamine-mediated reinforcement learning (RL) by gradient descent to optimize the vocal imitation process.

Strengths:

A key strength of the model, due in part to the fact that his model was generated by a computational laboratory that has also contributed significantly to the collection of experiment-driven empirical data, is that the model is biologically constrained and incorporates a considerable amount of experimental data, including some of the latest findings in the field. In addition to providing a compelling model for the acquisition of vocal learning, this biologically based RL model outperforms many current models. A key feature of this model, which makes it unique, is the implementation of a synaptic volatility variable within the BG pathway that aims to mimic published work showing that juvenile birds exhibit post-sleep deterioration. The model uses a motor output to drive a biophysical model of the avian vocal organ (syrinx) and explores not only the ability to copy song acoustic units (syllables) but also the underlying neural dynamics in both the cortical and BG pathways, showing that each converges onto the types of neural activity patterns that are observed experimentally. Because of the richness of experimental data in this system, the authors can perform "computational experiments" where they can block sleep-driven synaptic volatility or lesion various pathways to replicate experimental observations.

In addition to providing important computational insights to our understanding of vocal learning in the songbird, this study provides key insights into the general architectures that are optimal for RL by gradient descent. These include the conclusion that effective RL requires adaptive regulation of exploration and exploitation, that cortical consolidation must occur at a slower timescale than BG-driven exploration, and intriguingly that the introduction of synaptic volatility prevents RL models of incomplete learning by getting "stuck" in local minima.

Weaknesses:

In the methods section, the authors state "... HVC and RA layers are fully connected, as are the HVC and BG layers. Synaptic weights in these pathways are plastic, reflecting activity-dependent plasticity at RA and BG synapses." Unless I missed it, it is unclear how much the authors consider the synaptic differences between HVC and BG inputs to RA. This seems like an important feature to highlight, especially given that HVC-RA connections are primarily AMPA-mediated whereas those from LMAN are predominantly NMDA. The authors should be clearer about how they model these synapses and better highlight (and describe) the importance of these synaptic differences in their modeling efforts. Ideally, they should evaluate whether the differences in synapse type influence the outcome of their model. It would be interesting, for example, to test the effect on learning of synaptic conductance substitution (i.e., replacing NMADA with AMPA) on the LMAN-RA synapse.

The model focuses exclusively on the interaction of two converging pathways, and learning is based purely on acoustic feature properties of what seem like four independent syllables of similar or identical duration. For this model, this is fine. But it would be helpful for the authors to state more clearly that they are not modeling respiratory influences on syllable production, which include amplitude modulation of the syllables and expiratory pulse duration. It should be noted that the authors do not (unless I missed it) mention the existence or role of recurrent loops in song initiation (and possibly syllable sequencing). They should at least mention this in the discussion, perhaps as a limitation and item for future versions of the model.

Reviewer #3 (Public review):

Summary:

This study modeled vocal learning in zebra finches with a network of three components: a pathway with delayed/slow Hebbian learning that mimics the HVC-RA projection, a pathway with reinforcement learning that mimics the HVC-BG-RA projection, and a motor unit that mimics the syrinx and produces song output. The model convincingly reproduces key features of song learning and is a valuable step towards understanding how vocal learning is substantiated in the song system. Further examination of model assumptions and presentation of testable predictions would increase the impact of the study.

Strengths:

(1) The model reproduces several key features of song learning, including learning in a non-convex performance landscape, decreasing motor variability during learning, the relative importance of the HVC-RA and HVC-BG-RA pathways at different learning stages, and sleep-related deterioration.

(2) The model incorporates several key physiological properties of the song system, including performance-dependent dopamine signals to the BG, the neural variability in the system, and multiple global/local optima of the motor production landscape.

(3) The study convincingly demonstrates the advantages of a dual-pathway network over a single-pathway one.

(4) The model demonstrates the counterintuitive benefit of sleep-related performance deterioration for facilitating the escape from local optima to reach the global optimum.

(5) The model seems robust to some variations in model parameters and task structure.

Weaknesses:

(1) The study could be more impactful if the model can generate new, testable predictions. The predictions provided in the Discussion are not well justified. For example, with the HVC spine turnover, it seems unlikely that BG lesions would abolish overnight performance deterioration. Because the volatility term is inversely related to learning during the day, the changes in LMAN and RA during sleep are not necessarily larger than those during the day.

(2) The section "Neural activity patterns in the model parallels song system neurophysiology" seems fully anticipated because the model construction is based on the known neural activity patterns. Are there new testable predictions from the model?

(3) Certain model assumptions lack explanation or justification.<br /> a) The authors treat "the delayed maturation of the cortical pathway" as an important component. However, it is unclear if/how this component was implemented in the model. If it was not included in the model, the authors should remove statements related to the delayed maturation idea.<br /> b) How critical is the inverse relationship between learning and sleep-deterioration? If it is known that sleep-related deterioration is inversely related to learning during the previous day, a citation should be added. Similarly, it should be clarified if the spine turnover observed in HVC depends on previous plastic changes, like the assumed inverse relationship in the model.<br /> c) Spine turnover has been demonstrated in HVC and not yet in Area X, but the model implements volatility only in the HVC-BG pathway. It would be important to compare the effects of sleep-related volatility in the HVC-RA and HVC-BG-RA projections.<br /> d) In Table 1/Figure 8, the learning rate for HVC-BG is 10^4 times bigger than the learning rate for HVC-RA. What is the biological justification for this difference?

(4) Related to #3, it is unclear how changing those assumptions would affect model performance.

(5) It is not explained/shown why cross-day exploration (with sleep, sporadic) is better than continuous, non-sleep-related ones. Figure 7B presents results with different noise levels, which may approximate non-sleep-related, continuous volatility, but only for the single-pathway model. Comparable simulations by adding continuous volatility in the dual-pathway model would be helpful. For example, would just a bigger intrinsic noise in either pathway confer the same benefit in escaping local optima, e.g., epsilon-greedy exploration? If the authors can establish the advantages of sleep-specific volatility in its particular form (based on day learning) and relate it to other sensorimotor learning behaviors, it could increase the general impact of the study.

(6) The model description can be improved.<br /> a) What are mBG and mRA in Eqs 6/7?<br /> b) Where is the learning rate specified in Equations 1-8?<br /> c) It is unexplained why Equations 1-3 are not in the same form: Equations 1 and 2 normalize by activity, but Equation 3 normalizes by weight (Equation 8 also normalizes by weight). The authors should confirm that these equations are correct.<br /> d) How J_HVC is generated should be defined.<br /> e) How is R calculated in Equation 10? Does this model maintain a PPE for each syllable or for the overall performance? Would it make any difference in learning?<br /> f) Are the weight updates in Equations 9 and 13 added at different time points (e.g., immediately after each spike versus after a syllable). This should be clarified (perhaps a diagram would help).<br /> g) How are the optimal threshold and slope determined for Equation 14?<br /> h) Parameters in Equation 20 are not defined.

Reviewer #1 (Public review):

Summary:

This study presents an interesting behavioral paradigm and reveals interactive effects of social hierarchy and threat type on defensive behaviors. However, addressing the aforementioned points regarding methodological detail, rigor in behavioral classification, depth of result interpretation, and focus of the discussion is essential to strengthen the reliability and impact of the conclusions in a revised manuscript.

Strengths:

The paper is logically sound, featuring detailed classification and analysis of behaviors, with a focus on behavioral categories and transitions, thereby establishing a relatively robust research framework.

Comments on revised version.

I think the authors have addressed all of my comments.

Reviewer #2 (Public review):

Summary

The authors examine how dominance hierarchy modulates defensive strategies in mice exposed to two naturalistic threats: a transient visual looming stimulus and a sustained live rat. By comparing single versus paired testing conditions, they demonstrate that social presence attenuates fear responses, and that dominant and subordinate mice display distinct behavioral and social patterns depending on threat type. The study offers a rich behavioral dataset and a potentially valuable framework for investigating hierarchical influences on innate fear.

Strengths

(1) The use of two ecologically relevant threat paradigms allows for meaningful comparisons across transient and sustained contexts.

(2) Behavioral quantification is thorough, incorporating manual annotation of multiple behavior types and transition‑matrix analyses.

(3) The comparison between dominant and subordinate pairs is novel within the innate‑fear literature.

(5) The manuscript is well structured and clearly written, with figures that are visually informative and effectively support the main conclusions.

Weaknesses

The investigation of neural mechanisms underlying the observed behavioral effects remains limited.

Reviewer #3 (Public review):

Summary:

This study examines how dominance hierarchy influences innate defensive behaviors in pair-housed male mice exposed to two types of naturalistic threats: a transient looming stimulus and a sustained live rat. The authors show that social presence reduces fear-related behaviors and promotes active defense, with dominant mice benefiting more prominently. They also demonstrate that threat exposure reinforces social roles and increases group cohesion. The work highlights the bidirectional interaction between social structure and defensive behavior.

Strengths:

This study makes a valuable contribution to behavioral neuroscience through its well-designed examination of socially modulated fear. A key strength is the use of two ethologically relevant threat paradigms - a transient looming stimulus and a sustained live predator, enabling a nuanced comparison of defensive behaviors. The experimental design is robust, systematically comparing animals tested alone versus with their cage mate to cleanly isolate social effects. The behavioral analysis is sophisticated, employing detailed transition maps that reveal how social context reshapes behavioral sequences, going beyond simple duration measurements. The finding that social modulation is rank-dependent adds significant depth, linking social hierarchy to adaptive defense strategies. Furthermore, the demonstration that threat exposure reciprocally enhances social cohesion provides a compelling systems-level perspective. Together, these elements establish a strong behavioral framework for future investigations into the neural circuits underlying socially modulated innate fear.

Comments on revised version.

The authors have addressed the initial major criticism regarding the lack of causal evidence for neural mechanisms, which has alleviated our concerns. This provides a more solid behavioral foundation for future investigations into neural circuit mechanisms.

Reviewer #1 (Public review):

Summary:

In this work, the authors investigate the mechanisms of low-frequency synaptic depression at cerebellar parallel fiber to interneuron synapses using unitary recordings that allow direct quantification of synaptic vesicle release. They show that sparse stimulation can induce robust synaptic depression even in the absence of substantial vesicle consumption, and that this depressed state is rapidly reversed when stimulation frequency is increased. To account for these observations, the authors propose a model in which low-frequency depression reflects a redistribution of vesicles within the readily releasable pool, in particular a reduction in docking site occupancy due to vesicle undocking.

Strengths:

I found the experimental work to be of high quality throughout. The use of simple synapse recordings to count individual vesicle release events is particularly powerful in this context and allows questions to be addressed that are difficult to approach with more conventional approaches. The demonstration that low-frequency depression can occur independently of prior vesicle release, together with the rapid recovery observed during high-frequency stimulation, places strong constraints on possible underlying mechanisms and represents a clear strength of the study.

The modeling framework is clearly laid out and helps organize a broad set of observations across stimulation frequencies. Several of the experimental tests appear well motivated by the model, including the recovery train experiments, the analysis of failures, and the use of doublet stimulation. Taken together, the data provide a coherent phenomenological description of low-frequency depression and its relationship to vesicle availability within the readily releasable pool.

Weaknesses:

The major concerns raised during the initial review have been successfully addressed through substantial revisions of both the manuscript and the presentation of the model. The distinction between experimental observations and model-based interpretation is now considerably clearer, and the discussion more appropriately reflects the explanatory nature of the modeling framework.

Reviewer #2 (Public review):

Summary:

Silva and co-workers exploit their previously established methods of analyzing release events at single parallel fiber to molecular layer interneuron synapses. They observed synaptic depression at low transmission frequencies (< 5 Hz) which rapidly recovers during high-frequency transmission. Analysis of the time course of low-frequency depression revealed an initial rapid and a slow linearly increasing time course. Strikingly, the initial depression occurred even in the absence of proceeding release arguing against vesicle depletion as the underlying mechanism.

Strengths:

The main strength of the study is the careful demonstration of an interesting synaptic phenomenon challenging the classical vesicle-centered interpretation of synaptic depression.

Weaknesses:

There are no weaknesses.

Reviewer #3 (Public review):

Summary:

The manuscript builds on the observation that, at some synapses, low-frequency stimulation causes synaptic depression which can be reversed by subsequent high-frequency stimulation. Such low-frequency depression (LFD) cannot be easily explained by the depletion of a single vesicle pool. Here, Silva and colleagues propose a model of activity-dependent vesicle trafficking to explain LFD at synapses between cerebellar granule cells and molecular layer interneurons.

Strengths:

Overall, LFD is interesting and worthy of examination, and the authors provide new experimental results that are of the high quality expected from this group.

Weaknesses:

The study proposes a novel model of vesicle trafficking that is not explained by known biological mechanisms, and the manuscript does not adequately compare or discuss alternative models.

I have several concerns about how the authors interpret the data. First, the manuscript's primary conceptual advance is the idea that LFD involves vesicle undocking, rather than depletion. However, most experiments were performed under conditions that promote vesicle depletion (3 mM extracellular Ca2+). When experiments were repeated in physiological Ca2+, there appeared to be little or no LFD (stats are not provided). Second, the RS/DS/DU/undocking model, though not outside the realm of possibility, is not readily explained by known mechanisms and is only loosely supported by experimental findings. Third, when simulating LFD, the authors do not compare alternative models and use inappropriate language to imply that a model fit represents the truth (e.g. "the finding of identical experimental and simulated values confirms that the undocking mechanism accounts for LFD"). Finally, the model is presented in an overly complicated manner. The sheer amount of terms and nomenclature makes the manuscript confusing and difficult to read. Overall, the manuscript would benefit from added experiments and more statistics, a better justification and evaluation of the model, and more nuanced language.

Comments on revised version.

I appreciate the authors' detailed responses to my initial review of the manuscript. My suggestions reflect a sincere attempt to improve the clarity and accuracy of the paper. I disagree with some of the authors conclusions and their responses to my comments, but do not wish to make any further suggestions. The authors are entitled to their own views.

A final note: If the authors want to refrain from making definitive statements on underlying cellular mechanisms, they should consider amending the title of the manuscript.

Reviewer #1 (Public review):

Summary

This manuscript addresses an important question in auditory neuroscience and neuroprosthetics: whether cortical responses to cochlear-implant stimulation resemble those evoked by natural acoustic stimulation, or whether electrical stimulation engages a distinct cortical population representation. The authors use high-density intracranial EEG recordings in rats to compare responses to pure tones in normal-hearing animals with responses to single-channel cochlear-implant stimulation in deafened animals. They combine analyses of event-related potentials, high-gamma activity, trial-by-trial variability, PCA/TCA-based dimensionality reduction, and decoder-based measures of stimulus information.

Strengths

A major strength of the study is the question it addresses. Understanding how electrical cochlear stimulation is represented centrally is highly relevant for cochlear-implant design, fitting strategies, rehabilitation, and broader theories of sensory neuroprosthetics. The comparison between acoustic and electrical stimulation, including within-animal comparisons in a subset of cases, is valuable because it directly asks whether implant-evoked cortical activity can be interpreted within the same framework as normal acoustic responses.

The methodological approach is also a strength. Dense cortical surface recordings provide simultaneous access to spatial and temporal features of auditory cortical responses. The combination of PCA, TCA, and decoder analyses gives complementary views of the data, and the information-transfer analysis provides an interesting way to test whether representations learned from acoustic stimulation generalise to electrical stimulation.

The revision has improved the manuscript considerably. The use of mixed-effects models better matches the partially paired experimental design. The expanded Methods improve reproducibility. The revised cohort descriptions and figure legends make the experimental design easier to follow. The clarification of Figure 8 strengthens the interpretation of the poor cross-modal transfer result, and the more cautious framing of spatial organisation better reflects the data.

Weaknesses

The main remaining limitation concerns experimental validation. The authors now clearly state that deafening was not verified with ABRs, hair-cell counts, or behavioural confirmation in the animals used for the main iEEG dataset, and that support for deafening efficacy in this cohort relies on prior validation of the same procedure in separate cohorts. This transparency is welcome and improves the manuscript, but the lack of direct validation in the main cohort remains a limitation, particularly given the importance of complete deafening and reliable cochlear implantation for interpreting implant-evoked cortical responses.

Appraisal

This does not undermine the main conclusions, but it should be kept in mind when interpreting the strength of the cochlear-implant comparisons. Overall, the revised manuscript is much stronger and more appropriately framed than the previous version. The study addresses an important problem, uses useful analytical approaches, and provides convincing evidence that acoustic and acute cochlear-implant stimulation evoke cortical responses with poor representational transfer.

Likely impact of the work on the field

The work is likely to be of interest to auditory neuroscientists, cochlear-implant researchers, and neuroengineers. Even where some conclusions require caution, the dataset and analytical framework may be useful for future studies aiming to relate central neural responses to implant programming, perceptual learning, or closed-loop neuroprosthetic strategies.

Comments on revised version.

The revised manuscript is substantially improved. The authors have clarified the methodological details, improved the statistical treatment of partially paired data, provided a clearer account of the animal cohorts, clarified the Figure 8 cross-modal decoding framework, and moderated several claims about spatial organisation and perceptual interpretation. Importantly, the manuscript now distinguishes more clearly between non-random spatial organisation, coarse cochleotopic structure, and sharply graded tonotopy or cochleotopy.

The results support the conclusion that acoustic and cochlear-implant stimulation evoke cortical responses with different properties. In particular, acoustic responses support better single-trial stimulus decoding than cochlear-implant responses, and decoders trained on acoustic responses generalise poorly to implant-evoked responses. The evidence for spatial organisation is more nuanced: the cochlear-implant condition shows evidence of coarse, non-random spatial structure, but not strong evidence for consistent, sharply graded cochleotopy. Overall, the revised manuscript makes a valuable contribution, provided that the conclusions remain framed around acute cortical responses and poor representational transfer rather than definitive claims about long-term perceptual experience.

Reviewer #2 (Public review):

Summary:

This article reports measurements of iEEG signals on the rat auditory cortex during cochlear implant or sound stimulation in separate groups of rats. The observations indicate some spatial organization of cochlear implant stimuli, but that is very different from cochlear implants.

Strengths:

The study includes some interesting analyses of the sound and cochlear implant representation structure based on decoders.

Weaknesses:

The observation that responses to cochlear implant stimulation (stimulation) is spatially organized but not exactly as sound-driven responses is not new.

The analyses in Fig. 8 supporting the claim that there is a mismatch between cochlear implant and normal sound representations remain hard to evaluate. The shuffle control now provided by the authors indicates that the information transfers between normal hearing representations is at chance level and between cochlear implant and normal hearing is below chance (Fig 8H). This clearly indicates, unlike the authors suggest in their response, that the analysis used to make this claim is not sensitive enough. Therefore, the claim does not seem to be supported.

Reviewer #3 (Public review):

Summary:

Through micro-electroencephalography, Hight and colleagues studied how the auditory cortex in its ensemble respond to cochlear implant stimulation compared to the classic pure tones. Taking advantage of a double implanted rat model (Micro-ECoG and Cochlear Implant), they tracked and analyzed changes happening in the temporal and spatial aspects of the cortical evoked responses in both normal hearing and cochlear-implanted animals. After establishing that single trial responses were sufficient to encode the stimuli properties, the authors then explored several decoder architectures to study the cortex ability to encode each stimuli modality in a similar or different manner. They conclude that a) intracranial EEG evoked responses can be accurately recorded and did not differed between normal hearing and cochlear-implanted rats; b) Although coarsely spatially organized, CI-evoked responses had higher trial-by-trial variability than pure tones; c) Stimulus identity is independently represented by temporal and spatial aspect of cortical representations and can be accurately decoded by various means from single trials; d) and that Pure tones trained decoder can't decode CI-stimulus identity accurately.

Strength:

The model combining micro-eCoG and cochlear implantation and the methodology to extract both the Event Related Potentials (ERPs) and High-Gammas (HGs) is well designed and appropriately analyzed. Likewise, the PCA-LDA and TCA-LDA are powerful tools that take full advantage of the information provided by the cortical ensembles.

The overall structure of the paper, with a paced and exhaustive progress through each step and evolution of the decoder is very appreciable and easy to follow. The exploration of single trial encoding and stimulus identity through temporal and spatial domains is providing new avenues to characterize the cortical responses to CI stimulations and their central representation. The fact that single trials suffice to decode the stimulus identity regardless of their modality is of great interest and noteworthy. Although the authors confirm that iEEG remains difficult to transpose in clinic, the insights provided by the study confirm the potential benefit of using central decoders to help in clinic settings.

Weakness:

The conclusion of the paper, especially the concept of distinct cortical encoding for each modality, is unfortunately only partially supported by the results. Although acknowledged by the authors, fundamental limitations related to CI stimulation might have weaken the results.

The authors stimulated in a Monopolar mode which, albeit being clinically relevant, notoriously generates a high current spread in rodent models. Thus, it seems possible that current spread ended stimulating indistinctly higher turns of the cochlea or even the modiolus in a non-specific manner, greatly reducing (or smearing) the place-coding/frequency resolution of each electrode, which in turn could explain the coarse topographic (or non-random) organization of the cortical responses.

Although the authors acknowledge that post-lingual CI users always have an adaptation period, their conclusion is based on measurements that are relatively "early" in the CI-use timeline so to speak since iEEG were collected a) acutely right after mono-aural implantation and stimulation, b) under anesthesia, c) using unmodulated pulse train fixed at 900pps regardless of the electrode used and thus lacking any temporal information shifts in relationship to electrode cochleotopic placement. Basically, all CI electrodes had the same rate whereas you would expect basal CI electrodes to be amplitude modulated at higher frequencies than apical electrodes.

Nevertheless, the reviewer wants to reiterate that the study proposed by Hight et al. is well constructed, relevant to the field and that the overall proposal of improving patient performances and help their adaptation in the first months of CI use by studying central responses should be pursued as it might help establish new guidelines or create new clinical tools.

Comments on revised version.

The reviewer would like to thank the Authors for their work on this new version. The reviewer is satisfied with the current state of manuscript and its associated public review and has no further comment.

Reviewer #1 (Public review):

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

Summary:

This study aimed at replicating two previous findings that showed (1) a link between prediction tendencies and neural speech tracking, and (2) that eye movements track speech. The main findings were replicated which supports the robustness of these results. The authors also investigated interactions between prediction tendencies and ocular speech tracking, but the data did not reveal clear relationships. The authors propose a framework that integrates the findings of the study and proposes how eye movements and prediction tendencies shape perception.

Strengths:

This is a well-written paper that addresses interesting research questions, bringing together two subfields that are usually studied in separation: auditory speech and eye movements. The authors aimed at replicating findings from two of their previous studies, which was overall successful and speaks for the robustness of the findings. The overall approach is convincing, methods and analyses appear to be thorough, and results are compelling.

Weaknesses:

Eye movement behavior could have presented in more detail and the authors could have attempted to understand whether there is a particular component in eye movement behavior (e.g., blinks, microsaccades) that drives the observed effects.

Reviewer #2 (Public review):

Summary

Schubert et al. recorded MEG and eye tracking activity while participants were listening to stories in single-speaker or multi-speaker speech. In a separate task, MEG was recorded while the same participants were listening to four types of pure tones in either structured (75% predictable) or random (25%) sequences. The MEG data from this task was used to quantify individual 'prediction tendency': the amount by which the neural signal is modulated by whether or not a repeated tone was (un)predictable, given the context. In a replication of earlier work, this prediction tendency was found to correlate with 'neural speech tracking' during the main task. Neural speech tracking is quantified as the multivariate relationship between MEG activity and speech amplitude envelope. Prediction tendency did not correlate with 'ocular speech tracking' during the main task. Neural speech tracking was further modulated by local semantic violations in the speech material and by whether or not a distracting speaker was present. The authors suggest that part of the neural speech tracking is mediated by ocular speech tracking. Story comprehension was negatively related with ocular speech tracking.

Strengths

This is an ambitious study, and the authors' attempt to integrate the many reported findings related to prediction and attention in one framework is laudable. The data acquisition and analyses appear to be done with great attention to methodological detail. Furthermore, the experimental paradigm used is more naturalistic than was previously done in similar setups (i.e.: stories instead of sentences).

Weaknesses

While the analysis pipeline is outlined in much detail, some analysis choices appear ad-hoc and could have been more uniform and/or better motivated (other than this is what was done before).

Reviewer #3 (Public review):

I thank the authors for their extensive revision of this paper, and I found some elements greatly improved.

In particular, the authors do embrace a somewhat more speculative tone in the current version, which I think is fitting for this work, as the data seem (to me) to be not fully conclusive. The data set collected here is clearly valuable and unique (and I would encourage the authors to make it publicly available!), however, my overall impression is that the specific analyses reported here might not fully.

Despite the revised description of methods, results and figures, I still have trouble understanding many of the results and the authors conclusive interpretation of them. These are my main reservations:

(1) Regarding "individual prediction tendency" - thank you for adding clarifying methodological details and showing the data in a new Figure (#2). Honestly, however, I still can't say that I fully understand the result. For example, why is there also a significant response in the random condition as well? And how do you interpret the interesting time-course (with a peak ~200ms prior to the stimulus, and a reduction overtime from there?<br /> Also (I may have missed this, but...) what neural data was used to train the classifier and derive the "prediction tendency" index? Was it just the broadband neural response? Is there a way to know which sensors contributed to this metric (e.g., are they predominantly auditory? Frontal?)? And is there a way to establish the statistical significance of this metric (e.g., how good the decoder actually was in predicting behavioral sensitivity?). I don't see any statistics in the results section describing the individual prediction tendency.

(2) Regarding the TRF analysis - Thanks for clarifying the approach used to obtain 2-second long "segments" of speech tracking. This is an interesting approach, however I think quite new(?) , and for me it raises a whole new set of questions, as well as additional controls and data that I would have liked to see, to be convinced that results are significant. I will elaborate:

- Do I understand correctly that you segment the real and predicted neural response into 2-second-long segments and then calculate the Pearsons' correlation between them to assess the goodness of the model? This is very unclear, since in the methods section you state only that "the same" analysis was performed as for the full data - but what exactly? Clearly, values will be very different when using such short segments. I feel that additional details are still required (and perhaps data shown) to fully understand the "semantic violation" analysis of TRFs.

- I would like to reiterate my previous comment regarding the use of permutation tests to verify the validity of TRF-based measures derived. This would be especially important when using new approaches (such as the segmentation used here). The authors argue that this is not needed since this was not done in their previously published study. However, this sounds a bit like "two wrongs make a right" argument... why not just do it, and let us know that this 2-second segmentation approach allows estimating reliable speech tracking?

- Following up on my previous comment that defining "clusters" as at least two neighboring channels (Figure 3) - the fact that this is a default in Fieldtrip is by no means sufficient justification! This seems quite liberal to me, especially given the many comparisons performed. Here too, permutations can help to determine the necessary data-driven threshold for corrections. This is of course critical for interpreting the result shown in Figures 3E&G that are critical "take home messages" of the paper - i.e., that the prediction-index from the first part of the experiment is related to speech tracking in the second part of the experiment. To my eyes, this does not look extremely convincing, but perhaps the authors can show more conclusive data to support this (e.g., scatter plots of the betas across participant?).<br /> - A similar point can be made for the effect of semantic violations (though here the scalp-level result is somewhat more clustered). The authors point out that the semantic effect is a "replication" of their result reported in Schubert et al. 2023, but if I am not mistaken the results there were somewhat different (as was the manipulation). It would be nice to explicitly discuss the similarity/difference between these effects.

(3) Regarding the ocular-TRFs -

- Maybe this is just me, but I believe that effects that are robust should be clearly visible in the data, without the need for fancy "black-box" statistical models. In the case of the ocular TRFs, it is hard for me to see how these time-courses are not just noise (and, again, a permutation test would have helped to convince me...). The inconsistent results for horizontal and vertical eye-movements vis a vis the experimental conditions (single vs. multi-speaker conditions) don't help either, despite the authors argument that these are "independent" - but why should this be the case, especially if there is nothing really to look at in this task?<br /> - I remain with this scepticism for the mediation-portion of the analysis as well... But perhaps replications from other groups or making the data public will help shed further light on this in the future.

Minor<br /> - Thanks for adding information about the creation of semantic-violation stimuli. Since the violations and lexical-controls were taken from different audio recordings, it would have been nice to verify that differences between neural responses cannot be attributed to differences in articulations (e.g., by comparing their spectro-temporal properties).

Reviewer #1 (Public review):

The idea behind this paper is to have an alternate, reliable and quantitative approach to assess cell-cell metabolic heterogeneity. This study tries to achieve that using translationally-coupled energetic responses to metabolic stress. This is interesting because, in general, most quantitative measurements of metabolic outputs are 'bulk' and average for many cells. To overcome this, many recent studies use some read-outs of translation (presuming that translation is the single major energy sink in cells - however, this is objectively correct only in rapidly proliferating cells). That said, the authors take an interesting approach - to use two clickable methionine analogs, and assess baseline vs metabolically coupled translation within the same cell.

The highlight is the methodology development where two distinct, clickable CMAs are used (to replace methionine in proteins). The labelling and approach are clever, and can be useful if carefully used. But there is going to be a challenge in using this, since the depletion of methionine itself (required for labelling), and a bias towards incorporation in some proteins (because these reagents are not highly permeable) will make it challenging to obtain precise metabolic state information - which otherwise can be obtained directly and far more precisely using a combination of other methods (ATP/flux measurement, respiratory capacity, translation rates etc). I therefore only make broader comments in my review below - to help structure this study better, clearly identify key limitations (and there are several that are clearly seen), and better clarify what MARBL may be useful for.

(1) These reagents used for MARBL are not highly cell permeable/transported into cells, and largely work on the surface proteome. Which means the measurements related to changes in translation are indirect - quantified based on changes on the surface proteome (seen with labelling), and not the entire proteome.

(2) A major limitation - which will confound any interpretation- is the need to use methionine-free media; this can be a problem beyond protein synthesis/met incorporation since this will almost instantly deplete SAM pools in cells. There is little data provided on the impact of using this approach on SAM pools (time kinetics, how quickly SAM pools are affected, how much of the impact on metabolism comes from purely that, etc).

This is important to establish because (i) of the continuous, very high flux of SAM -> SAH (eg. in the folate pathway, other methylations), and a constant need for SAM synthesis from methionine. This information will set the limits of capabilities of this method, as well as help delineate how much you can interpret results related to metabolic states between compared cells/states, etc. The labelling process is ~2 hours, while effects on SAM can be seen within minutes of methionine starvation in media, in metabolically active cells.

(3) What is the effect on overall adenylate charge/ATP due to shifting to methionine-free media + label addition? How does it vary between the cells tested (suspension vs adherent)? This should be established before data related to Fig. 2. Does it correlate with extent of AHA incorporation?

The primary conclusion that this method suitably reflects overall changes in energetics comes from the titration of 2DG/glycolytic inhibition.

(4) Relatedly, if this label incorporation experiment is carried out (for ~2 hrs), and subsequently there is a washout/replacement with fresh, methionine-supplemented medium, (how quickly) do the cells recover and restore their energetic allocations?

(5) One possible advantage of a system like this can be to address questions in single cells/study cell metabolic heterogeneity. However, these are best done if the attaching moiety has a (selective) fluorescence increase and/or other read-out that can be quantitatively obtained at a single cell level. Largely, using AHA or HPG effectively only leads to bulk estimates (which can be sub-sorted towards single-cell estimates indirectly). This means that this method cannot really be used to study cell-cell metabolic heterogeneity effectively - compared to far simpler approaches, for example using a mitochondrial potentiometric dye with high fluorescence, or reporters for glycolytic activity, etc. This would also be related to Figure 5 - at best, this approach may complement existing approaches towards identifying heterogeneous sub-populations of cells.

However, I do agree that MARBL is flexible, stable, and can be internally normalised and used through flow-based platforms. It can supplement existing approaches to perturb bioenergetics, and also supplement existing approaches to understand metabolic state in live cells, particularly in suspension cells.

Reviewer #2 (Public review):

Summary:

Delacruz et al. describe a new method, called "MARBL" (Methionine Analogues for Ratiometric Bioenergetics in Live cells) to measure metabolic activity in single cells. The concept is similar to the SCENITH (anti-puromycin flow cytometry) assay to measure energy metabolism by measuring protein translation activity, yet offers, in theory, two advantages: 1) it keeps cells alive for downstream biological assays and 2) it is a ratiometric measurement, measuring both baseline translation and translation in the presence of metabolic inhibitors to correct for inherent cell-to-cell translation differences.

Specifically, this method takes advantage of two click-chemistry-active methionine analogs, and then clicks fluorophores onto newly-synthesized surface proteins that have incorporated these analogs to measure translational activity. One methionine analog is given to cells for 2-4 hours to measure baseline translational activity, then metabolism is blocked using 2-deoxyglucose and oligomycin and the second methionine analog given to measure "metabolically-linked" translation activity. The authors establish this technique and show that mouse T cells polarized as pathogenic Th17 cells are more translationally active ("resilient") compared to non-pathogenic Th17 cells, and when sorted, the resilient cells produce more interferon-gamma. This latter finding requires live cells after the metabolic measurement assay, showcasing findings that are inaccessible to the SCENITH assay.

Strengths:

The approach used is conceptually clever. It is appealing to measure metabolic/translational activity and to then be able to carry out further assays on sorted cell populations with different degrees of metabolic activity. This would indeed represent a useful advance.

Weaknesses:

In principle, one key benefit of this technique is that cells can be used for biological assays after the metabolic measurement. Indeed, this would represent a valuable tool in the field.

However, in this technique, cells are subjected to methionine deprivation, addition of non-natural methionine analogs, click chemistry, and high doses of toxic metabolic inhibitors 2-deoxyglucose and oligomycin. Indeed, the authors show in Figure S5F that 1/3 more of the post-MARBL cells die relative to cells not subject to this technique (60% viability in unclicked control, 40% in MARBL-measured cells). This data suggests that cells after this technique may be stressed and not reflective of the biological function of unmanipulated cells. More controls on viability and cell function (e.g. cytokine production) at more time points after the MARBL assay would have been valuable to address this issue.

Another weakness of the paper is limited benchmarking against established metabolic assays in the field. The main assays used currently in the field are SCENITH and Seahorse. The authors do not compare their findings to SCENITH. They do compare their results to Seahorse, but the data shown don't address the key question: how does energy production measured by Seahorse, say in unmanipulated vs 2dg+oligomycin-treated cells, compare to the MARBL measurement? (Instead, they show a calculated "glucose dependence" metric in cells subjected to low vs high inhibitor dose, not showing the underlying data or cells that didn't receive an inhibitor).

Reviewer #1 (Public review):

Summary:

The authors investigate whether systematic manipulation of GABA-A and NMDA receptors influences large-scale cortical neuronal timescales and transient network dynamics. 57 healthy male participants completed placebo, lorazepam and D-cycloserine sessions in a double-blind, within-participant, cross-over design. The authors estimated timescales from the knee frequency of the aperiodic component of MEG power spectra, and identified transient large-scale cortical networks using a time-delay embedded hidden Markov model (TDE-HMM) analysis. The authors report that lorazepam increases estimated timescale across multiple cortical areas, with particularly pronounced changes in states interpreted as frontal default-mode network (DMN) and dorsal attention network (DAN). These changes include increased fractional occupancy of the DMN and decreased for DAN. D-cycloserine did not significantly affect neuronal timescales.

Strengths:

The study has several notable strengths:

(1) The pharma-MEG study is well designed and executed (e.g., a crossover design, collecting subjective, cardiovascular and additional control measurements).

(2) Neuronal-timescales maps are validated against previously published cortical timescale and hierarchy maps.

(3) TDE-HMM findings are examined using alternative numbers of hidden states and different preprocessing pipelines.

(4) The manuscript is generally clear and well written.

Weaknesses:

Nevertheless, several aspects of the primary neuronal timescale measure and the TDE-HMM analysis require further validation, and several points should be clarified:

(1) A central concern is whether the reported measure of neuronal timescale, on its own, is sufficient to support the interpretation assigned to it. Lorazepam has been shown to alter several spectral parameters, including oscillatory peaks and the aperiodic component of power spectra, in ways that could influence knee-frequency fitting. The manuscript, however, does not provide sufficient information about fit quality (across participants, parcels, and conditions), parcels within each participant/condition with identifiable knees, or the sensitivity of the results to the fitting range and peak settings (i.e., FOOOF parameters). Importantly, Figure S3 indicates that neither lorazepam nor D-cycloserine significantly affects knee frequency, although the neuronal timescale measure is mathematically derived from that knee frequency (i.e., tau = 1/(2*pi*f_knee)). Although such a result is mathematically possible, the pattern is unusual and requires explanation because timescales are not measured independently, but rather derived from the knee frequency. Furthermore, the neuronal timescale maps show a relatively homogenous increase across the cortex under lorazepam (Figure 2A), whereas the knee-frequency maps show a heterogeneous spatial pattern, with increased knee frequency under lorazepam in frontal regions, and decreased in occipital and temporal regions (Figure S3). A widespread significant effect appearing only after inversion could reflect a genuine effect, particularly in parcels with low knees to begin with. However, it could also arise if a small number of extremely low fitted knee frequencies (below 1Hz?) produce very long timescale estimates and disproportionately affect the statistical analysis.<br /> The authors should address this discrepancy by presenting the distributions of knee-frequencies and neuronal timescales, and their ranges. Additional information about outliers and the robustness of the findings to extreme fitted values would also be valuable.

(2) A second concern relates to the TDE-HMM analysis. The model identifies states from the covariance matrix of time-delayed time-courses. Consequently, states are differentiated partly on the basis of their spectral and temporal characteristics. Knee frequencies for each state are then estimated from the power spectra of the same states used to define them. Differences in neuronal timescales across states may therefore be expected, at least in part, simply by the way the states were inferred. This point weakens the claim that cortical states are independent, and "operate on distinct timescales". A clearer separation between state definition and neuronal timescale estimation would be needed to establish that the reported state-specific differences are not partly an expected consequence of the fitted model. This could be addressed using, e.g., cross-validation or simulations. This concern is less substantial for the drug-related changes in neuronal time scale within individual states.

(3) The result and methods sections provide insufficient detail and statistical reporting for the TDE-HMM analysis. In the result section (page 10), the authors report only the *range* of fractional occupancies across all states. A range of 1% to 48% is substantial. It is therefore important to determine whether some states occupied only 1% (corresponding to ~3sec) while others occupied a much larger proportion. The authors should report the mean fractional occupancy of each state, together with its SD and range across participants.

(4) The authors should explain the apparent discrepancy between the relatively homogeneous increase in neuronal timescales under lorazepam across the complete recording (Figure 2) and the heterogeneous state-specific changes (both increases and decreases; Figure 4B). Could this pattern be explained, at least in part, by the differences in fractional occupancy of individual states? This provides an additional reason to report state-specific occupancy values in greater detail.

(5) On page 13, 2nd paragraph, the authors state that the findings indicate that the frontal DMN is an important driver of the global prolongation of neuronal timescales, based on the strong similarities to the time-averaged timescales. However, Figure S5 appears to show that the spatial correlation between state-specific and time-averaged timescales is as high for State 8 and is similar for State 2. The current statement therefore appears to be an overinterpretation. Demonstrating that the correlation for State 3 is significantly larger than the correlations for the other states would provide stronger support for this claim.

(6) Spatial maps are presented inconsistently throughout the main manuscript and in the supplementary. Specifically, some figures only show thresholded maps (at p<0.05 or p<0.001; e.g., Figure 4b), whereas others show unthresholded maps. Reporting only the number of parcels exhibiting a significant effect, without presenting the corresponding thresholded maps, makes it difficult to evaluate the spatial distribution of the reported changes. At least for the main findings (e.g., lorazepam effects on neuronal timescales), I recommend presenting both thresholded and unthresholded maps within the same figure.

(7) Figure 3: The rationale for presenting the mean power between 3-30 Hz is unclear. The authors should explain why this metric was selected to represent each state. Visual inspection suggests that the state-specific power spectra differ across several dimensions, including the aperiodic exponent, offset, alpha power, etc. Characterizing states using these parameters may be more informative. Each of these features could potentially also influence the estimated knee frequency.

(8) The conclusion on page 17 (and similar statement in the intro): "...these findings provide causal evidence that microscale synaptic inhibition directly shapes both local neuronal timescale organization... ") appears to be overstated based on the evidence presented. Although the lorazepam manipulation supports a causal effect of the drug on MEG-derived cortical timescales and network dynamics, the study does not directly measure microscale synaptic inhibition or establish a direct mechanistic link across scales. The phrasing should therefore distinguish the observed pharmacological effects from the inferred role of GABAergic inhibition and be phrased more cautiously.

(9) The method section lacks several critical details, including: criteria for excluding MEG channels and noisy segments (see comment below), details on source reconstruction (e.g., number of vertices used to project the sensor-level data), procedures used to generate the null distributions for the spatial autocorrelation preserving permutation test, transition probability analysis and more. Relatedly, the preprocessing pipeline of the MEG data appears to be based on manual inspection for the removal of channels, ICA components and noisy segments. This procedure is inherently subjective. The authors should provide details on the exact criteria used to make these decisions. Critically, the authors should also report the duration of usable resting-state data remaining after segment rejection. The Methods section should provide sufficient detail to allow readers to evaluate the validity of the study and reproduce the analysis as closely as possible. In its current form, it does not do so.

Reviewer #2 (Public review):

This work provides empirical data on how GABA and NMDA agonists globally affect timescales as measured through MEG. The authors reproduce the previously observed gradient of intrinsic timescales in the placebo condition, as well as its relationship to cortical hierarchy in T1/T2w maps. Timescales were not fixed, but dynamic, as revealed by large-scale network analysis separating into discrete network states. Pharmacologically, GABA agonist Lorazepam produced a brain-wide increase in timescales while NMDA agonist D-cycloserine did not. Furthermore, the GABA-mediated increase in timescale was area- and state-dependent, and more detailed analyses show changes in state occupancy mainly for DMN and DAN.

Overall, the paper contributes valuable data on a relevant topic of research in understanding the timescales of network dynamics at the local and global level. The question is well-motivated, and the analyses are technically sound and described in a straightforward manner. The network-level analysis in TDE-HMM is interesting and provides a complementary and more fine-grained perspective to the global timescale gradient, both in terms of space and time. The hypotheses were straightforward since both GABA and NMDA have relatively long timescales (of the dominant synaptic currents), though the lack of effect from NMDA-agonist is quite surprising but reasonably explained by the voltage-dependence of NMDA receptors in such a task-free setting. The paper overall is clearly written, and the figures are of high-quality, though some things could be presented in slightly more informative ways (see below). I have some questions and minor suggestions, but don't have too much to criticize as a whole.

My biggest question is the following: the mixed effects model shows that lorazepam additionally mediates timescale over and above the hierarchy (myelination map). This leaves a very clear gap. What the authors also probably want to show is that greater GABA_A receptor expression (of any or all the subunits) results in greater change under lorazepam, not against the myelination map only, or that the residue can be explained by the GABA_A maps. This, of course, would not explain the non-stationary nature of the dynamics (and state-dependent timescale maps), but would give a more direct explanation of the spatial effect. Since you already compared to the prior MEG map in Shafiei et al., 2023, I guess it's not a huge technical effort to grab the gene maps from neuromaps (https://github.com/netneurolab/neuromaps). I think this could strengthen the current manuscript.

Reviewer #1 (Public review):

Summary:

The authors used extracellular field potential recordings and two-photon imaging to monitor neuronal network activity and intracellular chloride concentration ([Cl-]i) in organotypic hippocampal slices from mice expressing the genetically encoded chloride fluorophore Clomeleon. These slices were used as a model of acute traumatic brain injury and epileptogenesis in vitro. The study provides evidence that blocking the WNK-SPAK/OSR1 pathway with WNK463 alleviates epileptic activity, and that this anticonvulsant effect involves suppression of the chloride loader NKCC1 and enhancement of chloride extrusion via KCC2. Overall, this is a solid study with a comprehensive pharmacological analysis.

Strengths:

The conclusions are well supported by the detailed pharmacological analysis.

Weaknesses:

The only weakness I see is the absence of cellular-level electrophysiology, which precludes interpretation of the imaging data in the context of GABA action polarity.

Reviewer #2 (Public review):

Summary:

The authors investigate whether inhibition of the WNK-SPAK/OSR1 pathway using the allosteric inhibitor WNK463 improves neuronal chloride homeostasis and suppresses epileptiform activity in organotypic hippocampal slice cultures. Using Super Clomeleon imaging combined with extracellular field recordings, they demonstrate that WNK463 accelerates recovery of intracellular chloride following chloride loading, reduces interictal chloride accumulation, and progressively suppresses recurrent ictal-like discharges. Pharmacological inhibition and siRNA-mediated knockdown of NKCC1 and KCC2 are then used to investigate the contribution of these transporters to the anti-ictal effects of WNK463.

Strengths:

The study addresses an important question in the field of chloride homeostasis and epilepsy and combines complementary experimental approaches. In particular, the distinction between baseline chloride measured in the presence of TTX and activity-dependent interictal chloride accumulation provides a useful conceptual framework for interpreting previous studies of WNK-SPAK inhibition. The imaging, electrophysiological, and pharmacological data are internally consistent and support the conclusion that WNK463 alters chloride dynamics and substantially suppresses ictal-like activity in this model.

Weaknesses:

The principal limitation of the manuscript is that several mechanistic conclusions extend beyond the experimental observations. Throughout the results and discussion, the authors interpret the observed changes in intracellular chloride dynamics as evidence of enhanced CCC-mediated chloride extrusion, while the pharmacological and siRNA-mediated experiments are interpreted as supporting coordinated NKCC1 inhibition and KCC2 activation, ultimately leading to restoration of GABAergic inhibition and negative shifts in EGABA. While these interpretations are plausible and consistent with the data, they remain inferential because transporter phosphorylation or activity, EGABA, and inhibitory synaptic function were not directly assessed in the current study. Moreover, although the pharmacological and knockdown experiments support a contribution of NKCC1 and KCC2 to the actions of WNK463, they do not definitively establish coordinated modulation of both transporters as the primary mechanism underlying seizure suppression. These mechanistic conclusions should therefore be presented more cautiously.

The manuscript would also benefit from broader contextualization within the current literature. The introduction largely focuses on previous work from the authors' group and provides a relatively narrow overview of chloride homeostasis in epilepsy. In particular, the discussion would benefit from broader consideration of studies examining KCC2 dysfunction in human epilepsy and experimental models, alternative mechanisms regulating KCC2 activity following seizures, and recent therapeutic strategies targeting KCC2.

Finally, although the authors appropriately acknowledge that the experiments were performed exclusively in vitro, the discussion could more explicitly address the limitations of the organotypic hippocampal slice model, including how culture-induced network reorganization and spontaneous epileptiform activity may influence chloride homeostasis and the extent to which these findings generalize to traumatic brain injury and chronic epilepsy in vivo. In addition, the statistical analysis would benefit from clarification regarding the experimental unit and the treatment of repeated measurements.

Reviewer #3 (Public review):

Summary:

Dzhala and colleagues present findings from organotypic slice cultures suggesting that simultaneous modulation of the complementary cation-chloride cotransporters NKCC1 and KCC2 through inhibition of the WNK-SPAK/OSR1 pathway reduces seizure-like activity. The manuscript is generally well written, and the data support the conclusion that WNK463 exerts robust anti-ictal effects in this model. However, several issues should be addressed to strengthen the mechanistic interpretation and statistical rigor of the study, and improve confidence in the conclusions.

Strengths:

(1) The study addresses an important mechanistic question by investigating how inhibition of the WNK-SPAK/OSR1 pathway with WNK463 influences seizure activity and neuronal chloride homeostasis.

(2) The experimental design is logical and comprehensive, progressing from characterization of chloride dynamics to pharmacological and genetic interrogation of the underlying mechanism using multiple complementary approaches, including pharmacological inhibition, siRNA-mediated knockdown, electrophysiology, and chloride imaging.

(3) The combination of simultaneous extracellular electrophysiology and two-photon chloride imaging provides complementary functional and mechanistic information and represents a major technical strength of the study.

(4) The TTX experiments elegantly distinguish activity-dependent chloride accumulation from resting intracellular chloride concentration, substantially strengthening the central mechanistic conclusions.

Weaknesses:

(1) The mechanistic conclusions regarding KCC2 activation and NKCC1 inhibition are stronger than the data directly support. Throughout the manuscript, the authors conclude that WNK463 activates KCC2 and inhibits NKCC1. Although this interpretation is consistent with the established biology of the WNK-SPAK/OSR1 pathway, the evidence presented here is indirect. Specifically, the authors infer KCC2 activation and NKCC1 inhibition from the observation that pharmacological inhibition or siRNA-mediated knockdown of these transporters alters the effects of WNK463, together with measurements of chloride dynamics. While these findings are compatible with a KCC2- and NKCC1-dependent mechanism, they do not directly establish that WNK463 regulates either transporter. Direct evidence would require measurements of transporter activity, phosphorylation state, membrane expression, or other biochemical indices of transporter regulation. I therefore recommend that the authors both temper the mechanistic language throughout the manuscript and explicitly acknowledge in the Discussion that the proposed regulation of KCC2 and NKCC1 is inferred from indirect evidence rather than directly demonstrated in the present study.

(2) The conclusions drawn from the siRNA-mediated knockdown experiments should be interpreted more cautiously. First, it is unclear whether silencing NKCC1 or KCC2 induced compensatory changes in the expression or function of the complementary cotransporter. Given the well-established interplay between NKCC1 and KCC2 in regulating intracellular chloride homeostasis, compensatory adaptations could influence the interpretation of these experiments and should be addressed or acknowledged as a limitation. Second, the sample size for the siRNA experiments appears relatively small. It is unclear how many independent animals contributed slices to each experimental group, making it difficult to assess the degree of biological replication. In addition, effect sizes are not reported. Clarifying the number of biological replicates and reporting effect sizes would improve the rigor of the statistical analysis and increase confidence in these findings.

(3) The final pharmacological experiments require clarification, as the conclusions appear internally inconsistent. Earlier experiments suggest that the anticonvulsant effects of WNK463 depend on coordinated regulation of both NKCC1 and KCC2. However, in the final experiment, the authors state that simultaneous pharmacological inhibition of NKCC1 and KCC2 does not prevent the anticonvulsant effects of WNK463. In contrast, the accompanying statistical analysis indicates that combined transporter inhibition significantly reduces the effect of WNK463 relative to control conditions. These interpretations appear inconsistent and make it difficult to determine the extent to which the anticonvulsant action of WNK463 depends on NKCC1 and KCC2. The authors should clarify whether simultaneous inhibition of both transporters completely abolishes, partially attenuates, or merely reduces the magnitude of the WNK463 response, and revise the text accordingly. If the effect is only partially attenuated, alternative mechanisms contributing to the anticonvulsant actions of WNK463 should also be considered and discussed.

(4) The statistical analysis and reporting require further attention. First, median values should not be reported with standard deviations, as standard deviation describes variability around the mean rather than the median. For non-normally distributed data, the authors should report median values together with an appropriate measure of variability, such as the interquartile range (25th-75th percentile) or another suitable summary. Second, in several instances, ANOVA results are reported using only a single degree of freedom value (e.g., page 6, DF = 53). This is incomplete, as an F statistic is defined by two degrees of freedom: the numerator degrees of freedom (between-group variability) and the denominator degrees of freedom (within-group variability). Reporting statistical results using standard notation (F(df_between, df_within) = F statistic, p = value) would improve clarity and allow proper interpretation of the analyses.

Reviewer #1 (Public review):

Summary:

The authors developed a free flight perturbation assay that induces roll instability. They set out to understand how steering muscles control and stabilize roll instability. In addition to the previously reported changes in wing amplitude, they find that a pure roll perturbation induces changes in stroke deviation. They silence motor neurons of individual steering muscles to show that silencing any one muscle alone is not sufficient to disrupt the recovery from roll perturbations. Through aerodynamic modelling, they argue that this occurs despite the fact that each muscle alone can induce changes in wing amplitude and/or stroke deviation. They suggest that this robustness to roll perturbation may be due to redundancy in the motor control program. Finally, they confirm redundant projections from the published haltere connectome study and show that indeed muscles which produce similar effects on wing kinematics receive redundant connections from the haltere afferents.

Strengths:

This study's strength lies in integrating findings from several adjacent areas in insect flight control research. The authors combine steering muscles physiology, wing kinematic quantification, aerodynamic modelling, and sensory (haltere) control of wing kinematics. This integrated approach provides a comprehensive discussion of the mechanisms that may underlie recovery from roll perturbations.

Weaknesses:

The biggest weakness is that, although the authors generate a very plausible and interesting hypothesis, much of the supporting evidence already occurs in existing datasets. In fact, a distributed or redundant many-to-one muscle control for flight kinematics is not a new idea. It has been suggested wherever researchers have examined muscle control for wing kinematics, across studies in flies, moths and other insects (Heide and Gotz, 1996; Balint and Dickinson, 2001; Lindsay et al, 2017; Melis et al, 2024, etc; Wood et al., 2024, etc). Therefore, it is not particularly surprising that Drosophila employs a multi-muscle strategy for roll stabilization. Although it is interesting to see that inhibition of even the phasic muscles (b3/ I2) alone did not disrupt the recovery, further experiments are required to address how these muscles contribute to wing kinematics responsible for roll control. Unfortunately, although the new evidence provided in this manuscript strengthens the idea of redundant muscle control, it does not test it directly.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the kinematics, aerodynamics, and neural control of free-flight roll perturbation in fruit flies.

Strengths:

The paper employs a variety of appropriate methods, including magnetically sourced in-flight perturbations, free-flight wing kinematic measurement, and optogenetic silencing of specific motor units (and thus steering muscles). The results are generally consistent with prior work, showing that 5 different bilateral pairs of steering muscles contribute to the roll response, affecting the wing stroke amplitude and wing pitch. Furthermore, the roll response - both the overall animal performance and the details of the wing motion - is not detectably altered by knocking out any one of the five muscle pairs.

The reverse approach, optogenetic activation of specific phasic muscle pairs or silencing of tonic pairs, confirms that the selected muscles produce changes to wing kinematics appropriate for a roll response (confirmed by quasi-steady aerodynamic modeling). This set of results corroborates the main conclusions.

Weaknesses:

The authors refer to this as robust control of roll, though exactly what is meant by this is not clearly defined, and the word "detectably" may be important to understanding the limitations of the findings, since the large amount of variability in many of the experimental measurements would make it challenging to detect differences among treatments. As with the silencing experiments, the wing kinematics after optogenetic activation were highly varied, making it challenging to identify differences between the effects of individual muscles.

Reviewer #3 (Public review):

Summary:

Ludlow et al. investigate the control strategy that flies use to stabilize flight during small roll perturbations. Using 3D kinematic analysis of freely flying Drosophila, Ludlow et al. ask how manipulating steering motor neurons alters the fast stabilization reflex, which spans only a few wingbeats. Bilaterally activating i1 or i2 wing steering motor neurons during free flight pitches the fly down via decreases in the wing stroke amplitude, whereas inhibiting the b3 wing steering motor neurons pitches the fly up via increases in the wing stroke amplitude. These results suggest that asymmetric recruitment of these steering muscles may be used to rotate the fly around the roll axis. Then, using quasi-steady aerodynamic modeling, they linearly interpolate changes in six kinematic features of wing strokes to describe which wing parameters produce the greatest corrective roll torque. They repeated this analysis with data from optogenetic activation of wing steering motor neurons. They argue that modeling of these optogenetic perturbations supports the hypothesis that i1, i2, and b3 muscles contribute to rotation around the roll axis by calculating roll torque changes from changing kinematics of a single wing, despite bilateral optogenetic activation. Finally, they support their claims about the redundancy of steering motor neurons by presenting connectomic analyses of the direct pathways from haltere sensory neurons to wing steering motor neurons. This analysis reveals two independent pathways from halteres to two wing muscle groups (b1 and b2 vs. i1, i2, and b3). Taken together, these data provide some evidence for redundant roll stabilization control strategies in Drosophila.

Strengths:

The central strength of this work is the high-quality free-flight kinematic dataset, which provides a detailed picture of how wing-stroke parameters change during natural roll perturbations and correction. The integration of quasi-steady aerodynamic modeling with these kinematic data offers a principled framework for linking muscle activity to torque generation. The use of split-Gal4 lines to target individual wing steering motor neurons with cell-type specificity provides a potentially precise approach to probe the contribution of specific muscles to roll torque. Together, these tools position this study to make a meaningful contribution to understanding the sensorimotor control strategies underlying flight stabilization in Drosophila.

Weaknesses:

The GtACR1 silencing experiments lack validation that the optogenetic manipulation actually suppresses motor neuron activity. Without a positive control to calibrate light intensity and duration, the absence of kinematic effects cannot be interpreted with confidence. The confocal images of the driver lines provided are insufficient to uniquely identify the targeted motor neurons and do not clearly show expression in the brain and nerve cord. The kinematic modeling relies on flight profiles derived from a single fly and a single trial, raising questions about whether they capture the full range of natural variation. Finally, the connectomics analysis largely recapitulates prior work and provides little new insight.

There is no evidence that optogenetic silencing of wing motor neurons with GtACR1 is actually suppressing their activity. There are many factors that could impact the efficacy of this manipulation: transgene expression, light intensity and duration, etc. While electrophysiology experiments would be ideal, this would be challenging. Another option would be to use a positive control with an obvious phenotype to calibrate the light intensity and duration. For example, silencing all motor neurons (e.g., using OK371-Gal4 or another driver labeling glutamatergic neurons) should essentially paralyze the fly. Without additional evidence, the lack of a kinematic effect in the GtACR experiments is not convincing.

The confocal images in Figure 1C are of poor quality and do not help identify the motor neurons. They only point to cell body locations, and the morphologies of the neurons are not clear. The glial sheath also appears to be labeled. The images as they currently exist are not sufficient to uniquely identify the wing motor neurons and should be updated, including brains, since the experiments manipulate activity across the nervous system. It should also be clarified that these split Gal4 lines were not generated in this work.

The connectomics analysis does not add much on top of what was already known. It doesn't necessarily need to be removed, and the authors acknowledge that it is basically repeating prior analyses in prior publications from another connectome dataset. But it could be reduced to a schematic summarizing prior work. The one thing that should be clarified is what the "haltere afferents" actually are. Campaniform sensilla only or other sensory neurons (e.g., haltere chordotonal neurons)?

In Figures 3 and 4, 50 kinematic profiles are created using data from a single fly and a single trial. Are these kinematic profiles representative of the range that flies use? Would the results generalize across different bouts? Figure 1i-m shows a much narrower distribution of kinematic features compared to Figure 3, which might reduce the resulting torque calculations. Why not sample from the distribution of data in 1i-m in Figure 3? Could there be some sort of bootstrapping for the modeling in Figures 3-4?

Reviewer #1 (Public review):

Summary:

Tullo et al. address an important and currently unresolved mechanistic question: does the prion-like spreading of alpha-synuclein (aSyn) generalize across three biological factors: host genotype, preformed-fibril (PFF) species, and disease epicentre (brain region); and can the resulting neurodegeneration be predicted computationally? Using a longitudinal design, adult M83 A53T-hemizygous mice and wild-type littermates received intrastriatal human- or mouse-PFF or PBS, with in vivo brain MRI at 7T, motor testing, survival and weight followed to 120 days post-injection. A parallel experiment seeded human-PFF or PBS into the hippocampal dentate gyrus. Atrophy was quantified by deformation-based morphometry, brain-behaviour coupling by partial least squares, and spread was simulated with a Susceptible-Infected-Removed agent-based model constrained by the Allen mouse connectome and SNCA expression. The authors conclude that aSyn-associated atrophy generalizes across genotype and fibril species but is anatomically distinct for the two epicentres, emphasizing regional vulnerability. This is a technically strong and ambitious study, reflecting a substantial and well-executed research effort.

Strengths:

This is a technically accomplished and ambitious study from a group with clear expertise in mouse neuroimaging and network modelling. The study addresses a real knowledge gap, relevant for mechanistic explorations of alpha-synucleinopathies: genotype and fibril inoculum species have rarely been compared head-to-head, and the relationship between aSyn propagation and downstream atrophy outside the striatum has been under-examined so far. The central hypothesis, that regional vulnerability constrains aSyn-associated neurodegeneration, together with the first attempt to model aSyn-induced atrophy computationally in rodents, is conceptually and methodologically valuable and translationally relevant.

The longitudinal dataset is unusually rich (687 in vivo scans), and both the data and the analysis pipeline are openly shared (OpenNeuro ds007671, Zenodo, GitHub), which is extremely important for reproducibility and of clear value to the community. The work uses a multi-modal methodology in which the same phenomenon is examined across anatomical MRI, multivariate brain-behaviour modelling, and a mechanistic simulation, and is the first to model aSyn-induced atrophy computationally in mice.

Another strength is the consistent incorporation of sex as a biological variable throughout, including sex-stratified survival, behavioural, and voxelwise atrophy analyses. This allowed the authors to identify sex differences in disease progression, notably in survival and symptom onset, while indicating that the core PFF-induced atrophy pattern was largely preserved across sexes.

The core descriptive findings, that PFF-induced atrophy and motor impairment are reproducible across genotype and fibril species, and that striatal and hippocampal seeding yield distinct anatomical signatures, are convincingly supported and represent a very valuable advance.

Weaknesses:

The strongest mechanistic and epicentre interpretations would benefit from some additional support, although the study's core findings are robust.

First, the framing centres on aSyn propagation, but the sole in-cohort readout is MRI-derived atrophy assessment; no aSyn/phospho-Ser129 pathology is shown for these animals. The atrophy-propagation link remains inferential.

Second, the computational model's fit is reported as the peak correlation across simulation time steps and is not yet benchmarked against null or baseline models, so it is somewhat difficult to determine how much the connectome and dynamics contribute beyond gene expression alone; parameter provenance is also not described in the text.

Third, the epicentre difference is well supported empirically at matched inoculum, but the computational comparison (SIR) is so far inoculum-mismatched: the striatal model was evaluated against mouse-PFF atrophy while the hippocampal model used human-PFF. A matched striatal human-PFF map is already available, so this could be reconciled without new data. The reduced hippocampal vulnerability despite higher hippocampal SNCA expression also remains unexplained.

Appraisal and impact:

The authors largely achieve their aims, and the generalization of atrophy across genotype and fibril species, together with the epicentre-specific anatomy, is well supported by a strong and openly available dataset. The more mechanistic conclusions - aSyn propagation specifically, connectome-driven vulnerability, and epicentre-determined resistance - would be strengthened where feasible by pathology validation, model benchmarking, and completing the already-available matched computational comparison, and should be interpreted with corresponding caution. Even so, the combination of a large longitudinal imaging resource, a factorial in vivo design, and the first rodent computational model of aSyn-related atrophy makes this a valuable contribution that is likely to be a useful reference and methodological template for the synucleinopathy and network-neurodegeneration communities.

Reviewer #2 (Public review):

Summary:

This study explores risk factors for neural atrophy following alpha-synuclein injection from two complementary perspectives. First, it evaluates the effect of biological and experimental factors (genotype, alpha-synuclein species, biological sex, seeded brain region and time since injection) on the extent of neural atrophy. Second, it assesses whether regional biological features (gene expression and structural connectivity) can predict the spatial distribution of that atrophy. Using longitudinal in vivo MRI, the authors map brain volume changes over time. They relate the brain changes from striatum seeding to behavioral outcome, identifying factors associated with more severe pathology. Finally, the authors validate a previously developed in silico model for predicting brain atrophy from alpha-synuclein seeding. The model is based on the alpha-synuclein prion-like spreading hypothesis and uses local gene expression and structural connectivity to predict atrophy following the injection. They conclude that the model accurately predicts atrophy following striatal seeding but performs poorly for hippocampal seeding. They further show that structural connectivity alone is insufficient to explain the observed atrophy after striatal seeding, and that incorporating regional gene expression substantially improves model performance.

Strengths:

The authors have expanded on their previous work by systematically evaluating how multiple biological and experimental variables influence the development of brain atrophy. The use of MRI to map structural changes and the subsequent analysis is well validated by this group and enables comprehensive whole-brain quantification across a large number of experimental conditions. The evaluation of the in silico model linking regional gene expression and structural connectivity to patterns of atrophy under different experimental conditions is important for expanding our understanding of how atrophy develops in synucleinopathies.

Weaknesses:

My principal concern is that the manuscript is framed as an investigation of alpha-synuclein propagation, whereas the primary outcome measured throughout the study is a change in regional brain volume. Although atrophy is likely related to the underlying spread of pathological alpha-synuclein, the spatial distribution of alpha-synuclein pathology is not directly quantified. Conclusions regarding propagation of alpha-synuclein and the relationship with tissue loss are inferred from the performance of the in silico model in predicting atrophy. I think the manuscript could be revised to make this distinction clearer.

A second concern relates to the comparison between striatal and hippocampal seeding. A key conclusion of the manuscript is that the in silico model accurately predicts atrophy following striatal seeding but not hippocampal seeding. However, the two analyses use different experimental group comparisons (striatum: M83 Ms-PFF versus WT PBS; hippocampus: M83 Hu-PFF versus M83 PBS). It would be helpful to demonstrate that the observed difference in model performance is not attributable to these differing experimental/ control groups.

Reviewer #3 (Public review):

Summary:

This work studied the prion-like α-synuclein spreading hypothesis from the view of different host genotypes (M83 transgenic vs wild-type), fibril species (mouse vs human PFFs), and disease epicenter (striatum vs hippocampus). Major results include tracking neurodegeneration longitudinally with in vivo MRI, behavior, and survival in the same mice. Furthermore, this work sought to link atrophy patterns to structural connectivity and regional SNCA expression. Finally, the authors tested whether a connectome-based SIR spreading model could predict the atrophy in silico and generalize across seed sites.

Strengths:

(1) Same mice imaged repeatedly across four timepoints (−7, 30, 90, 120 dpi), giving true within-subject volumetric trajectories rather than cross-sectional snapshots.

(2) Investigate the atrophy pattern for striatal-vs-hippocampal seeding in PD.

Weaknesses:

(1) The hypothesis (regional vulnerability) is not novel, although the manuscript presents compelling and interesting results supporting it in Figures 2 and 3.

(2) The findings primarily establish statistical associations rather than causal mechanisms. This limitation appears inherent to the cross-cohort dataset utilized, which the authors should explicitly address in the discussion.

(3) The descriptions of the statistical analyses in Sections 2.5 and 2.6 lack sufficient detail. The authors should provide additional technical specifics to ensure reproducibility.

(4) Given that VBM was used to determine atrophy patterns, it is necessary to address how the multiple comparisons problem was handled in the statistical analysis to control for false positives.

Reviewer #1 (Public review):

Summary:

HIV can persist in brain microglia despite ART and is linked to ongoing neuroinflammation and altered cellular function.

Strengths:

The authors demonstrate an innovative cell-type-specific analysis of human postmortem brain tissue from aviremic and viremic people with HIV. It uses FANS, bulk and single-nucleus RNA-seq to show that HIV DNA is concentrated mainly in microglia and that inflammatory and synaptic abnormalities persist despite ART.

Weaknesses:

The evidence is exploratory, based on a small, heterogeneous postmortem cohort. Therefore, the findings are suggestive rather than definitive.

The study would be stronger if a larger cohort, particularly more HIV-negative controls, were included. Also, less heterogeneity would reduce confounding from co-infections and terminal illness. The findings would also be better supported by longitudinal or matched peripheral data, protein-level validation, and direct evidence of viral activity rather than proviral DNA alone. A larger sample size would improve statistical power and make the cell-type differences more reliable.

Reviewer #2 (Public review):

Summary:

The authors use FANS of rapidly obtained postmortem brain tissue from DPWH, seven aviremic, four viremic and three HIV-negative controls to characterize the CNS HIV reservoir and cell-type-specific transcriptional changes.

Strengths:

The study addresses a genuinely important and understudied question: the effect of viral suppression specifically on the CNS reservoir and transcriptome using a rare and well-characterized specimen set.

Weaknesses:

I have some reservations about the conclusions, because of the confounders, mechanistic narrative, and the data itself.

(1) With n = 3 negative, n = 4 viremic, and n = 6 aviremic (post-H5 exclusion), every DEG and enrichment result rests on very few individuals. Rather than HCA reporting effect-size distributions and per-gene sample support, the authors should consider sensitivity/leave-one-out analyses to show that results are not driven by single donors. To me, it is as in Figure 3: major changes in the DGE are between the viremic vs aviremic, interestingly not with the negative control.

(2) HIV-negative controls were significantly older (74,76 & 83, inflammaging) and entirely male (sex-based immune differences). Both bias the immune comparisons that anchor the paper. PCA reassurance with n = 3 is weak. The authors should address this quantitatively, e.g., age/sex as model covariates, or explicit discussion of directionality of bias for each key pathway.

(3) HIV DNA was detectable in only 5/11 DPWH, and the microglial reservoir signal comes from ~3 individuals. The 10³-10⁴ copies/million figure and "dominant reservoir" claim should be framed against this limited detection and the focal distribution of infection.

(4) Only two participants had documented cognitive symptoms, and histopathology showed no neuropathology in anyone. The transcriptome-to-HAND link is currently asserted rather than demonstrated. The authors should state this limitation prominently and avoid implying an established relationship.

(5) A large fraction of DPWH had TB (one TBM), and controls had SARS-CoV-2. TBM alone causes microglial activation. Excluding H5 does not remove the broader TB signal. The authors should analyze/discuss TB status as a potential driver of the microglial immune signature in the retained cohort.

(6) Sorting on IRF5 cannot distinguish microglia from perivascular macrophages, as correctly stated by the authors in the discussion, so the "microglial" reservoir may include other myeloid populations. The authors should change the cell-type attribution accordingly.

Reviewer #1 (Public review):

Summary:

The authors are trying to characterize the sources of H. pylori in an island system. They find that, like the humans, the bacteria are admixed, but there is no correlation within the island between human ancestry and bacterial ancestry.

Strengths:

The study has taken particular care to characterize the humans from which isolates were obtained. Thus, it is a particularly convincing demonstration of a bacterial "melting pot".

Weaknesses:

The GWAS is highly confounded by population structure. In particular, there is a large group of strains that lack the cag pathogenicity island, and also differ in frequencies of other genes. So it's not clear that these differences are other than in cag status.

Figure 1 seems very inconclusive.

Reviewer #2 (Public review):

Summary:

This study investigates the population structure and ancestry of Helicobacter pylori in Cabo Verde, where the human population has mixed West African and European ancestry. The authors combine a population survey, serum markers, bacterial genome analysis, and paired human-bacterial ancestry data. They report high H. pylori seropositivity, several distinct bacterial groups, and limited correlation between human ancestry and bacterial ancestry. They also identify one European-derived bacterial group that appears to have undergone a recent expansion and carries fewer well-known virulence-related genes.

The study is interesting, and the dataset is valuable, especially because population-based H. pylori genomic data from Cabo Verde and West Africa are limited. The results provide useful information on bacterial diversity, historical migration, and host-bacterial ancestry. However, some of the main conclusions are stronger than the evidence currently supports, particularly the claims of host adaptation, increased transmission, and reduced virulence.

Strengths:

(1) A major strength is the study population. Participants were recruited from the general population and not only from patients with gastrointestinal disease. This gives a broader view of H. pylori diversity in Cabo Verde than studies based only on hospital patients.

(2) The number of participants tested for H. pylori antibodies is substantial, and the authors also obtained a relatively large number of bacterial genomes. The combination of human and bacterial genomic information is another important strength. This allows the authors to directly examine whether human ancestry is related to the ancestry of the colonising bacteria.

(3) The population genetic analyses are extensive. The authors use several different approaches, and these generally support the existence of African-derived and European-derived bacterial groups in Cabo Verde. The identification of two low-diversity European-derived groups is also interesting and suggests a relatively recent expansion.

(4) The addition of new strains from Ghana and Portugal improves the reference dataset. The results may help future studies of H. pylori population structure in Africa, Europe, Cabo Verde, and populations affected by historical Atlantic migration.

(5) The finding that human ancestry and bacterial ancestry are only weakly related in this population is potentially important. It suggests that the long-term relationship between human and bacterial ancestry may be less stable in recently admixed populations.

Weaknesses:

The main weakness is that several biological conclusions are based on indirect evidence. The genomic results support recent expansion of one bacterial group, but they do not directly show that this expansion was caused by adaptation to local hosts or by increased transmission. Founder effects, population history, geographic clustering, household transmission, or random expansion may also explain the pattern. The wording should therefore be more cautious.

The conclusion of reduced virulence is also not fully supported. The expanded lineage often lacks the cag pathogenicity island and carries less virulent forms of vacA, which suggests lower virulence potential. However, this does not prove that the strains cause less gastric damage or lower disease risk. There are no endoscopic or histological data, and serum pepsinogen values are only indirect markers.

The description of the study population as having limited gastric inflammation is too strong. Serum pepsinogen measurements are useful for estimating gastric atrophy, but they do not directly measure the degree of histological gastritis. In addition, participants were recruited independently of symptoms, but this does not mean that they were all asymptomatic.

The epidemiological estimate is based on antibody testing. This measures seropositivity and cannot clearly distinguish current from previous infection. Therefore, terms such as active infection or colonisation should be used carefully.

The proposed new West-Central African bacterial group is based on a small number of reference strains from Ghana and Nigeria. The result is interesting, but broader sampling from African countries is needed before this group can be considered firmly established.

The interpretation related to the trans-Atlantic slave trade is plausible, but the data mainly show patterns consistent with known historical migration. They do not directly demonstrate when or how the bacterial lineages moved.

The gastric cancer comparison may also be affected by bacterial population structure. Differences between the Cabo Verdean lineage and gastric cancer strains may reflect ancestry or lineage differences rather than disease association alone.

Overall, the study achieves its main aim of describing H. pylori diversity and ancestry in Cabo Verde. The evidence is strong for the population structure and ancestry findings, but less strong for the proposed mechanisms of adaptation, transmission, and reduced disease-causing potential. The work will be useful to the field, but the main conclusions should be stated more carefully.

Reviewer #1 (Public review):

This interesting manuscript challenges the current interpretation of the well-established stop signal reaction time task (SSRT), commonly used in many research areas. SSRT has been traditionally thought of as primarily a measure of inhibitory control. In this work, the authors argue that this is influenced significantly by sensory and motor transmission times, and that these low-level processes may systematically confound SSRT estimates both in individual groups and also in many clinical populations.

Conceptually, this raises an important and significant question regarding the construct validity of one of the more widely used behavioral measures of response inhibition. The authors provide a clear theoretical framing and importantly address the overlooked assumptions in SSRT modelling, the underexplored source of variability.

Further, direct evidence separating peripheral sensory and motor contributions from central inhibitory processes is certainly needed, as well as a more balanced interpretation of prior methodological refinements in the field. Is a correlation between T0 and SSRT sufficient to conclude that SSSRT may be predominantly driven by peripheral delays? What proportion of SSRT variance remains unexplained after one accounts for T0? If T0 and inhibitory processes share neural processing speed, does that mean they covary? If one corrects T0, do the group differences become smaller or perhaps disappear?

Furthermore, how robust is the T0 estimate in noisy environments of all sorts and especially across modalities (visual and motor)? The authors argue that this relationship is clearer in "good quality data"; how do you define that, and what happens with noisy data? For instance, the authors refer to a range of clinical disorders such as ADHD and PD where noise is abundantly present, partly due to the disease itself or due to treatment.

In conclusion, this is certainly a thought-provoking and potentially influential contribution in the literature that raises important questions about the interpretation of the stop signal reaction time task.

Reviewer #2 (Public review):

Continuing their work distinguishing sensory latencies of "cognitive" processes, the authors turn their attention to "response inhibition". The "square quotes" are being used to highlight how this manuscript aims to challenge previous descriptions of performance data and inferred computational processes. The authors assert that previous descriptions of the measure known as "stop signal reaction time" (SSRT) are flawed because they did not account for sensory latencies empirically or theoretically.

Enthusiasm for the manuscript cannot be high in light of many weaknesses countering the possible strengths. Strengths include offering an opportunity to more carefully characterize the quantity SSRT and a specific empirical approach offered to the research community. However, these strengths are countered by the following structural, theoretical, and empirical weaknesses:

As announced by the elephant in the title, the writing could be described as excessively polemical. However, the characterization and interpretation of previous empirical and theoretical work is disputable.

The major theoretical claim regarding sensory delays inherent in SSRT is not novel. The authors assert, "...this corpus of work may have been misinterpreted because the SSRT is systematically influenced by low level sensory and motor transmission times, arguably more so than by inhibition or cognitive processes." This was certainly recognized by Logan and Cowan in their original work. They wrote, "An act of control, like any other act, must take time. The theory provides methods for measuring the latency of control even when the act of control is not directly observable." (page 298) Also, "... the estimate of stop-signal reaction time includes the latency of the internal response to the stop signal and the duration of the ballistic process." (page 316-317). Moreover, subsequent computational and empirical work, some noted by the authors, has distinguished the sensory encoding interval from the interval during which the STOP process interrupts the GO process.

The theoretical suggestion that an accounting for sensory delays undermines the functional interpretation of SSRT mischaracterizes the original literature. For example, in the Abstract the authors write "Sensory and motor contributions must be ruled out before linking SSRT results to inhibition or cognition". The original Logan and Cowan theory was about what happens at the end of SSRT, and that was described only as an "act of control", in perfectly positivist fashion. For example, Logan and Cowan wrote, "Estimates of stop-signal reaction time provide a measure of the latency of control." (page 315). Thus, the authors are misstating what was meant originally by SSRT. In addition, the authors offer no specific or formal definition to specify what they mean by "inhibition or cognitive processes".

Confidence in the new empirical conclusions of the manuscript must be low because the new performance data are of questionable quality. The first issue is that the stopping accuracy (or inhibition functions in original terminology) shown in Figure S3 is very problematic for the interpretation of the authors' empirical work in this manuscript. There are two problems. First, these plots should span from nearly 0% to nearly 100%. It is not possible to resolve the span of each individual in the figure, but it is clear that many, if not most, in both the Manual and Saccadic data span just 20-30%. Second, the plots should span the 50% success value. It is clear that the maximum or minimum values for many participants do not reach the 50% value. These two problems indicate that many (most?) participants were not really sensitive to the stop signal.

The second issue concerns the pattern of response times (RTs) on "ignore" trials. The authors portray performance as exemplifying a "pause-then-go" strategy. This is not uncommon, but it is not the only way participants perform. Many participants across multiple studies of selective stimulus stopping produce RTs on "Ignore" trials essentially indistinguishable from RTs on no-stop trials. The authors must acknowledge and account for such individual variability. In fact, the "T_s" value is measured by the difference in distributions of RT on no-signal and ignore trials. If these distributions are not different, then the measurement and interpretation of this quantity is questionable.

Related, the distributions of RT on stop trials, particularly for saccade responses, are portrayed with a second mode in the schematic illustrations and clearly peaking at SSRT in Figure S1. This second mode is not observed in other saccade stop signal studies. This indicates that the participants in this study were in a peculiar mode of performance.

Finally, given the pivotal role of measures of differences of RT distributions and the pronounced variation of stopping accuracy (Figure S3), the authors must show the distributions for all of their new participants. The authors' claim to higher resolution obliges them to reveal every step of analysis.

In its current form, this manuscript is unlikely to change the thinking of modelers or practitioners of the stop signal task.

Reviewer #3 (Public review):

Summary:

Statham and colleagues test an assumption underpinning a very large literature: that the stop-signal reaction time (SSRT) indexes the speed or efficacy of top-down inhibitory control. They argue instead, and support their claims with a total of eight datasets, that SSRT is substantially occupied by visuomotor deadtime (i.e., incompressible sensory and motor delays common to all visually guided responses), which varies across individuals, conditions and populations in ways that mimic effects usually attributed to inhibitory control. They propose two remedies: subtracting an independent estimate of visuomotor deadtime (T₀) from SSRT, and a new index, the selective stopping delay (ΔT), from the stimulus-selective stopping task.

Strengths:

The paper's principal strength is the combination of these components. That SSRT must contain peripheral delays is not itself new, as the authors point out (Boucher et al., 2007; Salinas and Stanford, 2013; Bompas et al., 2020). What is new is the quantification of the problem at scale, across seven archival datasets and a preregistered replication, together with the demonstration that T₀ can be recovered from existing stop-task data. That is important, as it provides a diagnostic that can be applied to data already collected. The authors' offer to assist others in doing so is exemplary. The supplementary analyses of trial numbers and participant pooling are very useful, and the paper provides important sanity checks, notably confirming that stop and ignore signals produce indistinguishable initial interference before pooling them.

Weaknesses:

The evidence for the central claim is strong but presented in a way that overstates it. Figure 2 reports 85% and 80% shared variance between SSRT and T₀, but these pool across datasets and, more critically, across response modality: manual and saccadic estimates from the same participants are plotted together with a single regression line through both. Because manual and saccadic deadtimes differ by roughly 130 ms, the resulting correlation largely reflects a between-condition difference rather than covariation among individuals. The numbers that speak to individual differences are more modest (40% for manual responses; 7% for saccades). The manual result is convincing and consequential; the saccadic result is not, and the explanation in terms of restricted range, while plausible, is offered after the fact and is directly testable by reporting the reliability of saccadic T₀ or correcting the correlation for attenuation. This limitation is arguably good news for the paper's practical message, since it implies saccadic measures are relatively protected, but the manuscript should make clear (including in the abstract) that the strong individual-differences case rests on the manual data, where motor execution delay is the main driver.

A related point concerns interpretation rather than analysis. Since SSRT is, on the authors' own account, approximately the sum of T₀ and a decision-related component, covariation between the two is expected on structural grounds; the preregistered correlation with reaction times from separate speeded blocks mitigates this, but the finding is less surprising than its current framing implies. What would determine whether past conclusions must be revised is not whether SSRT correlates with T₀ across individuals, but whether the decision-related component tracks the independent variable in any given study. The alcohol reanalysis could be a test case for this: the authors show that alcohol raises T₀ commensurately with SSRT and conclude the effects are "consistent with these effects being fully driven by visuomotor delays," yet (unless I missed something) they do not report the corrected measure for these data, while they do so for signal contrast and response modality. Running that analysis, and stating plainly what Campbell et al. (2017) would have concluded under the proposed treatment, would be an important demonstration.

The case for ΔT is the least developed part of the paper. ΔT is a difference between two independently estimated, individually noisy quantities, extracted by a non-trivial procedure (see also below), and no reliability estimates are reported for T₀, TS or ΔT. This would be possible based on the two-session design (and the group has prior work on the reliability of cognitive control measures). This matters because the argument that ΔT is superior rests, to some extent, on null findings: ΔT does not correlate with SSRT, with stopping accuracy, or with the differential response to stop and ignore trials. These null correlations are interpreted as freedom from confounds, but an unreliable measure would produce the same pattern, and the seven participants with implausible negative ΔT values indicate that noise is not negligible.

In addition, the subjective correction of dip onsets ("Departure points were visually inspected and adjusted if it was deemed that the algorithm had placed them in inappropriate places"), which is critical to the paper's central measurement, should be blinded to condition or show inter-rater agreement. Since T₀ and TS are compared across conditions and ΔT is their difference, this introduces researcher degrees of freedom.

This is particularly critical when a dip is not easy to extract. Figure 3 depicts an idealized ignore-trial distribution with a clean, deep dip. Real distributions are unlikely to look like this, and dip depth should depend on the behavioral relevance and salience of the ignored event; published work on rapid manual inhibition indicates that dips to behaviorally irrelevant events can be very shallow. Since ΔT is extractable only where the dip is resolvable, the generality of the method can be questioned. Ideally, the empirical distributions underlying every dataset analyzed should be shown to alleviate this concern.

One uncontrolled procedural difference also deserves comment. Corrective feedback about stopping too often or stopping too rarely was given after manual blocks only; saccadic blocks received none, and fixed rather than staircased delays were used throughout. Since the manual-saccadic contrast carries much of the argument, and saccadic blocks yielded both lower stopping accuracy (36% vs 52%) and far more exclusions (8 vs 1 of 37), this asymmetry offers an alternative to the interpretation that saccades are simply harder to inhibit.

A final point concerns the comparison between response modalities. Raw SSRT suggests that saccadic inhibition is faster than manual (174 vs 266 ms), while both proposed corrections reverse this, with SSRT−T₀ and ΔT each indicating that saccadic inhibition is slower (the latter consistently across nearly every participant). This is one of the clearest illustrations of the paper's thesis, but it is not taken up in the discussion, which returns to modality only to note that saccadic T₀ varies little (the one reference to variation across action modalities appears in the modeling section, without stating its direction). It would also benefit from a caveat. Both corrected measures subtract the same T₀, and manual and saccadic T₀ differ by roughly 130 ms, so the two do not corroborate one another independently (TS is itself longer for manual responses, and yields a shorter ΔT only once the larger manual T₀ is removed). The accuracy of the subtraction therefore matters here: if the manual regression slope of 0.75 reflects sub-additivity rather than attenuation, subtracting the full T₀ would overcorrect manual responses more than saccadic ones, and could produce the reversal on its own.

These concerns qualify rather than undermine the contribution. The core observation is robust, the diagnostic is practical and immediately applicable, and the case that a large body of work requires re-examination is well made. If the corrected analyses are carried through on the datasets already in hand, this will be an important paper for anyone who uses the stop-signal task.

Reviewer #1 (Public review):

Summary:

In this paper, Chen et al. identified a role for the circadian photoreceptor CRYPTOCHROME (CRY) in promoting wakefulness under short photoperiods. This research is potentially important as hypersomnolence is often seen in patients suffering from SAD during winter times. The mechanisms underlying these sleep effects are poorly known.

Strengths:

The authors clearly demonstrated that mutations in cry lead to elevated sleep under 4:20 Light-Dark (LD) cycles. Furthermore, using RNAi, they identified GABAergic neurons as a primary site of CRY action to promote wakefulness under short photoperiods. They then provide genetic and pharmacological evidence demonstrating that CRY acts on GABAergic transmission to modulate sleep under such conditions.

Weaknesses:

The authors then went on to identify the neuronal location of this CRY action on sleep. This is where this reviewer is much more circumspect about the data provided. The authors hypothesize that the l-LNvs which are known to be arousal promoting may be involved in the phenotypes they are observing. To investigate this, they undertook several imaging and genetic experiments.

While the authors have made improvements in this resubmitted manuscript, there are still multiple concerns about the paper. I think the authors provide enough evidence suggesting that CRY plays a role in sleep under short photoperiod. The data also supports that CRY acts in GABAergic neurons. However, the identity of the GABAergic neurons involved in this CRY dependent sleep mechanism remains unclear. Similarly, whether l-LNvs are the target of this GABA mediated sleep regulation under short photoperiod is not fully demonstrated. The data presented suggests that but does not conclusively prove it.

Reviewer #3 (Public review):

Summary:

In humans, short photoperiods are associated with hypersomnolence. The mechanisms underlying these effects is, however, unknown. Chen et al. use the fly Drosophila to determine the mechanisms regulating sleep under short photoperiods. They find that mutations in the circadian photoreceptor cryptochrome (cry) increase sleep specifically under short photoperiods (e.g. 4h light : 20 h dark). They go on to show that cry is required in GABAergic neurons and that the effects of the cry mutation on sleep are mediated by alterations in GABA signalling. Further, they suggest that the relevant subset of GABAergic neurons are the well-studied small ventral lateral neurons that they suggest inhibit the arousal promoting large ventral neurons via GABA signalling.

Strengths:

Genetic analysis to show that cryptochrome (but not other core clock genes) mediates the increase in sleep in short photoperiods, and circuit analysis to localise cry function to GABAergic neurons.

Weaknesses:

The authors' have substantially revised their manuscript, and the manuscript is much better for the revisions. However, the idea that the sLNvs are GABAergic is unfortunately still not well supported by the data. The authors have acknowledged the limitations of their methods though which is very welcome, and a substantial improvement.

Reviewer #4 (Public review):

Summary:

Short photoperiod is an important experimental manipulation in neurobiology, endocrinology, and metabolism studies. However, the molecular mechanisms by which short photoperiod gives rise to behavioral phenotypes that are seen in seasonal affective disorders remain unknown. Using the classic circadian model organism Drosophila, this study examines short photoperiod-induced hypersomnolence and identifies the circadian photoreceptor cryptochrome as a regulator of GABAergic tone within the clock neural circuit to promote wakefulness under short photoperiod conditions. The discovery has broad implications for understanding how short photoperiod modulates neural inhibition in circadian circuits in regulating sleep.

Strengths:

The Drosophila model provided a powerful platform to dissect the molecular mechanisms underlying short photoperiod-induced hypersomnolence. A battery of behavioral, imaging, circuit-manipulation approaches were employed to test the novel hypothesis that the circadian photoreceptor cryptochrome modulates GABAergic tone within the clock neural circuit to promote wakefulness under short photoperiod conditions.

Weaknesses:

The current model proposed by the authors suggests that the small ventral lateral neurons of the Drosophila clock circuit are GABAergic; however, this remains unclear. At present, the field lacks sufficient data and validated reagents to definitively establish the GABAergic identity of these neuropeptidergic neurons.

Reviewer #2 (Public review):

Chong Wang et al. investigated the role of H3K4me2 during the reprogramming processes in mouse preimplantation embryos. The authors show that H3K4me2 is erased from GV to MII oocytes and re-established in the late 2-cell stage by performing Cut & Run H3K4me2 and immunofluorescence staining. Erasure and re-establishment of H3K4me2 have not been studied well, and profiling of H3K4me2 in germ cells and preimplantation embryos is valuable to understanding the reprogramming process and epigenetic inheritance.

(1) "The authors' assertion that H3K27me3 did not change from GV to MII stage (Author response) is noted; however, this data was not provided. To validate the technical success of the CUT&RUN protocol in MII oocytes-a stage characterized by chromatin condensation and low DNA input-it is essential that the authors provide their internal H3K27me3 data as a positive control.

Without showing that a stable mark (like H3K27me3) can be successfully mapped in their MII samples, the 'disappearance' of H3K4me2 peaks cannot be distinguished from a technical failure of the assay at this developmental stage. Furthermore, I remain skeptical of the inclusion of the first polar body as a DNA quantity, as polar body chromatin is often undergoing degradation and may not reflect the epigenetic state of the oocyte itself.

(2) I remain concerned by the inconsistencies regarding KDM1A (LSD1) expression. The authors claim in the text and Figure 4A that KDM1A is 'rarely expressed' during mouse embryonic development. However, their own Extended Data Figure 3A shows high expression of KDM1A in oocytes, 4-cell, and 8-cell stages. Both figures reportedly use published RNA-seq data. The authors must explain how the same gene in the same developmental stages can appear 'rarely expressed' in one figure and 'expressed' in another.

(3) Page 6 (Line 161-165) The H3K4me2 demethylases KDM1B (LSD2) were also highly expressed in growing oocytes but showed decreased expression in MII oocytes (Figure S3A, see also revised heatmap). We have re-analyzed the expression data and corrected the heatmap normalization; the revised Figure S3A now accurately shows that Kdm1b is highly expressed in growing oocytes, with lower levels in 8- week and MII oocytes, consistent with its role in maternal imprint establishment.

The expression data in growing oocytes are missing in Fig S3A.

(4) The authors' proposal to use transcriptome data to confirm TCP specificity is insufficient (Author reply). Since TCP is a small-molecule inhibitor of protein activity, its primary effect is not the reduction of mRNA transcripts, but the inhibition of the enzymes themselves.

To support their claim that the observed H3K4me2 resetting and developmental arrest are specifically due to KDM1B inhibition, I recommend:

a. Perform Western Blot for KDM1A/B

b. Use a Selective Inhibitor: Use a more selective KDM1A inhibitor (e.g. GSK-LSD1)

c. Address the Discrepancy: Provide a clear biological explanation for why chemical inhibition leads to 4-cell arrest while a maternal KO of KDM1B survives to E10."

(5) "The authors' response that IF and CUT&RUN cannot be compared because of 'different analysis models' is not scientifically sound in this context.

Quantitative Contradiction: A 1,000-fold increase in global IF signal must be reflected in the genomic landscape. If only 251 genes show a gain in H3K4me2 in the CUT&RUN data, this represents a negligible fraction of the genome, directly contradicting the 'dramatic increase' claimed in the IF data.

Lack of Spike-in Normalization: Did the authors use a spike-in for their CUT&RUN? Without global normalization, CUT&RUN only reports relative changes. If H3K4me2 increased everywhere, a non-normalized CUT&RUN library would fail to show it, making the data misleading. Currently, the two datasets provide two different versions of biological reality. A third validation (e.g., Spike-in Normalized CUT&RUN) would be recommended to determine which dataset is accurate."

Reviewer #3 (Public review):

Summary:

The study "Resetting of H3K4me2 during mammalian parental-to-zygote transition" provides valuable insights into the dynamic changes in H3K4me2 during early embryonic development.

Strengths:

The findings provide valuable insights into the temporal and spatial dynamics of H3K4me2 and its potential role in zygotic genome activation (ZGA).

Weaknesses:

Key areas for improvement include enhancing the innovation and novelty of the study, providing robust functional validation, establishing a clear model for H3K4me2's role, and addressing technical and presentation issues. While the findings are significant, the current manuscript falls short in several critical areas. Addressing these major and minor issues will significantly strengthen the study's contribution to the field of epigenetic reprogramming and embryonic development.

Comment on revised version:

It would be better for the author to directly provide some experimental or analytical data rather than discussing and defending.

Reviewer #1 (Public review):

Summary:

This paper asks how the NK cell receptor KIR2DL4 binds HLA-G and undergoes endocytosis. The authors propose that an allosteric disulfide-bond switch controls whether the receptor is in a ligand-binding or non-binding state, and they support this model using mutagenesis, imaging, mass spectrometry, and structural prediction.

Strengths:

A major strength is the use of diverse, complementary approaches to validate the central claim. The authors combined unbiased random mutagenesis to identify key residues, confocal microscopy to track cellular localization , and mass spectrometry to quantify the redox states of specific disulfide bonds. These methods consistently support a single model: an allosteric disulfide switch. The transition between a Cys10-Cys28 bond and a Cys28-Cys74 bond serves as a functional switch that controls whether the receptor resides at the plasma membrane to bind ligand or remains inactive in endosomes.

Comments on revised version.

The revision substantively addresses the core weaknesses I raised, particularly on direct binding evidence, which was the most important gap. The remaining points (oligomerization confound in the SPR comparison, C10L not independently validated by imaging, and the 293T-only functional readout) are real but secondary so I'd suggest they be addressed with a sentence or two of acknowledgment in the Discussion rather than additional experiments.

Reviewer #2 (Public review):

Summary:

Rajagopalan et al. shows how extracellular domain features regulate KIR2DL4 internalization. The trafficking phenotypes of cysteine mutants are logically organized and well summarized in Table. The disulfide mapping and differential alkylation strategy is appropriate and provides strong support for alternative disulfide configurations in D0. The higher accessibility or more selective reduction of Cys10-Cys28 as compared to Cys28-Cys74 by PDI is a key mechanistic anchor.

Strengths:

The identification of a conformational switch in KIR2DL4 is conceptually novel. Experimental elegance, detailed and well written.

Reviewer #1 (Public review):

Summary:

Despite setting global conservation goals for the conservation of genetic diversity, gathering enough genetic data to assess its current status or monitor change over time for all species would require substantial time and resources. Finding reliable proxies or predictors of genetic diversity change would be a valuable alternative. Selmoni and Schuman test for patterns and trends in coral reef animals' genetic diversity across space and time and evaluate how well environmental variables predict their genetic diversity.

Strengths:

The authors have compiled a large genomic dataset from 19 studies, 18 species, and 173 reefs, and use a standardized k-mer-based pipeline to process all datasets. The authors propose that using k-mers may be especially suitable for macrogenomic analyses because they are less computationally intense and do not require a reference genome. This is interesting because there are currently few examples of macrogenetics studies using genomic data, likely due in part to the difficulty associated with processing multiple disparate datasets in an efficient and standardized way.

Weaknesses:

I have concerns that the models don't fit the data structure, the data are not suitable to test for temporal change, and that the values being predicted do not reflect meaningful genetic diversity. The manuscript would also benefit from a more cohesive narrative structure, clearly stated research goals, and better engagement with previous literature on this topic.

Effects of time: The data are not suitable to test for temporal changes across all species. 11 of 19 datasets were only sampled over 1-2 years, and only 5 were sampled across 5 or more years. I don't know generation times for these species offhand, but this is generally too short to draw any conclusions about genetic diversity change over time. While it makes sense to include year in the models as a control variable, in my opinion these patterns should not be interpreted and removed from the discussion, or at least the conclusions should be tempered (for example the section "Are coral reef populations rapidly losing their genetic diversity?").

Environmental effects models: I'm not convinced that the geographic random intercept term (response variable) represents a local genetic distance that should be related to environments. How many species were sampled at each reef? If only 1 species is sampled, then the reef's average diversity reflects that species, not necessarily environmental effects. If multiple species are sampled at a reef, then I suspect this value is the average diversity of all the species sampled there. Knowing that species have different levels of diversity (e.g., Toczydlowski et al 2025 cited here), what does this metric mean and how is it affected by species composition vs environments? I do not see the value in predicting this variable without considering species identity.

Introduction: The macrogenetics literature is not well described in the introduction. Most of the cited studies indeed use raw data rather than summary statistics, and most do not aim to predict genetic diversity.

I wouldn't say that explaining more than 20% of variation in data is a major challenge in macrogenetics. The choice of how many, and which, predictors to use depends on the research question (e.g., on the spectrum of understanding to predicting, see Shmueli 2010 "To Explain or To Predict?"; Arif & MacNeil 2022 "Predictive models aren't for causal inference"). It's proposed here that explained variance can be increased by adding more, or more relevant, environmental predictors-but particularly for macrogenetics studies reusing data from different sources with different study and sampling designs and different species, much of the variation in the data will be due to these factors. That is why conditional R2s are typically quite high (>80%) in mixed models with species/study as random effects, see e.g. Clark & Pinsky 2024 or Karachaliou 2025. Having strong effects of particular variables would also require that all species respond to the predictor in the same way, which is not necessarily expected. Low explanatory capacity alone is not an issue if the goal is not prediction.

Reviewer #2 (Public review):

Summary:

The authors measured whether a phylogenetically wide sample of marine species was experiencing declining genetic diversity, as one might expect from widespread habitat threat, over a recent 2-decade time span. They next identified key environmental variables that are impacting genetic diversity within and between reefs. A key insight was to apply k-mer-based genetic distances to massively speed up the reanalysis of genetic data into a common pipeline, which is otherwise onerous. The manuscript ends by highlighting key seascape variables that were associated with increases or decreases with genetic diversity through time. The authors achieved their overall aims, though I remain unsure of how well the identified seascape variable-genetic diversity predictions can be generalized, as implied in the abstract.

Strengths:

A key strength of the study was its rigor in vetting the k-mer based distance metrics, checking whether they give population structure patterns (Figure S2) and correlated with nucleotide diversity. This surpasses previous studies that used a similar method. The modeling procedure of genetic diversity predictions from environmental variables is also rigorous and presented with some appropriate nuance.

Weaknesses:

I noted five weaknesses, listed below in order of potentially more severe at the top to more minor at the bottom.

First, only one k-mer distance metric was tested. There are many k-mer distance metrics that will potentially give different weight to different frequencies of polymorphism, like how Watterson's theta and nucleotide diversity weight polymorphisms, depending on their frequency. It would be interesting to see if the seascape variable predictions hold with a Jaccard or cosine distance metric, or if the observed results are purely restricted to the choice of Bray-Curtis distance. Another option would be to use mash, skmer, or (very recent development) re-skmer distances, which attempt to more directly approximate the average nucleotide identity between two sequence sets based on the k-mer sets of their reads while also being faster than traditional alignment. This would potentially give cleaner trends, seeing as the goal is to have a proxy for nucleotide diversity, with the downside of not including the impacts of non-SNP variation.

Second, it is unclear to me whether the number of species analyzed, 18, can accurately identify important environmental variables in early warning systems, as claimed in the abstract. While this likely represents the best available balance of evidence, it is worth highlighting the manuscript's note that the environment-diversity predictions did not scale across marine realms. This is likely a limitation of data availability, rather than a study design flaw, but is nonetheless important for readers to keep in mind. Would recommend that the abstract acknowledge this limitation.

Third, it is unclear to me how the included datasets compare in terms of genome-wide coverage. Figure S1 gives sequencing depths in terms of read number, but what is the range normalized for genome size - are the datasets 1x, 5x, 10x, on average, etc? This is probably most key to how the k-mer distance metrics will perform, because low coverage will make two samples appear artificially distant due to rarity of sampling the same k-mer multiple times. However, the correlation between k-mer distance and regular alignment-based SNP distance (Figure 2C) gives some confidence that this effect could be small.

Fourth, though k-mer distances may in some sense better capture the breadth of DNA sequence diversity, they lack a concrete interpretation of what loci may/may not be under selection as marine environments are increasingly threatened over time. This is a different question than what the manuscript tries to address, but is of interest to the field and is something that would be seemingly difficult to do with k-mers.

Finally, I had a more minor concern: the k-mer-based distances use only k-mers that are mapped to a reference genome. This is done to thoroughly remove contaminant sequences, which is important, but is a double-edged sword because there is potentially additional pangenomic variation that is real but does not map to a single reference. The authors state in the first paragraph of the discussion that this choice did not affect the results, but I did not find an associated analysis in the supplement or main text. It would be interesting to compare distance metrics based on screening against all known microbial+human genomes vs screening against the reference genomes.

Reviewer #1 (Public review):

The authors use metabolic reconstruction to build genome-scale models from TARA metagenomes.

This is not the first paper that attempts a reconstruction of metabolism on this scale. And like those that came before, its weakness is in the analysis of the resulting model.

I was initially quite excited about this manuscript as the reconstruction itself seems well done. However, the analysis does not deliver major or minor results.

It could be said that the modelling cuts some corners, for example in the identification of the flux modes. I was not quite able to follow why this is necessary, as numerical linear algebra methods seem to be able to handle problems of this size. But then again, I am quite confident that the sampling approach that the authors use produces a good enough approximation.

The weak point of the manuscript is the analysis that follows. The conclusions contain very few hard results, and the results that are presented talk mostly about concepts that are somewhat arbitrarily introduced and hard to relate back to nature. The authors construct a mix of their own concepts and others borrowed from cancer research, and neither of the metrics that are used is very convincing.

For example, the authors say they contribute to the complexity-stability debate, but that debate focuses on a particular notion of stability. What the authors call stability here is an entirely different concept that is completely unrelated to the stability of the complexity-stability debate; I would rather describe it as a resilience metric rather than stability.

The problem is that the metrics that are used lack an underlying firm grounding. Ecology was for a long time struggling with the same problem (and to some extent still is), but eventually much progress has been made by rigorously deriving metrics that are easy to relate back. The same would be possible here. Using Steuer's structural kinetic method, the reconstructed metabolism could be described dynamically, which would allow genuine stability analysis as well as other crucial metrics such as the observability and controllability, impact and sensitivity, which would make it far easier to relate results back.

Reviewer #2 (Public review):

This study used publicly available Tara Oceans and Tara Oceans Polar Circle metagenomic and metatranscriptomic datasets, including viromes, to construct sample-specific, gene-scale metabolic models. This reviewer understood that, for each sample, genes or transcripts associated with metabolic pathways were integrated into a single virtual "superorganism" or "community cell." These sample-level models were then compared across global ocean regions to investigate spatial patterns in heterotrophic prokaryotic metabolism, metabolic synergy, and the potential effects of virus-encoded auxiliary metabolic genes. However, it was not clear whether archaeal genes were also included in the heterotrophic prokaryotic fraction.

The study represents an ambitious and potentially valuable attempt to connect large-scale environmental omics data with constraint-based metabolic modeling. The authors handled a very large dataset and introduced quantitative approaches based on flux sampling, Reaction Cumulative Correlation, and synergy scores to describe community-level metabolic phenotypes using several mathematical formulations. The recovery of previously defined oceanic ecological zones from reaction-based models may provide preliminary support for the ecological relevance of the framework, although only approximately 26% of prokaryotic genes and 7% of viral genes were mapped to known metabolic reactions.

However, the framework should be understood as gene- or reaction-resolved, sample-level metabolic modeling rather than genome-resolved community modeling. By combining all detected genes within a sample into a single superorganism, the approach likely loses taxon-specific metabolic information and cannot directly distinguish intracellular metabolism from interspecies metabolic exchange. Consequently, several ecological interpretations, including cooperation, stability, reaction essentiality, and viral impacts, remain strongly dependent on the underlying model assumptions.

Overall, the manuscript presents a novel and scalable framework with considerable potential. However, greater methodological clarification and more cautious interpretation are needed before the ecological and biogeochemical conclusions can be fully supported.

Reviewer #1 (Public review):

Summary:

This manuscript seeks to make use of information about Ct values from PCR testing of mosquito pools for West Nile virus infection to make inferences about mosquito prevalence and West Nile risk. It does so through analysis of empirical data and simulated data with a realistic agent-based model.

Strengths:

This work is conceptually innovative for mosquito-borne viruses, building on ideas developed primarily during work on SARS-CoV-2. Exploring this topic is worthwhile regardless of the outcome. The use of data, testing in multiple labs, and the complementarity of modeling and empirical data analysis are all strengths of the approach.

Weaknesses:

Some of the primary weaknesses include a dependence of the results on relatively narrow model assumptions, and a lack of compelling improvement over existing methods. None of these weaknesses are fatal flaws; they are modest weaknesses that limit the potential of or excitement about the method.

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.

Weaknesses:

(1) It would be helpful if the authors could provide plots showing variation across locations and over time. This would further support the claim made in the paragraph at lines 101-107.

(2) Figure 2: The model schematic is clear in terms of workflow, but it would benefit from more information on model parameterization. In particular, it would be helpful to clarify which parameters or migration rates were estimated from the data and which were assumed based on prior literature.

(3) Figure 4: I wonder whether the authors examined how changes in the proportion of mosquitoes with static viral-kinetics trajectories would affect the observed bimodal distribution. Relatedly, it would be useful to know whether there is a threshold proportion at which the method becomes less able to distinguish active from static viral-kinetics patterns.

Conclusion:

Overall, the evidence is reasonably strong for demonstrating the feasibility and biological plausibility of the proposed framework. Some conclusions would be further strengthened by additional sensitivity analyses on key assumptions, especially the proportion of static viral-kinetics trajectories and spatial-temporal heterogeneity across surveillance sites.

值得关注的不是模型自己要求人类复核这句漂亮话，而是复核链条本身：初审的两位数学家是 Anthropic 自己人，外部专家只是「短时间内看了一下」。自证清白式验证在纯数学里勉强够用（有 Lean 兜底），换到别的学科就不成立。

这里其实留了口子：高风险改动仍要逐行读。可惜没给判据——哪些属于高风险、按什么分级，得团队自己定。缺了这层分级标准，"还有别的办法"在实践中很容易退化成默认全部走轻量路径。

锋芒藏在"从来"两个字：人工逐行审查的失效远早于 AI，只是 AI 把问题暴露出来了。但这句最容易被滑坡引用——从"不必逐行看"到"干脆不看"只差一步，后者就是同期在讨论的 meat proxy。区别在于是否用别的手段补上了验证。

门槛被从"看懂代码"挪到了"下清指令 + 设计验证手段"。隐含前提是验证必须可执行——测试、日志、可复现步骤，而不是凭手感扫一遍。对本来就没有测试基建的团队，这个转变不会提效，反而会把原有的质量漏洞放大一个数量级。

Reviewer #1 (Public review):

Summary:

The authors hypothesized that "RVM neurons operate across multiple temporal scales, integrating fast responses associated with reflex-linked control with slower fluctuations reflecting ongoing network or state-dependent modulation". The hypothesis was tested with the established ON/OFF-cell model and probabilistic modeling. The study is conceptually interesting and methodologically sophisticated. The findings build toward the conclusion that pain-control circuits operate across multiple timescales.

Strengths:

The use of Bayesian regression and Gaussian process modeling to quantify and characterize recovery dynamics and ongoing oscillatory activity.

The authors show that slow rhythmic activity appears preferentially in ON- and OFF-cells but not in NEUTRAL-cells, suggesting that the oscillations are related to pain-modulatory circuitry rather than being a generic feature of all recorded neurons.

The observation that some oscillatory activity is coherent with autonomic measures aligns with broader views of the RVM as a hub integrating nociceptive and homeostatic regulation.

Some pitfalls are appreciated and discussed by the authors, including the functional significance of slow fluctuations, the influence of anesthetics on global brain-state dynamics, the molecular profiles of the studied ON- and OFF-cells, and the heart rate as a covarying signal of RVM neuronal activity.

Weaknesses:

A general weakness is that the work is mostly descriptive and relies on anesthetized preparations. Whether the observed rhythms occur in awake animals and are linked to fluctuations in pain behavior needs to be confirmed in future studies.

The study measures limited autonomic variables. The causal relationship between "slow fluctuations" and "ongoing physiological state" is unclear and overstated, since the data presented appear correlational.

ON- and OFF-cells in the RVM are identified by their responses correlated with reflexive activity. The significance of the observed oscillations in spontaneous pain conditions is unclear.

It is uncertain whether the observed rhythms truly reflect intrinsic RVM organization rather than anesthesia-dependent phenomena; the authors appreciated this pitfall, though.

The Gaussian process analysis suggests predictability and quasi-periodicity, but predictability alone does not necessarily imply a true biological oscillator.

Conclusion:

The results support the authors' hypothesis. The findings provide a compelling conceptual message about the multiscale organization and dynamics of descending pain-control circuits and encourage further studies on the topic.

Reviewer #2 (Public review):

Using electrophysiological recordings in a well-characterized animal model of acute pain, and analytical and modeling methods, the authors show that descending pain-modulatory neurons in the rostral ventromedial medulla (RVM) operate across various timescales. They have both rapid multi-phase responses to noxious stimuli that unfold over tens of seconds, with distinct fast and slow recovery dynamics. Additionally, they generate slow quasi-periodic oscillations with approximately 5-minute periods during ongoing activity. These oscillations are statistically predictable and cell-type specific, demonstrating that descending pain control is organized through structured temporal dynamics that encompass immediate stimulus-evoked responses and slower fluctuations associated with physiological state.

A novel discovery is a ~5-minute quasi-periodic oscillation in ongoing ON- and OFF-cell activity. This oscillation, along with its coherence with heart rate, forms the basis for the claim that descending pain circuits exhibit intrinsic multi-timescale organization. However, it's crucial to demonstrate that this periodicity is independent of external experimental cycles such as methohexital infusion pharmacokinetics, servo-controlled temperature regulation, or slow autonomic feedback loops, all of which operate on similar timescales. For instance, the 300-second period closely matches typical drug infusion cycling and thermoregulatory feedback intervals. Therefore, heart-rate coherence peaks at multiples of this period could equally reflect a shared external driver rather than intrinsic RVM organization. Although the absence of this cyclic structure in Neutral cells argues against this possibility, the authors might want to explicitly discuss this potential confound.

The findings are important and novel in that they characterize an intriguing structure in the activity of ON and OFF neurons in the RVM. However, in the absence of a causal manipulation causality can only be inferred. That there is no phase-dependence of withdrawal latency argues against a causal role. The author are encouraged to qualify their conclusions (and their title) accordingly.

Because anesthesia can affect global dynamics, this might affect the oscillations reported. Without awake validation, it remains uncertain whether these rhythms reflect an intrinsic property or an anesthesia-induced regime. Again, the absence of oscillations in Neutral cells argues against this possibility, but it is still possible that ON/OFF cells are embedded in different circuits that are affected differently by anesthesia.

Analyses of many of the ON-cells had longer training windows (>1 sec) compared to those for the NEUTRAL cells. Could this have reduced the ability to fit and validate periodicity for the latter cell type?

Reviewer #3 (Public review):

Summary:

In this manuscript by Ashworth and colleagues, the authors investigate the temporal dynamics of the rostral ventromedial medulla (RVM), a key output node in a major descending pain-modulation circuit. Using data from extrasellar single-unit recordings of RVM ON, OFF, and NEUTRAL cells in lightly anesthetized rats, the authors' computational modeling yielded two major findings: (1) heat-evoked ON burst and OFF pause, followed by exponential recovery components in10s of seconds; and (2) ON and OFF cells exhibit periodic fluctuations in ~5-minute cycles that are statistically predictable.

Strengths:

The manuscript's concept is innovative, offering the first quantitative analysis of multi-timescale dynamics in physiologically characterized RVM pain-modulating neurons. This advances a field that has mostly depended on qualitative or single-timescale descriptions. The authors use contemporary Gaussian process and probabilistic models to capture statistically predictable slow dynamics. The study is further strengthened by identifying ON-, OFF-, and NEUTRAL-type cells using well-established criteria grounded in decades of RVM research. The combination of rapid reflex-related responses and slower ongoing rhythms supports a dual-timescale framework, providing a more integrated understanding of how these neurons may regulate reflex activity and state-dependent processes.

Weaknesses:

Several limitations are noted. Incomplete characterization of light anesthesia during recording sessions, such as methohexital stability and clear criteria for identifying "lightly anesthetized" states. While the NEUTRAL cell control is helpful, it does not fully address concerns about circuit specificity or systemic confounds. The findings are male-dominant, which may limit their generalizability. The synchrony between ON and OFF cells was suggested but not directly tested. The heart rate coherence with ON, OFF, and NEUTRAL cell activity results is intriguing but does not fully clarify how these neurons influence heart rate, particularly within the "lightly anesthetized" model.

Reviewer #1 (Public review):

Summary:

Liao et al. present SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), a method for reconstructing spatial organization from diffusion-defined DNA barcode interactions without the use of optical imaging. In SCOPE, hydrogel beads bearing unique DNA barcodes contain both "sender" and "receiver" oligonucleotides. Upon enzymatic release, sender oligos diffuse locally and hybridize to receiver oligos on neighboring beads, forming chimeric molecules that encode spatial proximity. Sequencing these products yields an interaction matrix, which is then used to reconstruct a spatial coordinate map.

The authors demonstrate reconstruction of synthetic two-dimensional shapes, a large multicolor Snellen eye chart, and the interior surface of three-dimensional molds. The work expands the conceptual and experimental landscape of optics-free spatial sequencing.

Strengths:

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

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

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

The revised manuscript also strengthens the interpretation of these three-dimensional experiments by providing additional evidence that the limited recovery of interior regions primarily reflects technical constraints associated with molecular recovery from the hydrogel beads on the interior rather than an inherent limitation of the SCOPE framework.

Weaknesses:

Although the method is discussed in the context of spatial genomics and potential tissue applications, it is currently demonstrated only on engineered two-dimensional bead arrays and three-dimensional shapes fabricated in molds. The authors appropriately acknowledge that additional work will be required to establish performance in heterogeneous biological tissues, where diffusion and molecular recovery are likely to be more complex.

The revised manuscript substantially clarifies the limitations of the current three-dimensional implementation by providing additional discussion and control experiments supporting the interpretation that reduced recovery of interior beads is primarily a technical limitation of the present protocol. Although limited volumetric sampling remains a current constraint of the method, the authors appropriately discuss applications in which surface-resolved reconstruction may still be informative.

The computational reconstruction workflow is now described in greater detail, including automated parameter selection, fixed versus optimized hyperparameters, and explicit discussion of the manual flattening step required for the largest Snellen reconstruction. The revised manuscript also explains that many anticipated tissue-section applications present a more constrained reconstruction problem than the intentionally challenging proof-of-concept demonstrations presented here, providing additional context for the expected performance of the method in future biological applications.

Reviewer #1 (Public review):

Summary:

This study by Akhtar et al. aims to investigate the link between systemic metabolism and respiratory demands, and how sleep and circadian clock regulate metabolic states and respiratory dynamics. The authors leverage genetic mutants that are defective in sleep and circadian behavior in combination with indirect respirometry and steady-state LC-MS-based metabolomics to address this question in the Drosophila model.

First, the authors performed respirometry (on groups of 25 flies) to measure oxygen consumption (VO2) and carbon dioxide production (VCO2) to calculate the respiratory quotient (RQ) across the 24-hour day (12h:12h light-dark cycle) and assess metabolic fuel utilization. They observed that among all the genotypes tested, wild type (WT) flies and per0 flies in LD and WT flies in DD exhibit RQ >1. They concluded the >1 RQ is consistent with active lipogenesis. In contrast, the short-sleep mutants fumin (fmn) and sleepless (sss) showed significantly different RQ; the fmn exhibits a slight reduction in RQ values, suggesting increased reliance on carbohydrate metabolism, while sss exhibit even lower RQ (0.94), consistent with a shift toward lipid and protein catabolism.

The authors then proceeded to bin these measurements in 12-hour partitions, ZT0-12 and ZT12-24, to assess diurnal differences in average values of VO2, VCO2, and RQ. They observed significant day-night differences in metabolic rates in WT-LD flies, with higher rates during the day. The diurnal differences remain in the short-sleep mutants, but the overall metabolic rates are higher. WT-DD flies exhibit the lowest respiratory activity although the day-night differences remain in free-running conditions. Finally, per01 mutants exhibit no significant change in day-night respiratory rates, suggesting that a functional circadian clock is necessary for diurnal differences in metabolic rates.

They then performed finer resolution 24-hours rhythmic analysis (RAIN and JTK) to determine if VO2, VCO2, and RQ exhibit 24-hour rhythmic and if there are genotype-specific differences. Based on their criteria, VCO2 is rhythmic in all conditions tested while VO2 is rhythmic in all conditions except in fmn-LD. Finally, RQ is rhythmic in all 3 mutants but not in WT-LD and WT-DD. Peak phases for the rhythms were deduced using JTK lag values.

The authors proceeded to leverage a previously published steady-state metabolite datasets to investigate potential association of RQ with metabolite profiles. Spearman correlation was performed to identify metabolites that exhibit coupling to respiratory output. Positive and negative lag analysis were subsequently performed to further characterize these associations based on the timing of the metabolite peak changes relative to RQ fluctuations. The authors suggest that a positive lag indicates that metabolite changes occur after shifts in RQ, and a negative lag signifies that metabolite changes precede RQ changes. To visualize metabolic pathways that exhibit these temporal relationships, clustered heatmap and enrichment analysis were performed. Through these analyses, they concluded that both sleep and circadian systems are essential for aligning metabolic substrate selection with energy demands, and different metabolic pathways are misregulated in the different mutants with sleep and circadian defects.

Strength:

The research questions this study explore are significant given metabolism and respiratory demand are central to animal biology. The experimental methods used, including the well characterized fly genetic mutants, the newly developed method for indirect calorimetry measurements, and LC-MS based metabolomics, are all appropriate. This study provides insights into the impact of sleep and circadian rhythm disruption on metabolism and respiratory demand and serves as a foundation for future mechanistic investigations.

Comments on revised version.

The authors have thoughtfully revised the manuscript. They have now provided clarifications regarding perceived conceptual flaws in the original version. They have also performed additional data analysis to support their conclusions and provide clarifications on statistical methods when appropriate. Overall, the revised manuscript is much improved and the conclusions are generally well supported by their results.

Reviewer #2 (Public review):

This is an innovative and technically strong study that integrates dual-gas respirometry with LC-MS metabolomics to examine how sleep and circadian disruption shape metabolism in Drosophila. The combination of continuous O₂/CO₂ measurements with high-temporal-resolution metabolite profiling is novel and provides fresh insight into how wild-type flies maintain anticipatory fuel alignment, while mutants shift to reactive or misaligned metabolism. The use of lag-shift correlation analysis is particularly clever, as it highlights temporal coordination rather than static associations. Together, the findings advance our understanding of how circadian clocks and sleep contribute to metabolic efficiency and redox balance.

However, there are several areas where the manuscript could be strengthened. The authors should acknowledge that their findings may be gene-specific. Because sleep deprivation was not performed, it remains uncertain whether the observed metabolic shifts generalize to sleep loss broadly or are restricted to the fmn and sss mutants. This concern also connects to the finding of metabolic misalignment under constant darkness despite an intact clock. The conclusion that external entrainment is essential for maintaining energy homeostasis in flies may not translate to mammals. It would help to reference supporting data for the finding and discuss differences across species. Ideally, complementary circadian (light-dark cycle disruption) or sleep deprivation (for several hours) experiments, or citation of comparable studies, would strengthen the generality of the findings. Figures 1-4 are straightforward and clear, but when the manuscript transitions to the metabolite-respiration correlations, there is little description of the metabolomics methods or datasets, which should be clarified. The Discussion is at times repetitive and could be tightened, with the main message (i.e., wild-type flies align metabolism in advance, while mutants do not) kept front and center. Terms such as "anticipatory" and "reactive" should be defined early and used consistently throughout.

Overall, this is a strong and novel contribution. With clarification of scope, refinement of presentation, and a more focused Discussion, the paper will make a significant impact.

Comments on revised version.

The authors have satisfactorily addressed my concerns in the revised manuscript

Reviewer #3 (Public review):

Summary:

The authors investigate how sleep loss and circadian disruption affect whole-organism metabolism in Drosophila melanogaster. They used chamber-based flow-through respirometry to measure oxygen consumption, carbon dioxide production, in wild-type flies and in mutants with impaired sleep or circadian function. These measurements were then integrated with a previously published metabolomics dataset to explore how respiratory dynamics align with metabolic pathways. The central claim is that wild-type flies display anticipatory coordination of metabolic processes with circadian time, while mutants exhibit reactive shifts in substrate use, redox imbalance, and signs of mitochondrial stress.

Strengths:

The study has several strengths. Continuous high-resolution respirometry in flies is challenging, and its application across multiple genotypes provides good comparative insight. The conceptual framework distinguishing anticipatory from reactive metabolic regulation is interesting. The translational framing helps place the work in a broader context of sleep, circadian biology, and metabolic health.

Weaknesses:

At the same time, the evidence supporting the conclusions is somewhat limited. The metabolomics data were not newly generated but repurposed from prior work, reducing novelty. The biological replication in the respirometry assays is low, with only a small number of chambers per genotype. Importantly, respiratory parameters in flies are strongly influenced by locomotor activity, yet no direct measurements of activity were included, making it difficult to separate intrinsic metabolic changes from behavioral differences in mutants. In addition, repeated claims of "mitochondrial stress" are not directly substantiated by assays of mitochondrial function. The study also excluded female flies entirely, despite well-documented sex differences in metabolism, which narrows the generality of the findings.

Reviewer #1 (Public review):

Strengths:

Thorough reanalysis of the experimental results obtained in previous studies, which led to the publication of the PNAS paper in 2016.

New experimental evidence to confirm that enzymes previously considered as participating in the ED, actually are not catalyzing the ED biochemical reactions, but are involved in other metabolic pathways. Also, the authors completely discarded the occurrence of the GDH/GK shunt in Synechocystis PCC 6803. Generally speaking, the manuscript is very clearly written, with a precise description of the previous findings, the mistakes which took place in the 2016 paper, and the strategies they have used to address those issues, in order to reach a thoroughly revised vision of the glucose metabolic pathways in Synechocystis PCC 6803. In this regard, the drawings shown in Figures 1 and 7 are very helpful for the reader to follow the story and understand the possible metabolic transformations depending on the working hypothesis.

Also, I commend the authors for openly describing previous mistakes. In this paper, they reassess past observations under the light of more recent findings, and to integrate the information in this manuscript. The scientific conclusions are solid and very interesting, and besides they use the opportunity to offer valuable advice to researchers. This is especially focused on the importance of careful biochemical characterization of enzymes, which should always be carried out when studying proteins which have been identified as a specific enzyme on the basis of sequence homology. In a similar way, they found that an insertional mutant was the cause for the absence of specific metabolites, which had been attributed to particularities of a metabolic pathway in that mutant, when it was actually due to a nucleotide insertion; given that currently, genome sequencing is an affordable technique, this kind of mistakes can now be easily prevented by confirming the correct generation of the mutant by DNA sequencing, as proposed by the authors in a recently published preprint (Theune et al, bioRxiv 10.64898/2026.04.08.717167).

Weaknesses:

The authors propose that EDA might be involved in the PEP-pyruvate-OAA node, or in the proline metabolism, but this requires further experimental work for clarification; what their results indicate clearly is that this enzyme is not actually catalyzing the transformation of KDPG to GAP, which is the second specific enzyme of the ED pathway. But the real physiological function in this cyanobacterium is still unconfirmed.

Another aspect which could be improved is that the recombinant expression of some genes was carried out in E. coli; even if this is a useful and valid research strategy, in studies like this (where there is a strong focus on the physiological function of enzymes in the original organism, Synechocystis PCC 6803), I think it would have been more appropriate to express the 6803 genes in another cyanobacterium easily amenable for genetic transformation and gene expression, which would produce the protein in a physiological environment more similar to another cyanobacterium (compared to E. coli, which is an heterotrophic bacterium). I am not sure this would change any of the obtained results, but certainly would confer additional robustness to the enzymatic results.

Comments on revised version.

The authors have provided satisfactory replies to all my suggestions and corrections, and I have no further changes to suggest.

Reviewer #2 (Public review):

Summary:

The study presents novel results on the presence of the Entner Doudoroff pathway in Synechocystis sp. PCC 6803. In contrast to an earlier study, compelling evidence is given that this strain lacks both an ED pathway and a glucose dehydrogenase/glucokinase bypass but contains a promiscuous aldolase, which also decarboxylates oxaloacetate and cleaves 2-keto-4-hydroxyglutarate (as it occurs in proline degradation). The study concludes with successfully reconciling data of different studies and with lessons learned from the previous misconception.

Strengths:

Solid biochemical data is presented to reconcile contradicting data of earlier studies and to serve as basis for disclosing possible functions of a promiscuous aldolase. Earlier misconceptions and lessons to be learned are well discussed.

Weaknesses:

The materials and methods section is rather lengthy, suffering from a lack of conciseness and repetitions, and nevertheless misses some specifications.

Comments on revised version.

The materials and methods section has been significantly improved. The revised manuscript is now recommended for publication as it is.

Reviewer #1 (Public review):

Summary:

The authors test specific but related hypotheses regarding anti-predator responses of wild marmoset groups to predator and human playback sounds triggered to play via a motion sensor on camera-trap devices. The differential responses they observe to human noises and natural predator sounds are interesting, but greater inferences are limited due to a lack of clarity in the methods and analyses.

Strengths:

The authors create an excellent experimental design using a customised ABR system for testing the behavioural responses of wild, social-living, tiny, arboreal primates: pygmy marmosets. Much of the work is described with great transparency, and figures and tables are helpful in facilitating this.

Weaknesses:

The current study requires improvement in three areas, in my opinion, to permit readers to better evaluate the validity and importance of these results.

(1) Improve the framing of the paper:

The current title and justification for this study appear to point to a lack of previous studies testing specific hypotheses (line 51/52: "ABRs have not been applied to hypothesis testing". I find this a rather strange argument to make, given that a quick read through of other ABR papers, cited by the authors (e.g., Kasper et al., 2025, Epperly et al., 2021), are testing predictions set by ecological theory in the cascading effects of predator-prey dynamics. To me, even if these are not explicitly stating "X hypothesis" in their paper, they still appear to be studies guided by implicit hypotheses. To say that previous work with ABR did not test hypotheses is presumptuous, in my opinion. The entire paper would be much better appreciated if the authors could reframe the study for its importance to arboreal mammal/ tropical ecology, anthropogenic effects, and so on. Similarly, the authors should avoid use of phrasing such as "this study is the first direct test of .... " (lines 59/60) and should emphasize the true significance of their work, beyond it being the 'first' of something.

Similarly, on line 87, "demonstrating that the ABR system can be used to generate data for hypothesis testing" should be removed, as firstly sufficient sample size for any study depends on a number of study-specific parameters, and the authors do not actually demonstrate this in my opinion, given that many of their models end up suffering from singular fit. This is due to a lack of sample size, and also because they do not actually do any type of power analysis or something similar to demonstrate that they actually assessed sample size. So again, my suggestion is to reframe the paper to focus on the behavioural ecology and conservation-related impacts rather than this emphasis on methodology.

(2) Methods:

The authors generally do a great job providing sufficient detail on the ABR system and how each experiment was designed. Still, there is room for improvement, as I was confused a number of times. I also would recommend that the authors include a limitations section somewhere which considers the drawbacks of their study, in particular the lack of individual identity for behavioural responses of marmosets (especially given that they used focals, it seems), the groups being in close vicinity of one another/potentially related (?), the specific stimuli used, etc.

Points of confusion for me included what the control was for Experiment 1. Line 93 - 70 videos without playbacks are used (Table 1), but it is not clear how these videos were selected, and it is not a suitable control comparison for assessing the difference in behaviour associated with playbacks (playbacks with control sounds are). At most, these videos will give basal rates of behaviour (like vocalizations, etc.), but then it is not clear why these '70 videos' and how they were chosen to avoid bias. So, for example, in line 105 the authors write that focals were more likely to flee when hearing playback stimuli than in these "control" videos, but this is not convincing. If there was no fleeing after playback of control sounds (cicadas, macaws) - i.e., the true control in this experiment - then this should be the comparison that is emphasized.

Can the authors also clarify how they considered/assessed the sound playback level (normally done in Decibels) and if they did not normalize the sound level across the playback stimuli, why not, and what potential effect this could have on the results (i.e., something else to consider for the limitations section)?

Something else not discussed is the rate of exposure to predator and human noise for these wild monkeys. Are these rates within normal range/expectation for these monkeys? Thinking here of the number of videos captured for each group presumably means exposure to a playback unless 'control' videos were videos where no playback sound was emitted (see question above re: control videos). There were a lot more unsuccessful videos than successful ones that the authors could use, so trying to understand the potential impacts of this (see question re: trial/video # below as well).

(3) Analysis:

A few things are unclear and need more explanation in the way the authors conducted their analyses, although they do well to detail all steps of their statistical methods, which was great.

For assessing model fit, it is not clear what exactly was assessed with the 'performance package' line 467, as the authors do not go on to provide us with any results of the performance/fit. Instead, they tell us that the models did not fit well, with no parameter provided (e.g. lines 481-488). I'm familiar with overdispersion as a parameter that is reported for Poisson models (that does not seem to be provided here). Or by looking at changes in model estimates if one datapoint (and/or one group) is removed after another (with replacement, so keeps sample size static). On line 468/469, it says that model fit was assessed via conditional R2; could the authors provide a citation for this practice, and then provide the R2 parameter for the other models that were used/included in the end?

Also, please standardize how the GLMM results are presented. There should be the estimate, SE, Z or t, then p value. (line 99, 137).

Given the high rates of exposure to playbacks, I think the authors should test trial # (or video #) for a potential habituation effect, with earlier captures more likely to draw stronger responses than later video captures for each group.

Reviewer #2 (Public review):

Summary:

The article describes an interesting methodology to test hypotheses about the impact of anthropogenic noise on a small arboreal primate, the pygmy marmoset. The authors used a motion-triggered combination of camera traps and speakers to play back control sounds, avian predator calls, and anthropogenic noise to test the risk-disturbance hypothesis and the distracted prey hypothesis. In addition, the authors implemented a technique that is usually used for larger mammals and has not been used before for smaller arboreal animals. The authors are careful in their interpretation of the results and do not favor one hypothesis over the other. The authors also elaborate extensively in their discussion on how to improve this kind of data collection in the future.

Strengths:

This study provides a method for rapid data collection while minimizing observer impact. The sample size is comparatively large for a wild animal in a reserve, given the overall observation time. The article also benefits from a solid analysis of the data.

Weaknesses:

Though the authors tested two contrasting hypotheses, the discussion would benefit from more detail on the ecological relevance of the observed behaviors.

Reviewer #1 (Public review):

Summary:

In this manuscript, Du et al. identify a putative URS (nucleotides −709 to −229) that contributes to CY/MMS-induced DDI2/3 transcription in the promoter of S. cerevisiae DDI2 through promoter mapping. They further showed that CY/MMS leads to histone loss, and that genetic depletion of histone triggers DDI2/3 expression in an Fzf1-dependent manner. Using MNase-seq, the authors demonstrate that CY treatment leads to nucleosome loss at the DDI2/3 promoter and coding sequences, and that this chromatin remodeling process requires Fzf1. Based on these results, the authors propose that Fzf1 promotes CY-induced DDI2/3 expression through two distinct mechanisms: as a transcription factor and as a regulator of nucleosome occupancy. This dual mode of regulation may contribute to the exceptionally high induction of DDI2/3 relative to other Fzf1 target genes in response to CY.

Strengths:

This manuscript identifies the URS region at the DDI2 promoter that regulates CY/MMS-induced DDI2 expression. In addition, the authors revealed an important role of nucleosome occupancy in regulating DDI2/3 transcription, proposing the intriguing dual regulation model of Fzf1. Overall, this work furthers our understanding of how Fzf1 mediates the increase of DDI2/3 expression in response to CY/MMS treatment.

Weaknesses:

Overall, the study is of interest, and the data are generally convincing; however, several conclusions would benefit from further experimental validation. Certain controls are necessary for several experiments to improve the strength of evidence. Several major points are listed below:

(1) For Figure 6A and Figure 7A, the authors concluded that there are 'joint effects' of CY treatment and histone depletion. However, it is unclear whether CY treatment acts dependently or independently of histone depletion. As shown in Figure 2, both CY and MMS can cause histone reduction. In addition, the depletion system in the RMY102 strain only depletes about half of the H3 (based on the western blots in Figure 5D, E). It would be necessary to test whether H3 abundance is further depleted in CY-treated RMY102 by Western blot.

(2) Proper controls are missing in Figure 6A and Figure 7A. The authors compare gene expression levels in RMY102 + histone depletion + CY/MMS treatment with RMY102 + non-histone depletion. There are two variables here: histone depletion and CY/MMS treatment. It would be more convincing to include RMY102 YPGal+ CY/MMS treatment (5-40 mM), so that the impact of histone depletion and CY/MMS treatment on Fzf1 target gene expression levels would be clearer.

(3) In lines 242-249 and 269-272, the authors compared RMY102 versus BY4741 to conclude that histone depletion affects dose dependency of CY/MMS treatment. However, RMY102 and BY4741 might have different responses to CY/MMS due to strain background differences. Thus, in line with point 2, showing the expression level curves for non-histone depleting RMY102 treated with different doses of CY/MMS would be necessary.

(4) Figure 4 shows that CY and MMS have differential impacts on cell growth, which is intriguing. However, the rest of the data did not provide further insights in regard to this observation. It might be helpful to speculate possible underlying mechanisms in the Discussion session.

Reviewer #2 (Public review):

Summary:

Previous work established that FZF1 is both necessary and sufficient for activation of FZF1 target genes through the CS2 sequence motif, which is present upstream of FZF1-responsive targets. This study extends that model by demonstrating that, in addition to direct binding of FZF1 to CS2 elements, FZF1 can also promote reduced nucleosome-mediated repression, thereby contributing an additional layer of transcriptional regulation.

The authors investigate why FZF1-dependent transcriptional responses exhibit different magnitudes despite FZF1 binding to CS2 elements with similar affinity. Using promoter constructs derived from the DDI2-3 gene, the authors identify a region upstream of the CS2 element that functions as a repressive regulatory element. Based on this observation and publicly available datasets, the authors propose that this repression may be mediated through nucleosome occupancy.

Strengths:

The authors demonstrate that the DDI2-3 promoter contains positioned nucleosomes and show that chemical stress results in decreased histone protein levels and reduced histone-associated transcripts. They further examine whether histone depletion alone is sufficient to activate the DDI2-3 response and find that reduced histone levels increase expression, although chemical treatment produces an additional increase that remains dependent on FZF1. These findings suggest that FZF1 contributes to reductions in nucleosome occupancy at DDI2-3 and SSU1, revealing a second, potentially independent mechanism by which FZF1 regulates transcriptional responses to chemical stress.

Overall, the authors provide strong evidence that nucleosome occupancy influences the magnitude of FZF1-mediated DDI2-3 responses to chemical stress. This work has important implications for understanding how transcriptional networks evolve to generate highly tuned responses by combining multiple regulatory mechanisms acting on shared molecular components.

Weaknesses:

However, several additional considerations should be addressed. While histone depletion may contribute to differential FZF1-mediated responses, alternative mechanisms may also influence the observed transcriptional differences. For example, YHB1 exhibits basal expression that is independent of FZF1, and SSU1 contains the CS1 regulatory element, which can promote increased expression independently of FZF1 responsiveness. Therefore, differences in promoter architecture and the presence of alternative regulatory sequences may also contribute to differential FZF1 responses and should be discussed.

Additionally, the authors should clarify whether nucleosome depletion is directly mediated by the FZF1 ZF5 domain or occurs indirectly as a consequence of RNA polymerase II (Pol II) recruitment. Although the data presented in Figure 9 are consistent with a direct interaction model, the current evidence does not fully exclude the possibility that Pol II recruitment contributes to subsequent nucleosome/histone depletion. Unless there is direct experimental evidence demonstrating that FZF1 ZF5 independently promotes nucleosome remodeling, this alternative mechanism should be acknowledged and considered in the discussion.

Reviewer #3 (Public review):

Summary:

In the manuscript titled "Dual regulation of chemical stress-induced DDI2/1 3 expression by a transcription factor Fzf1 and nucleosome in Saccharomyces cerevisiae" Du et al have discovered a dual role of Fzf1 in transcriptional control of DDI2/3 during cyanamide (CY) or MMS treatment. While previous literature established that Fzf1 regulates multiple targets (DDI2/3, SSU1, YHB1, and YNR064C) by binding the CS2 consensus sequence, it remained unclear why DDI2/3 uniquely undergoes a massive 1,000-fold induction under cyanamide (CY) stress, whereas the others show only a 20- to 40-fold induction. In this work, the authors showed that Fzf1 functions beyond standard transcriptional activation. Using MNase-seq and a series of promoter truncation mutants, the authors mapped Upstream Repressing Sequences (URS) in the DDI2/3 promoter that are heavily occupied by nucleosomes. The authors showed that Fzf1 is essential for chromatin remodelling and nucleosome eviction (specifically at the -2 nucleosome position) to de-repress the DDI2/DDI3 expression.

Strengths:

The two-tier mechanism of action of Fzf1 in controlling the DDI2/3 expression during CY/MMS stress is compelling and novel.

Weaknesses:

While the authors presented the in vivo MNase-seq data that show Fzf1 is necessary for nucleosome displacement, the current study lacks any in vitro mechanistic proof. As the authors acknowledge, it remains unknown whether Fzf1 directly displaces nucleosomes on its own (perhaps through unmapped post-translational modifications induced by chemical stress) or whether its ZF5 activation domain merely acts as a scaffold to recruit separate chromatin remodelling complexes.

To test nucleosome repression, the authors utilised extreme global interventions, such as deleting the SPT10 gene or halting de novo histone synthesis using a galactose-to-glucose medium shift in the RMY102 strain. While these methods effectively deplete histones and induce DDI2/3 up to 30- to 350-fold, completely depleting cellular histones causes massive, pleiotropic secondary effects across the entire genome, which can obscure specific regulatory relationships.

Reviewer #1 (Public review):

Summary:

In this manuscript, Tuñí-Domínguez et al. present a large-scale, cross-study meta-analysis of 2,356 cell-free RNA sequencing (cfRNA-Seq) samples. The authors aimed to systematically evaluate the impact of pre-analytical variables and library preparation protocols on biological interpretation. By harmonizing publicly available and internally generated datasets through a uniform bioinformatics pipeline, the study seeks to establish standard quality control (QC) metrics and provide evidence-based guidelines for protocol selection in cfRNA-Seq biomarker discovery.

Strengths:

A major strength of the study is the scale and breadth of the harmonized dataset. The use of a common computational pipeline reduces variation arising from differences in bioinformatic processing and enables more direct comparisons among published datasets. The authors examine multiple complementary dimensions of data quality rather than relying on a single sequencing metric. The inclusion of variance-partition analyses, healthy-control-only analyses, and analyses restricted to samples with low gDNA contamination strengthens the evaluation of technical heterogeneity. The public availability of the analysis code and processing configurations further increases the reproducibility and potential utility of this work. The study convincingly demonstrates that technical and pre-analytical factors are major sources of variation across existing plasma cfRNA-seq datasets.

Weaknesses:

Several central conclusions are broader than the current cross-study design can fully support. Protocol category is strongly associated with dataset, laboratory, sample source, and collection procedure, making intrinsic protocol effects difficult to separate from study-specific effects, particularly for categories represented by only one or a few studies. The conclusion that technical variation generally overwhelms biological variation also requires qualification because diverse diseases and cancer types are combined into broad phenotype categories, and many phenotypes are concentrated within individual datasets. The interpretation and additional value of the proposed NG80 and NP80/NG80 metrics require further support, especially given their variable behavior across datasets. Most importantly, the thresholds used in the proposed universal QC framework are not independently validated and are strongly influenced by library-preparation strategy. The current findings therefore support protocol-dependent comparisons and identification of technical trade-offs more strongly than they support a universal definition of library quality.

Major concerns:

(1) Protocol effects remain difficult to distinguish from study-specific effects.

The manuscript interprets Broad Protocol Category as a major determinant of transcriptomic variation. However, protocol, dataset, laboratory, sample source, and collection procedure are strongly interconnected. Although both Dataset and BPC are included in the variance-partition model, several protocol categories are represented by only a limited number of studies.

This is particularly problematic for WRO, which is represented by a single study. Its apparent characteristics therefore cannot be separated from Reggiardo-specific laboratory, cohort, provider, or sample-processing effects. The authors should clarify the stability of the variance-partition results and, where feasible, provide sensitivity analyses. Alternatively, conclusions based on protocol categories represented by one or very few studies should be explicitly presented as study-specific observations rather than broadly validated protocol properties.

(2) The interpretation and robustness of the proposed library-diversity metrics require further support.

The manuscript attributes the high NG80 values in the Block and Sun datasets to gDNA contamination. However, other datasets with similarly low FSR values, including Wang and Giráldez, do not show comparably high NG80 values. This suggests that gDNA contamination alone is insufficient to explain library diversity and that sequencing depth, fragment length, mapping behavior, or library construction may also contribute.

The NP80/NG80 ratio is conceptually reasonable, but its additional value beyond the RNA-biotype composition shown in Figure 3C is unclear, particularly given the large variability observed in WRR datasets. The authors should further examine the relationships among FSR, NG80, sequencing depth, and protocol characteristics and assess the stability of NP80/NG80. If additional validation is not feasible, the interpretation and generality of these metrics should be moderated.

(3) The conclusion that technical variation generally overwhelms biological variation requires qualification.

The manuscript provides convincing evidence that technical heterogeneity is a major source of variation in the combined cross-study dataset. However, the conclusion that donor phenotype contributes only negligible variation may be broader than the analysis supports.

Phenotypes are reduced to healthy, cancer, and non-cancer disease categories despite substantial biological heterogeneity, and many disease groups are concentrated within particular studies. Consequently, disease-specific biological variation may partly be assigned to the Dataset term. A similar issue affects the cellular-origin analysis, where collection center and phenotype are substantially associated in the Chen dataset, making their individual contributions difficult to distinguish.

Where sample sizes permit, the authors should examine more specific disease categories or perform within-dataset analyses. Otherwise, the conclusions should be narrowed to state that technical variation dominates the present heterogeneous cross-study aggregation, rather than implying that phenotype-associated cfRNA signals are generally negligible.

(4) The proposed universal QC framework is insufficiently justified and protocol-dependent.

Figure 6 defines high-quality libraries using NG80 >1,000 together with FSR >20% or FER >75%. However, the manuscript does not explain how these thresholds were selected or validate them against an independent measure of reproducibility, analytical performance, or biomarker utility.

These criteria are also strongly affected by library-preparation strategy. FER and FSR favor libraries enriched for exonic or spliced RNA, whereas the NG80 cutoff disadvantages WRR libraries containing abundant noncoding transcripts. This is difficult to reconcile with the recommendation of WRR for exploratory transcriptomic and microbial analyses.

The authors should justify the threshold selection and assess its sensitivity and protocol dependence. Ideally, the criteria should be validated against an independent performance endpoint. Otherwise, Figure 6 should be reframed as a descriptive comparison, and protocol- or application-specific guidance should replace a universal binary definition of library quality.

Reviewer #2 (Public review):

Summary:

This manuscript systematically evaluates the impact of experimental workflows on plasma cell-free transcriptome sequencing (cfRNA-seq) data. The authors integrate a large number of cfRNA sequencing datasets from multiple publicly available studies and establish a unified bioinformatics framework to systematically assess the effects of experimental workflows, genomic DNA contamination, library diversity, and preanalytical factors on cfRNA transcriptomic profiles.

Strengths:

This study addresses the technical heterogeneity that may hinder cfRNA biomarker discovery and clinical translation and is of substantial value.

Weaknesses:

The effects of disease phenotype, technical confounding, criteria for library quality, and conclusions regarding DNase treatment require further clarification and validation.

Major Points:

(1) The conclusion that donor phenotype explains only a small fraction of transcriptomic variation requires further support from within-study analyses.

The authors conclude from variance partitioning across all studies that phenotype explains only a small fraction of cfRNA transcriptomic variation. However, the included studies encompass different diseases, while phenotype is simplified into healthy, cancer, and non-cancer disease categories, and the overall transcriptomic variation is strongly influenced by study-specific and experimental workflow batch effects. Therefore, the cross-study pooled analysis may underestimate genuine disease-associated cfRNA differences within individual studies conducted under the same experimental workflow.

Recommendation: The authors are encouraged to perform within-study phenotype analyses in datasets that include both healthy controls and disease samples and have sufficient sample size, and to quantitatively estimate the proportion of transcriptomic variance explained by phenotype. For example, the Zhu dataset includes healthy controls and liver cancer samples, and the authors have already observed relatively clear phenotype-associated clustering between the two groups. The contribution of healthy-versus-liver-cancer phenotype to transcriptomic variation could therefore be quantified within this dataset. If similar results are obtained across multiple independent cohorts, the findings could then be summarized across studies. The authors should also note that a low contribution of phenotype to global transcriptomic variance does not necessarily imply that disease-associated cfRNA signals lack biological or clinical relevance.

(2) Comparison of the relative contributions of technical factors and disease phenotype may be affected by confounding.

Figure 1C shows that phenotype is strongly or even completely confounded with technical variables such as collection center and centrifugation protocol in some cohorts. Nevertheless, the variance partitioning analysis across all samples is used to conclude that technical factors are the primary sources of variation, whereas phenotype contributes little. In the presence of such confounding, technical effects and disease-associated biological effects may not be reliably estimated independently, and this conclusion therefore requires more direct validation.

Recommendation: The authors are encouraged to perform an independent within-study variance analysis in cohorts in which technical variables and phenotype are relatively balanced. For example, in the Moufarrej cohort, phenotype is essentially unconfounded with collection center/centrifugation protocol (Cramer's V = 0). Phenotype and relevant technical variables could be included simultaneously in a within-cohort model to quantify their respective contributions to cfRNA transcriptomic variation. If technical factors still explain a larger fraction of variance in such relatively unconfounded cohorts, this would provide stronger support for the central conclusion of the manuscript.

(3) The use of NG80 as a criterion for defining "high-quality libraries" requires further validation.

The authors use NG80 as a metric of library diversity and further apply NG80 > 1,000 as one criterion for defining high-quality libraries in Figure 6. However, because NG80 is based on gene counts, it may be affected by sequencing depth. In addition, gDNA contamination can artificially increase NG80, whereas genuinely abundant non-coding RNAs in WRR libraries can lower NG80. Therefore, a higher NG80 does not necessarily indicate better overall library quality, and the metric may reflect both technical quality and genuine RNA composition.

Recommendation: The authors are encouraged to re-evaluate NG80 after downsampling samples to a common number of mapped fragments and to examine the relationship between NG80 and sequencing depth. The rationale for the NG80 > 1,000 threshold should also be further justified, and the impact of alternative NG80 thresholds on high-quality library classification and the main conclusions should be assessed. Unless there is evidence that this threshold reliably predicts library reproducibility or biomarker-related information content, library diversity and overall library quality should be clearly distinguished, and NG80 should not be presented as a universal criterion for high-quality libraries.

(4) Conclusions regarding DNase treatment should be interpreted more cautiously.

The Toden study did not explicitly report DNase treatment. The manuscript infers that DNase digestion was performed based on the fact that this study originated from the same laboratory as other studies and used a similar workflow; this inference should not be treated as an established experimental fact. In addition, the authors state that double DNase treatment is the most effective approach among non-EB workflows, but this conclusion is mainly based on cross-study comparisons, in which DNase strategy varies together with laboratory, sample handling, and cohort-specific factors. The current evidence is therefore insufficient to establish that double DNase treatment itself is superior.

Recommendation: The authors are encouraged to label the DNase status of the Toden study as "not reported" or "inferred", unless confirmation can be obtained from the original authors. The conclusion that double DNase treatment is the most effective approach should also be tempered, with explicit acknowledgment that it requires direct parallel validation using the same samples under different DNase treatment strategies.

Reviewer #1 (Public review):

Summary:

In this study, the authors use patient-specific induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from six patients carrying three distinct pathogenic LMNA variants to investigate disease mechanisms underlying LMNA-associated dilated cardiomyopathy (DCM). The authors report shared abnormalities in nuclear morphology, electrophysiology, calcium handling, and contractility across all patient-derived lines and demonstrate that correction of representative LMNA variants by CRISPR/Cas9 rescues several of these phenotypes. They further develop a high-throughput phenotypic drug screening platform using calcium transient measurements and identify cyproheptadine as the only compound among 1,280 FDA-approved drugs that consistently improves calcium transient abnormalities across all patient-derived lines.

The study addresses an important clinical problem and establishes a technically sophisticated patient-derived screening platform. However, while the experimental work is generally well executed, several of the major biological and translational conclusions are not sufficiently supported by the presented data.

Strengths:

The major strength of this study is the development of a patient-derived functional screening platform using multiple LMNA mutations rather than focusing on a single pathogenic variant. The inclusion of six patient-derived iPSC lines representing three distinct mutations increases the generalizability of the observations and allows identification of disease features that appear reproducible across different genetic backgrounds.

The phenotypic characterization is comprehensive and includes nuclear morphology, transcriptomics, manual and automated electrophysiology, calcium imaging, and impedance-based contractility measurements. Importantly, CRISPR-mediated correction of representative LMNA variants provides convincing evidence that the observed cellular abnormalities are directly attributable to the pathogenic variants.

Finally, the implementation of an unbiased high-throughput drug screen using patient-derived cardiomyocytes represents a valuable technical advance that could facilitate therapeutic discovery in inherited cardiomyopathies.

Weaknesses:

The principal weakness of the manuscript is that the central conclusions substantially exceed what is directly demonstrated by the data.

The manuscript repeatedly concludes that dysregulated calcium handling represents a shared pathogenic mechanism underlying LMNA-associated cardiomyopathy. However, the presented experiments establish only that abnormal calcium handling is a shared cellular phenotype across the studied variants. The data do not distinguish whether calcium dysregulation is a primary disease mechanism or whether it is secondary to the numerous upstream abnormalities already known to result from LMNA dysfunction, including altered nuclear architecture, defective mechanotransduction, chromatin remodeling, and transcriptional dysregulation. This distinction is critical because the manuscript repeatedly interprets correction of calcium handling as correction of the underlying disease process without directly demonstrating this relationship.

A related concern is the interpretation of the drug screening results. The primary screen is entirely based on normalization of calcium transient parameters (CTD75, FWHM, and T75-25). Consequently, the screen identifies compounds capable of correcting calcium cycling rather than compounds that necessarily modify disease biology. Although cyproheptadine subsequently improves impedance-derived contractile parameters, it remains unknown whether treatment rescues other defining features of LMNA cardiomyopathy, including abnormal electrophysiology, nuclear defects, transcriptional alterations, or broader cellular stress responses. Thus, the conclusion that cyproheptadine represents a "novel treatment" for LMNA-associated cardiomyopathy is considerably stronger than the evidence presented.

The mechanistic studies are also insufficient to support the proposed mode of action. The manuscript proposes CHRM2 as the likely mediator of cyproheptadine activity primarily because it is the only appreciably expressed known target in the transcriptomic dataset. However, no functional experiments test this hypothesis. Without genetic or pharmacological interrogation of CHRM2, the proposed mechanism remains speculative. Likewise, alternative mechanisms of cyproheptadine action, including serotonergic signaling, histamine receptor antagonism, direct calcium channel modulation, or antioxidant effects, are not investigated.

Another conceptual weakness is that the manuscript promises to distinguish both shared and variant-specific disease mechanisms but ultimately focuses almost exclusively on shared phenotypes. The transcriptomic analyses remain largely descriptive and are not leveraged to identify mutation-specific biological pathways or explain differences among the three LMNA variants. Given the unique cohort assembled in this study, this represents a missed opportunity to generate broader biological insight into LMNA-associated disease.

The transcriptomic analyses themselves would also benefit from more rigorous interpretation. RNA from multiple independent differentiations was pooled prior to sequencing, limiting assessment of biological variability and reducing confidence in statistical inference. Similarly, many functional analyses emphasize the number of wells, recording sweeps, or individual cells while the number of independent biological differentiations is less prominently presented. Greater emphasis on biological replication would strengthen confidence in the robustness of the findings.

Finally, the translational implications of the work should be interpreted more cautiously. All therapeutic studies are performed in relatively immature two-dimensional iPSC-derived cardiomyocytes. No validation is presented in engineered heart tissues, multicellular cardiac organoids, animal models, or human tissue. The current evidence supports the conclusion that cyproheptadine is a promising in vitro phenotypic modifier rather than an established therapeutic candidate for LMNA-associated cardiomyopathy.

Overall, this study establishes a valuable patient-derived platform for investigating LMNA-associated cardiomyopathy and demonstrates the utility of functional high-throughput screening in identifying compounds that improve disease-associated cellular phenotypes. However, the manuscript currently overstates both the mechanistic significance of calcium dysregulation and the therapeutic implications of cyproheptadine. A more restrained interpretation of the findings together with additional mechanistic validation would substantially strengthen the impact of the work.

Reviewer #2 (Public review):

Summary:

The authors conducted a functional high-throughput drug screening using hiPSC-CMs derived from patients with LMNA-DCM. Cyproheptadine emerged as a therapeutic candidate.

Strengths:

The screen appears well designed.

Weaknesses:

The single candidate that emerged from the screen, cyproheptadine, raises issues with potency. In addition, validation studies that are both expected and necessary for a drug proposed as a novel therapeutic for human cardiomyopathy have not yet been performed. Rigor could be improved once the basic mechanistic and validation studies discussed below have been performed.

Reviewer #2 (Public review):

Summary:

This study addresses an important and timely question in colorectal cancer biology by systematically examining the effects of the common driver mutations APC, KRAS G12D, and TP53 in murine colorectal organoids, with particular emphasis on how the order of APC and TP53 acquisition influences tumor phenotype. These mutations are well known to be frequent, truncal, and often co-occurring in colorectal cancer. While it is increasingly appreciated that mutational order can shape tumor behavior, studies directly comparing the phenotypic consequences of alternative APC-TP53 mutation orders remain rare. This work therefore addresses a relevant and timely question.

Strengths:

A major strength of the study is its focus on previously unexplored biology, combined with the generation of multiple isogenic murine organoid models with controlled mutational sequences. The authors employ careful and robust quality control of the CRISPR-mediated alterations, and the inclusion of both in vitro and in vivo experiments strengthens the relevance of the work.

Weaknesses:

There are, however, several limitations that should be considered when interpreting the findings. First, KRAS G12D activation is used as the initiating alteration, whereas APC loss is generally believed to be the initiating event in most human colorectal cancers. Second, the analysis is restricted to comparing only two mutation orders (KAT versus KTA), which limits the breadth of conclusions that can be drawn about mutation ordering more generally. Finally, key RNA-sequencing and in vivo experiments rely on a limited number of isogenic lines, which constrains interpretability.

The study aimed to systematically investigate how the accumulation and sequence of driver mutations influence colorectal cancer initiation. The data provide intriguing evidence that the relative timing of APC and TP53 loss may impact tumor initiation and survival in a hostile microenvironment. However, given the limited number of biological replicates, these observations should be interpreted with caution and would benefit from further validation.

Reviewer #1 (Public review):

Summary:

The manuscript by Montenegro and colleagues reports a uniquely significant set of compelling results from a carefully designed study. The findings are fundamental and should substantially advance our understanding of whether prenatal exposure to high levels of alcohol produces neural changes that are precursors to the development of Alzheimer-like pathology in an animal model of Fetal Alcohol Spectrum Disorder (FASD). The quest was to test whether prenatal exposure to high-dose alcohol in the mouse would result in selective damage that would result in Alzheimer disease-like cellular disorder and mnemonic impairment. Both outcomes emerged and were exacerbated in relevant transgenic mice. The untoward effect on memory endured and even worsened with age.

Strengths:

The authors noted the importance of using a validated animal model to test their hypotheses related to AD-like outcomes because human postmortem data are unavailable and even in vivo data are limited to younger FASD cohorts. The authors further noted limitations, which also anticipate experiments that could chart the temporal course of the effect and windows of prenatal alcohol exposure that result in damage or resilience. In short, as the authors state on lines 435-7, "These data provide the first experimental evidence that developmental alcohol exposure impacts core proteolytic pathways central to AD/ADRD pathogenesis."

Weaknesses:

Addressing the following would clarify several points in an already well-written paper:

(1) It would be useful to have a timeline of the study, much like the one provided for the water maze protocol. On that timeline, please include the sample sizes examined, ages at exposure, and other pertinent procedures, indicating which animals remained alive for testing, etc.

(2) What were the attrition rates for each study group?

(3) What are the human age equivalents of the maternal mice?

(4) It would be helpful to see a graph of the BECs of each animal relative to the doses given. That would clarify how the alcohol exposure amount and timing are the same and where they are different for all exposed mice, given that the alcohol levels were somewhat different by group, as noted in the Methods. Were these BEC differences at all related to group differences in outcome measures or memory performance?

Reviewer #2 (Public review):

Summary:

In this study, the impact of prenatal alcohol (PAE) on amyloid precursor protein (APP) C-terminal fragments and notch intracellular domain (NICD) levels in adulthood is measured in 3xTg-AD mice.

Prenatal alcohol alters gamma secretase activity with development and aging. This could have implications for Alzheimer's disease risk in populations without inherited Alzheimer's risk genetics.

Strengths:

Strengths include the model, the use of orthogonal approaches, and the rigorous, high-quality data.

Weaknesses:

Some figures lack prenatal alcohol treatment in the 3xTg-AD mice.

Some overstatements should be tempered. For instance, one cannot conclude that the changes in CTFs are driving the changes in learning and memory (as suggested in the last line of the abstract) without a direct intervention testing this. For instance, though PAE caused a more robust learning deficit at 6 mo in WT, the impact on CTFs was less than it was at 3 mo. PAE did not significantly change CTFs or learning/memory in 3xTg-AD mice at 4 months, suggesting the genotype effect takes over at this point. The text should be adjusted to reflect this.

Conclusion:

In summary, this is a rigorous assessment of the long-term impacts of PAE on CTFs and learning/memory in adult WT and 3xTg-AD mice.

Reviewer #3 (Public review):

Summary:

The goal of this study was to test the hypothesis that prenatal alcohol exposure (PAE) can affect Alzheimer's disease (AD) pathogenesis using biochemical proxies, histology, and a behavioral paradigm sensitive to AD-related memory decline. Major strengths include the breadth of techniques used, consideration of different AD-related molecular markers, and the use of different ages as well as appropriate controls. The authors largely achieved their aims to show that PAE does affect amyloid precursor fragments (CTFs) and notch signaling very early on as well as long-term effects in adulthood, which may uncover a previously underappreciated mechanism that may contribute to AD-related neuropathology and behavioral outcomes during lifespan.

Strengths:

(1) Several techniques are used to address molecular, behavioral, and histological PAE-related changes.

(2) There is use of appropriate controls and an AD-relevant mouse model.

(3) Different ages are used to address age-related and long-term effects in AD and control mice.

(4) The novelty of results shows early changes in amyloid-related processes, affected by PAE.

Weaknesses:

(1) It is unclear as to whether there are sex differences, particularly in the adult cohort.

(2) More clarity is needed on sample size per cohort and whether mice that were used for anatomy and biochemical analyses were previously used for behavior. Including a table and noting any overlap would be useful.

(3) In many instances, two-way ANOVAs with treatment (PAE vs vehicle) and genotype as factors will be useful to report (e.g., Figure 1).

(4) In Figure 5 and line 253, it is stated that older mice have more severe deficits, but there are no direct statistical comparisons with younger AD mice.

(5 Lines 270-271 refer to mice as "presymptomatic", but these mice do have behavioral symptoms. Do the authors mean no neuropathology yet? Any data showing lack of robust neuropathology would be useful.

Reviewer #1 (Public review):

Vasilevskaya and Keller test different models of cortical function through the lens of predictive processing, a powerful framework for the brain to learn and predict the statistics of the world via generative internal models. The authors use a clever combination of behavioral perturbations in closed-loop and open-loop visuomotor virtual reality assays, a paradigm the Keller lab pioneered and used effectively in the past decade, in conjunction with two photon imaging of neuronal calcium responses and targeted optogenetic perturbations of activity. They specifically put to test proposed hierarchical vs. non-hierarchical circuit implementations of predictive processing by analyzing the logic of inter-lamina interactions (superficial vs. deep; L2/3 vs. L5/6).

The authors conclude that both versions of predictive processing architectures they analyze are likely invalid and instead formulate an alternative novel model of cortical function based on a recently developed machine learning algorithm for self-supervised learning (joint embeddings of predictive architectures, JEPA) and its further refinements. JEPA borrows elements from predictive processing engaging two encoder networks and training the output of one network to predict the output of the other. In their new model of cortical computations, prediction errors neurons in L2/3 compare the deep layers (L5/6) activity, which is taken as a teaching signal, to a local, L2/3 prediction of this latent representation.

Specifically, the authors build on their previous work and reports from other groups that different sets of L2/3 neurons compute positive prediction errors (fire when sensory stimuli appear unexpectedly with respect to the movements of the animal; e.g., grating onsets in the absence of locomotion) and respectively negative prediction errors (fire when sensory stimuli are absent, while the brain expected them to be present; e.g. mice locomote but visual flow is suddenly halted - visuomotor mismatches). These L2/3 positive and negative prediction error neurons exchange messages with neurons in the deeper cortical layers that, the authors propose, build an internal representation (R) of the sensory stimuli given the animals' movements.

In the hierarchical model, internal representation neurons (R) are supposed to act as a teaching signal for both types of prediction error neurons; the output of the positive prediction error neurons is assumed to suppress activity of R such that the error between the teaching signal and the prediction is minimized; similarly, in the non-hierarchical version, R serves as a prediction for the prediction error neurons, and in turn it receives excitatory drive from the positive prediction error neurons and negative input from the negative prediction error neurons.

The authors find that the functional impact of L5 neurons to L2/3 neurons is not compatible with the non-hierarchical architecture they and other groups proposed, but rather in accordance with the hierarchical model. At the same time, the functional impact of L2/3 neurons (positive vs. negative prediction error neurons) on L5 neurons (internal representation) appears not compatible with the hierarchical model, but rather in accordance with the non-hierarchical implementation.

They further hypothesize that L2/3 prediction error neurons don't use sensory input, but rather the L5 activity as a teaching signal, and test it using perturbations (halts) of optogenetic stimulation of L5 neurons coupled with locomotion (Fig.7).

All in all, the question is topical, and the new model addresses a decades-long quest to develop a unifying model of cortical function. The findings reported here transform our understanding of cortical computations, opening new exciting avenues for future investigation. The experimental design and execution are rigorous; the arguments are clearly laid out (in spite of ample potential for confusion given the numerous loops and sign flips). These include a discussion of why the non-hierarchical model proposed by the same group does not hold, as well as potential caveats in interpreting the results and novel testable proposed experiments emerging from the JEPA-like model.

Comments on revised version

I commend the authors for nicely provided answers and addressing my concerns and clarifying their points on the relationship of their findings to JEPA and current state of understanding.

In particular for Q5 -I meant if there is also a positive correlation between optomotor mismatch response and visuomotor mismatch response when looking only at neurons that the authors identify as PE-?<br /> The authors provided the answer on point.

Overall, I think this is a fundamental study and the strength of evidence for the claims is exceptional as an exemplary use of existing approaches.

Reviewer #2 (Public review):

This manuscript reveals functional connectivity of two different classed of cortical neurons that respond in opposite ways to mismatches between sensory and top-down inputs. These data are very valuable because different theories of information processing in the cortex make different predictions on the patterns of connectivity of these neurons. Therefore, these data strongly constrain possible theories of cortical processing.

Comments on revised version.

I thank the Authors for answering my questions and updating the manuscript.

Congratulations on this important work!

Reviewer #3 (Public review):

Vasilevskaya and Keller set out to experimentally distinguish between two variants of predictive processing: a hierarchical and a non-hierarchical variant. The hierarchical variant assumes a hierarchical organization in which internal representation neurons (believed to be a subset of layer 5 excitatory neurons) serve as a source of a teaching signal for local prediction error neurons as well as for the next higher level of the hierarchy, while simultaneously providing prediction signals to the preceding lower level. In contrast, the non-hierarchical variant posits that these layer 5 internal representation neurons provide local predictions to layer 2/3 prediction error neurons.

The interaction between internal representation neurons and prediction error neurons differs fundamentally between the two variants. In the hierarchical variant, internal representation neurons excite positive prediction error neurons and inhibit negative prediction error neurons, while at the same time being inhibited by positive prediction error neurons and excited by negative prediction error neurons. In the non-hierarchical variant, this pattern of connectivity is reversed.

This work is very exciting, timely, and carefully executed. The authors functionally, and later molecularly, identify layer 2/3 prediction error neurons in V1 and probe their interactions with genetically defined neuron types in cortical layers 5 and 6 using optogenetics. They demonstrate that the functional influence of putative prediction error neurons in layer 2/3 onto layer 5 is incompatible with the hierarchical variant, whereas the influence of layer 5 onto putative prediction error neurons in layer 2/3 is incompatible with the non-hierarchical variant. They then test an alternative hypothesis, in which layer 2/3 responses resemble prediction errors with respect to perturbations of artificial layer 5 activity patterns. To investigate this, they designed an experiment in which optogenetic activation of L5 IT neurons was closed-loop coupled to the mouse's locomotion speed in the absence of visual feedback, allowing them to probe the causal influence of L5 activity on layer 2/3 responses.

Finally, the authors hypothesize that their data are more consistent with a joint embedding predictive architecture (JEPA) and outline experimentally testable predictions arising from this framework.

While the work is overall convincing and provides important insights into the circuit-level implementation of predictive processing, I think the connection to JEPA networks would benefit from a more in-depth discussion of its relationship to recently proposed and implemented models. Below, I address the specific points raised by the authors (flanked by ' ... ' to make the author's statements stand out), in particular in relation to the model proposed by Nejad et al. (2025):

- 'The two proposals indeed share similarities in assuming that bottom-up input for both L2/3 and L5 arrives from thalamus, and that representations formed in L2/3 are used for predicting the activity of L5. However, there are a few important differences between the JEPA implementation proposal formulated here and the Nejad et al. model.

(1) There is no proposed mapping of computations in the Nejad et al. model onto different JEPA networks. We assume that the suggested mapping would be L4 and L5 as encoder networks, and L2/3 as a predictor network? In that case, it is different to our proposal, in which L2/3 is part of the encoder network.'

I think there is some confusion here. In Nejad et al., both L2/3 and L5 function as encoder networks. Each receives sensory input (with L2/3 receiving this input delayed via L4) and computes a latent representation of that input, denoted z_{L2/3} and z_{L5}, respectively. The prediction is obtained by comparing the output of L2/3 with L5 latent representations (via W_{L2/3->L5} * z_{L2/3}). In other words, L5 is the target in the learning objective.

Although, they did not explicitly state the mapping with JEPA, their model has the same fundamental property - predictive learning happens in the latent space. Also, this appears very similar to the roles assigned to L2/3 and L5 in your Figure 9B. The fact that you made this more explicit and the new data included, is in my view, a very interesting contribution. However, from an architectural perspective, it appears that your proposal and the model of Nejad et al. are conceptually very similar, and I do not see a substantial difference between the two. This should be made more clear in the Discussion.

- '2. Our proposal contains explicit prediction error neuron cell types within L2/3, while prediction errors in Nejad et al. are encoded in the gradients, and the layer origin of these signals is hypothesized to be L5 ('the learning-driving error signal originates in L5'). Hence, also the role of L5-L2/3 connection is distinct between the two proposals. In Nejad et al. this connection serves the role of error propagation and update for predictions in L2/3, while in our proposal this connection contains teaching signal (target representations) that are compared to predictions within L2/3. Similarly, the functional role of L2/3-L5 connection is also different, since in Nejad et al, it is supposed to carry predictions of L5 activity, whereas in our proposal we expect it to drive plasticity in L5 encoder.'

Indeed, in Nejad et al., the layer-dependent mismatch responses are modeled as gradients with respect to neuronal activity, and the model does not explicitly include prediction error neurons. However, this appears to be a modeling choice rather than a fundamental aspect of the proposal, and it does not preclude an implementation with explicit prediction error neurons. In fact, the authors explicitly acknowledge this possibility in the Discussion:

"The second approach would be to recast our model within a predictive coding framework... Predictive coding jointly optimizes both model parameters and neuronal activities, which could naturally lead to prediction errors observable in the activity of both L2/3 and L5 neurons. Note that these two views are not mutually exclusive."

While Nejad et al. hypothesize that the learning-driving error signals (that are distinct from their mismatch responses) originate in L5, the abstract loss function itself does not uniquely specify where the underlying comparison between the predicted representation (W_{L2/3 -> L5} z_{L2/3}) and the target representation (z_{L5}) must be implemented. The proposed biological implementation places this computation in L5, but from my understanding, the computational objective itself does not require this specific localization.

That said, I agree that your proposed model introduces a genuine difference. The functional roles assigned to the vertical projections are effectively reversed: in Nejad et al., the L2/3->L5 projection carries the prediction, whereas the L5->L2/3 projection conveys the error/gradient. In your architecture, by contrast, the L5->L2/3 projection carries the teaching signal (target). This is, in my view, a real and testable interpretational divergence that is worth stating clearly.

Therefore, I think the novelty lies less in the computational architecture itself and more in committing to a particular biological implementation-one that adds cell-type-specific detail to an implementation that Nejad et al. hypothesised as being compatible with their framework.

- '3. The difference outlined above also makes it evident that the two proposals should differ in how deep and superficial layers are expected to influence the activity of one another. Indeed, the proposal in Nejad et al. is based on the cortical column idea, and according to eq. 2 and 3 in the Methods, activity in L5 is a function of activity in L2/3, while activity in L2/3 is not a function of activity in L5. Our proposal is based on idea of layers forming parallel networks, where horizontal communication is the dominant mode of cortico-cortical interactions, and activity in deep layers serve as a teaching signal for L2/3. In our case, we expect the opposite - that activity in L2/3 depends on activity of L5, while activity of L5 is not immediately dependent on activity of L2/3 (only via plasticity route). This led us to propose one of direct tests for our framework - silencing L2/3 in a familiar setting should result in no immediate changes to L5 activity and behavior of the animal.'

My reading of Nejad et al. is consistent with your interpretation of the equations. Specifically, Eq. 3 makes L5 activity depend on L2/3 activity (albeit weakly, with a = 0.3), whereas Eq. 2 contains no L5 term, so L2/3 activity does not depend directly on L5 activity. In that model, the L5->L2/3 pathway carries the learning gradient rather than contributing to the activity dynamics. By contrast, in your proposed model, L2/3 activity depends on L5 activity, whereas L5 activity is not immediately dependent on L2/3 activity (except indirectly through learning/plasticity). So, you state that "silencing L2/3 in a familiar setting should result in no immediate changes in L5 activity.<br /> [...].

However, I am unsure how to reconcile this prediction with the results shown in Fig. 6. If I understand the figure correctly, optogenetic activation of Rrad-positive (positive prediction error) L2/3 neurons produces a small increase in L5 activity, whereas activation of Adamts2-positive (negative prediction error) L2/3 neurons produces a decrease in L5 activity. Although these experiments involve activation rather than silencing, they nevertheless suggest that perturbing L2/3 activity can have an immediate effect on L5 activity. Could you clarify how this is consistent with the proposed model? In other words, what aspect of the proposed circuitry makes activation effective while silencing is predicted to have no immediate consequence?

For comparison, Nejad et al. performed a related perturbation analysis in Fig. S15 by scaling the output of L2/3 neurons exhibiting positive mismatch signals (defined through the activity gradients), which increased L5 activity, whereas scaling neurons with negative mismatch signals produced the opposite effect. I am not entirely sure how directly these simulations map onto the experiments shown in your Fig. 6, since the Nejad simulations were performed during mismatch conditions, if I have understood them correctly.

- '4. The proposal in Nejad et al. relies on input reconstruction or variance maximization within the L5 autoencoder network to avoid collapse. Instead, our proposal has no reconstruction objective.'

Nejad et al. only require two encoders (like in JEPA), how these two are learnt can be done in several ways. While Nejad et al. focus on using a reconstruction loss to learn the L5 target, they also show that it works equally well with non-reconstruction objectives. Therefore, I do not think the presence or absence of a reconstruction objective constitutes a fundamental distinction between the two proposals.

As your current work presents a conceptual architecture rather than a fully implemented learning algorithm (in a model), the mechanism that would prevent representational collapse has not yet been defined. From my understanding, every joint-embedding approach must address this issue, whether through reconstruction, variance/covariance regularization, stop-gradient or EMA mechanisms, or other approaches. Thus, the absence of a reconstruction objective (or another anti-collapse mechanism) is not, in itself, a distinguishing feature of the proposed architecture, but rather an as-yet unspecified design choice within the learning objective.

- '5. Lastly, there is time-delay between inputs to L5 and L2/3 that is proposed in Nejad et al., while this is not something inherent to our proposal.'

I agree that the temporal delay introduced by L4 is a key component of the Nejad et al. model and is currently absent from your proposal. However, I would expect temporal delays to emerge naturally in your framework as well, given the multisynaptic and highly parallel organization of cortical circuits. More generally, implementing predictive learning over time (as in JEPA) requires comparing representations at times t and t+1, which seems to require some form of temporal delay. How else would you suggest this is done?

In general, I think the manuscript would benefit from a clearer discussion of its relationship to the model proposed by Nejad et al. (perhaps following the discussion above), including both the shared conceptual claims and the aspects that genuinely differ between the two frameworks. At present, some of the claims are presented as novel, although at least some of these core ideas have already been proposed in Nejad et al.

For example, the authors state: "Thus, we propose that layer 2/3 functions to predict layer 5 activity, not sensory input per se, hence making predictions in the internal representation space, not input space." This appears to be exactly what Nejad et al. proposed as discussed above - in their model L2/3 predicts L5 activity (purely in the latent space), as they state in the abstract.

That said, there are some interesting differences, and I think the community would greatly benefit from making these clear, including the roles assigned to interlaminar connections and the interpretation of the signals carried by these pathways. These differences are interesting and potentially testable, and I think the manuscript would be strengthened by explicitly distinguishing which aspects are in line with the ideas already present in Nejad et al. and which aspects represent new contributions.

Reviewer #1 (Public review):

Summary:

This work asks the question of how different organelles and structures in the apicomplexan parasite Toxoplasma gondii are recycled and/or segregated to the daughter cells during cell replication. In particular, they consider an unusual cell structure called the residual body that links replicating cells during the intracellular infection stage of this parasite. The residual body has historically been considered a 'dumping ground' for unnecessary relics of the mother cell during division, but this notion is increasingly being revised. Indeed, cell replication in Toxoplasma is often misinterpreted as cell division (cytokinesis), but in fact, the cell replicates its organelles and structures to multiple 10s of copies in seemingly distinctly formed daughter cells, but cytokinesis is delayed for many such cycles and typically only occurs simultaneously with parasite egress from its host cell. The residual body is, in fact, the connection between these pre-cytokinetic replicated daughters, and effectively, this is still a single cell at this stage. The authors have previously shown that an actin network extends through the residual body between these daughter cells, and ER and mitochondria common to all cells are also linked through this structure. This study examining the fates of organelles during cell replication is timely for continuing our understanding of how this fascinating component of the cell participates in these processes. The authors use Halo-tags as their principal tool to track discrete populations of proteins, labelling their organelle locations, and this provides beautiful insight into these processes.

Strengths:

Using dyes conjugated to Halo tags this work elegantly tracks the fates of proteins synthesised by an original 'mother' cell over several replication cycles of pre-cytokinetic 'daughters'. Using this tool, they show that some organelles are made intact just once and that some of these can be subsequently sorted to the daughters (micronemes and rhoptries) while others are dismantled (IMC) and the daughters must make their own. A third set of organelles (largely synthesis, sorting and metabolic compartments) are divided and inherited, and new daughter-synthesised proteins are added to the preexisting maternal proteins in these structures. A role for actin and myosin is clearly demonstrated for micronemes and rhoptries, and this correlates with their relatively late inheritance into the developing daughters. Overall, this work gives clarity to the behaviours of several cell structures during replication and paves the way to better understanding the mechanisms that drive the differences between structures and the universality of these processes in other apicomplexan parasites. In particular, this study shows that the residue body is a region of the cell syncytium that organelles can be actively transported from. Therefore, it is a space that can actively contribute to the segregation of the late segregating micronemes and rhoptries.

Weaknesses:

In addressing the question of residual body participation in sorting of organelles, a clear definition of this structure is required including when and where it is delineated from the posterior of a mother cell during the formation of daughter structures. The authors' definition is as follows: 'The RB originates from the collapse of the maternal parasite during daughter cell budding and occupies the space previously occupied by the mother cell.' As such, a clear marker of the mother cell 'collapse' is required, but such a marker is not identified or used in the study to separate what might be considered an active part of the mother cell during early daughter formation, and the residual body. This might seem like moot a point, but it would help to give clarity to notions of recycling and 'reservoirs'. Mother cells retain their active invasion apparatus until very late in daughter formation and the need for micronemes and rhoptries to be released from this service late in the process might explain why they are only then trafficked to the cell posterior and then into the daughters. So, is this a distinct 'residual body' body function/reservoir or just a spatial constraint of this sequence of daughter formation? The authors elegantly show that MyoF is necessary for segregation of micronemes and rhoptries into daughters, and that MyoF depletion leads to accumulation of these organelles within the residual body. Moreover, restored expression of MyoF can then recover these organelles. This clearly demonstrates the activity of the residual body as part of the syncytium space that participates in the maintenance of the vacuole. But does it imply that this space necessarily handles all inherited micronemes and rhoptries as a 'trafficking hub'? My concern with the lack of a clear definition could provide some misinterpretation or overinterpretation of the contribution residual body.

A further, remarkable conclusion is that maternal micronemes are evenly segregated into daughters through an active process for 'balanced microneme inheritance'. The proportion of maternal micronemes is quantified up to the 8-cell stage and shown to be not significantly different between cells. But would this result be expected with random assortment at this stage? The authors model the probability of a 32-cell stage vacuole occurring with each daughter having within 0-3 maternal micronemes and this is considered unlikely. However, the authors neither present the modelling for the 8-cell stage or show quantification of 32-cell vacuoles. They do show some images of large vacuoles, but it is not possible to determine the distribution of maternal micronemes in these images. A regulated process of segregation would require a complex mechanism where some form of microneme counting would be required to create the proposed balance. It is, therefore, important to have strong data supporting such a hypothesis, but this is not currently presented.

Reviewer #2 (Public review):

Summary:

Toxoplasma gondii is an obligate intracellular parasite and the causative agent of toxoplasmosis. Parasite invasion of host cells, intracellular replication, and subsequent egress, which results in destruction of the infected cell, are central to pathogenicity. This manuscript focuses on understanding how maternal resources, specifically cellular organelles, are shared between daughter parasites during cell division. Many organelles are present as a single copy, making their division and inheritance essential for successful replication. In T. gondii, our understanding of how organelles are divided during cell division remains limited, and this study helps address this important knowledge gap.

Strengths:

The major strength of this study is the use of a Halo-based pulse-chase assay to characterize patterns of organelle inheritance and to monitor protein synthesis, turnover, and movement. This approach will be of considerable interest to the field. Using this method, the authors identify three major modes of organelle inheritance:

(1) Organelles present in multiple copies (such as micronemes and rhoptries) are partitioned between daughter parasites, with additional contributions from newly formed vesicles. Newly synthesized and pre-existing material remain as distinct populations within the cell.

(2) Single-copy organelles, such as the Golgi and apicoplast, are expanded through the incorporation of newly synthesized material before division.

(3) Cytoskeletal structures are synthesized de novo during each round of cell division.

These findings provide a more refined understanding of organelle inheritance and demonstrate that secretory organelles are not generated entirely de novo during each round of division, as was previously thought.

The paper places particular emphasis on the fate of maternal micronemes and rhoptries during division. The data show that (1) during division in wild-type cells, maternal micronemes and rhoptries are detectable in the residual body (RB); however, the majority of these organelles are localized within the parasite body, either at the apical or basal ends of the daughter parasites (Fig. 6). (2) In the absence of the myosin motor MyoF, micronemes and rhoptries accumulate in the residual body and are not properly trafficked to the daughter cells. Upon restoration of MyoF protein levels, these organelles redistribute to the daughter cells, although in an uneven manner.

Weaknesses:

The second half of the paper focuses on a more detailed characterization of microneme and rhoptry recycling. The authors strongly argue that the RB is a central hub for recycling micronemes and rhoptries; however, this conclusion is not fully supported by the data. For example, the authors state:

Line 227:<br /> "Notably, after endodyogeny was completed, M-MIC2 was redistributed from the RB to the apical tip of the daughter cells (Figure 6A, 11:30, 16:00 h), confirming that the RB serves as a temporary reservoir during microneme recycling (Periz et al., 2019)."

Line 231:<br /> "In approximately 90% of parasites undergoing replication, M-RON2 was integrated into daughter rhoptries prior to mother cell collapse and formation of the RB (Figure 6B, 4:15-4:30 h and 10:30-10:45 h). Like M-MIC2, M-RON2 was occasionally detected in the RB, though less prominently, suggesting more rapid, tightly regulated, or more efficient recycling due to their lower number."

Line 326:<br /> "However, we show that the RB temporarily stores maternal secretory organelles, such as micronemes and rhoptries, which are later redistributed to daughter cells in a MyoF-dependent manner (Figure 9B)."

Thus, the model that all microneme and rhoptry trafficking is RB-dependent is based primarily on the MyoF depletion phenotype (which results in RB accumulation) together with the observation that a relatively small amount of maternal microneme and rhoptry material is detectable in the RB of wild-type parasites. Although the authors' interpretation-that recycling is RB-dependent-is one possible explanation, alternative models are not discussed.<br /> For example, an alternative possibility is that the majority of micronemes and rhoptries are trafficked directly from the apical end of the mother parasite to the daughter cells without passing through the RB. In this scenario, only a subset of the organelles would enter the residual body, perhaps reflecting imperfect trafficking efficiency rather than an obligatory recycling step. Loss of MyoF would impair this trafficking pathway, resulting in the accumulation of secretory organelles within the RB. In other words, RB accumulation could be a consequence of MyoF depletion rather than evidence that all trafficking in wild-type parasites normally proceeds through the RB.

This alternative interpretation seems particularly relevant for the rhoptries, given that the authors themselves state that "M-RON2 was integrated into daughter rhoptries prior to mother cell collapse and formation of the RB."

Other comments:

Figure S10C<br /> To determine whether microneme degradation occurs in the RB, the authors quantified the fluorescence intensity of individual micronemes in control parasites and following auxin washout, showing that after redistribution the fluorescence intensity of individual vesicles is unchanged. However, this is not the appropriate analysis to address the question being asked. To conclude that micronemes are not degraded, the authors would need to quantify the total fluorescence intensity within the entire vacuole. For example, if half of the micronemes were degraded, the remaining micronemes would be expected to retain the same fluorescence intensity as those in the control parasites. Thus, unchanged fluorescence intensity of individual vesicles does not exclude the possibility that degradation has occurred.

Reviewer #3 (Public review):

Summary:

Knoerzer-Suckow et al. explore the mechanisms of organelle inheritance during endodyogeny in Toxoplasma gondii using an innovative dual-labeling approach to track the distribution of maternal organelles into daughter parasites. They can clearly distinguish between maternal and daughter-derived organelles using their dual-labeling Halo Tag approach. They reveal that different organelles are trafficked to daughter parasites in three broad patterns they have binned into groups. Their findings reveal a role for MyoF in the inheritance of micronemes and rhoptries, and notably, they observe that the inner membrane complex (IMC) is not recycled. Instead, the IMC undergoes a pronounced relocalization to the posterior of the maternal cell, where it is likely targeted for degradation.

Strength:

The data surrounding their MyoF knockdown experiments, IMC degradation, and trafficking of MIC2 after auxin washout are convincing. These data add to the knowledge of how organelle inheritance occurs in T. gondii, increasing the field's understanding of endodyogeny.

Weakness:

The inability to achieve higher temporal resolution due to phototoxicity precluded tracking of single micronemes, thus it remains possible that some micronemes follow a path similar to rhoptries and enter daughter cells before development of the residual body while others are recycled via the residual body.

Reviewer #2 (Public review):

The authors use a library of influenza A viruses from different strains, classified in lab-adapted, human, avian, and swine according to the animal from which they were isolated. They propose that the cow mammary gland serves as a mixing vessel for influenza A viruses. As a first approach, the authors assess susceptibility to infection across different cell types, including continuous and primary cell lines, bovine mammary cells, and mammary explants. All these cells support polymerase activity. Then, they analyzed changes in the bovine virus's viral fitness relative to an avian precursor. The authors use single-gene replacement to study whether and which RNP segments improve viral transcription. As part of this section, they also test IFN-specific antagonism by NS1 to assess the input of segment 8. Quantitative glycomic analysis was performed on the continuous bovine mammary cell line to demonstrate the presence of both a2,3 and a2,6, which is consistent with their observation that these cells can be co-infected with human and avian IAVs simultaneously. The main question, however, is: what is the glycome in the explants, or directly from tissues?

Overall, the manuscript is clearly written and provides new insights into the behaviour of the cattle isolate, now compared with a representative group of model or precursor HAs of different origins.

It would be great if a consistent nomenclature for the IAV strains could be used in the study. There is a mix of origin (Texas), animal from which the virus was isolated (mallard), or abbreviations that do not follow guidelines (IAV07). Are the USSR and Udorn not lab-adapted?

The experimental setup includes bovine mammary primary and continuous cells, as well as mammary explants. Some of the most significant differences, for example, in viral fitness studies and co-infection experiments, are observed in these explants. Perhaps there could be some additional focus on this observation. The implications in comparison to the results obtained in cultured cells could be described. How will the human and other HA subtype viruses fare in the explants?

Comments on revised version.

The authors have satisfactorily addressed the reviewers' comments.

Reviewer #3 (Public review):

Summary:

This excellent manuscript by Pinto, Sharp, and colleagues examines bovine tissue tropism for influenza viruses. They find that bovine flu, as well as other strains, have strong replication in mammary tissue. They also map the genetic changes to influenza that improve replication in bovine cells. Overall, the study is well designed and executed and the results are very timely.

Strengths:

(1) The experiments are well-controlled.

(2) The figures are well-constructed and easy to follow.

(3) The Methods and legends are detailed, with sufficient information.

Comment on revised version.

The authors have strengthened the manuscript by addressing comments from the three reviewers and I have no additional concerns/suggestions.

Reviewer #1 (Public review):

In this article, the authors set out to understand how evolutionary selection could introduce structural priors into neural networks that act as an inductive bias to accelerate learning. To do so, the authors propose an evolutionary conditioning (ED) algorithm and analyse its properties. I find the conceptual framing of the paper very interesting, and it addresses an important question. Since the paper adopts a mostly theoretical approach with no comparison to empirical biological data, I do have a couple of concerns regarding the setup of the computational framework/method, which I think is incomplete and limits how much we can conclude from the current results.

Major Concerns:

(1) The evolutionary conditioning (EC) algorithm proposed works as fine-tuning training, plus propagation of the best parent network's weights to the next generation with added Gaussian noise. Conceptually, I find this to be a fairly implausible mechanism for evolution, since it requires carrying the entire set of network weights at some precision. The authors themselves point out that direct weight transfer could be problematic in the introduction.

(2) More importantly, I would like the authors to conduct a baseline / null model comparison, in which the "evolution" process consists simply of training a neural network for a small number of iterations, adding Gaussian noise, and repeating. The resulting network at each step serves as the "generations", which is then trained further. The same learning speed and dynamics analyses should be applied to this null model. What I am getting at is that I am not sure to what extent the EC algorithm can be thought of as "running a few iterations of SGD" and chaining them together; how much work is the selection process in the GA actually doing?

(3) I am also unclear on why the EC algorithm does not improve throughout learning. Is this behavior the result of applying only a small number of fine-tuning steps? Presumably, with longer fine-tuning, the individual networks in the middle generations would also improve in performance?

(4) The EC algorithm applied to a single problem seems somewhat artificial in its setup. I would conceptualize evolution as learning a prior that conditions the network for a range of survival-related tasks. A more realistic setup would apply EC to an ensemble of tasks and then examine its impact on learning a specific task afterwards.

Reviewer #2 (Public review):

Summary:

This paper studies the interplay of evolutionary and in-lifetime learning. The authors develop a neural network model in which initial weight configurations evolve under selective pressure, while fitness is determined by the network's performance after a learning period. They show that such a network displays very distinct learning dynamics from those trained by either gradient descent or genetic algorithms alone: in particular, they do not learn the task, but they show evidence of learning-to-learn and unusual representational structure.

Strengths:

(1) The writing, figures, and presentation of ideas were clear.

(2) The question of how evolution on initial weights combines with learning from within-lifetime experience to structure a learning trajectory seems interesting.

(3) The analysis of existing experiments was well-done, highlighting that though these networks did not really learn, they show latent learning structure that makes the network perform better from less data.

(4) The interplay between Baldwin & learning dynamics seemed novel and interesting, presenting many attractive puzzles.

Weaknesses:

First, the authors point to an important distinction between performing evolutionary selection on the weights pre- or post- lifetime training, the latter of which is Lamarckian. They argue, correctly, that their model is interesting because it selects on the weight initialisation, unlike, for example, Shuvaev et al. However, my understanding is that a long line of papers beginning perhaps with Hinton & Nowlan also do non-Lamarckian evolution: Hinton & Nowlan have unspecified weights (denoted '?' in the paper) that can be inherited and then learnt. Is this not exactly inheritance of initial conditions (in this case, whether learnable or not)? This novelty is a primary motivation of the paper, whereas to me it seems it was already apparent in Hinton & Nowlan, and developed further in what seems to be a long line of uncited literature (see next paragraph). As such, this paper's conclusions seem poorly positioned within the existing state of knowledge/literature.

Second, the algorithm is framed as novel, but I think it is a rediscovery. This framework is very close to MAML, in which an initial weight configuration is optimised by gradient descent to be good after a few steps of fine-tuning (Finn et al., 2017). The authors' approach differs in using a genetic algorithm to perform the training of the initial weights, avoiding some of the computational complexities of MAML, especially after long fine-tuning. In this, the authors have, I think, rediscovered ES-MAML, MAML where the inner optimisation loop is gradient descent, while the outer is genetic (Song et al., 2020). Other similar work is "Meta-Learning by the Baldwin Effect" (Fernando et al., 2018).

Further, within Fernando et al. there is a rich literature review, almost none of which are cited by the authors. I point especially to Keesing & Stork, 1990, which appears to show a strong dependence of Baldwin-like improvements on the amount of data, something this paper also shows but explores less thoroughly.

To summarise my critique thus far: I think the literature already answers the main concern of the motivation (i.e. non-Lamarckian neural network evolution and learning), I think it has already discovered this particular algorithm, and I think past work has more thoroughly analysed behaviours similar to those presented in this paper. Without positioning correctly within this literature, the more general contribution of the paper is hard to establish. The true novelty of the authors' analysis seems to be the emphasis on Saxe et al.-like learning dynamics and its interplay with the Baldwin effect, but I am not certain of this without knowing the literature better.

Regarding experiments, there was an interesting effect where the EC networks didn't learn but did show latent learning (Figure 2, Figure 3), which sped up later learning (Figure 4). There were a few details I was surprised by on which I would appreciate clarity:

(1) The main result has basically no headline learning under EC. This will clearly be very dependent on parameters (e.g. if you add or remove enough training steps, the algorithm becomes SGD/GA, which both show learning). It seems like a natural analysis would examine this (e.g. a plot of final performance of EC after 4000 generations with different per-generation learning budgets).

(2) It is then shown that after 4000 generations EC can learn very quickly to perform the semantic task perfectly, at least within 200 generations (Figure 4C, and perhaps much sooner, Figure 4D, Figure 4F last panel; it was hard to say. This and the previous point seem somewhat inconsistent; was it just that Figure 3 used only 100 fine-tuning steps while the perfect-task-performing networks in Figure 4 required somewhere between 100 and 200? This seems to point to extreme parameter dependence. More broadly, how should I square this inconsistency/near-inconsistency?

(3) Figure 3k, and especially Figure 4f bottom right panel, seem to show networks that correctly separate all stimuli but cannot classify them. Should I understand this as networks learning to just push apart all pairs of datapoints without structure?

(4) If I understood the genetic algorithm correctly, only three individuals from each population seeded the next generation. This seems another important parameter to tune, since I think it is far lower than standard evolutionary work, but I am not sure.

Finally, the paper most interested me as a neural network learning puzzle: how can the network perform so badly, yet lead to such different post-fine-tuning results? The paper pointed to these as 'distinct' learning phenomena without explaining what was causing those differences. The only way I could square these results in my head was as above: that the 4000 generations pushed the initial representation to represent all datapoints differntly, effectively changing the learning problem gradient descent faces from one with a lot of structure (the semantic task) that leads to stepwise learning, to one in which it was basically linear regression on a set of well separated stimuli without the structure necessary for stepwise learning. Since the paper focuses so much on learning dynamics, and studies a task where such things can be precisely probed, it would have been nice to pin down exactly what was happening slightly more.

Reviewer #1 (Public review):

Summary:

This paper proposes a non-decision time (NDT)-informed approach to estimating time-varying decision thresholds in diffusion models of decision making. The manuscript motivates the method well, outlines the identifiability issues it is intended to address, and evaluates it using simulations and two empirical datasets. The aim is clear, the scope is deliberately focused, and the manuscript is well written. The core idea is interesting, technically grounded, and a meaningful contribution to ongoing work on collapsing thresholds.

Strengths:

The manuscript is logically structured and easy to follow. The emphasis on parameter recovery is appropriate and appreciated. The finding that the exponential NDT-informed function produces substantially better recovery than the hyperbolic form is useful, given the importance placed on identifiability earlier in the paper. The threshold visualisations are also helpful for interpreting what the models are doing. Overall, the work offers a well-defined, methodologically oriented contribution that will interest researchers working on time-varying thresholds.

Weaknesses / Areas for Clarification:

A few points would benefit from additional clarification following the previous revision:

Returning to one point from my original review. The applications to empirical data describe 6 models, including the FT-DDM with across-trial variability described as a benchmark. Yet the modelling results for both studies (Tables 2 and 3) show only 5 models, omitting the benchmark model. I acknowledge the comment about this in the response letter (no other FT-DDM models in the main text have across-trial variability). Nevertheless, for this to serve as a benchmark, the modelling results really should be reported in Tables 2 and 3 with the other 5 models, and the model goodness of fit figures currently shown in Appendix 7 incorporated into Figures 10 and 13 of the main text. Unfortunately, as it currently reads, it looks as if something is being obscured, which I don't believe is the intention. This won't change the primary conclusions, but it will increase transparency and ease of interpretation.

Many thanks for introducing Appendix 1 to the revised manuscript. Additional motivation for the central issue is always helpful. However, I'm not (yet) convinced the simulation study in Appendix 1 achieves the intended aim. The simulation study shows that NDT misspecification leads to poorer recovery of other CT parameters (i.e., if a generating value is perturbed and then held fixed at that perturbed value during estimation, recovery of the remaining parameters deteriorates). This demonstrates the consequences of fixing NDT incorrectly, but it does not seem to address the central claim of the manuscript: that NDT and the CT threshold parameters trade off when they are estimated simultaneously. I had expected the simulation study to estimate NDT alongside the remaining model parameters, mirroring the estimation procedure used in the empirical analyses. Such a simulation would directly test whether the two sets of parameters compensate for one another during estimation, and whether this results in poor recovery of the NDT parameters.

Reviewer #2 (Public review):

Summary:

The authors use simulations and empirical data fitting in order to demonstrate that informing a decision model using noisy single-trial estimates of an underlying fixed non-decision time can guide the model to more reliable parameter estimates, especially when the model has collapsing bounds.

Strengths:

The paper is well written and motivated, with clear depth of knowledge in the areas of neurophysiology of decision-making, sequential sampling models, and in particular, the phenomenon of collapsing decision bounds.

Two large-scale simulations are run to test parameter recovery, and two empirical datasets are fit and assessed; the fitting procedures themselves are state-of-the-art, and the study makes use of a very new and well-designed ERP decomposition algorithm that provides single-trial estimates of the duration of diffusion; the results provide inferences about the operation of decision bound collapse - all of this is impressive.

Weaknesses:

This is an interesting and promising idea, but a very important issue is not clear: it is an intuitive principle that information from an external empirical source can enhance the reliability of parameter estimates for a given model, but how can the overall BIC improve, unless it is in fact a different model?

Comment on revised version.

Thanks to the authors for their responses and inclusion of additional analyses and simulations. Thanks, in particular for clarifying a crucial detail, that the ndt-informed model actually assumes, like the uninformed model, that there is no variability in the non-decision time, and the idea is that the variable single-trial measurements of non-decision time are noisy estimates of an underlying, constant ndt. The revised paper itself has not made this clear - for example, throughout the Intro, there is no statement that the behavioural model assumes a trial-invariant ndt, and line 231 still calls tau the 'mean' non decision time, implying there is a distribution rather than an invariant single value in the behavioural model.

One implication of the above is that if the lognormal sigma is purely measurement noise that does not relate to actual variation in the underlying decision process generating behaviour, then the HMP latencies should not relate to behaviour, e.g. shorter latencies predicting shorter RT. I assume that even if the authors did find such a relationship, the principle still stands that a model with fixed ndt is more accurately fit when there are single-trial ndt estimates whose mean provides a constraint on that ndt value, than without such measurements. Still, given ndt variability is a core feature of many decision models, the authors could comment on whether the strategy would work in theory for a model with ndt variability (in the behavioural part), where the single trial estimates would then presumably reflect a mix of measurement noise and genuine ndt variability.

Another more important implication is that since it is in fact the same model being compared with and without the HMP data guiding the fixed ndt estimate, the reason the fit quality improves with HMP-information is not because it is a better model per se (it is the same model) but because without the HMP guidance, the search algorithm somehow gets lost and fails to find the 'optimal' parameter vector. That is, the parameter vector (just the parameters that relate to the behavioural model itself, not the HMP lognormally-distributed noise associated with VEP measurements) identified as optimal in the HMP-informed version of the model exists in the parameter space of the model without HMP information, but it is just not found? I raised this implication before, and it is still not clear whether it applies. I'm sorry to press on it, but it is critical for readers to understand why it is that neural information can improve overall model fit. Again, the enhancement of parameter recovery (like in Nunez 2025) makes sense, but the enhancement of the "model's fit to behavioural data" does not, without pointing to a deficiency in the search algorithm / fitting procedure.

The authors state in their replies that the onset of bound collapse is set at accumulation onset and imply that setting it instead at stimulus onset could "mathematically resolve the issue" but they don't do it because it is implausible. It is in fact not only plausible but clearly evidenced in empirical data - collapsing bounds are implemented neurally through urgency signals, and these can begin to dynamically build toward threshold well before, let alone at, stimulus onset. There is nothing bizarre about this - we can prepare movements without sensory input, and indeed even if choosing actions based on a sensory discrimination, motor preparation can launch well before the sensory evidence (e.g. Stanford, Salinas et al 2010) and this in effect collapses the bound on cumulative evidence for triggering action before any evidence actually arrives. So, Urgency/bound-collapse does not need to be triggered by a stimulus; it can start in anticipation of the stimulus. It seems critical, therefore, for the authors to clarify this point - does re-defining the onset of the collapse at stimulus onset remove the trade-off and render unnecessary the neurally-informed ndt estimation?

Related to this, it is still not clear how bias in the estimation of nondecision time would not be a problem. What if, for example, it is the end of the N2 rather than the peak of the N2 that marks accumulation onset, and/or there is an additional fixed motor time that adds to the N2-based marker to make the full nondecision time that applies in the underlying decision process. By definition (and I think this is essentially what the authors' new simulations verify), because of the trade-offs, this bias would simply be absorbed in shifted estimates of theta and lambda describing the bound collapse function. But wasn't the whole point of the exercise to more accurately estimate those parameters? The obvious implication is that the parameter-estimation accuracy of the ERP-informed model is determined by the accuracy with which the proposed ERP marker directly pinpoints the full nondecision time without bias, but this is not at all obvious in the paper as written. Importantly, in the example scenario I describe above where accumulation onsets when N2 ends, there may still be a perfect correlation of N2 peak latency with underlying ndt across trials - they could still be very strongly "linked" statistically, but we can't know what size offset might be involved.

Reviewer #3 (Public review):

The current paper addresses an important issue in evidence accumulation models: many modelers implement flat decision boundaries because the collapsing alternatives are hard to reliably estimate. Here, using simulations the authors demonstrate that parameter recovery can be drastically improved by providing the model with additional data (specifically, an EEG-informed estimate of non-decision time). Moreover, in two empirical datasets it is shown that those EEG-informed models provide a better fit to the data. The method seems sound and promising and might inform future work on the debate regarding flat vs collapsing choice boundaries. As an evidence-accumulation enthusiast, I am quite excited about this work, although for the more broader audience the immediate applicability of this approach seems limited because it does require EEG data (i.e. limiting widespread use of the method or e.g. answering questions about individual differences that require very large N).

Comments on revised version.

I thought the authors carefully addressed my concerns.

Reviewer #1 (Public review):

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

Summary:

Combining in vitro refolding, SEC-based assembly assays, peptide-library screening, MALDI-TOF, LC-MS/MS, structural analysis and immunopeptidomics, this manuscript investigates the peptide-binding principles of the promiscuous chicken MHC-I molecule BF2*21:01.

Strengths:

Although the peptide motif of BF2*21:01 is highly complex, this manuscript identified several principles, including a preference for 10-mer peptides, co-variation between P2 and Pc-2, effects of P3 and Pc-3, and a strong cellular preference for Leu at Pc. The results are important for avian MHC biology and poultry vaccine epitope prediction.

Reviewer #2 (Public review):

Summary:

The study presents an in-depth analysis of the peptide repertoire bound by a promiscuous chicken MHC molecule using mass spectrometry, x-ray crystallography and modelling. While the MHC can bind a very diverse set of peptides, the authors have found some new rules that govern peptide binding to this MHC that could help to build a predictive model to study the repertoire of pathogen-derived peptides.

Strengths:

The study uses a range of well performed experiment across multiple techniques and provides an in-depth analysis of the peptide repertoire, including peptide sequences, length, preferred residues, stability and MHC presentation.

Reviewer #1 (Public review):

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

Summary:

This paper presents a toolkit for the transformation of Blastocystis. The authors have screened a number of selectable agents, promoters and reporter genes and present their findings. This resource will be of immense use to those in Blastocystsis field, as well as those seeking to establish transformation tools in other species where such tools do not yet exist. Establishing new transformation tools is extremely challenging, and the authors have done an excellent job.

Strengths:

The authors have carried out a systematic screen of promoters, reporter genes and selectable agents. They have screened numerous for each, and all the data is presented. It is good to see when things did not work as well as when things did - so this data set is extremely useful indeed.

Comments on previous version.

The authors have revised their manuscript to clarify that molecular analyses have not yet occurred and have resolved the technical/publication issues with the figures. I look forward to seeing these tools used in future publications to answer important questions in Blastocystsis research.

Reviewer #3 (Public review):

Summary:

The primary objective of this study was to establish a practical and functional framework for propagation of stable transgenic cell lines of Blastocystis, a common animal gut microeukaryote. Although the work focused on Blastocystis ST7-B, a subtype with relatively low prevalence in humans, this choice is justified by its association with more frequent negative health effects. Beyond their relevance to the medical field, the methodological advances described here have the potential to also expand cell biology studies of this anaerobic organism, including its unusual mitochondria and redox metabolism.

Strengths:

Prior to this work, genetic tools for Blastocystis were very limited, relying on a single strong promoter-terminator combination. The authors successfully expanded the available promoter set across a range of expression strengths by testing two dozen variants in luciferase-based assays. Critically, they developed an integrated workflow from a modular transgenic construct design to an expanded inventory of molecular components (promoters, reporters), optimized DNA delivery, stepwise antibiotic resistance-mediated clonal selection and propagation, and to reporter validation. The evaluation of several anaerobiosis-compatible labeling strategies for live (and fixed) cell optical imaging will be particularly useful, with the SNAP-tag system appearing especially promising for Blastocystis.

Reviewer #1 (Public review):

This study by Vitar et al. probes the molecular identity and functional specialization of pH-sensing channels in cerebrospinal fluid-contacting neurons (CSFcNs). Combining patch-clamp electrophysiology, laser-based local acidification, immunohistochemistry, and confocal imaging, the authors propose that PKD2L1 channels localized to the apical protrusion (ApPr) function as the predominant dual-mode pH sensor in these cells.

The work establishes a compelling spatial-physiological link between channel localization and chemosensory behavior. The integration of optical and electrical approaches is technically strong, and the separation of phasic and sustained response modes offers a useful conceptual advance for understanding how CSF composition is monitored.

Comments on revised version:

I thank the authors for their extensive revisions and detailed responses to the reviewers' comments. The manuscript has been substantially improved, and most of the major concerns raised in the initial review have been adequately addressed. In particular, the additional analyses of PKD2L1 channel activity, the incorporation of physiologically relevant pH conditions, the clarification of ASIC involvement, and the expanded Discussion have significantly strengthened the study.

Major scientific concerns largely addressed:

Quantification of PKD2L1 channel activity<br /> The authors appropriately addressed my previous concerns regarding the use of Po as the sole measure of channel activity. The inclusion of additional parameters such as apparent Po, open time, nmax, holding current, and membrane charge provides a more robust assessment of PKD2L1 activity and substantially strengthens the conclusions.

Physiological relevance of pH modulation<br /> The inclusion of experiments at pH 6.5 and the additional analyses of holding current and resting membrane potential are valuable additions. These experiments considerably improve the physiological relevance of the study.

ASIC contribution<br /> The additional pharmacological experiments using ASIC blockers are helpful and support the conclusion that the photolysis-evoked response in the apical process is predominantly mediated by PKD2L1 channels.

Functional implications<br /> The expanded Discussion regarding Ca2+-dependent signaling, neurosecretion, and the potential physiological roles of CSFcNs considerably improves the manuscript.

Remaining concerns:<br /> Continued overstatement regarding "exclusive" localization and function:

Although the authors softened some statements in the revised manuscript, the term "exclusive" remains in several key locations, including the title.

For example:<br /> "PKD2L1 channels segregated to the apical compartment are the exclusive dual-mode pH sensor..."

The data clearly demonstrate strong enrichment of functional PKD2L1 channels in the apical process. However, the available evidence does not fully justify the term "exclusive," particularly because:

- PKD2L1 immunoreactivity is still detectable outside the apical process.<br /> - ASIC-mediated responses are present in CSFcNs.<br /> - The authors themselves use more appropriate terminology such as "predominantly located" in the Discussion.

Therefore, I recommend replacing "exclusive" with more conservative terminology such as:

- predominant<br /> - predominantly localized<br /> - enriched<br /> - functionally segregated

throughout the manuscript, including the title, Abstract, Introduction, Results, and Discussion.

Use of the term "tonic current"

The manuscript continues to use the term "PKD2L1 tonic current."

While the dibucaine-sensitive holding current is clearly present, the precise mechanism generating this current remains uncertain. Indeed, the authors themselves acknowledge in the Discussion that:

- an alternative conducting state may exist, or<br /> - unresolved brief channel openings may account for the current.

Therefore, the data support the existence of a sustained PKD2L1-associated current, but do not yet definitively establish a distinct tonic gating mode of the channel.

I therefore recommend replacing:

"tonic current"

with a more neutral expression such as:

- sustained current<br /> - PKD2L1-associated holding current<br /> - sustained PKD2L1-mediated current throughout the manuscript.

Continued use of "off-current" and "off-response":<br /> The revised manuscript has improved considerably in this regard. However, the terms "off-current" and "off-response" still remain in portions of the text and figure legends.

Because the manuscript itself demonstrates that the response reflects recovery from transient acidification rather than a separate OFF signaling mechanism, these terms remain potentially misleading.

I recommend replacing them with terminology such as:<br /> - photolysis-evoked PKD2L1 current<br /> - recovery current<br /> - proton-removal-induced current

throughout the manuscript, including figure legends.

Minor editorial corrections<br /> Figure 1Bd Please change: "po" to "Po" for consistency with standard channel physiology nomenclature.<br /> Figure 1Ca Please add units (mV) to the voltage labels shown on the left side of the traces.<br /> Figure 3E Please change: "Norm po" to "Norm Po".<br /> Figure 4Fb Please replace: "sec" with "s" to conform with SI unit conventions.

The authors have addressed the majority of my previous concerns and the manuscript has been substantially improved. The remaining issues are primarily related to terminology and overinterpretation rather than experimental deficiencies.