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  1. Oct 2026
    1. Reviewer #1 (Public review):

      Summary:

      This study investigates how temporal and spatial discrepancies between an action and its visual feedback are combined when people judge whether an observed movement corresponds to their own action. Using a virtual-reality reaching task and computational modeling, the authors show that both temporal and spatial discrepancies reduce self-attribution and that a Multiplicative model better explains judgments than the prespecified Minimum and Mean alternatives. Importantly, the main model comparison was confirmed in a preregistered replication.

      Strengths:

      The study addresses an important question about how multiple sources of sensorimotor information contribute to the sense of agency. The factorial manipulation of temporal and spatial discrepancies provides a clear experimental approach to this question, and the distinction between an exploratory study and a preregistered replication is a major strength. The behavioral effects were consistent across the two samples, and the replication provides confirmatory support for the advantage of the Multiplicative model among the prespecified candidate models.

      Weaknesses:

      The main limitation concerns the strength of the theoretical conclusions drawn from the model comparison. Although the Multiplicative model outperformed the prespecified Minimum and Mean models, this does not uniquely establish multiplication as the underlying computational operation, because other nonlinear integration rules were not tested. The evidence therefore more directly supports cross-domain integration than a specifically multiplicative computation.

      Similarly, the behavioral results do not by themselves establish that this integration occurs within a domain-general representation of agency. The binary judgment used in the task is also closely related to judging whether the seen and performed movements match, leaving open whether the observed integration reflects agency computation itself or a later decision process. These considerations do not undermine the main empirical findings, but they suggest that the broader computational and representational claims should be stated more cautiously.

    2. Reviewer #2 (Public review):

      Summary:

      Harduf et al. investigated the mechanisms underlying the sense of agency (SoA), the feeling of being in control of our actions. SoA is thought to arise from the comparison of action outcome predictions, emerging from forward models, with the actually observed outcomes. Current literature has not elucidated how cues from different forward models predicting different features of actions are combined to form a final agency judgement. The authors investigate this gap by introducing various combinations of spatial and temporal mismatches during a virtual reality (VR) based reaching task, and recording participants' agency judgements. Then, they fit different models of cue combination to behavioural data and compare them. The authors find compelling evidence that a model in which the effects of spatial and temporal incongruence are multiplied provides the best fit, outperforming models that do not combine spatial and temporal incongruences, and one model that combines them by averaging rather than multiplying. The authors argue that sense of agency emerges from the domain-general integration of different forward models.

      Strengths:

      The experiments are well designed, and model validation is compelling thanks to the comparison with an appropriate set of alternative models and replication of the results in a first exploratory cohort and in a pre-registered validation cohort. The writing is generally very clear, and the presentation of the results is exhaustive yet compact, making the paper very readable. I also commend the authors for empirically addressing whether and how the integration of different cues contribute to the sense of agency, an idea which has been more widely proposed conceptually than empirically investigated.

      Weaknesses:

      (1) The terminology is not always clearly defined, and the authors are not sufficiently rigorous in specifying what gap their study aims to address exactly, and to what extent it succeeds. The authors acknowledge that the idea that cue combination underlies sense of agency was already proposed, yet frame their contribution as showing that "integration" underlies sense of agency. However, it is unclear what the authors exactly mean by "integration". Also, it is not well specified what a domain-general representation is exactly, and how such a notion is not already included in the previously proposed cue combination theory. Overall, the authors seem to imply the main contribution of the paper is to elucidate both whether and how cue integration is performed. However, the most novel contribution of the study appears to concern the "how" rather than the "whether".

      (2) The authors should have discussed their findings in light of Bayesian inference, which has already been proposed as the basis of cue combination for sense of agency. The fact that a multiplication of the different cues fits the data better than an averaging makes it too tempting to think about Bayesian inference to ignore such idea. Indeed, if cues are assumed to provide statistically independent evidence about the probability of self-causation, then they should combine in a multiplicative way rather than by averaging. Even if the experimental design or pre-registration constraints preclude a formal Bayesian analysis, this point should be discussed and related to previous literature.

    3. Author response:

      We thank both reviewers for their constructive and thoughtful evaluations, and the editors for the positive assessment. We are grateful that the reviewers found the question important, the design clear, and the exploratory-replication structure a strength. Below we briefly outline the directions of our planned revision; a full point-by-point response to the public reviews and the recommendations will accompany our resubmission.

      The reviewers raise several points we find valuable and intend to address. First, we will clarify the scope of our central claim. We will distinguish our confirmatory result, that the Multiplicative model best accounts for the data among the preregistered candidates, from stronger statements about the underlying computation, making explicit that the evidence most directly supports cross-domain integration, with multiplication the best-supported rule among those tested. Second, we agree on the importance of situating our findings within Bayesian accounts of cue integration, and will add a discussion of how the multiplicative combination of statistically independent cues relates to probabilistic inference about self-causation and to prior cue-integration work on the sense of agency. Third, we will sharpen definitions of key terms such as ‘integration’ and ‘domain-general’ and the framing of our contribution, clarifying that it concerns how multi-feature prediction errors are combined. Finally, we will address and discuss the distinction between the integration we demonstrate and its interpretation as a domain-general representation of agency.

      We believe these revisions will strengthen the manuscript, and we thank the reviewers again for their helpful guidance.

    1. eLife Assessment

      This valuable study evaluates how different learning curricula in a complex planning task impact performance. The authors provide convincing evidence across eight behavioral studies that practicing increasingly difficult examples of a concept before moving to the next concept benefits immediate performance, despite participants having a preference for the reverse - learning new concepts before practicing. However, there is incomplete evidence for claims about relevance to real-world learning settings, as the curriculum effects did not persist across a delay or support transfer to new problem types. The paper will be of interest to researchers studying learning curricula, problem solving, planning, and education.

    2. Reviewer #1 (Public review):

      This work uses a compelling pairing of controlled task design with rich, naturalistic behavior. It is ambitious, with eight experiments, both correlative and causal demonstrations, delayed assessments, and generalization of the phenomena to a very different domain.

      The stated goal of the paper is to understand how learning different curricula "promote successful learning of complex tasks in human adults," which is a very broad scope. The evidence for generalization to a non-spatial task is compelling, but the structure of the two tasks is isomorphic, and it is still difficult to claim likely generalization to all complex tasks. For example, one could wonder to what extent demands of the task or online setting may have explicitly or implicitly encouraged participants to prefer trials that can be completed more quickly, biasing them towards choosing discovery transitions. Is it possible that they believed that the experiment might end more quickly if they chose trials that were faster to complete?

    3. Reviewer #2 (Public review):

      Summary:

      This paper investigates how curricula should be structured to support learning in complex tasks, focusing on the distinction between concept discovery (learning new components) and concept implementation (learning how to use them in increasingly complex sequences). The main finding is that participants tend to prioritize discovering new concepts, even though learning outcomes are better when they instead practice implementing known concepts in increasingly complex ways.

      Strengths:

      A major strength of the paper is the experimental paradigm. The grid-world task is a novel way of independently manipulating two dimensions of complexity that are often confounded: the number of concepts that need to be learned and the complexity with which those concepts must be deployed. Participants have to learn how the underlying tools operate and use them in different configurations, which provides an interesting setting for studying variability in how individuals structure their own learning. Additionally, the experiments progress from correlational analyses of self-directed learning, to randomized experimental comparisons of curricula, and finally to transfer and adaptive curriculum tests. Overall, this paper addresses an important question about whether the complexity of concept discovery versus implementation is better for structuring learning.

      Weaknesses:

      The main concern is that the paper's conclusions do not seem to fully match the evidence. The authors present the results as identifying a relatively general principle of complex learning, such that we should master a concept's implementation before introducing new concepts. There was no implementation-first advantage in the transfer phase, and when testing was delayed by one week, the curriculum difference was no longer detectable. Although the implementation-first curriculum improves immediate performance within the constraints of these experimental paradigms, it is less clear that it produces more generalizable learning. Additionally, it is not sufficiently clear how the "flow" measure was created, what "flow" means in the context of subjective task experience, or more broadly in everyday learning, and what inferential role the measure plays in the paper.

    4. Reviewer #3 (Public review):

      Summary:

      The authors report findings from a set of experiments that compare learning outcomes from two broad classes of training regimens. The first, concept discovery, emphasizes the identification of task components. The second, concept implementation, emphasizes the procedural deployment of task components. Learners tend to self-schedule the former, but task performance and performance on an immediate test are supported by the latter.

      Strengths:

      The authors address a novel research question with clear application value. The experiments that are reported in the manuscript are effectively designed to answer the research questions of interest. However, as I will expound on below, the authors ultimately misinterpret their findings.

      Weaknesses:

      This manuscript makes no mention of the distinction between learning and performance (e.g., Schmidt & Bjork, 1992; Soderstrom & Bjork, 2015). It is well-known in the human learning literature that valid tests of learning are ones that entail a separation of context from an initial acquisition phase. This separation may be temporal (i.e., a delayed test) or feature-based (i.e., a test of transfer on novel iterations of a task). In any case, contextual differentiation between test and acquisition is critical to provide an accurate assessment of learning. To the authors' credit, they do report results from both a delayed test and tests of transfer. However, they do not appropriately interpret the results of those tests, which demonstrate no impact of training schedule on learning. They instead rely on findings from immediate tests to support their claims that graduated concept implementation supports learning. In truth, concept implementation is neither preferred by learners nor beneficial to learning. These findings are accordingly quite limited in terms of generating insights for effective training conditions.

      Compounding my primary concern above, I will note that the authors report fitting Bayesian multilevel models (MLMs) to their data, yet rely extensively on analyses of participant means (e.g., permutation t-tests) when a proper MLM would suffice, and in fact would be more appropriate. Bayesian MLMs are an incredibly flexible and robust analytic tool (e.g., Fiechter, 2024), and I would have liked to have seen a more comprehensive approach in this manuscript.

    1. eLife Assessment

      The primary aim of this study is to measure the different dimensions of executive function in non-human primates and the classification of animals into distinct cognitive phenotypes. To achieve this, it uses a multi-task touchscreen battery for six rhesus macaques with repeated measurements to examine how performance across inhibitory control, shifting/learning, working memory, and updating covaries within and across individuals. Overall, this is an important finding with relevance for anyone interested in behavioural assessment of cognitive functions in non-human primates or dimensionality of executive functions. The strength of evidence is overall convincing, with some minor methodological weaknesses that should be addressable in future versions.

    2. Reviewer #1 (Public review):

      Summary:

      The authors aim to characterize how executive function (EF) subdomains (shifting/learning, inhibitory control, working memory maintenance, and working memory updating) are organized in NHPs. They train six macaques on four touchscreen tasks per session and derive a battery of behavioral metrics. They use reliability screening, correlation analyses, unsupervised clustering, and latent factor models to argue for separable EF factors and consistent cross-session cognitive phenotypes.

      Strengths:

      (1) Clear behavioral effects within tasks.

      (2) Battery approach with repeated sampling.

      (3) Scalable.

      Weaknesses:

      (1) Fixed task order may be a confound for between-task correlations. Curious about the potential impact of motivational changes, reward satiety, fatigue, etc., especially if later tasks have lower accuracy or higher variance?

      (2) The claim about the common EF factor is limited with only 6 animals.

      (3) The claim about phenotypes and clustering is limited as one cluster is a single animal (subject F).

      (4) Framing of the AS task as such is inappropriate, as it is a manual response task and is not interchangeable with classic oculomotor AS paradigms.

      The authors demonstrate with solid evidence that a multi-task battery can yield repeatable performance metrics across multiple EF-relevant tasks in macaques. The data support the idea that multiple separable dimensions can summarize performance in this cohort and that individuals differ consistently across sessions. However, the claims about the three cognitive phenotypes, four-factor EF architecture, and lack of common EF factor in NHPs are only partially supported given the fixed task order, small sample size, and ambiguity about whether factors reflect stable traits or covariance between sessions. These claims would be strengthened if the authors analyze effects of the fixed task order and time since session start.

    3. Reviewer #2 (Public review):

      Summary:

      The authors set out to characterize how four studied components of executive function (Inhibitory Control, Shifting, Updating, and Working Memory) are organized in rhesus monkeys, using a battery of four touchscreen tasks performed repeatedly across many sessions. They report that individual monkeys fall into distinct "cognitive phenotypes," and that statistical modeling of the task scores reveals four separable latent factors, which they interpret as evidence against a single shared "general" executive function factor in this species. The four latent factors (Shifting/Learning, Exogenous Inhibitory Control, Endogenous Inhibitory Control, and Working Memory Updating) are slightly different from those mentioned in the introduction, which is of interest to refine the definition and assessment of executive functions in non-human primates.

      Strengths:

      (1) The multi-task, repeated-measures design is a substantial undertaking and yields a large, richly characterized behavioral dataset from the same animals across many sessions. The use of 4 different cognitive tasks is uncommon in this field and a real asset to the study.

      (2) The overall approach of testing the reliability of each behavioral measure before including it in further analysis is a sound and appreciated practice, backed up with overall appropriate data analysis strategies.

      (3) The attempt to move beyond single-task designs, which dominate the non-human primate literature, toward a broader profiling of executive sub-functions is conceptually valuable and timely.

      Weaknesses:

      (1) The four factors introduced early in the paper are not the same four factors that emerge from the final statistical analysis, and this shift is not clearly flagged or explained to the reader, which is likely to cause confusion about what the paper is actually demonstrating. In addition, working memory vs. updating are not clearly defined or distinguished. The introduction and discussion sometimes treat "updating" and "working memory" as separate executive functions, but the literature the authors themselves cite (e.g., Friedman & Miyake, 2017) treats updating as a component nested within working memory rather than a fully independent dimension. This conceptual ambiguity carries through the paper and affects how the reported four-factor structure should be interpreted.

      (2) The grouping of very different types of behavioral measures (accuracy-based executive function metrics, reaction-time/processing-speed metrics, and reward/loss motivational-effect metrics) under a single "cognitive metric" label in Figure 2 is potentially misleading, since these are not conceptually equivalent, and the paper later treats them separately in supplementary analyses. This could give readers the impression that all reported metrics are equally informative tests of the four-domain hypothesis, when only a subset actually is.

      (3) The rationale for how motivational & valence effects (gains/losses) were analyzed is not well supported. Effects of loss versus gain may reflect risk attitudes or loss-aversion documented in the primate literature, rather than motivation per se, and this alternative explanation is not discussed.

      (4) The Discussion would benefit from acknowledging other factors that could influence performance and phenotype membership across sessions (such as age, sex, social rank, or stress) and from clarifying whether the identified cognitive phenotypes are expected to be stable traits or could shift with further training. Additionally, some interpretive statements appear in the Results section rather than the Discussion, which is inconsistent with how results and interpretation are normally separated, and could give undue weight to preliminary interpretations.

      (5) Concerns about the reliability of some response-time-based measures. In the Continuous Updating task, reaction times appear to drop unexpectedly at the highest difficulty levels for each animal, which may reflect animals disengaging from the task (e.g., random choice heuristic) rather than a genuine cognitive effect, and could also reflect thinner, unevenly sampled data at these levels rather than a true average. If so, the slope measures derived from these data may need to be recalculated after appropriately filtering the trials. Related to this, individual differences in hand-resting position in front of the screen are acknowledged by the authors as a source of noise in reaction times across tasks, but no correction or normalization for this is applied.

      (6) The Supplementary Methods referenced in the text (e.g., for the variance decomposition and additional factor analyses) do not appear to be included anywhere in the submitted materials, only Supplementary Results, making some analyses difficult to fully evaluate. There is no information about the subject age, sex, species, etc.

      Achievement of aims and impact:

      The authors largely achieve their stated aim of demonstrating that monkeys' executive function performance is reliable and structured in different dimensions rather than guided by a single factor, and the discovery of stable individual "cognitive phenotypes" across sessions is a genuinely interesting and useful contribution. However, the conceptual framework of the study needs to be better clarified (i.e. are working memory and updating truly separable?) and enriched with relevant inputs from the fields of comparative cognition and cognitive ethology in non-human primates and beyond. The conclusions about Response Times (RT) may change after proper or further analysis of these metrics; additional relevant dimensions may then be disentangled by considering the sources of variability in these RT data (i.e., motivation, emotional state). With clarification of these points, this work has the potential to become a useful reference and methodological resource for the field of comparative cognition and non-human primate cognitive neuroscience.

    1. eLife Assessment

      This valuable study uses chemogenetic inactivation techniques in behaving macaque monkeys to ask whether the locus coeruleus (LC), an area that is involved in the control of arousal, is also involved in regulating access to mental resources for a task that requires working memory. The data show that moderate locus coeruleus inhibition impairs foraging efficiency without affecting response times, total rewards accrued, or overall motivation. However, the evidence supporting the more specific claim that the locus coeruleus regulates cognitive and/or working memory function is currently incomplete, as the experimental design is confounded by elapsed time, lacks formal statistical interaction tests, and relies on behavioral measures that cannot cleanly separate effort allocation from working memory capacity or strategy shifts. These limitations may be ameliorated with further analyses.

    2. Reviewer #1 (Public review):

      Summary:

      This study has a very well-posed question and interesting proposed finding. It proposes that because LC activity has been shown to correlate with arousal, perhaps its activity is related to mobilizing resources for cognition and action. They use chemogenetics to inactivate LC neurons while monkeys perform a foraging task with homogeneous reward options and a working memory component. They use two task conditions, one where the grid of reward wells is visible and one where they are opaque, and the monkey has to open them to retrieve the food inside and remember where he has been. The authors claim that inactivation of LC had a selective effect on the opaque well task by increasing the number of revisits to the same wells without affecting total rewards, response times, or overall motivation, which they interpreted as reduced efficiency rather than disengagement. This is a particularly appealing outcome because it implies that LC suppression, which should lower arousal, does not cause disengagement, and rather affects working memory in some fashion. However, there are many issues with this study that make the abstract misleading, in a concerning way. The abstract and introduction are written well, but the data do not support the overall claims, and there are fundamental flaws that aren't easily resolvable.

      Strengths:

      The bigger question is very interesting and has broad appeal. The writing of the abstract and introduction sets the paper up clearly.

      Weaknesses:

      (1) The title and abstract claim effort; the paper repeatedly discusses working memory. It does not appear that any metric of effort was measured or manipulated in this study (no pupillometry, no effort-cost model, no offered choice between effort levels).

      (2) The authors cite their previous paper as strong evidence of chemogenetic manipulation of LC neurons. They use that paper to justify the dose they use of DCZ, the activator of the chemogenetic construct. That study recorded only 5 neurons at that dose, and 3 of them had a reduction in rate. The statistical significance of that result is questionable (given that I think the authors averaged % change across 3 neurons and it was 15%). The authors treat that as a robust result in multiple places, for example:

      "...induced a reliable 15% decrease in the firing of LC neurons..." The only significance test reported in the source paper for that data is t(5.8) = -3.6, p = 0.01, and that test compares low-dose against high-dose neurons. It establishes dose-dependence. It does not establish that 0.1 mg/kg produced a reliable decrease in firing.

      "...the dose of DCZ was chosen based on its known influence on LC activity in monkey Jf, i.e. a 15% decrease in firing lasting over 30 minutes...".

      This is misleading to readers with regard to the effectiveness of chemogenetics in LC and for creating a foundation for this paper.

      (3) The authors seem to undermine their main argument that the opaque task requires working memory at all, and I am not convinced the monkeys were not just operating like a random agent. The random agent model seems to sample uniformly with replacement and an arbitrary termination rule, which is a straw man argument for a model. It does not allow for testing memoryless vs. memory. A monkey executing a systematic scan with short, coherent moves will avoid revisits without using working memory at all. The correct control is an agent matched to the animal's own movement statistics (step-length and turn-coherence distributions) with no memory of visited wells and with memory of visited wells. There's currently no evidence that the monkeys are using working memory in the opaque well task.

      (4) The models used are not described sufficiently for interpretation or replication. The methods are brief, and the models seem pulled out of thin air with no reference to other work in foraging or any mathematical construct. If the authors have used these models before, then they should cite them, but the statements "...performance of a random agent is harder to evaluate intuitively...", "random agents have no reason to stop generating actions...", "...the number of trials was set arbitrarily...", and the qualitative comparison of performance "was intermediate between that of the optimal and average random agent..." raised concern as to whether the models were principled at all. The distance model was strange, although fine, but why not use geometry? It is also not clear why a reach would have this kind of spatial relationship when the monkey eats food between visiting locations and does not go from one box to the other without a reach to his mouth and a reach back to the board in between.

      (5) The figures in this paper need work; axes are not labeled properly, or have labels that are undefined. Legends are not labeled clearly or described properly in the figure legends to interpret the figures. In multiple cases, the box and whisker plots have too little data, and the edges of the boxes collapse on themselves because IQR doesn't make sense for only 6-7 data points.

      (6) The statistics seem questionable; although they used parametric and non-parametric tests, they report results of tests that assume normality for small sample sizes <10. Other choices regarding the trial number to stop counting data (e.g., on the 20th or 23rd) are suspicious and seem arbitrary, not data-driven. Some choices regarding analysis are stated in the text as arbitrary when they could be derived from hypotheses or rigor. The extremely small p-values for the ANOVA result testing performance made me suspect that this was combining all trials across sessions (given there were only 6 or 7 sessions per animal) and there was no repeated measures or nesting to account for repeated sampling within an animal. Sessions within an animal under matched conditions are the natural paired unit. So a paired or mixed-model treatment would be both more powerful and more appropriate.

      (7) Lastly, there is no evidence that chemogenetics actually worked in this experiment in a way that tests their hypothesis. In Figure 7, they show their only effects that passed statistical significance (p=0.03 and p=0.028) for two monkeys, comparing across saline (D) and DCZ (D) sessions, and show a small effect on mean performance. I don't think there were corrections for multiple tests across all their monkeys and performances. But that aside, the monkeys' performances were not different from those of their random agents on mean performance, as shown in Figure 5(A). So if they are performing randomly, what would inactivation be affecting?

      (8) There seems to be an ordering confound of weeks 1 and 2 being saline and weeks 3 and 4 being entirely DCZ, and only the last 2 weeks are randomized, so for 20 of the 28 sessions per animal, treatment is confounded with time.

      (9) There does not appear to be any group x treatment interaction test for any analyses. Claims rest on statements like "significant in monkey 1 but not in monkey 3," etc.

      (10) There are duplicated statistics between pages: "The effect reached significance for monkey Jf (T-tests, Jf: t(10.79) = -2.31, p = 0.042), but only approached significance for monkey Js (t(8.89) = 2.04, p = 0.072)." on page 12, "The number of trials performed after the 23rd reward increased under DCZ for monkey Js (T-test, t(10.79)=-2.31, p=0.042) and Jf only showed a non-significant trend (T-test, t(8.89)=-2.04, p=0.072)." on page 13 for a separate section.

    3. Reviewer #2 (Public review):

      Summary:

      The authors investigate the causal role of the LC in mobilizing cognitive resources. Two rhesus macaques expressing TH-dependent inhibitory (hM4Di) DREADDs in the LC, alongside one non-transduced control, performed a spatial foraging task under visible (transparent) and hidden (opaque) reward conditions. The authors report that systemic DCZ (0.1 mg/kg) administration selectively impaired performance in the memory-guided opaque condition in the DREADD animals, concluding that LC activity is causally required for allocating cognitive effort

      Strengths:

      (1) Achieving cell-type-specific, reversible chemogenetic inhibition in the macaque LC is a substantial technical achievement that successfully bypasses the limitations of systemic noradrenergic pharmacology.

      (2) The experimental setup elegantly isolates memory-guided action selection from global arousal, sedation, or motivational deficits, providing a useful quantitative constraint for LC functional models.

      (3) The introduction and discussion clearly identify the limitations of using indirect pupillometry to assess locus coeruleus activity. The authors thoughtfully frame their targeted manipulation as a critical complement to existing literature without overstating their claims.

      (4) Demonstrating that the DREADD animals navigated the board similarly regardless of reward visibility is a valuable null finding. This specific result provides crucial evidence for addressing the memory-versus-effort debate. Elevating this insight from a subordinate clause to a more prominent position will further maximize the paper's impact.

      Weaknesses:

      (1) The current behavioral metrics-specifically mean inter-choice distance and success rates bounded by a "random agent" baseline-cannot distinguish between a working memory deficit and a fundamental shift in search strategy. Because performance is a many-to-one function of memory quality and search policy, animals could achieve the observed outcomes either by executing a tight local sweep with "leaky" memory for visited wells, or by abandoning tight spatial locality for a dispersed, near-random walk with intact memory. Without a detailed characterization of the animals' actual step-by-step visit sequences and spatial consistency across conditions, the data cannot adjudicate whether LC inhibition degrades an existing habitual policy (a memory/maintenance deficit) or impairs the adoption of a structured policy (an effort-mobilization/task-set deficit). Clarifying this is critical, as the two interpretations engage distinctly different LC-prefrontal mechanisms.

      (2) The analyses of within-session behaviors rely on arbitrary reward-inclusion thresholds (e.g., 20 or 23 rewards) and censoring rules that obscure the temporal dynamics of the task. Because wells are depleted without replacement, the difficulty and working memory burden rise steeply as the board empties. A constant, time-invariant memory deficit could readily produce the observed early vs. late performance pattern, as the detectable cost of forgetting accrues heavily after the 20th reward. Furthermore, early-session failures to maintain an effective policy naturally lead to continued responding after depletion. However, the imposed time caps compress variance exactly where this within-session causal chain would be most evident, leaving the claimed independence of the early and late deficits unsupported.

      (3) The experimental timeline heavily confounds chemogenetic manipulation with elapsed time. Test weeks 1-2 were exclusively saline, and weeks 3-4 were exclusively DCZ, with treatment alternation only occurring in weeks 5-6. Consequently, the critical contrast between opaque saline and DCZ conditions is largely a between-block comparison across a month of testing. Plausible time-varying factors-such as habituation, declining novelty of a daily home-cage task, and accumulating satiety-all directionally align with the reported DCZ impairment, undermining the isolation of the chemogenetic effect.

    1. eLife Assessment

      This study suggests that caspases strengthen the antiviral response by cleaving Drp1, which otherwise restrains mitochondria-mediated type I interferon signaling. The authors report an important finding supported by solid evidence. However, further mechanistic work is needed to firmly establish whether apoptotic caspases are indeed required for Drp1 depletion in virus-infected cells.

    2. Reviewer #1 (Public review):

      Summary:

      Drp1 promotes mitochondrial fission, whereas viral sensing can trigger mitochondrial fusion, promoting MAVS aggregation and the type I interferon response. In this manuscript, Fang et al. describe an unexpected role of caspases induced upon Rift Valley fever virus (RVFV) infection in inactivating Drp1. They show that multiple caspases cleave Drp1 in virus-infected cells and propose that this cleavage boosts mitochondrial elongation and type I interferon signaling. Finally, the authors establish the generalisability of the proposed mechanism in the context of cellular infections with H1N1, SeV, and HSV-1. Caspase-dependent and caspase-independent cell death are important components of host defense against viruses. This work suggests an additional antiviral role for caspases through the mitochondrial type I interferon axis. Although the authors have addressed most of the reviewers' concerns in a rigorously revised manuscript, one aspect of the proposed mechanism requires further validation.

      The data in Figures 6E and 6F remain difficult to reconcile with the study's central thesis. The authors propose that caspase-mediated cleavage of full-length Drp1 enhances the type I interferon response in virus-infected cells. To test this model, they overexpressed the caspase-resistant mutant Drp1-3DE. Yet RVFV infection reduced Drp1-WT and Drp1-3DE levels to a similar extent (Fig. 6E), while progeny virus yield was higher in cells overexpressing Drp1-3DE (Fig. 6F). This observation contradicts the notion that the abundance of Drp1 in virus-infected cells is primarily regulated by Caspases and also weakens the potential link between Caspases and viral propagation through Drp1. The authors' rebuttal that the transcription-inhibitory function of NSs may have contributed to the reduction of both Drp1-WT and Drp1-3DE is neither persuasive nor helpful in resolving the mechanistic discrepancy. Because this is a very significant point, clarity on this matter will be critical.

    3. Reviewer #2 (Public review):

      In the present study, the authors report the role of virus-induced apoptosis in positively regulating innate immune response. Upon infection, host cell apoptosis is triggered as a defense mechanism against virus replication. Culmination of infected-cell death impairs replicative potential for viruses hence attenuating virus propagation. Reports exist denoting inhibitory effect of apoptosis upon innate immune signaling. Contrary to that, findings of this manuscript underscore possible role of apoptosis in enhancing innate immune signaling and effector response. Infection-induced activation of caspases (3, 6, 7 and 8) has been demonstrated to cleave DRP1 protein. DRP1, acting as positive regulator for mitochondrial fission, upon degradation leads to altered mitochondrial morphology (elongation).

      Mitochondria being a hub for innate immune signaling (via operation of RLR-MAVS-downstream effector molecule-axis), upon elongation as a result of DRP1 depletion results in greater innate immune signal flux and interferon induction. Increased interferon induction thus acts to inhibit virus propagation, as demonstrated by the authors using cell-culture models.

      Strengths:

      (1) The findings presented by the authors have been validated by employing elaborate biochemical experimental approaches. The study entails extensive biochemical characterization of DRP1 residues targeted by activated caspases, in vitro assays validating caspase-mediated DRP1 cleavage & caspase-DRP1 interaction.

      (2) This study possesses broad implications since authors demonstrate role of caspase-mediated DRP1 cleavage in promoting innate immunity, in context of infection by diverse viruses (both RNA and DNA viruses).

      Weakness:

      Although the authors undertook a thorough experimental approach attempting to validate their findings, all the experiments were performed using either cell-culture models for infection or in vitro biochemical assays (cleavage and protein-protein interaction). Additional experimentation using animal models (in vivo) will further help strengthen biological significance of their findings under more physiological settings.

      Comments on revised version.

      I am satisfied with the revised version of the manuscript.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Drp1 supports mitochondrial fission (doi: 10.1038/s41586-019-1296-y). Viral sensing triggers mitochondrial fusion, leading to MAVS aggregation and improved type-1 IFN response. It was suggested that impairment of Drp1 upon phosphorylation by Tbk1 enhances mitochondrial fusion in virus-infected cells (doi.org/10.1016/j.molcel.2020.10.018). In this manuscript, Fang et al. describe an unexpected role of caspases activated upon Rift Valley fever virus (RVFV) infection in inactivating Drp1. They show that Drp1 is targeted by multiple caspases, including caspase-3, -6, -7 and -8. Indeed, cleavage of Drp1 leads to mitochondrial elongation, boosting the type-1 IFN response of infected cells. Finally, the authors establish the general disability of the proposed mechanism in the context of cellular infections with H1N1, SeV, and HSV-1. Caspase-dependent and independent cell death processes provide important host defence mechanisms against obligatorily intracellular viral pathogens. This work suggests that caspases reinforce antiviral response involving also the mitochondria-type 1 IFN axis. As such, the manuscript is well written, and the proposal pertaining to caspase-mediated targeting of Drp1 may have implications beyond host-virus interaction studies. However, several loose ends remain, and these concerns need to be addressed to substantiate the mechanistic model.

      Reviewer #2 (Public Review):

      In the present study, authors report the role of virus-induced apoptosis in positively regulating the innate immune response. Upon infection, host cell apoptosis is triggered as a defence mechanism against virus replication. Culmination of infected-cell death impairs replicative potential for viruses, hence attenuating virus propagation. Reports exist denoting the inhibitory effect of apoptosis upon innate immune signalling. Contrary to that, the findings of this manuscript underscore the possible role of apoptosis in enhancing innate immune signalling and effector response. Infection-induced activation of caspases (3, 6, 7, and 8) has been demonstrated to cleave DRP1 protein. DRP1, a positive regulator for mitochondrial fission, degradation leads to altered mitochondrial morphology (elongation).

      Mitochondria, being a hub for innate immune signalling (via operation of RLR-MAVS-downstream effector molecule-axis), upon elongation as a result of DRP1 depletion, results in greater innate immune signal flux and interferon induction. Increased interferon induction thus acts to inhibit virus propagation, as demonstrated by the authors using cell-culture models.

      Strengths:

      (1) The findings presented by the authors have been validated by employing elaborate biochemical experimental approaches. The study entails extensive biochemical characterization of DRP1 residues targeted by activated caspases, in vitro assays validating caspase-mediated DRP1 cleavage & caspase-DRP1 interaction.

      (2) This study possesses broad implications since the authors demonstrate the role of caspase-mediated DRP1 cleavage in promoting innate immunity in the context of infection by diverse viruses (both RNA and DNA viruses).

      Weaknesses:

      Although the authors undertook a thorough experimental approach attempting to validate their findings, all the experiments were performed using either cell-culture models for infection or in vitro biochemical assays (cleavage and protein-protein interaction). Additional experimentation using animal models (in vivo) will further help strengthen the biological significance of their findings under more physiological settings.

      Reviewer #3 (Public Review):

      Summary:

      The authors demonstrated that the NSs protein of RVFV triggers the activation of apoptotic caspases, which cleave the mitochondrial fission factor DRP1 resulting in mitochondrial elongation.

      Strengths:

      The manuscript provides an insightful investigation into a novel mechanism through which apoptotic caspases promote anti-viral immunity by regulating mitochondrial morphodynamics.

      We are very grateful for this constructive review. Our specific responses are enclosed below.

      Recommendations for the authors:

      Reviewing Editor Comments:

      It is advised that the deficiencies and weaknesses pointed out by all three reviewers are meticulously addressed. This will be important to mechanistically substantiate the proposed model. While the absence of animal data represents a deficiency, the authors may graphically/textually articulate future courses of in vivo experiments that may shed further light on the physiological significance of the proposed mechanism.

      Reviewer #1 (Recommendations for the Authors):

      (1) Figure 1: While Figure 1 is convincing, could it be possible to add some quantification for mitochondrial morphodynamics? For example, the average number, size, perimeter, interconnectivity, and elongation of mitochondria from uninfected and infected cells may be compared in a violin plot. This approach should then be followed throughout the manuscript.

      We appreciate the reviewer’s suggestion. We agree that quantitative analysis of mitochondrial morphology would be valuable to the study. However, it seems that a widely accepted quantification standard for the mitochondrial elongation phenotype is currently not available. We also tried to use the MiNA (Mitochondrial Network Analysis) plugin (ImageJ; 2017 Acta Histochem, doi:10.1016/j.acthis.2017.03.001) to analyze mitochondrial morphology. Unfortunately, this plugin could not be properly executed due to unknown reasons. For this reason, we propose not to include the quantitative data in the main manuscript, also because the immunofluorescence analysis showed a rather distinctive phenotype. If the reviewer can suggest a method for this kind of quantification, we could try with the suggested method.

      (2) Figure 2: NSs of RVFV interact with the general transcription factor TF-IIH and primarily reside in the nucleus. Then, what could be the mode of caspase activation by NSs? Is the interaction between NSs and TF-IIH important for caspase activation? In general, the link between NSs and caspases may be discussed more elaborately.

      Regarding the mode of caspase activation, our recent publication (together with other’s previous work) showed that the NSs-mediated TFIIH degradation leads to transcriptional inhibition resulting in rapid downregulation of the short-lived pro-survival protein MCL-1, contributing to BAK activation (2024 Cell, doi: 10.1016/j.cell.2024.09.008; 2024 PLoS Pathog, DOI: 10.1371/journal.ppat.1012387). Activation of BAK may trigger mitochondrial apoptosis (2022 Nat Commun. doi: 10.1038/s41467-021-27851-y).

      (3) Figure 3: Some data quantification will be important for immunoblot analyses. This is relevant for other figure panels as well.

      We have now included semi-quantitative analyses for the key immunoblot data in Figure 3.

      (4) Figure 3: Previous studies implicated MITOL-mediated pathway in Drp1 degradation (doi: 10.1242/jcs.257808). Importantly, the ubiquitin-proteasome system was shown to degrade PKR in RVFV-infected cells (doi.org/10.1371/journal.ppat.1005437). It could be important to determine the PKR level in immunoblot analyses performed in Figure 3A to ensure that MG-132-mediated inhibition was not inadequate in those experiments.

      We have now included detection of PKR in the revised Figure 3A and updated the corresponding text in the manuscript. The results showed that MG132 treatment effectively rescued the RVFV-induced degradation of PKR. In contrast, MG132 treatment did not inhibit RVFV-induced DRP1 downregulation.

      These results indicate that while proteasomal degradation of DRP1 may occur in other contexts, it is not the primary mechanism for DRP1 downregulation in the context of RVFV infection.

      (5) Figure S2F and Figure S2G assure the necessity and sufficiency of NSs in caspase activation and should be placed in the main text. These figures MUST also include an additional panel comparing the level of full-length and cleaved Drp1.

      We have reorganized the figures in the revised manuscript accordingly. The changes are as following:

      (1) The original Figure 3G (showing caspase activation during RVFV infection) has been moved to the Supplementary Information as new Figure S2F to streamline the main figures.

      (2) The data showing that NSs expression alone is sufficient to trigger caspase activation and PARP-1 cleavage remains as Figure S2G, with DRP1 downregulation shown separately in Figure 2E from the same samples. This arrangement was maintained for narrative flow in the main text, but we have updated the text to explicitly guide the reader between these figures.

      (3) We have put the original Figure S2F to the main text as new Figure 3G. This figure now includes the DRP1 cleavage data from the same experiment. And we compared the level of full-length and cleaved Drp1.

      (6) Figure 4: Figure 4A needs a positive control to show that caspase 1, -4, and -9 were active in the in vitro assay condition.

      As suggested, the corresponding positive controls of caspase-1, -4, and -9 have now been included in the revised Supplementary Figure S3.

      The enzymatic activities were confirmed as follows:

      Caspase-1 and Caspase-4: We tested cGAS as a reported substrate (2017 Immunity, doi:10.1016/j.immuni.2017.02.011). While the cleavage fragments for cGAS were not observed potentially due to the epitope affinity of the antibody, a clear reduction in full-length cGAS level was observed upon incubation with both caspase-1 and caspase-4 (Figure S3A), indicating substrate degradation by these caspases.

      Caspase-9: Its activity was demonstrated by its ability to process a catalytically inactive mutant of procaspase-3 (Figure S3C).

      (7) There are some anomalies related to the size of the cleaved Drp1, and this needs serious attention. Figure 3E suggests the size of the cleaved Drp1 produced in RVFV-infected cells to be ~ 55 kDa. However, Figure 4A and Figure 4D indicate that the size of the cleavage products is around 15 kDa [Drp1(1)] and 35 kDa [Drp1(2)]. This must be clarified. Did the Drp1(D556E) mutant still produce the 55 kDa band upon virus infection?

      Below, we provide a point-by-point response.

      (1) Clarification on the apparent size discrepancy of cleaved DRP1 fragments:

      The apparent size difference arises from the use of different antibodies targeting distinct regions of the DRP1 protein.

      In Figure 3E, we detected endogenous DRP1 using an anti-DRP1 monoclonal antibody (CST, #5391) by Western blot. Under these conditions, this monoclonal antibody recognize a single linear epitope. The detection of the ~55 kDa fragment provides evidence that the epitope for this monoclonal antibody is located N-terminal to the D500/D503 cleavage site. Therefore, the ~55 kDa band represents the large N-terminal fragment generated after caspase cleavage.

      In contrast, Figures 4A and 4D analyze exogenously expressed, C-terminally strep-tagged DRP1 (DRP1-strep) using an anti-strep antibody. This setup visualizes the C-terminal cleavage fragments (~15 kDa or ~35 kDa) that contain the strep tag.

      These experiments are designed to detect different portions of the cleaved DRP1 protein. We have also revised the respective figure legends to state the protein source and detection method to prevent confusion.

      Specific Cleavage Pattern of the DRP1(D556E) Mutant:

      The full-length DRP1-strep protein is 83.2 kDa. Cleavage at D556 produces an estimated ~61.9 kDa N-terminal fragment and an estimated ~21.3 kDa C-terminal fragment (containing the strep-tag). Cleavage at D503 produces an estimated ~56.3 kDa N-terminal fragment and an estimated ~26.9 kDa C-terminal fragment (containing the strep-tag). Therefore, the DRP1(D556E) mutant would not produce the N-terminal ~61.9 kDa fragment (cleaved DRP1 (1), Author response image 1) and the C-terminal ~21.3 kDa fragment. However, since the D503 site remains intact, this mutant can still be cleaved at D503, yielding the ~56.3 kDa N-terminal fragment (cleaved DRP1 (2), shown in Author response image 1) and the ~26.9 kDa C-terminal fragment. When detected with an anti-DRP1 antibody, the ~56.3 kDa N-terminal fragment from D503 cleavage is observed. This fragment migrates at approximately 55 kDa on immunoblots. The result (see Author response image 1) are consistent with this interpretation and reflect the situation during virus infection.

      Author response image 1.

      (8) Figure 5: It will be meaningful to add a panel on mitochondrial morphodynamics upon Drp1 knockdown in this Figure. This may provide a correlative argument linking mitochondria to type 1 IFN response through Drp1.

      According to this suggestion, we performed this experiment and included a new panel showing mitochondrial morphology in DRP1 knockout cells. The new Figure 5D demonstrates that DRP1 knockout induces significant mitochondrial elongation compared to control cells.

      (9) Figure 6 and Figure 7: One may consider combining these in one consolidated panel.

      We have now combined the original Figure 6 and Figure 7 into a single consolidated panel (new Figure 6 in the revised manuscript).

      (10) Figure 7: Figure 7B needs particular attention. It seems that RVFV infection causes a similar downmodulation of both Drp1-wt and Drp1-3DE. However, Drp1-3DE did not produce the 15 kDa cleavage product and did not support enhanced type 1 IFN response as much as its WT counterpart did. Could the proteasomal pathway have a role in degrading caspase-resistant Drp1? Could the cleavage product have a role in enhancing anti-viral pathways? This must be meticulously addressed involving additional experimental designs, keeping in mind the results obtained using Drp1 knockdown cells (Figure 5).

      We thank the reviewer for this comment. (1) Regarding the similar downmodulation of DRP1-WT and DRP1-3DE, the RVFV NSs has a potent function to inhibit host transcription. The exogenous DRP1-WT and DRP1-3DE are expressed from a CMV promoter via lentiviral transduction. This high-level exogenous expression system is sensitive to the transcriptional inhibition caused by RVFV-NSs, potentially explaining the observed similar reduction in protein levels. (2) Regarding the role of proteasomal pathway in degrading caspase-resistant Drp1, our data indicate that the proteasomal pathway is not the primary mechanism for the downregulation of the caspase-resistant DRP1-3DE. This is supported by the observation that the downregulation of endogenous DRP1 was largely insensitive to MG132 treatment (Figure 3A). (3) Whether the cleavage product plays a role in enhancing anti-viral pathways is an interesting question, which we propose to address in future study considering the scope of the current study.

      (11) Figure 7: A panel may be added to compare the effect of overexpression of WT and mutant Drp1 on mitochondrial morphodynamics.

      According to this suggestion, we have performed the experiment, and the data are included as a new panel in the revised Figure 6H. The results indicate that upon RVFV infection, control cells (PCDH) exhibited significant mitochondrial elongation. In contrast, cells overexpressing either DRP1-WT or DRP1-3DE did not show significant mitochondrial elongation morphology.

      (12) Figure 8: Considering the known role of Drp1 in MAVS aggregation (doi.org/10.1016/j.molcel.2020.10.018), this figure in its present form could appear less informative. One may argue that a comparison of WT and mutant Drp1 for controlling virus-induced MAVS aggregation could be more meaningful.

      We thank the reviewer for this comment. The major finding of our work is revealing a novel, caspase-dependent mechanism of DRP1 down-regulation. Figure 8 (revised Figure 7) showed that depletion of Drp1 promoted MAVS aggregation resulting in a strong antiviral effect. Regarding the suggestion to compare DRP1-WT and DRP1-3DE for MAVS aggregation during virus infection, we have focused our analysis on the DRP1 knockout system. This approach mimics the natural outcome of infection, where virus-induced caspase activation leads to the depletion of endogenous DRP1. Overexpression of WT and mutant Drp1 could hamper virus-induced depletion of Drp1 due to it’s high abundance, and may therefore affect the MAVS aggregation analysis.

      (13) Figure 9: This figure establishes that Drp1 is cleaved by caspases in response to cellular infection by multiple viruses. However, the significance of caspase-mediated Drp1 cleavage per se in the progeny virus titre remains unclear.

      We agree with this comment. The purpose of the original Figure 9 (revised Figure 8) is to show that the caspase-dependent Drp1 cleavage may occur in response to different virus infection. Whether and how this may affect progeny virus production, would be an interesting topic for future investigation. According to this comment, we reworded our description in the corresponding Results section.

      (14) Would caspase inhibition impact mitochondrial morphodynamics and progeny virus titres? This question needs to be experimentally addressed.

      Our data in Figures 3C, 3D, and 3F showed that the pan-caspase inhibitor Z-VAD-FMK effectively blocks virus-induced DRP1 cleavage in a dose-dependent manner. However, this upstream inhibition did not result in a significantly variation of nucleoprotein (NP) expression under RVFV infection. It is possible that the broad-spectrum inhibition of caspases by Z-VAD-FMK may simultaneously affect multiple aspects of the host cell and affect virus-host interactions.

      For this reason, we employed the caspase-resistant DRP1-3DE mutant (revised Figure 6), which allowed us to specifically analyze the functional impact of Drp1 cleavage without introducing the effects of global caspase inhibition and caspase-dependent processes. The result that DRP1-3DE enhances viral replication provides evidence for the role of DRP1 cleavage in restricting virus production.

      (15) One weakness is a lack of in vivo data despite the availability of Drp1-floxed mice. What are the results that one may anticipate based on the current study? This should be indicated in the Discussion section.

      We thank the reviewer for this suggestion. According to this suggestion, we have now added discussion of the anticipated outcomes of in vivo studies.

      Reviewer #2 (Recommendations for the Authors):

      (1) Based on the DRP1-residues that caspase-3 targets for cleavage (500/503 and 556), 3 fragments having molecular weights of 55, 5.5 and 22 kDa, approximately, should be generated. In Figure 3E, the authors show the presence of DRP1 cleavage product having the size of approx. 50 Kda. Since the antibody (anti-DRP1) used by the authors recognizes the C-terminal region of DRP1, immunoblot for DRP1 under RVFV infection context should also demonstrate the presence of ~20 Kda DRP1 fragment. The authors should provide immunoblots showing the same.

      We agree with the reviewer that, based on the identified cleavage sites at D500/D503 and D556, a C-terminal fragment of approximately 20 kDa should be detectable.

      In our experiments using whole-cell lysates, we consistently observed the ~55 kDa cleaved DRP1 fragment but were unable to detect the smaller ~20 kDa fragment with the anti-DRP1 monoclonal antibody (CST, #5391). According to the manufacturer’s information, this antibody was raised against the carboxy-terminal region of human DRP1; however, the exact epitope has not been disclosed (we tried to contact the company, but did not get an answer). Since this is a monoclonal antibody, we infer that this single epitope likely resides upstream of the caspase-3 cleavage sites (D500/D503 and D556). Consequently, the C-terminal cleavage fragment may lack this epitope and is not recognized by the antibody.

      To verify the generation of this C-terminal fragment, we performed in vitro cleavage assays using C-terminally strep-tagged DRP1 (DRP1-strep), where an anti-strep antibody clearly detects the ~20 kDa band (as shown in Figures 4A and 4D–4F). We have included this information in the manuscript for clarity.

      (2) In Figure 4A, although the band for active caspase3 (from in vitro cleavage assay) is missing, DRP1 shows cleavage. Similarly, in Figure 4E, the band for active caspase3 is also missing. The authors should provide explanations for these data.

      The reason is that under our standardized in vitro cleavage assay conditions, the anti-His antibody did not efficiently detect these particular commercial recombinant proteins, likely due to variations in epitope accessibility, protein conformation, or the detection sensitivity of the Western blot. The enzymatic activity of caspases used in our assays was verified using established positive control substrates. For caspase-3, activity was confirmed by cleavage of the well-characterized substrate PARP1 See Figure S3B.

      (3) In Figure 4F, WT-DRP1 full length-band exhibits reduction in intensity upon treatment with caspase3, which is expected given the ability of caspase3 to cleave DRP1. However, although mutating 'D500/556E & D500/503/556E' abolishes DRP1 cleavage by caspase3, total DRP1 band intensity exhibits marked reduction in both cases. The authors should provide an explanation for this anomaly.

      The recombinant protein used in this assay is C-terminally strep-tagged DRP1 (DRP1-strep), detected with an anti-strep antibody. The mutation of sites D500/503/556E prevented cleavage at these positions. However, the observed decrease in full-length protein indicates that caspase-3 cleavage may occur at other, alternative sites within DRP1, such as D221 (Figure 4C). The detection of cleavage fragment is dependent on the anti-strep antibody recognizing the C-terminal tag. Cleavage at alternative sites located between the mutated sites and the C-terminus is likely to produce shorter C-terminal fragments that may be difficult to be detected. Therefore, it is possible that although caspase-3 cleavage proceeds at these alternative sites in the mutant proteins, the fragments remain undetectable with our detection method.

      (4) In Figures 7B and 7D, infection by RVFV of cells overexpressing DRP1-3DE (caspase-mediated cleavage-resistant) also leads to a reduction in full-length DRP1, although no cleavage product was seen. Does this indicate a possible role for other DRP1 residues being targeted by caspases upon virus infection? Treatment of DRP1-3DE-overexpressing RVFV-infected cells with a pan-caspase inhibitor will further provide insight into whether other DRP1 sites can also be targeted by caspase activity. Moreover, the authors should treat RVFV-infected cells with pan-caspase inhibitor and verify whether WT-DRP1 cleavage is restored concomitant with the abrogation of interferon induction and signaling.

      We thank the reviewer for this comment. (1) For the reduction of full-length DRP1-3DE in RVFV-infected cells, similar with the response to the above comment, it is possible that the transcription inhibition effect of RVFV lead to reduction of Drp1-3DE expression level, an effect which may be more pronounced in the exogenous overexpression system. Additionally, the existence of other potential caspase cleavage sites, such as D221 (Figure 4C), may also in part account for the observed reduction. (2) Regarding the suggestion to use pan-caspase inhibition to analyze interferon signaling, we feel that blocking multiple caspase activities with the pan-caspase inhibitor Z-VAD-FMK will likely affect the global caspase activity in the cell affecting the scope beyond the interferon signaling pathways and is likely to complicate the interpretation of the results. Therefore, we employed the DRP1-3DE mutant for the analysis (revised Figure 6). This way we would not introduce the unspecific effects of global inhibition of multiple caspase-dependent processes. The result that DRP1-3DE enhances viral replication (revised Figures 6E-6G) provides evidence for the role of DRP1 cleavage in antiviral immunity.

      (5) MAVS aggregation shown by SDD-AGE in Figures 8B, 8C & 8D does not display a typical appearance characterizing MAVS aggregation. The pattern shown in the above-mentioned immunoblots is very atypical for aggregates and indicates an increased MAVS abundance in RVFV-infected (8B)/DPR1-KO (8C, 8D) lanes. The aggregation of MAVS when demonstrated in SDD-PAGE gels exhibits a completely different pattern. The authors should include positions for molecular weight markers indicating their corresponding molecular sizes in the immunoblot. That will help in differentiating MAVS monomer from the aggregates and will help assess the extent of MAVS aggregation upon infection/DPR1-KO.

      We thank the reviewer for this comment.

      In the meantime, we checked literatures detecting the pattern of MAVS aggregation using the same technique (e.g., 2011 Cell, DOI: 10.1016/j.cell.2011.06.041; 2017 Nature Communications, DOI: 10.1038/ncomms15138), where, if we understood correctly, MAVS aggregation showed a similar high-molecular-weight smear. It is common in SDD-AGE not to use protein markers, as this method separates by molecular size under semi-denaturing conditions and is designed to resolve high-molecular-weight aggregates rather than provide precise molecular weights.

      (6) In Figure 9D, the authors should also immunostain cells with virus-specific antibodies. This will help delineate that only virus-infected, but not uninfected cells exhibit elongation of mitochondria (as indicated by the appearance of TOM20 staining). In addition to that, the authors should provide an immunoblot for SeV antigen in Figure 9F. Instead of upregulating the H1N1 antigen, caspase inhibition by zVAD-FMK displayed a reduction in viral protein concomitant with DRP1 cleavage inhibition. Whereas, HSV-1 antigen abundance was seen to be enhanced upon the inhibition of apoptosis, which is consistent with the activity of axis entailing apoptosis-DRP1 cleavage-mitochondrial elongation-enhanced innate immunity- reduced virus propagation. The authors should explain this discrepancy.

      Regarding Figure 9D (revised Figure 8D) and virus-specific staining, we now provided the viral antigen (NP) staining for RVFV and H1N1 (See Author response image 2). However, highly specific antibodies for SeV and HSV-1 immunofluorescence (IFA) are not available for us at the moment. We therefore propose to keep the original layout of the figure.

      Author response image 2.

      Regarding the immunoblot for SeV antigen in Figure 9F, we now provided the immunoblot for SeV antigen. See Figures 8F.

      Regarding the H1N1-NP level upon Z-VAD-FMK treatment in Figure 9E, the reduction in NP signal with Z-VAD-FMK can be explained by the fact that the H1N1-NP protein itself is a substrate for caspase cleavage, as previously reported (DOI: 10.1007/s00705-007-0003-8). A zoom-in view of the blot in Figure 9E reveals a smaller migrating band corresponding to the cleaved NP. Z-VAD-FMK treatment inhibited the generation of this cleavage band, concurrently with the inhibition of DRP1 cleavage. When the ratio of internal control tubulin was considered, the NP level in the Z-VAD-FMK treatment group would not indicate a reduction of signal.

      Figures 8E in the revised manuscript and Author response image 3.

      Author response image 3.

      (7) Previous report (Wu et al, 2022, Nat cell death & disease, DOI: 10.1038/s41419-022-05156-2) demonstrates the inhibitory role of apoptosis in interferon-induction upon HSV/SeV in macrophage cells. However, in this study, the authors suggest apoptosis positively regulates interferon response (upon SeV/HSV-1 infection of macrophage cells). The authors should provide an explanation for possible reasons accounting for this difference in the discussions section.

      We propose that the functional consequence of caspases activation, which process different substrates, will likely pose two-sided effects on immune signaling as a two-sided “sword”. While the established caspase-dependent cleavage of molecules such as cGAS, MAVS, or NF-κB may pose inhibitory effect, the caspase-dependent cleavage of other substrates (such as Drp1 and yet-to-be identified other substrates) may pose the opposite effect. The overall effect would need to be determined in a context-dependent manner. We have included this notion in the revised Discussion section.

      Reviewer #3 (Recommendations for the Authors):

      (1) Please add the full name in the Abstract.

      We have now added the full names for all relevant abbreviations upon their first appearance in both the Abstract and the Introduction sections.

      (2) There is no scale bar in Figure 1A, 2A, 2B, 9D upper panel, Figure 8A.

      We have now added scale bars to the revised figures: upper panels of Figure 1A, 2A, 2B, and 8D (original 9D), and Figure 7A (original 8A), as suggested.

      (3) Can the author explain more in the text why DRP1, but not other mitochondria proteins?

      Our initial investigation simultaneously examined the key regulators of both mitochondrial fusion (MFN1, MFN2, OPA1) and fission (DRP1) using the same set of cell samples. This comparison showed that DRP1 was specifically down-regulated upon RVFV infection (Figure 2C).

      (4) Can the author explain more in the text why further study H1N1, SeV, HSV-1, but not other viruses?

      H1N1, SeV and HSV-1 are model RNA or DNA viruses that are commonly used and were selected as model viruses in our system. We propose that other viruses could also be investigated in further studies.

    1. eLife Assessment

      This manuscript addresses an important and conceptually ambitious question by using a synthetic biology strategy to perturb ATP homeostasis in yeast and examine its relationship with replicative lifespan. While the lifespan phenotype is robust and the experimental approach is generally solid, the current evidence is limited to establish intracellular ATP abundance as the causal determinant of lifespan, particularly given the apparent uncoupling from NTT1-dependent ATP import and single-cell ATP levels, as well as the possibility of extracellular ATP-induced stress responses. Further clarification of the mechanistic interpretation, including more cautious treatment of Snf1/AMPK signaling and NTT1 transport directionality, will be needed before the biological significance of the findings can be fully assessed.

    2. Reviewer #1 (Public review):

      I thank the authors for a thorough revision. The correction to the QUEEN reporter sign convention resolves what was the central issue in my original review: with the reciprocal (1/488 nm) now plotted in Figure 5, the single-cell reporter data and the LC-MS/MS measurements in Figure 2 are internally consistent, and I appreciate the added metabolomics (Figure S2B) and transcriptomics (Figure S2A, S1-S3) that support reduced de novo purine biosynthesis as the source of the lower baseline ATP in NTT1 cells, as well as the quantified Hog1 phosphorylation data (Figure 4C, Figure S8). However, several points from my original review are not yet fully resolved, and I would like the authors to address them before I can recommend acceptance.

      (1) A residual, uncorrected statement about QUEEN signal directionality. The Results section (subsection "Altered intracellular ATP level modulates lifespan") still states: "...only 488 nm excitation was used in these experiments, and the resulting QUEEN fluorescence signal is inversely correlated with intracellular ATP levels (Fig. 5A-C)." This directly contradicts the corrected Methods section ("...plotted the inverse of fluorescence measurements (1/488nm), as an indicator of ATP abundance, in Fig. 5, A-C") and the Figure 5 legend and color scale, both of which now correctly state that the plotted quantity increases with ATP abundance. Given that this exact ambiguity was the source of the original contradiction the reviewers raised, I ask the authors to correct this sentence and re-check the rest of the text for any other place where the old (pre-correction) sign convention may still be described.

      (2) AMPK/Snf1 validation remains indirect. I had asked for AMPK-pathway controls alongside the Hog1 quantification. The revision addresses this only through transcriptomic inference - downregulation of Snf1 target genes (MIG1, HXK1, HXK2, HXT3; Figure S7) is used to argue for low Snf1 activity under high ATP. This is a reasonable correlative observation but is not a direct validation of Snf1/AMPK activity (e.g., Snf1 Thr210 phosphorylation status or a kinase activity assay), which is what I intended by "AMPK controls." Please either add a direct biochemical readout of Snf1 activation status analogous to the Hog1 blots or explicitly temper the claims about Snf1-mediated metabolic rewiring to reflect that they rest on transcriptomic inference alone.

      (3) Bidirectional-transport hypothesis addressed indirectly, not experimentally. The new metabolomics (ATP/ADP/AMP all reduced in untreated NTT1 cells) and the coordinated downregulation of the purine biosynthesis pathway are a coherent case for "reduced synthesis" as the parsimonious explanation, and I appreciate this analysis. However, this remains a correlative/transcriptomic argument rather than the direct flux experiment I suggested was feasible (e.g., an isotope-labeled ATP efflux/uptake assay to directly test whether NTT1 exports as well as imports ATP). A contraction of the adenylate pool via reduced synthesis and a bidirectional transporter are not mutually exclusive, and the current data cannot fully distinguish them. I would ask the authors to either perform such a direct test or explicitly acknowledge this as a limitation and an alternative that current data do not rule out.

      In summary, the revision has substantially improved the manuscript and resolved the central ATP-directionality contradiction, but points 1-3 above should be addressed, particularly point 1, since it re-introduces the same ambiguity, the revision was meant to eliminate, before I can consider my concerns fully resolved.

    3. Reviewer #2 (Public review):

      Summary:

      This work presents interesting findings where addition of exogenous ATP extends replicative lifespan of yeast cells in a way that seems uncorrelated with actual increased intracellular ATP levels or mitochondria. To be clear, addition of ATP to yeast growth media increases the number of cell divisions per cell in yeast. Expression of the NTT1 ATP transporter gene increases intracellular ATP levels according to LCMS analysis, but the effect on replicative lifespan works without the NTT1 gene and without an intracellular increase in ATP (possibly with a decrease in intracellular ATP), so the effect appears to be independent of the effect on intracellular ATP levels or mitochondria, as mitochondria-less R0 yeast cells also have increased numbers of cell division when grown with extracellular ATP. I would not state that the data support a role for ATP availability regulating aging because human cells do not import ATP from the environment; maybe for yeast or microbes that import ATP unlike normal human cells that make all their own. I would not state that the data support a role for ATP in increasing replicative lifespan since there is no correlation between increased lifespan an increased ATP- rather the opposite is shown - you got no correlation between intracellular ATP at replicative lifespan. What you show is that you can increase replicative lifespan when you increase extracellular ATP in a way that doesn't matter if you further increase intracellular ATP with NTT1, but maybe you were saturated. But then, intracellular ATP doesn't correlate either. So, it doesn't make sense to say that the paper shows that increasing ATP increases replicative lifespan. You gave the cells what seems like an osmotic stress and it didn't even matter if they had the mitochondria to respond. Lifespan didn't correlate with more ATP at all. So, it seems incorrect to indicate that ATP regulates lifespan in yeast, when you haven't shown that it just wasn't osmotic stress and you have shown that it doesn't really have anything to do with how much ATP each cell has. Rather, it seems more like it's the robustness of some other response to the ATP and not how much they imported.

      The authors point out in their rebuttal that, paraphrased, with the QUEEN reporter gene that quantifies ATP via ratiometric fluorescence, some cells increase atp with age, some cells decrease it and others show no age related changes, so it would not be meaningful to make an ATP measurement in old cells. Essentially, that line of reasoning further supports that, while adding extracellular atp seems to increase replicative lifespan, the lack of correlation between intracellular ATP and replicative aging suggest that they are eliciting a stress response to having high extracellular ATP. The increased replicative lifespan seems to be caused by some kind of stress response to the extracellular ATP, possibly osmotic. So, this is an interesting result that suggests that there are mechanisms to respond to high extracellular ATP that result in increased replicative lifespan that aren't caused by the ATP per say, but by having a high concentration of ATP, outside the cell. This paper seems like it might relate to the Kaeberlein paper about high osmolarity increasing replicative lifespan, but I don't know if this condition qualifies as high osmolarity.

      As an aside, this seems like a better model for increased tumor cell growth in the presence of increased extracellular ATP, which happens in some cancers.

      Strengths:

      Longitudinal imaging of single cells. Analyzed ATP levels with two approaches. Creative approach to use NTT1 transporter to increase intracellular ATP levels (that did nothing for lifespan). Solid replicative lifespan data. Seemingly clear lack of correlation between ATP and replicative lifespan among individual cells strongly pointing so some kind of stress response, possibly some kind of osmolarity response like Kaeberlein 2002.

      Weaknesses/inconclusive data:

      The LCMS data seems better than the microscopy data on ATP because it does not suffer from signal interference/befuddlement from autofluorescence or pH or age-related changes in either of those parameters. The authors seem to have subtracted an average autofluorescence from each cell in their revised analysis, but it is unclear if this value changes with age to account for age related changes in autofluorescence; it is also unclear if signal attenuation as a function of age-related intracellular pH change was accounted for. They may have age-related values for autofluorescence or age-related changes in intracellular pH that affect fluorescence of GFP if they go back to their raw data, which might be useful for signal cleanup.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Summary of the Key Clarification Underlying This Revision

      Both reviewers independently identified what appeared to be a direct contradiction between our LC-MS/MS measurements of intracellular ATP (Figure 2) and our single-cell QUEEN reporter measurements of intracellular ATP (Figure 5): NTT1 expression appeared to lower rather than raise intracellular ATP, and exogenous ATP treatment appeared to further lower the QUEEN signal even though LC-MS/MS showed the opposite. We have carefully reviewed the reviewers’ comments alongside our original figures and legends, and we agree that the source of this apparent contradiction was insufficiently explained in the original submission.

      To make the reported quantity intuitively track ATP abundance, we have now:

      - Plotted the reciprocal of the 488 nm signal (1/488 nm) in the updated Figure 5, eliminating the inverse correlation between AU values and ATP abundance;

      - Revised the Figure 5 legend, axis labels, Methods, and Results to explicitly state that the plotted value is directly proportional to intracellular ATP abundance, removing any ambiguity about directionality;

      - Subtracted autofluorescence from the raw QUEEN reporter fluorescence signal;

      - Expanded the Methods and Results to explain, side by side, why the QUEEN-based and LC-MS/MS based measurements are complementary but not numerically identical (different dynamic ranges; live single-cell sampling over time versus bulk lysates at fixed timepoints); and

      - Re-examined Figures 2, 5, and 7 together to confirm that, once the QUEEN directionality is correctly read, the two datasets are internally consistent: NTT1 expression lowers baseline intracellular ATP relative to Wt, and exogenous ATP supplementation raises intracellular ATP in NTT1 cells (and, more modestly and via a distinct mechanism, in Wt cells), by both methods.

      Because this misunderstanding affected interpretation of the foundational ATP data, it appears to have influenced evaluation of all downstream conclusions. For example, neither reviewer meaningfully engaged with:

      - The identification of distinct cell death trajectories of aged cells;

      - The mitochondrial dependency of NTT1-associated toxicity and the observed lifespan differences; and

      - The distinction between intracellular ATP manipulation and extracellular ATP sensing mechanisms.

      Example of a Data Discrepancy Resolved by This Clarification

      In some instances, we think the original figures did not present the data clearly enough to support the intended reading, which we address below.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aim to engineer a synthetic system for manipulating ATP homeostasis in budding yeast by expressing the microsporidian nucleotide transporter NTT1, thereby enabling ATP import from the extracellular environment. Using this system, they attempt to test whether intracellular ATP abundance causally regulates replicative lifespan and whether extracellular ATP sensing contributes independently to longevity pathways. The manuscript presents data from ATP biosensing, transcriptomics, mitochondrial perturbations, and microfluidic aging assays to build a dual-mechanism model linking ATP availability, MAPK signaling, mitochondrial function, and aging trajectories.

      Strengths:

      A major strength of the study is its creative application of xenotopic synthetic biology to directly manipulate ATP homeostasis-an ambitious approach that addresses an important and difficult question in aging biology. The use of complementary methods, including single-cell ATP reporters, microfluidic lifespan measurements, and RNA-seq, generates a rich experimental dataset with the potential to reveal multiple layers of ATP-dependent physiological regulation. The manuscript also raises interesting hypotheses regarding extracellular nucleotide sensing and HOG/MAPK pathway involvement, opening conceptual space for future exploration of ATP-based signaling in yeast.

      Weaknesses:

      Despite these strengths, the manuscript suffers from several critical weaknesses that undermine the central conclusions. Foremost, the intracellular ATP measurements contradict key interpretations: NTT1 expression lowers ATP levels, yet multiple sections assert or assume that NTT1 increases intracellular ATP via import. This unresolved contradiction propagates throughout the mechanistic model. The authors do not consider or experimentally address the more parsimonious explanation that NTT1 may be a bidirectional ATP transporter, which would unify many perplexing results. Several important analyses are missing (e.g., transcriptomic comparison of NTT1 cells with vs. without ATP), and key signaling claims lack proper validation (e.g., Hog1 quantification, AMPK controls). Additionally, inconsistencies in figures-such as incorrect scale bars, mismatched ATP measurements, and a conceptual model contradicted by the data-further detract from clarity. As a result, the manuscript does not yet convincingly achieve its stated aims, and the current evidence does not adequately support the proposed causal relationships between ATP homeostasis and lifespan.

      We thank the reviewer for a rigorous and constructive critique. The central objection that our intracellular ATP measurements contradict our interpretation arose from a presentational error in how the QUEEN reporter data were plotted. As described in our Methods (QUEEN reporter section), the QUEEN biosensor is a ratiometric reporter whose 410 nm/488 nm excitation ratio increases with ATP binding. Because repeated 410 nm excitation causes phototoxicity over the multi-day time course of a replicative aging experiment, our longitudinal single-cell measurements (Figure 5) used only the 488 nm channel, for which fluorescence intensity decreases as ATP binds the reporter. In the original Figure 5, arbitrary fluorescence units (AU) were therefore inversely correlated with intracellular ATP abundance, such that higher AU values corresponded to lower ATP levels. We described this inverse relationship in the Results section and labeled the relevant figure panels “Low versus High,” but on reflection we agree this was not stated clearly enough in the figure legend itself, where a reader would most naturally look for it. As a result, the two datasets read as contradictory even though they are fully consistent, and we take responsibility for that lack of clarity.

      The reviewer proposes that NTT1 may function as a bidirectional transporter, with net ATP export accounting for the reduced ATP we observe in untreated NTT1-expressing cells. We considered this possibility, and our expanded metabolite data argue against it. LC-MS/MS shows that ATP, ADP and AMP are all significantly reduced in untreated NTT1-expressing cells relative to wild type (new Figure S2B), with AMP most severely affected a contraction of the entire adenylate pool rather than a shift in its distribution.

      Our transcriptome data identify the source of this contraction. NTT1 expression produces a coordinated downregulation of de novo purine biosynthesis, spanning the pathway (new Figure S2A), consistent with the pronounced AMP deficit. Parallel pyrimidine biosynthesis genes are unaffected, indicating a purine-specific response rather than a general biosynthetic shutdown. We therefore conclude that reduced de novo purine synthesis, not reverse transport, is the parsimonious explanation for the lower baseline nucleotide levels in NTT1-expressing cells, and we have added this analysis and interpretation to the Results and Discussion.

      Under ATP-replete conditions, both LC-MS/MS and the corrected QUEEN readout show a clear increase in intracellular ATP in NTT1-expressing cells, consistent with import. We have additionally added the transcriptomic comparison of NTT1-expressing cells with and without exogenous ATP that the reviewer requested (new Figures S1–S3), quantified Hog1 phosphorylation, and audited every figure, legend and numerical value in the manuscript.

      Reviewer #2 (Public review):

      Summary:

      This work presents interesting findings where the addition of exogenous ATP extends the replicative lifespan of yeast cells in a way that seems uncorrelated with actual increased intracellular ATP levels or mitochondria. To be clear, the addition of ATP to yeast growth media increases the number of cell divisions per cell in yeast. Expression of the NTT1 ATP transporter gene increases intracellular ATP levels according to LCMS analysis, but the effect on replicative lifespan works without the NTT1 gene and without an intracellular increase in ATP (possibly with a decrease in intracellular ATP), so the effect appears to be independent of the effect on intracellular ATP levels or mitochondria, as mitochondria-less R0 yeast cells also have increased numbers of cell division when grown with extracellular ATP. The plots in Figure 5 make it seem like exogenous ATP addition lowers intracellular ATP for both the NTT1 cells and the wild-type cells, and that is not what the data in Figure 2d with LCMS shows.

      As an aside, this seems like a better model for increased tumor cell growth in the presence of increased extracellular ATP, which happens in some cancers.

      Restated, the data suggest they were successful in increasing intracellular ATP by LCMS, but not by queen reporter, and that the seemingly likely increased intracellular ATP was not causative, as cells that did not have an increase in intracellular ATP, but had the same exogenous ATP addition, also gained an increase in replicative lifespan. There could also be two distinct mechanisms extending replicative lifespan to the same degree in these two different strains. More measurements, controls, and analyses are needed to accurately determine what is happening with intracellular ATP levels with age. It is currently unknown if there is any correlation between ATP levels and replicative aging (with properly controlled longitudinal measurements).

      Strengths:

      Longitudinal imaging of single cells. Analyzed ATP levels with two approaches. Creative approach to use NTT1 transporter to increase intracellular ATP levels. Solid replicative lifespan data.

      Weaknesses:

      Mostly unclear about ATP levels with age and the relationship, or lack thereo,f between intracellular ATP levels and replicative lifespan. No idea what this effect depends on, but some ideas what it does not depend on (mitochondria or increased intracellular ATP). Experiments seem to lack biological controls (cells without gfp) for age related changes in autofluorescence (and pH that can affect gfp signal) for the fluorescent microscopy quantifying ATP with age using the QUEEN reporter (seems that way as written); conflicting evidence on ATP levels; lack of LC-MS measurements in old cells; no apparent correlation between ATP levels and replicative lifespan, but that could be wrong - just not apparent from the longitudinal data plots. The LCMS data seems better than the microscopy data on ATP because the microscopy approach seems to lack proper biological controls, and the selection of only the top 40% of pixels to quantify signal seems unjustified as written, and possibly prone to technical artifacts. Figure 2 B&C plots of ATP levels should show what the cells were normalized to. The figures also seem too diluted and should probably be combined or put in the supplements (hog1 western) if they do not relate to the lifespan effect. There seem to be some technical scientific editorial errors, like in Figure 7.

      We appreciate this careful and, in most respects, accurate reading. The reviewer concludes that the lifespan extension conferred by extracellular ATP does not depend on an increase in intracellular ATP, since it occurs in cells lacking NTT1 and in rho<sup>0</sup> cells, and suggests that two distinct mechanisms may extend lifespan in the two strains. This is precisely the model our data supports, and we are encouraged that the reviewer arrived at it independently: we propose an intracellular, import-dependent and mitochondria-dependent mechanism operating in NTT1-expressing cells, and a separate extracellular ATP-sensing mechanism that acts without import and without functional mitochondria. We have rewritten the Results and Discussion to make this dual-mechanism structure explicit rather than leaving it to be inferred, and Figure 8 has been revised so that the model matches the data.

      The apparent conflict between the QUEEN and LC-MS/MS measurements, which underlies much of the reviewer's uncertainty about ATP levels with age, is resolved by the correction described below. We have additionally subtracted autofluorescence from the reporter signal, stated the normalization for the ATP measurements in the Figure 2 legends, and added a justification in the Methods for restricting quantification to the brightest pixels. The QUEEN reporter is a soluble cytoplasmic protein excluded from the vacuole, which occupies an increasing fraction of cell volume in ageing mother cells, so whole-cell quantification systematically underestimates cytoplasmic signal in old cells for reasons unrelated to ATP. On the request for LC-MS/MS on sorted old cells, we agree this would be valuable in principle, but because individual cells follow divergent ATP trajectories with age, a bulk measurement of a sorted population would average across those trajectories and would not be directly comparable to our single-cell data.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The intracellular ATP measurements contradict key interpretations: NTT1 expression lowers ATP levels, yet multiple sections assert or assume that NTT1 increases intracellular ATP via import. This unresolved contradiction propagates throughout the mechanistic model. The authors do not consider or experimentally address the more parsimonious explanation that NTT1 may be a bidirectional ATP transporter… Several important analyses are missing (e.g., transcriptomic comparison of NTT1 cells with vs. without ATP), and key signaling claims lack proper validation (e.g., Hog1 quantification, AMPK controls). Additionally, inconsistencies in figures, such as incorrect scale bars, mismatched ATP measurements, and a conceptual model contradicted by the data, further detract from clarity.

      We thank the reviewer for this central and constructive critique. As explained above, the apparent contradiction in intracellular ATP directionality stemmed from an underexplained sign convention in the QUEEN reporter readout in Figure 5, which we have now corrected and clarified throughout the text, legends, and axis labels. With this correction, NTT1 cells show reduced baseline ATP and a clear, dose-dependent increase in intracellular ATP upon exogenous ATP treatment, consistent between the QUEEN and LC-MS/MS assays. There is a discrepancy between the two methods (QUEEN versus LC-MS/MS) specifically in the Wt cell ATP measurements, which we discuss in the Results section.

      The authors seem to assume that NTT1 expression should result in ATP influx… the authors should consider an alternative interpretation: the ATP transporter is bidirectional… this interpretation seems to be consistent with many of the observations in the manuscript, and this bidirectional ATP flux can be experimentally tested.

      We thank the reviewer for this insightful alternative hypothesis. With the QUEEN directionality now corrected (see summary above), the core observation motivating this suggestion lower ATP in NTT1 cells alongside evidence of import is substantially resolved: NTT1 cells have lower baseline intracellular ATP than Wt, and both LC-MS/MS and the corrected QUEEN readout show that exogenous ATP raises intracellular ATP specifically in NTT1 cells, consistent with net import under ATP-replete conditions. In addition, our expanded LCMS/MS data (Figure S2) suggest that NTT1 overexpression results in decreased de novo purine biosynthesis, offering a possible explanation for the decreased ATP levels in these cells.

      The RNA-seq analysis compared the effects of adding exogenous ATP in WT and NTT1 cells. However, analysis can also be done to compare the effects of NTT1 under conditions with or without exogenous ATP.

      We have added the missing transcriptomic comparison of NTT1 cells with versus without exogenous ATP, and the corresponding data are now presented in three new supplementary figures (Figures S1–S3).

      “In NTT1-expressing cells, ATP is directly imported from the extracellular environment…” the authors are assuming NTT1 cell imports ATP from media with ATP supplementation. This should be reconciled with the lower intracellular ATP concentration and lifespan shortening.

      This statement refers specifically to the ATP-treated condition, in which both LC-MS/MS and the corrected QUEEN measurements show elevated intracellular ATP in NTT1 cells relative to untreated NTT1 cells and to Wt. We have revised the text to make this conditional context explicit.

      The western blots for Hog1p and Hog1 should be quantified… I suggest using p-Hog1 or phos-Hog1 to indicate the phosphorylated form.

      We have now quantified Hog1 phosphorylation by western blot analysis from two independent biological replicate experiments, each probed with a different commercial phospho-Hog1 antibody, to confirm that the result is not antibody-specific (Figure 4, Figure S8; Methods). We have also adopted the reviewer’s suggested nomenclature and now consistently denote the phosphorylated form as Hog1ᵖ throughout the text and figures to avoid ambiguity with the Hog1p gene product naming convention.

      The model in Figure 8 indicates a larger lifespan extension in the NTT1 cells. This is not consistent with the data presented in Figure 5-7.

      The statement regarding a larger lifespan extension is consistent with the data presented in Figures 5 and 7, but we agree this connection was not spelled out clearly enough in the original text. These two figures differ in their treatment conditions. In Figure 6D, 10 µM ATP treatment resulted in a significant median lifespan extension in both Wt and NTT1 cells, though the magnitude of the extension differed: As stated in the Results, Wt cells showed a median RLS increase of 41% (p = 1.28E-12) upon 10 µM ATP supplementation, while NTT1 cells, which also showed an increase in ATP levels (Fig. 6B, C), showed an even more pronounced extension (median RLS increase = 60%, p = 4.03E-18) in lifespan (Fig. 6D). In Figure 7, we analyzed the higher dose of ATP treatment (10 mM) to examine its toxic effect on lifespan. Figure 7A shows that the lifespan-extension effect observed at the low ATP dose (Fig. 6) diminished under the 10 mM treatment condition for NTT1 cells but not for Wt cells. Figure 7B shows that elimination of functional mitochondria rescues this mitochondrial toxicity in NTT1 cells, such that these cells extend their lifespan to a magnitude comparable to Wt under 10 mM ATP treatment conditions.

      Reviewer #2 (Recommendations for the authors):

      The fluorescence imaging shows that cells with exogenous ATP have lower ATP levels, and that is not what was shown in Figure 2 by LCMS. So, as it is, this is confusing and not conclusive and may have to do with technical artifacts.

      We would like to clarify that our data do not in fact conflict. Figure 2B (fluorescence imaging) shows that NTT1 cells have lower intracellular ATP than Wt under baseline (no exogenous ATP) conditions, and that exogenous ATP treatment increases intracellular ATP abundance in both cell types. Figure 2C shows that under 2-deoxyglucose (2DG) treatment, Wt cells display decreased ATP levels regardless of exogenous ATP treatment, whereas NTT1 cells show increased intracellular ATP upon ATP treatment, further supporting the import function of NTT1. Our LC-MS/MS data (Fig. 2D, left panel) independently corroborate the fluorescence results in Figure 2B: NTT1 cells show lower ATP abundance than Wt cells at baseline, and exogenous ATP treatment raises intracellular ATP in NTT1 cells but not in Wt cells. We discuss the modest discrepancy between the LC-MS/MS and QUEEN measurements specifically for Wt cells in the Results section.

      The plots in Figure 5 make it seem like exogenous ATP addition lowers intracellular ATP for both the NTT1 cells and the wild-type cells, and that is not what the data in Figure 2d with LCMS shows… Restated, the data suggest they were successful in increasing intracellular ATP by LCMS, but not by queen reporter, and that the seemingly likely increased intracellular ATP was not causative…

      We appreciate the reviewer’s detailed reading of Figures 2 and 5. As described above, this apparent conflict arose because Figure 5 originally plotted a raw fluorescence value that is inversely related to ATP abundance without stating this clearly enough in the legend. We have corrected the legend, axis labels, and Results text so that the plotted quantity in Figure 5 (1/488 nm QUEEN signal) increases with ATP abundance, matching the LC-MS/MS data in Figure 2. With this correction, both assays agree that exogenous ATP treatment raises intracellular ATP in NTT1 cells; we discuss in the Results the more modest and assay-dependent behavior we observe in Wt cells, where LC-MS/MS and QUEEN differ somewhat in sensitivity and time-resolution.

      It needs a bit more experimental clarification and some thinking about what it means to disrupt ATP homeostasis with high extracellular levels of ATP, a generally biologically unnatural/pathological situation.

      The central premise of our study is that altered intracellular ATP is associated with lifespan. Mitochondrial functional decline is a hallmark of ageing, but whether this correlates with decreased ATP, and whether age-associated ATP changes are linked to cellular aging, remains an overlooked question. Here, for the first time, we show that not every cell exhibits a decreasing ATP trajectory with age, and that maintaining ATP homeostasis can extend lifespan. Interestingly, extracellular ATP sensing is directly relevant to cell-to-cell communication (purinergic signaling); however, this has never been characterized in yeast. Our data indicate that a similar, conserved mechanism may also be present in yeast, although the exact components of this sensing cascade require further study. This information is included in the Introduction and Discussion sections of the manuscript.

      The data for ATP levels with age needs to be cleaned up/refined… Sort some high bud scar cells and test them with LCMS… run wild-type cells to subtract out the autofluorescence… or redo it on a scope with linear unmixing.

      Our data indicates that each cell follows a different ATP-abundance trajectory during aging: while some cells show decreasing ATP with age, others show no change, and still others show increasing ATP. Population-level testing of sorted old cells would therefore not yield results that are directly comparable to our microfluidic experiments, in which we track individual cell trajectories over time. In the updated figure, we have subtracted autofluorescence.

      Figures 1 and 2 could be combined, and so could 3 and 5, 6 and 7. Figure 4 should probably be a supplement or part of another figure since upregulating HOG1 has nothing to do with the lifespan extension effect.

      We appreciate this suggestion for streamlining the figures. Our data show only decreased Hog1 activity under ATP treatment conditions; we did not test whether this decline in Hog1 activity is associated with lifespan extension. However, we would like to include this figure in the main text, because it directly validates the effect of extracellular ATP sensing on modulation of Hog1 activity.

      Figure 7 seems like it is showing that the NTT1 strain’s shorter replicative lifespan is rescued by losing mitochondria, and not that toxicity from ATP in the NTT1 is alleviated by losing mitochondria.

      Both the NTT1 strain’s shorter replicative lifespan and its ATP toxicity (10 mM) are rescued by eliminating respiratory mitochondria. In Figure 7, we analyzed the higher dose of ATP treatment (10 mM) to examine its toxic effect on lifespan. Figure 7A shows that the lifespan-extension effect observed at the low ATP dose (Fig. 5) diminished under the 10 mM treatment condition for NTT1 cells but not for Wt cells. Figure 7B shows that elimination of functional mitochondria rescues this mitochondrial toxicity in NTT1 cells, such that these cells extend their lifespan to a magnitude comparable to Wt under 10 mM ATP treatment conditions.

      I think you should make sure your pinhole is opened up to catch all the signal in z from each cell… It seems like taking the mean intensity of the top 40% of pixels in a cell can create technical artifacts… Please justify why you use that approach.

      We have added justification for the top-40%-pixel approach to the Methods: because the QUEEN reporter is a soluble cytoplasmic protein excluded from the large vacuoles that occupy an increasing fraction of cell volume as mother cells age, whole-cell or full-z-stack quantification systematically underestimates cytoplasmic ATP signal in older cells for reasons unrelated to ATP abundance. Restricting analysis to the brightest pixels focuses quantification on reporter-containing cytoplasm and avoids this age-confounded dilution artifact.

    1. eLife Assessment

      This important large-scale comparative study of odorant receptor (OR) genes across more than 100 insect species, combining sequence- and structure-based approaches, represents a significant contribution in the exploration of the evolution of this large gene family involved in the detection of odorant signals by olfactory neurons. Most notably, the authors uncover a structural feature unique to the odorant receptor co-receptor Orco that reduces ligand binding affinity. From this solid data emerges a general integrative framework to apprehend the diversity and evolution of odorant receptor genes in insects.

    2. Reviewer #1 (Public review):

      [Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. All the reviewers' comments have been adequately addressed and taken into account.]

      Objectives of the study and impact of the work

      The authors of this article primarily aim to reconstruct the evolutionary history of the insect odorant receptor (OR) family, which is responsible for the detection of odorant signals by olfactory neurons. Due to the lack of phylogenetic signal present in the sequences of this multigene family, which evolves very rapidly, phylogenetic analyses have so far never made it possible to precisely retrace how ORs diversified prior to the appearance of present-day insect orders, and what the drivers of this diversification were. For example, one may suspect that the adaptation of ORs to odors emitted by plants constituted a critical step in insect evolution during the "angiosperm terrestrial revolution," which occurred at the end of the Cretaceous, but nothing currently allows this to be asserted.

      There are very nice examples, notably in drosophilids, derived from comparisons between closely related species and documenting mechanisms of OR adaptation to certain signals. However, what the authors attempt to do in this work is to produce a macroevolutionary analysis at the scale of insects as a whole, based almost exclusively on bioinformatic analyses. To do this, they annotated OR genes in about one hundred insect species and developed pipelines for analyzing sequence similarity, structural similarity and functional similarity, the latter being estimated through a molecular docking approach. An important element in the evolution of insect ORs is the appearance of a unique co-receptor, called Orco, which appears to be an OR that has lost the ability to bind odorants. In addition to the large-scale bioinformatic analysis, the authors also aim to explore more specifically the factors that favored the emergence of Orco and the selective advantage conferred by the existence of OR-Orco complexes.

      Given the importance of odorant receptors in insect biology and in their adaptation to different environments and lifestyles, retracing their evolutionary history is indeed a major question in evolutionary biology. In principle, this type of work therefore has the potential to become a reference in the field and to provide a basis for significant scientific advances.

      Major strengths and weaknesses

      The sampling chosen for collecting OR sequences is very impressive, with more than 100 insect families represented, covering most of the major orders. This sampling appears appropriate for the question being addressed. The analysis pipeline used to collect the sequences makes sense, relying on homology-based annotation tools coupled with a structure-based filter. Nevertheless, one can note aberrant numbers of ORs for certain species (much lower than reality). A lower number of OR genes is somewhat expected, as the authors chose to apply a fairly stringent filter on sequence quality (based on predicted 3D structure), which reduces the number from 14,000 to 9,000. This choice seems logical given the subsequent use of these data, but it inevitably leads to data loss. However, the low number of genes also results from the fact that the pipeline did not function correctly for all genomes.

      In the revised version of the manuscript, the authors included a benchmarking step, which is a good point. They compared their OR gene annotations with previous reports in the same species. Unfortunately, this comparison is irrelevant because the chosen reference OR repertoires are actually a mix of transcriptome and genome annotations. Furthermore, comparisons with entire OR gene repertoires essentially demonstrate that their annotation was of good quality for species in which OR sequences were already present in the query OR database, but poor for species in which they were not. Therefore, these supplementary analyses made by the authors are not particularly in favor of an overall good quality of OR gene repertoires in the >110 species studied. The fact that some OR genes may be missing and that the total number may not be exact for each species is not prohibitive for studying the evolution of the family on a broad scale. However, it does call into question the correlation between the number of ORs and lifestyles and diets.

      From the dataset collected, the authors attempted to categorize ORs in several ways, starting with the reconstruction of sequence similarity networks. The approach is interesting, but fails to reveal homology relationships between ORs from species belonging to different insect orders. So, it is unclear what the advantage of this approach is compared with the "classical" phylogenetic approach.

      The clustering based on structure also leads to the identification of a majority of "order-specific" clusters, which does not provide major insights into the evolution of ORs. However, the authors highlight a group of ORs in flies that appear to possess an unusual intracellular region, as well as a cluster of OR shared across many insect orders that exhibit a larger binding cavity. This is really interesting, although more relevant to OR structure than to their evolution.

      The analysis of structural diversity then leads the authors to focus on the Orco co-receptors, which are characterized by modifications of the binding pocket and the appearance of an extracellular loop that could explain the loss of the ability to bind odorant molecules. This part, which relies on in vitro experiments, is interesting and constitutes the most striking result of this study, which could in itself have been the subject of a separate manuscript.

      The rest of the manuscript is based on the prediction of OR response spectra using molecular docking. The work that has been carried out is extremely substantial, and the objective of linking clusters based on sequence similarity or 3D structural similarity with functional categories is entirely relevant. The docking score threshold used was chosen thoughtfully, which is very good, and according to the calculation performed should ensure a true positive rate of more than 20%, which is excellent in such a docking analysis. But in the absence of functional validation, this 20% true positive rate is not sufficient to extrapolate OR function, and docking-derived binding breadth measures used in the remaining of the manuscript have to be taken with caution, as acknowledged by the authors themselves. Consequently, the chance that results of this part of the work will enlighten the evolution of OR on a broad scale is low. For example, the fact that insect lineages that emerged after the Permian-Triassic extinction have more broadly-tuned OR is an interesting observation, yet remains highly speculative.

      In summary, despite the large number of analyses performed, the authors do not really succeed in achieving the stated objective of reconstructing the evolutionary history of insect ORs, and the results obtained do not strongly support all the conclusions regarding the links between OR repertoires and environment or lifestyle.

    3. Reviewer #2 (Public review):

      The remarkable evolvability of the olfactory system enables animals to rapidly adapt to dynamic and chemically complex environments. Over the past two decades, substantial effort has been devoted to uncovering the evolutionary principles that drive the diversification of odorant receptors (ORs), yielding key insights into the forces shaping their striking variability in both vertebrates and insects. In this manuscript, Zhang and colleagues analyze the OR repertoires of over 100 insect species, leveraging sequence and structural similarity to infer patterns of gene family evolution within this diverse and ecologically important clade. By integrating sequence-based and structure-based comparisons, their study builds on a compelling and recently emerging line of research made possible by the advent of AlphaFold, which has previously clarified the phylogenetic relationship between insect Ors and the gustatory receptor gene family and revealed the unexpectedly deep evolutionary origins of this ancient structural fold.

      Applying this approach to a large set of ORs derived from species throughout the insect phylogeny, the authors confirm many previously reported patterns of OR evolution. More importantly, with their large-scale approach the authors found new structural features of different Or clades, resulting in intriguing testable hypotheses about Or evolution that have the potential to support future directions in studying this important gene family. For example, the authors identify a structural feature mostly unique to the OR co-receptor ORco, a beta-sheet in EL2, which they functionally show reduces odorant binding affinity - a key aspect of ORco, which does not bind ligands in the ancestral ligand-biding site. This is a particularly strong part of the manuscript, since the authors support their in silico-derived hypothesis with functional data.

      In an attempt to assess the relationship between sequence identity and structure on one hand and function on the other, the authors perform an in-silico structure prediction and chemical docking analysis. While not suitable to assess the actual function of individual receptors without functional testing, this strategy revealed potential receptor clades with differences in the number and type of docked chemicals. As a response to the previous reviews, the authors now changed their writing to refer to these results as 'docking-derived function' instead of just 'function'. While I appreciate that the authors changed their wording, this semantic change still can be read as referring to docking-derived predictions as conclusions of actual biological function. However, this is not the case. Any functional claims based solely on the docking approach need to be interpreted with caution, as in silico-only approaches are currently not well enough predictors for actual receptor function. Accordingly, I strongly recommend to remove the parts that connect receptor function to structure or ecology.

      Assessing their docking analyses with respect to species ecology, the authors identify correlations suggesting that Or repertoires might follow predictable evolutionary trajectories broadly aligned with species ecology. These are interesting hypotheses. However, due to the inability to predict actual receptor function the author's claims about ecology-function relationships are not well supported. Accordingly, the authors should change the respective parts of the manuscript in way that it is clear these are currently untested hypotheses. This includes adjusting the title, since the work does not find any experimental support for ecological tuning.

      I believe that future studies combining functional testing with an explicit evo-eco framework could benefit from this work and the explicit hypotheses it generates. But the authors should be careful to not overstate their in-silico derived results.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Objectives of the study and impact of the work

      The authors of this article primarily aim to reconstruct the evolutionary history of the insect odorant receptor (OR) family, which is responsible for the detection of odorant signals by olfactory neurons. Due to the lack of phylogenetic signal present in the sequences of this multigene family, which evolves very rapidly, phylogenetic analyses have so far never made it possible to precisely retrace how ORs diversified prior to the appearance of present-day insect orders, and what the drivers of this diversification were. For example, one may suspect that the adaptation of ORs to odors emitted by plants constituted a critical step in insect evolution during the "angiosperm terrestrial revolution," which occurred at the end of the Cretaceous, but nothing currently allows this to be asserted.

      There are very nice examples, notably in drosophilids, derived from comparisons between closely related species and documenting mechanisms of OR adaptation to certain signals. However, what the authors attempt to do in this work is to produce a macroevolutionary analysis at the scale of insects as a whole, based almost exclusively on bioinformatic analyses. To do this, they annotated OR genes in about one hundred insect species and developed pipelines for analyzing sequence similarity, structural similarity and functional similarity, the latter being estimated through a molecular docking approach. An important element in the evolution of insect ORs is the appearance of a unique co-receptor, called Orco, which appears to be an OR that has lost the ability to bind odorants. In addition to the large-scale bioinformatic analysis, the authors also aim to explore more specifically the factors that favored the emergence of Orco and the selective advantage conferred by the existence of OR-Orco complexes.

      Given the importance of odorant receptors in insect biology and in their adaptation to different environments and lifestyles, retracing their evolutionary history is indeed a major question in evolutionary biology. In principle, this type of work therefore has the potential to become a reference in the field and to provide a basis for significant scientific advances.

      Major strengths and weaknesses

      The sampling chosen for collecting OR sequences is very impressive, with more than 100 insect families represented, covering most of the major orders. This sampling appears appropriate for the question being addressed. The analysis pipeline used to collect the sequences makes sense, relying on homology-based annotation tools coupled with a structure-based filter. Nevertheless, one can note aberrant numbers of ORs for certain species (much lower than reality). A lower number of OR genes is somewhat expected, as the authors chose to apply a fairly stringent filter on sequence quality (based on predicted 3D structure), which reduces the number from 14,000 to 9,000. This choice seems logical given the subsequent use of these data, but it inevitably leads to data loss. However, the low number of genes also results from the fact that the pipeline did not function correctly for all genomes.

      In the revised version of the manuscript, the authors included a benchmarking step, which is a good point. They compared their OR gene annotations with previous reports in the same species. Unfortunately, this comparison is irrelevant because the chosen reference OR repertoires are actually a mix of transcriptome and genome annotations. Furthermore, comparisons with entire OR gene repertoires essentially demonstrate that their annotation was of good quality for species in which OR sequences were already present in the query OR database, but poor for species in which they were not. Therefore, these supplementary analyses made by the authors are not particularly in favor of an overall good quality of OR gene repertoires in the >110 species studied. The fact that some OR genes may be missing and that the total number may not be exact for each species is not prohibitive for studying the evolution of the family on a broad scale. However, it does call into question the correlation between the number of ORs and lifestyles and diets.

      Thank you for this important comment. We agree that it is inappropriate to directly compare genome-based OR annotations with OR repertoires derived solely from transcriptomes, because transcriptome-only datasets generally do not capture the complete OR gene complement of a species. We therefore repeated the benchmarking analysis and collected, wherever available for the species included in this study, published OR repertoires based on genome annotation or genome annotation supported by transcriptomic evidence. Transcriptome-only OR datasets are no longer included in this comparison.

      The updated benchmarking analysis shows that, for most species with comparable genome-based reference repertoires, the number of ORs identified in this study exceeds 70% of the previously reported number, with good agreement in OR repertoire size for most species (Table S1). Diabrotica virgifera is an exception: a previous manual annotation reported 124 complete ORs, whereas we identified 78, approximately two-thirds of the previously reported number. This result indicates that our automated annotation and stringent structural filtering pipeline may indeed underestimate OR repertoire size in some species. The large, repeat-rich genome of this species may further complicate gene prediction and the reconstruction of OR loci. We have therefore explicitly stated in the revised manuscript that incorporating transcriptomic evidence and manual curation may help recover more complete OR repertoires from such complex genomes.

      Another notable outlier is Anagrus nilaparvatae. A previous study reported 88 ORs, whereas we identified 330. Examination of the methods used in that study revealed that its OR search relied primarily on homology searches using known OR sequences from two other hymenopteran species. Because insect ORs constitute a rapidly evolving gene family with pronounced lineage-specific expansions, a limited query set may fail to detect highly divergent or species-specific ORs. In comparison, our study used OR queries from a broader range of insects and a genome-level annotation pipeline specifically designed for insect ORs, which may have enabled the detection of more highly divergent candidate ORs. Because the protein sequences of the 88 ORs were not made available in the original study, we are currently unable to perform a one-to-one comparison to verify whether all of these ORs are included in our annotations.

      You also noted that our better performance in species such as Drosophila melanogaster, Anopheles gambiae, and Bombyx mori might partly reflect the inclusion of known OR sequences from these species in the query database. We consider this an important and directly testable question. We therefore divided the benchmark species into two groups according to whether conspecific OR sequences were represented in the query database, and used ln(OR count_this study / OR count_previous) to quantify differences in OR counts relative to previous genome-based annotations. No significant difference was detected between the two groups (P = 0.841, Fig. S1 B). Given the unusually large difference in OR counts between the two studies for A. nilaparvatae, we also performed a sensitivity analysis excluding this species, which yielded the same conclusion (P = 0.413, Fig.S1 C). Thus, among the species for which comparable genome-based reference repertoires are currently available, we found no evidence that the inclusion of conspecific ORs in the query database systematically affected the final OR repertoire size. We have added this analysis to the supplementary material of the revised manuscript.

      Finally, we agree that annotation biases in OR repertoire size may affect analyses of its correlations with ecological traits. We therefore repeated all relevant ecological sensitivity analyses using an updated OR count dataset, taking the larger of the OR counts from our study and the previous annotation for each species (Fig. S1 D- G). The main statistical results remained consistent with the original analyses: OR counts were still significantly associated with adult diet, larval diet, and habitat, whereas no significant association was detected with circadian rhythm. We therefore consider the statistical associations between OR counts and the main ecological variables to be reasonably robust within the range of annotation discrepancies detectable using the currently available genome-based benchmarks, although automated annotation and stringent structural filtering may underestimate OR counts in some species. The updated benchmarking analyses are described in Supplementary Text S1 and summarized in Table S1.

      The following revision has been added (lines 103-107): “Benchmarking against curated OR repertoires showed that the automated pipeline recovered most previously reported ORs and that stringent structural filtering produced a high-confidence set of structurally intact receptors (Supplementary Text S1, Tables S1–S3, and fig. S1B–C). For most species with comparable genome-based references, retained OR counts exceeded 70% of published values.”

      From the dataset collected, the authors attempted to categorize ORs in several ways, starting with the reconstruction of sequence similarity networks. The approach is interesting, but fails to reveal homology relationships between ORs from species belonging to different insect orders. So it is unclear what the advantage of this approach is compared with the "classical" phylogenetic approach.

      Thank you for raising this important question. Our intention was not to replace classical phylogenetic analysis with sequence similarity networks. Rather, we used network-based approaches as a class-wide framework to classify ORs independently according to sequence similarity, structural similarity, and docking profiles, and then compared how these different classifications correspond across insect taxa and major macroevolutionary transitions.

      For smaller gene families with limited sequence divergence, defining subfamilies based on gene trees is a well-established and highly effective approach, because phylogenetic trees can both identify subfamilies and reveal their evolutionary relationships. However, the insect OR superfamily exhibits pronounced birth-and-death evolution, with frequent gene duplications and losses across lineages, and comprises a vast number of highly divergent sequences. Consequently, when a single phylogenetic tree is constructed for the entire OR superfamily, deep nodes often lack sufficient support, and relationships among subfamilies may be difficult to resolve consistently. It is therefore challenging to obtain a stable and consistent classification of thousands of ORs into subfamilies across insects using a single gene tree alone. We therefore used sequence similarity networks as a complementary classification approach. Their main advantage is that they allow large datasets to be grouped according to sequence similarity among all ORs without requiring the resolution of every deep phylogenetic relationship. More importantly, similar network frameworks can be used to construct sequence similarity, structural similarity, and docking-profile similarity networks, allowing SeqC, StrC, and dPC to be compared directly within a common analytical framework. This is difficult to achieve using conventional gene trees alone and is the main reason for our choice of a network-based approach.

      Sequence similarity networks alone cannot directly resolve homology relationships among ORs from different insect orders. During the previous revision, we therefore sought to link sequence communities to phylogenetic relationships. We conducted additional phylogenetic analyses of shared sequence communities containing multiple insect orders to search for potentially conserved OR lineages spanning different orders. However, among these shared communities, we identified only one conserved OR lineage spanning four insect orders with relatively clear phylogenetic support. We have added this result to the Supplementary Data S6. This finding further indicates that clearly identifiable orthology relationships spanning multiple orders are very limited among extant insect ORs.

      The following revision has also been added (lines 150-154): “The SSN is not intended to replace phylogenetic reconstruction, but rather to provide a complementary framework for summarizing sequence relationships among thousands of highly divergent ORs at a class-wide scale. It allows major patterns of OR sequence similarity and diversification to be visualized more clearly across the entire dataset. Phylogenetic analyses can then be used to further resolve evolutionary relationships within specific sequence communities.”

      The clustering based on structure also leads to the identification of a majority of "orderspecific" clusters, which does not provide major insights into the evolution of ORs. However, the authors highlight a group of ORs in flies that appear to possess an unusual intracellular region, as well as a cluster of OR shared across many insect orders that exhibit a larger binding cavity. This is really interesting, although more relevant to OR structure than to their evolution.

      Thank you for this important comment. We agree that the previous version of the manuscript focused primarily on structural differences among structural communities and did not adequately explore their potential evolutionary implications. StrC23 is the most widely distributed structural community, spanning 12 insect orders and containing 46.3% of the ORs. We further found that its putative binding pockets were significantly larger than those of other ORs, which we previously interpreted as a potentially conserved structural pattern across taxa. Following your suggestion, we conducted additional analyses of the phylogenetic and orthology relationships within StrC23. The results showed that StrC23 does not correspond to a single, broadly conserved orthogroup spanning insect orders, but instead contains 156 distinct orthologous groups. Of these, 24 encompass more than 10 species. Only a few of these larger groups span multiple insect orders: one contains ORs from Hymenoptera, Diptera, and Trichoptera, and two contain ORs from both Lepidoptera and Trichoptera. The remaining groups show pronounced order specificity, including nine Hymenoptera-specific groups, eight Lepidoptera-specific groups, and four Diptera-specific groups.

      These results indicate that the large putative binding pocket shared by StrC23 members cannot simply be attributed to a single conserved orthologous lineage spanning multiple orders, but is distributed across several phylogenetically independent OR lineages. This pattern may reflect convergent evolution toward similar structural features in different OR lineages, or the retention of an older structural feature in multiple OR lineages that subsequently underwent substantial sequence divergence. The current data do not allow us to distinguish conclusively between these two possibilities. We have added the phylogenetic and orthologous-group analyses of StrC23 to the revised manuscript and supplementary material to further clarify the evolutionary implications of this structural community shared across insect orders.

      The following revision has been added (lines 208-218): “To further investigate the evolutionary basis of this broadly shared structural feature, we examined the phylogenetic and orthology relationships among StrC23 receptors. StrC23 was divided into 156 orthologous groups, 24 of which contained ORs from more than 10 species. However, only a few of these larger groups spanned multiple insect orders: one included ORs from Hymenoptera, Diptera, and Trichoptera, while two included ORs from both Lepidoptera and Trichoptera (Supplementary Data S6). Thus, StrC23 does not represent a single conserved cross-order orthologous lineage, but instead comprises multiple phylogenetically distinct OR lineages. Its shared large-pocket architecture therefore cannot be explained solely by conservation of a single orthologous lineage and may reflect either structural convergence or retention of an ancient structural feature followed by substantial sequence divergence.”

      The analysis of structural diversity then leads the authors to focus on the Orco co-receptors, which are characterized by modifications of the binding pocket and the appearance of an extracellular loop that could explain the loss of the ability to bind odorant molecules. This part, which relies on in vitro experiments, is interesting and constitutes the most striking result of this study, which could in itself have been the subject of a separate manuscript.

      We thank the reviewer for the positive assessment of this part of our work.

      The rest of the manuscript is based on the prediction of OR response spectra using molecular docking. The work that has been carried out is extremely substantial, and the objective of linking clusters based on sequence similarity or 3D structural similarity with functional categories is entirely relevant. The docking score threshold used was chosen thoughtfully, which is very good, and according to the calculation performed should ensure a true positive rate of more than 20%, which is excellent in such a docking analysis. But in the absence of functional validation, this 20% true positive rate is not sufficient to extrapolate OR function, and docking-derived binding breadth measures used in the remaining of the manuscript have to be taken with caution, as acknowledged by the authors themselves. Consequently, the chance that results of this part of the work will enlighten the evolution of OR on a broad scale is low. For example, the fact that insect lineages that emerged after the Permian-Triassic extinction have more broadly-tuned OR is an interesting observation, yet remains highly speculative.

      Thank you for this important comment. We agree that, even after optimizing the docking threshold, the current hit rate of approximately 20% remains insufficient to interpret molecular docking results as reliable predictions of actual OR ligand responses or functions. We also acknowledge that the biological implications of some docking results were still overstated in the previous revision, potentially leading readers to interpret these computational results as approximations of actual OR function. To avoid this misunderstanding, we have further revised the relevant terminology and interpretation of results throughout the latest manuscript. For results derived entirely from molecular docking, we no longer use terms such as ‘docking-derived function’, ‘functional community’, or other expressions that might imply experimental evidence of function. Instead, we consistently use ‘docking profile’ to make clear that these analyses reflect only similarities in computational docking patterns among receptors.

      In addition, following your comments and the related suggestions from Reviewer 2, we have removed all results and discussion that directly linked docking-based predictions of OR function or recognition breadth to species ecology. We have explicitly presented the few remaining macroevolutionary comparisons as hypothesis-generating observations rather than evidence of functional evolution. For example, differences in docking-profile composition among extant insect lineages that originated before or after the end-Permian mass extinction (EPME) are now described only as a broad statistical pattern, with any interpretation framed as an experimentally untested hypothesis. Whether this pattern reflects actual differences in receptor tuning, and whether it is evolutionarily linked to environmental changes associated with the EPME, still requires testing through broader taxonomic sampling, reliable ancestral-state reconstruction, and functional experiments. We hope that these revisions restrict the interpretation of molecular docking results to what the evidence can support and bring the corresponding conclusions into closer alignment with the current strength of evidence.

      The following revision has been added in Result section (lines 367-369): “These observations should be interpreted as a docking-derived comparative pattern among extant lineages rather than direct evidence of ancestral functional changes across the

      EPME.”

      And Discussion section (lines 410-414): “However, this interpretation remains hypothetical. Because rapid OR evolution and limited cross-order orthology preclude reliable reconstruction of deep ancestral repertoires, the EPME-related pattern should be viewed as a descriptive comparison among extant lineages. Further validation will require broader taxon sampling, ancestral reconstruction, and experimental testing of receptor tuning breadth.”

      In summary, despite the large number of analyses performed, the authors do not really succeed in achieving the stated objective of reconstructing the evolutionary history of insect ORs, and the results obtained do not strongly support all the conclusions regarding the links between OR repertoires and environment or lifestyle.

      Thank you for this important comment. We agree that ‘reconstructing the evolutionary history of insect ORs’ was too broad a formulation and exceeded what our methods could reliably address. A rigorous reconstruction of evolutionary history requires well-resolved phylogenetic relationships across taxa, ancestral-state inference, and systematic analysis of gene duplication and loss events. We have therefore redefined the scientific objectives of this study. The revised manuscript no longer claims to have reconstructed the evolutionary history of insect ORs. Instead, it aims to systematically characterize insect OR diversity and classification patterns at the levels of sequence, structure, and docking profile, compare the correspondence among these levels, and describe differences in their distribution across insect taxa and broad macroevolutionary contexts.

      We have also reassessed the strength of different types of evidence concerning the relationships between OR repertoires and environment or lifestyle. For relationships between OR repertoire size and ecological traits, we repeated the genome-based benchmarking. After rerunning the ecological correlation sensitivity analyses using the updated OR count dataset, the main statistical results remained consistent with the original analyses. We therefore consider the current data to continue to support statistical associations between OR counts and ecological traits, with a degree of robustness. By contrast, we have removed results and discussion directly linking predicted OR function or recognition breadth to species ecology, and no longer use terms such as ‘docking-derived function’ or ‘functional community’ that might imply experimental evidence of function. The remaining macroevolutionary comparisons of docking profiles, such as differences in docking-profile composition among groups considered in relation to the EPME, are treated only as hypothesis-generating comparative patterns, rather than direct evidence of receptor functional evolution. We explicitly state that these hypotheses require future testing through functional experiments and more reliable ancestral-state reconstruction.

      In summary, we have redefined the study objectives and no longer describe this work as a reconstruction of insect OR evolutionary history. We have retained the statistical associations between OR counts and ecological traits that remained supported after reassessment, while removing results and discussion linking receptor function to ecology on the basis of molecular docking. We believe these revisions better align the scientific objectives and scope of the conclusions with the evidence provided by the available data and methods.

      We have revised the final paragraph of the Introduction to clarify the scope and objectives of this study (lines 80-91): “In this study, we characterized large-scale patterns of OR diversification across 115 species spanning 16 insect orders. Using a framework that integrates network analysis, protein structure prediction, and molecular docking, we constructed sequence-similarity (SSN), structural-similarity (StSN), and docking-profile similarity networks (dPSN) to classify ORs from three distinct perspectives. We identified differences in OR sequence-community diversity between holometabolous and non-holometabolous insects, as well as differences in docking-profile community composition among extant lineages whose order-level origins fall on opposite sides of the end-Permian mass extinction (EPME). Across this class-wide sampling, OR repertoire size was associated with adult diet, larval diet, and habitat, but not with circadian rhythm. Moreover, the conserved β-sheet in the extracellular loop 2 (EL2) region of Orco, together with its specialized binding-pocket architecture, is associated with its reduced ligand-binding capacity and represents a prominent structural specialization of the co-receptor lineage.”

      Reviewer #2 (Public review):

      The remarkable evolvability of the olfactory system enables animals to rapidly adapt to dynamic and chemically complex environments. Over the past two decades, substantial effort has been devoted to uncovering the evolutionary principles that drive the diversification of odorant receptors (ORs), yielding key insights into the forces shaping their striking variability in both vertebrates and insects. In this manuscript, Zhang and colleagues analyze the OR repertoires of over 100 insect species, leveraging sequence and structural similarity to infer patterns of gene family evolution within this diverse and ecologically important clade. By integrating sequence-based and structure-based comparisons, their study builds on a compelling and recently emerging line of research made possible by the advent of AlphaFold, which has previously clarified the phylogenetic relationship between insect Ors and the gustatory receptor gene family and revealed the unexpectedly deep evolutionary origins of this ancient structural fold.

      Applying this approach to a large set of ORs derived from species throughout the insect phylogeny, the authors confirm many previously reported patterns of OR evolution. More importantly, with their large-scale approach the authors found new structural features of different Or clades, resulting in intriguing testable hypotheses about Or evolution that have the potential to support future directions in studying this important gene family. For example, the authors identify a structural feature mostly unique to the OR co-receptor ORco, a beta-sheet in EL2, which they functionally show reduces odorant binding affinity - a key aspect of ORco, which does not bind ligands in the ancestral ligand-biding site. This is a particularly strong part of the manuscript, since the authors support their in silico-derived hypothesis with functional data.

      We thank the reviewer for the positive assessment of this part of our work.

      In an attempt to assess the relationship between sequence identity and structure on one hand and function on the other, the authors perform an in-silico structure prediction and chemical docking analysis. While not suitable to assess the actual function of individual receptors without functional testing, this strategy revealed potential receptor clades with differences in the number and type of docked chemicals. As a response to the previous reviews, the authors now changed their writing to refer to these results as 'docking-derived function' instead of just 'function'. While I appreciate that the authors changed their wording, this semantic change still can be read as referring to dockingderived predictions as conclusions of actual biological function. However, this is not the case. Any functional claims based solely on the docking approach need to be interpreted with caution, as in silico-only approaches are currently not well enough predictors for actual receptor function. Accordingly, I strongly recommend to remove the parts that connect receptor function to structure or ecology.

      Thank you for highlighting this important issue again. We agree with your comments and acknowledge that the presentation of molecular docking results in the previous revision still overstated their biological implications. Following your suggestion, we have removed all results and discussion that directly linked receptor function to species ecology solely on the basis of molecular docking, to avoid interpreting computational predictions as actual biological functions. We then systematically reviewed and revised docking-related terminology throughout the manuscript. For results derived entirely from docking analyses, we no longer use ‘docking-derived function’, ‘functional community’, or other expressions that might imply experimental evidence of function. Instead, we consistently use ‘docking profile’ to clarify that these analyses reflect similarities in computational docking patterns among receptors, rather than experimentally validated functional categories.

      Through these revisions, we hope to distinguish more clearly between computational docking profiles and experimentally validated receptor function, and to avoid biological interpretations of docking results that exceed the available evidence. We believe these changes bring the manuscript's conclusions into closer alignment with the strength of the evidence.

      Assessing their docking analyses with respect to species ecology, the authors identify correlations suggesting that Or repertoires might follow predictable evolutionary trajectories broadly aligned with species ecology. These are interesting hypotheses. However, due to the inability to predict actual receptor function the author's claims about ecology-function relationships are not well supported. Accordingly, the authors should change the respective parts of the manuscript in way that it is clear these are currently untested hypotheses. This includes adjusting the title, since the work does not find any experimental support for ecological tuning.

      Thank you for this important suggestion. We agree that the current molecular docking analyses cannot support direct conclusions about relationships between actual receptor function and species ecology. Following your suggestion, we have removed the results and discussion directly linking docking-based predictions of OR function to species ecology. For the remaining macroevolutionary comparisons, such as differences in docking-profile composition among groups considered in relation to the EPME, we explicitly state that these results represent only computational patterns observed in extant taxa. The evolutionary interpretations proposed from these patterns remain experimentally untested hypotheses and cannot be considered direct evidence of changes in actual receptor function.

      In addition, following your suggestion, we have changed the manuscript title to ‘Classwide sequence, structural, and docking-profile diversity of insect odorant receptors and structural specialization of Orco’. The new title no longer emphasizes ‘ecological tuning’ or ‘olfactory perception’, but instead focuses on class-wide diversification of insect OR sequences, structures, and docking profiles, together with the experimentally supported structural specialization of Orco.

      The following revision has been added in Result section (lines 367-369): “These observations should be interpreted as a docking-derived comparative pattern among extant lineages rather than direct evidence of ancestral functional changes across the EPME.”

      And Discussion section (lines 410-414): “However, this interpretation remains hypothetical. Because rapid OR evolution and limited cross-order orthology preclude reliable reconstruction of deep ancestral repertoires, the EPME-related pattern should be viewed as a descriptive comparison among extant lineages. Further validation will require broader taxon sampling, ancestral reconstruction, and experimental testing of receptor tuning breadth.”

      I believe that future studies combining functional testing with an explicit evo-eco framework could benefit from this work and the explicit hypotheses it generates. But the authors should be careful to not overstate their in-silico derived results.

      Thank you for this suggestion. We have revised the manuscript accordingly.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Below are my comments in response to the rebuttal letter. I thank the authors for their careful revision and detailed responses to the points raised in my review.

      (1) OR annotation pipeline and correlation analyses

      Previous comment: "One can note aberrant numbers of ORs for certain species (much lower than reality), which indicates that the pipeline probably did not function correctly for all genomes. In the absence of a validation step comparing the results with already known OR repertoires, it is difficult to estimate the overall quality of the data. [...] It calls into question certain results that rely on this total number, such as the correlation between the number of ORs and genome size, lifestyle, and diet."

      The response made by the authors on that point is not particularly convincing. They compared their OR gene annotations with previous reports in the same species, but this comparison is irrelevant because the chosen reference OR repertoires are actually a mix of transcriptome and genome annotations (the OR counts for Harmonia axyridis, Plutella xylostella, Pierus brassicae, Dendrolimus kikuchii, Carposina sasakii, Hermetia illucens, Chlorops oryzae and Propsilocerus akamusi are from transcriptomes). As not all OR genes are expressed in a given tissue, the number of ORs in transcriptomes is always lower than in genomes. Furthermore, comparisons with entire OR gene repertoires essentially demonstrate that their annotation was of good quality for species in which OR sequences were already present in the query OR database (Bombyx mori, Drosophila melanogaster, Anopheles gambiae) but was of bad quality for species in which OR sequences were NOT in the query (Diabrotica virgifera). However, re-annotation of Diabrotica led to an OR repertoire of 78 complete genes, vs. 124 in the manually curated genome annotation (1/3rd of the genes are missing). So these supplementary analyses made by the authors are not particularly in favor of an overall good quality of OR gene repertoires in the >110 species studied. They have also repeated the correlation analyses using 'full' OR gene sets where available. This showed that the correlation of OR count with genome size is no longer valid, but the correlation with diet or lifestyle is. However, if a mix of gene numbers from transcriptomes and genomes was used (see my comment above), I am afraid that these novel correlation analyses are not relevant. In conclusion, I would disagree with the claim that "These new benchmark and sensitivity analyses directly strengthen the evidence supporting our dataset and its ecological interpretations".

      Thank you for further highlighting this issue. As detailed in our response to the Public Review, we repeated the OR annotation benchmarking, excluded transcriptome-only repertoires, and restricted comparisons to genome-based reference datasets or genome-based datasets supported by transcriptomic evidence. The updated results show that, for most species with comparable reference data, the number of ORs identified in this study exceeds 70% of the previously reported number. We also explicitly acknowledge that repertoire size may still be underestimated in some species.

      We also directly tested whether query database composition systematically affected the number of annotated ORs. No significant difference was detected between species with and without conspecific OR queries (P = 0.841; P = 0.413 after excluding Anagrus nilaparvatae). Thus, we currently find no evidence that conspecific queries systematically increase the final OR count.

      In addition, we repeated the sensitivity analyses of ecological correlations using the updated OR count dataset. The main statistical results remained unchanged: OR counts were still significantly associated with adult diet, larval diet, and habitat, but not with circadian rhythm. We therefore consider these statistical associations between OR counts and ecological traits to be reasonably robust to the annotation differences currently detectable. Detailed results are provided in our response to the Public Review and in the revised supplementary text S1.

      (2) sequence similarity-based clusters

      Previous comment: "The authors attempted to categorize ORs in several ways, starting with the reconstruction of sequence similarity networks. The approach is interesting, but in the end, the results do not seem to be sufficiently exploited. [...] For example, clusters SeqC26 or SeqC28, which appear to be shared by many insects, are potentially very interesting. It might have been relevant to combine this similarity-based clustering approach with phylogenetic reconstructions within each shared cluster."

      The authors have followed this suggestion and identified an orthogroup conserved across several insect orders, which is an interesting observation. However the phylogeny of this orthogroup should be provided in supplementary data, which does not seem to be the case.

      We thank the reviewer for the positive assessment. The corresponding phylogenetic tree has been added to the supplementary data S6.

      (3) structure-based clusters

      Previous comment: "The clusters shared by several orders are not truly exploited, which does not provide new insight into the evolution of ORs"

      The authors show an interesting feature for a cluster of ORs from several insect orders, with a large putative binding pocket. However, it is not clear whether they verified or not if this cluster also correspond to an orthogroup or if this shared feature (a large binding pocket) is a convergence. They also improved the discussion of their results on a cluster of fly ORs with a long intracellular domain, which is a good thing.

      Thank you for this important question. To determine whether StrC23, a structural community shared across multiple insect orders, corresponds to a conserved orthogroup, we performed additional phylogenetic analyses of its OR sequences and identified orthology relationships within the community based on the gene tree. The results showed that StrC23 does not correspond to a single orthogroup spanning insect orders, but instead contains 156 distinct orthologous groups. Of these, 24 encompass more than 10 species. Among these 24 larger groups, one contains ORs from Hymenoptera, Diptera, and Trichoptera, and two contain ORs from both Lepidoptera and Trichoptera. The remaining groups show pronounced order specificity, including nine Hymenoptera-specific groups, eight Lepidoptera-specific groups, and four Diptera-specific groups.

      These results indicate that the large putative binding pocket characteristic of StrC23 is not explained by a single, broadly conserved orthogroup spanning multiple orders, but is distributed across several phylogenetically independent OR lineages. We therefore consider that this shared structural feature cannot simply be interpreted as direct retention within a conserved orthologous lineage. It may reflect convergent evolution toward similar structural features in different OR lineages, or the retention of an older structural feature in multiple OR lineages that subsequently underwent substantial sequence divergence. The current data do not allow us to distinguish conclusively between these two possibilities. We have added the phylogenetic analysis of StrC23 to the revised manuscript to clarify the evolutionary relationships within this structural community.

      The following revision has been added (lines 208-218): “To further investigate the evolutionary basis of this broadly shared structural feature, we examined the phylogenetic and orthology relationships among StrC23 receptors. StrC23 was divided into 156 orthologous groups, 24 of which contained ORs from more than 10 species. However, only a few of these larger groups spanned multiple insect orders: one included ORs from Hymenoptera, Diptera, and Trichoptera, while two included ORs from both Lepidoptera and Trichoptera (Supplementary Data S6). Thus, StrC23 does not represent a single conserved cross-order orthologous lineage, but instead comprises multiple phylogenetically distinct OR lineages. Its shared large-pocket architecture therefore cannot be explained solely by conservation of a single orthologous lineage and may reflect either structural convergence or retention of an ancient structural feature followed by substantial sequence divergence.”

      (4) molecular dynamics simulation

      Previous comment: "The molecular dynamics modelling does not add anything in the way it is conducted (5 ns is too short)."

      The authors argue that such a short simulation is sufficient for testing the interaction between a compound and the extracellular beta-sheet of the coreceptor Orco. I'm not a specialist enough to confirm whether this is actually true, but I trust the author's expertise in this area.

      We thank the reviewer for the positive assessment.

      (5 / 6) molecular docking and functional extrapolation

      Previous comment: "in the absence of functional validation, this 20% true positive rate is not sufficient to extrapolate OR function"

      The authors argue that molecular docking was not used to identify specific OR/ligand pairs, but rather to "estimate the potential binding tendencies of OR repertoires". I agree with this point; correlations were made using "binding breadth index values" that are calculated for entire OR repertoires. The text of the manuscript has been edited to refer to "docking-derived" functional clusters and binding breadth index, and to emphasise that the results should be interpreted with caution, which is a good point as it will prevent potential misinterpretation by the reader. However, this does not change the fact that interpreting the olfactory capacity of one species based on docking-derived binding breadth values of its OR repertoire is highly speculative (especially when OR repertoires are incomplete) and the chance that results of this part of the work will enlighten the evolution of OR on a broad scale is low.

      Thank you for raising this concern. We agree that repertoire-level analyses do not resolve the inherent limitations of molecular docking in predicting actual receptor function. Although such analyses summarize overall docking patterns rather than individual OR–ligand interactions, they still cannot reliably define a species’ true olfactory capabilities. It therefore remains insufficient to describe a species' actual olfactory capabilities solely on the basis of docking-derived binding breadth. As detailed in our response to the Public Review, we have addressed this issue by following your suggestions and those of Reviewer 2 and removing the results and discussion that used the binding breadth index to directly link predicted receptor function to ecological traits such as diet, habitat, and circadian rhythm. We have also further standardized the terminology, replacing ‘docking-derived function’, ‘functional community’, and other expressions that could imply experimentally established function with ‘docking profile’, to emphasize that these results describe computational docking patterns rather than experimentally determined receptor functions. Through these revisions, we hope to distinguish more clearly between computational docking patterns and experimentally established receptor functions, and to restrict the interpretation of the related macroevolutionary comparisons to what the current data can support.

      (7) EPME

      Previous comment: "ORs of non-holometabolous insects cannot be considered equivalent to those of species that existed before the Permian-Triassic extinction"

      The authors have now clarified the fact that they compared extant insect orders rather than ancestral and derived OR repertoires.

      We thank the reviewer for the positive assessment of the revised manuscript.

      (8 to 16)

      The authors have carefully considered all the points raised in the recommendations for the authors, improving the manuscript on several aspects.

      We thank the reviewer for the positive assessment of the revised manuscript.

      Minor comment

      Line 217: Orco did not emerge in the ancestor of Zygentoma, it emerged in the common ancestor of Zygentoma and Pterygota.

      Thank you for pointing this out. We have corrected the statement.

      Reviewer #2 (Recommendations for the authors):

      The authors have done a good job in their revision of the manuscript. They addressed many of the reviewers' concerns well.

      We thank the reviewer for the positive assessment of our previous revision.

      However, the functional claims are still too strong and need to be removed or tempered down as described in the public review.

      Thank you for this suggestion. We have removed or tempered the functional predictions in the manuscript in accordance with the Public Review. Please see our response to the Public Review for details.

      Further, the manuscript title is not a good fit for the content. I would argue that the paper does not 'reveal ecological tuning shaping olfactory perception', since Or structurefunction relationships are involved in olfactory sensation or detection. Perception, however, requires interpretation, which is done by neural circuits but not receptors expressed within. Further, I still think that referring to 'ecological tuning' is far-fetched based on the docking approach that cannot really resolve the tuning of receptor sets. I would think that the work reveals interesting correlations between docking-derived population tuning and ecology. But that does not necessarily have anything to do with actual function and thus may not make much sense to advertise in the title or abstract.

      Thank you for your suggestions concerning the manuscript title. Following this comment, we have changed the title from ‘Evolutionary dynamics of insect odorant receptors reveal ecological tuning shaping olfactory perception’ to ‘Class-wide sequence, structural, and docking-profile diversity of insect odorant receptors and structural specialization of Orco’ The new title no longer emphasizes ecological tuning or olfactory perception, but instead focuses on the two aspects of this study supported by the most direct evidence: class-wide patterns of sequence and structural diversification in insect ORs, and the experimentally supported structural specialization of Orco.

      Consistent with the revised title, we have further adjusted the relevant wording in the Abstract, Results, and Discussion, and removed content directly linking receptor function to species ecology on the basis of docking results. We believe that the revised title and abstract more accurately reflect the main content of the study and the scope of its evidence, while avoiding functional or ecological interpretations of computational predictions that exceed their evidential support.

      I suggest selecting a title that fits the sequence/structure/in silico docking nature of their large macroevolutionary approach and findings better. After all, apart from the orco work this manuscript is mostly a resource of in silico predictions.

      Thank you for this suggestion. We have revised the manuscript title.

      Author response table 1.

      Numbers of OR genes in previously annotated species.

      Reference

      Carey AF, Wang G, Su CY, Zwiebel LJ, Carlson JR. 2010. Odorant reception in the malaria mosquito Anopheles gambiae. Nature 464: 66-71.

      Engsontia P, Sangket U, Chotigeat W, Satasook C. 2014. Molecular evolution of the odorant and gustatory receptor genes in lepidopteran insects: implications for their adaptation and speciation. J Mol Evol 79: 21-39.

      Ma Y, Guo Z, Wang L, Wang B, Huang T, Tang B, Zhang G, Zhou Q. 2022. The genome of the rice planthopper egg parasitoid wasp Anagrus nilaparvatae casts light on the chemo- and mechanosensation in parasitism. BMC Genomics 23: 541.

      Mier P, Fontaine JF, Stoldt M, Libbrecht R, Martelli C, Foitzik S, Andrade-Navarro MA. 2022. Annotation and Analysis of 3902 Odorant Receptor Protein Sequences from 21 Insect Species Provide Insights into the Evolution of Odorant Receptor Gene Families in Solitary and Social Insects. Genes (Basel) 13.

      Paddock KJ, Corcoran JA. 2025. Life-stage dependent behavior mimics chemosensory repertoire diversity in a belowground, specialist herbivore. G3 (Bethesda) 15.

      Robertson HM, Warr CG, Carlson JR. 2003. Molecular evolution of the insect chemoreceptor gene superfamily in Drosophila melanogaster. Proc Natl Acad Sci U S A 100 Suppl 2: 14537-14542.

      Tanaka K, Uda Y, Ono Y, Nakagawa T, Suwa M, Yamaoka R, Touhara K. 2009. Highly selective tuning of a silkworm olfactory receptor to a key mulberry leaf volatile. Curr Biol 19: 881-890.

      Wang Q, Dicke M, Haverkamp A. 2023. Sympatric Pieris butterfly species exhibit a high conservation of chemoreceptors. Front Cell Neurosci 17: 1155405.

      Xiao S, Ye X, Wang S, Yang Y, Fang Q, Wang F, Ye G. 2023. Genome assembly of the ectoparasitoid wasp Theocolax elegans. Sci Data 10: 159.

    1. eLife Assessment

      This paper presents important findings on how the shapes of leaves might be biased towards simpler shapes due to biases in how variation is generated by developmental processes rather than selection. The authors present convincing evidence that combines image analysis of a herbarium dataset and computational analysis of a model of leaf development. The paper should be of interest to diverse researchers, ranging from plant development to the evolution of complexity more broadly.

    2. Reviewer #1 (Public review):

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

      Summary:

      The authors aim to understand, in the context of leaf shape, how the constraints imposed by development inform evolution. Leaf shape is a good place to study the influence of development on evolution because it is a trait that exhibits a lot of diversity, and the developmental mechanisms that give rise to leaf shapes are apparently rather conserved across angiosperms.

      As part of the motivation for their work, the authors cite a previous study (Geeta et al), which found that in angiosperm phylogenies, transitions from complex to simple leaf shapes occur through evolution more often than transitions in the opposite direction. Is this due to developmental constraints or adaptation?

      The authors undertake two parallel lines of work:

      (1) Extending the study of Geeta et al with more data, consisting of both phylogenies and a shape classification dataset. The conclusion from this line of inquiry is that transitions from lobed to unlobed leaves are more common than transitions away from unlobed leaves.

      (2) The authors conduct evolution simulations in a computational model of leaf development. Here, they look at {\it neutral} mutations and whether simply neutral evolution is sufficient to drive the observed trend.

      The conclusion of the second part of the work is that the driver of the evolution toward simple leaf shape is entropy: there are more ways to make unlobed leaves than to make lobed leaves (at least in terms of gene regulation parameters that will produce the two leaf types). The argument is that random gene regulatory networks are more likely to produce unlobed leaves than lobed leaves; therefore, neutral evolution drives this trend.

      Data Analysis:

      Roughly $9000$ images of leaves were classified into 4 categories: unlobed, lobed, dissected, and compound. These labels were applied to the tips of 5 phylogenetic trees of angiosperms (3 resolved at the genus level and 2 at the species level). By fitting a continuous-time Markov chain to the labelled trees, the authors claim that there is a significantly higher rate of transition to the unlobed leaf shape compared to transitions to more complex shapes.

      Simulation:

      First, the authors validate a computational model (Runions et al) for leaf growth on an experimental dataset. By changing parameters in the model, they can recapitulate the morphological changes in the shapes of Arabidopsis leaves engendered by expression of two particular genes.

      Then the authors run an evolutionary model (without selection, just random mutations) on top of the computational leaf development model. As the random walk in parameter space reaches a stationary distribution, they look at both the proportions of the leaf categories in the steady state as well as the transition rates between different categories. The result is that transitions to unlobed leaves are more common than from unlobed leaves.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aim to understand, in the context of leaf shape, how the constraints imposed by development inform evolution. Leaf shape is a good place to study the influence of development on evolution because it is a trait that exhibits a lot of diversity, and the developmental mechanisms that give rise to leaf shapes are apparently rather conserved across angiosperms.

      As part of the motivation for their work, the authors cite a previous study (Geeta et al), which found that in angiosperm phylogenies, transitions from complex to simple leaf shapes occur through evolution more often than transitions in the opposite direction. Is this due to developmental constraints or adaptation?

      The authors undertake two parallel lines of work:

      (1) Extending the study of Geeta et al with more data, consisting of both phylogenies and a shape classification dataset. The conclusion from this line of inquiry is that transitions from lobed to unlobed leaves are more common than transitions away from unlobed leaves.

      (2) The authors conduct evolution simulations in a computational model of leaf development. Here, they look at {\it neutral} mutations and whether simply neutral evolution is sufficient to drive the observed trend.

      The conclusion of the second part of the work is that the driver of the evolution toward simple leaf shape is entropy: there are more ways to make unlobed leaves than to make lobed leaves (at least in terms of gene regulation parameters that will produce the two leaf types). The argument is that random gene regulatory networks are more likely to produce unlobed leaves than lobed leaves; therefore, neutral evolution drives this trend.

      Data Analysis

      Roughly $9000$ images of leaves were classified into 4 categories: unlobed, lobed, dissected, and compound. These labels were applied to the tips of 5 phylogenetic trees of angiosperms (3 resolved at the genus level and 2 at the species level). By fitting a continuous-time Markov chain to the labelled trees, the authors claim that there is a significantly higher rate of transition to the unlobed leaf shape compared to transitions to more complex shapes.

      Simulation

      First, the authors validate a computational model (Runions et al) for leaf growth on an experimental dataset. By changing parameters in the model, they can recapitulate the morphological changes in the shapes of Arabidopsis leaves engendered by expression of two particular genes.

      Then the authors run an evolutionary model (without selection, just random mutations) on top of the computational leaf development model. As the random walk in parameter space reaches a stationary distribution, they look at both the proportions of the leaf categories in the steady state as well as the transition rates between different categories. The result is that transitions to unlobed leaves are more common than from unlobed leaves.

      We thank the reviewer for the helpful and clear summary of our work.

      General Comments

      The authors use angiosperm phylogenies from other works as the basis for the data analysis part of their work. Given the centrality of these phylogenies for their conclusions, more information is needed about how these phylogenies were constructed and what they mean. What is the timescale that they span? What method is used to infer them? What regions of DNA were sequenced in order to build the phylogenies? Also, maybe some more discussion of angiosperm evolution (e.g., when was the most recent common ancestor of all angiosperms?) would help put the study in context.

      We also need a more in-depth discussion of the computational model. What are all the $>100$ parameters doing, and what informs the seemingly strange mutational model that changes parameters by 3 orders of magnitude?

      I am confused about how the rates of transitions were inferred from the phylogeny. Here, one has a phylogeny inferred by some method (which needs to be described in more detail), and just the leaves are labelled. It is stated in the methods that BayesTraits was used to infer the transition rates. I realize this method is probably documented elsewhere, but a bit of a summary of how it works and how to interpret its results would (1) make the paper more selfcontained and (2) if the algorithm is credible, make the results firmer.

      We thank the referee for the suggestion to make the paper more accessible. The tool we use to infer transition rates from the phylogenies, BayesTraits, is standard in the field. However, the referee is right that for an interdisciplinary journal, it may be helpful to more fully flesh out how these methods work. To that end, we have added an additional section S2.A "Phylogenetic rate inference" in the supplementary information that includes a longer description of how BayesTraits works, and how we used it to infer transition rates from phylogenies.

      All trees are shown in the supplementary information section S2.B "Phylogenetic trees" with scale bars showing the amount of time or genetic change that the trees span. We have added details of the specific genes used to construct the trees in our analysis in Methods section F. For a broader discussion of angiosperm evolution, there is supplementary information section S4 "A short literature review of the adaptive significance of leaf shape".

      Regarding the more in-depth discussion of the computational model, we have added supplementary information section S1.A "Leaf model details" to give a more detailed description of the leaf model. We chose our mutational scheme over other methods, such as uniform or Latin hypercube sampling, as the biologically relevant ranges for each parameter were unknown. Mutations of multiple orders of magnitude are a way to sample both parameters with large and small ranges with the same scheme.

      Additionally, larger mutations help to move a walk out of a local region if it is stuck while small mutations ensure sensitive parameters are still varied. We use multiple schemes for additional robustness.

      I am a bit skeptical of the authors' interpretation of the biological trend (of complex to simple leaf shapes) as being driven by neutral evolution. Why does one expect that the mutations generated by the random walk models described in the work are in fact neutral mutations?

      A random walk is a well-established way of modelling the dynamics of neutral evolution in the monomorphic regime, where the population has a narrow diversity of different genotypes. In the higher mutation rate polymorphic regime, where the diversity of genotypes in the population is larger, we also expect that a random walk should still recapitulate the correct average transition rates. The purpose of the simulations is not to model every aspect of population genetics, but to ask whether developmental bias alone is sufficient to generate the observed directional asymmetry. By assigning equal fitness to all viable leaves, we isolate the contribution of development from that of selection. The agreement with the phylogenetic transition rates therefore demonstrates sufficiency rather than exclusivity: selection may also contribute, but it is not required to explain the observed bias. We discuss the evidence for the role of adaptation in leaf shape further in supplementary information section S4 "A short literature review of the adaptive significance of leaf shape".

      - If the entropy of simple leaf shapes is higher than that of complex leaf shapes, why did we have complex leaves at all? I suspect the authors might argue that this is due to selection. In that case, what allows these complex shapes to become simpler? Wouldn't they be losing the selective advantage that drove them to be more complex in the first place? Or maybe the idea is that the rates are inferred assuming some steady state that generates the phylogeny? I did not understand this point.

      The entropy language is a useful framing. Within that framework, one can view our study as showing that the entropy (defined here as the logarithm of the volume of parameter space mapping to a phenotype) of simple leaf shapes is higher than that of complex leaf shapes. If this entropy were to be ignored, and were we not to take fitness differences into account, then all states would be equally likely in our simulations. What we show is that the differences in entropy, which are related to differences in volumes of the parameter space that map to different phenotypes, also affect the rates. The inferred transition rates for both simulation and phylogeny from unlobed to more complex shapes are lower than the reverse but not zero. Therefore, complex leaf shapes arise stochastically through mutation and in this model would eventually reach a steady-state proportion, even in the absence of selection. This steady state proportion for complex leaves would be smaller than that of unlobed leaves, but non-zero.

      Are the rates of transitions between leaf types inferred for the phylogeny assuming that the phylogeny is generated by the steady state of some Markov process? (I think the answer is no: in that case, how does one explain the initial condition?)

      The tool we use to infer transition rates from phylogenies—BayesTraits—allows the initial state at the root of the tree to vary during the numerical optimisation (Pagel, 1994). Therefore, it is not assumed that the initial state is generated by the steady state of the Markov process.

      If I take the mutation model (random walk) seriously, then shouldn't I expect that this steady state obeys detailed balance? In that case I should have $p_i r_{i\to j} = p_j r_{j\to i}$ for each of the occupancies $\{ p_i\}$ and transition rates $r_{i\to j}$ for the shape categories. How close are the rates inferred from the phylogenies to obeying detailed balance? Presumably, the Markov chain fitted to the simulation data obeys detailed balance because the mutation model itself does?

      BayesTraits allows off-diagonal transition rates of the rate matrix to vary freely during numerical optimisation (Pagel, 1994). Therefore, there is no requirement for detailed balance to hold for the inferred rate matrix. For our simulations, the mutations are symmetric at the parameter level, therefore at this level, the process would be expected to obey detailed balance. We investigated this further by calculating the posterior signed equilibrium flux difference $\pi_iq_{ij}-\pi_jq_{ji}$ for all inferred rate matrices (see new material in supplementary information Fig S23). The posterior signed equilibrium fluxes for each transition pair in the simulations have 95% credible intervals overlapping with 0, which is consistent with detailed balance. Some phylogenetic state pairs are inconsistent with detailed balance. This could be due to something other than symmetric neutral drift acting on these specific transitions, such as selection or mutation biases not captured by the model. However, the identity of the transition pairs that are inconsistent with detailed balance varies between phylogenies, indicating that these apparent violations could be noise due to phylogenetic uncertainty. In any case, a violation of detailed balance does not disprove a developmental bias explanation, it just suggests that the underlying process is more complex than the reversible evolutionary algorithm we use in our leaf-model simulations. However, introducing non-reversible mutation dynamics into our leaf-model simulations would require additional assumptions and parameters describing mutational asymmetries, which are not necessary to reproduce the key bias towards unlobed.

      I find it hard to take the discussion of development seriously without some consideration of mechanics. Presumably, the mechanics are hidden in the computational leaf development model, but this model is not discussed in enough detail for the reader to know. It seems to me that the interesting question is: what are the {\it mechanical} constraints on development that drive the apparent trend in evolution towards simpler leaf shapes? Maybe it is something about the type of differential growth needed to make complex leaf shapes less robust to mutation. But in this case, I would assume that selection plays a role in the complexity of shape. In any case, a better understanding (or explanation) of the computational model is needed to make this interpretation.

      We thank the referee for the suggestion to make the paper more accessible. We have added a new, more detailed and pedagogical description of the model from (Runions, Tsiantis and Prusinkiewicz, 2017) in the supplementary information section S1.A “Leaf model details". We also note that Fig. 5 in the methods gives an overview of how the model works, including some mechanical aspects of development and growth.

      More generally, mechanics is one component of the developmental map that determines which parameter combinations produce viable leaf morphologies. Our analysis concerns the geometry of this complete developmental map, irrespective of whether its constraints arise from gene regulation, tissue mechanics, or their interaction.

      On the interesting question of what is causal, perhaps the example in figure 2 is helpful. We focus on two parameters, a morphogen repression strength, and a duration of growth. A key physical process here is called webbing, where cellular growth fills in the gaps between branching veins. This process flattens the leaf structure and creates a continuous, solid leaf blade (lamina). Strong webbing, characterized by a significant resistance to stretching and bending, results in a smoother margin (Runions, Tsiantis and Prusinkiewicz, 2017). The morphogen repression strength affects the physical parameters that determine how strong the webbing is. The duration of growth determines how long the leaf has to grow. If an unlobed leaf has some marginal patterning that results in small protrusions, such as serrations, increasing the duration of growth will amplify these protrusions, eventually forming lobes. Similarly, increasing the strength of morphogens that repress growth along the margin will deepen the sinuses between protrusions, resulting in lobes. Combinations can result in highly dissected and compound leaves, as illustrated in figure 2. Conversely, decreasing either or both will result in a smoother margin and eventually leaves with an unlobed shape. Thus, varying these two developmental parameters varies the physical processes that determine leaf shape. The mechanics of growth operate downstream of these parameters that we vary in our evolutionary simulations according to the details of the leaf developmental model.

      Some discussion of timescales is needed, especially when invoking neutral evolutionary arguments. If a neutral mutation occurs, its time to fix in a population of size $N$ is $\sim N$ generations. What are the relevant angiosperm population sizes and the number of mutations that separate branches on the tree? Are timescales remotely consistent with e.g., the age of angiosperms on Earth?

      Neutral processes have a well-established role in key aspects of angiosperm evolution, for example genome complexity (Lynch and Conery, 2003). This would suggest that the relevant timescales and generation times are not completely prohibitive of neutral processes also playing a role in the evolution of angiosperm leaf shape. Effective population sizes in plants are highly variable but estimates span 10^3-10^6. Assuming diploidy (and therefore average fixation time of 4Ne) and generation times of 1-10 years, this gives fixation timescales of 10^3-10^7 years. This is within the timescales of the trees we analyse, which span >150 million years.

      Reviewer #2 (Public review):

      Strengths:

      The paper's underlying question is interesting, extending the authors' prior work on RNA along similar conceptual lines. The paper combines both image analysis of leaves and a computational analysis of a simple model of leaf development.

      Weaknesses:

      The entire paper is based on the Runion model. More intuition about the Runion model would be useful for a broader readership that cares about the evolutionary aspect of this, but may not know the developmental model in question. Obviously, this is prior well-established work, but 2 - 3 sentences highlighting the key structural aspects of such a model would be great.

      Currently, that intuition is found implicitly in a sentence on page 2 ("complex leaf shapes need more specificity in their GRNs than their simpler unlobed leaf shape"), but the reader is left wondering - is the Runion model a detailed mechanistic one with multiple interacting genes/proteins? If so, how many? Or is it just 2 - 3 genes but with complexity entirely in how long they are each expressed/when they are turned off, etc.

      We thank the referee for the suggestion to make the paper more useful for a broader readership. To that end, we have added a more detailed description of the (Runions, Tsiantis and Prusinkiewicz, 2017) model in supplementary information section S1.A "Leaf model details".

      The Runions model has nearly 100 free parameters. Random walks in 100dimensional spaces have generic properties like a tendency to move toward regions of larger volume that have nothing to do with leaf biology. How do you disentangle the geometry of high-dimensional random walks from genuinely biological developmental bias? Would a toy model with 100 parameters and arbitrary phenotype categories also show "bias toward simplicity" if "simple" phenotypes occupy more volume?

      Our argument is largely independent of the number of parameters. While it is true that most of the volume is near the surface in a high-dimensional space, our argument is about the relative volumes of the sets of parameters that map to each of the four phenotypes, an entropic argument if you wish. The basic intuition is that a simple phenotype needs fewer parameters to be fine-tuned, and so a larger volume of parameter space will map to a simpler phenotype.

      The question about a toy model with arbitrary phenotypes is helpful because it allows us to clarify that what we are illustrating here with the biologically realistic example of leaf shapes is a much more generic principle. We can say with confidence that if the toy model generates a many-to-one set of outputs (phenotypes) through an algorithmic process whose description length does not grow faster than logarithmically with the size of the genotype space, then it should produce a bias towards simplicity regardless of the number of dimensions, see for example Johnston et al. (2022) and Dingle, Camargo and Louis (2018) for a longer discussion of this more general point which is based on arguments from algorithmic information theory (AIT). We do not use that framing in the current paper because the basic intuition for GRNs, that more complex phenotypes need more parameters to be fine-tuned and so have relatively smaller volumes, is more straightforward to understand than the more abstract AIT arguments. Our general prediction that this principle should hold more widely for GRNs can be made both by the more formal AIT route, or via the more heuristic fine-tuned parameter route.

      It is also true that random walks can have biases towards regions of parameter space with high robustness (Van Nimwegen et al., 1999). For our model, this would mean a bias towards valid leaves, however we have no reason to expect this would bias the rates between different valid leaf shapes. We address this point in methods section C.

      The discussion of Figure 4 (PCA of parameter space) uses "area" loosely when what's actually being measured is bin count in a 2D projection of a highdimensional space. I would think that, in general, PCA projections can be misleading about volume in the full parameter space, but I can't tell if that's an issue in this case. Some comments/thoughts here would be useful.

      The quantitative estimate of phenotype frequencies is computed directly in the full parameter space and does not depend on PCA. We estimate that the total volume of viable leaves maps to simple unlobed leaves about 80% of the time. However, the volume is extremely high-dimensional, and so hard to visualise. PCA is used solely to provide an interpretable visualization of this otherwise high-dimensional structure. The PCA plots in Fig 4 and Fig S16 are there to be illustrative, not quantitative. Because the volume differences are large, we do not think that the projections of the main PCA components would be misleading on at least the ordering of the sizes of the parameter space components that map to each leaf shape. We provided a similar analysis for other projections -- PC1-PC6 (supplementary information section S3.B "PCA occupancy for higher dimensions"), finding the same trend. To make this point clearer, we have now changed the sentence in the Fig. 4 caption slightly “This (reveals that --> illustrates how) unlobed leaves occupy a larger region of model parameter space than more complex shapes and that this larger space also contains the majority of more complex leaves.”

      The classifier validation section is in the Methods section, but it seems critical to the whole story. The < 80% agreement with manual classification could propagate to the rest of the estimates in the paper. Again, some comments/thoughts here would be useful.

      The review raises a valid concern. In response, we have repeated the analysis of the agreement between by-eye and automatic morphometric classification and produced a confusion matrix for the two classification methods to make the uncertainty clearer (see supplementary information Fig S7, section S1.E). The confusion matrices show that the agreement between the by-eye and automatic morphometric methods is high for unlobed, dissected and compound. The main source of disagreement is leaves that were classified as lobed by eye being classified as either unlobed or dissected by the automatic morphometric method. The proportion of by-eye lobed leaves classified by the automatic-morphometric method as either unlobed (27%) or dissected (23%) is relatively balanced, which we think will cancel out some error as well. Moreover, we find that the agreement between the automatic-morphometric method and by-eye classification increases to 90.0% when using the categories unlobed and all other categories grouped into one (complex). This is the most important classification for our finding that development and phylogeny are both biased towards unlobed.

      The authors should explain Mut2 and Mut5 in the main paper with a sentence or two, at least schematically, because how you mutate is obviously very relevant to interpreting a paper about biases in variation.

      We agree that this information should be readily available in the main text. In response, we have added a sentence to the results section C for more detail on the random walk.

      "[We mutated the initial sample using a random walk algorithm with two different mutational schemes, MUT2 (alg. 1) and MUT5 (alg. S2).] These algorithms work by iterating through model parameters one by one and perturbing the value by a small amount. We then [automatically classified the resulting shapes...]"

      However, we note that in methods section C there is already a more detailed description of both algorithms.

      “MUT2 (alg. 1) iterates through the parameters in a random order, and attempts to change the parameter by a value selected at random from an array of numbers randomly generated at 3 different orders of magnitude. MUT5 (alg. S2) is the same as MUT2 except that each mutation is additionally scaled by 10% of the current parameter’s range within the initial leaves (fig. S6). The aim here was to provide some way of accounting for the biologically relevant sampling range."

      Moreover, the MUT2 algorithm is described in pseudocode in Algorithm 1 in the main text, and the pseudocode for MUT5 is in supplementary information section S1.C, as algorithm S2.

      The two mutational schemes use additive perturbations to individual parameters. Real mutations presumably affect regulatory networks in more structured ways (e.g., changing binding affinities that affect multiple parameters simultaneously). How sensitive are the results to the assumption of independent single-parameter mutations?

      The referee raises an interesting and well-known issue concerning this widely studied class of GRN models. Without a detailed understanding of how individual genetic mutations map onto model parameters, it is difficult to determine with confidence whether a mutation would produce correlated changes in certain sets of parameters. Our main argument, however, is that the primary source of the observed bias is geometric: the volume of parameter space (or equivalently, the entropy) corresponding to simple leaf morphologies is substantially larger than that corresponding to complex morphologies. As long as mutations explore parameter space approximately symmetrically, even if they involve correlated changes in multiple parameters, larger phenotype regions will tend to be encountered more frequently and retained for longer than smaller regions. We therefore expect the observed bias to be robust to many alternative mutation models, as is already supported by the agreement between our two mutational schemes, although quantifying this robustness is an interesting direction for future work.

      The connectedness argument is made using a 2D PCA projection. Is there a way to check this statement in the full parameter space or perhaps in higherdimensional projections to test the robustness of this result? Connected components can merge/split under different projections.

      We agree that the 2D projection alone is limited. We therefore performed a new analysis using the same method but in the full dimensional parameter space. The nearest neighbour graph for the full-dimensional data has the following numbers of connected components: unlobed-146, lobed-273, dissected-255, compound-315. This follows the same pattern identified for the PC1-PC2 projection, that unlobed splits into fewer connected components than other leaf shape categories. We have added this result to the caption in figure 9.

      References:

      Dingle, K., Camargo, C.Q. and Louis, A.A. (2018) ‘Input–output maps are strongly biased towards simple outputs’, Nature Communications, 9(1), p. 761. Available at: https://doi.org/10.1038/s41467-018-03101-6.

      Johnston, I.G. et al. (2022) ‘Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution’, Proceedings of the National Academy of Sciences, 119(11), p. e2113883119. Available at: https://doi.org/10.1073/pnas.2113883119.

      Lynch, M. and Conery, J.S. (2003) ‘The Origins of Genome Complexity’, Science, 302(5649), pp. 1401–1404. Available at: https://doi.org/10.1126/science.1089370.

      Pagel, M. (1994) ‘Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters’, Proceedings of the Royal Society of London. Series B: Biological Sciences, 255(1342), pp. 37–45. Available at: https://doi.org/10.1098/rspb.1994.0006.

      Runions, A., Tsiantis, M. and Prusinkiewicz, P. (2017) ‘A common developmental program can produce diverse leaf shapes’, New Phytologist, 216(2), pp. 401–418. Available at: https://doi.org/10.1111/nph.14449.

      Van Nimwegen, E., Crutchfield, J.P. and Huynen, M. (1999) ‘Neutral evolution of mutational robustness’, Proceedings of the National Academy of Sciences, 96(17), pp. 9716–9720. Available at: https://doi.org/10.1073/pnas.96.17.9716.

    1. eLife Assessment

      This important study demonstrates how individual taste preferences change over time, how these changes are reflected in cortical activity, and how sensory experience contributes to reshaping both. The evidence is convincing and broadly supports the main conclusions. The findings should be of interest to neuroscientists studying sensory processing and cortical plasticity.

    2. 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:

      Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. They first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in taste cortex to the same tastes on two consecutive days.

      Strengths:

      The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses.

      Weaknesses:

      (1) Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.

      (2) Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.

    3. Reviewer #2 (Public review):

      Summary:

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

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

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

      Strengths:

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

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

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

    4. Reviewer #3 (Public review):

      Summary:

      Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.

      Strengths:

      (1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.

      (2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given trial or test session can differ from canonical palatability.

      (3) The animal's preferences for various tastes are reflected in the neurophysiological recordings, despite that behavior and physiology sessions are separated by several weeks, with an intervening surgery, across multiple delivery methods (active licking vs passive delivery), and across homeostatic states (thirsty vs sated).

      (4) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time.<br /> The findings are novel and impactful, and the results are relatively complete.

      Weaknesses:

      (1) The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.

    5. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The authors collect extensive behavioral data and show that preference vary between animals and days, but little insight is provided into what underlies these changes and to what extent they reflect "preference". Two animals drank equal amounts of sucrose and quinine on day one of preference testing, suggesting that behavior does not reflect preference but (lack of) habituation to/proficiency with the testing environment.

      Rats underwent several days of habituation and were checked for lick proficiency before testing. A note regarding this fact has been added to the text, and to the fact that day one consumption of NaCl and water was high in the rat that did not lick much for sucrose, making it clear that the rat was proficiently licking for tastes that it wanted (lines 102-104). We have also added prose to the Discussion section about what lick data tells us about preferences (lines 360-371).

      Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.

      The proposed experiment is suggested as a future direction (lines 389-393); performing this experiment is currently impossible, as the experimenters involved have moved on.

      Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.

      Although this is an excellent idea, the limitations of the current dataset render the analysis impossible. This is now explained in the Discussion (389-393).

      Reviewer #3 (Public review):

      The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.

      Alas, we are unable to address this concern, although the robustness of our results renders it unlikely that we are observing a phenomenon that is particularly sensitive to specifics of the taste battery.

      It is not clear how the analyses in Figure 3 emerge from the behavioral patterns across Figures 2A2 and 2B2. Along these lines, citric acid responses for R1 and R2 and saccharine responses for R7 and R8 are not shown in Figures 2A2 and 2B2, thus, we cannot determine if they change from the first to final BAT sessions. Salt, even at low concentrations, may not be palatable when rats are in a water-restricted state. It would be interesting to see Figure 3's analysis exclusively for sweet or for bitter tastes.

      We have clarified the text in the results (line 149), methods (lines 531-548), and figure legend (lines 639-644) to better explain Figure 3. The figure itself was updated such that each taste is now color coded, and an analysis looking exclusively at sweet or bitter tests has been run (and found nonsignificant; lines 157-158).

      A potential reason why a single taste experience session causes taste palatability responses in GC neurons to revert to a reflection of canonical palatability ranks, as is seemingly shown in Figure 6, is not discussed.

      This prose, which we apologize for failing to include previously, has been added to the Discussion section (lines 364-371).

    1. eLife Assessment

      This study proposes that fitness level, but not sex, influences exercise-induced hypoalgesia in women. However, the evidence to support this claim is incomplete: the conclusions rely on a small-effect interaction that emerges only under specific conditions, the findings are inconsistent across pain modalities and stimulus intensities, and the absence of a baseline condition limits the interpretability of results as reflecting true hypoalgesia. Additionally, the methods by which fitness level was categorized across cohorts can be questioned. The results of this study could be useful to improve future experimental designs exploring these factors in more depth.

    2. Reviewer #2 (Public review):

      I appreciate the substantial amount of work the authors have invested in revising the manuscript. The response is extensive, and the addition of pooled frequentist and Bayesian analyses represents a considerable and technically sophisticated effort to address the concerns raised during review. The authors have also improved the use and presentation of the continuous pain-rating data and have responded constructively to several methodological points.

      Nevertheless, with respect to my original review, I do not think the revisions have yet aligned the manuscript's central claims with the specificity and uncertainty of the evidence. The additional analyses provide some evidence consistent with a contribution of fitness to heat-pain modulation, but they cannot fully resolve the limited internal replication, selected sample or differences in measurement procedures between studies. Nor do they establish that sex has no effect on pain modulation. In my view, the evidence supports a modest, exploratory conclusion that fitness may contribute to variability in heat-pain modulation under specific conditions, rather than the more categorical conclusion that exercise-induced pain modulation depends on fitness but not sex. The manuscript would therefore be strengthened by reflecting this distinction more consistently in the title, abstract, Discussion and Conclusion. My remaining concerns, organised in relation to the original review, are detailed below.

      (1) The core interpretation may not be fully supported by the data

      (1.1) Lack of baseline condition

      I am satisfied with the authors' substantive response to Comment 1.1, however I note that the authors are still attributing their findings to "hypoalgesia" in the Discussion.

      (1.2) Lack of internal replication across conditions

      The pooled analyses do not resolve the original lack of internal replication. The substantive effect remains largely restricted to heat pain at the highest stimulus intensity and is not clearly reproduced across stimulus intensities or for pressure pain. The authors' response describes the evidence more cautiously, stating that fitness "contributes to variability in exercise-induced pain modulation". However, the title and principal conclusions do not reflect the exploratory, modality-specific and statistically uncertain nature of the evidence.

      (1.3) Deviations from the original study and selective use of data

      I appreciate the addition of analyses using the continuous pain ratings, which addresses my concern that the richness of the data from the current study was not being fully used. However, the decision to pool the current and previous datasets introduces a related and potentially more consequential comparability problem. The previous study used retrospective post-stimulus ratings, whereas the current study used continuous ratings from which peak values were extracted. Rescaling these measures to a common numerical range does not establish their measurement equivalence. This is particularly important because study membership is closely confounded with the participant characteristics central to the manuscript: the higher-fitness female group was recruited and assessed in the current study, whereas the comparison groups were assessed using the earlier procedure. Consequently, apparent effects of fitness may partly reflect differences in outcome acquisition between studies. Indeed, including study membership improves model fit substantially more than including fitness.

      (1.4) Over-reliance on null results regarding sex differences

      I do not think the new Bayesian analyses resolve my original concern regarding the interpretation of the null sex effect. The authors state that the analyses provide no evidence for a moderating role of sex, but they do not report a Bayes factor clearly supporting the null hypothesis for the exercise intensity × sex interaction. In fact, Figure 5 clearly shows that the association between fitness and pain modulation is significant in males but not females. Panels D-F indicate uncertainty regarding a differently specified exercise intensity × sex interaction, rather than evidence for its absence. The figure therefore suggests that the role of sex remains unresolved and may qualify the association attributed to fitness. The authors should therefore distinguish absence of evidence from evidence of absence and temper the conclusion that exercise-induced pain modulation depends on fitness "but not sex".

      (2) Limited sample and lack of diversity

      The manuscript does not acknowledge the limited generalisability of this small, highly selected sample of physically active women recruited primarily from cycling clubs. Although the rebuttal states that the findings should be interpreted within the context of the sample studied, this qualification is absent from the Limitations section. It should be added, and the conclusions restricted accordingly.

      (3) Methodological choices limit interpretability of the data

      (3.1) The use of a non-standard VAS scale (0-150 with a fixed pain threshold at 50) is unconventional and may influence how participants report pain, while limiting comparability with related literature.<br /> The authors' response explains the rationale for the modified scale but does not fully address its implications for measurement comparability. In particular, setting all ratings below 50 to zero is not merely rescaling: it collapses distinct responses into a single value and may alter the distribution of the outcome. The cited study addresses the relationship between real-time and retrospective ratings, but does not validate this scale or transformation.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The current study is a follow-up to a previously published study by the same research group (Nold et al. 2025). In the previous study, the authors had included a set of exploratory analyses which assessed the effects of fitness level (denominated by a relative FTP), sex, and drug treatment (Naxolone versus placebo). In this previous study, the authors state that "exploratory analysis showed a significant main effect of fitness level on differences in pain ratings in the [saline] condition... suggesting increased hypoalgesia with increasing fitness levels, pooled across all stimulus intensities".

      In the current study, the authors have recruited an additional 22 female participants (21 included in analysis) from local cycling clubs to assess if fitness level does indeed impact exercise-induced hypoalgesia responses to experimental thermal and pressure pain models.

      Strengths:

      The current study has the potential to present a convincing argument about the effect of fitness level and potentially other factors (e.g., sex) on exercise-induced hypoalgesia responses. Combining data across two of their primary studies would be highly fruitful to the research community interested in this area. Specifically, it has the potential to inform sports medicine practitioners and how they administer exercise protocols to help those experiencing pain with a further consideration for the fitness level (and maybe sex) of their patients.

      Weaknesses:

      However, the current study makes several bold claims about the role of fitness level and sex on exercise-induced hypoalgesia, which I do not believe that this study on its own - or in conjunction with the previously published study by the same authors - can make at present. Namely, the current study does not appear to conduct any specific analyses between the cohorts from either study (current and present). The results mention a difference in the group mean values in "fitness level" between cohorts, but the analysis itself on pain responses/exercise-induced hypoalgesia is limited only to the cohort from the current study. If the authors wanted to provide a convincing argument that fitness level has an effect on exercise-induced hypoalgesia, then the analysis of this study would have to include an analysis between the groups considered to be of "high" and "low" fitness level. I do not think the current study does this. Instead, it makes an assumption from the previous study (Nold et al. 2025) which only states that "exploratory analysis showed a significant main effect of fitness level on differences in pain ratings in the [saline] condition... suggesting increased hypoalgesia with increasing fitness levels, pooled across all stimulus intensities". The analysis of this study would have to include fitness level "high fitness" versus "low fitness" of participants across both studies in its statistical model to properly discern if fitness level has an impact on exercise-induced hypoalgesia.

      A similar comment can be made with respect to sex differences, as these have not been assessed in the analysis of this study either.

      Another area of weakness in this study is how "fitness level" has been demarcated across participants. One issue is how authors have assumed that the current cohort is 'fit', whereas the previous cohort was 'less fit', meaning that the authors could be coming to false conclusions about fitness level. In detail, figures within the current study show a large overlap between the 'fit' and 'less fit' cohorts, where some participants have a higher relative functional threshold power (FTP) in the 'less fit' cohort than the 'fit' cohort and vice versa. Therefore, I believe the authors should better demarcate between those that are in the 'more fit' and 'less fit' groups according to a validated and well-established criterion from the kinesiology and sport science literature. That being said, I think this may be problematic in some ways as FTP is considered a relatively poor measure to denote fitness levels, a limitation highlighted in the previous study's review.

      Altogether, whilst I commend the researchers on their body of work across the two studies, the current methods and analysis provide an incomplete assessment of their primary research question, and therefore, I would urge the authors to reconsider some of their methods/analysis and the framing of their results to better reflect the main research question they have attempted to answer. Likewise, I would recommend that readers ensure they consider the current results with caution until the authors have addressed some areas of concern which currently limit their main conclusions.

      We sincerely thank the reviewer for their thorough and insightful comments and for the time invested in reviewing our work. In response, we have made several important revisions, particularly to address the major concerns regarding fitness level and sex differences.

      Specifically, we have:

      (1) Conducted additional pooled analyses to directly evaluate fitness and sex effects.

      We pooled the data from the previous and current studies and conducted linear mixed-effects models investigating the effect of exercise intensity between the subgroups, as well as specifically testing the effect of fitness level and sex across the pooled data. This allowed us to directly test the effects of fitness level and sex on a common scale rather than infer these effects from separate cohorts. To quantify evidence both for and against the presence of exercise-induced pain modulation and its moderation by fitness, sex, and study sample, we estimated Bayes factors comparing nested models of increasing complexity. In addition, we examined the posterior distributions of the regression coefficients (β) and subgroup- specific (males from previous study, females from previous study, females from current study) exercise effects to estimate the magnitude and uncertainty of these effects. The Bayesian analyses provided some evidence that fitness level moderated exercise-induced pain modulation, but sex did not. Importantly, these findings were consistent with the overall pattern of results obtained in the frequentist analyses. All corresponding statistics in the main manuscript and Supplementary Materials have been updated accordingly. To improve transparency, the pooled analyses are now presented in the dedicated Results subsections and accompanied by new figures (Figures 4 and 5). Furthermore, we have adapted the Title, Discussion and Conclusion to more closely reflect the results.

      (2) Clarified the assessment of fitness level.

      We expanded our consideration of fitness level and training status by discussing training volume as an additional indicator of training status. We also acknowledge that fitness is a continuous construct and that some overlap in FTP values exists between cohorts. To address this concern, we conducted pooled analyses using FTP as a continuous predictor rather than relying solely on cohort membership. For consistency and comparability with the previous study, FTP remained our primary indicator of fitness level, while analyses using training volume are now reported in the Supplementary Materials.

      (3) Expanded the mechanistic background on the effect of fitness level and sex on EIH.

      We expanded the mechanistic background in both the Introduction and Discussion to provide a clearer theoretical rationale for how fitness level and sex may influence exercise-induced hypoalgesia (EIH), drawing on relevant experimental and clinical literature.

      Reviewer #2 (Public review):

      This study addresses an important question regarding exercise-induced modulation of pain in women, but the conclusions appear to be based on relatively limited and selective evidence. The authors report an interaction between exercise intensity and stimulus intensity, which they interpret as evidence for exercise-induced hypoalgesia and conclude that fitness, but not sex, modulates this effect. However, this main result relies on a relatively small interaction that emerges only under specific conditions, with inconsistent findings across pain modalities and stimulus intensities, and an analysis approach that does not fully exploit the continuous pain ratings collected. The lack of a baseline condition further limits the interpretability of the findings as reflecting hypoalgesia, and overall, the data provide a rather constrained basis for drawing broader conclusions.

      Strengths:

      (1) The focus on women is important and timely, particularly given the ambiguity in prior findings and the historical bias toward male-dominated samples.

      (2) The attempt to revisit previous findings in a new cohort is valuable in principle.

      Weaknesses:

      (1) The core interpretation may not be fully supported by the data

      The central claim-that the results demonstrate exercise-induced hypoalgesia and its dependence on fitness but not sex-does not appear to be fully supported by the evidence presented.

      (1.1) Lack of baseline condition

      The absence of a no-exercise baseline substantially limits interpretation. The study compares high- and low-intensity exercise, but without a baseline, it is not possible to determine whether either condition produces hypoalgesia or hyperalgesia relative to calibration. The observed HI-LI difference, therefore, reflects only a relative contrast between exercise intensities, not an absolute reduction in pain. As a result, attributing the findings to "hypoalgesia" may be difficult to justify fully.

      (1.2) Lack of internal replication across conditions

      The reported effect is highly specific and does not clearly generalise across the experimental design. It emerges significantly only for heat pain at the highest stimulus intensity, with no clear effects for other intensities and for pressure pain. Moreover, the main statistical result is a relatively small interaction effect with a modest p value, which translates into a difference of approximately 6-8 VAS units on a 150 scale. This combination-a small effect size, limited statistical strength, and restriction to a single condition-substantially weakens the evidence for a robust or generalisable effect.

      (1.3) Deviations from the original study and selective use of data

      Although framed as a follow-up to previous work, the current study introduces substantial methodological changes, particularly in the acquisition and scaling of pain ratings (continuous vs post-hoc ratings, modified VAS with sub-threshold range). Despite collecting rich continuous data, the analysis focuses on peak responses to approximate the previous study. While this may aid comparability, it results in a strong emphasis on a single data point (highest intensity), rather than leveraging the full dataset. This limits both interpretability and comparability.

      (1.4) Over-reliance on null results regarding sex differences

      The conclusion that fitness, but not sex, modulates exercise-induced pain may not be directly supported by the data presented. The current study includes only highly fit women, and comparisons with men or less-fit women rely on non-significant differences in a previous cohort. The absence of a significant difference does not provide evidence for equivalence, and no formal statistical support for a null effect is provided. As such, conclusions about the absence of sex differences would unfortunately benefit from more cautious interpretation.

      (2) Limited sample and lack of diversity

      The dataset is narrow in scope, comprising a small sample (N = 21) of healthy, highly fit women. Key demographic characteristics (e.g. age range, BMI distribution) are not fully presented, explored or discussed. This limits generalisability and makes it difficult to draw broader conclusions about exercise-induced pain modulation in women, as the main focus of the study.

      (3) Methodological choices limit the interpretability of the data

      Several methodological decisions would benefit from stronger justification:

      (3.1) The use of a non-standard VAS scale (0-150 with a fixed pain threshold at 50) is unconventional and may influence how participants report pain, while limiting comparability with related literature.

      (3.2) Participants explicitly reported expecting exercise to reduce pain, introducing a potential confound that is not presently addressed.

      (3.3) A more comprehensive use of the full time series of pain ratings would provide a stronger and more transparent basis for interpretation of the present findings.

      We sincerely thank the reviewer for their time and effort invested in reviewing our manuscript and appreciate the comments. In response to the points raised by the reviewer, we have now updated our work. Specifically, we have:

      (1) Pooled samples and strengthened inferential framework:

      We pooled the current and previous datasets to provide a stronger basis for the primary interpretation of the findings. On this combined dataset, we conducted both frequentist linear mixed-effects models and complementary Bayesian analyses, allowing us to directly test effects of fitness level, sex, and exercise intensity within a unified framework and to quantify both effect sizes and evidence for/against key hypotheses. The Title, Results, Methods and Discussion have been adapted to more closely reflect the results.

      (2) Addressed sample limitations through pooled analysis:

      To mitigate limitations related to sample size and composition in the individual studies, we extended all primary inferential analyses to the pooled dataset. This approach increases statistical power, improves the stability of estimates, and enables a more reliable examination of between-group effects across fitness level and sex, thereby strengthening the generalisability of the findings.

      (3) Expanded analyses of continuous pain ratings:

      To address methodological concerns regarding the use of a summary pain metric, we supplemented the primary peak-based analyses with additional linear mixed-effects models on the full time-series data. Specifically, we modelled time × exercise intensity interactions for both heat and pressure pain, thereby capturing the dynamic structure of pain responses and ensuring that conclusions are supported by the richness of the continuous rating data. These analyses have been added to the Supplementary Materials.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Synopsis

      The current study is a follow-up to a previously published study by the same research group (Nold et al. 2025). In the previous study, the authors had included a set of exploratory analyses which assessed the effects of fitness level (denominated by a relative FTP), sex, and drug treatment (Naxolone versus placebo).

      In the current study, the authors have recruited an additional 22 female participants (21 included in analysis) from local cycling clubs to assess if fitness level impacts exercise-induced hypoalgesia responses to experimental thermal and pressure pain models.

      All the analysis of this study seems to be specific to the "fit" cohort from this study. No analysis or figures seem to show differences between cohorts from the current and previous studies. For this study, the authors found that maximum pain ratings after application of a thermal stimulus and pressure pain stimulus were lower after a high-intensity exercise compared to a lower-intensity exercise. There was also an exercise intensity × stimulus intensity interaction when thermal stimulations were applied, suggesting that the exercise-induced hypoalgesia response varies across exercise intensities as the stimulus intensity increases/decreases. The same cannot be said for pressure pain according the statistical analysis outputs of this study, although inspection of Figure 3B (top row) does illustrate that at higher stimulus intensities, there is a difference between exercise intensities for maximal pain ratings, whereas there is no difference at lower stimulus intensities (30 and 50 VAS). However, observing the changes in maximum pain ratings (Figure 3C) does show similar changes across all stimulus intensities for pressure pain, unlike thermal pain. Therefore, the authors claim that there is an effect of fitness level on the exercise-induced hypoalgesia response.

      Overall, the authors have completed a neat supplementary study to their previous study. However, there are several areas that I believe should be addressed within the current manuscript.

      First, the authors regularly state "...the hypoalgesic response to exercise depends on fitness level but not on sex". As mentioned earlier, the results section of the previous study states that "exploratory analysis showed a significant main effect of fitness level on differences in pain ratings in the [saline] condition... suggesting increased hypoalgesia with increasing fitness levels, pooled across all stimulus intensities". In this study, the authors do not seem to have completed a direct comparison between the two study cohorts. The results for the current study only seem to discuss the effects of exercise intensity within this study cohort. If this study is to be treated as a stand-alone study, then the data that is presented should make it clear that the effect of fitness level is being assessed. Currently, the results and the figures only illustrate the effects of exercise intensity and stimulus intensity, not fitness level (or sex), which are mentioned in the title, abstract, and most discussions.

      Second, on the topic of sex-related effects, as authors repeatedly state ""...the hypoalgesic response to exercise depends on fitness level but not on sex", I think it is worth noting that this statement would require an a priori assessment of sex differences on exercise-induced hypoalgesia responses. As the previous study (exploratory) and this study (no sex difference analysis) do not include this a priori assessment, I do not think they can conclude that sex is not a factor in the exercise-induced hypoalgesia response, though they can surmise that fitness level may have an influencing role if it was specifically tested in this study. If the authors wish to judge the role of sex, there ought to analysis on this specifically.

      Third, whilst the authors have tested the effects of "fitness level", I urge the authors to consider how they have demarcated between fitness levels in this study and any other (validated) criterion that could be used in place of their current method to distinguish between "fit" and "less fit" participants. To explain the cohort of the current study is judged to be "fitter" than the previous study based on the group mean relative FTP. Yet, observing Figure 1, there are several participants with lower relative FTP in the "fit" cohort than the "less fit" cohort, and vice versa. I would recommend the authors to consult with some kinesiologists/sport scientists about this issue. As the central topic of this study is the effect of fitness level, this warrants careful consideration, as the current method to split the groups is problematic, especially as there is clear overlap between the two groups in the metric that the authors have used (relative FTP).

      Fourth, I think it would be highly beneficial for the authors to add more discussion around the theoretical and mechanistic accounts about why fitness level (and sex) would impact exerciseinduced hypoalgesia responses. The current manuscript provides a nice account of the current literature in terms of the direction of each study's results, yet they do not explore why fitness level or sex has such effects. This would be highly valuable to a reader to set the tone of the study rationale in the introduction and provide more depth to the authors' discussion of their results.

      Below I have highlighted all my comments that have been separated into MAJOR and MINOR comment sections.

      I hope my comments are of use to the authors, and I would like to thank them and the journal for the opportunity to review their paper on a truly interesting and insightful topic.

      We thank the reviewer for their insightful comments and appreciate the time invested in reviewing our work. In the following, we have addressed all comments raised by the reviewer and marked all changes in the manuscript.

      Major Comments

      (1) In my opinion, it would enhance the manuscript if the authors provided a theoretical or mechanistic account of why fitness levels or sex would impact the exercise-related hypoalgesia effects. Though some reasoning about the mechanisms of fitness level and sex is mentioned in the discussion of the previous study (Nold et al. 2025), and even if the overall research findings are mixed at present, I do not think it would be overkill to provide the rationale for why the authors do/do not predict a certain response in the current study after their analyses. On this note, the authors may argue that the manuscript content from L52-61 is an account of the previous literature. However, whilst this literature sets out the directions of previous research findings, as I read this, I do not think there is a theoretical framework to underpin why fitness level or sex would impact exercise-induced hypoalgesia (EIH). References already included in the manuscript should have some discussions around this (e.g., Sluka et al. 2018; Koltyn 2000; Koltyn et al. 2014).

      We thank the reviewer for their comment. We agree that the mechanistic rationale for why fitness level and sex may influence EIH could be described in more detail. Accordingly, we have expanded the Introduction/Discussion to provide a more comprehensive overview of the potential mechanisms underlying these effects, drawing on previous EIH literature (e.g., Sluka et al., 2018; Koltyn, 2000; Koltyn et al., 2014) as well as evidence relating to changes in endogenous modulatory systems due to repeated exercise (Anderson & Shivakumar, 2013; Gajic et al., 2026; Matei et al., 2023; Saanijoki et al., 2022; Song et al., 2022) as well as sex differences in these neuromodulatory systems potentially contributing to sex differences in EIH (Blanton et al., 2021; Castelli et al., 2014; Fullerton et al., 2018; Levine et al., 2021; Loyd et al., 2008; Zubieta et al., 2002).

      (2) The study sets out an aim, but I do not see any (a priori) hypotheses in the intro or methods. If no hypotheses were registered, this can be reported in the discussion and factored into the discussion about why certain results would/would not be expected.

      The reviewer is correct that no formal, pre-registered hypotheses were specified for this study. As this study was designed as a follow-up to our previous study, which reported no exercise-induced pain modulation in relatively less fit females, the present investigation was primarily exploratory in nature and aimed to examine whether exercise-induced pain modulations would be observed in a sample of females with significantly higher fitness levels. We have now clarified this in the discussion and acknowledge the exploratory nature of the study when interpreting the findings.

      (3) Is there a sample size justification, sensitivity analysis, or post-hoc calculation for the additional 21 participants to detect effects for fitness level and sex?

      We thank the reviewer for this question. No formal a priori sample size calculation was performed for the recruitment of the additional 21 participants. The current study was designed as a follow-up to our previous work, in which female participants were, on average, slightly less fit than male participants. To better disentangle the effects of fitness and sex, we therefore aimed to recruit a comparable number of female participants with higher fitness levels. Accordingly, we targeted a sample size similar to that of the female subgroup in the previous study.

      (4) I have a wider question about where there has truly been a test between fitness levels.

      Definition and measurement of "fitness level".

      Is there an appropriate rationale that relative FTP is an appropriate measure to distinguish between fitness levels? Understandably, this is done for consistency between the two studies, and there may be mention of the use of the FTP measure in the original study; however, it would be worth justifying the use of the relative FTP measure to categorise fitness level with previous literature to back this up.

      We thank the reviewer for this very valid and important point regarding both the validity of FTP as a proxy for fitness and the distinction between fitness levels. Previous research has shown that the FTP and FTP20 tests are reliable and convenient methods to estimate approximate measures of VO2max (Denham et al., 2020) and that the FTP test is a useful test for performance prediction in moderately trained cyclists (Sørensen et al., 2019). Furthermore, we argue that FTP represents a continuous performance measure in our studies instead of a categorical variable distinguishing between fit and unfit individuals, and we have strengthened the manuscript by providing a clearer justification for its use as a field-based proxy of endurance fitness, supported by previous literature linking FTP to more direct physiological markers such as VO<sub>2max</sub> (Denham et al., 2020). Since we have used FTP in the previous sample as a measure of fitness level, we maintained this for consistency in the current study.

      We also acknowledge that, despite statistically significant group differences, there is meaningful overlap between the males of the previous study and the females of the current study and that “fitness level” should be interpreted as a continuum rather than a strict categorical separation. Accordingly, the labels “fit” and “unfit” should be interpreted relative to the samples included in the current and previous studies rather than as absolute classifications. To reflect this more accurately, we have revised the manuscript to refer to participants as having higher or lower fitness levels, which should be interpreted within the context of the pooled sample rather than as discrete fitness categories.

      Classification of fitness level.

      In L110-111 - The statistical analysis does show a difference between groups, but Figure 1A does show that there is a crossover with around half the sample from each study cohort. That is, the current study had ~16 participants who had a lower relative FTP than the highest relative FTP from the previous study. When removing the highest participant (outlier) in the previous study, there are still 7 participants (a third of the sample) with a lower relative FTP than participants considered of a 'lower' fitness level than the previous study.

      Essentially, on the group level, there is a statistical difference in relative FTP, but there is notable overlap between the two groups.

      We thank the reviewer for this observation. Our aim was not to create entirely non-overlapping fitness categories, but rather to recruit a female sample with significantly higher average fitness levels than the females included in the previous study. As shown in the revised manuscript, females from the current study had, on average, significantly higher relative FTP values than females from the previous study, whereas no significant difference was observed between females from the current study and males from the previous study. We also report training volume as an additional indicator of training status; however, because training volume was self-reported, we consider FTP to provide a more objective and sensitive measure of fitness. The partial overlap in FTP values between samples is not unexpected and, in fact, facilitates comparisons across studies by ensuring that the samples are not entirely distinct populations.

      Could another distinction be made based on:

      - A set value (e.g., 2.0 W/kg-1);

      - Another value from previous literature, and the analysis be re-run with this classification of participants? The approach to use a set value is consistent with kinesiology literature that uses set values to demarcate fitness level for other more common, measures like VO2max (see ACSM guidelines and de Pauw et al. 2013);

      - Or by dissociating between training volume and slightly tweaking the wording to "training status/volume" (although I am cognisant that this would introduce an incongruence in terminology between the authors' studies, but other studies that have been referenced [e.g., Geva and Defrin, do specifically test between training volume]).

      We thank the reviewer for their suggestions. We would like to refrain from making another distinction based on a set value and dividing the group into “fit” and “unfit” individuals. Instead, we have pooled the samples and treated FTP as a continuous variable, which preserves statistical power and avoids the loss of information associated with dichotomising a continuous measure.

      To address the reviewers' suggestion of including training volume as a measure of fitness level, we have repeated our new analyses, namely the LMER model using training volume as a predictor for difference pain ratings (LI – HI Exercise pain ratings), in the pooled sample. These analyses yielded no significant effects for training volume, the interaction of training volume x sex, or training volume x subgroup in either heat or pressure pain. Furthermore, the training volume is potentially influenced by outliers in the current study sample. For completeness, we have added these analyses and according visualisation to the Supplements (Fig. S6).

      We believe that the FTP provides a more sensitive and objective measure of cardiovascular fitness level in our study, as it is not susceptible to recall bias, differences in training intensity and differences in training efficacy. We also note that other studies (Geva & Defrin, 2013) did not rely solely on self-reported training volume as a measure of fitness level, but recruited participants based on training history and athletic status (e.g. competitive triathletes vs. active controls).

      For these reasons, we consider the FTP the most appropriate primary indicator of fitness level in the current (and previous) study, while still reporting training volume in the main manuscript to consider.

      The above suggestions would also allow to select matched pairs from the male cohort when comparing effects between sexes. Altogether, though, I believe this comment to be the most important in my review of this paper, as the primary question depends strongly on how fitness level is defined and categorised by the researchers, and this requires careful consideration and validation from previous literature if such decisive conclusions are to be drawn from the results. Therefore, a clearer description of the reason why certain effects are expected (theory/mechanisms) and how participants have been categorised will be highly valuable for readers. It may be useful for the authors to reach out to kinesiologists/sport scientists within their peer groups to help with this comment.

      To further address the concern regarding between-cohort comparability, we have now pooled the samples from the previous and current study and re-analysed the data fitness level as a continuous variable rather than relying on cohort-based comparisons. Specifically, we conducted regression analyses examining fitness level, sex, and their interaction, as well as subgroup analyses including males from the previous study, females from the previous study, and females from the current study. These analyses were complemented by Bayesian model comparison and posterior inference. The results are now reported in the Results section. We also acknowledge that fitness is best viewed as a continuum rather than a discrete characteristic and have revised the manuscript accordingly, replacing references to “fit” and “unfit” participants with descriptions of relatively higher and lower fitness levels within the pooled sample.

      Finally, as mentioned in the previous comment (major comment #1), we have expanded the Introduction and Discussion to more clearly outline the mechanistic and theoretical rationale for expecting effects of fitness level and sex on exercise-induced pain modulation, drawing on relevant literature (see major comment #1). We believe these additions substantially strengthen both the conceptual justification and the analytical robustness of the manuscript.

      (5) The abstract states that "...further investigation to determine if and to what extent exercise induced hypoalgesia depends on fitness level and/or sex." However, I cannot see any analysis relating to sex in the results of this study that takes the female sample of this study and the male sample of the previous study and then carries out another LNER model? I also see no report of results about sex in any of the figures/tables. If sex is part of the abstract and main research question, then I would argue it ought to be in the results and feature in figures within the main manuscript, not supplementary material.

      We agree with the reviewer that, given the emphasis on fitness and sex in the Abstract and research aims, analyses directly examining sex effects should be presented in the main manuscript. In response, we pooled the samples from the previous and current studies and repeated the linear mixed-effects regression analyses used in the previous study to examine the effects of fitness and sex on exercise-induced pain modulation. We further complemented these analyses with Bayesian model comparison and parameter estimation to assess the moderating effects of fitness level and sex in the pooled dataset. The results of these pooled analyses are now reported in the corresponding Results section and are presented in the main manuscript as Figure 5. The Abstract, Results, Discussion, and Conclusions have been revised accordingly.

      (6) The current abstract and main manuscript make it seem that this study completed an additional analysis on the effect of sex, but this does not appear to be the case. Statements like "together with our previous findings, this suggests that exercise-induced hypoalgesia depends on fitness level but not on sex" are quite decisive, although it does not seem that any analysis was completed between sexes in an a priori fashion. The only data I can see that assesses between sex effects is an exploratory analysis from the previous study, which was part of a larger exploration of sex - fitness level - and drug interactions.

      Therefore could the authors either:

      - Run this analysis between sexes;

      - Or make it explicit that this study did not conduct any specific a priori analysis on sex differences in EIH at high and low intensities.

      - And/or temper how decisive their conclusions on the (null) effects for sex, unless there are statistical analyses to support this.

      We thank the reviewer for this suggestion. We agree that the manuscript did not include a dedicated analysis of sex effects in the combined dataset. In response, we pooled the samples from the previous and current studies and conducted additional frequentist (LMER) and Bayesian (Bayes Factor and posterior distributions) analyses examining the effects of fitness and sex on exercise-induced pain modulation. The frequentist analyses indicated an association between fitness and EIH, but not sex, while the Bayesian analyses similarly provided little evidence for sex-dependent effects. These new analyses have been added to the manuscript, and the Abstract, Results, Discussion, and Conclusions have been revised accordingly.

      Section-specific Comments:

      (1) Abstract

      L38-40: See major comment #6 above.

      As described above, we have now conducted additional analyses on the moderating effect of fitness level and sex using frequentist and Bayesian analyses and adapted the Abstract accordingly.

      (2) Introduction

      L68 - What is meant by "robust" in this sentence? Robust to describe the direction and magnitude of the differences in effects between sexes seems a little misrepresentative.

      We thank the reviewer for their comment and agree that the wording is vague. To emphasise clarity, we have rephrased this to “[…] whereas others (Koltyn, 2000; Sternberg et al., 2001; Vaegter et al., 2014) suggest that hypoalgesia following exercise is more pronounced in females, but overall results remain inconclusive (Rice et al., 2019).”

      (3) Results

      L124 - What is the online rating scale? It is the word "online" in particular that is confusing here, as it is described in the methods, but just seems to be a standard handheld device with a cursor.

      We thank the reviewer for their comment. In this case, “online” refers to the continuous ratings whilst the stimulus is ongoing, using a handheld rating device and using button presses. We clarified this further at the beginning of the Results and Methods sections to include “real-time (continuous) pain ratings” to avoid confusion.

      L136 - I think there is a missing word here before the set of parentheses. RPE?

      We thank the reviewer for detecting this and have added “RPE” before the parentheses.

      L136-138 - I would amend to read something like... "This confirms the exercise intensity protocols elicited a different output, relative power, physiological demand (HR), and perceived demand (RPE)", just to be clear on what the differences have been physiologically and psychologically for the participants when exercising at the two intensities.

      We agree with the reviewer and have rephrased the sentence according to their suggestion for clarity.

      (4) Discussion

      L190 - "results suggest that fit females show a hypoalgesic response to HI compared to LI exercise". I think there is a word missing in this sentence to make it clear what the results are.

      We have revised the sentence in accordance with the new (pooled) analysis results.

      L250-253 - The authors have given a fantastic account of the previous literature and the trends that each study has observed. However, in line with major comment #1, could the authors include some discussion to speculate why there are expected differences between training level/fitness level and sex (plus interactions)? Some studies that are referenced are theoretical papers, and even if the current literature is unsure about the likely mechanisms behind fitness and sex differences in EIH responses, stating this would also be useful to a reader but the authors could signpost them to specific references that would broaden their understanding after reading this study.

      We have now provided a more detailed account of the potential mechanistic background on why fitness level and/or sex could influence exercise-induced modulation in the Discussion (see major comment #1).

      (5) Limitations

      L258-259 - If I have understood this sentence correctly and the content in L301-304, the heat stimulus (temperature of the thermal probe) that was applied to induce a 30 VAS pain intensity rating at rest actually induced no pain at all, as 30 VAS is considered below the pain threshold of "minimally painful". In retrospect, this seems like an odd choice of measurement.

      It means that 30 VAS is simply a measure of warm (non-painful but detecting heat) and perturbation to the skin (pressure but not hurting) in the heat and pressure pain conditions, respectively. Therefore, to include the 30 VAS values in the analysis to dissociate whether HIGH or LIGHT intensities, fitness level, or sex have an impact on EIH seems misplaced, as there is no actual pain for which a hypoalgesia response can be tested at this intensity.

      Making this clearer in the results or discussion would be worthwhile for the readers, in my opinion.

      We thank the reviewer for highlighting this. In both the previous and current studies, participants were calibrated using the same procedure and rating scale (0 – 100) to identify painful stimulus intensities corresponding to VAS 30, 50, and 70. On the main experimental day, participants in the current study rated on a modified scale ranging from 0 to 150, with 50 marking the pain threshold, such that ratings below 50 reflected a non-painful rating. Importantly, the calibrated VAS 30, 50, and 70 stimuli refer to the target pain intensities established during the calibration procedure and not to the numerical values displayed on the experimental rating scale. Participants were familiarised with the scale and anchor points prior to testing and viewed only the visual analogue scale rather than numerical values during the experiment. We have adapted the wording, referring to VAS 30, 50, and 70 as intensities and not numerical ratings.

      (6) Conclusion

      L276-277 - Similar mention as before, can the authors please make it clear if there is a sex effect in their analyses or how this has properly been tested in a prior study through an a priori assessment to be certain that "fitness level rather than sex plays a central role in modulating EIH".

      We have now tested the effect of sex in frequentist and Bayesian analyses in the pooled samples and revised the conclusion in accordance with the results.

      (7) Methods

      L284 - What denotes an individual being "physically active"?

      We thank the reviewer for this question. “Physically active” was defined based on training volume, operationalised as regular participation in structured cycling training sessions (i.e., consistent engagement in planned cycling training units per week). This criterion was used to ensure that participants had an established and ongoing training routine relevant to the study context. We have clarified this in the referenced section.

      Table 1 - Female participants in the current study are noticeably older than the other cohorts from the previous study. While all are considered "young" (<40 y.o.), if one were to take a matched pair of females from each cohort (previous vs current) in terms of relative FTP, then the years of experience and exposure to training are likely a strong influence on EIH responses, not "fitness level" as it is described in this study. Just a speculative thought.

      The reviewer raises an interesting point. However, since we have not acquired any information on the years of training and volume, we are unable to answer this question in the current framework. Future research should investigate the effect of training years (which potentially strongly correlates with fitness level) on exercise-induced pain modulation.

      L313-315 - For future reference, there are some nice papers on how to administer the RPE scales with clear instructions to be certain that perceived effort is being measured appropriately (Halperin & Emanuel 2020; Halperin & Visgotsky 2025; Malleron et al. 2023). I always have a habit of signposting to these studies with regard to RPE because it can often be misrepresented in many different areas of research where RPE scales are used.

      We thank the reviewer for this suggestion and for highlighting these relevant papers. In the present study, participants were extensively familiarised with the rating of perceived exertion scale, and the instructions closely followed the original definition and recommendations provided by Borg (1998). The scale was presented in accordance with its intended anchor points to promote accurate reporting of perceived exertion. We have now added a citation (Borg, 1998) in the Methods section to clarify this procedure.

      (8) Code Availability

      Thank you for including open source code in your submission. I am very appreciative of researchers who champion open research principles.

      We appreciate the reviewer’s comment and support of open research practices. We have updated the code containing the updated analyses and datasets.

      Reviewer #2 (Recommendations for the authors):

      (1) Core interpretation may not be fully supported by the data

      We thank the reviewer for this comment. To investigate the role of fitness level and sex in exercise-induced pain modulation, we have now pooled the samples from the previous and current study and conducted frequentist (linear mixed effects models) analyses. Specifically, we tested for the effect of exercise intensity between the subgroups, as well as (consistent with those conducted in the previous study (Nold et al., 2026) testing the effect of fitness level and sex across the pooled data. This allowed us to directly test the effects of fitness level and sex on a common scale rather than infer these effects from separate cohorts. Furthermore, we conducted Bayesian analyses to quantify evidence both for and against the presence of exercise-induced pain modulation and its moderation by fitness, sex, and study sample, utilising the Bayes factors comparing nested models of increasing complexity. In addition, we examined the posterior distributions of the regression coefficients (β) and subgroup-specific exercise effects to estimate the magnitude and uncertainty of these effects. The Bayesian analyses provided some evidence that fitness level moderated exercise-induced pain modulation, but sex did not. Importantly, these findings were consistent with the overall pattern of results obtained in the frequentist analyses. All corresponding statistics in the main manuscript and Supplementary Materials have been updated accordingly. To improve clarity, the pooled analyses are now presented in dedicated Result subsections and accompanied by new figures (Fig. 4 and Fig. 5).

      (1.1) Lack of baseline condition

      We thank the reviewer for this important comment. We agree that, in the absence of a no-exercise control condition, our design does not allow us to determine whether either exercise condition produced hypoalgesia or hyperalgesia relative to no exercise. Rather, the observed differences reflect the relative effects of high- versus low-intensity exercise on pain ratings.

      Our study was not designed to assess the overall effect of exercise compared with rest, but rather to investigate whether exercise intensity influences exercise-induced pain modulation. Specifically, we hypothesised that high-intensity exercise would produce greater pain modulation than low-intensity exercise. This approach was motivated by previous work demonstrating intensity-dependent differences in post-exercise pain responses (Jones et al., 2019; Vaegter et al., 2014).

      In response to the reviewer's comment, we have revised the manuscript to clarify this distinction and have replaced several instances of "exercise-induced hypoalgesia" with the more neutral term "exercise-induced pain modulation" where appropriate. We also explicitly acknowledge in the Limitations that the lack of a no-exercise condition precludes conclusions regarding absolute hypoalgesic/hyperalgesic effects of either exercise condition.

      (1.2) Lack of internal replication across conditions

      We thank the reviewer for this comment. We agree that the observed effect was most pronounced for heat pain at the highest stimulus intensity and was not consistently observed across all stimulus intensities or pain modalities. Importantly, however, this pattern mirrors the findings of our previous study, in which exercise intensity effects were likewise evident for heat pain but not pressure pain. We therefore interpret the current findings as a replication of the modality-specific pattern observed previously rather than an isolated positive result.

      To further address concerns regarding robustness and sample size, we have now pooled the data from the previous and current studies and conducted additional frequentist and Bayesian analyses examining the moderating roles of fitness level and sex on exercise-induced pain modulation. These analyses yielded converging evidence that fitness level contributes to variability in exercise-induced pain modulation, thereby strengthening confidence in the overall findings.

      With respect to pressure pain, previous meta-analyses have reported comparable or even larger exercise effects for pressure pain than heat pain (Naugle et al., 2012). However, most studies included in these analyses employed handheld pressure algometry, whereas our study used cuff pressure stimulation. Consequently, the pressure pain measures used here may not be directly comparable to those reported in the broader literature.

      (1.3) Deviations from the original study and selective use of data

      We agree with the reviewer that we have adapted the rating scale to reflect a real-time pain rating. To provide a comprehensive account of participants’ pain experience, we have included analyses corresponding to the visualisations on how online ratings evolve over the course of the pain administered in Supplemental Figure S7 – S8. Specifically, we conducted LMER models with exercise intensity × time on heat and pressure pain ratings, separately (Supplemental Fig. S7). The results yielded a significant interaction of exercise intensity × time on heat (β = -0.13, SE = 0. 06, t(7577) = 2.12, P = 0.03) but not pressure pain ratings (β = -0.07, SE = 0.10, t(7956) = -0.71, P = 0.48). Furthermore, we conducted LMER models with exercise intensity × time × stimulus intensity on heat and pressure pain ratings (Supplemental Fig. S8), suggesting no significant interaction on pressure pain ratings (β = -0.0002, SE = 0.004, t(2353) = -0.06, P = 0.95) but a significant interaction on heat pain ratings (β = -0.01, SE = 0.003, t(2201) = -3.24, P = 0.001).

      As the reviewer correctly notes, to facilitate comparison with the previous study, we conducted our primary analyses using peak pain ratings. We acknowledge that this approach does not fully capture the richness of the continuous data but allows for comparability.

      (1.4) Over-reliance on null results regarding sex differences

      We thank the reviewer for this important point regarding the interpretation of null effects, particularly in relation to sex differences. We agree that the absence of a statistically significant difference does not, in itself, provide evidence for equivalence between groups.

      To address this issue, we have pooled the samples and supplemented our frequentist analyses with Bayesian analyses. Bayes factors were computed to quantify the relative evidence for the null versus alternative hypotheses, providing a continuous measure of support that complements frequentist inference, particularly in cases of non-significant effects where evidence for the absence of an effect is of interest. Furthermore, posterior distributions of the regression coefficient of interest (in this case, the moderating role of sex on exercise-induced pain modulation) provided no evidence for a moderating role of sex on exercise-induced pain modulation. This approach allows for a more nuanced interpretation of the data, especially concerning null effects. We have adapted the Methods, Results, and Discussion accordingly.

      (2) Limited sample and lack of diversity

      We thank the reviewer for this comment. The study was intentionally designed to include only female participants with significantly higher fitness levels compared to the females from the previous study, as our previous study included females who were slightly less fit than males, making it difficult to separate the effects of sex and fitness. We, therefore, aimed to examine exercise-induced pain modulation in a more homogeneous sample. However, much of the EIH literature has been conducted in highly trained male participants (Janal et al., 1984; Saanijoki et al., 2022; Scheef et al., 2012). As such, studies in women of higher fitness are important to improve our understanding of potential sex differences in exercise-induced pain modulation. While the present findings should be interpreted within the context of the sample studied, when analysed and considered alongside our previous work, they contribute to a broader understanding of exercise-induced pain modulation across sexes and fitness levels.

      (3) Methodological choices limit the interpretability of the data

      (3.1) The use of a non-standard VAS scale (0-150 with a fixed pain threshold at 50) is unconventional and may influence how participants report pain, while limiting comparability with related literature.

      We thank the reviewer for their comment. Since we introduced real-time pain ratings in the current study, while the pain is ongoing, we aimed to introduce a scale that allowed us to pinpoint the period when a painful stimulus reaches painfulness (exceeds 50 on the scale). To allow for comparability between the studies, we have calculated the peak pain rating provided during the real-time pain ratings, as this corresponds to the instruction participants received in the previous study to rate the most painful point of the previous stimulation. Previous research (Koyama et al., 2004) has shown that, in heat pain, real-time ratings account for R<sup>2</sup> = 0.89 of the variability of post-stimulus rating, where much of the variability for the post-stimulus intensity pain rating is accounted for by the peak response during the online ratings (adj. R<sup>2</sup> = 0.3 – 0.4), suggesting good comparability of both pain measures. Furthermore, as we have now pooled the samples and conducted analyses, we rescaled the maximum pain ratings so that ratings below 50 (below the pain threshold) were set to 0, corresponding to VAS 0 in the previous study, resulting in pain ratings ranging between 0 and 100.

      (3.2) Participants explicitly reported expecting exercise to reduce pain, introducing a potential confound that is not presently addressed.

      We thank the reviewer for this comment. Indeed, participants reported expecting lower whole-body pain in response to acute aerobic exercise, but no changes in joint or muscle pain. In the context of exercise, previous research (Colloca et al., 2018) showed that exercise-unrelated expectations and exercise itself induce comparable hypoalgesic effects, though they might work through distinct mechanisms. Studies directly manipulating exercise-related expectations showed that education about EIH, compared with general education about exercise and pain, could induce hypoalgesia following aerobic exercise (Jones et al., 2017). Interestingly, another study (Vaegter et al., 2020) investigated the interplay of exercise-related expectations (through verbal instructions) and resistance exercise on pain perception and showed that only explicit negative instructions induced hyperalgesia, whereas positive or neutral exercise-related instructions did not change the magnitude of EIH.

      Since participants in the current study reported highly specific positive expectations only for whole-body pain, but not for joint or muscle pain, expectations are unlikely to fully explain our findings. Consistent with this, in our previous study using the same prospective expectation questionnaire, we found no significant expectation effects for any pain domain (Nold et al., 2025), although we acknowledge that the samples differed, especially with respect to fitness levels.

      Nevertheless, the current study was not designed to disentangle the effects of exercise and expectations, as expectation ratings were measured prospectively but not manipulated. Therefore, we cannot determine to what extent positive expectations of exercise have contributed to the observed effects, and we have now acknowledged this as a limitation in the Discussion.

      (3.3) A more comprehensive use of the full time series of pain ratings would provide a stronger and more transparent basis for interpretation of the present findings.

      We thank the reviewer for this suggestion. As noted in our response to Comment 1.3, we have included analyses and visualisations of the full time series of online pain ratings in the Supplementary Materials (Fig. S7–S8), providing a more comprehensive understanding of pain dynamics over time.

      Figures are very low in quality - artefacts, small text, insufficient labelling.

      We thank the reviewer for highlighting this and, indeed, rendering the PDF reduced the quality of the images. For the reviewed version, we have been specifically attentive to image quality by increasing font sizes and adding labels.

      There was not enough detail in the methods section for the number of cycling blocks, and how low and high intensity were distributed throughout the session.

      We thank the reviewer for their comment. Participants completed four cycling blocks in total, two HI and two LI exercise blocks, presented in a pseudo-randomised order in the session. To improve clarity, we have added the relevant details to the Methods section.

    1. eLife Assessment

      This study presents important findings linking peripheral inflammation to the remodeling of perinodal adipose tissue and draining lymph nodes, suggesting a mechanism by which local tissue inflammation can reshape LN structure and metabolism. The evidence is solid and supported by observations, future studies will address any outstanding questions.

    2. Reviewer #1 (Public review):

      The idea is super interesting, and the subsequent work is potentially significant because it links peripheral inflammation to remodelling of perinodal adipose tissue and draining lymph nodes. This suggests an antigen-independent manner by which local tissue inflammation can communicate with and reshape immune organ structure and tissue metabolism. However, the evidence is suggestive. For instance, many conclusions rely on correlational weight/cellularity relationships, models with confounders (spontaneous wounding; potentially systemic IMQ), and macrophage dependence inferred from a single pharmacologic approach without definitive depletion/lineage or tracer-based causal link.

      Comments on revised version:

      Overall, the rebuttal addresses most of the original concerns constructively, with additional data and appropriate moderation of several claims. However, some of the key mechanistic conclusions remain over-stated. In particular, macrophage origin, the contribution of systemic inflammation, and the mechanistic relationship between fat-pad remodelling and lymph-node expansion remain incompletely resolved. The manuscript would nevertheless be acceptable if these limitations are clearly acknowledged and the mechanistic language toned down. That said, I think the rebuttal is broadly satisfactory.

    3. Reviewer #2 (Public review):

      Summary:

      The authors aim to demonstrate skin inflammation is associated with fat pad atrophy and lymph node expansion. They further propose that these phenotypes are driven by the recruitment and lipid metabolism of CCR2-independent macrophages.

      The authors took advantage of two skin inflammation models, fight-induced and imauimod-induced skin inflammation and analyzed multiple tissues, including skin, fat pads, and lymph nodes. Using a macropahge-depletion method (e.g., CSF-1R inhibitor), the authors further suggest the inverse correlation between fat pads atrophy and lymph node expansion is macropahge-dependent.

      Comments on revised version:

      The authors improved their manuscript with additional data and discussion. However, the authors need to clarify their terminology in the experiments. To strengthen their conclusion, authors need some additional experiments addressed below.

      Clarify cohousing conditions in Fig. 1 through 3: the authors used 'resting' ,'alone', 'together'. These are unclear. Please state whether 'resting' mice were singly housed or co-housed in Fig. 1. If they singly house, specify when they were separated from their littermate or cage mates. In Fig. 1-3. If they were co-housed ('together'), specify whether their cage mates were littermate or non-littermate Ccr2-/- mice in the manuscript, including line 107.

      Strengthen the link between fighting and the observed phenotypes: Co-housed Ccr2-/- males had increased lymph node (LN) weight and reduced fat pad weight. To test whether these changes are associated with fighting-induced skin inflammation, please consider comparing mice that are singly housed after three weeks of co-housing with mice that are singly housed throughout the same period.

      Clarify the monocyte analysis: At line 141, please use 'monocytes' rather than 'inflammatory monocytes' if the analysis does not include inflammatory markers/phenotypes. Please also label the monocytes and macrophage populations in Fig. 3Di and 3Dii as was done for neutrophils and eosinophils in Fig. 3B.

      Recruitment vs. Self-renewal: At line 144, CD11b expression alone does not distinguish recruited cells from cells expanded by local proliferation; Inflammation itself can alter CD11b expression. Please consider providing the evidence that distinguish these possibilities. Fate mapping would be an ideal approach, while Ki67 staining or BrdU incorporation assay could alternatively assess proliferation.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Major Comments:

      (1) "Wounding/fighting" evidence is confounding.

      Unless I am mistaken, a large part of the argument for inflammation-driven perinodal fat pad atrophy and LN expansion relies on spontaneous fighting injuries in co-housed CCR2-/- males, including animals "culled...due to excessive wounding." Because wound severity, duration, infection load, stress, and cage dynamics are uncontrolled, isn't it difficult to assign causality to "cutaneous inflammation"?

      We thank the reviewer for this point and fully understand the concerns here. We have carefully gone through the text now and ensured that we refer to wounding-associated inflammation throughout. This therefore takes into account potential wound infections etc. We are also confident that the phenotype relates to cutaneous inflammation because it is only seen in lymph nodes and fat pads draining inflamed skin. We have adapted the discussion accordingly to address this important issue (lines 169-172 of the revised text).

      (2) The "CCR2-independent macrophage" conclusion.

      The manuscript interprets persistence/accumulation of macrophages despite reduced inflammatory monocytes as CCR2-independent recruitment or local proliferation. However, CCR2 deficiency can alter immune baselines and long-term tissue remodelling. Perhaps consider bone marrow chimeras (WT to CCR2-/-, CCR2-/- to WT ????) or an inducible CCR2 deletion approach to separate developmental/systemic effects from acute inflammation-driven mechanisms. If "in situ proliferation" is proposed, include a direct readout (e.g., Ki67 in ATMs in the fat pad).

      We have previously published that CCR2-/- mice display no alterations in any myeloid or lymphoid cell subsets with the exception of Ly6C+ monocytes. We have now referred to this in the revised text (lines 135 and 136) and hope that this addresses the reviewer’s concerns. In terms of whether the CCR2-independent increase in macrophages as a consequence of recruitment or local proliferation, the fact that the macrophages are CD11b+, along with the relative increase in inflammatory monocyte content in co-housed CCR2-/- mouse skin, strongly suggests that they derive from peripheral blood monocytes and therefore from recruited cells. We have clarified these points on page 7 of the revised text.

      (3) IMQ and systemic effects.

      The work relies on topical Aldara/imiquimod as an "inflammation without antigen" driver of distal LN/fat-pad remodelling. But IMQ is well known (and cited by the authors) to enter circulation and drive systemic responses, which could blur whether effects are truly draining-site specific vs systemic metabolic/inflammatory effects. It would be ideal to provide systemic context: plasma cytokines and/or metabolic readouts (e.g., circulating FFAs) to distinguish local vs systemic drivers.

      This is an important point. We have now included plasma cytokine data showing that these are indeed elevated systemically in Aldara-treated mice (new Supplementary Figure 5). We have altered our discussion of this point to reflect these new data (lines 228-229 of the revised text).

      (4) Macrophage dependence is inferred from CSF1R inhibitor treatment.

      However, validation of macrophage depletion and specificity is incomplete. The manuscript uses AZD7507 (CSF1R inhibitor) and observes partial rescue of fat pad/LN phenotype while skin severity (PASI) is unaffected. But, to this reviewer, the data shown do not clearly quantify actual macrophage depletion efficiency in the target fat pad, and LN at endpoint, and CSF1R blockade can affect multiple myeloid populations. Therefore, show absolute macrophage counts (and likely other myeloid populations) in fat pad and LN with/without AZD7507 at the analysed timepoints, not only outcome weights. (The methods describe dosing but not endpoint depletion quantification??)

      We have now added these data (new Figure 7D and Supplementary Figure S6 and discussed on page 13 of the revised manuscript). What we show is that fat pads from Aldara-treated mice show a large reduction in macrophages and also a reduction in dendritic cells, although the number of dendritic cells was already low in the fat pad of Aldara-treated females. The lymph nodes of Aldara-treated females with AZD7507 also show reduced macrophage counts, but the reduction is also seen in all other immune subsets (lymph nodes were smaller so reduced counts in all subsets). We hope that these new data satisfy the reviewer’s concerns.

      (5) Fat pad atrophy/LN expansion is a correlation.

      The paper emphasises negative correlations between fat pad and LN weights/cellularity at baseline and with inflammation. But correlation does not establish whether fat pad lipolysis drives LN expansion, whether LN changes drive fat remodelling, or whether both reflect systemic mediators. Add tissue-level evidence distinguishing true adipocyte loss vs other contributors to "weight change" (e.g., oedema/fibrosis).

      We agree that our data do not establish a direct causal relationship between adipose tissue loss and LN expansion, but the observations do demonstrate a strong temporal association. As seen from the added graph (new Figure 8B and related text on page 13 of the revised manuscript), the rapid and concurrent occurrence of both processes (approximately 25% reduction in fat pad weight and 30% increase in LN size within 24 h of Aldara treatment) suggests that they are closely linked biological events occurring within the same tissue microenvironment. Determining whether these changes arise through direct local interactions or reflect broader inflammatory mediators would require additional mechanistic investigation.

      (6) Evidence for "fatty acid donation" from fat pad to LN.

      The lipid data are described as "exemplary," and the inference that LN fatty acids originate from the fat pad is based on temporal ordering and relative abundance. This does not rule out plasma spillover, LN-intrinsic metabolism, or altered lymph flow.

      We agree with reviewer and have amended our discussion of this point appropriately (page 16 of the revised manuscript).

      Reviewer #2 (Public review):

      The authors aim to demonstrate skin inflammation is associated with fat pad atrophy and lymph node expansion. They further propose that these phenotypes are driven by the recruitment and lipid metabolism of CCR2-independent macrophages.

      The authors took advantage of two skin inflammation models, fight-induced and imauimod-induced skin inflammation and analyzed multiple tissues, including skin, fat pads, and lymph nodes. Using a macropahge-depletion method (e.g., CSF-1R inhibitor), the authors further suggest the inverse correlation between fat pads atrophy and lymph node expansion is macropahge-dependent. While the study identifies this intriguing inverse correlation during skin inflammation, the causal pathway linking fat pad atrophy and lymph nodes enlargement has not been clearly established.

      To improve the rigor of the manuscript, the authors address the following concerns;

      (1) CCR2-deficient mice showed reduced inflammatory monocytes and monocyte-derived macrophages (PMID:16462739; 16341265). During tissue inflammation, CCR2+ classical monocytes are typically recruited to the injured peripheral tissues, including skin, where they differentiate into monocyte-derived macrophages (PMID:38474365). While inflammatory monocytes were reduced in the skin (Figure 3 d), fat pads (Figure 4a, S2D) of CCR2-deficient mice, macrophage numbers were significantly increased in these mice. It remains unclear whether CCR2-independent macrophages were newly recruited from alternative sources or tissue-resident macrophages underwent local self-proliferation to compensate for the loss of CCR2+ monocyte-derived macrophages.

      We agree with reviewer and have expanded our discussion of this important point (see text on pages 7 and 8 of the revised manuscript). As mentioned in response to reviewer 1 we have inferred from the expression of CD11b and c on recruited macrophages that they are derived from peripheral blood monocytes and therefore ultimately are recruited and have not arisen due to local proliferation. We hope that the reviewer is happy with this expanded discussion.

      (2) In line 258, the authors state that there was "a significant reduction in CD11C- CD206+ anti-inflammatory macrophages (Figure 4b i-iii)". However, the quantification data in Figure 4b iii do not appear to show any reduction in anti-inflammatory macrophages in either males or females. Please reconcile this discrepancy between the text and the figure

      We apologise for our carelessness here and this is the consequence of not having normalised some of the data presented. We have now done this and updated the graph which now, hopefully, more convincingly demonstrate reduction in CD11c- macrophages.

      (3) Although CD11C and CD206 were historically used as markers of inflammatory and anti-inflammatory markers, respectively. These markers are no longer considered sufficient to define the macrophage polarization state, particularly in adipose tissue, where they are constitutively expressed by resident macrophages (PMID:34210853). Numerous studies have demonstrated substantial macrophage diversity/heterogeneity across iWAT, eWAT, and brown fat tissues. The authors should discuss adipose macrophage diversity beyond the outdated M1/M2 frame.

      We thank the reviewer for pointing this out and apologise for the superficial way in which we have addressed macrophage heterogeneity in the fat pads. We selected CD11c, as it is clearly an important discriminatory marker for tissue macrophages, and CD206 as it presents itself as a common marker of both CD11c-positive and CD11c-negative macrophages in our analyses. We fully acknowledge that the understanding of macrophages has moved on beyond the simple pro-and anti-inflammatory categories have therefore amended the discussion appropriately (please see text on page 8 of the revised manuscript). We hope that these changes are satisfactory for the reviewer.

      Minor issues

      (1) Clarify which claims are "antigen-independent" vs "no intentional antigen." The introduction/abstract emphasises "absence of antigen presentation," but inflammation models (wounds/IMQ) do not exclude endogenous/self-antigens. Rephrase or add controls aligned to the claim.

      We fully accept reviewer’s point here and have removed references to ‘antigen-independent’ throughout.

      (2) Provide more explicit randomisation/blinding details for subjective readouts (e.g., PASI scoring).

      We agree that additional methodological detail would improve transparency. Inflammation scoring was performed by an independent collaborator blinded to treatment allocation. Animals were not formally randomized between groups because AZD-treated mice required additional gavage procedures and handling distinct from control animals. We have now included additional details in the Materials and Methods section (see page 22 of the revised text).

      (3) Increase transparency on sample sizes and independence when pooling tissues (e.g., pooled fat pads, pooled macrophage RNA samples).

      Lymph nodes and fat pads were used for different analyses throughout the study, resulting in differences in sample processing and pooling strategies between experiments. Information regarding biological replication and any sample pooling is provided within the corresponding figure legends for each experiment.

      In most cases, both fat pads and lymph nodes of each mouse were pooled for analysis, this is specified in each figure legend (i.e. ‘each point represents the combined weight of both inguinal lymph nodes/fat pads for each mouse’

      Pooling of fat pads from multiple individual mice was only done for RNA sequencing due to low numbers of adipose tissue macrophages. Again, details are in the figure legends.

      We hope that this is acceptable to the reviewer.

      (4) Quantify systemic effects of IMQ (weight loss is mentioned in monitoring; include the data and interpret its relevance).

      Body weight was monitored throughout the study for animal welfare purposes but was not systematically recorded for analysis. While we cannot formally exclude systemic effects, we believe the observed phenotype is unlikely to be driven primarily by general sickness behaviour, as substantial tissue changes were already evident by day 1 following treatment, including an approximately 25% reduction in fat pad weight and a 30% increase in lymph node weight (please see revised figure 8B). The rapid onset and tissue-specific nature of these changes suggest an early biological response rather than a secondary consequence of generalized illness.

      (5) Tone down mechanistic language ("donation," "feeds the LN") unless tracer/intervention experiments are added; keep as "consistent with" until proven.

      We thank the reviewer for pointing this out and have amended the language appropriately.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      Minor concerns;

      (1) Please delete 'i-iii' from Figure 3b, as these panel labels are not referenced anywhere in the manuscript.

      We have carefully edited, and improved, figures throughout the text and hope that they are now acceptable.

      (2) Please change '1D' to '2D' in Figure S2.

      This has now been done and we apologise for this carelessness.

    1. eLife Assessment

      This is a useful and clearly presented study showing how simple feedback between cell crowding and cell growth can maintain tissue balance and lead to the spontaneous formation of stem-cell clusters. The evidence supporting the behavior of the model is solid, but the significance of the clustering result would be strengthened by relating the emphasizing the differences to prior studies, and potentially relating the clustering mechanism to properties of critical birth-death processes in two dimensions.

    2. Reviewer #1 (Public review):

      Summary:

      This theoretical study of a simple system of stem, transient progenitors and fully differentiated cells provides interesting results about how tissues can reach homeostasis with different proportions of these cell types. The key ingredient is the feedback due to pressure, which regulates the proliferation rate. The study shows that by varying the rates at which stem cells switch to progenitor cells, and progenitor cells to differentiated cells, the final composition of the tissue can be tuned. In addition, the model shows that clusters of stem cells can arise in this model due to their effective immobilization and therefore that stem-cell niches can arise even in the absence of specific adhesion between different cell types.

      The model is calculated using both cell-based simulations and continuum dynamics, and is thoroughly analyzed in its spatial and temporal characteristics.

      Strengths:

      The model is simple, yet leads to very rich results. The authors demonstrate the results of the model using both particle-based simulations and a continuum model, thereby enhancing our understanding of this complex system.

      Weaknesses:

      The origin of the stem cell niche is not so clearly explained.

    3. Reviewer #2 (Public review):

      Summary:

      This is a theory/computational paper that explores a model of a two-dimensional tissue undergoing homeostatic turnover. The motivation is to explore how the rates of cell division and cell loss in a tissue can be precisely balanced to maintain tissue integrity. It is already well appreciated that this can be achieved by a feedback mechanism whereby cells tune their division or loss rate in response to a change in cell density relative to a homeostatic set point (e.g. PMID 30269903 and other papers cited in the introduction to this new manuscript). Cell density can be sensed locally by mechanical pressure.

      Here, the question asked is whether pressure-dependent homeostatic control can maintain not only cell density (as previously known) but also cell type proportions when a tissue has a proliferative hierarchy. The specific example considered is a 3-tier hierarchy with stem cells (S) that give rise to transit-amplifying cells (T) that give rise to terminally differentiated cells (D), which are then lost. Next, the authors notice in simulations that in such a model of density-dependent cell division, stem cells undergo spontaneous clustering without any need for cell adhesion. Thus, the key result of the paper is that a simple pressure-based homeostatic feedback control leads to spontaneous formation of stem cell clusters, which the authors call "niche-like". They characterize these clusters numerically.

      Overall, the work is elegant and presented in a pedagogical manner. But I think its central result has already been previously described (DOI 10.1103/PhysRevE.77.031907), albeit without the use of terms such as "niche-like" that increase the relevance of the work, and using a different detailed choice of model. Essentially, clustering is the outcome of a log-divergence of the density of random walkers undergoing a critical birth-death process in two dimensions, as discussed in the 2008 paper. Essentially, once stem cell division is coupled to total density (rather than just stem cell density), then the stem cells follow a critical birth-death process in 2D, and the clustering is an inevitable outcome. This is the precise point of the previous paper. As such, my review will focus on novelty compared to prior work.

      Strengths:

      The first strength of the paper is that it gives a clear and biologically oriented presentation of how pressure-dependent (or more simply, density-dependent) feedback can be sufficient to maintain steady-state homeostasis with defined proportions of stem, transit-amplifying, and differentiated cells. The authors scan parameters in numerical simulations using an established agent-based model of cells under mechanical tension, and show that tissue can be maintained provided that stem cell division rates are able to match loss rates. This is not very surprising, but I believe it has not been laid out before in this way, and it is clearly presented.

      A second strength is that the paper may make a useful contribution by drawing attention to the connection between mechanical homeostatic feedback and the spatial organization of stem/progenitor cells. Even though there is a precedent for this point (see overview above), the work is more biologically oriented than the prior paper, and may therefore be useful to readers interested in how simple local rules can shape tissue organization. The current paper uses a more biophysically informed particle-based model for simulations than the prior work, and it provides a useful framework for studying the interaction between mechanical feedback, tissue homeostasis, and cell-type hierarchy that could be generalized. The simulations are well motivated, and the results are presented in a pedagogical manner. More on the comparison to the 2008 paper below.

      Weaknesses (or rather, comparison to prior work):

      The main weakness is that the central result of the paper appears to have already been previously described, albeit using different terminology and a different detailed choice of model. I will describe the similarities and differences between the two papers below.

      The 2008 paper...

      • ...is motivated by asking how a critical birth-death process can maintain uniform density in 2D. This is different from the current paper, which asks whether a birth-death process can be tuned by total density and still maintain cell type proportions. As such, the questions motivating the work are subtly different, although both worry about how homeostasis is maintained.

      • ...considers a 2-tier model consisting of progenitor and differentiated cells (A->B->0). This is different from the current paper, which considers a 3-tier model (S->T->D->0). But the current paper is aiming to be general rather than specific, and I would propose that complexifying the model from a 2-tier hierarchy to a 3-tier hierarchy doesn't make the new paper qualitatively different.

      • ...shows that "the population of progenitor cells should cluster over time" (quoted from the paper). This is shown for A cells in the 2008 paper, and is shown for stem (S) cells in this new paper. As such, the prior paper already shows the key result of this new paper.

      • ...uses slightly different rules, which are clunkier: it allows for symmetric and asymmetric division of A cells, does not allow direct A cell differentiation (no A->B allowed), and it uses a hard-core lattice-based model with hole-diffusion to model density-dependent cell division. By contrast, the current paper uses a more biophysically informed two-particle agent-based model for simulations, and it only considers symmetric divisions and direct differentiation. As such, the current paper is a more realistic treatment than a lattice-based model, and simply different (not more or less realistic) in terms of ignoring epigenetic heritable sister cell correlations. But since clustering is a large-scale phenomenon, as is often the case in statistical physics, the microscopic implementations should not be important. As such, I would not expect the difference in rules to be important to the clustering.

      • ...derives a long-term asymptotic form for the clustering dynamics of progenitor cells (Equation 15 of that paper, and the unnumbered equation at the top of column 2 of page 7 of the paper). This goes beyond the new paper, which only describes the phenomenon numerically. The 2008 paper also explains the phenomenon in terms of prior work on random walkers undergoing birth-death processes, which better links the phenomenon to prior statistical literature.

      • ...shows experimental evidence for clustering of progenitors in mouse epidermis. This goes substantially beyond the current paper, which is entirely computational. That said, despite the excellent fit of data to the theory, I am myself not convinced that the clustering seen in epidermis is the result of this phenomenon alone.

      • ...studies clonal dynamics and shows that it is consistent with a voter-model-like behavior, with clone size growing as t/log(t). The scaling law is different from that reported in the current paper.<br /> Thus, the key result that progenitor cells undergo clustering seems to have been mathematically described in the 2008 paper and even shown in that paper to be consistent with experimental data in one tissue.

      That said, I do think that this new paper may make a useful contribution. It is certainly more biologically oriented (despite lacking experimental data, which the prior paper has). And it may be that the quantitative dynamics of clustering and clonal competition are subtly different in this model. So, with certain revisions, I am supportive of this work.

      Specific suggestions:

      (1) The new paper should appropriately cite precedent. The paper should avoid making claims that progenitor clustering in this model of birth-death with spatial density control is a major novel discovery. This was a key result of the previous paper. The new paper should also cite the 2008 paper for evidence that clustering of this type has been proposed to be seen experimentally. The differences between the papers discussed above could be used as needed in highlighting the contribution of the new work.

      (2) There is still a question about whether microscopic details of the simulation may alter the asymptotic dynamics of the clusters and clones. The authors could compare the dynamics of clustering between their simulations and the 2008 lattice-based result (Equation 15 of that paper, the "1/log(t)" behavior of the density radial correlation function) derived in the original paper, and similarly compare clonal dynamics in that paper to this new paper. Whatever they find is interesting: if they confirm that the same dynamic behaviours occur, it will reinforce the results of the prior work, rephrase the problem in more relevant terms, and help make it accessible to a broader audience. If they show that their model leads to different quantitative dynamics, they could explore (in a supplement perhaps?) what is the origin of the difference, given that the models are superficially similar.

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript studies a minimal model of tissue homeostasis in which stem (S), progenitor/transit-amplifying (T), and differentiated (D) cells interact through stochastic fate decisions and mechanically regulated proliferation. Combining a compartment-level analytical description with particle-based simulations, the authors show that pressure-dependent growth can stabilise cell populations and maintain tissue homeostasis over a broad parameter range. They derive homeostatic cell fractions and identify distinct regimes, including tissues maintained by rare stem cells or by long-lived progenitors. Most notably, the simulations reveal the spontaneous emergence of persistent stem-cell clusters and renewal-unit-like structures in the absence of explicit niche signals or differential adhesion. The authors further analyse spatial correlations, cluster statistics, lineage dynamics and neighbourhood compositions, and argue that stochastic fate decisions qualitatively alter the spatial organisation of mechanically regulated tissues. Based on the annotated manuscript, several of these central claims concern the emergence and stability of stem-cell clusters and their interpretation as self-organised structures.

      Strengths:

      The manuscript is clearly written, technically competent, and combines analytical calculations with large-scale agent-based simulations in an elegant way. The systematic analysis of spatial observables, including pair correlations, cluster-size distributions, void statistics, neighbourhood composition, and lineage tracing, provides a rich characterisation of tissue organisation. The comparison with the authors' previous work on deterministic stem-cell hierarchies is particularly interesting, as it demonstrates that altering the fate-choice scheme can qualitatively change the resulting tissue architecture from stem-cell repulsion to stem-cell clustering. The study therefore generates a number of experimentally testable predictions regarding stem-cell spatial organisation and lineage dynamics. I also found the attempt to connect microscopic fate decisions to mesoscopic tissue-level organisation valuable, as this remains a relatively underexplored area at the interface of stem-cell biology and tissue mechanics.

      Weaknesses:

      My main concern relates to novelty and positioning within the existing literature. The manuscript frequently presents mechanical feedback as a mechanism capable of generating tissue homeostasis and robust cell-type proportions. However, the idea that crowding- or pressure-dependent feedback can robustly stabilise renewing tissues is already well established, both in mechanical homeostasis models and in more general feedback-based theories of tissue regulation. Consequently, the homeostasis aspect of the present manuscript appears more as an application of existing principles to a particular mechanical framework than as a fundamentally new conceptual advance. The authors should position their work more clearly with respect to these studies and explain more explicitly what genuinely new biological or mathematical insights are provided beyond the existence of homeostatic steady states.

      The manuscript therefore derives its novelty primarily from the spatial phenomena emerging from the coupling of stochastic fate decisions and mechanical interactions. While the observed stem-cell clustering is intriguing, the mechanistic explanation remains incomplete. The authors attribute clustering to stochastic symmetric divisions combined with mechanical control, yet it remains unclear whether the observed structures genuinely arise from a new self-organised mechanical mechanism or simply reflect local genealogical correlations generated by repeated symmetric divisions. Several of the strongest conclusions, including long-range order, renewal-unit formation, and stable niche-like organisation, require more rigorous support. In particular, the manuscript currently lacks appropriate control models that would disentangle the effects of mechanics, stochasticity, and daughter-cell placement. Furthermore, the definition of clusters via a specific distance threshold appears somewhat arbitrary and may itself influence the inferred cluster sizes and conclusions regarding finite renewal units. It would be important to demonstrate robustness with respect to this choice.

      I also have concerns regarding the simulation methodology and interpretation of the spatial statistics. Large tissues are assembled from smaller pre-equilibrated patches. While this may be computationally convenient, it raises concerns about whether long-range structures can emerge naturally and whether the reported spatial organisation could be affected by the construction procedure. Given that one of the central claims concerns spatial ordering over large scales, it would be important to demonstrate explicitly that the patching procedure does not influence the results. Similarly, the manuscript interprets pair-correlation and void statistics as evidence for long-range ordering of stem-cell clusters, but does not sufficiently exclude alternative explanations arising from finite system size, patch construction, or simple genealogical clustering.

      Conclusion:

      Overall, I find the manuscript interesting, whilst I also believe it should have a major revision. The most valuable contribution of the study is not the demonstration that mechanical feedback can maintain homeostasis, but rather the suggestion that stochastic fate decisions can qualitatively reshape the spatial organisation produced by mechanically regulated tissues. For a broad-interest journal, however, the authors should sharpen the positioning of the work relative to prior homeostasis theory, establish more clearly the mechanistic origin of the reported stem-cell clustering, and provide additional evidence that the observed structures are genuine emergent features of the model rather than artefacts of modelling assumptions or analysis choices.

    5. Author response:

      Thank you for providing detailed and positive feedback. This will help us to improve manuscript and its accessibility for a broad audience.

      We first want to highlight what we believe is the main aspect, and therefore novelty, of our work: Combining cell-cell mechanics, mechanical regulation of growth and stochastic cell fate decisions leads to a stable population of stem cells in a stable balance with more differentiated cells, avoiding neutral drift, extinction or overgrowth. Furthermore, this combination leads to stable clusters, i.e. without further coarsening. We call these structures “niche-like”, as we believe stability is fundamental for biological function.

      As the referees point out, this work builds on previous works in the literature, and highlighting the differences and advances will benefit the manuscript. As referee 3 points out, homeostatic regulation by mechanics, in particular pressure, has been discussed intensively in the literature, by us and many others. The novelty in our work is that we apply this regulatory mechanism to a multi-component tissue, and there can stabilize not only the total number of cells, but also their relative composition. In particular, we derive analytical equations relating composition to differentiation rates, and demonstrate their validity in agent-based simulations. In the revised manuscript we will highlight more clearly that this is an application of previous works on a novel system.

      Referee 2 points to Klein et al. (2008). We do agree that their model also shows clustering, and therefore that we should have compared and contrasted our model more thoroughly with this work. However, as we will show more clearly in a revised manuscript, our results differ crucially from this form of clustering without detailed mechanical interactions. As also discussed in the original work by Klein et al., the mechanism therein results in clusters which are not stable, but continue to grow indefinitely over time (although at a slow rate). We show that our simulations display stable clusters which do not grow further, even when simulating for very long time scales, and that these clusters are well-defined, by structurally different correlation functions showing clear inter-cluster separation. In the revision, we will demonstrate that even when starting from a perfect cluster, the stem cells separate, resulting in finite stable clustering. Thus, our clusters are true “fix points” of the stem cell dynamics. Referee 3 rightfully recommends that we should identify more clearly which mechanism is responsible for the stable niche-like structures. To this end, we will compare more closely to Klein2008 as a model without mechanics leading to log(t) coarsening, and our earlier work (Krämer et al., 2024) where stem cells were immortal. There, we had found that mechanics alone produces the reverse result, i.e. leads to enhanced separation of stem cells, which anti-cluster and are dispersed as single cells. We will add new simulations as an additional control that contain cell-cell mechanics, but no mechanical regulation of growth. This combination will allow us to disentangle how the combination of cell mechanics, mechanical feedback on growth and stochastic differentiation results in the formation of stable niche-like structures.

      Note that neither Klein2008 nor Krämer2024 have a physical mechanism that maintains the overall stem cell population number stable. Krämer2024 simply fixes the number of stem cells, whereas the model in Klein 2008 results in stochastic drift in stem cell number.

      Evidentially, we will also address the more technical weaknesses of the manuscript identified by the referees. In particular, we will demonstrate more clearly that the observed structures are genuine emergent features of the model rather than artefacts of modelling assumptions or analysis choices, especially with respect to the patching technique used to obtain large simulations.

    1. eLife Assessment

      This valuable study investigates the role of dinucleoside polyphosphates in regulating the cGAS-STING-TBK1 axis of innate immunity. The data collected using multiple biochemical and cellular assays, plus X-ray crystallography, are solid, but testing additional polyphosphates and using NUDT2KO cells will further strengthen the main conclusion. This paper will be broadly relevant to those studying various innate immune pathways.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript entitled "NUDT2 loss defines quantitative limits for dinucleoside polyphosphate action on the cGAS-STING-TBK1 axis" investigates whether intracellular levels of dinucleoside polyphosphates can influence signaling through the cGAS-STING pathway. The study was motivated in part by a previous report proposing that Ap4A can bind to STING and inhibit its activity.

      In the present work, the authors prepared recombinant human and mouse cGAS, STING, and TBK1 and systematically investigated their interactions with Ap3A, Ap4A, and Ap4G, which represent biologically relevant dinucleoside polyphosphates. To characterize these interactions, they employed a broad range of biochemical and structural approaches, including isothermal titration calorimetry (ITC), HPLC analysis of enzymatic reactions, X-ray crystallography, and a time-resolved fluorescence kinase assay.

      First, the authors prepared human and mouse STING in both wild-type form and as specific mutants previously associated with altered binding of the canonical STING ligand cGAMP. ITC measurements showed that, whereas cGAMP binds STING with nanomolar affinity, none of the tested dinucleoside polyphosphates displayed detectable binding to any of the STING variants. The authors then investigated binding of ATP, GTP, and the dinucleoside polyphosphates to a mutant form of cGAS by ITC. Interestingly, all three dinucleoside polyphosphates showed apparent binding in a range similar to that of the tested NTPs. However, subsequent structural analysis did not reveal electron density corresponding to the dinucleoside polyphosphates in the active site.

      The authors further examined the effect of Ap4A on TBK1 and found that Ap4A can inhibit TBK1 activity, but only at very high concentrations. Finally, they quantified intracellular ATP, GTP, Ap3A, Ap4A, and Ap4G levels in NUDT2-knockout cells. Although Ap4A and Ap4G concentrations increased substantially upon NUDT2 deletion, the measured intracellular concentrations generally remained below those required to significantly affect components of the cGAS-STING-TBK1 pathway in vitro.

      Overall, the manuscript is clearly written and represents a careful and rigorous piece of biochemical work. I particularly appreciate the authors' effort to critically examine and clarify conclusions from previous studies. The experimental work is comprehensive.

      Strengths:

      A major strength of the study is the breadth and rigor of the experimental strategy. The authors combine binding measurements, enzymatic assays, structural analysis, kinase assays, and direct measurements of intracellular metabolite concentrations. Their data provide convincing evidence that the tested dinucleoside polyphosphates do not bind STING with physiologically relevant affinity and that inhibition of TBK1 by Ap4A occurs only at concentrations substantially higher than those normally reached in cells. Importantly, the authors also quantify the intracellular concentrations of these metabolites in cells lacking NUDT2 and show that, although Ap4A and Ap4G levels increase significantly, they generally remain below the concentrations required to affect the tested signaling proteins in vitro. This quantitative comparison between biochemical effects and intracellular metabolite levels is particularly valuable and strengthens the physiological interpretation of the study.

      Weaknesses:

      Interpretation of the pppGpA density in the cGAS structure.

      Importantly, when Ap4G was used in the crystallographic experiments with cGAS, the authors observed density corresponding only to pppGpA. They interpret this observation as potentially resulting from processing of Ap4G by DNA-bound cGAS to generate a canonical reaction intermediate.

      Based on my direct experience with structural studies of an unrelated enzyme using commercially supplied Ap4G from Jena Bioscience, I believe that an alternative explanation should be carefully considered. In our experience, Ap4G preparations can contain small amounts of pppGpA, likely at the per mille level, as a side product of the chemical synthesis. If the studied protein has substantially higher affinity for pppGpA than for Ap4G, even such a minor contaminant can become strongly enriched in the active site during crystallization and consequently dominate the observed electron density.

      Indeed, traces of a corresponding contaminant may already be visible in the HPLC profile presented in Figure S2.3C. I therefore strongly suggest repurifying Ap4G by HPLC and repeating, at minimum, the HPLC-based experiments shown in Figure S2.3C using the purified material. Ideally, the purity of the Ap4G preparation used for crystallography should also be carefully documented.

      Until this possibility has been excluded experimentally, I would recommend that the authors substantially temper the conclusion that pppGpA observed in the crystal structure originates from cGAS-mediated processing of Ap4G.

    3. Reviewer #2 (Public review):

      Summary:

      Dinucleoside polyphosphates have been found to increase in responses to cellular stress and are involved in the regulation of transcription, antibiotic resistance, DNA replication and innate immunity in metazoa. Increased Ap4A levels have been previously found to regulate the cGAS/STING pathway, leading to the proposal that modulation of Ap4A levels may be a suitable pathway to pharmacologically modulate cGAS/STING signalling (Guerra et al., 2020). Here, Weiland et al. present compelling biochemical, biophysical and structural evidence that dinucleoside polyphosphates (Ap3/4A and Ap4G) do not interact with STING nor are likely to affect cGAS signalling in vivo.

      The evidence that they present evaluates the interactions of Ap3/4A and Ap4G s with cGAS, STING and TBK1 kinase as key signalling molecules in the cGAS/STING innate immune response. Previous work has identified Ap4A as a regulator of cGAS/STING signalling, and through precise characterisation of the biophysical interaction, crystallographic as well as modelling data provide evidence that these effects are too weak to affect these processes in vivo. Their work identifies that there are no direct interactions between STING and dinucleoside polyphosphates at physiological concentrations and at hyperactivated levels in NUDT2 mutants. There are detectable interactions of both Ap4A/and Ap4G with cGAS. In the presence of DNA, cGAS shows either ATP when bound with Ap4A or an intermediate of the cGAMP synthesis when crystallised with Ap4G. This is an interesting observation, showing Ap4G appears to serve as a weak substrate for canonical cGAMP production in the presence of DNA. Finally, the effect of Ap4A on the catalytic activity of TBK1 was evaluated and showed very weak effects. Competition assays in the presence of ATP and Ap4A/Ap4G showed a higher inhibitory effect for Ap4G, but only at concentrations 5x or greater than intracellular ATP levels and much weaker effects for Ap4A (~32x ATP levels).

      Strengths:

      The approaches used provide robust data for a subset of physiological Ap4Ns. The strength of their evidence comes from the complementary structural and biophysical approaches investigating the interactions of cGAS, STING and TBK1 independently. These data, including the dissociation constants for Ap4A and Ap4G with cGAS, allow for comparison with physiological levels of the dinucleoside polyphosphate in vivo. The use of quantitative mass spectrometry provides some increased rigor to the conclusions by integrating the in vitro and in vivo studies.

      Weaknesses:

      The data presented for Ap4G and Ap4A are convincing, and conclusions are consistent with the evidence presented. However, there are other Ap4Ns (Ap4C/U) that may be active against cGAS/STING/TBK1 that are not included in this study, which weakens the conclusions. This could be rectified relatively simply. The data would be more robust if the effects of Ap4C and Ap4U were included for cGAS using ITC, and also the additive effect of competition assays with physiological ATP and the levels of all Ap4Ns in the same reaction as in Figure 3A. At present, each Ap4N is investigated in isolation. To determine the effect of physiological Ap4N levels, reactions with all 4 Ap4Ns at maximum physiological levels as determined from NUDT2 KO cells would provide a robust test of their activities.

      Overall, the in vivo data suggest that Ap4A and Ap4G can reduce cGAS activity, but only weakly and only at levels that are unachievable even where NUDT2 is inactivated. The data suggest that Ap4A/Ap4G do not affect cGAS/STING/TBK1 signalling at physiological levels and that NUDT2 targeting would be ineffective for the pharmacological modulation of cGAS/STING/TBK1 signalling. This conclusion is accurate only if Ap4C/U do not contribute to this inhibition. At present in the absence of these data, this conclusion cannot be made, as their activity could in principle affect cGAS/STING signaling.

    4. Reviewer #3 (Public review):

      Recent work reported that STING binds Ap4A and proposed that elevated Ap4A levels could modulate the cGAS-STING pathway. Intracellular Ap4A levels are controlled by the hydrolase NUDT2, itself a cancer drug target, and loss of NUDT2 raises cellular Ap4A pools. Building on this, the authors set out to investigate whether and how Ap4A modulates the cGAS-STING pathway. This question was motivated by the fact that the mechanism by which STING might bind a linear dinucleotide such as Ap4A remained unclear, and by the existence of alternative routes of crosstalk between Ap4A and the pathway. These include the chemical similarity between Ap4Ns and pppGpA, the linear intermediate generated by cGAS during cGAMP synthesis, raising the possibility that Ap4A could inhibit cGAS directly, as well as a potential downstream effect on the kinase TBK1.

      Using in vitro biochemistry, the authors show that STING does not, in fact, bind Ap4A, contrary to what was previously claimed, and they further examine the impact of NUDT2 loss on the cGAS-STING pathway. This clarifies for the field whether Ap4A could influence the pathway in any way, whether through binding to cGAS, STING, or TBK1.

      The authors find that cGAS can bind Ap4A but cannot process it. cGAS also binds Ap4G, but processes it only inefficiently and only when supplied at very high concentrations in vitro. This argues against physiological relevance in cells, since inhibition of canonical cGAMP synthesis would require Ap4Ns to be present in excess over ATP and GTP, a condition unlikely to be met at physiological nucleotide concentrations even when NUDT2 is absent. The authors also test for effects on TBK1, showing that while it can bind Ap4A, the concentrations required for inhibition are similarly not physiological.

      The authors conduct a thorough series of biochemical and biophysical experiments to clarify the underlying mechanism, systematically ruling out a plausible effect of Ap4A in cells at each step. Even where an effect on cGAS or TBK1 can be detected at very high, non-physiological concentrations, this remains confined to in vitro conditions. The experiments are sound and conducted with scientific rigor, and the results support the authors' conclusions.

      This work provides clear evidence that STING does not bind Ap4Ns, and that even where binding does occur elsewhere in the pathway, there is no plausible mechanism by which Ap4Ns could modulate the cGAS-STING pathway under physiological conditions. This resolves an open question for the field: if Ap4Ns do have biological effects related to this pathway, these are unlikely to arise from a direct interaction with cGAS, STING, or TBK1, and must instead be mediated through other processes. The methods and data will be a useful reference for the community when evaluating future claims of nucleotide second-messenger crosstalk.

      Prior studies have shown that NUDT2 disruption, which elevates cellular Ap4A levels, is associated with down-regulation of interferon-response pathways. This is notable in light of the current findings, which show no direct biochemical interaction between Ap4A and cGAS, STING, or TBK1. Together, this suggests that if elevated Ap4A does suppress IFN production, it likely does so indirectly, through mechanisms other than direct engagement of the cGAS-STING-TBK1 axis.

    1. eLife Assessment

      This valuable study tests to what extent dopamine neuron stimulation produces striatal serotonin release, and vice versa - a topic of interest to neuroscientists studying the basal ganglia, neuromodulation, and reward learning. Employing a combination of modern optical and genetic methods, it provides solid evidence that the mesolimbic dopamine and serotonin systems operate primarily in parallel, with little crosstalk observed when each of the pathways are activated individually. The results are generally convincing, although the reviewers were still concerned about the absence of control experiments examining light artifacts.

    2. Reviewer #1 (Public review):

      In this study, Chaudun et al use optogenetics and genetically encoded neuromodulator sensors to test to what extent dopamine neuron stimulation produces striatal serotonin release, and vice versa. The study is timely given growing interest in dopamine/serotonin interactions, but important controls are missing. The authors find that striatal dopamine and serotonin afferents function largely independently, with dopamine neuron stimulation producing no striatal serotonin release and serotonin neuron stimulation (possibly) producing minimal striatal dopamine release.

      In the revised manuscript draft, the authors increased sample sizes for some important experiments, removed inconclusive electrophysiology data, reorganized the figures, and added new anatomical tracing experiments. The critical fluorophore-only controls for the optostimulation experiments requested by reviewers were not done.

      Unfortunately, with the exception of increased samples sizes, these revisions have done little to substantially improve the strength of the evidence behind the authors' conclusions. These conclusions, as reflected in the new title of the paper, have also slightly changed in a manner that puts them further at odds with the data presented. As a result, my opinion continues to be that the study is interesting and the experiments are useful, but important controls are missing, a more cautious interpretation is warranted, and the organization of the study is confusing. I explain my reasoning below.

      (1) A major criticism of the original manuscript was that the electrophysiology experiments were only tangentially related to the question under study. The manuscript has now been reorganized in such a way that the cocaine experiments are similarly irrelevant. In the authors' own words, the central question of the study is "does activity in one [DA or 5-HT] system directly recruit transmitter release from the other, or do DA and 5-HT operate mainly through parallel circuits?". Optogenetically stimulating release of dopamine while recording serotonin activity (or vice versa) begins to answer this question, and in the original draft, cocaine-evoked dopamine and serotonin recordings were presented side by side with the opto-evoked dopamine/serotonin data. In this configuration, the drug-evoked recording data allowed readers to compare the magnitude of dopamine/serotonin release evoked by an artificial optogenetic manipulation to that evoked by a behaviorally relevant pharmacological treatment. This serves a purpose. In the revised manuscript, the cocaine experiments are presented in a standalone first figure. As the authors themselves note in their response to reviewer 2, cocaine independently evokes dopamine and serotonin accumulation is therefore poorly suited on its own to test how activating one neuromodulatory system affects the other. Thus, the cocaine data presented alone in this way neither address the paper's central question nor support the paper's conclusions.

      (2 The original manuscript suggested that one way by which striatal serotonin activity could recruit striatal dopamine release would be if the same serotonin neurons project to both VTA and striatum. To test this, the authors use retrobeads to identify DS-, NAc-, and VTA- projecting dorsal raphe neurons. However, they neither identify whether the labeled neurons are serotonergic nor evaluate colocalization between the retrograde tracers as would be required to ascertain whether the same serotonin send axon collaterals to more than one region. Thus, the anatomical data presented do not answer the question of whether the same serotonin neurons innervate both targets.

      (3) As noted in the original round of review, applying light stimulation to mice expressing a fluorophore-only construct without opsin is an important control in optostimulation experiments that is widely standard in the field. The omission of such a control is a weakness of the paper. The authors suggest that other results presented - such as the lack of green serotonin sensor activation when red light is applied in the midbrain in Fig 2G-H - argues against cross-talk between the red and green optical channels. This is technically correct, but ruling out cross-talk between optical channels is not the only reason such a control is necessary. The high intensity red light used for optostim travels several millimeters through tissue and can reach the retina. Although mice do not have cones to detect red light (and therefore cannot distinguish the color red from other colors), their rods are sensitive to red light. As a result, bright red intracranial optostim can be perceived by the animal as a flashing light, and this sensory stimulus can produce a neural response. The absence of such a response in the serotonin system in Fig 2G-H does not rule out the possibility that the small response in the dopamine system observed in Fig 3G-H is driven by sensory input rather than a bona fide physiological effect of serotonin neuron activation on striatal dopamine release. Crucially, sensory responses to the stimulating light would also show 2Hz entrainment to the frequency of the stimulating laser trains. In other words, without a fluorophore-only control, the entirety of the authors' results pertaining to serotonin-evoked dopamine release can be explained by artifactual effects of the optostimulation. If this opsin-negative control is absolutely impossible for the authors to perform, at the very least this major limitation should be clearly and explicitly addressed in the discussion because it pertains directly to the study's main conclusions. This brings me to my final point.

      (4) Taken together, the authors' data appear to support two primary conclusions. First, optogenetic dopamine stimulation has no effect on striatal serotonin release. Second, synchronous and supra-physiological activation of the serotonin system has a very modest effect on striatal dopamine release that might be entirely explained by artifacts of the optostimulation protocol. The miniscule magnitude of the evoked dopamine response observed following maximally strong (15-20 mW) optogenetic serotonin stimulation also bears consideration here. It argues that even if the observed dopamine release effect is not an artifact of the optostimulation protocol, serotonin-evoked dopamine release is likely to be vanishingly small or absent in other conditions when serotonin neuron firing is neither supraphysiological nor synchronous. The new title strongly implies the existence of significant cross-talk, which is not well supported by the data. Claims throughout the text that call the cross-talk effect reliable or otherwise robust are similarly overstated.

    3. Reviewer #3 (Public review):

      The authors suggest that the small release of DA may be due to a release of glutamate from DRN 5-HT neurons to the VTA that stimulates weakly and in a transient fashion the VTA DA neurons, which in the end, produce a transient and small release of DA in the NAc.

      Their findings give more information on the previously reported complex and partial known crosstalk between 5-HT and DA in the NAc.

      Comments on revised version.

      The authors have added several new experiments that address some of the prior concerns. However, there are still points of confusion. For example, the authors repeatedly argue that 5HT or DA are synaptic when in fact most of the transduction mechanisms for these neuromodulators and their transporters are extra synaptic, as indicative of volume transmission. This is not well articulated in the authors' interpretative scheme.

    4. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment:

      This useful study employs optogenetics, genetically-encoded dopamine and serotonin sensors, and patch-clamp electrophysiology to investigate modulations of neurotransmitter release between striatal dopamine and serotonin neurons - a topic of interest to neuroscientists studying the basal ganglia. The results suggest that the dopamine and serotonin systems operate largely in parallel, with the activation of serotonin neurons resulting in a small, transient dopamine release. The authors suggest that this interaction occurs via glutamate release in the ventral tegmental area, findings that are closely related to previous work. Some conclusions are incomplete requiring larger samples-sizes and controls.

      We sincerely thank the editor and reviewers for their thoughtful and constructive comments. Addressing their concerns required extensive additional experimental work and the involvement of new personnel. We increased sample sizes, completed previously missing experimental groups, generated a largely new dataset, and performed additional anatomical experiments and analyses. We also removed the patch-clamp electrophysiology experiments to sharpen the study’s focus and extensively revised and reorganized the manuscript to address methodological and interpretative inconsistencies. Although the central concept remains unchanged, the revised manuscript provides a substantially more rigorous and consistent account of the limited and directionally asymmetric circuit-level coupling between striatal dopamine and serotonin release.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this study, Liu et al use optogenetics and genetically encoded neuromodulator sensors to test the extent to which dopamine neuron stimulation produces striatal serotonin release, and vice versa. The study is timely given growing interest in dopamine/serotonin interactions and in the context of recent work showing bidirectional and dynamic regulation of striatal dopamine by another neuromodulator, acetylcholine. The authors find that striatal dopamine and serotonin afferents function largely independently, with dopamine neuron stimulation producing no striatal serotonin release and serotonin neuron stimulation producing minimal striatal dopamine release. This work will inform future work seeking to dissect the contributions of striatal dopamine, serotonin, and their interactions to various motivated behaviors. While the paper's main conclusions are adequately supported (see Strengths), additional controls and experiments would significantly broaden the paper's impact (see Weaknesses). Finally, this draft of the work is poorly presented with numerous errors, omissions, and inconsistencies evident throughout the text and the figures that should be addressed.

      Strengths:

      The study employs optogenetic stimulation simultaneously with fiber photometry recording of dopamine or serotonin release measured with genetically encoded sensors. These methods are state-of-the-art, offering tighter temporal control compared to pharmacological methods for manipulating dopamine and serotonin and improved selectivity over techniques like electrochemistry and microdialysis used to record neuromodulator release in previous studies on the subject. As a result, the paper's main conclusions are well supported.

      Weaknesses:

      (1) The electrophysiology experiments in Figure 3 are only tangentially related to the focus of the study, and their findings are almost entirely irrelevant to the paper's main conclusions. The results of these experiments are also not novel. Glutamate corelease from 5-HT neurons has been previously shown, including in the OFC and VTA (Ren et al, 2018, Cell, McDevitt et al, 2014, Cell Rep, Liu et al 2014, Neuron; and others). The authors should explain more clearly what they think these data add to the manuscript and/or consider removing them altogether.

      We thank the reviewer for their insightful comments regarding the patch-clamp electrophysiology data. We acknowledge the concerns raised by multiple reviewers and have therefore removed these data from the revised manuscript to improve clarity and focus.

      (2) Related to the point above, as far as I can tell, the only value the electrophysiology data add is to suggest that perhaps activation of serotonin neurons may drive minimal striatal dopamine release via glutamate corelease in the VTA. The evidence provided in this version of the manuscript is insufficient to support that claim, but the manuscript would be significantly strengthened if the authors tested this hypothesis more directly. One way to do that could be to stimulate serotonin axons in the striatum (as opposed to the serotonin cell bodies) and record striatal dopamine release.

      This is an interesting approach. However, given the divergent projection of serotonin fibers and the need for a strict spectral separation between excitatory opsin and transmitter sensor fluorescence, we retained the current approach, which allows us to investigate circuit-level interactions while minimizing technical confounds.

      A complementary anatomical approach would be to use retrograde tracing to test whether the DR 5-HT neurons projecting to the striatum are the same or different from the VTA projecting population.

      We performed the suggested experiment and have included the new data in the revised manuscript (Figure 1F–H). This approach revealed a clear topographical organization. These results indicate that the identified neuronal populations are largely segregated.

      (3) The findings would be strengthened by the addition of a fluorophore-only control group lacking opsin expression in all experiments in Figures 1 and 2.

      We thank the reviewer for this important suggestion. We requested authorization to add an opsin-negative control cohort, but this request was not approved under the applicable animal-welfare regulations. We therefore cannot fully exclude nonspecific physiological effects of light delivery and now acknowledge this limitation in the Discussion. Nevertheless, the stimulation-locked response was confined to the biosensor channel and absent from the isosbestic channel, arguing against direct optical contamination. Moreover, application of the same optical stimulation protocol in the VTA did not generate a detectable stimulation-locked 5-HT signal. Although these observations do not fully replace an opsin-negative control, they support the interpretation that the response reflects a biological signal rather than direct optical interference.

      (4) The experiment of stimulating serotonin neurons and recording serotonin release in the NAc was not performed. It would be useful to be able to compare the magnitudes of evoked serotonin release in these two striatal regions, though it is not central to the main claims of the paper.

      This point has now been addressed with additional experiments, and the figure has been reformatted accordingly in the revised manuscript.

      (5) The interpretation of the results from Figure 2 is described inconsistently throughout the manuscript. The title implies there is significant crosstalk between the dopamine and serotonin systems. The abstract calls the crosstalk "transient", which is a description of its temporal dynamics, not its magnitude. Then the introduction figures and discussion all suggest the crosstalk is minimal. I suggest the authors describe the main findings - minimal crosstalk between the dopamine and serotonin systems - clearly and consistently in the title, abstract, and main text.

      We agree with the reviewer and have revised the title, abstract, Results, figure legends, and Discussion to describe the magnitude and directionality of the interaction consistently. The revised manuscript now emphasizes that striatal dopamine and serotonin release operate largely in parallel, with limited and asymmetric crosstalk: activation of VTA dopamine neurons did not produce detectable serotonin release, whereas activation of dorsal raphe serotonergic neurons induced only a weak, stimulation-locked dopamine signal without a sustained increase in dopamine amplitude. The revised title, “Asymmetric Circuit-Level Crosstalk Between Dopamine and Serotonin Release in the Striatum,” and the corresponding changes throughout the manuscript now more accurately reflect these findings.

      Reviewer #2 (Public review):

      Summary:

      This brief communication aims to clarify interactions between the dopamine (DA) and serotonin (5-HT) systems of mice. The authors use optogenetic stimulation of DA neurons in the VTA or of 5-HT neurons in the DRN, while monitoring the fluorescence of DA and 5-HT sensors in the nucleus accumbens (NAc) and dorsal striatum (DS) using fiber photometry. The authors report on a small release of DA in the NAc following DRN stimulation, which they attribute to glutamate co-release onto VTA DA neurons using slice electrophysiology. The authors also report on cocaine-induced 5-HT release in the striatum.

      Strengths:

      This is a topic well worth studying.

      Weaknesses:

      In its current form, this is an incomplete and underpowered study that does little to clarify the complicated relationship that exists between DA and 5-HT in the mammalian brain under physiological conditions or during cocaine use.

      We appreciate the reviewer’s thorough evaluation and constructive efforts. We acknowledge that the original manuscript did not meet the expected standards and have therefore undertaken a complete revision. We hope that the revised version addresses the concerns raised and provides a clearer and more rigorous presentation of our findings.

      Reviewer #3 (Public review):

      The authors suggest that the small release of DA may be due to a release of glutamate from DRN 5-HT neurons to the VTA that stimulates weakly and in a transient fashion the VTA DA neurons, which in the end, produce a transient and small release of DA in the NAc.

      Their findings give more information on the previously reported complex and partial known crosstalk between 5-HT and DA in the NAc.

      I only have some minor concerns about the manuscript:

      (1) In Figure 2F, there is a missing curve for 5-HT in NAc. Besides, the legend shows n=2, making it difficult to perform statistical analysis with that data.

      We repeated the experiments and analysis with a new and expanded dataset. We believe this revised dataset now provides sufficient power for robust statistical analysis.

      (2) In Figure 3, the use of NBQX/AP5 is shown, but it is not mentioned either in the methodology or in the discussion. What is the meaning of those results?

      Based on the reviewers’ suggestions, we have removed this figure entirely from the revised manuscript.

      (3) Line 98 compares results from two different places of stimulation. The results are related to stimulation in the VTA, but the comparison indicates that the stimulation was made in the DRN.

      The manuscript has been extensively revised, and all relevant sections of the text have been updated accordingly.

      (4) If the release of 5-HT in Nac does not occur, it needs to be precise in the abstract that 5-HT is released in the dorsal striatum (DS) but not in the NAc (line 19).

      We performed an additional set of experiments and confirmed the presence of 5-HT release in the NAc.

      (5) Be consistent with the way you mention the 5-HT neurons. For example, in lines from 106 to 119, SERT neurons are used. Previously, 5-HT neurons were used.

      We have carefully considered this issue throughout the revised manuscript and have taken steps to ensure consistency, as highlighted by the reviewer.

      (6) There are several points of confusion when referring to the figures, making the text difficult to follow because the text explains something that is not shown in the figure cited.

      We have carefully considered this issue throughout the revised manuscript and have taken steps to ensure consistency, as highlighted by the reviewer.

      Recommendations for the authors:

      Reviewing Editor Comments:

      Additional comments from the reviewers recommend that the authors increase sample sizes, carry out control experiments to exclude non-specific effects, and modify the title and abstract to reflect the minimal cross-talk observed between dopamine and serotonin. Finally, we note that the text of the manuscript does not meet minimum standards in the field for clarity, interpretability, or reproducibility.

      We acquired a new experimental cohort, and performed additional experiments suggested by the reviewer. The manuscript has been revised and reformatted, incorporating all reviewer comments. We hope that this new version now meets the expected standards for clarity, interpretability, and reproducibility in the field.

      Reviewer #1 (Recommendations for the authors):

      (1) For angled stereotactic injections, the direction of the angle (ML, AP, etc) is not defined.

      Added to the revised manuscript.

      (2) The manuscript is full of grammatical errors and is very difficult to follow in some places, especially the results section for Figures 2 and 3. The following is an incomplete list of the most obvious errors to give examples of what I mean, but I strongly urge the authors to thoroughly revise the entire text.

      (a) There appear to be photometry traces copied and pasted by mistake over the x-axis labels in the bar graph in Figure 1E.

      (b) Line 41 states that fiber photometry recordings were used in reference 17, which is a human study that used voltammetry.

      (c) Figures are referenced out of order throughout.

      (d) Acronyms are used without being defined on the first usage.

      (e) Lines 54-56, missing references.

      (f) Line 82, the crosstalk reported in this paper is unidirectional, not bidirectional.

      (g) Line 92 describes the virus as AAV-dLIGHTX.XX, where presumably the exes are placeholders for numbers that were never replaced.

      (h) The text of the results section for Figure 2 in some cases directly contradicts the data it describes, and likely has some mistaken figure panel references.

      (i) Line 123 is missing a reference and just has the placeholder REF.

      (j) Lines 182-183 are a copy-paste mistake from lines 185-186.

      (k) Scalebars in figure legends are in units of micromolar, not micrometers.

      The manuscript has been extensively rewritten to address and incorporate the reviewers’ comments.

      Reviewer #2 (Recommendations for the authors):

      This is one of the least impressive and compelling manuscripts I have ever been asked to review. It reads in many ways like a first draft, not like a document that was meticulously prepared and proofread prior to submission. The data and analyses are shockingly light (with some conclusions supported by an N of 1 or 2) yet frustratingly difficult to interpret. The text and figures are full of typos, inconsistencies, and factual errors in interpretation. Most concerningly, this study does little to clarify the complicated relationship that exists between DA and 5-HT, including providing compelling evidence for their key finding of 'crosstalk' between both modulators or mechanistically implicating glutamate co-release. In fact, it might add to the noise. It is therefore unclear what the value is of this study in its current form.

      We thank the reviewer for their detailed and thorough comments, which we saw as an opportunity to substantially improve the manuscript. We have therefore undertaken a comprehensive revision to improve clarity, coherence, and overall quality. As part of these revisions, we have removed the original Figure 3 presenting the in vitro patch-clamp electrophysiology data. We hope that the extensive changes implemented in the revised manuscript adequately address the reviewer’s concerns and significantly strengthen the study.

      General comments:

      (1) Limited scope: the design of the experiments performed offers very few opportunities for identifying 'crosstalk' between DA and 5-HT:

      (a) To most, such cross-talk might best be imagined at the level of cells expressing receptors for both modulators, yet this is not studied. Cross-talk may also manifest in complex circuit-level interactions, downstream of brain areas directly targeted by DA and 5-HT. This is not studied either. Instead, only one type of experiment is performed: optogenetically stimulating cell bodies in the midbrain while monitoring DA/5-HT levels in the striatum. Opportunities for 'cross-talk' in this preparation are exceedingly limited. For example, the only way that optogenetically-evoked DA release may impact 5-HT levels (assuming classical, direct projections from DA neurons to the striatum) is via presynaptic modulation of 5-HT release from axons. Though potentially interesting, it is not exhaustive.

      We agree that many types of crosstalk can be investigated; however, we chose to first adopt a broader approach that integrates neuronal circuit-level mechanisms. We believe that an important discrepancy remains in the literature that needs to be addressed. While our findings do not exclude the involvement of other forms of crosstalk, they aim to clarify whether crosstalk already exists at the level of neuronal circuits, which our results support.

      (b) Any negative modulation of DA or 5-HT release would require a baseline tone of either modulator within the dynamic range of the sensor, which is not demonstrated. Thus, in both cases, a negative influence cannot be excluded, limiting any finding to 'positive' modulation. This is problematic for a study set on setting the record straight on 'cross-talk' between DA and 5-HT, given that prior studies pointed to antagonistic influences.

      Previous studies using the same type of sensor have already demonstrated inhibitory crosstalk. For example, the study “Opponent Control of Reinforcement by Striatal Dopamine and Serotonin” successfully used this approach to reveal opposing interactions between dopaminergic and serotonergic signalling. Therefore, our findings are consistent with the possibility of circuit-level crosstalk and further contribute to resolving this ongoing question.

      (c) Only one stimulation paradigm is tested, and its biological (ir)relevance is not discussed.

      The stimulation paradigm used in this study has been extensively validated in multiple publications from our laboratory and has consistently been shown to promote reinforcement. Our recent review provides a comprehensive overview of this approach and its established behavioral effects. (Lüscher et al, Neuron, 2026, PMID: 42462721).

      (d) Only one brain region is investigated (striatum), with little justification for omitting other regions where both modulators coexist (e.g., amygdala, cortex, DRN).

      Not all brain structures can be investigated within the scope of a single study, and we acknowledge this limitation in the Discussion section.

      (e) The suitability of the chosen behavior and cocaine administration to reveal interactions is not mentioned.

      We have now clarified the use of i.p. cocaine injection to study the different temporal dynamics of 5-HT and DA release.

      (2) Scarcity of data and questionable analyses and interpretation:

      We thank the reviewer for the detailed comments listed below (a-m). In response, we repeated many experiments, increased sample sizes, completed the experimental groups, fully reanalysed the dataset, and removed the patch-clamp electrophysiology data. The manuscript has been extensively rewritten and reformatted based on a largely new dataset. In addition, we now focus specifically on one form of crosstalk and have expanded the discussion to address other potential mechanisms, as suggested by the reviewer. We hope this revised version adequately addresses the concerns raised. Because of the many changes in the text, the concerns listed below were addressed.

      (a) Conclusions for Figures 3B and C are based on a N of 1 whole cell recording each.

      (b) The relevance of data in Figures 3D and 3E to test the proposed hypothesis is unclear.

      (c) Figure 2F is an average of N=2 recordings.

      (d) Lines 98-100: Concluding statement not supported by experiment performed.

      (e) Apparent contraction: Lines 112-113 indicate that 5-HT neuron stimulation failed to evoke 'any significant change in DA', while lines 117-118 talk about a small but significant increase in DA in response to the same manipulation.

      (f) Line 114: conclusion not supported by data; Figure 2E points to DA, not 5-HT, and Figure 2F-H only shows 5-HT in DS, not NAc. Legend specifies that only 2 recordings are averaged in Figure 2F, and n=4 are quantified in Figures G and H, despite the plot containing 5 data points. What is going on?

      (g) The key finding of this study is a transient, barely-detectable increase in NAc DA for ~1s during the 10s stimulation of 5-HT neurons in N=4 mice (Figure 2D). It is not clear if a significant increase was observed in all mice tested, or if this increase is significantly different from what is seen in DS. Was this effect seen in multiple testing sessions?

      (h) Concerningly, the authors do not exclude the possibility of a 'startle' response to strong light delivery in mice expressing tdTomato-only. This is an essential control that is missing.

      (i) Very few details regarding this DA response are offered. How soon does it appear following DRN stimulation? If mediated by glutamate co-release, it ought to appear with the very first light stimulus.

      (j) The physiological significance of this stimulation paradigm is not established. Comparing it to cocaine-evoked DA release is not satisfactory, as cocaine elevates DA beyond levels encountered physiologically.

      (k) Correlative evidence to implicate glutamate is insufficient. Does co-release depress when driven with the same stimulus train as that applied in vivo? Is it strong enough to drive DA neuron firing? Does it not affect DS-projecting DA neurons? Does this DA 'blip' disappear in mice genetically deficient in glutamate co-release (using SERT-Cre vglut2-floxed mice, which are commercially available)?

      (l) The authors don't find evidence for DA stimulation affecting 5-HT release in the striatum. Is this sufficient to exclude any influence of DA on 5-HT? Isn't the fact that cocaine evokes 5-HT release evidence of an interaction? As such, it is unclear if the experiments performed significantly advance our understanding of whether and how DA and 5-HT influence one another.

      We respectfully disagree with this statement. Cocaine directly binds to the serotonin transporter (SERT), thereby blocking serotonin reuptake into the presynaptic terminal and increasing extracellular 5-HT levels in the synaptic cleft. Furthermore, dopamine and serotonin release exhibit distinct temporal dynamics, suggesting that their release is mediated through different molecular mechanisms and neural circuit pathways rather than through a single shared process.

      (m) The evidence that 5-HT stimulation alters DA release in the striatum is not compelling, but also not sufficient to exclude or support cross-talk between both modulators. The evidence provided is simply insufficient to strongly validate or challenge earlier studies.

      (3) Minor:

      In this revised manuscript, we have carefully implemented the reviewers’ suggestions and addressed the concerns they raised. We believe that this revised version addresses a significant proportion of the reviewers’ concerns and hope that these fundamental improvements are fully recognized during the evaluation.

      Because of the many changes in the revised manuscript, the concerns listed below do no longer apply or were addressed.

      (a) In the intro paragraph, line 19, specify that you are monitoring DA/5-HT in the striatum of mice.

      (b) In the results section, indicate the preparation: in vivo, awake, what were the mice doing...

      (c) Inconsistent nomenclature: VTA dopamine neurons are referred to as 'VTA DA', while dorsal raphe serotonergic neurons are referred to as 'DRN SERT' or 'DR SERT'. Why the difference? Is there any reason to believe that SERT-Cre mice label non-serotonergic neurons? Please adopt a consistent nomenclature and/or justify your choice. Also, if neurons are optogenetically stimulated, specify the word 'neuron' (e.g., line 118 refers to 'DR SERT stimulation', which could be interpreted to suggest the transporter is being activated).

      (d) Line 103 and elsewhere: Confusing terminology referring to VTA DA neurons stimulation as 'modulating' or 'altering' DA release in the striatum. Isn't it directly evoked by the stimulation of DA-releasing neurons? Please change the terminology so as not to make it unnecessarily vague.

      (e) Lines 111-112: comparison between DA and 5-HT fluorescence devoid of biological meaning. Relative increases in the fluorescence of 2 sensors that are molecularly distinct and separately-expressed in different cohorts of mice cannot be meaningfully compared.

      (f) The timeline in Figure 1A suggests that the optogenetic stimulation protocol is initiated 5 min prior to cocaine injection. Figure 1D (right) plots this 5 min baseline, yet little/no DA release can be seen, despite Figure 1F indicating peak DA ratios of 0.5-1. Please explain. Since both sets of recordings were performed back to back and are directly compared, make sure to show them both on the same graph for direct comparison.

      (g) Figure 1E contains 2 misplaced data traces.

      (h) Figure 2D. Please repeat for the full stimulation window, not just for the few seconds where a tiny change in DA is seen.

      (i) Figure 2E is misleading. It suggests that the peak extracellular DA concentration in NAC following DRN stimulation is approximately half of what cocaine evokes and similar to what VTA DA stim evokes (Figure 1F). But this is simply not true. There must be an error in calculation, and one should wait more than 20 minutes to properly estimate peak DA concentration in cocaine.

      (j) What is the value of comparing optogenetically-evoked neuromodulator release to cocaine-evoked release? Can the authors be sure that the DA levels reached in cocaine are not saturating the DA sensor? Wouldn't this be required to permit comparison?

      (k) Line 123: Please include a citation. Rephrase the sentence to avoid sounding dismissive.

      (l) Line 124: Vague/confusing sentence. What is meant by 'optogenetic projection targeting' and 'parsing the synaptic currents in identified regions'?

      (m) Figures 1D, 1G, 2C, 2F: confirm y-axis unit of measure. Are DA transients really only 0.2% in amplitude?

      (n) Figure 3B, 3C: number of recordings performed in DA and NAcSh, anterior not specified. Only one trace showed, and no summary plot. Insufficient data presented to draw conclusion.

      (o) How does the presence of EPSCs in OFC or NAc help support the author's finding of a brief elevation of DA upon DRN stimulation?

      (p) Line 358: Wrong unit.

      Reviewer #3 (Recommendations for the authors):

      Some minor concerns are the following:

      (1) In line 31, define what DS is.

      (2) In line 57, the word "neurons" is missing in the phrase ".....decreases 5-HT neuron spontaneous firing in DRN,...".

      (3) In line 111, the comparison needs to be between 5-HT release due to optogenetic stimulation with the 5-HT release due to cocaine infusion, not with DA release due to cocaine infusion.

      (4) In line 112, it should be Figure 2F.

      (5) In line 114, instead of 5-HT, it should be DA. In addition, it is not Figure 2E, but 2C.

      (6) In line 123, add the appropriate reference instead of "REF".

      (7) In lines 182 and 183, the text is out of context.

      (8) In lines 250 - 251, the text is out of context.

      (9) Why was Rabit anti-GFP if you do not use GFP in the procedures?

      While the manuscript underwent a complete comprehensive revision to improve clarity, coherence, and overall quality, we have carefully implemented the reviewers’ suggestions and addressed the concerns they raised.

    1. eLife Assessment

      This study demonstrates a critical role of the glycolipid membrane protein insertase (MPIase) in the twin-arginine translocation (TAT) pathway, a protein translocation system that is conserved across all domains of life. The successful reconstitution of the bacterial TAT system in both bacteria-derived and artificial liposomes provides solid experimental evidence that MPIase has a broader role in membrane protein translocation than previously recognized. By revealing the essential function of a nonproteinaceous membrane component in catalyzing a core cellular process, this work offers fundamental insights into the molecular mechanisms of protein translocation.

    2. Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

      Strengths:

      The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation.

      Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway.

      Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

    3. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      [Editors' note: Overall, the reviewing editor and reviewers agreed that authors rigorously addressed reviewers' major scientific concerns.]

      Reviewer #1 (Public review):

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

      Comments on revised version from Reviewer 1 and the Reviewing Editor:

      In Reviewer 1's points 1 and 2 regarding statistical significance of immunoblotting data (in recommendation to authors), authors stated that they repeated the experiments three times in the rebuttal letter but not in the figure legends. We recommend authors to add a statement such as "errors denote +-SD (N = 3)" in the final version, which will make the data more solid and reliable.

      Thank you for the comments. We added the suggested sentence.

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors investigated the relationship between the Tat system and MPIase, a glycolipid that facilitates protein integration into the bacterial cell membrane. The TAT (twin-arginine translocation) system is a unique membrane transport machinery that exports fully folded proteins containing a twin-arginine signal peptide. Using both in vivo and in vitro approaches, the authors demonstrated that a sufficient amount of MPIase is required for Tat-dependent protein translocation. Furthermore, the authors successfully reconstituted the Tat transport system by combining recombinant TatA, TatB, TatC, MPIase, and FoF1-ATP synthase.

      Strengths:

      The reconstituted system clearly demonstrated the requirement for each component, as substrate translocation occurred only when all components were present. Based on these findings, the authors proposed a mechanistic role for MPIase in facilitating Tat-mediated membrane translocation. Previous studies have shown that MPIase is involved in Sec-dependent protein translocation and membrane protein integration, as well as YidC-dependent membrane insertion. The present study further demonstrated that MPIase also plays an essential role in the Tat translocation pathway. Overall, this work highlights the central importance of MPIase in bacterial membrane protein biogenesis and provides new insights into the molecular mechanism of Tat-dependent protein transport.

      Comments on revised version from Reviewer 2 and Reviewing Editor:

      (Reviewer 2) It is nice to find the mistake in Fig. 4B (RR, EK413, (not +MPIase)) and corrected it in the revised version. However, the data of Fig. 4B and the related modified text on P.9 L256-269 are not very easy to understand what the authors are trying to argue. This reviewer believes that the magnitudes of MPIase expression level are Ek413 > +MPIase > delta-MPIase. Therefore, the means of supernatant in the delta-MPIase and EK413 represents mostly cytosol and periplasm, respectively. On the other hand, the +MPIase shows less supernatant that means a large part of TorA-GFP are trapped nearby membrane but not aggressively transported. Similar results are found also in (KK) case, whereas the sup level in EK413 is lower than that of (RR) case due to the lack of twin-arginine motif in the substrate.

      Although these results are convincing in explaining the model in Figure 5, authors might be able to attempt to refine them more understandable. Especially, the revised text on P.9 L256-269 is not enough for leading readers to the author's claim. So that, the authors should revise the text about Fig. 4B again and the related description in the manuscript. Another option is it would be possible to delete whole Fig. 4B, because Fig. 4A, C already well support the model in Fig. 5.

      (Reviewing Editor) I agree with Reviewer 2. The first paragraph in the section "MPIase serves as the TAT signal receptor" runs very long and the clarity of the descriptions on Fig. 4B could be further improved. Fig. 4A, C would sufficiently support author's arguments. I recommend that authors consider these options for the final version.

      We appreciate the critical comments. In Fig. 4B, we aimed to show that the amount of membrane-targeted TorA-GFP would increase due to the overproduction of MPIase caused by TatABC. At the same time, TorA-GFP translocation was also stimulated. However, since the translocated TorA-GFP was recovered in the same fractions as the cytosolic TorA-GFP, interpreting the results would be complicated. Therefore, we deleted Fig. 4B, as suggested by the reviewer and editor. The corresponding text and the right-hand side of Fig. S9 were also deleted.

    1. eLife Assessment

      This study is a valuable contribution that comprehensively identifies and characterizes LC3B-binding peptides through a bacterial cell-surface display screen covering approximately 500,000 human peptides. The data presented are solid, although this approach has some limitations. For example, it cannot assess the effects of post-translational modifications, which are often relevant for LIR-mediated interactions. Validation of the newly identified binding peptides by demonstrating their interactions with full-length proteins in cells would further strengthen this work.

    2. Reviewer #1 (Public review):

      Summary:

      This study uses high-throughput bacterial cell surface display to identify LC3B interacting peptides in the human proteome. The screen is unbiased and this type of assay has not previously been used for selecting LC3B interacting peptides. The screen was done with library of 500,000 peptides and they ended up with 427 peptides that they scored as high-confidence LC3B binders. The experiments performed are solid and data are analyzed using well documented methods and statistics.

      The aim for the authors was to isolate LC3B interacting peptides from the human proteome, and the screen succeeded in doing so. The selected set of peptides included several previously reported LIR motifs, but also many novel LC3B binding peptides that either contained or did not contain the canonical core LIR motif [WFY]xx[LVI].

      Another aim was to identify binding determinants important for the LC3B interaction, and they made an interesting sequence logo based on selected LIR containing peptides. This study does not really extend our knowledge related to binding determinants essential for LIR motifs in LC3B binding, but it expands the number of exceptions to the canonical core LIR motif.

      Strengths:

      The approach used here (high throughput bacterial-surface-display) is new. The screen is unbiased and the fact that peptides are directly tested for LC3B binding may facilitate the discovery of non-canonical LIR motifs. The screen appears to be highly selective and manage to distinguish between peptides that interact with LC3B and peptides that do not interact.

      Weaknesses:

      It is a limitation that no proteins are analyzed in this study. Further work is therefore needed to verify that identified LIR motifs are functional in full-length proteins and in cells.

      Comments on revised version.

      It remains a weakness that none of the identified LIR motifs are tested in full-length proteins or cells, but all the specific comments in my initial review have been addressed.

    3. Reviewer #2 (Public review):

      Summary:

      To discover peptides that interact with autophagy-related protein LC3B and profile the key binding determinants, the authors screened a library of ~500,000 36-residue peptides derived from the human proteome using bacterial cell-surface display. Analysis of the screening data revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR.<br /> These results support a refinement the LIR motif definition and expand the network of candidate LC3B interaction partners.

      Strengths:

      High-throughput approach

      Weaknesses:

      Lack of in vitro data and molecular dynamics simulations

      Comments on revised version.

      In the revised manuscript the authors did not provide in vitro and molecular dynamics simulations data.

    4. Reviewer #3 (Public review):

      Summary:

      The LC3 family of proteins, which includes LC3B, are ubiquitin-like proteins that are covalently linked to phosphatidylethanolamine in the expanding autophagosomal membrane during autophagy. LC3 family members bind to short sequences of amino acids that reside within dynamic regions in a wide variety of proteins. These sequences, termed LC3 Interacting Regions (LIRs), were initially thought to function primarily to link LIR containing autophagy cargo receptors to LC3 family members to help facilitate their capture during autophagy. However, the functional importance of LIRs in autophagy has broadened to include more general functions in autophagy as well. While a general consensus for LIR sequences has been described as [FWY]0-X1-X2-[LVI]3 recent work has suggested that additional sequences outside of the canonical LIR sequence can bind LC3 family members and play important roles in autophagy. In this manuscript by Kosmatka et al, the authors perform a high throughput screen using bacterial surface display coupled with fluorescence associated cell sorting to identify which human sequences can bind to LC3B. They identify a variety of peptides capable of binding LC3B including peptides from proteins that have not previously been described as LC3B binding proteins. The results from the bacterial surface display were then used to guide sequence analysis, mutational analysis and structural studies to further characterize the range of LIR sequences that are capable of binding LC3B. Taken together, this work adds to the growing knowledge of how LIR sequences interact with LC3 family members and demonstrates which amino acids both inside and outside of the LIR sequence aid in binding. This work also identifies new potential LC3 binding proteins which may play unknown roles in autophagy regulation. Lastly, this work reinforces the importance of alternative LIR sequences such as the [WFY]0-X1-X2-[WFY]3 sequence which the authors have dubbed the LIR+ sequence.

      Strengths:

      The manuscript uses a robust approach to identify and characterize different peptide sequences that can interact with LC3B. They validate a large number of sequences using biolayer interferometry (BLI). The large number of LC3B binding sequences and their dissociation constants adds significant new information to the field that will help others understand what sequences can bind to LC3B. The authors are also very careful to accurately report on their data and not overly interpret their findings.

      Weaknesses:

      After the authors identify proteins from their bacterial display assay, the remainder of the manuscript is focused on characterizing the different types of sequences that are identified in addition to validating the LC3B-LIR interactions using BLI and X-ray crystallography. However, it's not entirely clear if the screen identified novel LC3B binders that interact with LC3B in cells. While I acknowledge that the focus of the manuscript is on the characterization of LIR sequences that can bind LC3B it seems like a missed opportunity to not validate a few of the novel LC3B binders in vivo.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study uses high-throughput bacterial cell-surface display to identify LC3B-interacting peptides in the human proteome. The screen is unbiased, and this type of assay has not previously been used for selecting LC3B-interacting peptides. The screen was done with a library of 500,000 peptides, and they ended up with 427 peptides that they scored as high-confidence LC3B binders. The experiments performed are solid, and data are analyzed using well-documented methods and statistics.

      The aim of the authors was to isolate LC3B-interacting peptides from the human proteome, and the screen succeeded in doing so. The selected set of peptides included several previously reported LIR motifs, but also many novel LC3B binding peptides that either contained or did not contain the canonical core LIR motif [WFY]xx[LVI].

      Another aim was to identify binding determinants important for the LC3B interaction, and they made an interesting sequence logo based on selected LIR-containing peptides. However, this study does not really extend our knowledge related to binding determinants essential for LIR motifs in LC3B binding. They basically verify known characteristics, including the importance of varied types of electrostatic interactions supporting the docking of the core LIR into the LDS of LC3B.

      Strengths:

      The approach used here (high-throughput bacterial-surface-display) is new. The screen is unbiased, and the fact that peptides are directly tested for LC3B binding may facilitate the discovery of non-canonical LIR motifs. The screen appears to be highly selective and manages to distinguish between peptides that interact with LC3B and peptides that do not interact.

      Weaknesses:

      It is a limitation that no proteins are analyzed in this study. Further work is therefore needed to verify that identified LIR motifs are functional in full-length proteins and in cells.

      Reviewer #2 (Public review):

      Summary:

      To discover peptides that interact with autophagy-related protein LC3B and profile the key binding determinants, the authors screened a library of ~500,000 36-residue peptides derived from the human proteome using bacterial cell-surface display. Analysis of the screening data revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR.

      These results support a refinement of the LIR motif definition and expand the network of candidate LC3B interaction partners.

      Strengths:

      High-throughput approach.

      Weaknesses:

      Lack of in vitro data and molecular dynamics simulations.

      Reviewer #3 (Public review):

      Summary:

      The LC3 family of proteins, which includes LC3B, are ubiquitin-like proteins that are covalently linked to phosphatidylethanolamine in the expanding autophagosomal membrane during autophagy. LC3 family members bind to short sequences of amino acids that reside within dynamic regions in a wide variety of proteins. These sequences, termed LC3 Interacting Regions (LIRs), were initially thought to function primarily to link LIR-containing autophagy cargo receptors to LC3 family members to help facilitate their capture during autophagy. However, the functional importance of LIRs in autophagy has broadened to include more general functions in autophagy as well. While a general consensus for LIR sequences has been described as [FWY]0-X1-X2-[LVI]3, recent work has suggested that additional sequences outside of the canonical LIR sequence can bind LC3 family members and play important roles in autophagy. In this manuscript by Kosmatka et al, the authors perform a high-throughput screen using bacterial surface display coupled with fluorescence-associated cell sorting to identify which human sequences can bind to LC3B. They identify a variety of peptides capable of binding LC3B, including peptides from proteins that have not previously been described as LC3B-binding proteins. The results from the bacterial surface display were then used to guide sequence analysis, mutational analysis, and structural studies to further characterize the range of LIR sequences that are capable of binding LC3B. Taken together, this work adds to the growing knowledge of how LIR sequences interact with LC3 family members and demonstrates which amino acids both inside and outside of the LIR sequence aid in binding. This work also identifies new potential LC3 binding proteins, which may play unknown roles in autophagy regulation. Lastly, this work reinforces the importance of alternative LIR sequences such as the [WFY]0-X1-X2-[WFY]3 sequence, which the authors have dubbed the LIR+ sequence.

      Strengths:

      The manuscript uses a robust approach to identify and characterize different peptide sequences that can interact with LC3B. They validate a large number of sequences using biolayer interferometry (BLI) and attempt to correlate different amino acids with their binding affinity for LC3B. The large number of LC3B binding sequences and their dissociation constants adds significant new information to the field that will help others understand what sequences can bind to LC3B. The authors are also very careful to accurately report on their data and not overly interpret their findings.

      Weaknesses:

      After the authors identify proteins from their bacterial display assay, the remainder of the manuscript is focused on characterizing the different types of sequences that are identified in addition to validating the LC3B-LIR interactions using biochemical approaches, including BLI and X-ray crystallography. However, it's not entirely clear if the screen identified novel LC3B binders that interact with LC3B in cells. While I acknowledge that the focus of the manuscript is on the characterization of LIR sequences that can bind LC3B, it seems like a missed opportunity not to validate a few of the novel LC3B binders in vivo. This may result in the demonstration of novel binders of LC3B in cells and may further demonstrate the strength of this approach for identifying LC3 family member binding partners. Therefore, it would be helpful to look at a few proteins identified in the HC set that have not previously been identified as LC3B binders in cells to determine if they CO-IP with LC3B or interact with LC3B using a different approach.

      The reviewers appreciated the contribution of our screen and our biochemical analyses and indicated there would be additional value in performing follow-up studies in cells to investigate the functional relevance of interactions we detected. We agree that such work will be important, but it is beyond the scope of our current study. We expect the data and analyses included in this paper, and the lists of peptide binders that we identified, will inspire such future efforts.

      We appreciate the reviewers’ careful reading of our manuscript, and we have revised the text and figures to improve the clarity and presentation of the data in accordance with their recommendations.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Main comments:

      (1) In the abstract, the authors correctly conclude in the final sentence: "our results refine the LIR motif definition and expand the network of candidate LC3B interaction partners", but I disagree that their study "highlights how mutations at the LC3B-LIR interface can modulate affinity and specificity". They probably refer to the data in Figure 5, where they found that some peptides were positively affected by a mutation of the LDS in LC3B. This is interesting, but four peptides are listed in Figure 5B, and for these peptides, no experimental data are included to verify that their interactions with LC3B are LIR-LDS interactions. LIR-mutated peptides are, for instance, not tested. Therefore, it is possible that these interactions are not LIR-LDS interactions but depend on another binding surface in LC3B than LDS.

      Although LIR-mutated peptides were not tested in the experiments reported in Figure 5B, the LIR docking site (LDS) mutations F52A/L53A change the peptide-LC3B affinity. This suggests that the LDS is involved in the binding interaction, even if the interaction is not with a LIR motif. We have changed the statement to “highlight how mutations at the LIR docking site can modulate affinity and specificity,” to be more explicit.

      (2) Another text line in the abstract is that their study "revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR", and I find this text line a bit misleading. Yes, their structural data for the BLM peptide nicely shows the importance of electrostatic interactions, and R70 in LC3B binds to an E in the +1 position. This is different from several other reported LIR-LDS structures where R70 in LC3B binds to an acidic +7 residue in the LIR motif. However, R70 is conserved in all human ATG8s (R67 in GABARAP) and in many reported ATG8-LIR structures found to interact with an E in the +1 position, exactly as shown for the LC3B-BLM structure shown in Figure 3. The presented structure is therefore a very typical example rather than an exception to previously reported LIR motifs. The authors also select peptides with a so-called LIR+ motif, but this is a previously reported variant, and the alphafold model for the CTSL2 peptide in Figure 4 suggests a quite normal LIR-LDS interaction, except that W is docked into pocket 2.

      While the structural analysis of the BLM peptide bound to LC3B supports the importance of electrostatic interactions, it is not the primary basis for the statement regarding a “strong preference for negatively charged residues adjacent to the LIR.” Rather, this conclusion results from the sequence features enriched across the selected peptides in the HC-set, which revealed a consistent enrichment of negatively charged residues flanking the core motif and in the +4 position.

      With respect to the statement that the study “revealed exceptions to the reported LIR motif,” the intent was not to suggest that these interactions are unprecedented in the literature, but rather that we expanded the number of known exceptions to the most canonically defined LIR motif ([FWY]xx[LVI]). We do not claim these are unprecedented; we cite prior examples of exceptions in the text (Li 2018, Knævelsrud 2013, Chatzichristofi 2023). To avoid confusion, we revised the abstract to state that the study “revealed exceptions to the canonical LIR motif ([FWY]xx[LVI]).”

      (3) A limitation related to the search for binding determinants can be that the screen is only performed with LC3B. The impact of several determinants beyond the core LIR is known to be ATG8 specific. For instance, LC3-specific R10 and R11 are known to form electrostatic interactions with acidic residues in positions -2, -3, and -4 relative to core LIR, and the -3 and -4 positions can be a determinant for LC3 binding (Rogov et al, 2023; Autophagy Rep. 2: 2188523). Here, the authors made a consensus peptide based on the sequence logo derived from HC-set (Figure 3C). It is interesting that this peptide had a very high affinity for LC3B (0,1 µM) that was found to depend on the D in position -3. In the sequence logo shown in Figure 3A, D, and E are strongly favored in position -3, but position -4 is not included in the logo. Are acidic residues similarly enriched in the -4 position?

      The high affinity of the consensus peptide for LC3B is not dependent on the D in position -3. The peptides pCONS<sub>W-Nterm-S</sub> (EEDDDDEDWEILDIDEDEDGSGNEFYE) and pCONS<sub>W-Nterm-S-D-A</sub>(EEDDDAEDWEILDIDEDEDGSGNEFYE), where D in position -3 is mutated to A, have the same affinity (0.12 µM). The truncated peptide, pCONS<sub>W-Nterm-L</sub> (EDWEILDIDEDEDGSGNEFYE), which lacks position -3, has a weaker affinity of 0.66 ± 0.1 µM. These results illustrate that the flanking acidic residues, rather than specifically D in position -3, contribute to affinity (Figure 3C, Supplementary Table 3). The logo corresponding to the entire consensus peptide is shown in Supplementary Figure 5A. Acidic residues are not as enriched in the -4 position as they are in positions -1 through -3, although D is still the predominant residue in position -4 and is thus present in the consensus peptide.

      Extending this type of screen and analysis to other ATG8 family members has the potential to illuminate interesting paralog-specific differences.

      (4) Will the full list of peptides in the HC-set be included in a final manuscript? This is a very interesting set of peptides for scientists interested in LC3B-interacting proteins, and this includes those interested in noncanonical LIR interactions.

      Yes, all Supplementary Files (1-5) are available at Zenodo (10.5281/zenodo.17943925). Supplementary File 4 contains all of the peptides that were enriched through the sixth sort, and those peptides where “Average Z-score Across Sorts 4-6” is greater than or equal to (≥) 1.70 correspond to the full list of peptides in the HC-set.

      Minor comments:

      (5) When reading the result section, it is confusing that the bacterial membrane protein is named "circularly permuted OmpX" in the text on page 2, and "eCPX" in the figure legend. I suggest including both names in at least one place, as done in the methods section.

      We changed the text on page 2 to use the full name, which is “enhanced circularly permuted OmpX (eCPX)”, to address the confusion.

      (6) Figure S3A: The list of 10 peptides in the input library that have a KD in the LIRcentral database includes 7 with a KD below 5 µm (if I am correct), but is said to be 6 in the text on page 3.

      We corrected the text on page 3 to 7 peptides.

      (7) Error in the text. Data for the BLM peptide is shown in Figure 2B, but replaced by MAP1B in the text on page 3.

      We tested four peptides reported to co-immunoprecipitate with LC3B without an experimentally validated LIR motif (MAP1B<sup>823-858</sup>, ATG4A<sup>363-398</sup>, SCYL1<sup>640-675</sup>, and HEAT3<sup>377-412</sup>). Of these four, binding of three (ATG4A<sup>363-398</sup>, SCYL1<sup>640-675</sup>, and HEAT3<sup>377-412</sup>) is illustrated in Figure 2B, in addition to BLM<sup>552-587</sup>, which has not been previously reported to co-IP with LC3B. We added a note that not all curves are shown. Dissociation constants for these 5 peptides are included in Supplementary Table 1.

      Reviewer #2 (Recommendations for the authors):

      Human MAP1LC3B (LC3B) binds proteins involved in autophagy and other cellular processes using a short linear motif known as the LC3-interacting region (LIR).

      To discover peptides that interact with LC3B and profile the key binding determinants, the authors screened a library of ~500,000 36-residue peptides derived from the human proteome using bacterial cell-surface display. Analysis of the screening data, coupled with structural studies and site-directed mutagenesis, revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR.

      The authors also suggest that mutations in LC3B, shown to abrogate binding of LIR-containing peptides, instead alter LC3B binding specificity, leading to enhanced binding of some LIR-containing sequences.

      This is an interesting study, well structured, with solid data.

      My major concern is that the conclusion about the effect of known LC3B mutations on the binding of LIR-containing proteins is not strongly supported by the presented data. Did the authors try molecular dynamics simulations in such LC3B mutations to examine if F52A/L53A substitutions create alternative hydrophobic interfaces that modulate LIR motif peptide binding in a sequence-dependent manner?

      Figure 5C includes data that demonstrate that mutations to the LIR docking site (F52A, L53A) change the binding affinity of several peptides. Our structural analysis of the F52A/L53A LC3B mutants using ChimeraX (Figure 5A) provides a model for the changes in the hydrophobic pockets and a possible explanation for how these mutations introduce altered hydrophobic contacts and sequence specificity at the LDS. It would be challenging to exhaustively sample binding modes for peptides with different sequences using MD, which is best suited for exploring local minima. We do not claim to have a detailed structural explanation for the observed changes in binding specificity.

      Additionally, in vitro cellular data on the expression and localization of some of their strongest LC3B binding proteins (and their relevant novel residue mutations) would be useful.

      Assessing the expression and localization of our strongest LC3B-binding proteins and testing the effects of novel residue mutations in cells would be a valuable follow-up to further explore the implications of our findings. However, these experiments are beyond the scope of the current study, which focuses on high-throughput characterization of LC3B binding determinants using biochemical methods. We close the paper with a statement highlighting this as a promising direction for future investigation.

      Finally, the suggested refinement of the LIR motif sequence is not easily identifiable in the main text.

      We added a clause to clarify this in the Discussion: “Although a small number of LC3B-binding peptides bearing this LIR+ motif have been reported (~1% of cataloged hAtg8-binding peptides) (Li et al., 2018; Chatzichristofi et al., 2023), their prevalence appears underappreciated given their relatively high frequency in the HC-set (Supplementary Figure 10), leading to the refined motif [FWY]xx[LVIFWY].”

      Reviewer #3 (Recommendations for the authors):

      Please find my specific comments below.

      (1) The authors identify a large number of peptides from their screen, including several strong binders as shown in Figure 2C, that appear to bind LC3B but are from proteins that have not been previously annotated as LC3B binders in cells. Therefore, it would be helpful to validate whether some of these proteins are indeed LC3B binders in cells using CO-IPs or another related technique. This would significantly strengthen the manuscript, further validate their approach for characterizing LIR LC3B binding interactions, and demonstrate novel potential autophagy regulators.

      Validating candidate LC3B binders in cells using co-immunoprecipitation or related techniques will be a valuable follow-up and could provide important insight into novel autophagy regulators. Such experiments are beyond the scope of the current manuscript, which focuses on the high-throughput characterization of LC3B binding determinants in vitro. We mention this in the discussion as a promising avenue for future investigation.

      (2) In Figure 2C, there are two proteins that have high mean-Z scores but no detectable binding to LC3B (top right quadrant). It would be helpful for the authors to acknowledge this and speculate on why binders are isolated but cannot have detectable binding. Is this expected for a screen of this size? Is this below the non-specific binding expected or above? 

      Some apparent false-positive binders may have been isolated and exhibit high mean Z-scores due to avidity effects from the pre-tetramerized LC3B used in the bacterial display screen. Binding was validated using monomeric LC3B in BLI experiments, which eliminates these multivalent effects. Consequently, weak interactions stabilized by LC3B tetramerization that enrich in the bacterial display assay may not be detectable at the monomeric LC3B concentrations tested in BLI. Given the differences in multimerization between the screening and validation assays, as well as the large size of the library, both false positives and false negatives are expected. We added a comment about the apparent false positives to section Strongly enriched peptides include novel candidate LC3B interactors.

      (3) In Figure 2C, it says four peptides are colored as in 1C, but I can only see three colored that way, and the yellow one is very faint and hard to see.

      We corrected the text in Figure 2C to three peptides and have darkened the yellow point for easier visualization.

      (4) The authors determine the crystal structure of a peptide from BLM bound to LC3B. However, no electron density is shown in the manuscript. Therefore, it is unclear how ordered the side chains are that are shown, including for D564-D566. The authors should include an electron density omit map for the BLM peptide so that readers can accurately interpret how ordered each side chain is in the peptide.

      Supplementary Figure 9 shows an ensemble of conformations consistent with the diffraction data, as well as an electron density omit map. Data to support visualization of the electron density for the structure is included with the PDB deposition (structure 9p3e).

      (5) In Figure 3C, the authors include numbers in the bar graph. What are the numbers that are included? Is this the mean Kd value? If so, the numbers may be misleading without a second significant figure since some values of 0.1 are far away from 0.2, while others are very close to 0.2. In addition, if these values are the Kd values, it would be helpful to include these values in all other bar graphs throughout the manuscript, including Figures 4A and 5C.

      The values reported are the mean K<sub>D</sub> values, in micromolar, which is now indicated in the legend of Figure 3C; we also added a second significant figure. The mean K<sub>D</sub> values have also been included for the bar graphs in Figures 4A and 5C.

      (6) On page 7, the authors state that "we found strong evidence for enrichment of LIR sequences, but not for these closely related motifs, indicating that whereas such LIR-related motifs may support binding in some sequence contexts, they are not sufficiently strong binding determinants to be enriched in our screen". I found this statement to be unclear. Do the authors mean that while LIR sequences were strongly enriched in the HC-set, LIR+ sequences were not? This statement should be clarified.

      The statement on page 7 is in reference to Supplementary Figure 10, which shows that peptides containing the canonical LIR [FWY]xx[LVI] are enriched in the HC-set relative to their presence in the input library. In contrast, peptides containing LIR-related motifs ([WFY]xx[WFY], [LVI]xx[FWY], and [LVI]xx[LVI]) are not strongly enriched in the HC-set relative to their presence in the input library. We clarified this statement to read “We found strong evidence that canonical LIR motifs ([FWY]xx[LVI]) are enriched in the HC-set relative to their frequency in the input library, whereas LIR-related motifs ([WFY]xx[WFY], [LVI]xx[FWY], [LVI]xx[LVI]) are not (Supplementary Figure 10), indicating that although LIR-related motifs can support LC3B binding in certain sequence contexts, they are not sufficiently strong determinants to drive enrichment in our screen.”

      (7) How different is the alphafold prediction of the LIR+ engaging with LC3 compared to the LIR LC3 interaction? Are they largely similar or are there unique features to each? Does the presence of a second large aromatic residue push the backbone of the peptide farther away from the hydrophobic binding pocket?

      To address this, we predicted structures of the LIR+ motif of CTSL2 (EEDWEVFDPY) and a canonical LIR version (EEDWEVIDPY) to LC3B using AlphaFold3. Structural superposition shows that LC3B adopts a conserved conformation in both complexes (Cα RMSD 1.55 Å). The backbones of the core LIR / LIR+ motif also align closely to each other (1.39 Å RMSD), indicating preservation of the canonical binding mode. The LIR+ peptide exhibits a modest increase (~2 Å) in Cα distance between the aromatic residue Phe and LC3B F52 in HP2 compared to the corresponding Ile in the canonical LIR (8.3 Å vs 6.3 Å). Thus, a subtle backbone repositioning is predicted to accommodate the aromatic side chain. We added a comment about this to the section Highly enriched peptides lacking a canonical LIR can bind LC3B.

    1. eLife Assessment

      This valuable study substantially improves the phylogenetic framework for Kinetoplastea through greatly expanded sampling of free-living neobodonids; the phylogenomic analyses provide convincing evidence that neobodonids are paraphyletic and establish a substantially revised framework for deep relationships within Metakinetoplastina. The work forms the foundation for future comparative studies of kinetoplastid evolution and systematics, although its implications for the origins of parasitism should not be overstated. An 18S rRNA phylogeny documenting the placement of the newly sampled strains within previously recognized neobodonid lineages and/or confirmation of the principal topology using a well-documented phylogenomic set of marker genes would further strengthen the study. The work will be of particular interest to researchers studying protist evolution and the evolutionary context of trypanosomatids.

    2. Reviewer #1 (Public review):

      Summary:

      The authors present the most comprehensive phylogenomic study of free-living kinetoplastids to date, including newly generated transcriptomic data from 11 species. This dataset provides a solid foundation for future comparative evolutionary studies of kinetoplastids, a group that includes important human pathogens such as Trypanosoma brucei.

      Strengths:

      The study is clearly written, and the analyses are thoroughly described, well documented, and reproducible. The phylogenomic dataset comprises 444 proteins, which is larger than those used in many comparable phylogenomic studies. The main strength of the study is the substantially improved taxon sampling of neobodonids, which provides a much stronger framework for resolving evolutionary relationships among free-living kinetoplastids.

      Weaknesses:

      The phylogenomic analysis is based on a newly assembled dataset comprising 444 single-copy orthogroups. Using an established phylogenomic dataset, expanded to include the newly studied species, to confirm the inferred relationships would provide additional support for the robustness of the recovered topology.

    3. Reviewer #2 (Public review):

      Summary:

      This is a highly competent and very throough phylogenomic study of the position of neobodonids within the Kinetoplastid phylogeny. The addition of new transcriptomic data for diverse neobodonids allows their relationships to be better understood, leading to convincing changes to neobodonid taxonomy.

      Strengths:

      The generation of transcriptomic data produces more phylogenetic characters than ever before from several, diverse neobodonid lineages. This represents a substantial increase in the data available for this relatively unstudied group. The methodology used to define the character set and then estimate the phylogeny is conventional, very thoroughly described and expertly executed. The phylogeny is highly robust, and we can be very confident of the result.

      Weaknesses:

      These changes to neobodonid taxonomy, or essentially the discovery that some 'neobodonids' actually represent distinct lineages at the base of the Metakinetoplastida, are not surprising. So undersampled are the Kinetoplastida, both in terms of molecular markers and phyletic diversity, that an investment in sampling on this scale is always likely to bring about changes. The significance of these changes is not immediately apparent. Since they do not concern the Eubodonidae, they have no implications for the trypanosomatids and the new Metakinetoplastid lineages are very distant from the origins of parasitism, such that comments on "preadaptations for disease" are not compelling.

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript by Zavadska et al. presents a phylogenomic study focused on the relationships among metakinetoplastids, specifically addressing the question whether Neobodonida are monophyletic. The authors collected eleven kinetoplastid strains affiliated with five out of six neobodonid clades identified in previous studies and sequenced their transcriptomes. These data along with SRAs available from public databases were processed via assembly, annotation and decontamination. The final dataset, representing all (non-trypanosomatid) kinetoplastids with genome and/or transcriptome data available as well as selected trypanosomatids and discoban outgroups, was used for protein orthology inference and identification of single-copy orthologs present in the majority of taxa, followed by phylogenetic analysis in maximum likelihood and Bayesian frameworks.

      Strengths:

      I find the new data and resulting phylogenetic reconstruction valuable and interesting, particularly given the paucity of genomic/transcriptomic data from free-living kinetoplastids. The study is well-designed methodologically, and the main finding (paraphyly of Neobodonida) appears indisputable, considering the provided phylogenimic evidence. Following best practices, the authors shared their primary data, scripts, and files resulting from the conducted analyses.

      Weaknesses:

      Several aspects of the presentation and interpretation would benefit from revision. In particular, the composition of the neobodonid lineages central to the study is insufficiently explained, and an 18S rRNA gene-based tree showing these lineages and the placement of the newly analysed strains would substantially improve the clarity of the study. The Introduction would also benefit from a more concise and better-structured account of previous phylogenetic evidence and a clearer explanation of the broader biological significance of kinetoplastids, while the Methods could be streamlined by removing redundant information and presenting some details in tables. Finally, the rationale for including the electron microscopy images in the supplement should be clarified, as these are currently not discussed in the text and represent only a single studied strain.

    5. Author response:

      Reviewer #1 (Public review):

      Summary:

      The authors present the most comprehensive phylogenomic study of free-living kinetoplastids to date, including newly generated transcriptomic data from 11 species. This dataset provides a solid foundation for future comparative evolutionary studies of kinetoplastids, a group that includes important human pathogens such as Trypanosoma brucei.

      Strengths:

      The study is clearly written, and the analyses are thoroughly described, well documented, and reproducible. The phylogenomic dataset comprises 444 proteins, which is larger than those used in many comparable phylogenomic studies. The main strength of the study is the substantially improved taxon sampling of neobodonids, which provides a much stronger framework for resolving evolutionary relationships among free-living kinetoplastids.

      Weaknesses:

      The phylogenomic analysis is based on a newly assembled dataset comprising 444 single-copy orthogroups. Using an established phylogenomic dataset, expanded to include the newly studied species, to confirm the inferred relationships would provide additional support for the robustness of the recovered topology.

      We thank the reviewer for their comments on our work. We appreciate the suggestion to confirm the topology that we recovered by using an established phylogenomic dataset. We note that we did not choose to use an established dataset for the main phylogenomic analysis, as we found that free-living kinetoplastid orthologs are systematically excluded from commonly used “universal pan-eukaryotic” datasets (e.g., Fig. S4), despite the fact that we were able to identify sufficient orthologous proteins to robustly reconstruct interspecies relationships among euglenozoans. In our revised manuscript, we will implement the reviewer’s suggestion by comparing our orthogroup set with commonly used pan-eukaryotic orthogroup sets.

      Reviewer #2 (Public review):

      Summary:

      This is a highly competent and very throough phylogenomic study of the position of neobodonids within the Kinetoplastid phylogeny. The addition of new transcriptomic data for diverse neobodonids allows their relationships to be better understood, leading to convincing changes to neobodonid taxonomy.

      Strengths:

      The generation of transcriptomic data produces more phylogenetic characters than ever before from several, diverse neobodonid lineages. This represents a substantial increase in the data available for this relatively unstudied group. The methodology used to define the character set and then estimate the phylogeny is conventional, very thoroughly described and expertly executed. The phylogeny is highly robust, and we can be very confident of the result.

      Weaknesses:

      These changes to neobodonid taxonomy, or essentially the discovery that some 'neobodonids' actually represent distinct lineages at the base of the Metakinetoplastida, are not surprising. So undersampled are the Kinetoplastida, both in terms of molecular markers and phyletic diversity, that an investment in sampling on this scale is always likely to bring about changes. The significance of these changes is not immediately apparent. Since they do not concern the Eubodonidae, they have no implications for the trypanosomatids and the new Metakinetoplastid lineages are very distant from the origins of parasitism, such that comments on "preadaptations for disease" are not compelling.

      We agree that comments and discussion on how our study may shed light on the preadaptations of trypanosomatids to parasitism were excessive. We were influenced by prior studies of free-living metakinetoplastids, which highlighted their importance and applied value in the context of understanding evolution of parasitism in kinetoplastids (Deschamps et al., 2011; Dolezel et al., 2000; Flegontova et al., 2017). In our revised manuscript, we will remove/shorten the corresponding parts of discussion and results, and place more emphasis on the relevance of our study to understand the multiple origins of parasitism in metakinetoplastids, not just trypanosomatids.

      The datasets published in our study can be used in the future to investigate, for example, how gene repertoire of trypanosomatids differs from the free-living species, or how specific gene families evolved, and whether their diversification/origin predated the origin of the largest parasitic clade. We acknowledge that the data from the closest free-living relative of trypanosomatids that was available before our study is already extremely valuable, and that our datasets would be of most use for reconstructing relatively ancient preadaptations.

      Reviewer #3 (Public review):

      Summary:

      The manuscript by Zavadska et al. presents a phylogenomic study focused on the relationships among metakinetoplastids, specifically addressing the question whether Neobodonida are monophyletic. The authors collected eleven kinetoplastid strains affiliated with five out of six neobodonid clades identified in previous studies and sequenced their transcriptomes. These data along with SRAs available from public databases were processed via assembly, annotation and decontamination. The final dataset, representing all (non-trypanosomatid) kinetoplastids with genome and/or transcriptome data available as well as selected trypanosomatids and discoban outgroups, was used for protein orthology inference and identification of single-copy orthologs present in the majority of taxa, followed by phylogenetic analysis in maximum likelihood and Bayesian frameworks.

      Strengths:

      I find the new data and resulting phylogenetic reconstruction valuable and interesting, particularly given the paucity of genomic/transcriptomic data from free-living kinetoplastids. The study is well-designed methodologically, and the main finding (paraphyly of Neobodonida) appears indisputable, considering the provided phylogenimic evidence. Following best practices, the authors shared their primary data, scripts, and files resulting from the conducted analyses.

      Weaknesses:

      Several aspects of the presentation and interpretation would benefit from revision. In particular, the composition of the neobodonid lineages central to the study is insufficiently explained, and an 18S rRNA gene-based tree showing these lineages and the placement of the newly analysed strains would substantially improve the clarity of the study. The Introduction would also benefit from a more concise and better-structured account of previous phylogenetic evidence and a clearer explanation of the broader biological significance of kinetoplastids, while the Methods could be streamlined by removing redundant information and presenting some details in tables. Finally, the rationale for including the electron microscopy images in the supplement should be clarified, as these are currently not discussed in the text and represent only a single studied strain.

      We thank the reviewer for their suggestions on how our manuscript could be improved. Regarding the selection of species, our study followed a common procedure in protistology: we attempted to isolate, culture and sequence as many free-living kinetoplastids as possible, and the resulting manuscript includes all the cases in which we were successful. We appreciate the reviewer’s suggestion of including an 18S rRNA gene tree, which we will implement in our revised manuscript. We will revise the introduction to improve the presentation of previous phylogenetic evidence in metakinetoplastids and to provide a more clear explanation of their broader significance. We also plan to reduce redundant information in the Methods and relocate information on strains and culturing conditions to tables.

      Electron microscopy images in the Supplement display the ultrastructure of an isolate from the 1D clade. They were included because the information on morphology of this clade remains scarce in comparison to other clades; thus, we added these images because we believe they could be useful for the community. We intend to clarify this in our revised manuscript.

      References

      Deschamps P, Lara E, Marande W, López-García P, Ekelund F, Moreira D (2011) Phylogenomic analysis of kinetoplastids supports that trypanosomatids arose from within bodonids Mol Biol Evol 28:53-58 https://doi.org/10.1093/molbev/msq289

      Dolezel D, Jirků M, Maslov DA, Lukes J (2000) Phylogeny of the bodonid flagellates (Kinetoplastida) based on small-subunit rRNA gene sequences Int J Syst Evol Microbiol 50:1943-1951 https://doi.org/10.1099/00207713-50-5-1943

      Flegontova O, Flegontov P, Malviya S, Poulain J, de Vargas C, Bowler C, Lukeš J, Horák A (2018) Neobodonids are dominant kinetoplastids in the global ocean Environ Microbiol 20:878-889 https://doi.org/10.1111/1462-2920.14034

    1. eLife Assessment

      This valuable study applies a hidden Markov model to freezing, ultrasonic vocalizations, and lever pressing in male rats to ask whether fear extinction restores normal behavior or only suppresses defensive responding. The evidence is incomplete because the model states are not related back to raw behavior or group manipulations, and the states are not tested as predictors of relapse. The work will be of interest to researchers studying fear extinction and computational approaches to behavior.

    2. Reviewer #1 (Public review):

      Summary:

      This study addresses an important question of how recovery from conditioned fear can be characterized beyond the reduction of defensive responses. The authors combine multiple behavioral measures with a theory-guided hidden Markov model to identify recurring behavioral modes and examine how the organization of behavior changes during fear conditioning and extinction. The study provides an interesting and potentially valuable framework for examining recovery from conditioned fear as a broader change in behavioral repertoire rather than simply as a reduction in defensive behavior.

      Strengths:

      A major strength of the study is the attempt to characterize fear-related behavior as a dynamic organization of multiple behavioral measures rather than relying on a single conventional measure such as freezing. The integration of freezing, ultrasonic vocalizations, and food-reinforced behavior into recurring behavioral modes provides a potentially useful approach for capturing behavioral states that may be missed by analyses of individual responses. The study also presents a theoretically motivated framework for distinguishing defensive, quiescent, exploratory, and goal-directed behavioral modes, which could provide a useful conceptual basis for studying functional recovery following aversive experiences.

      Weaknesses:

      The main concern is whether the analyses and behavioral definitions provide sufficient support for the interpretation of these behavioral modes and, in particular, for the proposed concept of functional recovery. First, the data-driven model selection indicates substantially better predictive performance for eight states, whereas the authors retain four states primarily to conform to their theoretical framework. A stronger justification for prioritizing the four-state solution would therefore strengthen the interpretation of the results. Second, it is difficult to determine from the current behavioral measures whether the Quiescent state reflects safety and rest or behavioral inhibition associated with residual fear or anxiety. The marked reduction in exploratory and goal-directed behavior could potentially be consistent with either interpretation, making it important to consider alternative explanations for this state.

      The definition of functional recovery also warrants further consideration. The authors propose the reinitiation of food-seeking behavior in the previously threatening environment as an important indicator of recovery. From an evolutionary and ethological perspective, however, an animal that has experienced a threat while foraging in a particular location may benefit from shifting its foraging to an alternative location rather than returning to the previously threatening location, even after extinction learning. Thus, the resumption of goal-directed behavior in the previously threatening environment may not necessarily represent a biologically appropriate benchmark for successful fear extinction or functional recovery, as functional recovery may involve substantial changes in motivational processes that cannot be captured by the simple return of a previously suppressed behavior. More broadly, the results may support the value of examining behavioral repertoire during extinction, but they do not yet establish that restoration of the broader repertoire, or specifically the return of goal-directed behavior in the previously threatening environment, is required for functional recovery.

      Finally, the use of the no-shock control group could be more informative. The observed reduction in exploratory behavior during conditioning cannot by itself distinguish associative fear learning from habituation to novelty. A direct comparison between shock and no-shock groups would therefore help determine whether the changes in behavioral modes are specifically related to conditioning.

      Overall, the study provides a potentially useful framework and an interesting conceptual perspective on recovery from conditioned fear. However, the strength of the evidence supporting the interpretation of the identified behavioral modes and the proposed definition of functional recovery is currently limited. Addressing these interpretational and analytical issues would strengthen the manuscript and help establish the extent to which this framework provides a meaningful advance in the study of fear extinction and recovery.

    3. Reviewer #2 (Public review):

      Summary:

      The authors aimed to develop and validate a preclinical computational framework for assessing functional recovery after aversive learning. They argue that a reduction in defensive behavior (e.g., freezing) is insufficient to conclude recovery, and that a comprehensive assessment must also track the re-emergence of exploration and goal-directed action. To test this, they adapted a multimodal paradigm in rats, measuring freezing, 22-kHz and 50-kHz ultrasonic vocalizations (USVs), and food-reinforced lever pressing. Using a theory-guided hidden Markov model (HMM) with four states derived from tri-reference-point theory (Fear/Danger, Quiescent, Exploratory, Goal-directed), they decoded behavioral trajectories across fear conditioning, extinction, delay, and contextual relapse experiments.

      Strengths:

      (1) The study tackles a significant gap in animal models of fear and trauma-related disorders. It correctly identifies that a focus on a single defensive response like freezing may be misleading and that a broader behavioral assessment is necessary to understand the impact of interventions.

      (2) The application of a hidden Markov model to combine multiple behavioral channels into a cohesive, theory-driven framework is a strength. This approach moves beyond simple summary statistics to capture the temporal dynamics of behavior and allows for the identification of distinct, recurring states.

      (3) The authors demonstrate commendable rigor in their model selection and validation. They employ discrete-aware scoring to address the boundary issues of count data and conduct extensive stability analyses across restarts, variance floors, and studies. The association of the Fear/Danger mode occupancy with fecal boli, an independent autonomic measure, provides a convincing external validation.

      (4) The study provides several important findings. For instance, it shows that during the first extinction session, although Fear/Danger occupancy declined, the dominant state was Quiescent, rather than Exploratory or Goal-directed. This directly supports the core premise that the absence of fear does not equal functional recovery.

      Weaknesses:

      (1) The authors transparently report that their results are not emission-family invariant. The failure of count-native models (e.g., negative binomial) to converge or produce the expected four-state solution is a major limitation. This suggests the theory-guided state structure is tied to the specific Gaussian representation chosen and may not be a robust and universal feature of the behavioral data. This limits the strength of the claim that the data naturally resolve into these four modes.

      (2) The exclusive use of male rats from a single laboratory limits the generalizability of the findings to female animals and other experimental settings. The cross-study recurrence analysis is a helpful sensitivity check but does not substitute for true model transfer to an independent, out-of-sample dataset.

      (3) Testing K=4 vs. K=8 raises the question of whether the four-state solution is the most appropriate level of abstraction for the questions being asked.

      (4) The interpretation of the 50-kHz USV band as an Exploratory state is somewhat broad. 50-kHz calls are emitted in a variety of contexts, including social interaction and reward. While the use of the band is justified, it remains an indirect measure of exploration and should be interpreted with caution. The interpretation and link to the exploratory state should be toned down and discussed.

    4. Author response:

      We thank the editors and both reviewers for their careful evaluation and for recognizing the value of examining the wider behavioral repertoire after aversive learning. Our aim is to follow what an individual does after danger and intervention, rather than infer recovery from a decline in one defensive response. The reviews identify where the present computational account needs stronger connections to observed behavior, experimental comparisons, and subsequent outcomes. We are preparing a substantive revision addressing these issues. This provisional response outlines the work planned; it does not report completed revisions or new findings.

      Response to Reviewer 1:

      (1) Four-state versus eight-state resolution

      We agree that the better held-out performance of K=8 must be central to the interpretation. K=4 is a theory-guided coarse descriptive scale, not the predictively optimal model or evidence that behavior contains exactly four natural states. We will explain this distinction more clearly and show what the additional states resolve, particularly within the low-event-output configurations. Comparisons of experimental change at both resolutions will help establish which conclusions depend on the coarse scale and which persist at finer resolution. We will not treat agreement within one emission representation as proof of a universal four-mode organization.

      (2) Interpreting Quiescent

      The reviewer's concern is important: Quiescent denotes low vocal and lever output, not an absence of freezing, a safe internal state, or successful recovery. Likewise, a decline in Fear/Danger occupancy does not establish that defensive responding has disappeared. We will present raw freezing, vocalizations, and lever pressing alongside state occupancy, with experimental groups distinguished. The higher-resolution subdivision of count-silent observations will make differences in freezing more visible. These comparisons can describe residual defensive behavior, but cannot by themselves determine whether quietness reflects rest, inhibition, anxiety, or another internal condition. We will retain these alternatives explicitly rather than infer safety from silence.

      (3) The ethological meaning of functional recovery

      We appreciate the distinction between returning to a previously rewarded action and adapting appropriately after danger. Resuming food-directed responding in the original setting is one task-specific outcome, not a necessary or sufficient definition of recovery. Avoiding that location or pursuing resources elsewhere could also be adaptive. The present task does not offer the alternatives needed to distinguish those possibilities. We will therefore frame the measured repertoire as an assay of context-dependent functional change, not a universal standard to which animals should return. The translational question remains whether an intervention changes the broader organization of behavior, but judging successful adaptation also requires evidence about the opportunities and risks available to the individual. Pooled state percentages will not be presented as normal or desirable recovery targets.

      (4) Shock versus no-shock comparisons

      We will report direct comparisons between shock and no-shock animals for the raw channels and inferred occupancies, including changes over conditioning. This will allow the reader to distinguish changes associated with aversive conditioning from changes shared with unshocked animals, such as familiarization or declining novelty. The revision will also separate extinction from time-matched exposure and present renewal by group rather than pooling treatment histories. Comparisons will use rats as the experimental units, report uncertainty, and make differences in observation coverage explicit.

      (5) Connecting post-extinction behavior to later outcomes

      We will examine whether the available paired records permit associations between post-extinction configurations and subsequent defensive responding, alongside the corresponding raw behavioral measures. These analyses will respect test chronology and intervening treatment. They will be identified as retrospective and exploratory, not as independent validation of individual prediction: the current shared representation was fitted using the full dataset. We will report limited sample size, missing modalities, or insufficient variation when these prevent a question from being estimated. A failure to establish an association will not be treated as evidence that no biological relationship exists. If these data cannot support a prognostic claim, we will state that limitation and identify the prospective test needed.

      Response to Reviewer 2:

      (1) Emission-family dependence

      We agree that the count-native sensitivities materially limit the interpretation. Alternative emission models also retain temporal dependence; their disagreement with the Gaussian model therefore does not show that temporal modeling uniquely produces the four labels, or that the Gaussian account is biologically privileged. We will explain the count transformation and scaling, and distinguish numerical fit eligibility from recovery of the complete behavioral-label configuration. We will also make clearer which findings are conditional on the Gaussian representation. Stable classification within that representation is useful evidence for a reproducible reference, but is not evidence for an emission-family-invariant taxonomy.

      (2) Generalizability and independent transfer

      All animals were male and the experiments came from one laboratory. Recurrence across related study datasets, including a retest of the same animals, does not substitute for transfer to independent subjects or settings. We will strengthen the prospective validation roadmap: fix preprocessing and model parameters before testing independent animals, include both sexes and an independent setting, measure individual baselines, and evaluate later outcomes without using those outcomes to fit the representation. Direct exploration measures and tests of instrumental outcome sensitivity would provide independent checks on functional interpretation. This is a proposed validation design, not generalization demonstrated by the present study.

      (3) The appropriate level of abstraction

      The revision will distinguish predictive resolution from interpretive scale throughout, as described in our response to Reviewer 1. K=8's advantage will remain explicit. The relevant question for K=4 is whether it offers an informative coarse summary of experimental change, not whether it wins model selection. Conclusions that require a finer distinction will be presented at that resolution rather than forced into four categories.

      (4) Vocal and instrumental labels

      We agree that 50-kHz calls are a context-sensitive vocal measure, not a direct observation of exploration or a unique indicator of positive affect. We will qualify the Exploratory label consistently by its measured emission pattern. Similarly, food-reinforced lever pressing establishes instrumental responding in this task; without outcome-sensitivity tests it does not establish goal-directed rather than habitual control in the technical sense. The labels will describe observed behavioral configurations, not subjective feelings, neural mechanisms, or independently validated motivational states. A simplified behavior-to-model mapping will make these distinctions easier to follow.

      Together, these revisions will place the raw behavior and experimental contrasts alongside the model, clarify the limits of the labels and recovery interpretation, and make the next validation steps concrete. The intended contribution is a reproducible framework for describing history-dependent changes in the measured repertoire and testing their functional relevance, without equating a model category with recovery or claiming individualized prognosis that the present evidence has not established.

    1. eLife Assessment

      This useful study presents Penumbria, a method for segmenting cells in three-dimensional microscopy images, released with publicly available code and documentation. The implementation is technically sound, and the reported performance gains would be a welcome contribution; the advance is incremental, since regression on distances to boundaries is already widespread in the field. The evidence is incomplete: the main text does not state which setting it used for the main competing method, and the setting used is the one that handles data of this kind poorly, so the central claim of better accuracy is unsettled; current leading methods are missing from the comparison; and five architectural and training choices are combined without attributing the gains to any of them. Every evaluation dataset labels nuclei, so the work should present itself as nuclei segmentation; describing and completing the comparisons, and attributing the gains, would raise the strength of evidence.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript proposes an architecture and distance-regression-based logic for 3D cell segmentation.

      The architecture combines a U-Net backbone with an xLSTM bottleneck layer and two novel components: a Zernike Phase Layer that is used for input normalization and the Scaled Grid Geocaps that link the input and bottleneck, inspired by the Capsule mechanism.

      During training, the network predicts foreground vs. background and regresses a distance transform as output. During inference, these predictions are used as input to a watershed procedure to segment cells in 3D.

      The method is evaluated by training on four different 3D cell/nucleus segmentation tasks and compared to StarDist3D and CellPoseSAM baselines, which are also trained on the respective datasets using the same train / val / test splits.

      The authors report improved segmentation quality compared to the baselines.

      Strengths:

      The experiments are solid; ablation studies evaluate the main architectural components, and the software appears to be well documented (I did not have time to test it).

      Weaknesses:

      (1) This is not really a weakness but a general point: the main issue in microscopy segmentation is that a lot of training data is needed if training a network from scratch. This results in a very laborious data annotation process. This is what modern approaches like CellPoseSAM and microSAM address by providing strong pre-trained networks that work well out-of-the-box for many imaging settings and can then be fine-tuned with a few corrected images to achieve state-of-the-art results. Having a better 3D segmentation approach is of course still worthwhile, but does not address the main practical challenge. I would suggest clarifying this in the introduction.

      (2) The authors implement a well-known strategy to segmentation in microscopy: regressing the distance transform. They acknowledge some prior work (Scherr et al., Lux and Matula), but this approach is more prevalent than acknowledged, including for 3D cell segmentation. For example, microSAM [1] implements cell instance segmentation by regressing the distance to the cell boundary and centroid, which is applied in 3D by CochleaNet [2]. See also [3] for an overview of different distance- or flow-field-regression-based cell segmentation approaches. I would suggest performing a more thorough literature review and acknowledging more prior work.

      (3) The baseline comparisons are not described in any detail, are questionable for CellPoseSAM, and lack current state-of-the-art models:

      3.1) The details on training and inference for the baseline methods are missing. This is particularly missing for CellPoseSAM. Here, it would be important to clarify whether the model is initialized randomly or with CellPoseSAM's pretrained weights and which of the two different 3D segmentation modes is used (see next point). It would also make sense to report both CellPoseSAM in zero-shot mode and finetuned for all cases.

      3.2) CellPoseSAM supports two different 3D segmentation modes: either a "2.5D" segmentation mode, where the network is applied across all three axes, the resulting flow maps are merged, and then used for segmentation. And an "overlap-based stitching mode", where segmentation is applied only in the XY plane and slices are then merged based on overlap. It is unclear which of these modes is used. Ideally both should be reported; the "2.5D" mode often leads to worse results for anisotropic data, and we almost always prefer the overlap-based stitching in practice.

      3.3) Two important state-of-the-art baselines are missing:

      i) microSAM [1], which is widely used for cell segmentation based on a pretrained 2D network that is extended to 3D using a similar strategy as CellPoseSAM's overlap-based stitching mode. It would make sense to report both zero-shot and fine-tuned performance.

      ii) uSeg3D [4]: this method implements a generalized 3D stitching approach that works independently of the underlying architecture and reports significant improvements.<br /> Improving and extending the baseline experiments would give a much better picture of the practical relevance of this work.

      References:

      [1] https://www.nature.com/articles/s41592-024-02580-4

      [2] https://www.biorxiv.org/content/10.1101/2025.11.16.688700v1.abstract

      [3] https://doi.org/10.6084/m9.figshare.32782521

      [4] https://www.nature.com/articles/s41592-025-02887-w

    3. Reviewer #2 (Public review):

      Summary:

      In their manuscript "Penumbria: Advanced 3D cell segmentation for biomedical imaging", the authors describe their 3D nuclei segmentation pipeline and compare it against state-of-the-art methods for nuclei and cell segmentation.

      The introduction is clear, with state-of-the-art methods, for instance, segmentation of cells from microscopy images, and motivates the method with science that can be done with cell segmentation. Related work is appropriately mentioned with the caveat that it conflates cell and nuclei segmentation, and some related work is missing.

      The Results section describes the loss, the architecture novelties, and evaluation results on nuclei segmentation, not cell segmentation. Here it becomes clear that Penumbria addresses 3D nuclei segmentation, i.e., instance segmentation of medium-sized, mostly convex-ish and well-separated objects in single-channel 3D volumes.

      Penumbria combines several choices and innovations:

      - loss: boundary distance regression<br /> - architecture<br /> - 3D U-VixLSTM<br /> - Zernike Layer<br /> - SGG<br /> - combine multiple scales in SGG<br /> - sampling strategy to look for bright and dark objects.

      The authors show on four nuclei segmentation benchmarks from confocal images that Penumbria outperforms CellPose-SAM (in either zero-shot or fine-tuned mode) and Stardist-3D in most cases, except for some where Stardist-3D performs better. They report results of an IoU threshold sweep to measure instance segmentation performance.

      Penumbria is available on GitHub under a non-commercial license. I have not tested it yet, and therefore assume that the code is in full working condition and reproduces the reported results.

      Strengths:

      (1) The method may outperform state-of-the-art methods for cell segmentation as shown in three of four 3D benchmarks.

      (2) The code is available.

      Weaknesses:

      Major

      (1) Similar to the established confusion across the literature, all the authors' evaluation datasets do not label cells, but cellular nuclei. This is important to understand because the methods they compare against, CellPose-SAM and Stardist-3D, demonstrate their performance on a variety of datasets, some of which label cellular membranes instead of nuclei. The cell segmentation task is often considered harder than the nuclei segmentation task, because objects consistently touch each other, the separation may have noise-induced holes, and object shapes may be more diverse than for nuclei.

      (2) The addition of U-VixLSTM, SGG, multi-scale input, Zernike layers, and distance transform regression sounds great, but a comparison to architectures or conventional losses used to train any combination is missing. Five innovations/choices combined simultaneously without an attempt to attribute.

      (3) There is no clear delineation of 2D and 3D approaches. E.g. CellPose-SAM is not 3D, but sequential 2D execution with optional averaging of doing this along all axes.

      (4) For the 3-fold cross-validated evaluation: It sounds like they were all trained for each benchmark independently? Did Stardist-3D and CellPose SAM start from their existing 'general' weights or from scratch (not possible with SAM, I know). Not described at all.

      (5) Experiments to evaluate generalization performance are missing (train on all datasets, compare across datasets, ...).

      (6) The Zernike layer optimizes only coefficients {3,4,12}; the evaluation could be described as rudimentary. Table 3 is missing cross-validation. Since mouse_skull is (a lot) different and differences for the others are minor, I would be curious how significant these results are. The following text is unclear: "This reflects how we developed these modifications in turn." How so?

      (7) Training and inference: input and output block size missing, GPU memory requirements undefined, discuss block size vs. batch size. 50% overlap block-inference with feathering not compared to other overlap ratios, just "works best". Test-time augmentation 8x rotation (what are those rotations? Why 8? How much does it contribute? Compare the cost for 8xs test time augmentation against skipping "background" heuristic.)

      Minor

      (1) What is the incoming contrast range and bit depth?

      (2) Stardist-3D's weakness is not that it requires convexity (it requires star-convexity) but that it picks one hypothesis leading to low-resolution outlines. This has been overcome by Starfinity (https://linkinghub.elsevier.com/retrieve/pii/S0092867421013398; https://github.com/multiFISH/EASI-FISH).

      (3) Heatmap regression is not the same as distance regression; the manuscript does not explain what it is and uses terms interchangeably. This should be clarified. The reference for heatmap is 14; for distances, there are others, e.g. doi:10.1007/978-3-030-00934-2_36.

      (4) Only one evaluation example compares against CellPose-SAM zero-shot; I would add this to all of them. The mentioned place switch would be interesting to see in a plot instead of text.

      (5) "We replicated this failure on multiple machines." How does the machine matter here? Did the authors do cross-validation across different computers, instead of different random seeds? This sounds expensive!

      (6) Brightness-based sampling could be achieved by augmentation?

      (7) In the text "We use the following bicubic upsampling factors[...]", I think the authors mean tricubic, or cubic (because they upscale only one axis). Bicubic makes no sense, but people say this in the literature; we can do better. Why integer upscaling factors instead of exact ones? Are the authors concerned about that one incomplete border pixel?

      (8) "Furthermore, it is not confined to Linux-based environments such as Segmamba [32], an important consideration given that biologists often work with Windows machines."<br /> This is confusing. Is this a comment about somebody's publicly available repository? This is not relevant to evaluate the method and should be discussed separately. Does it work in WSL?

      (9) Downsampling by factors >2 with bicubic interpolation sounds wrong; should the authors use averaging?

    4. Author response:

      We thank the reviewers for their thoughtful comments. We will address the substantive concerns by broadening our evaluation to additional 3D datasets, strengthening comparisons against competitive baselines, and systematically ablating Penumbria's architectural components. We are confident that these experiments will establish Penumbria as a state-of-the-art 3D instance-segmentation method across a substantially broader range of segmentation challenges in biological imaging, without the pronounced dataset-specific failure modes observed for several competing approaches.

      Scope and dataset difficulty.

      We will expand our benchmark to include additional 3D datasets spanning nuclei, whole cells, and organelles. However, we do not accept two assertions made in the review: first, that nuclear segmentation is inherently easier than whole-cell segmentation; and second, that our existing datasets contain “mostly convex-ish and well-separated” objects (which we address separately below). Parhyale illustrates why the first assertion is unwarranted: despite being a nuclear-label dataset, its nuclei are extremely densely packed, with boundaries frequently so poorly distinguishable that even manual identification of individual instances is challenging. Historically, StarDist-3D has been the clear state-of-the-art method on this dataset, with little effective competition, whereas Penumbria matches its performance without imposing a star-convex shape prior. Conversely, the PlantSeg study reported an Adapted Rand error of only 0.075 on 3D cell-wall-labelled leaf tissue and approximately 90–99% correctly segmented whole cells across six leaf-surface specimens [1].

      Segmentation difficulty is an empirical property, not a consequence of what is being segmented or how it is stained.

      Diversity in biological targets or staining modalities does not necessarily translate into diversity of segmentation challenges, let alone greater difficulty. Constructing a meaningful benchmark therefore requires expert judgment in selecting datasets spanning challenging conditions, including irregular morphology, crowding, poor contrast, anisotropy, and imaging artifacts, informed by previous benchmarking results. The same empirical standard must apply to the reviewers' characterization of our datasets as “mostly convex-ish and well-separated.” Neither characterization is supported by quantitative evidence or relevant citations, and even if both were established, they would not by themselves demonstrate that the datasets are easy to segment. We will therefore quantitatively characterize the datasets, including their morphology and crowding, and examine how these properties relate to segmentation performance across methods.

      Zero-shot generalization.

      We will evaluate Cellpose-SAM zero-shot across all datasets, using segmentation accuracy (SA) at IoU 0.5 below 0.30 as a deliberately permissive indicator of severe instance-segmentation failure. Even at this threshold, substantial merging or missed instances can make the output unsuitable for reliable instance-level quantitative analysis without extensive correction.

      Cell pose-SAM comparison and baselines.

      We appreciate that the reviewers have since acknowledged the baseline training configurations reported in our Supplementary Material, which specified that Cellpose-SAM was fine-tuned from pretrained weights, while StarDist-3D was trained from scratch. In retrospect, these details would have benefited from greater visibility in the main text, and we will move them there in the revision. We also identified an important omission in our Cellpose-SAM inference configuration. The Cellpose-SAM paper specifies 1,000 dynamics iterations, flow3D_smooth=2, and removal of masks below 1,000 pixels for its 3D experiments. These settings differ substantially from generic API defaults and materially improve performance on several of our datasets. We will therefore repeat the evaluation using the published 3D settings and transparently report both the original and corrected configurations, with the latter serving as the principal comparison where applicable.

      u-Segment3D.

      The reviewers recommend u-Segment3D because it “reports large improvements,” without specifying over which methods. Its comparisons with Cellpose involve earlier cyto models, not Cellpose-SAM, which was introduced nearly a year after u-Segment3D's original preprint. Moreover, u-Segment3D is a 2D-to-3D aggregation framework rather than an independent segmentation model. Its supplementary material reports dataset-specific gradient smoothing and a 200-voxel size filter, without documenting equivalent settings for its Cellpose 3D baseline. As our own experiments demonstrate, such settings can materially affect 3D accuracy. The reported improvements therefore do not establish competitiveness with a properly configured and fine-tuned Cellpose-SAM. We will instead prioritize StarDist-3D, micro-SAM, and a corrected Cellpose-SAM comparison, reporting all inference configurations transparently.

      Architectural testing.

      While distance-transform regression is an established technique, as acknowledged in our manuscript, Penumbria's principal methodological contributions lie in its architecture and the integration of its components. We will expand our ablations by removing key architectural elements individually and measuring the resulting changes in performance. These experiments will quantify the contribution of each element within the complete pipeline. We look forward to submitting the revised manuscript incorporating these improvements.

      (1) Wolny, A., Cerrone, L., Vijayan, A., Tofanelli, R., Barro, A. V., Louveaux, M., et al. (2020). Accurate and versatile 3D segmentation of plant tissues at cellular resolution. eLife, 9, e57613.

    1. eLife Assessment

      This important study uses coarse-grained molecular simulations to investigate how phosphorylation and small-molecule ligands reshape the interactions underlying condensation and multidomain assembly of HDAC6. The findings provide mechanistic insights into how modifications of intrinsically disordered regions and ligand binding may regulate protein condensation. The evidence is solid: although the simulations broadly agree with previous experimental observations, concerns about model robustness and sampling in full-length assemblies limit support for the proposed mechanism and its pharmacological implications.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript authors have used Martini 3-based CG simulations to study how S22 phosphorylation and two catalytic-pocket inhibitors, Nexturastat A and HPOB, regulate HDAC6 condensation.

      In isolated IDR1 slabs, phosphorylation lowers the apparent saturation concentration without compacting single chains, and the manuscript attributes this to new contacts between the phospho-S22/D26 region and Arg/Lys-rich patches. The two ligands perturb these contacts differently. In full-length multichain systems, the C-terminal FD3 domain is the most frequent contact partner, and the ligands shift contacts from FD3-FD3 toward pocket-FD3. The manuscript proposes a two-layer mechanism by which pocket-binding ligands regulate multidomain condensates.

      Strengths:

      The question is well motivated by published experiments, the simulated IDR1 construct matches the experimental one, the phosphorylation contact pattern recurs across independent salt conditions, and the Discussion offers testable predictions. My main reservations are whether the full-length results are robust to the model and to sampling, and whether the evidence reaches the mechanistic and pharmacological conclusions drawn from it.

      Weaknesses:

      (1) The full-length conclusions rest on FD3-FD3 being the dominant domain contact. The water-interaction correction applies only to the disordered regions, so the folded domains retain standard Martini 3 behavior, which is known to overestimate protein association (e.g., Thomasen et al., J. Chem. Theory Comput. 2022). The contact maps are also normalized per bead, which favors a small domain such as FD3. Please test whether FD3-FD3 dominance survives a folded-domain correction, and show raw contact counts next to the normalized maps. Without this test, and without experimental evidence that this domain self-associates, frame the FD3 result as a model prediction.

      (2) The ligand-treated IDR1 slabs are single trajectories with no statistical uncertainty on the saturation concentrations or on the contact-difference maps. The full-length systems have three replicates; contact numbers are still rising at the end of several trajectories (Figure S9), and most differences between Nexturastat A and HPOB overlap within error. The wild-type controls also reverse the ligand ordering (Figs. S12, S14), which suggests that trajectory-to-trajectory variation may drive the domain-level differences. Please add independent replicas for the ligand comparisons, show convergence over consecutive time blocks, and report uncertainties on the difference maps. Claims should then be limited to the differences that are resolved.

      (3) The Discussion presents the simulations as explaining why Nexturastat A suppresses HDAC6 condensation more strongly than HPOB. However, every full-length system assembles at a high protein concentration, and total contacts and morphologies are similar across conditions, so the simulations do not show suppression. The IDR-level and domain-level mechanisms are studied in separate systems, and ligand distribution and IDR1 contacts are not analyzed in the full-length assemblies, so the coupling of the two layers named in the title is not tested. Please add an analysis that bears on condensation propensity and on this coupling, or recast "explains" as "is consistent with" and revise the title and abstract accordingly.

      (4) For phosphorylation, the IDR1 fragment is strongly basic, so a simple reduction of net charge could lower the saturation concentration without a position-specific contact node. The phosphoserine charge state used is also lower than expected at neutral pH. Moving the same charge to a distal serine, and testing the fully charged phosphoserine, would distinguish these readings. For the ligands, the coarse-grained models show only weak pocket engagement, lack a non-pocket control, have unvalidated nonbonded parameters, and are simulated at millimolar concentrations. The ligand-IDR1 mechanism could therefore reflect nonspecific association. Please add a non-pocket surface control, validate the ligand parameters, and relate the simulated concentrations to those of the motivating experiments.

    3. Reviewer #2 (Public review):

      Summary:

      The authors investigated the phosphorylation- and ligand-dependent regulation of HDAC6 condensates using explicit-solvent coarse-grained simulations. They found these regulations can be attributed to the reorganization of the interaction networks between multiple domains within the condensate.

      Strengths:

      Large-scale condensate simulations with multiple domains using explicit-solvent models are nontrivial, impressive, and provide a useful example of what can currently be studied using such simulation models.

      Comparison between simulations with/without ligand and with/without phosphorylation at different salt concentrations provides a comprehensive framework for understanding conditional residue- and domain-level condensate organization.

      The simulation results are mostly consistent with previous experimental observations and offer potential mechanistic understandings that extend those findings.

      The proposed FD3-centered interaction network for phosphorylation- and ligand-dependent regulations is a novel hypothesis.

      The study generates a few experimentally testable predictions regarding the IDR- and FD3-mediated interactions.

      Weaknesses:

      When going through the manuscript, especially the result section, I sometimes found it challenging to distinguish what has already been reported experimentally (mostly from Lu et al., Nature Cancer) from what is newly learned from the simulations. I do appreciate that the authors mentioned which results reproduce experimental trends; however, this is often after all simulations. It may help if each subsection first briefly states the known experimental observation and then explains the specific question addressed by or new mechanisms learned by the simulation. For instance, experiments have shown that phosphorylation enhanced and ligand suppressed phase separation.

      The authors directly used Automartini to generate the ligand parameters and then checked the contact fraction between the ligand and the pocket. Such analysis only tells whether the ligand visits the pocket but does not test either specificity or binding affinity, and therefore does not support the claim of ligand pocket recognition. The simulation concentration is also in the millimolar regime, probably much higher than the experimental range, so it's not surprising to see ~10% to 20% contact fraction. Considering that no native interactions are used to restrain the ligand-pocket interactions, I suspect the ligand might also have similar contacts to the other regions of HDAC. The authors might want to be careful about their discussions.

      The claim regarding the IDR dimension might be problematic. It's still an open question whether a coarse-grained model, or even current all-atom force fields, can reliably resolve the effect of a single phosphorylation on IDR dimensions. I agree that all current results, including the error estimate, support that Rg do not change that much, but the authors might still want to be careful about such claim considering such small perturbation in the sequence.

      The proposed electrostatic rewiring is based on the difference between the contact maps of WT and phosphorylation variant. It looks like the difference is relatively small to the contact probabilities. There is also no error estimation or pattern validation from multiple simulations. The authors might want to quantify the uncertainties of such patch-level relative changes by either checking some independent simulations or time-block variations.

      A similar comment applies to the claims on the charged-contact reorganization mechanism of two small inhibitors on LLLPS. Error estimates should be provided for these contacts when comparing different variant. In cases when errors are large (e.g. contact number with errors reported in Fig. 4K to N), statistical analysis (p-values) is needed to show the significance of the results.

      Regarding the proposed "phospho-S22-centered" contact network, I had some difficulty recognizing the specific regions discussed in Figures 2 and 3. It might be helpful to mark these regions directly on the contact maps. The authors may also wish to consider whether "S22-centered" is the most accurate description, or whether the observed changes are better described as involving a broader segment spanning several residues around S22. And since phosphorylation is introduced at S22, observing changes near S22 is not unexpected and may not be sufficient to establish phospho-S22-centered network.

      Within the last result section, the authors argued that FD3 plays an important role in both phosphorylation- and ligand-regulated HDAC assembly. However, only contact-based analysis instead of saturation concentration was presented. I understand the substantial challenge of obtaining converged saturation concentration for such system with multiple domains, if so, the authors need to discuss such caveats.

      Though direct experimental evidence of such FD3-centered interaction network does not exist, the experimental paper did provide measurements for a couple domain-deletion constructs, including the deletion of the FD3 (BUZ) region. This might be worth discussing, considering the manuscript focuses on the role of FD3 region. In particular, deletion of the BUZ region did not appear to affect LLPS. This does not necessarily exclude a role for FD3 in phosphorylation- or ligand-dependent regulation. However, it may complicate the description of FD3 as the center of the condensate network. The authors might want to discuss how their model can be reconciled with these domain-deletion results and consider their corresponding conclusion.

      Finally, the authors describe their mechanism as coupled networks. They might want to clarify the coupling they meant. If this refers to the coupling between different regions (e.g. S22 and FD3) or domains, they might want to consider more quantitive analysis, for instance by conditional/correlated contacts between domains. Changes in contact patterns at different conditions do not necessarily show the coupling between domains.

    4. Reviewer #3 (Public review):

      Summary:

      In this work, the authors probe the combined role of phosphorylation of an intrinsically disordered region (IDR) and domain pocket interactions with small molecules on the assembly of a multidomain protein HDAC6 using molecular simulations. Their find that IDR phosphorylation and ligand interactions with domain pockets can together reshaped in the interaction networks; thereby, expanding mechanistic understanding of multidomain protein assembly in the context of phase separation beyond an IDR centric grammar. The work is supported by extensive simulations featuring several controls and analysis of the underlying interaction networks.

      Strengths:

      (1) They find an important role of phosphorylation and ligand binding in shaping multidomain protein assembly.

      (2) They demonstrate that even if IDR:IDR interactions are important in isolated IDR systems, folded domain-folded domain interactions can dominate in the full-length protein. Furthermore, ligands can significantly perturb the interaction landscape, e.g., by redirecting folded domain-folded domain interactions to domain pocket ones.

      (3) They also show that different ligands can yield differing impact on the interaction networks by both competing with and compensating interactions to varying degrees. This is demonstrated for two ligands (Nexturastat A and HPOB).

      (4) They probe both the IDRs in isolation (including both the phosphorylated and unphosphorylated versions) and condensates formed via these IDRs. They also characterize assemblies of the full length proteins (with and without phosphorylation) in presence and absence of the ligands. The combined simulations, in general, support the key conclusions of the paper.

      Weaknesses:

      (1) There are areas where statistical tests can improve the quantitative presentation of the work. Specifically, the difference in the IDR ensembles for the isolated chains should be assessed for statistical significance.

      (2) There appears to be considerable finite-size effects in some of the slab simulations that may affect the conclusions. In particular, the chains along the long axis appear to be interacting across the periodic boundary. This can happen if the slab long axis is not long enough/if the ratio of dilute phase to dense phase is not large enough.

      (3) Some decisions in the simulation conditions require further justification/explanation. More specifically, the concentrations of small molecules used, as well as the decision to conduct simulations at 295 K, should be adequately justified.

      (4) Given the size of the full-length protein, it is understandable that the full dense phase is not probed for the full-length protein. However, it would be useful to comment on how the conclusions may change in the context of the full length. For instance, there is no real interface that the small molecules need to diffuse across and so discussions on the role of partitioning in the paper should be better contextualized in light of this.

    5. Author response:

      Reviewer #1 (Public review):

      (1) The full-length conclusions rest on FD3-FD3 being the dominant domain contact. The water-interaction correction applies only to the disordered regions, so the folded domains retain standard Martini 3 behavior, which is known to overestimate protein association (e.g., Thomasen et al., J. Chem. Theory Comput. 2022). The contact maps are also normalized per bead, which favors a small domain such as FD3. Please test whether FD3-FD3 dominance survives a folded-domain correction, and show raw contact counts next to the normalized maps. Without this test, and without experimental evidence that this domain self-associates, frame the FD3 result as a model prediction.

      We thank the reviewer for highlighting this important concern. We agree that the apparent prominence of FD3–FD3 interactions in the full-length simulations requires careful examination, particularly because the folded domains retain the standard Martini 3 protein–protein interaction strength whereas the water-interaction correction used in this study was applied to the disordered regions. We also agree that per-bead normalization may influence the apparent relative contributions of domains of different sizes.

      In the revised manuscript, we will examine the robustness of the FD3-centered interaction pattern to the treatment of folded-domain interactions. We will also report raw contact counts alongside the normalized contact maps and explicitly evaluate how domain size and normalization affect the relative contact frequencies.

      More generally, we agree that the simulations alone do not establish FD3 self-association as an experimentally validated property of HDAC6 condensates. We will therefore distinguish more clearly between simulation-derived predictions and experimentally established conclusions and will frame the proposed FD3-centered interaction network as a model prediction where appropriate.

      (2) The ligand-treated IDR1 slabs are single trajectories with no statistical uncertainty on the saturation concentrations or on the contact-difference maps. The full-length systems have three replicates; contact numbers are still rising at the end of several trajectories (Figure S9), and most differences between Nexturastat A and HPOB overlap within error. The wild-type controls also reverse the ligand ordering (Figs. S12, S14), which suggests that trajectory-to-trajectory variation may drive the domain-level differences. Please add independent replicas for the ligand comparisons, show convergence over consecutive time blocks, and report uncertainties on the difference maps. Claims should then be limited to the differences that are resolved.

      We agree that additional statistical characterization is required for the ligand-dependent comparisons. In particular, the current ligand-treated IDR1 slab simulations do not provide sufficient information about trajectory-to-trajectory variability, and differences between Nexturastat A and HPOB in the full-length simulations should not be interpreted without considering the uncertainty across independent trajectories.

      In the revised manuscript, we will rigorously assess the convergence and statistical robustness of the current simulations, including time-block analyses of the saturation concentrations and contact patterns. We will additionally perform independent replicate slab simulations to evaluate the robustness of the estimated saturation concentrations and interaction patterns. We will also increase the independent sampling of the full-length multichain systems to better characterize trajectory-to-trajectory variability and the robustness of the domain-level interaction patterns. Together, these analyses will allow us to provide uncertainty estimates and determine which ligand-dependent differences are reproducibly resolved.

      We will also reassess the differences attributed to Nexturastat A and HPOB in light of these uncertainties. Where the differences are not statistically resolved, we will refrain from assigning ligand-specific mechanistic interpretations and will revise the corresponding conclusions accordingly.

      (3) The Discussion presents the simulations as explaining why Nexturastat A suppresses HDAC6 condensation more strongly than HPOB. However, every full-length system assembles at a high protein concentration, and total contacts and morphologies are similar across conditions, so the simulations do not show suppression. The IDR-level and domain-level mechanisms are studied in separate systems, and ligand distribution and IDR1 contacts are not analyzed in the full-length assemblies, so the coupling of the two layers named in the title is not tested. Please add an analysis that bears on condensation propensity and on this coupling, or recast "explains" as "is consistent with" and revise the title and abstract accordingly.

      We agree that the current full-length simulations do not directly demonstrate suppression of condensation by either ligand. All full-length systems were simulated under conditions in which assemblies form, and similarities in the overall contact numbers and morphologies do not provide a direct measure of differences in saturation concentration or phase-separation propensity.

      We will therefore revise the title, Abstract, Results, and Discussion where necessary to avoid implying that the full-length simulations directly explain the experimentally observed suppression of HDAC6 condensation. Instead, we will describe the simulated interaction-network changes as being consistent with, or as providing a possible molecular basis for, the experimentally observed effects.

      We also agree that the coupling between the IDR-level and domain-level regulatory mechanisms needs to be more clearly established. We will extend the analysis of the full-length simulations to examine ligand distributions and interactions involving IDR1 and the folded domains and will assess whether conditional or correlated contact patterns provide quantitative evidence linking these interaction networks. If such analyses do not provide sufficient evidence for direct coupling, we will revise the terminology accordingly and describe the results as condition-dependent reorganization of multiple interaction networks rather than as a demonstrated coupled mechanism.

      (4) For phosphorylation, the IDR1 fragment is strongly basic, so a simple reduction of net charge could lower the saturation concentration without a position-specific contact node. The phosphoserine charge state used is also lower than expected at neutral pH. Moving the same charge to a distal serine, and testing the fully charged phosphoserine, would distinguish these readings. For the ligands, the coarse-grained models show only weak pocket engagement, lack a non-pocket control, have unvalidated nonbonded parameters, and are simulated at millimolar concentrations. The ligand-IDR1 mechanism could therefore reflect nonspecific association. Please add a non-pocket surface control, validate the ligand parameters, and relate the simulated concentrations to those of the motivating experiments.

      We thank the reviewer for raising these important points regarding the interpretation of both the phosphorylation- and ligand-dependent effects.

      For the phosphorylation-dependent mechanism, we would like to clarify that our simulations were motivated by prior experimental observations showing that phosphorylation at different serine residues of HDAC6 does not produce equivalent effects on phase separation. In particular, phosphorylation at S22 was found to have a pronounced effect on HDAC6 phase separation, whereas phosphorylation at the other tested serine sites did not show a comparable effect. These experimental observations argue against a simple model in which the effect of phosphorylation is determined solely by an increase in the overall negative charge of the protein, and instead indicate that S22 represents a site-specific regulatory position.

      Our simulations were therefore designed to explore the molecular basis of this experimentally established site specificity, rather than to establish S22 specificity from the simulations alone. In this context, the enhanced contacts involving the phosphorylated S22-containing region and positively charged segments of IDR1 provide a possible molecular explanation for why phosphorylation at this particular site can have a distinct effect on phase separation. We will clarify this experimental motivation and the scope of our interpretation in the revised manuscript, and will avoid implying that the simulations alone demonstrate the site specificity of S22 phosphorylation.

      We nevertheless agree that the representation of the phosphoserine charge is an important modeling consideration. We will examine the sensitivity of the phosphorylation-dependent results to the phosphoserine charge state and clarify the corresponding limitations of the coarse-grained representation in the revised manuscript.

      We also agree that the ligand simulations require a clearer assessment of specificity. In the revised manuscript, we will compare ligand contacts with the catalytic pocket to contacts with non-pocket regions of HDAC6 and further evaluate the extent of nonspecific protein association. We will also validate the physical reasonableness of the coarse-grained ligand parameterization, with particular attention to the nonbonded parameters relevant to ligand-protein interactions, and report the validation in the revised manuscript. We will explicitly state the limitations of using these models to infer binding specificity or affinity

      Finally, we will clarify the relationship between the ligand concentrations used in the simulations and those used in the motivating experiments. The high ligand concentrations used in the simulations were chosen to obtain sufficient sampling of ligand-protein encounters on accessible simulation timescales, and we will make clear that these simulations should not be interpreted as reproducing experimental binding equilibria at the corresponding concentrations.

      Reviewer #2 (Public review):

      When going through the manuscript, especially the result section, I sometimes found it challenging to distinguish what has already been reported experimentally (mostly from Lu et al., Nature Cancer) from what is newly learned from the simulations. I do appreciate that the authors mentioned which results reproduce experimental trends; however, this is often after all simulations. It may help if each subsection first briefly states the known experimental observation and then explains the specific question addressed by or new mechanisms learned by the simulation. For instance, experiments have shown that phosphorylation enhanced and ligand suppressed phase separation.

      We agree that the manuscript should more clearly separate the experimental observations reported previously from the questions addressed by the present simulations.

      In the revised manuscript, we will reorganize the relevant Results subsections so that each section first briefly states the pertinent experimental observation and then defines the specific mechanistic question addressed by the simulations. We will also distinguish more explicitly between trends reproduced by the simulations and hypotheses that emerge uniquely from the simulation analysis. This restructuring should make it clearer which conclusions are supported directly by prior experiments and which represent new mechanistic predictions of the present work.

      The authors directly used Automartini to generate the ligand parameters and then checked the contact fraction between the ligand and the pocket. Such analysis only tells whether the ligand visits the pocket but does not test either specificity or binding affinity, and therefore does not support the claim of ligand pocket recognition. The simulation concentration is also in the millimolar regime, probably much higher than the experimental range, so it's not surprising to see ~10% to 20% contact fraction. Considering that no native interactions are used to restrain the ligand-pocket interactions, I suspect the ligand might also have similar contacts to the other regions of HDAC. The authors might want to be careful about their discussions.

      We agree that observing contacts between the ligands and the catalytic pocket does not by itself establish specific recognition or binding affinity. The current coarse-grained simulations were designed primarily to examine how the presence of the inhibitors perturbs the protein interaction network rather than to calculate ligand-binding affinities.

      In response to this concern, we will analyze ligand interactions with non-pocket regions and compare these with the catalytic-pocket contacts, allowing us to better quantify the degree of preferential localization. We will also validate the physical reasonableness of the coarse-grained ligand parameterization, including the nonbonded parameters relevant to ligand-protein interactions, and report the validation and its limitations more explicitly in the revised manuscript.

      Throughout the manuscript, we will revise language implying that the coarse-grained simulations demonstrate specific ligand recognition or reproduce experimental binding affinities. We will also clarify the significance of the relatively high ligand concentrations used in the simulations and relate these conditions to the experimental concentrations.

      The claim regarding the IDR dimension might be problematic. It's still an open question whether a coarse-grained model, or even current all-atom force fields, can reliably resolve the effect of a single phosphorylation on IDR dimensions. I agree that all current results, including the error estimate, support that Rg do not change that much, but the authors might still want to be careful about such claim considering such small perturbation in the sequence.

      We appreciate this point and agree that the absence of a detectable change in radius of gyration in the present simulations should not be interpreted as evidence that a single phosphorylation cannot alter IDR dimensions more generally.

      We will provide appropriate uncertainty estimates and statistical comparisons for the single-chain conformational ensembles and revise the wording to state more narrowly that, within the resolution of the present Martini 3 simulations, we do not detect a substantial phosphorylation-dependent change in IDR1 dimensions. We will also acknowledge the limitations of current coarse-grained models in resolving subtle conformational effects associated with a single post-translational modification.

      The proposed electrostatic rewiring is based on the difference between the contact maps of WT and phosphorylation variant. It looks like the difference is relatively small to the contact probabilities. There is also no error estimation or pattern validation from multiple simulations. The authors might want to quantify the uncertainties of such patch-level relative changes by either checking some independent simulations or time-block variations.

      We agree that the relative changes visible in the phosphorylation-dependent contact maps need quantitative uncertainty estimates.

      In the revised manuscript, we will quantify the reproducibility of these contact changes using time-block analyses together with additional independent slab simulations, and provide uncertainty estimates for the difference maps. We will use these analyses to identify which phosphorylation-dependent contact changes are robust and will restrict the mechanistic interpretation to features that are reproducibly resolved.

      A similar comment applies to the claims on the charged-contact reorganization mechanism of two small inhibitors on LLLPS. Error estimates should be provided for these contacts when comparing different variant. In cases when errors are large (e.g. contact number with errors reported in Fig. 4K to N), statistical analysis (p-values) is needed to show the significance of the results.

      We agree that the same consideration applies to the comparison of the ligand-dependent interaction networks.

      We will report uncertainty estimates for the relevant contact observables and strengthen the statistical assessment using additional independent slab simulations together with expanded independent sampling of the full-length multichain systems. Statistical comparisons will be based on independent estimates, and where the variability is comparable to or greater than the apparent differences between conditions, we will explicitly report this and avoid assigning mechanistic significance to those differences.

      Regarding the proposed "phospho-S22-centered" contact network, I had some difficulty recognizing the specific regions discussed in Figures 2 and 3. It might be helpful to mark these regions directly on the contact maps. The authors may also wish to consider whether "S22-centered" is the most accurate description, or whether the observed changes are better described as involving a broader segment spanning several residues around S22. And since phosphorylation is introduced at S22, observing changes near S22 is not unexpected and may not be sufficient to establish phospho-S22-centered network.

      We appreciate this suggestion. We agree that describing the observed interaction changes as strictly "S22-centered" may be overly specific, because phosphorylation is introduced at S22 and the altered contacts involve a broader sequence region surrounding this residue.

      We will mark the relevant sequence regions directly on the contact maps to make these patterns easier to identify. We will also reconsider the terminology throughout the manuscript and, where more appropriate, describe the effect as a phosphorylation-responsive interaction region or network surrounding S22 rather than implying that S22 alone constitutes a discrete interaction node.

      Within the last result section, the authors argued that FD3 plays an important role in both phosphorylation- and ligand-regulated HDAC assembly. However, only contact-based analysis instead of saturation concentration was presented. I understand the substantial challenge of obtaining converged saturation concentration for such system with multiple domains, if so, the authors need to discuss such caveats.

      We agree that the full-length simulations provide information about the organization of the assembled state but do not directly determine the saturation concentration of full-length HDAC6. Given the size and complexity of the full-length system, obtaining converged coexistence concentrations would require substantially larger simulations and longer sampling than those performed here.

      We will make this distinction explicit in the revised manuscript and clarify that the full-length contact analysis addresses changes in the internal interaction network of the assembled state rather than directly measuring changes in phase-separation propensity.

      Though direct experimental evidence of such FD3-centered interaction network does not exist, the experimental paper did provide measurements for a couple domain-deletion constructs, including the deletion of the FD3 (BUZ) region. This might be worth discussing, considering the manuscript focuses on the role of FD3 region. In particular, deletion of the BUZ region did not appear to affect LLPS. This does not necessarily exclude a role for FD3 in phosphorylation- or ligand-dependent regulation. However, it may complicate the description of FD3 as the center of the condensate network. The authors might want to discuss how their model can be reconciled with these domain-deletion results and consider their corresponding conclusion.

      We appreciate the reviewer drawing attention to the previously reported BUZ/FD3 deletion experiments. We agree that the observation that deletion of the BUZ region does not abolish LLPS indicates that FD3 is not required for baseline phase separation.

      In the revised manuscript, we will discuss these experiments explicitly and will avoid describing FD3 as an obligatory center of the HDAC6 condensation network. Instead, we will discuss the possibility suggested by the simulations that FD3 contributes to the organization or regulation of interactions within the full-length assembly without being necessary for assembly itself. We will also clarify that this interpretation remains a model-derived hypothesis requiring experimental validation.

      Finally, the authors describe their mechanism as coupled networks. They might want to clarify the coupling they meant. If this refers to the coupling between different regions (e.g. S22 and FD3) or domains, they might want to consider more quantitive analysis, for instance by conditional/correlated contacts between domains. Changes in contact patterns at different conditions do not necessarily show the coupling between domains.

      We agree that condition-dependent changes in two interaction networks are not, by themselves, sufficient to demonstrate coupling between them.

      We will clarify what we mean by coupling and will examine whether conditional or correlated contact analyses in the full-length simulations provide quantitative evidence that changes involving one region or domain are associated with changes elsewhere in the interaction network. If these analyses do not support such a relationship, we will revise the terminology and describe the results more conservatively as coordinated or condition-dependent reorganization of multiple interaction networks.

      Reviewer #3 (Public review):

      (1) There are areas where statistical tests can improve the quantitative presentation of the work. Specifically, the difference in the IDR ensembles for the isolated chains should be assessed for statistical significance.

      We agree that the comparisons of the isolated-chain ensembles should include quantitative estimates of uncertainty and statistical support.

      In the revised manuscript, we will quantify the variability of the relevant conformational observables across independent simulations and perform appropriate statistical comparisons between the unmodified and phosphorylated IDR1 ensembles. We will use these results to refine the statement regarding the effect of phosphorylation on the single-chain dimensions and avoid interpreting differences that are not resolved within the uncertainty of the simulations.

      (2) There appears to be considerable finite-size effects in some of the slab simulations that may affect the conclusions. In particular, the chains along the long axis appear to be interacting across the periodic boundary. This can happen if the slab long axis is not long enough/if the ratio of dilute phase to dense phase is not large enough.

      We appreciate the reviewer raising this important issue. Interactions between the condensed slab and its periodic images could bias estimates of the dilute-phase concentration and therefore need to be explicitly evaluated.

      We will examine the trajectories and density profiles for evidence of interactions across the periodic boundary and quantify the separation between the condensed phase and its periodic images. We will also evaluate the sensitivity of the relevant conclusions to the simulation box dimensions and, where necessary, perform additional checks using enlarged simulation dimensions for representative systems. We will report the outcome of these tests and discuss any remaining finite-size limitations when interpreting the saturation concentrations.

      (3) Some decisions in the simulation conditions require further justification/explanation. More specifically, the concentrations of small molecules used, as well as the decision to conduct simulations at 295 K, should be adequately justified.

      We agree that these simulation conditions require clearer justification.

      The relatively high ligand concentrations were used to increase the number of ligand–protein encounters that can be sampled within the accessible coarse-grained simulation timescales, rather than to quantitatively reproduce the experimental ligand concentrations. In the revised manuscript, we will state this rationale more explicitly, compare the simulated concentrations with the experimental conditions, and discuss the implications of the elevated ligand concentrations for interpreting ligand–protein contact frequencies and possible nonspecific interactions.

      The simulations were conducted at 295 K to match the temperature used in the corresponding experimental measurements that motivated this study. We will clarify this point in the revised manuscript and explicitly state that the simulation temperature was chosen to maintain consistency with the experimental conditions.

      (4) Given the size of the full-length protein, it is understandable that the full dense phase is not probed for the full-length protein. However, it would be useful to comment on how the conclusions may change in the context of the full length. For instance, there is no real interface that the small molecules need to diffuse across and so discussions on the role of partitioning in the paper should be better contextualized in light of this.

      We agree that the full-length simulations should be distinguished more clearly from the slab simulations of IDR1. The full-length systems probe the organization of an assembled high-concentration state and do not contain a sufficiently large coexisting dilute phase and interface from which equilibrium partitioning or a saturation concentration can be determined.

      We will revise the relevant discussion of ligand partitioning to reflect this limitation. Where the current wording implies equilibrium partitioning between dilute and dense phases in the full-length simulations, we will instead describe the measured quantity directly—for example, ligand localization or enrichment within the simulated protein assembly.

      More generally, we will clarify throughout the manuscript which conclusions concern the internal organization of the full-length assembled state and which conclusions concern phase-separation propensity inferred from the IDR1 slab simulations.

    1. eLife Assessment

      The work presented represents important new evidence linking the cascade of neural processes triggered by memory-based prediction errors. The study uses an impressive collection of approaches and methods to characterize and measure cognitive control, arousal, and memory changes as a function of memory-based violations. The analyses are technically sophisticated and rigorous and, taken together, provide solid evidence that there are multiple processes accompanying prediction errors, and that they differentially relate to successful encoding.

    2. Reviewer #1 (Public review):

      This manuscript describes a multi-modal study of associative learning and memory in humans that combines scalp EEG, pupillometry and behavioral analysis to explore the construct of mnemonic prediction errors (MPEs), in terms of their relationship to attention and cognitive control. Across two pooled studies, participants performed associative memory tasks in which they learned the relationship between a cue word (action verb) and subsequent picture (animate or inanimate) with a strong vs. weak (4 or 1 repetitions) encoding manipulation. At test, participants were encouraged to generate a prediction following the cue word to determine whether the subsequently presented picture was a match or mismatch. The timecourse of pupillary responses during match decisions were decomposed using temporal principal components analysis, which identified 6 distinct and overlapping processes. Some of the components (PC3/PC4) exhibited sensitivity to both the strength and mismatch conditions, as well as behavior (both RT and accuracy) and retrieval success on the subsequent trial. Furthermore, relationships were also observed between pupillary responses (specifically for PC4) and both frontal theta and posterior alpha power measures obtained from scalp EEG in Experiment 2, as well as for frontal theta and subsequent learning from mismatch stimuli (assessed using subsequent memory findings from a surprise recognition test). The authors suggest the findings indicate that MPEs elicit changes in attention, arousal and cognitive control which impact subsequent learning.

      Strengths:

      This manuscript has many strengths, including a clever study design, thoughtful integration of multiple neurocognitive measures, and a set of rigorous and technically sophisticated analyses, which reveal a large set of relationships among the measures and behavior. The findings demonstrating brain/physiology-behavior relationships are particularly important, in that they point to potential functional consequences of MPEs.\

    3. Reviewer #2 (Public review):

      Summary:

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

      Strengths:

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

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

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Note from the authors:

      During review of the proofs and re-confirmation of all models and outcomes, we discovered that in the current version of the manuscript, the identity of the lagged time series in the exploratory cross-correlation analysis was misreported. In the cross-correlation plot (Fig. 4d), the posterior alpha time series was fixed and the frontal theta time series was lagged by the times indicated on the x-axis, not vice versa. The inset figures depicting the timeseries will be corrected accordingly in the Version of Record. The subsequent mediation model, in which we tested whether attention mediates the relationship between mnemonic prediction strength and cognitive control, was included as a follow-up to the previously reported cross-correlation outcomes and will therefore be removed. As noted in the Version of Record, the corrected interpretation of the temporal ordering is that in response to a strong mnemonic prediction error, increases in cognitive control precede increases in attention.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript describes a multi-modal study of associative learning and memory in humans, that combines scalp EEG, pupillometry and behavioral analysis to explore the construct of mnemonic prediction errors (MPEs), in terms of their relationship to attention and cognitive control. Across two pooled studies, participants performed associative memory tasks in which they learned the relationship between a cue word (action verb) and subsequent picture (animate or inanimate) with a strong vs. weak (4 or 1 repetitions) encoding manipulation. At test, participants were encouraged to generate a prediction following the cue word to determine whether the subsequently presented picture was a match or mismatch.

      The timecourse of pupillary responses during match decisions were decomposed using temporal principal components analysis, which identified 6 distinct and overlapping processes. Some of the components (PC3/PC4) exhibited sensitivity to both the strength and mismatch conditions, as well as behavior (both RT and accuracy) and retrieval success on the subsequent trial. Furthermore, relationships were also observed between pupillary responses (specifically for PC4) and both frontal theta and posterior alpha power measures obtained from scalp EEG in Experiment 2, as well as for frontal theta and subsequent learning from mismatch stimuli (assessed using subsequent memory findings from a surprise recognition test). The authors suggest the findings indicate that MPEs elicit changes in attention, arousal and cognitive control which impact subsequent learning.

      Strengths:

      This manuscript has many strengths, including a clever study design, thoughtful integration of multiple neurocognitive measures, and a set of rigorous and technically sophisticated analyses, which reveal a large set of relationships among the measures and behavior. The findings demonstrating brain/physiology-behavior relationships are particularly important, in that they point to potential functional consequences of MPEs.

      Weaknesses:

      The technical proficiency and complexity of the study and analysis also presents a clear limitation and challenge for interpretation. It is likely that readers, even those that are quite knowledgeable about the methods, constructs, and questions being addressed will often struggle (as this reviewer did) to keep the large set of findings in mind and gain understanding of how they all fit together.

      Indeed, it seems like there many threads running together in the paper which make it challenging to find the through-line of the key findings. The authors do address some of the key questions motivating the paper in the Introduction, but the results are somewhat ambiguous with regard to the primary question of the study as to whether the detection of MPEs leads to interaction among cognitive control, attention, and arousal. To their credit, the authors tackle this question through both cross-correlation and formal mediation analyses, and summarize these in diagrammatic figures (Figure 3, Figure 6). Yet it is not resolved whether the results represent a clear answer pointing to independence, or rather a lack of statistical power, or ill-resolved formulation of the mediational relationship. In particular, the cross-correlation suggests that posterior alpha suppression in response to MPEs does precede frontal theta, yet this indirect relationship does not explain the variation in trial-by-trial RTs on mismatches. This suggests a potential model misspecification.

      In addition to the primary interaction issue mentioned above (between cognitive control, attention & arousal), the Introduction lays out a number of claims:

      (1) That pupil size will be more sensitive to strong than weak MPEs.

      (2) That MPE-linked increases in attention (indexed with posterior alpha suppression) and arousal (indexed with pupil size) will be linked to learning.

      (3) That MPE learning will vary as a function of prediction strength.

      Given the focus on learning, it is somewhat surprising that learning is not included in the mediation models. As the authors indicate in the Discussion, the use of trial-by-trial RT variation to drive the mediation model might be problematic, given that the RTs are sensitive to a range of factors beyond mnemonic prediction strength and also are under competing pressures (longer for mismatches than matches, due to surprise-linked slowing, but also faster following stronger rather than weaker mnemonic predictions). Thus, an alternative possibility might be to use trial-by-trial recognition of mismatches as the outcome variable in mediation models rather than trial-by-trial RT as the independent variable.

      A large component of the results (Sections 2 and 3) is devoted to analyses of cue-linked pupil and EEG processes that putatively reflect mnemonic predictions (i.e., occurring before picture probes are presented and match/mismatch detection, i.e., MPEs occur). Yet these Results and the subsequent pupillary PCA components (PC1 and PC5) that are elicited are not well-integrated with the primary themes of the paper or the causal hypotheses. One finding that does seem to figure prominently (in that it is mentioned in Abstract, Introduction & Discussion) relates to the amount of attention allocated to the mnemonic prediction generation. Yet this finding is not well emphasized in the Results themselves. Possibly it refers to the negative relationship between posterior alpha during memory retrieval and the magnitude of pupillary PC3 component, described in Section 3. But it was quite challenging to identify amongst the wealth of results described in this Section as well as the others. More generally, the large amount of findings described across all four lengthy Results sections makes it challenging for readers to discern what are the key ones that the authors would like to highlight.

      It is recommended that the authors do another pass through the paper to better highlight the most critical findings that they want to emphasize or which are most interpretable from a mechanistic and causal flow perspective and then de-emphasize or move other findings to the Supplemental Materials. Although the authors are to be commended for such a rigorous and comprehensive set of analyses, there are so many of them and findings, that the key points get buried and the reader needs to struggle potentially unnecessarily to identify the key take-away points.

      We thank Reviewer 1 for the helpful feedback on how the manuscript can be further strengthened. We recognize that the rich set of findings can overwhelm the reader, resulting in difficulty discerning the main take aways about the effects of mnemonic prediction errors. We particularly appreciate Reviewer 1’s encouragement to restructure the manuscript so as to focus on the findings reported in Sections 1 and 4 of the original revision; the current revision now focuses on these key observations.

      As part of this restructuring, Reviewer 1 also proposed moving the content from Sections 2 and 3 of the original revision to the Supplement. We agree with the Reviewer that the questions addressed in these sections on retrieval-related processes are not the main focus of the paper, but that they are informative in their own right. To avoid their getting lost in the Supplement, we decided that these results would be better served in a separate manuscript and thus we have removed them entirely. 

      We acknowledge in the revised manuscript that the mediation and cross-correlation analyses were exploratory and that these specific analyses may not be well powered in the current experiments. With respect to Reviewer 1’s concerns about the specification of the mediation models, we were motivated to test whether MPEs trigger an increase in cognitive control that in turn, triggers an increase in attention and/or arousal (Fig. 4a); as such, we designed the model to assess whether, on strong MPE trials, frontal theta mediates the relationship between prediction strength and attention/arousal. As noted in the manuscript and raised by Reviewer 1, mismatch RT here is an imperfect measure of trial-level prediction strength. Future experiments that selectively elicit strong MPEs and have a more controlled measure of trial-level prediction strength may be better equipped to address these questions about interactions between control, attention, and arousal. We agree with Reviewer 1 that models assessing subsequent memory as an outcome would be desirable. However, given that (a) we did not find strong evidence for interactions at the time of a strong MPE and (b) we only observed a relationship between frontal theta and subsequent memory (but not posterior alpha or pupil), subsequent memory mediation models do not appear to be well justified. Altogether, these findings illuminate open avenues for future research.

      Reviewer #2 (Public Review):

      Summary:

      The authors studied cognitive control and attention in response to mnemonic prediction errors (MPEs): situations in which the external reality violates internal memory-based predictions. The behavioral task first established strong versus weak predictions, and then either confirmed or violated these predictions. The authors examined markers of cognitive control (frontal theta) and attention (posterior alpha suppression, pupil response) while strong and weak predictions were confirmed or violated. They found increased cognitive control (frontal theta) for strong MPEs, which correlated with subsequent memory. Markers of attention (alpha suppression, pupil response) also accompanied strong MPEs but did not correlate with subsequent memory.

      Pupil response was investigated using an interesting approach that decomposes the response into different components, finding that different components respond earlier or later and show different correlations with MPEs and their strength. The authors also investigated how EEG, reaction time, and pupil responses correlated with one another, providing further insight into the mechanism underlying the response to MPEs. Together, the study points toward multiple control and attention mechanisms involved in MPE response and memory.

      Strengths:

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

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

      Weaknesses:

      The methods are rigorous, and the data support the claims. The weaknesses are minor and are offered here as avenues for future research.

      (4) The relationships the authors find between brain measures and pupil components were largely not specific to mismatches/matches. Thus, the specificity of this relationship is untested.

      (5) The results with subsequent memory are important and address a major gap in the field that largely did not relate neural effects of MPE to subsequent memory. However, one major limitation of the study is that the authors did not test memory for matches. I understand the logic of avoiding testing matches. Because matches were repeated more times in the study, it’s not a fair comparison and could change participants’ overall criterion for old/new decisions. Future research could address this, e.g., by testing weak matches or potentially using a between-subject design.

      We appreciate Reviewer 2’s helpful feedback during the review process and encouraging comments on the strengths of the manuscript. We agree and note in the revision that it would be illuminating for future studies to contrast memory for events that violate and confirm mnemonic predictions.

      Comments on revised version

      The authors addressed all my concerns. I appreciate the authors’ thoughtful and detailed response.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      It was challenging to read the paper with key findings happening at the time of the MPE presented first, and then to go “backwards in time” to examine process that occurred at preceding time periods (i.e., prior to probe presentation), and then again forward in time to examine learning related processes.

      (1) Restructuring the Results

      In this regard two distinct recommendations are made:

      - Move Sections 2 and 3 to Supplemental Materials, to maintain the focus on the key findings related to detection of MPEs and their effects on subsequent learning.

      - An alternative structure would be to present the cue-locked findings first, which relate to mnemonic predictions, then to those that occur when those predictions get violated, and finally to the learning process that occur following MPEs and which can be detected with subsequent recognition tests.

      We thank the Reviewer for this encouragement to restructure the manuscript. To address concerns regarding the density of the paper and the cohesiveness of the findings, we removed the content that was in Sections 2 and 3 of the original revision. We will publish those results in a separate paper with additional analyses to more directly address previously raised questions regarding the mechanisms indexed by frontal theta and posterior alpha at the time of retrieval.  

      (2) Integration of the summary figures

      At the minimum, a recommendation would be to better integrate Figure 6, and maybe various versions of Figure 3, earlier into the text, preferably even in the Introduction, and then repeatedly refer to them throughout the results. For example, in Figure 6, linking the leftmost panel of the figure to Sections 2 and 3 is critical, and the righthand panel to Sections 1 with the rightmost part related to learning explicitly linked to Section 4.

      We moved the summary figure up to now be Figure 1; we reference this figure in the Introduction and throughout the manuscript; and we additionally make reference in the figure to the association between frontal theta and PC3 at the time of a strong MPE as well as the cross-correlation outcome. We hope these modifications further aid the reader in identifying the main findings of the manuscript.

      (3) Specification of the mediation models

      Additionally, for the mediation models it is quite unclear why mismatch RT is treated as the index of MPE magnitude, as this seems to be where the problem may lie in model fitting. Why not think of this as an outcome variable (since would seem to be a causal outcome of the underlying functional processes elicited by mismatch detection)?

      We thank the reviewer for these thoughtful comments. Our goal in designing the mediation models in Fig. 4a-c was to test our hypothesis that strong MPEs trigger an increase in cognitive control that, in turn, triggers an increase in attention and/or arousal; thus, attention/arousal should be the outcome and cognitive control should be the mediator. Given that increases in attention and cognitive control were selectively observed for strong and not weak MPEs, the models were restricted to strong MPEs. We therefore needed a trial-level measure of MPE magnitude to determine whether stronger MPEs in the strong condition elicit greater attention/arousal by engaging more cognitive control. As such, we decided to use mismatch RT as a proxy measure of MPE magnitude; we acknowledge in the text that this measure is imperfect. While a model with mismatch RT as the outcome variable is possible, the relative timing of the attention/arousal effects (which largely occur following responses) would render interpretation to be more challenging. Had stronger evidence of indirect effects emerged in Figs. 4b-c, we could have conducted model comparison with RT as an outcome. We acknowledge in the text that these analyses were exploratory and characterization of potential indirect effects will require more data and a more precise measure of MPE magnitude.

      Alternatively, examining trial-by-trial recognition memory of mismatches as the relevant outcome variable would also seem to capture the functional process of interest. In this regard, have the authors examined whether trial-by-trial RT on mismatches predicts subsequent recognition of these items? If this direct relationship holds, it could be a target for mediation analyses in itself.

      We agree with the reviewer that in theory, a mediation model predicting subsequent memory would be a desirable test of an integrated model of the mechanisms underlying MPE-driven learning. However, the mediation analyses conducted to address the functional relationships at the time of a prediction error (Fig. 4) are not well-powered to begin with; this limitation is raised in the Results and the Discussion. A mediation model predicting subsequent memory would be similarly underpowered; given that there is not strong evidence for indirect effects at the time of a strong MPE, and that only frontal theta – and not posterior alpha nor pupil – predicts subsequent memory, we think that such a mediation model is not well justified to include. Such a model should be more directly tested by well-powered designs in future studies.

      (4) Hippocampal theta in the Introduction

      The Introduction discusses hippocampal theta as well as frontal theta yet also makes clear that the former is not really well-detected or analyzed using scalp EEG. Consequently, a recommendation would be to remove this paragraph from the Introduction, since it can be misleading and a “red herring” for the reader, and instead only bring up this point in the Discussion section, as a pointer to the need for future research using methods that may be more sensitive to hippocampal interactions with PFC regions.

      We appreciate this point and moved discussion of the hippocampus from the Introduction to the Discussion.

    1. eLife Assessment

      This study provides valuable new insights into the functional differentiation of human amygdala subnuclei during the observation of negative images and emotional reappraisal. The evidence is convincing, particularly regarding differences in the time course of evoked responses and their partial correspondence with broad anatomical subdivisions. The work will be of interest to researchers studying the amygdala, emotional processing, and emotion regulation.

    2. Reviewer #1 (Public review):

      Summary:

      The authors aimed to re-analyse two fMRI datasets of studies investigating the effect of cognitive emotional regulation on amygdala response during the viewing of negative images, in order to assess whether such effects might have been missed in previous reports of the results of these studies. In order to accommodate potential non-typical response profiles in the amygdala, they used a modelling approach that is agnostic about the shape of the BOLD response (finite impulse response modelling). Furthermore, the authors sought to differentiate between amygdala subregions using i) two previously published probabilistic atlases of amygdala subnuclei; and ii) a data-driven analysis focusing on temporal response profiles, which identified four clusters that mostly overlapped with anatomical parcellations. The analyses confirmed their previous results insofar as they did not find any impact of emotional regulation on the amygdala response in any subregion. However, their approach revealed differences in temporal response profiles between amygdala subregions: the centromedial amygdala showed sustained activation extending into the post-stimulus rating period, whereas laterobasal and superficial subregions peaked earlier during stimulus presentation. Overall, this study helps to clarify whether cognitive emotional regulation during negative image viewing alters amygdala response, and demonstrates the value of flexible response modelling to differentiate between functional subregions of an important brain structure.

      Strengths:

      The strengths of this study lie in the amount of data used (a combined N of 358 participants is rather high), the flexibility of the modelling approach (FIR), the exploratory nature of the data-driven clustering based on temporal response profiles, and clarity of the text and of the findings. One weakness would be the fact that no fMRI data acquired at higher field strengths were used for the aim of differentiating responses of amygdala subunits.

      In my opinion, the authors achieved their aims of re-analysing these datasets with complementary methods, and helped to discover new aspects of the BOLD response in the amygdala during emotional regulation. The results support their conclusions.

      This work is likely to stimulate discussion of the response of amygdala subnuclei during emotional regulation, and might encourage researchers to resort to more flexible analysis methods by demonstrating the information gain achieved with the FIR approach.

      Weaknesses:

      There are 7T studies of the amygdala response that have looked at differences in BOLD response across subnuclei. While I don't know of any that have investigated emotional regulation, I think these studies should nevertheless be mentioned and discussed in the Discussion, at least to compare the differences observed across subnuclei.

    3. Reviewer #2 (Public review):

      Summary:

      In this paper, Bo et al. study the spatiotemporal organization of BOLD responses in the amygdala. They apply FIR modelling to a large sample (two cohorts tested at the same site and scanner, totalling >350 individuals) who completed a negative/neutral picture viewing and cognitive reappraisal task. Averaging voxelwise BOLD responses across two distinct probabilistic atlases of the amygdala, the authors found that a corresponding centromedial region showed sustained activation to negative stimuli beyond the stimulus period. Laterobasal and superficial subregions peaked earlier, within the stimulus presentation period. In all cases, reappraisal did not change these profiles. Lateralization effects (left-side dominance) were found in the CM region at later time points, and to a lesser extent in the superficial nucleus area at earlier time points. Data-driven clustering of voxelwise time courses could partly recover findings from probabilistic atlases, but nonetheless showed partial decoupling of anatomical and functional boundaries in the amygdala.

      Strengths:

      This is a well-written paper with a clear objective addressed by sound methods. The authors set out to address two main questions: (1) "do amygdala subregions exhibit distinct temporal dynamics in response to negative emotional stimuli?" and (2) "does cognitive reappraisal differentially modulate these temporal profiles across subregions?" Regarding question 1, FIR modelling results indicate strong differentiation of the centromedial region of the amygdala from other subdivisions, with strong and late responses to presented negative stimuli. On question 2, cognitive reappraisal does not seem to play a major role here, although I raise a question on this in the next section. Overall, all presented conclusions are supported by study findings. I would add that lateralization effects are not mentioned in the main objectives/questions of the paper, but this analysis represents a large section of the results that may warrant mention in the introduction as well.

      Weaknesses:

      (1) An important concern when looking at mesiotemporal regions such as the amygdala is signal-to-noise. Yet, this challenge in analyzing data from the amygdala was not addressed here but represents a potential confound if SNR varies within the amygdala along its major subdivisions.

      (2) The authors analyze BOLD evoked response time courses, but I did not find a GLM analysis showing that the task reliably engaged the amygdala, either when analyzed with a canonical HRF or with the FIR model used here. We can see from Figure 2 that the task may not engage the entire amygdala (e.g. LB/LA subregions seem to show lower amplitude evoked-responses), yet the authors focus equally on time courses of subregions engaged and not engaged in the task. As such, differences observed between subregions may reflect different underlying processes (e.g. different processes of engagement in the task, or high/low engagement).

      (3) The study uses two different probabilistic atlases to parse BOLD response time courses in the amygdala. However, methodological details on the processing of the atlas data are missing from the text. For example, it is unclear if the subregions were thresholded or overlap in any way, which would affect the strength of detected effects if there is substantial overlap between areas.

      (4) The authors found no effect of reappraisal on BOLD response time courses. However, if I understand correctly, not all participants showed successful reappraisal according to their behavioural ratings (lines 125-126: "Eighty-one percent of participants showed a regulation effect in the same direction as the group-level effect"). Were these participants excluded from the reappraisal analysis on evoked response time courses? This is currently unclear, but holds significance in reliably answering question 2.

    1. eLife Assessment

      This study provides important novel insight into how Wnt/beta-catenin signaling may regulate the responsiveness of blood vessels in the brain and retina to vascular endothelial growth factor, with potential relevance to Wnt surrogate therapies aimed at restoring vascular barriers. Solid evidence that Wnt/beta-catenin signaling changes the protein abundance and relative balance of two vascular endothelial growth factor receptors in brain blood vessels is provided by informative genetic mosaic experiments; in the retina, however, regulation of VEGFR2 was difficult to establish because expression by neighboring cells obscured its detection in blood vessels. Physiological implications are discussed, but evidence remains incomplete because the observed changes in receptor abundance were not directly linked to altered signaling or vascular function in living animals. A second mechanism, in which Wnt/beta-catenin signaling suppresses signaling through VEGFR2 with little or no change in receptor abundance, was inferred from reporter experiments in cell-based systems.

    2. Reviewer #1 (Public review):

      Summary:

      Wang et al. report that changes in the levels of canonical Wnt signaling in CNS ECs modulate protein expression levels of VEGFR2 and VEGFR1. An increase in Wnt/beta-catenin signaling results in a decrease in VEGFR2 and an increase in the decoy receptor VEGFR1; conversely, reduction of Wnt signaling leads to the opposite changes in VEGF receptors in brain CVOs (less clear in the retina). Next to the solid quantitative immunostaining of VEGFR2 and VEGFR1 in multiple CNS regions using genetic mosaics and loss-of-function mouse mutants, the authors employ HEK293T cells to investigate the effect of activation of Wnt/beta-catenin signaling on multiple RTK signaling systems and on the level of overexpressed VEGFR2. The principal conclusions are that Wnt/beta-catenin regulates VEGF signaling. Two distinct levels of regulation are reported: one acts in vivo on receptor protein expression levels, the other acts in 293T cells, and the precise mechanism remains to be identified.

      Strengths:

      Strengths of the study include the use of genetic mosaics that allow for quantification of immuno-staining intensity with an internal reference and the significance of understanding the interplay of beta-catenin-dependent and VEGF pathways.

      There are also some weaknesses noted:

      (1) Experiments to restore the VEGFR2/VEGFR1 ratio in vivo, or to modulate VEGF signaling in vivo, and assess to what extent Wnt/beta-catenin LOF phenotypes can be restored by modulation of VEGF signaling have not been conducted at this point. In addition, it remains to be determined to what degree an experimental recreation of the aberrant VEGFR2/VEGFR1 ratio is sufficient to recreate (partially) some of the phenotypes observed with loss of Wnt/beta-catenin signaling in ECs.

      (2) It would be useful to consider and discuss other essential components of the VEGF signaling system, especially Neuropilins. Wang et al. (2012, Figure 2, PMID: 23217714) reported that CNS ECs with loss of Norrin signaling gain Sema3F binding while not displaying VEGF-B binding sites, suggesting changes in VEGF/semaphorin co-receptor biology.

      (3) Martowicz et al. (2019) PMID: 31533473  also investigated the influence of Wnt/beta-catenin signaling in CNS ECs on VEGFR2 and VEGFR3 expression and reported findings regarding VEGFR2 regulation that appear to differ from the present study. This should be discussed.

      (4) Norrin/beta-catenin and Wnt/beta-catenin signaling have a critical role in CNS angiogenesis, and Wnt surrogates counteract neovascularization in the oxygen-induced retinopathy model. These findings highlight context-dependent roles of Wnt/beta-catenin signaling in normal and pathological ECs. How the modulation of VEGFR2/VEGFR1 may influence developmental and pathological angiogenesis could be discussed.

      (5) When looking up the publicly available scRNA-seq datasets from Heng et al. (2019, PMID: 30988181) from Norrin KO mice, it appears that there is transcriptional regulation of VEGFR1/VEGFR2 consistent with the protein regulation reported here. If transcriptional regulation is important, CMV-driven VEGFR2 expression in Fig.5 supplement 2 may not model the regulatory mechanisms observed in vivo. Does CHIR99021 modulate VEGFR2/VEGFR1 transcripts and proteins in HRMVECs?

    3. Reviewer #2 (Public review):

      The manuscript by Wang, Nathans, and colleagues presents a compelling investigation into the regulatory crosstalk between Wnt/beta-catenin and VEGF signaling within central nervous system (CNS) endothelial cells. Overall, it is a very solid paper from a very respected research lab. The use of in vivo genetic mosaics to directly compare endothelial cells (ECs) with differing beta-catenin states within the same tissue is a particularly elegant approach to mapping receptor dynamics.

      Major Comments:

      (1) Distinguishing Receptor Abundance from Functional Signaling:

      The study relies heavily on immunohistochemistry to assess total VEGFR1 and VEGFR2 levels, inferring that changes in receptor abundance directly equate to altered VEGF signaling. Because immunofluorescence does not establish plasma membrane localization, receptor phosphorylation, or downstream pathway activation, the definitive conclusion that beta-catenin "acts at the receptor level to reduce VEGF signaling" is currently overstated.

      Recommendation: The authors should tone down the functional claims throughout the text (e.g., state that the receptor changes are "predicted to reduce VEGF signaling") or provide direct functional readouts of VEGFR2 activation in vivo, such as phospho-VEGFR2, phospho-PLCγ, ERK, or AKT.

      (2) Limitations of the In Vitro HEK293T Model:

      The HEK293T STF reporter system is utilized to argue for a "post-receptor physiological suppression" of VEGF signaling. While experimentally practical, this non-endothelial system does not definitively establish a physiological mechanism for CNS ECs. Furthermore, NFAT-luciferase is not uniquely specific to VEGFR2 and can be activated by broader calcium/calcineurin pathways. The authors also show that high Wnt1 expression reduces total VEGFR2 protein in these cells, but only at plasmid concentrations much higher than those required to suppress the VEGFR2/NFAT reporter.

      Recommendation: Explicitly decouple the in vitro observations from the physiological in vivo claims. Remove the term "physiological" unless you validate post-receptor suppression in a primary endothelial system under endogenous signaling conditions.

      (3) Regional Heterogeneity (Brain vs. Retina):

      The retinal vascular mosaic data reveal that while VEGFR1 is reduced in BRB- ECs, VEGFR2 shows no obvious difference. The authors correctly acknowledge that Müller glial VEGFR2 expression complicates endothelial quantification, rendering Figure 4C a negative result with uncertain sensitivity. Consequently, the broad generalization that an "increase in Wnt/beta-catenin signaling produces an increase in VEGFR1 levels and a decrease in VEGFR2 levels" across all CNS ECs is not entirely accurate.

      Recommendation: Embrace this biological nuance. Revise the generalized claims to reflect that beta-catenin regulates VEGFR1 consistently across the brain and retina, but VEGFR2 regulation appears context-dependent and is not clearly demonstrated in the retina. To resolve the Müller glia interference, consider orthogonal approaches to isolate the EC signal, such as EC-specific RNAscope or flow cytometry on FACS-sorted ECs.

      (4) Statistical Rigor and Experimental Units:

      The figure legends (e.g., Figure 1) explicitly state that data points represent the mean values from single confocal images. If multiple images were obtained from the same animal, treating them as independent units. Furthermore, the authors utilized unpaired statistics (Wilcoxon rank-sum) to compare BBB+ and BBB- ECs within the same mosaic tissue sections.

      Recommendation: Reanalyze the data using the animal as the primary biological replicate if such data are available (averaging all image measurements within each animal). For mosaic comparisons within the same tissue image/animal, utilize paired statistical tests (e.g., paired t-test or paired Wilcoxon signed-rank test). Figure legends should clearly report the exact N (animals) and n (images/cells), the specific statistical tests used, and confidence intervals.

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript by Wang et al. describes the identification of a novel mechanism by which the canonical Wnt/beta-catenin pathway could regulate endothelial permeability in the central nervous system (CNS). Specifically, they show that in circumventricular organs (CVOs), in which the blood-brain barrier (BBB) is not established in vessels and endothelial cells are highly permeable, the previously reported low levels of Wnt/beta-catenin coincide with high levels of the vascular endothelial growth factor receptor 2 (VEGFR2, Flk-1), whereas the decoy receptor VEGFR1 (flt-1) is expressed at low levels. In order to test the hypothesis in vivo that the Wnt/beta-catenin pathway controls VEGF signaling in CNS vessels by regulating VEGF receptors, the authors analyzed mosaic cerebellar vessels of mice deficient for the Wnt receptor frizzled 4 (Fzd4), showing that BBB+ (normal condition) vessels show low VEGFR2 and high VEGFR1, whereas BBB- (Fzd4ko) vessels show high VEGFR2 and low VEGFR1. Similarly, although not as conclusively due to strong background from Müller cells, these effects were also visible in the retina of these mice. Conversely, dominant genetic activation of beta-catenin signaling in endothelial cells in vivo resulted in upregulation of VEGFR1 and downregulation of VEGFR2 in vessels of the CVOs as well as of the choroid plexus.

      In order to better understand the direct relevance of Wnt/beta-catenin signaling for this regulatory theme, the authors activated the pathway in HEK293 cells, stably harboring the Super-Top-FLASH reporter construct in the presence of VEGFR2 and VEGF. Moreover, by transfecting Wnt1 into HEK293 cells, the authors analysed the effect of TGFbeta, EGF, and FGF signaling. Together, these experiments showed that Wnt/beta-catenin signaling potently suppressed VEGF signaling at low concentrations of Wnt1 transfection, whereas VEGFR2 suppression was only visible at relatively high concentrations. Also, the other pathways were suppressed, although to a lower extent.

      In conclusion, the authors suggest a mechanism of VEGF signaling suppression by Wnt/beta-catenin 1) at the post-receptor level of VEGF receptors and 2) at the transcriptional level of VEGFR1 and VEGFR2, independent of VEGF signaling suppression.

      Strengths:

      (1) The present manuscript provides interesting additional information on top of the published work on the mechanistic function of vascular permeability control by Wnt/beta-catenin, specifically by the highlighted interaction with the VEGF pathway and the opposed regulation of the two VEGF receptors VEGFR1 and VEGFR2.

      (2) They have used state-of-the-art animal models, high-resolution imaging, and appropriate in vitro analysis.

      (3) This finding has potentially strong implications for future therapeutic strategies, aiming at Wnt/beta-catenin activation in various diseases of the CNS in which vascular repair is desired.

      Weaknesses:

      A detailed analysis of the post-receptor and transcriptional mechanisms remains largely unexplored and needs to be explored in the future.

    1. eLife assessment

      This study provides a useful inventory of genes that are up- or down-regulated during the early metamorphic development of male and female larvae and proposes that the microRNA cluster miR-277/34 is involved in the development of sexual differences during early metamorphosis of Drosophila melanogaster, although its precise role remains unclear. The strength of evidence, based on a combination of diverse methods including mRNA and small RNA sequencing, in silico analyses, in vitro assays, and loss-of-function experiments, is incomplete as it lacks a general model and an examination of the potential effects of the miR-277/34 mutations on phenotypes such as morphology or developmental time. This work will be of interest to developmental biologists interested in sexual dimorphism and in the interplay between hormones and microRNAs during development.

    2. Reviewer #1 (Public Review):

      Summary:

      In this paper, Li and colleagues have found mircoRNAs that affect levels of metamorphosis-regulating genes that can also affect levels of sesquiterpenoids (juvenile hormone and related compounds) and ecdysteriods, which regulate the timing and stages of insects, respectively. They first compared the transcriptomes of Drosophila at the third larval instar and at the white pre-pupa stage. They found thousands of differences in gene transcript levels between males and females, and between the two different stages. Among those genes that were differentially regulated they saw that genes involved in insect hormone biosynthesis were disproportionately represented. Many of the differentially regulated genes were involved in the insect hormone biosynthesis pathway and ascorbate and alderete metabolism. MicroRNAs were also differentially expressed during metamorphosis and were separately identified. The authors then considered genes and whether the differentially expressed microRNAs might regulate transcripts known to be involved in sesquiterpenoid production. In silico analysis of microRNAs predicted a list of 17 microRNAs that can regulate transcripts of sesquiterpenoid biosynthesis genes. The authors then used an in vitro luciferase assay to validate the binding and downregulation of 10 of the microRNAs to genes involved with sesquiterpenoid production in S2 cells.

      Li and colleagues then focus on two genes they found were bound by microRNAs that have established roles in metamorphosis. The microRNAs miR-34 and miR-277 bind transcripts of two protein-coding genes that regulate metamorphosis Kr-h1, which encodes a transcription factor that is a JH-inducible transcription factor, and Allatostatin C Receptor 1, (AstC-R1), a G-protein coupled receptor that regulates the corpora allatum, the gland that produces sesquiterpenoids. Using a LAMP assay, one of the microRNAs, miR-277 was shown to bind to both AstC-R1 and Kr-h1 in in vivo whole-animal extracts. There is no mention of binding between either protein-coding transcript and the miR-34 microRNA. Temporal expression of all four transcripts shows that their abundance is anti-correlated; stages of high miR-34 or miR-277 expression correlate with low AstC-R1 or Kr-h1 expression. Homozygous deletions of both mircroRNAs result in 23% lethality, five days after adult eclosion. The authors also generated specific mutants in miR-34 or miR-277 and find differences in the expression of AstC-R1 and Kr-h1 and sex-specific differences in both sesquiterpenoids and ecdysteroids in the knock-out lines. If there were phenotypes associated with the specific knock-outs, those were not mentioned. Next, the authors examined the transcriptomes of the miR-3277 and miR-34 mutants and found several other GO-terms enriched among the differentially expressed genes. However, the sesquiterpenoid pathway and ascorbate and alderete metabolism are not listed.

      Strengths:

      This is an interesting manuscript that could make an important contribution to our understanding of the roles of micro RNAs at metamorphosis, and potentially of how sex-specific differences arise during metamorphosis. Strengths of the paper include the functional validation of microRNA binding, in vitro and in vivo-, as well as the characterization of sesquiterpenoid and ecdysteroid titers. The authors have also used CRISPR to generate specific knock-outs of miR-34 and miR-277. The transcriptomes will be a resource for future work to mine for differences in gene expression during metamorphosis.

      Weaknesses:

      (1) Spatial Expression of miR-34 and miR-277. If miR-34 and miR-277 regulate AstC-R1 and Kr-h1, then they must be expressed in the same cells. Although the authors show that the microRNAs do bind to the transcripts of AstC-R1 and Kr-h1 in S2 cells, and miR-277 binds AstC-R1 and Kr-h1 in vivo whole-animal homogenates, we do not know if the microRNAs are ever in the cells where AstC-R1 or Kr-h1 are expressed. AstC-R1 is only expressed in a few cells in the brain, so it is not at all certain that it is co-expressed with either microRNA. The creation of enhancer lines or in situ hybridization in Drosophila is straightforward and would sort this out.

      (2) Phenotypes. Although a double deletion was used and specific knock-outs of both miR-34 and miR-277 were generated, the analysis of the mutants is very superficial. For the homozygous deletion of both microRNAs miR-34 and miR-277, only a decrease in survivorship was observed a full six days after adult eclosion - after the end of metamorphosis. No phenotype for either miR-34KO or miR-277-KO was given. The authors cite the work of others who have found specific phenotypes after manipulation of sesquiterpenoids or ecdysteroids, like Riddiford and Ashburner, but do not use any of these many studies to help them characterize the phenotype. If the loss of miR-34 and miR-277 affects so many pathways (including MAPK signaling, TGF-beta signaling, FoxO signaling, and Wnt signaling), as well as global titers of metamorphic hormones, then there shouldn't there be something different in the development to discuss?

      (3) I think the reliance on GO term enrichment is getting in the way of biology. For instance, I would not describe Kr-h1 as a sesquiterpenoid biosynthesis pathway gene. Yet the authors say they were motivated to examine microRNA regulation of Kr-h1 because they saw differences in levels of the sesquiterpenoid biosynthesis pathway between WL3 and WPP, a period which also saw differences in expression of some microRNAs. I understand that Kr-h1 expression is regulated by JH, a sesquiterpenoid, but it is not directly involved with JH production, so relying on GO term enrichment has made the decision to focus on Kr-h1 feel arbitrary.

      (4) The transcriptomes of miR-34 and miR-277 should have revealed genes encoding members of the sesquiterpenoid biosynthesis pathway as well as AstC-R1 and Kr-h1, but neither was mentioned. The functional tests of miR-34 and miR-277 were performed because they were shown to affect the levels of expression of genes in the sesquiterpenoid biosynthesis pathway. Figure 2 shows a significant decrease in AstC-R1 and Kr-h1 transcripts after the loss of miR-34 and miR-277. However, the results do not mention either (Lines 250-264). Instead, there is a list of 10 different GO terms (like arginine and proline metabolism or fatty acid degradation) that were enriched in miR-34 and miR-277 transcriptomes. If any of those ten types have any relationship to Kr-h1, AstC-R1, or metamorphosis, that has not been explained.

      (5) Not enough care was taken in describing the stages. The methods describe wandering larvae (WL3) and white pre-pupa (WPP) for the transcriptomes, but in the text, different terms are used, like "larva", "pupa" and "L3 larvae instars" "early pupae" "late L3". Also, it seems like the small RNA libraries for sequencing were taken from "L3 larvae", but the stage of the L3 larvae was not mentioned. Staging is important, especially during metamorphosis, since differences in expression are expected to exist between different stages of L3, between early vs late wandering, and between WPP and early pupal stages.

    3. Reviewer #2 (Public Review):

      Summary:

      This study proposes that the microRNA cluster miR-277/34 controls the generation of sexual dimorphism in Drosophila melanogaster during metamorphosis by acting on specific hormonal and developmental gene pathways.

      Strengths:

      Using a combination of mRNA and small RNA sequencing together with genome-wide in silico and in vitro analyses the authors identified a microRNA cluster that may be involved in metamorphosis and the generation of sexual dimorphism in Drosophila melanogaster.

      Weaknesses:

      Biological validation of the identified sexually dimorphic genes and a detailed understanding of how the microRNA cluster miR-277/34 might be involved in the regulation of sesquiterpenoids are needed.

      Major suggestions:

      (1) If AstC-R1 and Kr-h1 are targets of the miR-277/34 cluster and cause their downregulation, it is not clear why there would also be a decrease in the levels of these genes in the miR-277/34 mutants. This would suggest that the mechanism is not straightforward and that further epistatic experiments should be carried out in order to clarify this issue.

      (2) The changes in the expression levels of AstC-R1 in pupae of miR-277-KO and mir-34-KO flies must be accompanied by photos of the respective larvae and pupae, as well as an analysis of the larvae-pupa transition on the mutants by gender.

      (3) Biological validation of the identified sexually dimorphic genes in vivo will be necessary for the support of this work.

    4. Reviewer #3 (Public Review):

      Summary:

      The authors show convincingly the complexity of gene up- and down-regulation at the outset of metamorphosis and identify substantial differences between the two sexes, even at this early time in development. The complexity of microRNA expression and the difference between the sexes are also nicely laid out. The functional significance of these differences, though, is harder to establish. The authors have focused on the roles of two families of developmental hormones, the ecdysteroids, and the juvenile hormones. The emergence of sex-specific differentiation of organs during metamorphosis is clearly downstream of the action of ecdysteroids and/or JH, but there is no evidence that the presence or lack of these hormones has any effect on the sexual identity of organ systems - i.e., that manipulations of JH or ecdysteroid result in either the masculinization or feminization of individuals or their organs. The precedence for the linkage of these hormones to sex determination is the 2002, Belgacem & Martin study, which describes the effects of JH on fly locomotion. These authors show that the number of stop/start bouts is sexually dimorphic, and removal of JH in males shifts their frequency into the female range while giving treated males exogenous JH moves it back. While this is referred to as a "feminization" of male behavior, this quantitative shift in frequency is not as compelling as would be a qualitative shift -- for example, the removal of JH causing males to show egg-laying behavior (a result that has never been seen). Also, these effects are in a fully mature system, rather than at the early metamorphic time examined in the present paper. In driving and coordination metamorphosis, JH and ecdysteroids are intimately involved in sexual differentiation, but I know of no compelling evidence that they play a role in sex determination.

      While the summary of the effects or removal of specific microRNAs on the components of the biosynthetic pathway for the JHs and ecdysteroids (Figure 2E ,F) is quite compelling, I am concerned about the effects of the removal of mir-277 and mir-34 on the levels of both the JHs and 20E. My concern centers around the data from the control group (w[1118] animals in Figure 2D). These data are the first report of a marked sex difference in the titer of either JH or ecdysteroid at the start of metamorphosis in Drosophila. As expected, males show a 10-20 increase in levels of JH III, JHB3, and 20E between the L3 stage and the white puparium, but, surprisingly, the levels of these hormones in female L3 larvae are equal to or greater than that seen at pupariation! These data for females run counter to over 50 years of work on the effects of ecdysteroids in Drosophila!

      As far as I can gather from the paper, the L3 data were obtained using wandering larvae. This stage lasts for about 12 hours and ends with pupariation. Larvae from this period need to be used with caution for hormone studies. Levels of both JH and ecdysteroid are low as larvae leave the food but rapidly rise to their peak levels at the white puparium stage 12 hours later. To deal with the rapidly changing hormonal landscape through this period, the researchers have used physiological markers to track this progression. Initially, it was the sage of "puffing" of the giant salivary gland chromosomes, but, for bulk collection of staged larvae, larvae are fed on food containing a blue dye, and progression is tracked by the loss of blue coloring from the gut. I could not find if the authors had any criteria for selecting larvae during the wandering period. Male and female larvae grow to different sizes. Might this difference in growth be biased when larvae were selected during their wandering phase?

      The other hormone-related issue is the expression of Kr-h1 during larval stages and metamorphosis (Figure 1G). Kr-h1 is the main target of JH and Kr-h1 expression is often used as a proxy for the JH titer. The authors report that peak Kr-h1 expression occurs in the L3 (when the JH titer should be lowest!) and that it drops at wandering. This pattern is counter to that reported in the literature (e.g., FlyBase, ModEncode).

    5. Author response:

      [Editors’ note: An Expression of Concern has been published for this article. The authors’ responses submitted during revision have been appended for further context]

      eLife Assessment

      This study provides a useful inventory of genes that are up- or down-regulated during the early metamorphic development of male and female larvae and proposes that the microRNA cluster miR-277/34 is involved in the development of sexual differences during early metamorphosis of Drosophila melanogaster, although its precise role remains unclear. The strength of evidence, based on a combination of diverse methods including mRNA and small RNA sequencing, in silico analyses, in vitro assays, and loss-of-function experiments, is incomplete as it lacks a general model and an examination of the potential effects of the miR-277/34 mutations on phenotypes such as morphology or developmental time. This work will be of interest to developmental biologists interested in sexual dimorphism and in the interplay between hormones and microRNAs during development.

      We thank the editor and reviewers for the positive and constructive comments and suggestions. Point-to-point responses are listed below.

      Reviewer #1:

      Summary:

      In this paper, Li and colleagues have found mircoRNAs that affect levels of metamorphosis-regulating genes that can also affect levels of sesquiterpenoids (juvenile hormone and related compounds) and ecdysteriods, which regulate the timing and stages of insects, respectively. They first compared the transcriptomes of Drosophila at the third larval instar and at the white pre-pupa stage. They found thousands of differences in gene transcript levels between males and females, and between the two different stages. Among those genes that were differentially regulated they saw that genes involved in insect hormone biosynthesis were disproportionately represented. Many of the differentially regulated genes were involved in the insect hormone biosynthesis pathway and ascorbate and alderete metabolism. MicroRNAs were also differentially expressed during metamorphosis and were separately identified. The authors then considered genes and whether the differentially expressed microRNAs might regulate transcripts known to be involved in sesquiterpenoid production. In silico analysis of microRNAs predicted a list of 17 microRNAs that can regulate transcripts of sesquiterpenoid biosynthesis genes. The authors then used an in vitro luciferase assay to validate the binding and downregulation of 10 of the microRNAs to genes involved with sesquiterpenoid production in S2 cells.

      Li and colleagues then focus on two genes they found were bound by microRNAs that have established roles in metamorphosis. The microRNAs miR-34 and miR-277 bind transcripts of two protein-coding genes that regulate metamorphosis Kr-h1, which encodes a transcription factor that is a JH-inducible transcription factor, and Allatostatin C Receptor 1, (AstC-R1), a G-protein coupled receptor that regulates the corpora allatum, the gland that produces sesquiterpenoids. Using a LAMP assay, one of the microRNAs, miR-277 was shown to bind to both AstC-R1 and Kr-h1 in in vivo whole-animal extracts. There is no mention of binding between either protein-coding transcript and the miR-34 microRNA. Temporal expression of all four transcripts shows that their abundance is anti-correlated; stages of high miR-34 or miR-277 expression correlate with low AstC-R1 or Kr-h1 expression. Homozygous deletions of both mircroRNAs result in 23% lethality, five days after adult eclosion. The authors also generated specific mutants in miR-34 or miR-277 and find differences in the expression of AstC-R1 and Kr-h1 and sex-specific differences in both sesquiterpenoids and ecdysteroids in the knock-out lines. If there were phenotypes associated with the specific knock-outs, those were not mentioned. Next, the authors examined the transcriptomes of the miR-3277 and miR-34 mutants and found several other GO-terms enriched among the differentially expressed genes. However, the sesquiterpenoid pathway and ascorbate and alderete metabolism are not listed.

      Strengths:

      This is an interesting manuscript that could make an important contribution to our understanding of the roles of micro RNAs at metamorphosis, and potentially of how sex-specific differences arise during metamorphosis. Strengths of the paper include the functional validation of microRNA binding, in vitro and in vivo-, as well as the characterization of sesquiterpenoid and ecdysteroid titers. The authors have also used CRISPR to generate specific knock-outs of miR-34 and miR-277. The transcriptomes will be a resource for future work to mine for differences in gene expression during metamorphosis.

      Weaknesses:

      (1) Spatial Expression of miR-34 and miR-277. If miR-34 and miR-277 regulate AstC-R1 and Kr-h1, then they must be expressed in the same cells. Although the authors show that the microRNAs do bind to the transcripts of AstC-R1 and Kr-h1 in S2 cells, and miR-277 binds AstC-R1 and Kr-h1 in vivo whole-animal homogenates, we do not know if the microRNAs are ever in the cells where AstC-R1 or Kr-h1 are expressed. AstC-R1 is only expressed in a few cells in the brain, so it is not at all certain that it is co-expressed with either microRNA. The creation of enhancer lines or in situ hybridization in Drosophila is straightforward and would sort this out.

      We thank the reviewer for the positive and constructive comments. We agree with the reviewer, and have pointed out the limitation in the main text as follows:

      Lines 307-309 - “Further exploration of the spatial expression between miR-277, miR-34, Kr-h1 and AstC could bring new insights to the regulatory effects they have during metamorphosis”.

      (2) Phenotypes. Although a double deletion was used and specific knock-outs of both miR-34 and miR-277 were generated, the analysis of the mutants is very superficial. For the homozygous deletion of both microRNAs miR-34 and miR-277, only a decrease in survivorship was observed a full six days after adult eclosion - after the end of metamorphosis. No phenotype for either miR-34KO or miR-277-KO was given. The authors cite the work of others who have found specific phenotypes after manipulation of sesquiterpenoids or ecdysteroids, like Riddiford and Ashburner, but do not use any of these many studies to help them characterize the phenotype. If the loss of miR-34 and miR-277 affects so many pathways (including MAPK signaling, TGF-beta signaling, FoxO signaling, and Wnt signaling), as well as global titers of metamorphic hormones, then there shouldn't there be something different in the development to discuss?

      We thank for reviewer for pointing out the phenotypes. We did not detect morphological or developmental abnormalities during larval-pupal transitions in the different knockout mutants, and have now included the previously established research on miR-34 in the main text, shown as follows:

      Lines 305-307 - “MiR-34 has been documented to maintain the neural proteostasis while suppressing age-associated decline (Liu et al. 2012; Kennerdell, Liu and Bonini 2018; Srinivasan, Tran and Bonini 2022).”

      (3) I think the reliance on GO term enrichment is getting in the way of biology. For instance, I would not describe Kr-h1 as a sesquiterpenoid biosynthesis pathway gene. Yet the authors say they were motivated to examine microRNA regulation of Kr-h1 because they saw differences in levels of the sesquiterpenoid biosynthesis pathway between WL3 and WPP, a period which also saw differences in expression of some microRNAs. I understand that Kr-h1 expression is regulated by JH, a sesquiterpenoid, but it is not directly involved with JH production, so relying on GO term enrichment has made the decision to focus on Kr-h1 feel arbitrary.

      We have stated out the rationale and rephrased that Kr-h1 as sesquiterpenoid responsive gene in relevant sentences in the Results, shown as follows:

      Lines 135-145 - “Utilising an established cell culture dual-luciferase reporter assay (Hui et al 2013); we have validated 7 microRNAs (miR-8, miR-277, miR-958, miR-1000, miR-1002, miR-2283, miR-2280) that can downregulate the expression of Drosophila genes involved in JH synthesis, responsive, or signalling genes to varying degrees, including allatostatin C (AstC), allatostatin C receptor 1 (AstC-R1), allatostatin C receptor 2 (AstC-R2), Krüppel homolog 1 (Kr-h1), Chd64, juvenile hormone binding protein (JHBP), juvenile hormone diol kinase (JHDK) and juvenile hormone esterase (JHE) (Figure 1C). Among these genes, Kr-h1 or Kruppel homolog 1, is a JH-dependent transcription factor (responsive gene), and have been found to be regulated by another microRNA in cockroaches (Lozano et al 2015); while AstC-R1 or allatostatin C receptor 1, is a G-protein coupled receptor for neuropeptide allatostatin C which regulates juvenile hormone synthesis (Stay and Tobe 2007).”

      (4) The transcriptomes of miR-34 and miR-277 should have revealed genes encoding members of the sesquiterpenoid biosynthesis pathway as well as AstC-R1 and Kr-h1, but neither was mentioned. The functional tests of miR-34 and miR-277 were performed because they were shown to affect the levels of expression of genes in the sesquiterpenoid biosynthesis pathway. Figure 2 shows a significant decrease in AstC-R1 and Kr-h1 transcripts after the loss of miR-34 and miR-277. However, the results do not mention either (Lines 250-264). Instead, there is a list of 10 different GO terms (like arginine and proline metabolism or fatty acid degradation) that were enriched in miR-34 and miR-277 transcriptomes. If any of those ten types have any relationship to Kr-h1, AstC-R1, or metamorphosis, that has not been explained.

      We have now explicitly stated that Kr-h1 is significantly downregulated in the miR-277-KO transcriptomes consistent with our qPCR validation, as well as the transcriptomic analyses were used to understand genome-wide changes, shown as follows:

      Lines 211-217 - “Among these differentially regulated genes, Kr-h1 was significantly downregulated in the miR-277-KO mutants (Supplementary Table S2), consistent with our qPCR validations (Figure 2B). On the other hand, AstC-R1 was not classified as differentially regulated based on the in silico analyses threshold of the whole-body RNA-seq data, and whether this was due to its restricted expression in a small subset of brain neurons would require further experiments to differentiate between different scenarios.”

      Lines 255-259 - “It implies that miR-277 could have conserved regulatory roles of sesquiterpenoid and related pathways, and also divergent roles between sexes during metamorphosis with signaling pathways associated with cell proliferation, wing development, nutritional support, and endocrine regulation (Pan et al 2018; Jiang and Edgar 2009; Gibbens et al 2011; Toyama et al 2014).”

      (5) Not enough care was taken in describing the stages. The methods describe wandering larvae (WL3) and white pre-pupa (WPP) for the transcriptomes, but in the text, different terms are used, like "larva", "pupa" and "L3 larvae instars" "early pupae" "late L3". Also, it seems like the small RNA libraries for sequencing were taken from "L3 larvae", but the stage of the L3 larvae was not mentioned. Staging is important, especially during metamorphosis, since differences in expression are expected to exist between different stages of L3, between early vs late wandering, and between WPP and early pupal stages.

      We thank the reviewer for highlighting the importance of staging clarity, and have now revised both the Results and Methods to ensure all stages are defined explicitly and used consistently throughout the manuscript. In our dataset, all references to “L3 larvae” or “larva” correspond specifically to wandering third-instar larvae (WL3) which that have exited the food, display 1/3 to 1/4 gut contents, and show protruding anterior spiracles. All references to “pupa”, “pre-pupa”, or “early pupae” refer to white prepupae (WPP), defined as immobile individuals with fully everted spiracles and no movement when gently probed.

      Recommendations:

      (1) Lines 171-173. The genotype of the deletion line is given, but what is the genotype of the flies that were used? Were the balancers and chromosomes with mutations included in the analysis? I cannot tell from the description.

      The miR-277-34-KO line was used and significant decrease in survivorship was observed in double knockout line. The information is now included as follows:

      Lines 158-164 - “In the homozygous loss of function of miR-277/34 cluster fly (w[*]; Df(3R)mir-277-34-KO, TI{w[+mW.hs]=TI}mir-277-34-KO/TM3, P{w[+mC]=GAL4-twi.G}2.3, P{UAS-2xEGFP}AH2.3, Sb[1] Ser[1]; Chen et al 2014), we found that the heterozygous knockout individuals displayed low to almost no lethality, while the survivorship for the double knockout flies fell significantly with 77% of the double knockout flies dying within five days after eclosion (Figure 2A).”

      (2) The introduction and discussion are too broad. I would expect some information about what is happening to ecdysone and juvenile hormone titers during metamorphosis. Additional detail should be given to the regulation of the corpora allatum, since the Ast-C1 gene and sesquiterpenoid levels were a major part of the results. I had hoped the discussion would help to synthesize the details within the results, particularly the differences between sexes. Instead, the discussion is very general, citing other papers that are related, but without tying the results within those papers to specific results in this paper.

      We thank the reviewer. The following information is now included in the main text:

      Lines 47-53 - “Multiple ecdysone titre peaks were observed during Drosophila development with each pulse corresponds to a developmental transition (Kannangara et al., 2023; Scanlan et al., 2023). For instance, there are one small and another big one triggering wandering in larvae and pupariation, respectively. For sesquiterpenoid juvenile hormone, its titre in corpora allatum changes in different developmental stages (Zhang et al., 2022) and generally decreases closer to the end of larval stages for pupariation (Dubrovsky 2005).”

      (3) I found the text to be unclear, and important details were missing. There are many different cases where titers or gene expression went up or down, in males or females, but it is difficult to understand if these were expected or not, or what to make of all the differences. I also do not have a clear idea of what the hypothesis is. I had thought that the microRNAs were promoting the degradation of AstC-R1 or Kr-h1, so their loss should result in higher levels of AstC-R1 or Kr-h1 transcripts. However, Figures 2B and C show that there are lower levels of AstC-R1 and Kr-h1 transcripts in the loss of either miR-34 or miR-277. How does this fit with what is known of the progression of metamorphosis? A model built from the results to this point could help to organize the text.

      The following information is now included in the main text, shown as follows:

      - Lines 172-177 - “We then compared the expression of Kr-h1 and AstC-R1 of miR-277-KO, miR-34-KO, and between KO mutants and wild type flies using quantitative real-time PCR. The expression level of Kr-h1 was found to be significantly downregulated in both sexes of WL3 and WPP miR-277-KO mutants, coherent to the RNA-seq results; whereas a reduction was only noted in the male WL3 for miR-34-KO mutants but not in RNA-seq (Figure 2B; Supplementary Table S2).”

      - Lines 178-186 - “On the other hand, expression level of AstC-R1 determined by real-time PCR was found to be upregulated during metamorphosis in wild-type (w1118) flies, but not for either miR-277-KO or miR-34-KO mutants (Figure 2C; Supplementary Figure 6). This is in agreement with the transcriptomic analyses where AstC-R1 was only up-regulated during male and female metamorphosis in the wild-type fly (Supplementary Table S2). In addition, we also found that the expression level of AstC-R1 in WPP of both miR-277-KO and mir-34-KO flies were significantly different to that of the w1118 flies (Figure 2C). Together, these data demonstrated that Kr-h1 and AstC-R are stage-specifically regulated by miR-277/34 in vivo.”

      Reviewer #2:

      Summary:

      This study proposes that the microRNA cluster miR-277/34 controls the generation of sexual dimorphism in Drosophila melanogaster during metamorphosis by acting on specific hormonal and developmental gene pathways.

      Strengths:

      Using a combination of mRNA and small RNA sequencing together with genome-wide in silico and in vitro analyses the authors identified a microRNA cluster that may be involved in metamorphosis and the generation of sexual dimorphism in Drosophila melanogaster.

      Weaknesses:

      Biological validation of the identified sexually dimorphic genes and a detailed understanding of how the microRNA cluster miR-277/34 might be involved in the regulation of sesquiterpenoids are needed.

      Major suggestions:

      (1) If AstC-R1 and Kr-h1 are targets of the miR-277/34 cluster and cause their downregulation, it is not clear why there would also be a decrease in the levels of these genes in the miR-277/34 mutants. This would suggest that the mechanism is not straightforward and that further epistatic experiments should be carried out in order to clarify this issue.

      We thank the reviewer for the positive and constructive comments. We agree that targets participate in endocrine feedback loops is dynamic, which could be the case for Kr-h1 and AstC-R1 during the larvae-pupae transition as they are components of the sesquiterpenoid hormone regulatory network. Epistatic analyses would be useful for future work and seem to be beyond the current scope of this study. The following information is now included in the main text:

      Lines 184-187 - “Together, these data demonstrated that Kr-h1 and AstC-R are stage-specifically regulated by miR-277/34 in vivo. Further epistatic experiments would be needed to understand the dynamics between and the hormone actions.”

      (2) The changes in the expression levels of AstC-R1 in pupae of miR-277-KO and mir-34-KO flies must be accompanied by photos of the respective larvae and pupae, as well as an analysis of the larvae-pupa transition on the mutants by gender.

      We thank the reviewer for this suggestion. We have now added reference images illustrating the WL3 and WPP stages for w1118 in the Appendix as suggested (Author response image 1). We have analysed the larvae-pupae transition in both sexes of miR-277-KO and miR-34-KO mutants, directly comparing them with stage-matched w1118 controls, and we did not observe any detectable differences in larval morphology, pupal morphology, anterior spiracle eversion, gut-purge status, or timing of pupariation between the mutants and controls.

      Author response image 1.

      (3) Biological validation of the identified sexually dimorphic genes in vivo will be necessary for the support of this work.

      We have now included additional quantitative RT-PCR validation of key insect hormone pathway genes in male and female WL3 and WPP stages of wild-type, miR-277-KO, and miR-34-KO flies (Figure 5). These additional analyses confirm sex-specific expression differences in both wild-type and mutant backgrounds, supporting dimorphic endocrine regulation. The main text are also revised and shown as follows:

      Lines 276-287 - “To understand how different hormones such as juvenile hormone (Belgacem and Martin, 2002) involved during metamorphosis in both sexes, we selected and tested a selection of candidates from both sesquiterpenoid and ecdysteroid hormone pathways, and found JHAMT, Spok, CG40486, and CG10962 showed differential expression during transition from WL3 to WPP in mutant and/or wild-type fly lines in the transcriptome analyses (Supplementary Figure 16). To further examine these RNA-seq patterns, we performed RT-PCR on nine candidate genes from WL3 to WPP in WT and mutant fly lines. In the larval-pupal transition comparison, CG40486 was consistent down-regulated in WT males and females in both RNA-seq and RT-qPCR datasets. In the mutant-vs-WT comparison, CG9360 showed concordant downregulation in miR-277-KO WPP females. Other tested genes showed more condition-specific patterns matching between the transcriptomic and RT-qPCR analyses. Further validation and mechanistic studies will be required to define how these hormone-pathway changes contribute to the larval–pupal transition.”

      Reviewer #3 (Public Review):

      Summary:

      The authors show convincingly the complexity of gene up- and down-regulation at the outset of metamorphosis and identify substantial differences between the two sexes, even at this early time in development. The complexity of microRNA expression and the difference between the sexes are also nicely laid out. The functional significance of these differences, though, is harder to establish. The authors have focused on the roles of two families of developmental hormones, the ecdysteroids, and the juvenile hormones. The emergence of sex-specific differentiation of organs during metamorphosis is clearly downstream of the action of ecdysteroids and/or JH, but there is no evidence that the presence or lack of these hormones has any effect on the sexual identity of organ systems - i.e., that manipulations of JH or ecdysteroid result in either the masculinization or feminization of individuals or their organs. The precedence for the linkage of these hormones to sex determination is the 2002, Belgacem & Martin study, which describes the effects of JH on fly locomotion. These authors show that the number of stop/start bouts is sexually dimorphic, and removal of JH in males shifts their frequency into the female range while giving treated males exogenous JH moves it back. While this is referred to as a "feminization" of male behavior, this quantitative shift in frequency is not as compelling as would be a qualitative shift -- for example, the removal of JH causing males to show egg-laying behavior (a result that has never been seen). Also, these effects are in a fully mature system, rather than at the early metamorphic time examined in the present paper. In driving and coordination metamorphosis, JH and ecdysteroids are intimately involved in sexual differentiation, but I know of no compelling evidence that they play a role in sex determination.

      We thank the reviewer for the positive and constructive comments. We agree that our findings advanced on the sexual differences in gene regulation during metamorphosis rather than establishing direct evidence of juvenile hormone (JH) or ecdysteroids in influencing insect sex determination. In addition to rephrasing at various parts of the main text (see below), we have also carried out additional experiments in obtaining and analysing transcriptomes of late larvae and early pupae of both sexes in the wild-type of another species (Drosophila virilis), which showed similar KEGG pathways enriched. The revised parts are shown as follows:

      Lines 289-303 – Results section

      “Metamorphosis between different sexes in D. melanogaster and D. virilis

      In D. melanogaster, the transition from WL3 to WPP shows increased carbohydrate/energy metabolism and ER protein processing, with decreased lipid metabolism, detoxification (cytochrome P450), and redox/cofactor pathways (folate, glutathione, retinol); while PM-LM exhibits broader negative enrichment in translational and RNA-processing machinery (ribosome, spliceosome, proteasome, RNA degradation, ubiquitin-mediated proteolysis) and in signaling modules (mTOR, MAPK, FoxO, Hippo), whereas PF-LF shifts are smaller and less coordinated by GSEA (Supplementary Figure S15). On the other hand in D. virilis, core features match that of D. melanogaster from WL3 to WPP, carbohydrate/energy metabolism and ER protein processing increase, while lipid, detoxification, and redox/cofactor pathways decrease. The sex bias is inverted relative to D. melanogaster as PM-LM shows limited up-regulation, whereas PF-LF displays broader, coordinated repression involving amino-acid metabolism (glycine/serine/threonine; cysteine/methionine), lysosome, ABC transporters, and longevity-related signaling, yielding the largest DEG set in females (Supplementary Figure S15). These data strongly suggested the sexual differences in gene regulation during metamorphosis in insects is not species-specific.”

      Lines 354-361 – Methods section

      “D. melanogaster w1118 and D. virilis were reared at 25 C, 45-50% relative humidity and 14:10 hour light-dark cycle. Sexes of wandering L3 larvae instar (WL3) were differentiated under the light dissecting microscope by the presence (male) or absence (female) of gonads (Supplementary Figure 1). Collections were restricted to larvae wandering out of food and ready for pupariation. WPP were defined as immobile puparia with everted spiracles and a uniform white cuticle that were no longer moving even with external stimuli. A total of three biological replicates (Whole-body of 1 individual was served as per biological replicate) per stage and sex were obtained for D. melanogaster and D. virilis.”

      While the summary of the effects or removal of specific microRNAs on the components of the biosynthetic pathway for the JHs and ecdysteroids (Figure 2E ,F) is quite compelling, I am concerned about the effects of the removal of mir-277 and mir-34 on the levels of both the JHs and 20E. My concern centers around the data from the control group (w[1118] animals in Figure 2D). These data are the first report of a marked sex difference in the titer of either JH or ecdysteroid at the start of metamorphosis in Drosophila. As expected, males show a 10-20 increase in levels of JH III, JHB3, and 20E between the L3 stage and the white puparium, but, surprisingly, the levels of these hormones in female L3 larvae are equal to or greater than that seen at pupariation! These data for females run counter to over 50 years of work on the effects of ecdysteroids in Drosophila!

      We thank the reviewer and agree that the endocrine profiles of WL3 females require careful interpretation, given the late-L3 to WPP transition represents a highly dynamic window in which ecdysteroid and sesquiterpenoid titres can fluctuate over short developmental intervals. The revised text is shown as follows:

      Lines 322-326 – “Given the WL3 to WPP transition represents a highly dynamic window in which ecdysteroid and sesquiterpenoid titres can fluctuate over short developmental intervals, future experiments on narrower early-/mid-/late-wandering timepoints will be required to understand the dynamics of the microRNAs and hormones.”

      As far as I can gather from the paper, the L3 data were obtained using wandering larvae. This stage lasts for about 12 hours and ends with pupariation. Larvae from this period need to be used with caution for hormone studies. Levels of both JH and ecdysteroid are low as larvae leave the food but rapidly rise to their peak levels at the white puparium stage 12 hours later. To deal with the rapidly changing hormonal landscape through this period, the researchers have used physiological markers to track this progression. Initially, it was the sage of "puffing" of the giant salivary gland chromosomes, but, for bulk collection of staged larvae, larvae are fed on food containing a blue dye, and progression is tracked by the loss of blue coloring from the gut. I could not find if the authors had any criteria for selecting larvae during the wandering period. Male and female larvae grow to different sizes. Might this difference in growth be biased when larvae were selected during their wandering phase?

      We thank the reviewer and agree that WL3 represents a hormonally dynamic 12-hour window. To minimise developmental heterogeneity, WL3 larvae were selected for larvae that had completely exited the food and climbing upwards of the fly vials. To ensure tight developmental synchrony, pupating individuals from the same batch were collected 4 hours later, allowing us to match WL3 and WPP animals from a single, developmentally coordinated cohort. This paired-batch collection tried to minimise variation arising from differences in timing of wandering onset. As for the differences between the body sizes of male and female larvae, we minimise size-dependent bias, hormone titres measurement were measured to ng hormone per gram of tissue in our study, which corrects for sex-specific differences in mass. The information is now included in the main text as follows:

      Lines 276-287 – “To understand how different hormones such as juvenile hormone (Belgacem and Martin, 2002) involved during metamorphosis in both sexes, we selected and tested a selection of candidates from both sesquiterpenoid and ecdysteroid hormone pathways, and found JHAMT, Spok, CG40486, and CG10962 showed differential expression during transition from WL3 to WPP in mutant and/or wild-type fly lines in the transcriptome analyses (Supplementary Figure 16). To further examine these RNA-seq patterns, we performed RT-PCR on nine candidate genes from WL3 to WPP in WT and mutant fly lines. In the larval-pupal transition comparison, CG40486 was consistent down-regulated in WT males and females in both RNA-seq and RT-qPCR datasets. In the mutant-vs-WT comparison, CG9360 showed concordant downregulation in miR-277-KO WPP females. Other tested genes showed more condition-specific patterns matching between the transcriptomic and RT-qPCR analyses. Further validation and mechanistic studies will be required to define how these hormone-pathway changes contribute to the larval–pupal transition”.

      The other hormone-related issue is the expression of Kr-h1 during larval stages and metamorphosis (Figure 1G). Kr-h1 is the main target of JH and Kr-h1 expression is often used as a proxy for the JH titer. The authors report that peak Kr-h1 expression occurs in the L3 (when the JH titer should be lowest!) and that it drops at wandering. This pattern is counter to that reported in the literature (e.g., FlyBase, ModEncode).

      We have now revised the main text as follows:

      Lines 204-207 – “It should be noticed that the analyses of this study was based on RNA taken from whole-body rather than individual tissue, and further experiments will be required to dissect the dynamics of Kr-h1 and different hormones both spatially and temporally.”

      Besides dealing with the issues above, it would be useful in the background to establish the difference between "sex determination" and "sexual differentiation". I was surprised that the authors could deal with this subject without ever mentioning the sex determination pathway - e.g., double sex, transformer, etc.

      In this study, one major focus was on the differences between sex during metamorphosis rather than the sex determination pathway. We have included the reviewer’s suggestion by adding the following information in the main text, shown as follows:

      Lines 73-75 - “In insects, various genes related to sex determination or showing sexual dimorphic features have been revealed, including doublesex and transformer (Shukla and Nagaraju, 2010; Deng et al, 2025).”

      Line 61: at the time that the authors are considering, -- the transition from larva to pupa - the main controller of ecdysone production in the prothoracicotropic hormone, not ecdysis triggering hormone.

      Thanks for the reviewer’s suggestions. The main text is now written as follows:

      Lines 44-47 - “In general, these three hormones work in a cooperative manner, where ecdysteroid 20-hydroxyecdysone controls the timing of ecdysis or molting, prothoracicotropic hormone and the insulin-like peptides act as regulators of ecdysteroid synthesis, and sesquiterpenoid hormones such as juvenile hormone (JH) regulates metamorphosis based on their titre levels.”

      Line 71: it would be useful to name the main microRNAs that have been implicated in regulating insect metamorphosis.

      The main text is now written as follows:

      Lines 60-63 - “Previous studies have identified a number of microRNAs contributing to the regulation of insect metamorphosis such as let-7, miR-125, and miR-2 family (e.g. Caygill and Johnston 2008; Sokol et al 2008; Lozano et al 2015).”

      For Figures 2E and F as well as 3C (and a number of supplementary figures), it would be useful to provide some quantitative representation of the level of activation or suppression (as done in Figure 3B) rather than just Red or Green

      The trend of expression change of genes in selected pathways and detailed quantitative information can now be found in the supplementary file.

    1. eLife Assessment

      This important study used several genome-wide approaches to compare dormancy in S. pombe spores and killifish diapause embryos. The findings include a trade-off between longevity and heat resistance in yeast spores and, in both these systems, a relatively higher expression of ribosomal-protein mRNAs but not proteins. While the evidence for the findings is overall solid, some interpretations should still be tempered. The large-scale datasets provide an excellent resource for future research in the dormancy field.

      [Editors' note: this paper was reviewed by Review Commons.]

    2. Reviewer #1 (Public review):

      Summary:

      In the manuscript entitled "Gene function and expression profiling in yeast spores, killifish diapause embryos, and their post-dormant offspring cells" the authors analyze dormancy at three levels: functional, transcriptomic and proteomic, using Schizosaccharomyces pombe spores, diapausing killifish embryos and human dormant cancer cells as models. The authors performed a genome-wide Bar-seq screen for gene deletions in fission yeast spores and then compared changes in mRNA and protein expression in young and old and heat-stressed cells in yeast and killifish. The main conclusions of the work include that dormancy is a dynamic not static state. The authors conclude that specific biological processes, rather than particular genes, are conserved across organisms during the dormancy, and that there is a clear disconnect between the mRNA and protein kinetics and behavior, particularly for ribosomal proteins and processes related to autophagy and mitochondrial metabolism.

      In general, the manuscript is well structured and written, with carefully designed figures. The authors are also generally aware of the limitations of the work. The manuscript is interesting and valuable, but some interpretations should be moderated.

      Major comments:

      (1) The data presented provide in general good support for majority of conclusions presented in the manuscript. However, less support is provided for the theories that: there is a universal "core expression signature" of dormancy; the mRNA/ribosome protein uncoupling reflects a specific mechanism of preparation for exiting dormancy; and the observed changes provide evidence for an epigenetic "memory" of dormancy. These elements are currently more interpretive than evidentiary. I would recommend softening the language in these places and clearly labeling them as hypothetical.

      (2) The authors propose that spores and post-dormancy cells retain a memory of time spent in dormancy or stress exposure. However, sporulation is also generally expected to reset or remove accumulated damage. Please clarify whether the observed long-term effects reflect classical "memory" of the cell, persistent regulatory state, or selection of cells with different fitness (carry-over effects). I would be careful in interpreting the effects as a form of memory associated mostly with epigenetic control - the statement seems stronger than what the data directly demonstrate.

      (3) Validation of a small set of top hits by independent assays would substantially strengthen the conclusions. For example, targeted qPCR and/or biochemical assays for key genes (like ribosomal and autophagy) or pathways would help confirm the most important transcriptomic and proteomic trends. Without this, the suggestion of, for example, translational regulation remains possible but unproven.

      (4) Overall, the study appears reproducible, with detailed methods, biological replicates, and appropriate statistical analyses. However, reproducibility could be improved by more clearly describing the limitations of the comparisons (especially with the published datasets) and the data-processing parameters, particularly for correlation and functional enrichment analyses. Some comparative conclusions are based on data from different studies, under different conditions and often with different depths of coverage. It is not always clear to what extent interspecies comparisons are fully methodologically similar.

      Significance:

      This is an ambitious work that combines a genetic screen with multi-omics analyses and cross-species comparisons. The novelty is less in known roles of autophagy, mitochondria and translation, but rather in integrating them into a comparative framework across S. pombe spores, killifish diapause embryos, and dormant human cancer cells, combined with Bar-seq screening and multi-omics. Its greatest strength is its coherent, broad concept: dormant cells are not passive, and the regulated processes and functions during dormancy are evolutionarily conserved rather than the individual set of genes. The contribution of this work lies more in demonstrating process-level conservation than in identifying a single universal gene. This significantly expands the literature on dormancy, but rather in terms of a framework of biological organization rather than a single mechanism.

      The weakness of the manuscript is the tendency to draw broad, sometimes overly strong mechanistic conclusions based primarily on correlational data (sometimes with poor statistical significance) and on relative, rather than absolute, expression. The lack of biochemical validation, in my opinion, lowers the impact of the paper.

      Audience: This work will be of interest especially to aging and dormancy specialists and comparative biologists. It is important to note that this is not a purely technical or model-specific work, but it has the potential to impact researchers interested in homeostasis, translation and adaptation to stress.

      My expertise: My expertise is mostly in aging, sporulation, cell cycle control and ribosome biogenesis.

    3. Reviewer #2 (Public review):

      Summary:

      In this manuscript, Hassan et al. examine dormancy in fission yeast spores and killifish embryos in diapause. The authors first performed a genetic screen in yeast spores to identify genetic modulators of spore longevity and resilience to heat stress, finding that there is a trade-off between stress resilience and longevity. Next, the authors performed transcriptomic and proteomic profiling of yeast spores and killifish diapause embryos, comparing active and dormant states, early and late dormancy, and with and without heat stress in each model. Comparison between the two models identified a shared set of gene expression changes in dormancy. Lastly, the authors tested whether stress in dormancy is able to reshape the transcriptome or proteome following exit from dormancy, finding that heat stress altered gene expression post-dormancy in both yeast spores and killifish diapause embryos.

      This study is interesting because it provides valuable transcriptomic and proteomic datasets across a large number of conditions in both yeast spores and killifish embryos. Furthermore, these datasets reveal several interesting insights about dormancy, including shared gene expression changes in dormancy across diverse organisms, a trade-off between stress resilience and longevity in yeast spores, and memory of heat stress following exit from dormancy.

      This interesting manuscript would be strengthened by additional characterization of the functional consequences of these gene expression changes.

      Major comments:

      (1) The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?

      (2) The authors show that gene expression changes in dormancy (old vs. young spores and late vs. early diapause) are poorly correlated with aging in stationary-phase yeast cells (Figure 4E) and killifish brain (Figure 5E). The authors suggest that this poor correlation might reflect a lack of aging during dormancy. Interestingly, the authors also show that in both yeast and killifish models, both time and stress in dormancy result in gene expression changes following exit from dormancy. Are these post-dormancy differences correlated with aging in stationary-phase yeast cells and adult killifish? Additionally, could the authors compare these changes with age-related changes in different adult killifish organs?

      (3) In Figures 1C, 4F, and 5F, the authors find significant correlations between two comparisons with a shared condition (e.g. 2-week unstressed spores in Figure 4F). Using the same samples in both comparisons can inflate the correlation between the two sets of fold-changes because the two comparisons are not made independently. The authors should ensure that the correlations found in these figures remain significant even when different samples are used for the shared condition in the two comparisons.

      Significance:

      This study is interesting because it provides a comparison between different states of dormancy and identify shared pathways. This paper should appeal to a broad audience, including aging, stress pathways, and quiescence/dormancy.

    4. Reviewer #3 (Public review):

      Summary:

      In this study, the authors use several genome-wide approaches to find commonalities and differences between distinct dormancy models, including S. pombe spores, killifish diapause embryos, and human dormant cancer cells. As G0 states are ubiquitous in nature, yet still understudied, this study provides not only a clear demonstration of an evolutionarily-conserved component in G0 regulation, but also a rich resource of genomics datasets, that will be especially of interest to S. pombe and killifish researchers.

      The main conclusions of the study are that: (1) there are commonalities in dormancy programs across eukaryotic evolution; (2) the uncoupling between transcriptome and translatome is more pronounced in non-dividing cells, particularly at ribosomal protein coding genes; and (3) dormant cells retain dynamic responses to stress, changing their cellular state and maintaining this difference.

      Overall, the study is clearly written, provides large data resources for future research in the dormancy field, and the conclusions are supported by the presented evidence (although point 3 would require further experimental clarification, see below).

      Major comments:

      A key consideration when comparing transcriptomes of cycling and dormant cells is the overall RNA quantity in the cells; however, it is not made clear in the manuscript whether this overall level was compared and/or if RNA-seq used spike-in quantification. However, this will directly affect some conclusions; for example, there is a conceptual difference between a transcript upregulated in spores and a transcript that appears upregulated in spores but is in fact 'less repressed than average' (resulting in higher FC). While the overall transcript diversity as described will be not affected, this analysis can provide important context. For the highlighted example of ribosomal protein mRNAs, this scenario could be what resulted in an apparent mRNA increase but protein decrease (which would be interpreted as both mRNA stabilization and translation inhibition); if the total mRNA is much lower, then the mRNA levels may be similar (which would then be interpreted as no change in transcription nor mRNA stability, but highlight the importance of translation inhibition).

      This point was more clearly described in a previous seminal study by the authors (Marguerat et al 2012), and so should be made clearer here as well. What is the overall RNA level in cycling cells compared to quiescent cells, and compared to spores? Likewise, what is the overall reduction in total protein levels? Was the overall reduction comparable to the previous study? These overall levels should at least be quantified, which is a straight-forward experiment. The current mention of this limitation (two sentences in the last paragraph of the conclusion) is not enough.

      Accordingly, the term "induced" (in Fig 2 for example) may be misleading, and could be separated into truly "induced" genes and "relatively induced" genes, when normalized to absolute levels. Likewise, Fig S2A and S2I are misleading due to this effect (normalization will tend to show a similar number of up-regulated and down-regulated genes). These panels should be clarified by plotting next to the relative quantification (current panels) a comparison taking overall RNA level into account. This will very likely strongly increase the number of downregulated genes and decrease the number of upregulated genes, and the new lists can also be subjected to GO analysis.

      The same issue would be important to discuss for the killifish experiment as well.

      For spore offspring stress experiments, the estimated number of cell divisions after spore germination should be indicated. It appears over 20, which would confirm the authors' conclusion that an epigenetic state is induced and persists over multiple rounds of replication and underlie improved heat resistance, indeed hinting at transcriptional memory through chromatin modification. As the phenotype is tested at a population-level, it is unclear whether every spore is equally able to impart this phenotype, and for how long the stress resistance response persists. Moreover, it may be clearer to test heat resistance in log-phase cells, as in the current experimental setup cells are grown to stationary-phase before plating; however, stationary-phase cultures are more heterogenous and more heat-resistant.

      Note also that using 3-fold serial dilutions (instead of more commonly used 10-fold) means that the differences shown in Fig 3M are mild. Plating duplicates (at log-phase) and imaging at several times of growth would be necessary to have a clearer view of the result.

      Significance:

      Overall, the study provides a proof-of-concept that dormancy programs across vastly different eukaryotes display commonalities. This is not an entirely new concept, but this is the first demonstration of it using S. pombe and turquoise killifish and will therefore be of interest to a wide audience of researchers interested in quiescence and dormancy in general. A key finding (albeit also not entirely new) is the transcriptome/proteome difference, which appears increased in dormant cells; this hints at the existence of key post-transcriptional regulatory mechanisms in dormant and G0 cells.

      A current key limitation is that only relative quantifications are discussed, which can currently give a misleading idea of overall transcriptional activity in dormant cells. Another limitation is that differentially-regulated genes, proteins and GO processes are mentioned, but with relatively limited biological interpretation, making some sections of the manuscript lengthy and descriptive.

      Advance: The authors provide a vast amount of datasets for fission yeast spores (transcriptome, proteome, differential viability of a mutant collection) and killifish, which will be a very useful resource for researchers in the field. The authors identified several genes and processes that are commonly differentially regulated in dormant cells across several systems, including autophagy and ribosomal protein genes, which were previously known to be required for G0.

    5. Reviewer #4 (Public review):

      Summary:

      This manuscript from Hassan et al presents a comparative study of the hallmarks of dormant cells in different systems. The authors first use a bar-coded knockout set to identify genes important for persistence of fission yeast spores in the dormant state (up to 6 months) and then use RNA seq and Mass spec to examine changes in transcript and protein abundance as fission yeast spores enter and exit dormancy. They then perform a similar transcriptomic and proteomic profile of Killifish embryos in diapause. The authors note certain functional classes of genes (e.g. autophagy-related genes or ribosomal proteins) that are similarly upregulated during dormancy in both systems and show that this applies in human dormant cancer cells as well. Importantly, this pattern applies to genes broadly in these functional classes, but not necessarily to any particular gene within the class. Finally, the authors examine the effects of stress (heat shock) on the transcriptomes and proteomes of young and aged spores as well as mitotic populations germinated from those spores.

      Dormancy is relatively understudied at the cellular level and this study provides a number of potentially interesting observations, in particular the authors identify common features of dormancy-related expression changes in the different systems that may be a general hallmark of dormancy. Included in this is a fascinating disconnect between transcript and protein levels of ribosomal genes. That said, the work seems to be almost three different stories, none of which is entirely complete, and which only loosely fit together: (1) the identification of dormancy mutants in fission yeast; (2) comparative 'omics of dormancy in different systems; and (3) changes induced by stress in dormant cells.

      Specific comments:

      There is a dissonance between the apparent upregulation of autophagy genes during dormancy in both systems and the observation that loss of autophagy genes enhance dormancy. While the authors brush this off by noting "that genes regulated in a given cellular state often do not overlap with those functionally required for that state" (an argument that undermines their whole analysis), it raises the possibility that the process signatures they report are not, in fact, functionally linked to dormancy and could be coincidental. What would make the expression analysis more convincing would-be functional data showing that at least one of the upregulated genes in one of the systems is important for dormancy.

      It is not clear how the bar-coding experiment was controlled for mutants that make defective spores (e.g. poor spore walls) rather than being required for dormancy per se. As the authors note, several of the mutants with the strongest effects (shortening lifespan of the spores) have been previously identified in screens for sporulation or spore wall defects, consistent with those genes playing a specific role in proper spore assembly rather than a general function in dormancy.

      It would be helpful to make the data - or at least the lists of genes identified in the screen and the up and downregulated transcripts/proteins available as supplemental information.

      There are at least two different points of diapause in Killifish. It was not entirely clear which diapause was analyzed here. This should be clarified.

      The data that offspring from older or heat-shocked spores are more stress resistant (Figure 4M) is not convincing. Serial dilution assays as shown are more qualitative than quantitative and an actual quantitative measure (e.g. c.f.u.'s per cell plated) would be better and would allow statistical confidence to be calculated.

      Significance:

      This work provides a useful overview and comparison of gene expression changes occurring during dormancy in different systems. As this is a relatively understudied area the results are likely to be of great interest to researchers in this area. The primary weakness, as is often the case for systems studies, is that it provides a 'high level' view of changes, but it is unclear whether any of the specific changes documented are functionally relevant to dormancy.

      I am a cell biologist and not qualified to critically assess the quality/appropriateness of the different statistical measures used to analyze their expression data.

    6. Author response:

      General Statements

      We thank the four reviewers for their careful reading of the manuscript and for their constructive and insightful comments. Below, we outline the revisions we have already started or plan to make shortly in response to the reviewers' specific comments.

      Description of the planned revisions

      Reviewer #1:

      In general, the manuscript is well structured and written, with carefully designed figures. The authors are also generally aware of the limitations of the work. The manuscript is interesting and valuable, but some interpretations should be moderated.

      Major Comments:

      (1) The data presented provide in general good support for majority of conclusions presented in the manuscript. However, less support is provided for the theories that: there is a universal "core expression signature" of dormancy; the mRNA/ribosome protein uncoupling reflects a specific mechanism of preparation for exiting dormancy; and the observed changes provide evidence for an epigenetic "memory" of dormancy. These elements are currently more interpretive than evidentiary. I would recommend softening the language in these places and clearly labeling them as hypothetical.

      We agree, of course, that the core expression signatures determined for yeast, killifish and human may not extend to all other dormant states (and we do not call it universal in the manuscript). Also, the mechanistic nature of the ‘memory’ of dormancy is not known, and we have merely speculated on the possibility of an epigenetic memory for discussion, based on the known causes of similar phenomena. Similarly, we discussed plausible processes underlying the mRNA/ribosome protein uncoupling. We will make it clearer in the revised manuscript that these are only hypotheses at this point.  

      (2) The authors propose that spores and post-dormancy cells retain a memory of time spent in dormancy or stress exposure. However, sporulation is also generally expected to reset or remove accumulated damage. Please clarify whether the observed long-term effects reflect classical "memory" of the cell, persistent regulatory state, or selection of cells with different fitness (carry-over effects). I would be careful in interpreting the effects as a form of memory associated mostly with epigenetic control - the statement seems stronger than what the data directly demonstrate

      Our experiments focus on spores (post-sporulation) and their offspring cells, so these findings have no bearing on any reset that may occur during earlier sporulation. It is extremely unlikely that the observed ‘memory’ effects reflect the selection of cells with different fitness, because we started with genetically identical cells, findings were repeated independent biological replicates, and our conditions (stress and prolonged time) maintained the viability of most cells. However, we certainly agree that our results do not establish a mechanism for the observed post-dormancy ‘memory’ effects. Again, we merely discussed the plausible possibility of an epigenetic memory and will provide more nuanced interpretations in the revised manuscript.

      (3) Validation of a small set of top hits by independent assays would substantially strengthen the conclusions. For example, targeted qPCR and/or biochemical assays for key genes (like ribosomal and autophagy) or pathways would help confirm the most important transcriptomic and proteomic trends. Without this, the suggestion of, for example, translational regulation remains possible but unproven

      For transcriptome and proteome studies using current, reliable methods, transcripts or proteins from specific genes are not typically validated, especially when studies focus on global patterns and functional enrichments rather than on any specific genes. General induction of autophagy-related genes at both the RNA and protein levels is unlikely to occur by chance, and autophagy genes have previously been shown to be induced under similar conditions, including in the transcriptomes of killifish diapause embryos (see Fig. S2H). However, we agree that validation of the unexpected regulation of ribosomal genes in diapause embryos would strengthen this conclusion. In spores, the effect is weaker and depends on the control sample used. We have begun qPCR and Western validation of ribosomal protein gene expression in killifish embryos to independently confirm the antagonistic changes in relative expression at the RNA and protein levels. We will incorporate these results in the revised manuscript.

      (4) Overall, the study appears reproducible, with detailed methods, biological replicates, and appropriate statistical analyses. However, reproducibility could be improved by more clearly describing the limitations of the comparisons (especially with the published datasets) and the data-processing parameters, particularly for correlation and functional enrichment analyses. Some comparative conclusions are based on data from different studies, under different conditions and often with different depths of coverage. It is not always clear to what extent interspecies comparisons are fully methodologically similar

      We used two strategies to minimise the limitations of comparing our RNA-seq data with a published RNA-seq dataset (human dormant cancer cells): first, we considered only genes that were detected in all datasets being compared, so that any differences in sequencing depth would not bias the comparison; second, we used relative expression values normalized to their respective controls instead of raw values, using identical cutoffs for all studies (fold-change >1.5x; FDR <0.05), to make the cross-study comparisons more consistent. We will further clarify this and its limitations in the Methods and Results. Notably, we observed significant functional enrichments shared across all three model systems, something one would not expect if the comparison were strongly affected by differences in the analyses. A strength of our study is that the RNA-seq datasets used to compare killifish and yeast were both generated in our laboratory under standardised conditions and processed through the same bioinformatics pipeline, thus enhancing comparability. 

      Minor Comments:

      (1) Because the Bar-seq assay measures barcode abundance only after germination and regrowth, the phenotype may reflect not only spore survival but also germination efficiency and subsequent growth. Please discuss how the authors controlled for selection during regrowth and whether new mutations, clonal selection, or differences in ploidy could contribute to the observed effects.

      We highlight in the manuscript that “Bar-seq quantification of deletion mutants was carried out after spore germination and regrowth. Therefore, the abundance of each barcode is a product of the combined influence of spore survival, germination efficiency and growth, which may affect stress- or lifespan-associated phenotypes for some mutants, a compromise necessary to prevent barcodes of dead spores from being sequenced.” To partially correct for this effect, we used conservative cutoffs and normalised all barcode abundances to the 2-week reference timepoint, so mutants that were already depleted early on due to poor germination or growth should not confound the longevity analysis. Similarly, the heat-shocked samples were compared to unstressed control samples, and any differences between the two samples should therefore reflect the effect of heat stress. All timepoints and pools were grown under consistent, uniform outgrowth conditions. We used two independent biological replicates and pooled samples, so any effects arising from extremely rare events, such as new mutations or diploidization, should be negligible. Moreover, our genome-wide screens focus on global functional enrichments rather than any specific mutants. We will describe this strategy in more detail in the Methods section.

      (2) Please clarify whether spores were analyzed as pooled spores or after tetrad dissection, and how the authors ensured that colonies originated from single haploid spores rather than mixed events. Since ploidy can affect growth and lifespan, please indicate whether ploidy was checked in the recovered colonies or whether the experimental design excludes diploid formation during germination/regrowth.

      As indicated in the scheme in Fig. 1A, meiosis was triggered by crossing a haploid h+ with a haploid h- colony to generate diploid cells that immediately sporulate, using a colonypicking robot. All cells that do not form haploid spores after this step are then killed by heat. This is the same principle as applied for genetic interaction screens using synthetic genetic arrays (SGA). Thus, the haploid spores in each colony resulted from a cross between cells of different mating types carrying an identical deletion mutant. For both the Bar-seq screen and expressionbased spore analyses, spores were analysed as pooled populations without tetrad dissection. Our results refer to the population rather than the single-cell level. Unlike budding yeast, fission yeast cells do not normally grow as diploids and will not mate in the medium used for germination and regrowth. Naturally occurring, vegetatively growing diploid cells in fission yeast are rare and unstable; they would be at an equal growth disadvantage during regrowth from all conditions tested, thus minimising the possibility of a systematic bias caused by diploidy. Accordingly, we did not directly check for ploidy. We will describe this experimental design in greater detail in the Methods.  

      (3) The cross-species GO enrichment analyses may be affected by differences in annotation quality and gene coverage, especially between yeast and killifish. Please clarify how gene duplication, paralogs and annotation redundancy were handled and whether the conclusions remain robust after accounting for GO term similarity or annotation bias.

      Response:  The handling of orthology, paralogs, and annotation differences between species is described in the Methods. Importantly, yeast and killifish genes, and their GO terms, were not directly compared but via their corresponding human orthologs and GO associations, as these mappings are most reliable and comprehensive. Orthology relationships between fission yeast and humans were obtained from a manually curated list in PomBase (Wood et al. 2019), compiled from various sources. In some cases, the consensus ortholog from the major ortholog predictors (Compara, Inparanoid, OrthoMCL) is used. Distant orthologs have also been identified by PSI-BLAST matches. Other ortholog predictions come from experimental data demonstrating functional correspondence or involving membership of corresponding complexes. These predictions are aligned and submitted to Pfam before inclusion. PomBase’s approach ensures that the breadth of coverage exceeds that of any individual prediction method and includes many ortholog calls that are not detected by any automated method. Killifish-to-human orthology was inferred from BLASTp-based mapping to the human UniProt reference proteome, with additional hidden orthologs identified using the spotted gar proteome (Kelmer Sacramento et al. 2020). Spotted gar is a fish whose lineage diverged from teleosts before their genome duplication, and it was therefore used as a bridge to identify hidden orthologs between teleosts and mammals. All paralogs were included to ensure comprehensive coverage, although some may have specialised roles. 

      Thus, all genes were mapped to their human orthologs and their GO associations, and genes commonly regulated were identified by filtering for significantly induced or repressed genes across all models, as indicated. This procedure circumvents the bias in GO term annotation between yeast and killifish. Naturally, given the relatively poor annotation of the killifish genome, such comparative analyses may miss some orthologs. Nevertheless, we observed significant functional enrichments shared across all three model systems, something one would not expect if the comparison were strongly affected by differences in annotation quality or gene coverage. We will highlight this information more clearly in the revised manuscript. 

      (4) Please discuss whether the age of the mother cells could influence spore quality, lifespan, or stress resilience. Did the authors control for mother-cell age? I would like to know about a possible impact on spore quality and subsequent lifespan.

      Spores were always produced from freshly grown cells derived from frozen stocks, thereby controlling for any effects of cell age on spore quality and lifespan. Moreover, unlike budding yeast, fission yeast cells grow by symmetrical division, producing two daughter cells of equal size, so the concepts of mother cells and replicative ageing do not apply.  

      (5) It may be useful to briefly note how dormancy in plants differs from the fungal and animal systems studied here or to clarify that the present conclusions are limited to the models analyzed in this manuscript.

      We agree that this is an interesting topic, e.g. plant seeds. However, given that dormancy is an understudied cell state, there is not enough solid information to make meaningful comparisons at this point. Moreover, any comparative review of what is known in plants is beyond the scope of our study. In the revised manuscript, we will clarify that our findings are naturally limited to the fungal and animal cells analysed in our study.  

      (6) Please briefly justify the choice of heat shock as the acute stress condition. It would also be helpful to comment on whether oxidative stress (for example, by incubation with H2O2) might reveal similar or distinct aspects of dormant-cell resilience.

      Heat shock is a well-established stressor for yeast that elicits a conserved stress response and, most critically, avoids the spore wall permeability problem that complicates the interpretation of trials with chemical stressors. We have actually tried to stress spores with H2O2 at different doses, treatment times, and cell densities; under all tested conditions, spores maintained high viability similar to untreated spores, possibly due to their strong catalase activity (spore suspensions foamed after adding H2O2). Therefore, oxidative stress is not a suitable stressor for the resilient spores. In the revised manuscript, we will show these data in a supplement to justify the use of heat shock as an acute stress condition. 

      Reviewer #2:

      This study is interesting because it provides valuable transcriptomic and proteomic datasets across a large number of conditions in both yeast spores and killifish embryos. Furthermore, these datasets reveal several interesting insights about dormancy, including shared gene expression changes in dormancy across diverse organisms, a trade-off between stress resilience and longevity in yeast spores, and memory of heat stress following exit from dormancy.  This interesting manuscript would be strengthened by additional characterization of the functional consequences of these gene expression changes.

      Major Comments: 

      (1) The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?

      We used the genome-wide deletion library in fission yeast to systematically screen for genes required for spore resilience and longevity. A comparably large-scale functional interrogation is not possible for killifish diapause embryos. Generating deletion-mutant lines for even a single gene in killifish is quite a lengthy and costly effort and would not be realistic within the scope of this work. However, we agree that an exciting direction for future studies is to assess what conserved genes contribute to dormancy function across species.

      (2) The authors show that gene expression changes in dormancy (old vs. young spores and late vs. early diapause) are poorly correlated with aging in stationary-phase yeast cells (Figure 4E) and killifish brain (Figure 5E). The authors suggest that this poor correlation might reflect a lack of aging during dormancy. Interestingly, the authors also show that in both yeast and killifish models, both time and stress in dormancy result in gene expression changes following exit from dormancy. Are these post-dormancy differences correlated with aging in stationary-phase yeast cells and adult killifish? Additionally, could the authors compare these changes with age-related changes in different adult killifish organs?

      We conducted these analyses and did not observe any positive correlation between post-dormancy gene expression changes in yeast offspring from old spores and gene expression changes in stationary-phase ageing cells; if anything, the gene expression signatures showed a slight inverse correlation (R = -0.05; p = 9e-04). For killifish, postdormancy expression changes in embryos from late diapause showed no significant correlations with age-related expression changes in adult brain (R = −0.04, p = 0.84), liver (R = −0.03, p = 0.39), or skin (R = −0.06, p = 0.19). Changes in expression in killifish late diapause compared to early diapause showed no significant correlation with age-related changes in expression in adult killifish liver (R = −0.008, p = 0.53), but did show a weak correlation with old skin (R = 0.18 , p <2e−16), specifically repression of collagen and extracellular matrix genes. We will include the latter analyses in the revised manuscript as supplemental panels.

      (3) In Figures 1C, 4F, and 5F, the authors find significant correlations between two comparisons with a shared condition (e.g. 2-week unstressed spores in Figure 4F). Using the same samples in both comparisons can inflate the correlation between the two sets of fold-changes because the two comparisons are not made independently. The authors should ensure that the correlations found in these figures remain significant even when different samples are used for the shared condition in the two comparisons.

      To address this issue, we re-ran the correlation analyses shown in Figs 1C, 4F and 5F using independent replicates for the shared condition (split-replicate analyses). For example, in Fig 4F, replicate A of the 2-week unstressed spores was used for the old-versus-young comparison, while replicate B was used for the heat-shock-versus-control comparison. For Fig 1C, using independent batches, the correlations remained significant (Batch A vs B: R = −0.16, p = 4.e−16; Batch B vs A: R = −0.27, p <2e−16). For Fig 4F, using independent replicates of the unstressed spores, the correlations remained significant across all replicate pairs (Rep 1 vs Rep 2: R = 0.18, p <2e−16; Rep 2 vs Rep 3: R = 0.14, p = 3e−12; Rep 2 vs Rep 1: R = 0.23, p <2e−16). For Fig 5F, the correlations were also robust across independent pairs (Pair 1: R = 0.15, p = 4e−10; Pair 2: R = 0.13, p = 3e−08; Pair 3: R = 0.16, p = 2e−11). These results show that the correlations are not an artefact of shared samples.

      Minor Comments:

      (4) In Figure 1, it could be informative to present data from intermediate timepoints (i.e., 1, 2, and 5 months). Do mutants that affect spore longevity show a progressive enrichment or depletion over time in dormancy, as depicted in the schematic in Figure 1A? Are there differences in kinetics across the different mutants?

      We thank the reviewer for this suggestion. We will add line plots of the logCPM abundance of short-lived and long-lived mutants (relative to 2-week spore mutants) for all timepoints (2 weeks, 1, 2, 5 and 6 months) to Figure 1. The plots show that most short-lived mutants exhibit progressive depletion over time, while most long-lived mutants exhibit progressive enrichment.

      (5) The authors focus on shared gene expression changes in dormancy between yeast spores, killifish diapause embryos, and dormant cancer cells. Are any pathways enriched among yeast-, killifish-, or human-specific changes?

      We focused on pathways shared across species, but we have now also examined pathways enriched among genes that change specifically in each organism. Among the species-specific gene sets, only genes repressed in yeast spores were significantly enriched, with cytoplasmic translation being the most enriched process (p = 4e-11). The killifish- and human-specific gene sets were not significantly enriched for any functional terms. These results will be included as a supplementary figure.

      Reviewer #3:

      In this study, the authors use several genome-wide approaches to find commonalities and differences between distinct dormancy models, including S. pombe spores, killifish diapause embryos, and human dormant cancer cells. As G0 states are ubiquitous in nature, yet still understudied, this study provides not only a clear demonstration of an evolutionarily-conserved component in G0 regulation, but also a rich resource of genomics datasets, that will be especially of interest to S. pombe and killifish researchers.

      The main conclusions of the study are that: (1) there are commonalities in dormancy programs across eukaryotic evolution; (2) the uncoupling between transcriptome and translatome is more pronounced in non-dividing cells, particularly at ribosomal protein coding genes; and (3) dormant cells retain dynamic responses to stress, changing their cellular state and maintaining this difference.

      Overall, the study is clearly written, provides large data resources for future research in the dormancy field, and the conclusions are supported by the presented evidence (although point 3 would require further experimental clarification, see below).

      Major Comments:  

      A key consideration when comparing transcriptomes of cycling and dormant cells is the overall RNA quantity in the cells; however, it is not made clear in the manuscript whether this overall level was compared and/or if RNA-seq used spike-in quantification. However, this will directly affect some conclusions; for example there is a conceptual difference between a transcript upregulated in spores and a transcript that appears upregulated in spores but is in fact 'less repressed than average' (resulting in higher FC). While the overall transcript diversity as described will be not affected, this analysis can provide important context. For the highlighted example of ribosomal protein mRNAs, this scenario could be what resulted in an apparent mRNA increase but protein decrease (which would be interpreted as both mRNA stabilization and translation inhibition); if the total mRNA is much lower, then the mRNA levels may be similar (which would then be interpreted as no change in transcription nor mRNA stability, but highlight the importance of translation inhibition). This point was more clearly described in a previous seminal study by the authors

      (Marguerat et al 2012), and so should be made clearer here as well. What is the overall RNA level in cycling cells compared to quiescent cells, and compared to spores? Likewise, what is the overall reduction in total protein levels? Was the overall reduction comparable to the previous study? These overall levels should at least be quantified, which is a straightforward experiment. The current mention of this limitation (two sentences in the last paragraph of the conclusion) is not enough. 

      Accordingly, the term "induced" (in Fig 2 for example) may be misleading, and could be separated into truly "induced" genes and "relatively induced" genes, when normalized to absolute levels. Likewise, Fig S2A and S2I are misleading due to this effect (normalization will tend to show a similar number of up-regulated and down-regulated genes). These panels should be clarified by plotting next to the relative quantification (current panels) a comparison taking overall RNA level into account. This will very likely strongly increase the number of downregulated genes and decrease the number of upregulated genes, and the new lists can also be subjected to GO analysis. 

      The same issue would be important to discuss for the killifish experiment as well.

      We acknowledge that the overall quantities of transcripts and proteins in proliferating cells versus those in different dormant cells are important considerations. As described in the Results and Methods, our study presents relative expression changes between dormant and proliferating cells. We highlight this limitation in the Conclusions: “As is the case for most expression studies, our transcriptome and proteome data reflect relative expression levels between two conditions. It is likely that the absolute cellular numbers of most transcripts and proteins are reduced in dormant cells, as they are in quiescent S. pombe cells (Marguerat et al. 2012). Thus, genes induced in dormant cells relative to active cells might be repressed in absolute levels, but to a lesser extent than most other genes.” We also make it clear in the concluding sentences for different sections that our data refer to relative changes (e.g., “Accordingly, several transcripts and proteins were differentially expressed in offspring from 5month-old spores relative to offspring from 2-week-old spores, including 199 mRNAs, 223 proteins, and 33 long non-coding RNAs”). We will carefully review the manuscript, including the Abstract and figures, to further clarify this point, e.g., by using "relatively induced" or “higher relative abundance”. 

      We would like to note, however, that relative quantification, normalised to average expression, focusing on genes that become more or less enriched relative to average genes in the condition of interest compared to a control condition, is a widely accepted and biologically meaningful approach for identifying expression signatures that specify the condition of interest. This is also the principle for qPCR and western analyses, where housekeeping gene normalisation is common. If the absolute expression of all or most genes decreases in dormant cells (which is likely), it would not be meaningful to determine functional enrichments among these genes. Relative expression data help distinguish groups of genes that increase or decrease more than most other genes, reflecting cellular regulation and functional signatures.

      However, we fully agree that the overall quantities of transcripts and proteins in proliferating cells versus those in different dormant cells offer a complementary perspective. Therefore, we are quantifying the overall RNA and protein levels in yeast spores compared to proliferating cells as well as in killifish diapause embryos compared to actively developing embryos. If successful, these results will provide useful context for the relative expression data and will be included in the revised manuscript.  

      For spore offspring stress experiments, the estimated number of cell divisions after spore germination should be indicated. It appears over 20, which would confirm the authors' conclusion that an epigenetic state is induced and persists over multiple rounds of replication and underlie improved heat resistance, indeed hinting at transcriptional memory through chromatin modification. As the phenotype is tested at a population-level, it is unclear whether every spore is equally able to impart this phenotype, and for how long the stress resistance response persists. Moreover, it may be clearer to test heat resistance in log-phase cells, as in the current experimental setup cells are grown to stationary-phase before plating; however, stationary-phase cultures are more heterogenous and more heatresistant.

      We agree, and several of these issues are already addressed in the manuscript. First, regarding the number of cell divisions, the offspring cells were obtained by germinating spores in rich medium and allowing them to grow to mid-exponential phase for approximately 6 generations, as described in the Results (p. 24). Second, for the log-phase testing, we state in the Methods (p. 43) that cultures were adjusted to OD600 = 0.2 and grown to OD600 = 0.6 before stress treatment and plating. Thus, all assays were in fact already performed on log-phase cells.

      We will further emphasise these points in the revised manuscript.

      Yes, our experiments were conducted at the cell population level, and we therefore cannot distinguish whether there is uniform transmission of the transcriptional and phenotypic legacy of dormancy across all germinating spores or whether single cells show heterogeneity within the population in this respect. While this is an interesting question, it would be too technically challenging and time-consuming to pursue this project within the framework of the current study.

      Note also that using 3-fold serial dilutions (instead of more commonly used 10-fold) means that the differences shown in Fig 4M are mild. Plating duplicates (at log-phase) and imaging at several times of growth would be necessary to have a clearer view of the result.

      We fully agree that the serial dilution assay for stress resistance should be improved given the subtle differences. We are repeating the spot assay with 10-fold serial dilutions as suggested, imaging plates at multiple time points, and including sufficient repeats to strengthen the confidence in this result.

      Minor Comments:

      One point for clarification in the introduction is that there is a scale difference between cellular dormancy (at the level of a cell) and organismal dormancy such as diapause (in which the dormancy qualifies the whole organism; typically this will involve slower metabolism, but not necessarily the arrest of the cell cycle for every cell in the organism). As diapause can take different forms depending on organism, detailing this process for the turquoise killifish model may clarify the phenotypic commonalities between these models. Indeed, as pointed out by the authors, several mutants show opposite effects for stationary phase viability and for dormant spore viability, highlighting the diversity of G0 states.

      We agree that there may be a difference between cellular dormancy, in which individual cells arrest their cell cycle, and organismal dormancy, such as diapause, in which individual cells may differ in cell-cycle arrest. However, it has been shown in killifish that all cells throughout the diapause embryo exit the cell cycle and become quiescent (Dolfi et al. 2019), as may be expected given that these embryos can remain dormant for up to two years. We will highlight this aspect more clearly in the Introduction. 

      The title of the manuscript may gain from also mentioning some of the insights (instead of "Gene function and expression of X and Y", "Gene function and expression in X and Y shows Z").

      We agree and will change the title to “Gene function and expression profiling in yeast spores, killifish diapause embryos, and their offspring cells reveal common regulatory features of dormancy

      The Venn diagram in Fig 1D may not be the best way to represent the data; by definition, multiple overlaps here will be zero (between "long-lived spores" and "short-lived spores" and between "long-lived cells" and "short-lived cells"). A table or an upset plot may be useful there. Note that the gained space in Fig 1 may be used to provide examples of mutants in each GO category.

      We agree and will replace the Venn diagram in Figure 1D with an UpSet plot, which better represents the overlapping and non-overlapping categories. 

      Reviewer #4:

      Specific Comments:

      There is a dissonance between the apparent upregulation of autophagy genes during dormancy in both systems and the observation that loss of autophagy genes enhance dormancy. While the authors brush this off by noting "that genes regulated in a given cellular state often do not overlap with those functionally required for that state" (an argument that undermines their whole analysis), it raises the possibility that the process signatures they report are not, in fact, functionally-linked to dormancy and could be coincidental. What would make the expression analysis more convincing would be functional data showing that at least one of the upregulated genes in one of the systems is important for dormancy.

      We respectfully disagree with this assessment. Please note that this apparent paradox between autophagy gene regulation and spore phenotype is highlighted and discussed in the manuscript, and our data actually confirm the importance of autophagy in dormant spores when considered carefully. It is not correct to say that ‘loss of autophagy genes enhance dormancy’. Although autophagy mutants show increased lifespan, they also show decreased stress resiliency. Stress resilience is a key feature and purpose of dormancy, so the loss of autophagy function would clearly be a problem for dormant cells, consistent with the induction of autophagy genes in spores. The surprising trade-off between processes (including autophagy) that support spore longevity and those that support stress resilience is a key finding of our study.

      Regarding the request for functional data linking at least one upregulated gene to dormancy, we note that we have already provided this at a scale far beyond a single gene. In fact, we identified as many as 600 genes that are differentially expressed in spores and, when deleted, cause spore phenotypes (altered stress resilience and/or lifespan). Moreover, the processes significantly enriched among these 600 genes, including protein translation, ribosomes, TCA cycle, and autophagy, are the same processes most enriched among the differentially expressed genes. This significant process-level convergence between functional and expression data provides compelling evidence that our expression signatures are not coincidental but largely reflect roles in dormancy.

      It is not clear how the bar-coding experiment was controlled for mutants that make defective spores (eg poor spore walls) rather than being required for dormancy per se. As the authors note, several of the mutants with the strongest effects (shortening lifespan of the spores) have been previously identified in screens for sporulation or spore wall defects, consistent with those genes playing a specific role in proper spore assembly rather than a general function in dormancy.

      The experimental design was carefully set up to avoid the contribution of mutants that do not sporulate or fail to produce mature spores. After sporulation, samples were heatshocked at 43°C for 3 days and stored in water for at least 2 weeks prior to starting the longevity assay. These conditions will kill any remaining vegetative cells; hence, only spores that can withstand these treatments will be included in the barcode measurements. All abundance values were also normalised to the abundance of each mutant that regrew after two weeks of spore ageing, so any differences in abundance should be caused by our conditions of interest. Thus, mutants with sporulation defects or that fail to form viable spores would be substantially depleted before this normalisation point and would therefore not be included in the subsequent analysis; if they were included, they should not show different abundances across our postsporulation conditions. Nevertheless, we cannot rule out that some of the short-lived mutants reflect problems during sporulation rather than spore traits, as suggested by overlaps with the published sporulation screen. However, this overlap is limited to a very few genes (among 416 short-lived spore mutants), as described in the Results. 

      In this context, we also comment on a broader issue known in the ageing field. Short-lived mutants are less likely to function in ageing-related processes than long-lived mutants, as any perturbation that compromises cell integrity or general fitness – including poor spore wall assembly – will compromise survival regardless of its specific relevance to dormancy regulation. Thus, the long-lived mutants in our screen are more revealing of ageing-related processes. We will clarify and better describe these perspectives in the revised manuscript. 

      It would be helpful to make the data - or at least the lists of genes identified in the screen and the up and downregulated transcripts/proteins available as supplemental information.

      Please note that we provide extensive Supplemental Datasets S1 to S6 that include this very information in table form (genes identified in the Bar-seq screens, differentially expressed RNAs and proteins across the different conditions), along with additional useful data from our large-scale assays. 

      There are at least two different points of diapause in Killifish. It was not entirely clear which diapause was analyzed here. This should be clarified.

      We described the Diapause II stage in the Introduction as the most prominent dormant state and commonly studied state in N. furzeri. In the Results (p. 12), we stated, “To uncover conserved aspects of dormant states between yeast and vertebrates, we analyzed the transcript and protein signatures of N. furzeri diapause II embryos, henceforth called diapause embryos,….”. We also mentioned Diapause II in the Methods. 

      The data that offspring from older or heat-shocked spores are more stress resistant (Figure 4M) is not convincing. Serial dilution assays as shown are more qualitative than quantitative and an actual quantitative measure (eg. c.f.u.'s per cell plated) would be better and would allow statistical confidence to be calculated.

      Serial dilution assays are widely used and accepted to assess differences in stress resistance. The problem with CFUs is that they only determine the proportion of viable cells, while stress resistance also incorporates growth (e.g., stress-sensitive cells may survive the stress but grow more slowly in its presence). However, we agree that the current data in Fig. 4M are not convincing. We are repeating the spot assay with 10-fold serial dilutions, imaging plates at multiple time points, and including sufficient repeats to strengthen the confidence in this result. This is in response to a similar concern raised by Reviewer 3. We will also determine CFU counts, which would provide a more quantitative measure for statistical confidence, should the stress resistance be primarily reflected in cell viability. If necessary, for a more quantitative growth assay allowing statistical analysis, we may also analyse control and stressed cells growing in liquid cultures. 

      Description of the revisions that have already been incorporated in the transferred manuscript

      We have started conducting additional experiments and making text changes to the manuscript, as described in the specific points above. At this stage, we do not provide a partially revised manuscript, but we will provide a fully revised one when everything is ready. 

      Description of analyses that authors prefer not to carry out

      Reviewer 2:

      Comment 1: The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?

      We used the genome-wide deletion library in fission yeast to systematically screen for genes required for spore resilience and longevity. A comparably large-scale functional interrogation is not possible for killifish diapause embryos. Generating deletion-mutant lines for even a single gene in killifish is quite a lengthy and costly effort and would not be realistic within the scope of a revision. However, we agree that an exciting direction for future studies is to assess what conserved genes contribute to dormancy function across species.

    1. eLife Assessment

      The results of this study are important and the approach to dissect the developmental contribution of RIF1 function is convincing. The results that suggest a possibly separate control of replication timing and gene expression are intriguing and represent a strong foundation for future research. The work will be of interest for researchers both in the transcription and the replication field, especially for scientists investigating the interplay between the two processes.

    2. Reviewer #1 (Public review):

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

      The authors sought to determine how Rif1 contributes to DNA replication timing (RT), transcriptional regulation, and embryonic development using zebrafish. They generated a maternal-zygotic rif1 knockout line and examined developmental phenotypes, genome-wide replication timing profiles, RNA-seq, and nascent transcription (SLAM-seq) during early embryogenesis.

      Their major findings in this manuscript are.

      (1) Rif1 is not essential for zebrafish viability, unlike its partially essential role in mice.

      (2) Rif1 deficiency causes defects in female sex determination, delayed epiboly, and reduced primitive erythropoiesis.

      (3) Genome-wide RT is altered by Rif1, but developmental stage has a much larger influence than Rif1 itself.

      (4) Rif1 is required for the proper maturation ("sharpening") of the RT program during development rather than for specific developmental RT switches.

      (5) Rif1 has a much stronger effect on transcription during zygotic genome activation (ZGA) than on replication timing at these early stages.

      (6) Loss of Rif1 leads to increased expression of early zygotic genes, indicating that Rif1 normally suppresses widespread transcription during ZGA.

      Overall, the work proposes that Rif1 independently regulates replication timing and transcription, with these two functions becoming most prominent at different developmental stages.

      The major strengths of the manuscript are as follows.

      (1) the study combines multiple genome-wide approaches including whole-genome RT profiling, RNA-seq, SLAM-seq in combination with gene KO and developmental analyses.

      (2) One of the strongest points is that the authors conducted the analyses at multiple developmental stages rather than a single point.

      (3) The most important conclusion is that the Rif1 regulates transcription during development in a manner largely independent of its RT function, which was further strengthened by the additional data provided in the revised manuscript.

      Comments on revised version:

      The authors responded to my comments in a largely satisfactory manner. They have conducted additional analyses and concluded that Rif1 regulates transcription during ZGA largely independently of its classical RT function, which is an important finding.

      Although authors did not examine origin firing and replication fork rate in rif1 KO cells, which I suggested in my original review, this can be saved for their future studies.

      I think the revised manuscript has been improved and provides important basic information on the functions of the conserved Rif1 protein in RT and transcriptional regulation.

      I have no further recommendation for additional experiments or data analyses.

    3. Reviewer #2 (Public review):

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

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

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

      Comments on revised version:

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

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The authors sought to determine how Rif1 contributes to DNA replication timing (RT), transcriptional regulation, and embryonic development using zebrafish. They generated a maternal-zygotic rif1 knockout line and examined developmental phenotypes, genome-wide replication timing profiles, RNA-seq, and nascent transcription (SLAM-seq) during early embryogenesis.

      Their major findings in this manuscript are

      (1) Rif1 is not essential for zebrafish viability, unlike its partially essential role in mice.

      (2) Rif1 deficiency causes defects in female sex determination, delayed epiboly, and reduced primitive erythropoiesis.

      (3) Genome-wide RT is altered by Rif1, but developmental stage has a much larger influence than Rif1 itself.

      (4) Rif1 is required for the proper maturation ("sharpening") of the RT program during development rather than for specific developmental RT switches.

      (5) Rif1 has a much stronger effect on transcription during zygotic genome activation (ZGA) than on replication timing at these early stages.

      (6) Loss of Rif1 leads to increased expression of early zygotic genes, indicating that Rif1 normally suppresses widespread transcription during ZGA.

      Overall, the work proposes that Rif1 independently regulates replication timing and transcription, with these two functions becoming most prominent at different developmental stages.

      The major strengths of the manuscript are as follows.

      (1) the study combines multiple genome-wide approaches including whole-genome RT profiling, RNA-seq, SLAM-seq in combination with gene KO and developmental analyses.

      (2) One of the strongest points is that the authors conducted the analyses at multiple developmental stages rather than a single point.

      (3) The most important conclusion is that the Rif1 regulates transcription during development in a manner largely independent of its RT function, which was further strengthened by the additional data provided in the revised manuscript.

      On the other hand, the weakness of the manuscript includes the followings.

      (1) Limited mechanistic insight. The questions such as where Rif1 binds on the chromatin (in relation to the transcriptional promoters/ enhancers and replication origins).

      (2) Which functional domains of RIf1 are involved in regulation of transcription and replication (Is PP1 recruitment required for transcription regulation?) are not addressed.

      (3) Since Rif1 is known to be involved in chromatin organization/ nuclear architecture regulation, the studies addressing this (Hi-C, compartment analyses, ATAC seq etc) would provide important mechanistic information.

      (4) Female sex determination phenotype is intriguing, but it remains largely descriptive, and its mechanisms are elusive at the moment.

      Overall, the results support the authors' conclusions and they have successfully provided answers to the authors' original questions on developmental roles of Rif1 in RT and transcription in vertebrate.

      Comments on revised version:

      The authors responded to my comments in a largely satisfactory manner. They have conducted additional analyses and concluded that Rif1 regulates transcription during ZGA largely independently of its classical RT function, which is an important finding.

      Although authors did not examine origin firing and replication fork rate in rif1 KO cells, which I suggested in my original review, this can be saved for their future studies.

      I think the revised manuscript has been improved and provides important basic information on the functions of the conserved Rif1 protein in RT and transcriptional regulation.

      I have no further recommendation for additional experiments or data analyses.

      We thank the reviewers for their public evaluations of the manuscript.

      Reviewer #2 (Public review):

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

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

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

      Comments on revised version:

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

      We thank the reviewers for their public evaluations of the manuscript.

    1. eLife Assessment

      This important study explores the function of SIRT2 in inhibiting Japanese encephalitis virus infection and disease progression in rodent models. The findings presented are novel as sirtuins are known for their roles in aging, metabolism, and cell survival, but have not been studied in the context of viral infections until recently. The evidence supporting the claims is solid and reveals a mechanism for SIRT2 in modulating acetylated NF-kB transcription factor levels and additional inflammatory response mediators. The work will be of interest to biologists studying viruses, sirtuins, and inflammation.

    2. Reviewer #1 (Public review):

      Summary:

      Desingu et al. show that JEV infection reduces SIRT2 expression. Upon JEV infection, 10-day old SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival. Conversely SIRT2 overexpression reduced viral titer, clinical outcomes and improved survival. Transcriptional profiling show dysregulation of NF-KB and expression of inflammatory cytokines. Pharmacological NF-KB inhibition reduced viral titer. The authors conclude that SIRT2 is a regulator of JEV infection.

      Strengths:

      This paper is novel because sirtuins have been primarily studied for aging, metabolism, stem cells/regeneration. Their role in infection has not been explored until recently. Indeed, Barthez et al. showed that SIRT2 protects aged mice from SARS-CoV-2 infection (Barthez, Cell Reports 2025). Therefore, this is a timely and novel research topic. Mechanistically, the authors showed that SIRT2 suppresses the NF-KB pathway. Interesting, SIRT2 has also been shown recently to suppress other major inflammatory pathways, such as cGAS-STING (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Together, these findings support the emerging concept that SIRT2 is a master regulator of inflammation.

      Comments on revised version.

      The authors have addressed my comments.

    3. Reviewer #2 (Public review):

      The manuscript by Desingu et al., explores the role of SIRT2 in regulating Japanese Encephalitis Virus (JEV) replication and disease progression in rodent models. Using both an in vitro and an in vivo approach, the authors demonstrate that JEV infection leads to decreased SIRT2 expression, which they hypothesize is exploited by JEV for viral replication. To test this hypothesis, the authors utilize SIRT2 inhibition (via AGK2 or genetic knockout) and demonstrate that it leads to increased viral load and worsens clinical outcomes in JEV-infected mice. Conversely, SIRT2 overexpression via an AAV delivery system reduces viral replication and improves survival among infected mice. The study proposes a mechanism in which SIRT2 suppresses JEV-induced autophagy and inflammation by deacetylating NF-κB, thereby reducing Beclin-1 expression (an NF-κB-dependent gene) and autophagy, which the authors consider a pathway that JEV exploits for replication. Transcriptomic analysis further supports that SIRT2 deficiency leads to NF-κB-driven cytokine hyperactivation. Additionally, pharmacological inhibition of NF-κB using Bay 11 (an IKK inhibitor) results in reduced viral load and improved clinical pathology in WT and SIRT2 KO mice. Overall, the findings from Desingu et al. are generally supported by the data and suggest that targeting SIRT2 may serve as a promising therapeutic approach for JEV infection and potentially other RNA viruses that SIRT2 helps control.

      Comments on revised version.

      We believe the authors of Desingu et al. have adequately addressed the major concerns of each reviewer. Thus, we believe the current version of the manuscript is significantly improved, and the data more strongly support the authors' conclusions.

      We would like to point out that the statistics in Figure 8 are difficult to interpret and should be improved to make them useful to the reader and strengthen the paper. Also, line 70 has an extra period that should be removed.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Desingu et al. show that JEV infection reduces SIRT2 expression. Upon JEV infection, 10-day-old SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival. Conversely, SIRT2 overexpression reduced viral titer, clinical outcomes, and improved survival. Transcriptional profiling shows dysregulation of NF-KB and expression of inflammatory cytokines. Pharmacological NF-KB inhibition reduced viral titer. The authors conclude that SIRT2 is a regulator of JEV infection.

      This paper is novel because sirtuins have been primarily studied for aging, metabolism, stem cells/regeneration. Their role in infection has not been explored until recently. Indeed, Barthez et al. showed that SIRT2 protects aged mice from SARS-CoV-2 infection (Barthez, Cell Reports 2025). Therefore, this is a timely and novel research topic. Mechanistically, the authors showed that SIRT2 suppresses the NF-KB pathway. Interestingly, SIRT2 has also been shown recently to suppress other major inflammatory pathways, such as cGAS-STING (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Together, these findings support the emerging concept that SIRT2 is a master regulator of inflammation.

      We thank the reviewer for his/her valuable time, clear summary, and critical comments on the work.

      Weaknesses:

      (1) Figures 2 and 3. Although SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival upon JEV infection, the difference is modest because even WT mice exhibited very severe disease at this viral dose. The authors should perform the experiment using a sub-lethal viral dose for WT mice, to allow the assessment of increased clinical outcomes and reduced survival in KO mice.

      We thank the reviewer for the valuable recommendation. We conducted an experiment using a sub-lethal viral dose of 5 × 10^2 PFU in 14-day-old wild-type (WT) and SIRT2 knockout (KO) mice to evaluate clinical outcomes. Our findings showed a significant reduction in survival rates among the SIRT2-KO mice compared to the wild-type mice (Figure 2O).

      (2) Figure 5K-N, the authors examined the expression of inflammatory cytokines in WT and SIRT2 KO cells upon JEV infection, in line with the dysregulation of NF-kB. It has been shown recently that SIRT2 also regulates the cGAS-STING pathway (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Do you also observe increased IFNb, IL1b, and IL18 in SIRT2 KO cells upon JEV infection? This may indicate that SIRT2 regulates systemic inflammatory responses and represents a potent protection upon viral infection. This is particularly important because in Figure 7F, the authors showed that SIRT2 overexpression reduced viral load even when NF-KB is inhibited, suggesting that NF-KB is not the only mediator of SIRT2 to suppress viral infection.

      We agree with the suggestion made by the reviewers. We have quantified the transcript levels of IFNb, IL1b, and IL18 and found that the expression of these genes was increased in JEV-infected SIRT2-KO mice compared to wild-type mice (Figure S5D-S5F). Additionally, we acknowledge the reviewers' comments regarding the role of SIRT2 in regulating systemic inflammatory responses and its significant protective effect against viral infections. Furthermore, NF-κB is not the only mediator involved in SIRT2's suppression of viral infections; other molecular mechanisms may also contribute to this process.

      Reviewer #2 (Public review):

      The manuscript by Desingu et al., explores the role of SIRT2 in regulating Japanese Encephalitis Virus (JEV) replication and disease progression in rodent models. Using both an in vitro and an in vivo approach, the authors demonstrate that JEV infection leads to decreased SIRT2 expression, which they hypothesize is exploited by JEV for viral replication. To test this hypothesis, the authors utilize SIRT2 inhibition (via AGK2 or genetic knockout) and demonstrate that it leads to increased viral load and worsens clinical outcomes in JEV-infected mice. Conversely, SIRT2 overexpression via an AAV delivery system reduces viral replication and improves survival among infected mice. The study proposes a mechanism in which SIRT2 suppresses JEV-induced autophagy and inflammation by deacetylating NF-κB, thereby reducing Beclin-1 expression (an NF-κB-dependent gene) and autophagy, which the authors consider a pathway that JEV exploits for replication. Transcriptomic analysis further supports that SIRT2 deficiency leads to NF-κB-driven cytokine hyperactivation. Additionally, pharmacological inhibition of NF-κB using Bay 11 (an IKK inhibitor) results in reduced viral load and improved clinical pathology in WT and SIRT2 KO mice. Overall, the findings from Desingu et al. are generally supported by the data and suggest that targeting SIRT2 may serve as a promising therapeutic approach for JEV infection and potentially other RNA viruses that SIRT2 helps control. However, the paper does fall short in some areas. Please see below for our comments to help improve the paper.

      We thank the reviewer's valuable recommendation. We measured NF-kB acetylation in JEV-infected Neuro-2a cells treated with Ad-null or Ad-SIRT2. Our findings show that levels of NF-kB acetylation and JEV-NS5 were reduced in the SIRT2-overexpressing cells (Figure S6).

      We conducted an experiment using a sub-lethal viral dose of 5 × 10^2 PFU in 14-day-old wild-type (WT) and SIRT2 knockout (KO) mice to evaluate clinical outcomes. Our findings showed a significant reduction in survival rates among the SIRT2-KO mice compared to the wild-type mice (Figure 2O).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      In Figures 5C-F, the text is very small.

      Thanks for raising this point. We have increased the text size in the revised Figures 5C-F

      Reviewer #2 (Recommendations for the authors):

      Major concerns:

      (1) Line 71: The introduction does not adequately cite existing work demonstrating the role of SIRT2 in viral infections, including other RNA viruses. It would be more effective to summarize the current literature (similar to lines 162-165) rather than stating that the role of SIRT2 in RNA virus infection is largely unknown.

      We thank the reviewer for the valuable suggestion to improve the manuscript substantially. We have cited existing work demonstrating the role of SIRT2 in viral infections, including other RNA viruses, in the introduction, as suggested.

      (2) Figure 1C: The image should be quantified to state that the observation is statistically significant.

      Thank you for the valuable suggestion. We have quantified the proteins SIRT2, LC3 I, LC3 II, p62, and Beclin in Mock- and JEV-infected Neuro-2a cells, as well as in mouse brain Western blots. These results, along with representative blots (Figure 6I-6J) and n-number-related blots, are presented graphically along with p-values in supplementary figures 7A-7B. Our analysis indicates that these proteins are expressed at statistically significant levels.

      (3) Figure 1I: What tissue is 1I? not stated in the manuscript text or the figure legend.

      Thank you for pointing that out. These samples are from mouse brains. We have included this information in both the text and the figure legend of the revised manuscript.

      (4) Figure 2A: How does Sirt2 expression in WT-infected cells compare to the levels observed in the KD? Are they at comparable levels?

      Thank you for highlighting an important point. We used control siRNA to compare SIRT2 knockdown in SIRT2-siRNA-treated cells in JEV infection. In Figure 2A, “control siRNA” was labelled as “control.” In the revised Figure 2A, we updated this to read “control siRNA.”

      (5) Figure 2L-N: Clinical outcome comparing WT and SIRT2KO mice, JEV-infected mice seems modest. Can statistical analysis be performed to determine whether the results are statistically significant?

      We agree with the suggestion. Clinical signs (Figure 2L) and paralysis (Figure 2M) onset were earlier in SIRT2-KO mice compared to WT mice, and the survival curve (Figure 2N) shows statistical significance. We conducted an experiment using a sub-lethal viral dose of 5 × 10^2 PFU in 14-day-old wild-type (WT) and SIRT2 knockout (KO) mice to evaluate clinical outcomes. Our findings showed a significant reduction in survival rates among the SIRT2-KO mice compared to the wild-type mice (Figure 2O).

      (6) Figure 3: AGK2 can also inhibit other Sirtuins. Thus, to test the contribution of other Sirtuins, the experiment could be repeated using wild-type and Sirt2 KO mice.

      We agree with the suggestion. We repeated the experiment using wild-type and SIRT2-KO mice and found that the differences in survival rates were statistically insignificant between JEV-infected wild-type mice treated with AGK2 and JEV-infected SIRT2-KO mice treated with AGK2 (Figure 3K).

      (7) Figure 5: A-B: Clearly label diagrams and explain what is being compared. G-I: Please annotate genes in the heat maps and volcano plots.

      Thank you for pointing out an important aspect. The figures include several genes that may not be easily visible at readable levels in the main figure. To address this, we have enlarged the figures and labelled them with the gene names in a clearer format, as shown in the supplementary Figures S2-S4. Additionally, we have updated the figure legend for Figure 5 to include this information.

      (8) Line 230-232: "Taken together, these findings reveal that JEV infection negatively regulates SIRT2 levels, resulting in a concomitant increase in acetylated NF-κB mediated virus replication". Data does not support this statement. The experiments presented do not demonstrate that acetylated NF-kB mediates viral replication. In fact, it would be good to measure NF-kB acetylation in AdSIRT2 JEV-infected cells compared to WT-infected cells, to verify that the acetylation of NF-kB is truly linked to SIRT2 expression levels.

      We thank the reviewer for his/her valuable recommendation. We measured NF-kB acetylation in JEV-infected Neuro-2a cells treated with Ad-null or Ad-SIRT2. Our findings show that levels of NF-kB acetylation and JEV-NS5 were reduced in the SIRT2-overexpressing cells (Figure S6).

      (9) Figure 6E: The Graph is hard to interpret. It is also missing a figure legend/key, and also a misplaced A in the image.

      Thank you for highlighting an important point. We have highlighted NF-κB and other colour representations in the signalling network graph (Figure 6E). We revised the figure legend for clarity and removed the misplaced "A" from the figure.

      (10) Line 260-261: "These results indicate that SIRT2 regulates JEV replication through the NF-κB-beclin-autophagy axis.". Data does not support this statement.

      We modified the statement as follows: “These results suggest that SIRT2 regulates JEV replication through the NF-κB-autophagy axis.

      (11) Line 270: Do the authors mean decrease? Bay 11 is an NF-κB inhibitor.

      We agree with the suggestion. This is a typographical error. We have replaced “increase” with “decrease” in the revised manuscript.

      (12) Figure 8: The phenotype seems modest. Have the authors considered that Sirt2 may regulate JEV via other acylation targets?

      We agree with the reviewers' suggestion. NF-κB is not the only mediator involved in SIRT2's suppression of viral infections; other molecular mechanisms may also contribute to this process.

    1. eLife Assessment

      The characterization of a dissociable Mediator subunit implicated in cellular pathways, particularly lung alveolar function and HIV latency, would be conceptually interesting. The authors have preliminary evidence for a Med16 subcomplex that may regulate specific genes. This work is useful in that it points to interactions between Med16 and UBP1, but the evidence is somewhat preliminary and incomplete.

    2. Reviewer #1 (Public review):

      This work reports the finding of a dissociable human Mediator complex subunit, MED16, and its recruitment of transcription factors UBP1 and TFCP2 that form a trimeric complex regulating cellular gene transcription and inhibiting HIV promoter activity, thus promoting MED16-targeting inhibitors as HIV latency-reversing agents for anti-HIV therapy. In this revision, the authors provide new data indicating that MED16 action occurs in the holo-Mediator complex, not as a free form, for its functional association with UBP1 and TFCP2.

      Summary:

      Characterization of a dissociable Mediator subunit implicated in cellular pathways, particularly lung alveolar function, and HIV latency is conceptually interesting.

      Strengths:

      The strengths of this study are:

      (1) Demonstration of MED16 dissociation from the core Mediator complex and formation of a subcomplex containing MED16, upstream-binding protein 1 (UBP1), and transcription factor cellular promoter 2 (TFCP2) by elegant biochemical fractionation and immunoblotting analysis.

      (2) Defining nine N-terminal WD-40 repeats (WDRs) of MED16 as a Mediator-incorporating module and the C-terminal ⍺β-domain (157 amino acids) important for interaction with the UBP1-TFCP2 heterodimeric complex.

      (3) Illustration of a weak hydrophobic interaction between MED16 and the Mediator core that could be disrupted by 1,6-hexanediol, but not by its 2,5-hexanediol isomer nor by high salt (500 mM NaCl) disruption.

      (4) Classification of UBP1-upregulated cellular genes typically containing binding sites flanking the transcription start site (TSS) in contrast to UBP1-downregulated genes often containing a TSS-overlapping UBP1-binding site;

      (5) Presenting evidence for Mediator complex-dissociated free MED16-repressed HIV promoter activity through functional association with UBP1 and showing bromodomain-containing protein 4 (BRD4) inhibitor JQ1 that potentially disrupts BRD4-inhibited HIV-1 transcription elongation could lead to reversal of HIV-1 latency.

      Weaknesses:

      (1) No clear demonstration that MED16-UBP1-TFCP2 indeed form a trimeric core subcomplex in regulating cellular gene transcription and HIV-1 promoter inhibition.

      (2) No validation of transcriptomic datasets and pathways identified.

      (3) Use of mostly artificial reporter gene constructs and non-HIV host cells (e.g., human 293T embryotic kidney cells, human HeLa cervical cancer cells, and mouse HT pancreatic cancer cells) for examining MED16/UBP1-regulated HIV transcription.

      (4) Inconsistent use of 293T and HeLa cells in the characterization of dissociated MED16 interaction with UBP1 and TFCP2.

      (5) In vitro transcription using immobilized DNA templates was not performed to a high standard thus failing to convincingly show MED16/UBP1-inhibited HIV-1 transcription preinitiation complex formation.

      In this revision, the authors stipulate that the action of MED16-UBP1-TFCP2 is through the holo-Mediator complex, not as a free trimeric complex, since deletion of the N-terminal Mediator-incorporating module abolishes the functional effect of MED16 even though its interaction with UBP1-TFCP2 persists. The authors also show that a predicted UBP1-binding site is indeed present in the promoter region not overlapping with the TSS in each of the UBP1/MED16-upregulated respiratory gaseous exchange genes, such as surfactant protein A1 (SFTPA1), SFTPA2, SFTPB and SFTPC. To demonstrate UBP1/MED16-inhibited HIV transcription in a more biologically relevant system, the authors employ an HIV-1 latency model derived from CD4+ T cells. Additional experiments are also performed to address other issues raised by the reviewers.

      Overall, the revised manuscript has been improved by addressing some of the reviewer's comments. Additional issues that came up while reading this revision are: a) The statement, "UBP1 (as known as LBP-1) and TFCP2 (as known as LSF) belong to a subfamily of the TFCP2/Grainyhead transcription factor family" (lines 63-64), is confusing and needs to be clarified. My understanding is that LBP-1 has four family members encoded by two different genes (see Yoon et al., MCB 14: 1776-1785, 1994): LBP-1a and LBP-1b are probably UBP1, and LBP-1c and LBP-1d are likely TFCP2/LSF. If this is correct, the authors should clearly indicate which specific isoforms are analyzed in their UBP1 and TFCP2 proteins and clarify some of the text descriptions seemingly contradictory to the earlier findings.

      The use of mostly artificial reporter templates and transfection assays conducted mostly in 293T and HeLa cells remains concerning. It is also unclear why overexpression of MED16 only, but not MED23 or MED24, inhibits HIV promoter activity (Figure 5) if MED16 action is through the holo-Mediator complex and overexpression of MED16 may titrate out key cellular factors, thus diminishing their action through holo-Mediator. Results based on this squelching type of experiment relying on overexpression of a single component needs to be carefully interpreted.

    3. Reviewer #2 (Public review):

      Summary:

      The article from Zheng et al. proposes that the Med16 subunit of Mediator can dissociate from the complex, associate with transcription factor UBP1, and contribute to activation or repression of specific sets of genes in human cells. The question is potentially interesting, and the possibility of a distinct role for Med16 in UBP1-dependent transcription could be of interest to the field. However, I have substantial concerns about the experimental design, reproducibility, and interpretation of the data. In particular, several of the central conclusions rely on single experiments, indirect assays, or perturbations that are expected to cause broader changes in Mediator structure and transcription. In my view, the current data therefore do not provide sufficient evidence to support several of the principal mechanistic conclusions of the manuscript.

      Strengths:

      The authors provide preliminary evidence that Med16 may exist in an altered or partially dissociated Mediator complex and may be associated with specific transcriptional effects. The use of biochemical assays, reporter assays, RNA-seq, and Med16 knockout/rescue experiments provides several potentially useful approaches to investigating this question. In particular, the Med16 knockout/rescue RNA-seq experiment described in the rebuttal is a potentially informative experiment. Making the underlying IP-MS abundance and peptide-count data available is also an improvement over the previous version.

      However, important limitations of these experiments need to be addressed before the proposed mechanism can be established.

      Comments on the revised version:

      In the previous review, I raised concerns that the evidence for a specific UBP1-Med16 interaction and for a mechanistic role of this interaction was limited. The revised manuscript does not resolve these concerns. In particular, whether UBP1 interacts specifically with Med16, whether this interaction is required for activation of UBP1 target genes, and whether UBP1 represses HIV-1 transcription through Med16 remain unresolved.

      A stronger case for a specific UBP1-Med16 interaction would require more direct biochemical, structural, or biophysical evidence. The current experiments do not distinguish sufficiently between a specific Med16-dependent mechanism and broader consequences of perturbing Mediator structure or transcription.

      The revision also does not include the additional biological replicates needed to assess the reproducibility of several foundational experiments. Consequently, a number of the central conclusions remain based on single experiments, which substantially limits the strength of the conclusions that can be drawn.

      Major concerns:

      Figure 1:

      All of the data in Figure 1 are from single experiments without biological replicates. Because Figure 1 provides an important foundation for the proposed mechanism, this limits confidence in the reproducibility of the observations. Western blot experiments can show substantial experiment-to-experiment variability, and such variability appears relevant to several of the observations presented here.

      A key experiment is the western blot analysis of the gel-filtration experiment in Figure 1D. Rather than migrating with Mediator, the majority of the detected Med16 appears to elute at a lower molecular weight. The extract was maintained at super-physiological salt concentrations from preparation through column loading and elution, which could potentially contribute to this result. Other possibilities include experiment-to-experiment variation or detection of a cross-reacting protein by the Med16 antibody. The latter possibility warrants consideration because the bands detected in fractions 28-31 appear to migrate at a different molecular weight from the Med16 bands in fractions 16-19. Additional replicated experiments and appropriate controls would be needed to establish that the lower-molecular-weight signal represents Med16 in a distinct complex.

      Figure 2:

      The data in Figure 2 are also based on single experiments without biological replicates. The biochemical experiments use protein fragments that do not occur as such in the native protein. Such experiments can be useful for mapping possible protein-protein interaction surfaces, but they can also generate false-positive or false-negative interactions depending on the experimental configuration.

      Some of the results raise additional questions about the proposed interaction model. For example, the observations involving TFCP2 in panel B and Med1 and Med23 in panel F appear difficult to reconcile with a simple model in which UBP1 interacts with Mediator through Med16. Panel 2F is particularly relevant because Med1 and Med23 are not detected despite the proposed model involving UBP1 interaction with Mediator. These observations should be addressed when interpreting the specificity and mechanism of the proposed UBP1-Med16 interaction.

      Figure 3:

      The reporter assays provide potentially useful preliminary evidence for factors that influence transcription. However, the relatively long experimental timeframes (36-48 hours after transfection), use of artificial promoters, and overexpression of proteins introduce important limitations.

      The data are consistent with the authors' model, but they are also consistent with a simpler explanation in which Med16 depletion broadly affects Mediator structure and function. Previous work from the Gross, Robert, and Roeder laboratories has shown that loss of Med16 can result in dissociation of Med23, Med24, and Med25 from Mediator. Importantly, the authors' own data in the rebuttal document appear to show similar effects following Med16 loss. These observations are important because Med23, Med24, and Med25 provide major interfaces for DNA-binding transcription factors, and Med16, Med23, Med24, and Med25 together represent a substantial fraction of the molecular mass of Mediator. The functional properties and stability of the resulting Mediator complex therefore need to be considered when interpreting Med16 depletion experiments.

      The authors' data also suggest broader transcriptional effects: Med16 knockout causes loss of basal transcription in panel 3E. Conversely, overexpression of Med16 or Med16 fragments can cause broad defects in transcriptional activation, including through squelching. The relatively modest effects observed despite substantial increases in protein abundance are therefore also consistent with non-specific consequences of overexpression. These alternative explanations need to be considered when interpreting the reporter assays.

      Figure 4:

      RNA-seq is an appropriate approach for investigating possible effects of Med16 on gene expression. However, the experimental design has important limitations because Med16 depletion is expected to affect the composition of Mediator, including loss of Med23, Med24, and Med25. The knockdown also occurs over an extended timeframe (>24 hours), making it difficult to distinguish direct effects of Med16 from secondary effects arising from altered Mediator structure or broader changes in transcription.

      The RNA-seq results could therefore reflect Med16-specific functions, but they are also consistent with a model in which loss of Med16 compromises Mediator structure and function and produces downstream gene-expression changes.

      The presentation of the RNA-seq data in Figure 4E is also difficult to interpret. The volcano plot shows UBP1 overexpression versus the shCTRL vector, whereas the comparison central to the authors' interpretation concerns the effects of shMed16. Showing the volcano plot for the shMed16 comparison would allow readers to assess the statistical significance and magnitude of the relevant gene-expression changes directly. Instead, the authors superimpose "x" symbols for genes that "failed to be activated by UBP1 in the shMed16 cell line." Because the underlying Med16 comparison is not displayed, the p-values and fold changes for the comparison of interest cannot be evaluated. Moreover, positioning the "x" symbols according to the coordinates of the UBP1-overexpression volcano plot does not appear to represent their corresponding positions in a volcano plot of the Med16 comparison. This makes the figure difficult to interpret and could give a misleading visual impression of the underlying data.

      Finally, the RNA-seq data are not spike-in normalized. This is potentially important because Med16 depletion is expected to cause broad effects on transcription and could result in an overall reduction in transcription across the genome. Appropriate normalization is therefore important for distinguishing gene-specific effects from global changes. I also did not identify information in the manuscript indicating where the RNA-seq data have been deposited in a public repository.

      Figure 5:

      The reporter assays in Figure 5 are subject to similar concerns. The authors' model may explain the observations, but the data are also consistent with broader disruption of Mediator structure and function following Med16 depletion, or with more general transcriptional effects caused by overexpression of Med16 or Med16 fragments.

      A separate concern is the proposed mechanism for HIV-1 repression. The authors propose that UBP1 interacts with Med16/Mediator and that overexpression of Med16 enhances Mediator recruitment, although Mediator recruitment is not directly measured. The proposed increase in Mediator recruitment would, in isolation, be expected to promote transcription rather than repression. The authors instead invoke a UBP1-dependent mechanism to explain the repression. However, a simpler explanation is that overexpression of Med16 or selected Med16 fragments perturbs normal transcription through mechanisms including squelching. The authors themselves invoke a Med16 squelching model to explain reduced transcription at the UBP1 reporter in Figure 3G-I. The possibility that the same mechanism contributes to the results in Figure 5 should therefore be addressed.

      Finally, a central component of the proposed model is recruitment of Mediator by UBP1 through Med16, but neither UBP1 occupancy nor Mediator occupancy at the relevant promoters is directly measured. These experiments would provide important evidence for the proposed mechanism. There is also no legend for Figure 5G,H, and the legend for Figure 5C,D appears to be incorrect.

      Figure 6:

      The title of Figure 6 states that "The position of the UBP1 binding site relative to the TSS dictates whether MED16-UBP1 activates or represses transcription." The data presented do not establish this conclusion.

      Only one alternative location of the UBP1 binding site is tested, and the analysis in panel D compares only the 50 most up- or down-regulated genes in UBP1-overexpression experiments. The correlations shown are relatively weak, and a rigorous statistical analysis is not presented.

      The authors cite Duttke et al. (Nature 2024) in support of the concept that transcription-factor position can influence regulatory activity. That study used genome-wide mapping of native TSSs, MPRA analysis of approximately 2,000 promoter variants, and targeted follow-up experiments. By contrast, Figure 6 relies on an artificial reporter and a single change in UBP1 binding-site position. The current data therefore support the possibility that binding-site position influences the observed reporter activity, but they do not establish the broader conclusion that the position of the UBP1 binding site relative to the TSS dictates whether Med16-UBP1 activates or represses transcription.

      Figure 7:

      The title of Figure 7 states that Med16 "interferes with HIV-1 PIC assembly," but the data presented do not establish this mechanism.

      First, the proposed mechanism is described as acting through interaction with UBP1, but UBP1 is not measured in this experiment.

      Second, Med16 knockout or overexpression has relatively modest effects on the estimated levels of bound PIC components, and these changes do not consistently correspond to the transcriptional changes shown in panel C. For example, in the WT versus Med16 KO comparison, there is no apparent difference in the levels of Pol II, Med1, IIB, or IIH based on the western blot data. The reduction in Med16 and Med23 is expected following Med16 knockout and therefore does not by itself establish altered PIC assembly. In the WT versus Med16 overexpression comparison, panel C suggests a meaningful reduction in transcription, whereas the changes in PIC-associated proteins do not follow the direction predicted by the proposed model: Pol II and IIB increase with Med16 overexpression, while Med1, Med23, Med16, and IIH appear similar.

      Third, Cdk8 and Med12 are not PIC components. They are subunits of the CDK8 module, which can inhibit PIC formation. Even under a simplified interpretation in which reduced Cdk8 and Med12 levels were relevant to PIC formation, the direction of these changes is opposite to that expected from the "relative mRNA level" plots in panel C. Reduced levels of these proteins in the Med16 overexpression condition would, under this interpretation, be expected to promote rather than reduce transcription.

      Finally, this is a single-replicate experiment, and quantitative comparisons are made across immobilized-template pulldowns using different nuclear extracts (WT, Med16 KO, and Med16 OE). Differences in protein concentration between extracts, differences in pulldown efficiency, and experiment-to-experiment variation could all contribute to the observed differences. Replicated experiments with appropriate normalization would therefore be needed to support the proposed PIC mechanism.

      Figure 8:

      Panel F presents a model that, in my view, goes beyond what is supported by the experimental data. In particular, there is currently insufficient evidence to establish the proposed Med16-UBP1-TFCP2 complex as a distinct functional complex.

      The model also depicts the Mediator CDK8 module bound at promoters together with Pol II. The manuscript does not provide evidence supporting this arrangement, and there is substantial prior evidence that the CDK8 module can inhibit or prevent PIC formation. This aspect of the model therefore requires either experimental support or clarification.

      Finally, the model depicts a separate Med16-UBP1-TFCP2 complex. This appears inconsistent with the authors' statements in the rebuttal that they did not intend to propose such an unsupported isolated complex. If this is not the intended model, Figure 8 should be revised to avoid conveying that interpretation.

      Figure S1: IP-MS analysis:

      The IP-MS data raise substantial concerns regarding the specificity of the proposed UBP1-Med16 interaction.

      The authors state that they "have now reanalyzed the IP-MS data using a more precise quantification approach." However, the filtering procedure described in the Methods is difficult to evaluate and requires substantially more justification. The procedure includes multiple sequential criteria: detection in at least two of three replicates of the target bait; a minimum unique peptide count; detection in at least one additional Mediator bait; exclusion of Mediator/Pol II subunits, ribosomal proteins, histones, keratins, and common contaminants; and annotation with transcriptional or DNA-binding functions using GO terms. The rationale for this particular combination of filters and its impact on the candidate list should be explained and justified.

      The resulting "bait-preferential score" is also difficult to assess. The reported mean_target and mean_other values are frequently negative, with some positive values, and the method appears to derive a positive score from the difference between these values after taking an absolute value. The biological and statistical interpretation of this score is therefore unclear. The reported scores are generally clustered around relatively narrow values, and the analysis does not appear to provide the statistical framework normally used to assess enrichment and reproducibility in IP-MS experiments.

      Importantly, the candidate proteins identified by this approach do not appear to overlap with proteins identified in other published Med16 IP-MS analyses in human cell lines, including HCT116, MCF7, HEK293T, and HeLa. I was unable to identify a protein among the 37 Med16 candidates that was consistently observed in the cited resources, including BioPlex (Huttlin et al., Cell 2021) and Malovannaya et al. (Cell 2011). This lack of concordance raises questions about the specificity and physiological relevance of the proposed Med16-associated proteins and, in particular, about the evidence for a specific UBP1-Med16 interaction.

      The peptide counts for UBP1 also illustrate the limitation of the current analysis. The total UBP1 peptide counts across the Cdk8, Med1, and Med16 IP-MS experiments are 12, 10, and 14, respectively, yielding bait-preferential scores of 1.07, 0.76, and 1.85. Given these relatively small differences and the absence of statistical significance measures, it is difficult to determine whether these observations provide evidence for a specific biological interaction.

      The authors have now made the raw abundance values and peptide counts available as supplementary tables, which is useful. However, standard statistical measures for evaluating MS data, including FDR, p-values, and adjusted p-values, are not provided, nor is sufficient information given about how the reported scores were calculated. These additions would be important for readers to assess the robustness of the conclusions.

      Concerns about the rebuttal document:

      Several statements in the rebuttal appear inconsistent with the data shown in the revised manuscript or with the interpretation of the authors' own experiments. I highlight specific examples below.

      (1) The proposed Med16-UBP1 complex:

      I previously raised concerns that the manuscript overstated the evidence for a separate Med16 complex associated with UBP1 and lacked rigorous biochemical characterization of such a complex. The authors responded that the wording of the original manuscript may have created this interpretation and stated that this was not the mechanistic model they intended to convey. However, Figure 8 of the revised manuscript still depicts a separate Med16-UBP1-TFCP2 complex. The figure therefore appears to retain the interpretation that the authors stated they did not intend to convey. This should be clarified.

      (2) Replication and experimental rigor:

      I previously raised concerns about the absence of replication and the limitations of the experimental approaches used to infer roles for Med16 and UBP1. These concerns remain because no additional replicate experiments were performed for several of the key experiments. As a result, important conclusions continue to depend on single western blots, including foundational experiments in Figures 1, 2, and 7.<br /> Examples include the variable quantification in Figure 1A; the absence of detectable Med23 and Med1 in the UBP1 IP in Figure 2F despite the proposed interaction between UBP1 and Mediator; and the variable association of TFCP2 with Flag-MED16 fragments in Figure 2B, including lanes 7-9. Replication would be important for determining whether these observations are reproducible.

      (3) IP-MS analysis:

      The revised IP-MS analysis provides additional information compared with the previous version, but important concerns remain regarding the processing and interpretation of the data. The "bait-preferential score" approach is not sufficiently justified, and standard measures such as p-values, adjusted p-values, or FDR are not provided.

      The analysis also identifies no proteins among the 37 Med16 candidates that appear to be consistently observed in other published Med16 IP-MS analyses in human cell lines. This is important when assessing the specificity of the proposed interaction.

      The peptide counts for UBP1 are similarly low and do not show a clear Med16-specific enrichment: 12 peptides in the Cdk8 IP, 10 in the Med1 IP, and 14 in the Med16 IP, corresponding to scores of 1.07, 0.76, and 1.85, respectively. These observations do not by themselves provide strong evidence for a specific UBP1-Med16 interaction.<br /> In addition, the Med16 IP experiments identify approximately 800 proteins per replicate. This broad set of associated proteins highlights the importance of appropriate controls and statistical analysis when interpreting a co-IP as evidence for a specific protein-protein interaction.

      (4) Interpretation of the 1,6-HD experiments:

      I previously raised concerns that the IP-western data in Figure 1 did not establish a Med16-specific effect. The rebuttal states that "MED16 exhibits a uniquely pronounced dissociation by 1,6-HD that is consistent across three independent IPs using anti-CDK8, anti-MED1, and anti-MED12 antibodies." However, the quantitative data shown do not clearly support this interpretation.

      In the CDK8 IP, CDK8 and MED23 appear to be lost to a similar extent as Med16. In the Med1 IP, Med12 shows a similar loss to Med16, with Med23 also showing a comparable trend. In the Med12 IP, CDK8 and Med23 show changes comparable to Med16. There are also unexplained increases in some Mediator subunits with increasing 1,6-HD concentrations. For example, Med12 and Med1 are variable across the 1,6-HD conditions, and Med1 and Med24 increase at higher concentrations. Similar trends are present in the CDK8 IP.

      The Figure 1B data raise related concerns. Several Mediator subunits show substantial increases following the 2,5-HD condition compared with 1,6-HD, in some cases greater than the change observed for Med16. Thus, the available data do not clearly establish that Med16 exhibits a uniquely pronounced dissociation from Mediator.

      (5) Evidence for the proposed mechanism:

      I previously suggested that more rigorous approaches would be needed to distinguish a specific Med16-dependent mechanism from broader effects of Med16 perturbation. The rebuttal instead emphasizes that the authors' conclusion that the UBP1-TFCP2 heterodimer acts through Med16 is supported by multiple independent lines of evidence.

      However, the different experiments do not independently establish the same mechanistic conclusion. Many rely on long experimental timeframes, overexpression, reporter assays, limited controls, or lack of biological replication. These approaches can provide useful preliminary evidence for effects on transcription but do not establish that a specific UBP1-Med16 interaction is required for UBP1 target-gene expression.

      Additional approaches that directly test the proposed interaction and its requirement for transcriptional regulation would therefore be needed to establish this mechanism.

      (6) Med16 rescue experiment:

      The authors performed an RNA-seq comparison of WT, Med16 KO, and Med16 KO cells rescued by re-expression of WT Med16. This is a potentially informative experiment, and I appreciate that it was performed.

      However, the heatmap shown in the rebuttal does not appear to demonstrate a clear rescue of UBP1 target-gene expression. The figure contains approximately 60 genes but provides no legend beyond "Gene profiling of MED16 KO and rescue effect." The authors describe the result as showing that re-expression of Med16 can largely restore expression of UBP1 target genes, but this conclusion is not readily apparent from the figure as presented. More information about the analysis, normalization, and comparisons would be needed to evaluate the claimed rescue. It would also be useful to include the relevant analysis in the revised manuscript rather than only in the rebuttal.

      (7) Consequences of Med16 depletion:

      A continuing concern is that the manuscript does not adequately account for the broader consequences of Med16 depletion or knockout. Loss of Med16 is known to cause dissociation of other Mediator subunits, including Med23, Med24, and Med25. The authors acknowledge this in the rebuttal and state that their new Co-IP and IP-MS experiments confirm partial dissociation of these subunits following Med16 knockout. They also acknowledge that this structural perturbation must be considered when interpreting phenotypes from Med16 depletion and that long-term knockdown or knockout approaches are susceptible to secondary effects and cellular adaptation.<br /> These are important caveats, but they are not adequately incorporated into the interpretation of the revised manuscript, and the new data demonstrating loss of Med23, Med24, and Med25 are not shown in the article.

      This issue is particularly important for interpreting the transcriptional experiments. Loss of Med16 is expected to disrupt Mediator more broadly, including loss of Med23, Med24, and Med25, which provide important transcription-factor interaction surfaces. These changes could produce widespread transcriptional effects independently of a specific UBP1-Med16 mechanism. Given that the authors' own experiments now provide evidence for this structural perturbation, the possibility of indirect effects and altered Mediator function should be explicitly incorporated into the experimental interpretation.

      Overall assessment:

      The manuscript addresses an interesting question and presents several potentially useful observations concerning Med16, Mediator, and UBP1. However, the central mechanistic conclusions are currently not supported by sufficiently rigorous or independent evidence. The principal concerns are the extensive reliance on single-replicate experiments; the use of overexpression, artificial reporters, and long depletion timeframes; the absence of direct measurements of UBP1 and Mediator occupancy; the broad structural consequences of Med16 depletion; and the limited statistical support for the IP-MS analysis.

      Taken together, the current data do not distinguish adequately between the proposed specific UBP1-Med16 mechanism and alternative explanations involving broader disruption of Mediator structure or transcription. Addressing this distinction is important because it affects the interpretation of much of the manuscript, including the RNA-seq and reporter-assay results and the proposed HIV-1 mechanism.

      The authors have generated several useful datasets, including the Med16 knockout/rescue experiment and additional IP-MS information, but the manuscript would need to incorporate the associated caveats and provide stronger experimental evidence before the proposed mechanistic model can be established.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Characterization of a dissociable Mediator subunit implicated in cellular pathways, particularly lung alveolar function, and HIV latency is conceptually interesting.

      Strengths:

      The strengths of this study are:

      (1) Demonstration of MED16 dissociation from the core Mediator complex and formation of a subcomplex containing MED16, upstream-binding protein 1 (UBP1), and transcription factor cellular promoter 2 (TFCP2) by elegant biochemical fractionation and immunoblotting analysis.

      (2) Defining nine N-terminal WD-40 repeats (WDRs) of MED16 as a Mediator-incorporating module and the C-terminal ⍺β-domain (157 amino acids) important for interaction with the UBP1-TFCP2 heterodimeric complex.

      (3) Illustration of a weak hydrophobic interaction between MED16 and the Mediator core that could be disrupted by 1,6-hexanediol, but not by its 2,5-hexanediol isomer nor by high salt (500 mM NaCl) disruption.

      (4) Classification of UBP1-upregulated cellular genes typically containing binding sites flanking the transcription start site (TSS) in contrast to UBP1-downregulated genes often containing a TSS-overlapping UBP1-binding site

      (5) Presenting evidence for Mediator complex-dissociated free MED16-repressed HIV promoter activity through functional association with UBP1 and showing bromodomain-containing protein 4 (BRD4) inhibitor JQ1 that potentially disrupts BRD4-inhibited HIV-1 transcription elongation could lead to reversal of HIV-1 latency.

      Weaknesses:

      Nevertheless, foreseeable weaknesses include:

      (1) No clear demonstration of MED16-UBP1-TFCP2 indeed forming a trimeric core subcomplex in regulating cellular gene transcription and HIV-1 promoter inhibition

      We appreciate this insightful comment from the reviewer. The physical interaction between MED16 and the UBP1–TFCP2 heterodimer was uncovered serendipitously via biochemical fractionation. Specifically, UBP1 and TFCP2 were identified in gel-filtration fractions containing free MED16 that eluted separately from the intact core Mediator complex (Figures 1D–1F). This biochemical observation suggested to us a potential functional linkage between MED16 and these two transcription factors. Subsequently, our functional assays further clarified this regulatory relationship: UBP1 and TFCP2 do interact with MED16 that is required to be fully assembled into Mediator complexes to exert transcriptional control. Truncation of MED16’s N-terminal WD40-repeat (WDR) domain (MED16ΔWDR) eliminates MED16 integration into the Mediator complex. Notably, this MED16ΔWDR mutant still retains an intact C-terminal αβ-domain capable of binding UBP1 yet completely loses the capacity to support UBP1-dependent transcription. Collectively, these data demonstrate that UBP1–TFCP2 engage MED16 as a component of the complete Mediator complex to govern target gene expression in cells.

      We have updated the abstract and full manuscript to present this model more clearly and coherently throughout the text.

      (2) No validation of transcriptomic datasets and pathways identified.

      We thank the reviewer for this comment. Since our RNA-seq analysis identified SFTP family genes (SFTPA1, SFTPA2, SFTPB, SFTPD) as UBP1-MED16 co-regulated targets, and these are lung-specific surfactant protein genes, we have now performed qRT-PCR validation in the A549 human lung adenocarcinoma cell line. The results confirm that knockdown of MED16 largely attenuates the expression of these SFTP genes (see Author response image 1) and other UBP1 target genes identified in our RNA-seq dataset.

      Author response image 1.

      qRT-PCR validation of SFTP gene family expression (SFTPA1/A2/B/D) in A549 cells

      (3) Use of mostly artificial reporter gene constructs and non-HIV host cells (e.g., human 293T embryonic kidney cells, human HeLa cervical cancer cells, and mouse HT pancreatic cancer cells) for examining MED16/UBP1-regulated HIV transcription.

      To overcome the limitations of artificial reporter systems, we have managed to utilize multiple complementary approaches beyond reporter assays, including: (i) RNA-seq analysis of endogenous gene regulation, (ii) biochemical co-IP and gel filtration experiments, (iii) immobilized template transcription assays that directly measure PIC assembly and nascent RNA production, and (iv) validation in the J-Lat 10.6 CD4+ T cell line harboring an integrated latent HIV-1 provirus, where MED16 overexpression suppresses HIV-1 reactivation.

      Unfortunately, the direct infection experiments with replication-competent HIV-1 were not feasible as our institution does not have the biosafety level 3 (BSL-3) containment facilities.

      (4) Inconsistent use of 293T and HeLa cells in the characterization of dissociated MED16 interaction with UBP1 and TFCP2.

      For biochemical characterization in this study, HEK293T cells were primarily utilized for overexpression-based co-immunoprecipitation (Co-IP) and domain-mapping assays owing to their superior transfection efficiency. In contrast, HeLa cells were employed for gel-filtration chromatography and endogenous Co-IP, as fractionation of HeLa nuclear extracts represents a well-established workflow for purifying native protein complexes. Critically, the physical interaction between MED16 and the UBP1–TFCP2 heterodimer was consistently detected in both HEK293T whole-cell lysates and HeLa nuclear extracts across all assays.

      (5) In vitro transcription using immobilized DNA templates was not performed to a high standard, thus failing to convincingly show MED16/UBP1-inhibited HIV-1 transcription preinitiation complex formation.

      We appreciate the reviewer’s careful assessment of the immobilized template assay. While we recognize the technical challenges inherent in this approach, we want to emphasize that the proposed model—that MED16-UBP1 inhibits HIV-1 transcription at the level of PIC assembly—does not rest solely on this single experiment.

      Multiple independent lines of evidence converge on this model:

      (1) The MED16-UBP1 inhibition depends on the UBP1 binding site overlapping the TSS: a chimeric reporter with the UBP1 binding site placed at the TSS is inhibited by MED16, whereas placing it upstream does not show inhibition, demonstrating the UBP1 binding site-specific repression (Fig 6A-C).

      (2) MED16 overexpression inhibits HIV-1 reporter activity in a manner depending on interaction with UBP1. Deletion of the MED16 αβ-domain (MED16Δαβ), which abolishes MED16-UBP1 interaction, completely abrogates the inhibitory effect (Fig 5E-G).

      (3) The MED16-UBP1 inhibition of HIV is independent of TAR-mediated elongation (Fig S2B-C) and HDAC-mediated deacetylation (Fig S2A), consistent with an inhibition mechanism operating at the initiation stage rather than at the post-initiation steps.

      (4) Upon MED16 overexpression, the immobilized template assay shows reduced CDK8 binding and Pol II pSer5 signals, along with TFIIB retention, providing mechanistic evidence that is consistent with the aforementioned functional observations.

      Thus, the model is supported by convergent evidence from reporter assays, domain-mapping experiments, pharmacological inhibition, and in vitro biochemical reconstitution. In the revised manuscript, we have further clarified how these complementary approaches collectively support the PIC inhibition model.

      Reviewer #2 (Public review):

      Summary:

      The article from Zheng et al. proposes an interesting hypothesis that the Med16 subunit of Mediator detaches from the complex, associates with transcription factor UBP1, and this complex activates or represses specific sets of genes in human cells. Despite my excitement upon reading the abstract, I was concerned by the lack of rigor in the experimental design. The only statement in the abstract that has some experimental support is the finding that Med16 dissociates from the Mediator and forms a subcomplex, but the data shown remain incomplete.

      We would like to first address a core conceptual point that we believe underpins several of the reviewer’s concerns.

      We respectfully disagreed that the reviewer summarized our proposed model as a pathway where “Med16 detaches from the complex, associates with transcription factor UBP1, and this complex activates or represses specific sets of genes.” We acknowledge that phrasing in our original manuscript may have created this misleading interpretation, yet this is not the mechanistic model we intend to convey. We identified the MED16–UBP1–TFCP2 interaction serendipitously during biochemical fractionation: UBP1 and TFCP2 co-eluted with free MED16 in gel-filtration fractions that were fully separated from the intact core Mediator complex. While this biochemical result guided our subsequent functional investigation into MED16–UBP1–TFCP2 crosstalk, we never intend to propose that a standalone MED16–UBP1–TFCP2 subcomplex can drive transcriptional activation or repression independently of the intact Mediator complex.

      Most importantly, our functional experiments demonstrate that UBP1 and TFCP2 require MED16 as an integral part of the Mediator complex to regulate transcription as shown by domain mapping experiments. Specifically, MED16 uses its N-terminal WDR domain to integrate into the Mediator and its C-terminal αβ-domain to bind UBP1/TFCP2. Deletion of either WDR or αβ-domain is functionally inert in both cellular and HIV-1 reporter assays. Thus, the MED16-UBP1 interaction is functionally relevant because it links these transcription factors to the Mediator complex, not because it forms an autonomous regulatory complex. In another word, UBP1–TFCP2 target MED16 to recruit the whole Mediator complex for governing gene expression in cells.

      We have comprehensively revised the full manuscript to sharply distinguish our initial biochemical observation from the validated functional model. As all experimental approaches carry inherent limitations, we tried our best to validate every major conclusion using multiple orthogonal assays and to draw mechanistic interpretations when results from distinct experimental systems do converge. Our detailed replies to each specific concern from the reviewer are provided below.

      Strengths:

      The authors have preliminary evidence that a stable Med16 complex may exist and that it may regulate specific sets of genes.

      Weaknesses:

      The experiments are poorly designed and can only infer possible roles for Med16 or UBP1 at this point. Furthermore, the data are often of poor quality and lack replication and quantitation. In other cases, key data such as MS results aren't even shown. Instead, we are given a curated list of only about 6 proteins (Figure S1), a subset of which the authors chose to pursue with follow-up experiments. This is not the expected level of scientific process.

      As clarified in our Summary Response above, we thought this reviewer may have misinterpreted our data and model. Overall, our work demonstrates that MED16 acts as a molecular bridge, linking UBP1/TFCP2 to the Mediator complex, not that three proteins form a free-standing subcomplex, and we have extensively revised the manuscript to clarify this.

      Here we also want to address the specific concerns about data completeness and rigor. The full mass spectrometry datasets were not clearly presented in the original submission. We have now reanalyzed the IP-MS data using a more precise quantification approach. Rather than scoring proteins by simple presence or absence, we calculated a bait-preferential score for each protein by comparing its normalized abundance in the MED16 IP to that in the other three Mediator bait IPs (MED1, MED12, CDK8). This score reflects the degree to which a protein is preferentially enriched by MED16 relative to other Mediator subunits. The bait-specific candidates identified by this analysis are presented in Fig S1B, and the complete list of MED16-specific candidates is now included. In addition, the raw abundance values and peptide counts for all identified proteins have been compiled and uploaded as supplementary tables. A detailed description of the normalization and scoring procedure is provided in the Methods section

      Furthermore, we have added quantification analysis for key Western blot results throughout the revised figures.

      (1) The data supporting the Med16 dissociation and co-association with UBP1 are incomplete and not convincing at this stage. According to the Methods and text, the gel filtration column was run with "un-dialyzed HeLa cell nuclear extract" and eluted in 300mM KCl buffer. The extracts were generated with the Dignam/Roeder method according to the text. Undialyzed, that means the extract would be between 0.4 - 0.5M NaCl. Under these high salt conditions (not physiological), it's possible and even plausible that Mediator subunits could separate over time. This caveat is not mentioned or controlled for by the authors. Because a putative Med16 subcomplex is a foundational point of the article, this is concerning.

      The data are incomplete because a potential Med16 complex is not defined biochemically. The current state suggests a smaller Med16-containing complex that may also contain UBP1 and other factors, but its composition is not determined. This is important because if you're going to conclude a new and biologically relevant Med16 complex, which is a point of the article, then readers will expect you to do that.

      We appreciate the reviewer’s thorough feedback and address the two core concerns separately below.

      (1) Undialyzed nuclear extracts & high-salt conditions

      Published work confirms Mediator undergoes massive precipitation (~70% loss) upon dialysis to low-salt 100 mM KCl buffer (1), justifying our use of undialyzed HeLa nuclear extracts for biochemical assays. Prior study also demonstrated intact Mediator remains stable in up to 1 M KCl (1). Therefore, MED16 dissociation seen at 300 mM KCl arises from its unique biochemical property, rather than non-specific high-salt damage.

      (2) Biochemical interpretation of MED16-containing fractions

      We have revised ambiguous original wording to clarify our model: we do not intend to propose an autonomous MED16–UBP1–TFCP2 subcomplex functions independently in cells. Gel filtration only captures a biochemical state where MED16 co-elutes with UBP1/TFCP2 separate from core Mediator, which merely inspired our functional follow-up. Three lines of evidence prove UBP1/TFCP2 require fully assembled Mediator to function:

      (i) MED16’s N-terminal WDR domain mediates Mediator integration, while its C-terminal αβ-domain binds UBP1; both interactions can occur simultaneously (Fig 2B).

      (ii) MED16Δαβ incorporates into Mediator but loses UBP1 binding, showing no activity in UBP1-driven and HIV-1 reporter assays (Fig 3G, 5G).

      (iii) MED16ΔWDR retains UBP1 binding yet cannot assemble into Mediator, thus fails to repress HIV-1 transcription (Fig 5F).

      In vitro MED16 separation only reflects its extractable biochemical behavior, not a functional standalone complex. Collectively, our data establish UBP1/TFCP2 recruit the entire Mediator complex via MED16. All relevant manuscript sections have been rewritten to distinguish this preliminary biochemical observation from our validated functional model.

      Equally concerning are the IP-western results shown in Figure 1. In my opinion, these experiments do nothing to support the claims of the authors. The authors use hexanediols at 5% or 10% in an effort to disrupt the Mediator complex. Assuming this was weight/volume, that means ~400 to 800mM hexanediol solution, which is fairly high and can be expected to disrupt protein complexes, but the effects haven't been carefully assessed as far as I'm aware. The 2,5 HD (Figure 1B) experiments appear to simply contain greater protein loading, and this may contribute to the apparent differential results. In fact, in looking at the data, it seems that all MED subunits probed show the same trend as Med16. They are all reduced in the 1,6HD experiment relative to the 2,5 HD experiment. But it's hard to know, because replicates weren't completed and quantitation was not done. There aren't even loading controls. Other concerns about the IP-Western experiments are outlined in point 2.

      Treatment with 10% (w/v) 1,6-hexanediol (1,6-HD) is a well-established method to selectively disrupt weak hydrophobic interactions while maintaining stable protein assemblies. A comparable strategy was recently applied to dissect NEAT1-dependent paraspeckle formation driven by liquid–liquid phase separation (2). Notably, we included 2,5-hexanediol (2,5-HD) as a critical specificity control: as a structural isomer with distinct hydroxyl group positioning, 2,5-HD cannot efficiently disrupt weak hydrophobic contacts and failed to trigger MED16 dissociation (Figure 1B). The divergent outcomes observed side-by-side with equal sample input therefore serve as an internal control, validating that MED16 dissociation is a specific consequence of 1,6-HD treatment.

      We acknowledge mild signal reductions for several other Mediator subunits following 1,6-HD exposure under certain immunoprecipitation (IP) conditions (Figures 1A, 1B). Nevertheless, MED16 exhibits a uniquely pronounced dissociation by 1,6-HD that is consistent across three independent IPs using anti-CDK8, anti-MED1, and anti-MED12 antibodies, in both HEK293T whole-cell lysates and HeLa nuclear extracts. By contrast, the mild depletion of other Mediator subunits varies substantially depending on the antibody and cell extract used, whereas robust MED16 loss is universally observed in all experimental setups we tested.

      (2) At no point do the authors apply rigorous methods to test their hypothesis. Instead, methods are applied that have been largely discredited over time and can only serve as preliminary data for pilot studies, and cannot be used to draw definitive conclusions about protein function.

      We greatly appreciate the reviewer’s emphasis on methodological rigor. We fully concur that individual techniques including protein fractionation, IP-WB, transient transfection, shRNA knockdown, immobilized template and luciferase reporter assays each carry intrinsic limitations when deployed alone. To address this, we employed a panel of orthogonal experimental strategies throughout our work and only draw conclusions supported by convergent results across distinct assays. Our core mechanistic conclusion—that MED16 acts as a molecular bridge linking the UBP1–TFCP2 heterodimer to the intact Mediator complex to control transcription—is validated by multiple independent lines of evidence, rather than resting on a single experimental approach, as elaborated in detail below.

      (a) IP-westerns are fraught with caveats, especially the way they were performed here, in which the beads were washed at relatively low salt and then eluted by boiling the beads in loading buffer. This will "elute" bound proteins, but also proteins that non-specifically interact with or precipitate on the beads. And because Westerns are so sensitive, it is easy to generate positive results. It's just not a rigorous experiment.

      We acknowledge that IP-Western has inherent limitations regarding specificity. To minimize non-specific signals, we included IgG controls in our CDK8 IP experiments (available in raw data) and the boiling elution method has been extensively validated in our hands. More importantly, the interaction between MED16 and UBP1/TFCP2 is not solely supported by IP-Western.

      This interaction was initially observed through co-elution of MED16 with UBP1 and TFCP2 in gel filtration fractions (Fig 1D), confirmed by reciprocal co-IP with anti-MED16 and anti-TFCP2 antibodies in both HeLa NE and the resolved fractions (Fig 1E-F), and further corroborated by in vitro GST pull-down assays showing direct binding between MED16 αβ-domain and recombinant UBP1 (Fig 2C). IP-MS analysis with three biological replicates independently identified UBP1 and TFCP2 as MED16-associated proteins. Each of these methods has different sources of potential artifacts, yet they all converge on the same conclusion.

      (b) Many conclusions relied on transient transfection experiments, which are problematic because they require long timeframes, during which secondary/indirect effects from expression/overexpression will result. This is especially true if the proteins being artificially expressed/overexpressed are major transcription regulators, which is the case here. It is simply impossible to separate direct from indirect effects with these types of experiments. Another concern is that there was no effort to assess whether the induced protein levels were near physiological levels. Protein overexpression, especially if the protein is a known regulator of pol2 transcription (e.g., UBP1 or Med16), will create many unintended consequences.

      The reviewer raises a valid concern about indirect effects from overexpression. To address this, our key conclusions were validated by multiple loss-of-function approaches that do not rely on overexpression alone. Specifically, the conclusion that MED16 is required for UBP1 transcriptional activity is supported by:

      (i) shRNA-mediated MED16 knockdown (Fig 3C);

      (ii) CRISPR-mediated MED16 knockout in five independent frameshift clones with consistent phenotypes (Fig 3E-F);

      (iii) Using a domain-disruption approach, we demonstrated that a UBP1-binding-deficient MED16 mutant (Δαβ) fails to exert the dominant-negative effect on UBP1 activity, as seen with wild-type MED16 (Fig 3G and 6A). All these different methods point to the same conclusion. This makes it unlikely that the effects we see are simply artifacts of overexpression or indirect consequences.

      (c) Many conclusions were made based upon shRNA knockdown experiments, which are problematic because they require long timeframes (see above point), which makes it nearly impossible to identify effects that are direct vs. indirect/secondary/tertiary effects. Also, shRNA experiments will have off-target effects, which have been widely reported for well over a decade. An advantage of shRNA knockdowns is that they prevent genetic adaptation (a caveat with KO cell lines). A minimal test would be to show phenotypic rescue of the knockdown by expressing a knockdown-resistant Med16 (for example), but these types of experiments were not done.

      We agree that shRNA has potential off-target effects, and that is why we did not rely on shRNA as our sole loss-of-function approach. We have generated five independent CRISPR MED16 knockout clones, each with a distinct frameshift mutation, and all five clones showed consistent reduction in UBP1 reporter activity (Fig 3E-F). Furthermore, we performed RNA-seq on MED16 knockout cells in which wild-type MED16 was re-introduced. As presented in Author response image 2, re-expressing MED16 in KO cells can largely restore the expression of UBP1 target genes. These rescue data provide strong evidence that the transcriptional defects are specifically attributable to MED16 loss rather than off-target effects or clonal adaptation.

      Author response image 2.

      Gene profiling of MED16 KO and rescue effect

      (d) Many experiments used reporter assays, which involved artificial, non-native promoters. Reporters are good for pilot studies, but they aren't a rigorous test of direct regulatory roles for Med16 or other proteins. Reporters don't even measure transcription directly. In fact, no experiment in this study directly measures transcription. An RNA-seq experiment was done with overexpressed or Med16 knockdown cells, but these required long timeframes and RNA-seq measures steady-state mRNA, which doesn't test the potential direct effects of these proteins on nascent transcription.

      The two principal reporters used in this study are both based on native promoters. The UBP1 reporter is derived from the mouse α-globin gene promoter, a well-established natural target of UBP1/TFCP2 that contains two endogenous UBP1 binding sites. To enhance sensitivity, we expanded these to six copies, as the α-globin promoter has low basal activity in most cell lines. Critically, this expansion preserved the original promoter architecture, including the CCAAT box, TATA box, and TSS. The HIV-1 reporter is driven by the HIV-1 core promoter (-78 to +60), which contains all elements required for basal transcription and Tat-mediated activation. Thus, both reporters retain the native sequence context of the regulatory elements under study.

      These reporter assays served as initial screening and hypothesis-testing tools. The key functional insights from reporters were then validated in more physiological contexts:

      (i) The position-dependent effect of UBP1 binding (activation when upstream of TSS, repression when overlapping TSS) observed in chimeric reporters (Fig 6A-C) was independently confirmed by motif enrichment analysis of endogenous genes from RNA-seq data (Fig 6D-E).

      (ii) Our immobilized template transcription assays directly measure nascent RNA production from the HIV-1 promoter, showing that MED16 overexpression reduces transcriptional output (Fig 7C). Thus, while reporter assays alone would be insufficient, our conclusion on UBP1-MED16 function are further corroborated by both global genomic analysis and direct in vitro transcription measurements.

      (e) The MS experiments show promise, but the data were not shown, so it's hard to judge. The reader cannot compare/contrast the experiments, and we have no indication of the statistical confidence of the proteins identified. How many biological replicate MS experiments were performed?

      The IP-MS experiments were performed with three biological replicates for each of the four antibodies against MED1, MED12, CDK8, and MED16 respectively. The complete datasets are now provided in the revised manuscript (Supplementary Table 1).

      In addition, we have reanalyzed the data using a more quantitative approach. In the original submission, we classified proteins by a binary cutoff—detected or not detected in MED16 relative to other baits. In the revised analysis, we calculated a bait-preferential score for each protein by comparing its normalized abundance in the MED16 IP to its mean abundance across the other three Mediator baits. This score reflects how strongly a protein is enriched by MED16, rather than simply whether it meets an arbitrary detection threshold. A detailed description of this method is provided in the revised Methods section.

      Notably, using this quantitative method, UBP1 and TFCP2 can also be detected at lower levels in some replicates of the other Mediator baits. This is consistent with our functional model: UBP1 and TFCP2 associate with the whole Mediator complex through MED16. The MED16-biased enrichment pattern, now supported by the bait-preferential score, reinforces the conclusion that MED16 serves as the primary anchor for UBP1/TFCP2 to associate with the Mediator complex.

      (3) The data are over-interpreted, and alternative (and more plausible) hypotheses are ignored. Many examples of this, some of which are alluded to in the points above. For example, Med16 loss or overexpression will cause compensatory responses in cells. An expected result is that Mediator composition will be disrupted, since Med16 directly interacts with several other subunits. Also in yeast, the Robert, Gross, and Morse labs showed that loss of Med16/Sin4 causes loss of other tail module subunits, and this would be expected to cause major changes in the transcriptome. The authors also mention that yeast Med16/Sin4 "alters chromatin accessibility globally" and this would be expected to cause major changes in the transcriptome, leading to unintended consequences that will make data analysis and identification of direct Med16 effects impossible. The unintended consequences will be magnified with prolonged disruption of MED16 levels in cells (e.g., longer than 4h). These unintended consequences are hard to predict or define, and are likely to be widespread given the pivotal role of Mediator in gene expression. One unintended consequence appears to be loss of pol2 upon Med16 over-expression, as suggested by the western blot in Figure 8B. I point this out as just one example of the caveats/pitfalls associated with long-term knockdowns or over-expression.

      We thank the reviewer for raising this important point. Indeed, our new Co-IP and IP-MS data confirm that MED16 knockout leads to partial dissociation of MED23, MED24, and MED25 from the Mediator complex. We agree that this structural perturbation must be carefully considered when interpreting phenotypes from MED16 depletion experiments, and we acknowledge that long-term knockdown or knockout approaches are inherently susceptible to secondary effects and cellular adaptation.

      Author response image 3.

      MED16 knockout impacts on the Mediator complex

      However, our key conclusions are not based solely on long-term depletion experiments. Several lines of evidence, including experiments performed on short timescales without prolonged MED16 loss, converge on the same conclusion:

      First, the MED16 mutant lacking its C-terminal αβ-domain cleanly separates the two functions. This mutant retains the intact N-terminal WDR domain of MED16 and can integrate into the Mediator complex, preserving its structural role and preventing tail module dissociation. Yet it completely fails to exert the competitive inhibition (squelching) effect on the UBP1 reporter that is observed with wild-type MED16 overexpression (Fig 3G), and similarly fails to inhibit HIV-1 reporter activity (Fig 5G). The sole molecular defect of this mutant is its inability to bind UBP1 (Fig 2B). Critically, this overexpression experiment is performed in a wild-type background where the endogenous Mediator complex remains structurally intact. The only variable is whether the overexpressed MED16 can interact with UBP1. This provides strong evidence that the transcriptional effects are mediated specifically through the MED16-UBP1 interaction interface, independent of MED16's structural role in maintaining Mediator integrity.

      Second, the transcriptional changes we observe are not random but show specific functional coherence. If the transcriptomic changes in MED16 knockdown cells were merely secondary consequences of Mediator tail module disruption, we would expect broad, non-specific dysregulation. Instead, the UBP1-MED16 co-regulated genes are enriched for specific biological processes—angiogenesis—which largely align with the known biological functions of UBP1 (Fig 4D-E). This functional specificity is difficult to reconcile with the global, non-specific transcriptional disruption that would be expected from the total Mediator dysfunction.

      Finally, we wish to address the reviewer's observation regarding Pol II levels in Figure 8B. In preparing the revised manuscript, we carefully re-examined our original Western blot data and found that the reduction of Pol II upon MED16 overexpression was not consistently reproducible across independent experiments. We have therefore replaced new Pol II blot in Figure 8B. We note that this revision does not affect any of the conclusions drawn from Figure 8, which focus on the inhibitory effect of MED16 overexpression on HIV-1 latency reversal in J-Lat cells.

      We are grateful for the reviewer’s careful assessment, which encouraged us to refine our data presentation and enhance experimental rigor. The manuscript has been significantly improved as a result.

      Reviewer #3 (Public review):

      Weaknesses:

      (1) The decrease in 1,6-hexanediol-treated cells of MED16 is modest, variable, not quantified, and internally inconsistent. For example, in Figure 1A, 1,6-hexanediol treatment should not have an impact on the level of the protein being directly IP. For MED12 (and CDK8 and MED1 to a lesser extent), 1,6-hexanediol treatment alters the level of the target protein in the IP. Along these lines, Figure 1A shows a no 1,6H-D dependent decrease in MED1 or MED12 levels in the CDK8 IP, whereas Figure 1B does show a decrease. Figure 1A shows no 1,6H-D dependent decrease in CDK8 levels in the MED1 IP, whereas Figure 1B shows a dramatic decrease. MED24 levels in the MED12 IP increase upon 1,6H-D in Figure 1A, but decrease in Figure 1B. Internal inconsistencies of this nature persist in the other Figures.

      We thank the reviewer for this careful observation, and we here clarify the rationale behind our experimental design to resolve the apparent discrepancies between Figures 1A and 1B.

      We first performed co-immunoprecipitation (co-IP) assays using HEK293T whole-cell lysates with three distinct antibodies targeting CDK8, MED1, and MED12, respectively, to pull down intact Mediator complexes. This setup was designed to assess whether 1,6-hexanediol (1,6-HD) impairs Mediator complex integrity and, if so, whether this perturbation affects individual subunits unequally. Across all three independent IP replicates, we observed a uniform trend: most Mediator subunits displayed only mild signal attenuation, whereas MED16 exhibited a striking, dose-dependent reduction upon escalating 1,6-HD concentrations (Fig 1A). Quantitative densitometry of these immunoblot signals has now been incorporated into the revised Figures 1A and 1B to better visualize this difference. Thus, this initial result in Fig 1A indicated that MED16’s interaction with the core Mediator complex is uniquely vulnerable to 1,6-HD disruption, irrespective of which Mediator subunit was used for immunoprecipitation.

      To validate the reproducibility of this phenotype, we replicated the assay in an alternative biochemical system using HeLa nuclear extracts (NEs) (Fig 1B), with 2,5-hexanediol (2,5-HD) included as a critical negative control—this structural isomer cannot disrupt weak hydrophobic protein-protein contacts. Notably, the HeLa NE experiments in Fig 1B were carried out under elevated salt conditions (~300 mM NaCl), in contrast to the 150 mM NaCl buffer used for the HEK293T lysate assays in Figure 1A.

      We want to emphasize that despite these substantial technical disparities between the two experimental setups, both datasets converge on the identical core conclusion: MED16’s association with the core Mediator complex is selectively disrupted by 1,6-HD. This consistent trend is further strengthened by quantitative analysis of immunoblot band intensities from biological replicates, which we have added to the revised figures.

      Minor variability observed for other Mediator subunits between Fig 1A and 1B arises from intrinsic differences in cell type, extraction protocols, and buffer salt concentrations. For this reason, we did not draw mechanistic interpretations regarding the variable signal changes of other subunits across distinct experimental conditions; instead, our analysis focused on the robust, consistent loss of MED16 following 1,6-HD exposure.

      (2) Undermining the value of Figure 1E/F, UBP1 and TFCP2 may also associate with the small amount of MED16 in the 2MDa fractions. This is not tested, and therefore, the conclusion that they just associate with the dissociable form of MED16 is not supported.

      We thank the reviewer for this insightful observation. Indeed, UBP1 and TFCP2 can associate with MED16 in the context of the intact Mediator complex. Figure 1B directly supports this: under 2,5-HD conditions, where MED16 remains integrated into the Mediator, both UBP1 and TFCP2 are co-immunoprecipitated with the Mediator complex (via anti-MED1). This association is lost under 1,6-hexanediol conditions, where MED16 dissociates—suggesting that UBP1 and TFCP2 are tethered to the Mediator through MED16.

      The gel filtration experiment (Fig 1D) further reveals that a portion of MED16 resolves away from the core Mediator complex under these in vitro conditions, and Figure 1E-F shows that UBP1 and TFCP2 co-immunoprecipitated with MED16 in these lower molecular weight fractions. Taken together, these data indicate that UBP1 and TFCP2 can be found in association with MED16 in two biochemically distinguishable states: one integrated into the Mediator complex, and another that has dissociated from it under our experimental conditions. We did not state that UBP1 and TFCP2 associate exclusively with the dissociated form; rather, the dissociated fraction is where we initially identified this interaction, which then led us to investigate its functional significance. The whole manuscript has been extensively rewritten now, and hopefully the confusion has been clarified.

      Our functional data further demonstrate that the transcriptional activities of UBP1 and TFCP2 require the intact Mediator complex. MED16 bridges this interaction through its C-terminal αβ-domain, which binds UBP1/TFCP2, and its N-terminal WDR domain, which anchors MED16 into the Mediator. We have revised the manuscript to clearly distinguish the biochemical observation from the functional model, and we now explicitly state that UBP1 and TFCP2 associate with the Mediator complex through MED16.

      (3) Domain mapping studies in Figure 2 are overinterpreted. Since the interactions could be indirect, it is not accurate to conclude "Therefore, the N-terminal WDR domain of MED16 is crucial for its integration into the Mediator complex, while the C-terminal αβ-domain is essential for interacting with UBP1-TFCP2."

      We fully acknowledge that co-immunoprecipitation (co-IP) assays alone cannot discriminate direct physical binding from indirect protein associations mediated by intermediate partners. Nevertheless, our conclusion that MED16 directly interacts with UBP1 is supported by two orthogonal, independent experimental datasets.

      First, GST pull-down assays using purified recombinant proteins expressed in E. coli confirm direct binding (Figure 2C). This cell-free biochemical system removes all potential cellular bridging factors, unambiguously demonstrating a physical contact between the C-terminal αβ-domain of MED16 and UBP1.

      Second, our mutational functional analyses further rule out an indirect interaction model. Deletion of the unique 36-residue USP peptide in UBP1 completely abrogates MED16 binding (Figure 2F, comparison between ΔSAM and DBD truncations). Reciprocally, MED16 mutants lacking the C-terminal αβ-domain lose all capacity to associate with UBP1. Importantly, both truncation mutations yield identical functional defects in UBP1-driven and HIV-1 luciferase reporter assays (Figures 3G and 5G). If this complex assembly relied on an uncharacterized bridging protein, it would be statistically improbable for two distinct, structurally unrelated deletions (USP removal on the UBP1 side and αβ-domain truncation on the MED16 side) to independently disrupt the same indirect linkage and generate identical loss-of-function phenotypes.

      Collectively, the congruent results from cell-free in vitro biochemistry and targeted mutational functional assays provide robust evidence for a direct, specific physical interaction between MED16 and UBP1.

      (4) A close examination of Figure 2C undermines confidence in the association studies. The bait protein in lanes 5-8 should be equal. Also, there is significant binding of GST to UBP1 and TFCP2, in roughly the same patterns as they bind to GST-MED16 αβ. The absence of input samples makes the results even more difficult to interpret.

      We acknowledge the technical limitations the reviewer pointed out. Regarding the concern about unequal protein loading across lanes 5-8, we note that lanes 7 and 8 in fact contain more GST-M16αβ bait protein than lanes 5 and 6, yet TFCP2L1 and YY1 still show no appreciable binding above the GST-alone background. This loading asymmetry therefore strengthens the conclusion that these proteins do not directly interact with MED16. For UBP1 and TFCP2, we have now provided quantification of the Western blot signals, which confirms a clear difference in binding to GST-M16αβ versus GST alone. Furthermore, these GST pull-down experiments were performed under stringent conditions—300 mM NaCl and 0.5% Tween-20 in the wash buffer—and the UBP1 and TFCP2 interactions with GST-M16αβ persisted under these conditions (lane 5-8), whereas binding to GST alone was minimal (lane 1-4). These technical details support the specificity of the observed interactions.

      (5) The domain deletion mutants are utilized throughout the manuscript as evidence of the importance of the UBP1-MED16 interaction. However, in Figure 2F lanes 7 and 8, the delta-S mutant binds MED16 as well as full-length UBP1. This undermines much of the subsequent data and conclusions about specificity.

      We believe the reviewer may have a misunderstanding about the experimental design. The delta-S (ΔSAM) mutant deletes the SAM motif, which is required for UBP1-TFCP2 heterodimerization, but retains the UBP1-specific sequence (USP) that mediates MED16 binding. The observation that ΔSAM still binds MED16 at levels comparable to full-length UBP1 is therefore not a discrepancy—it is the expected result and confirms that USP is the region responsible for MED16 interaction. The DBD mutant, which further removes USP, completely loses MED16 binding. Together, these deletion constructs delineate two separable interaction surfaces on UBP1: USP for MED16 binding and the SAM motif for TFCP2 heterodimerization (see Figure 2 G). The functional analysis of these domains is reflected in Figure 3B: ΔSAM, which retains MED16 binding but loses TFCP2 dimerization, shows approximately half the transcriptional activity of full-length UBP1, suggesting that both interactions contribute to full UBP1 function. To avoid similar confusion, we have renamed ΔS to ΔSAM,UBP1-SP to USP in the revised manuscript.

      (6) Even if the delta-S mutant were defective for MED16 binding, the result in Figure 3B does not "confirm that MED16 is required for the transcriptional activity of UBP1,". Removal of that domain may have other effects.

      We wish to first clarify a factual point: the delta-S mutant (ΔSAM) is not defective for MED16 binding. ΔSAM deletes the SAM motif that is required for UBP1-TFCP2 heterodimerization, but retains the UBP1-specific sequence (USP) that mediates MED16 binding. As shown in Figure 2F, ΔSAM binds MED16 at levels comparable to full-length UBP1. The mutant that loses MED16 binding is DBD, which further deletes USP.

      The functional result in Figure 3B is therefore consistent with this biochemical mapping: ΔSAM retains MED16 binding but loses TFCP2 dimerization, and accordingly shows approximately half the transcriptional activity of full-length UBP1. This suggests that both the MED16 interaction (via USP) and the TFCP2 interaction (via the SAM motif) contribute to full UBP1 function.

      More importantly, the conclusion that MED16 is required for UBP1 transcriptional activity does not rest on domain mutant analysis alone. It is supported by independent loss-of-function approaches: shRNA-mediated MED16 knockdown (Fig 3C) and CRISPR-mediated MED16 knockout across five independent frameshift clones (Fig 3E-F), both of which consistently reduce UBP1 reporter activity. The domain deletion mutants complement these approaches by mapping the specific interaction interfaces.

      (7) As Mediator is critical for the activation of many genes, it is not accurate to assume that the impact of its deletion in Figure 3E/F demonstrates a direct requirement in UBP1-driven transcription. This could easily be an indirect effect.

      MED16 knockout data must be interpreted cautiously due to potential Mediator destabilization artifacts. However, our conclusion does not rely solely on loss-of-function data; transient competitive overexpression and domain-mapping assays bypass chronic MED16 depletion entirely. The MED16Δαβ mutant retains full Mediator integration but loses UBP1 binding. Unlike wild-type MED16, this mutant fails to change UBP1 or HIV-1 reporter activity in 36–48 h transient assays performed in wild-type cells with intact endogenous Mediator. Dose titration further confirms that only UBP1-interacting MED16 competes for UBP1 binding to repress target promoters (Fig 3G). Since these experiments involve no permanent MED16 ablation, the observed transcriptional effects cannot be attributed to indirect global Mediator dysfunction, verifying specific MED16–UBP1 regulation.

      (8) Without documenting the relative protein expression levels in Figure 3G/H, conclusions cannot be drawn about the titration experiments, nor the co-expression experiments. These findings are likely the result of squelching or some form of competition that is not directly related to the UBP1-mediated transcription. A great deal of validation would be required in order to support the model that these effects are a result of MED16 overexpression sequestering UBP1 away from holo-Mediator.

      We thank the reviewer for this insightful comment. In fact, the squelching interpretation that the reviewer proposes is fully consistent with our functional model and helps reinforce a key conclusion of our study.

      The reviewer correctly points out that overexpressed MED16 may compete with the endogenous Mediator complex for UBP1 binding, sequestering UBP1 away from the holo-Mediator and thereby inhibiting UBP1-driven transcription. This interpretation directly implies that UBP1 requires association with the intact Mediator complex—not merely free MED16—to activate transcription. This is precisely the model we advocate, and the squelching phenomenon observed in Figure 3G thus provides independent functional evidence for it.

      The specificity of this effect is confirmed by the MED16 Δαβ mutant: the Δαβ mutant integrates into the Mediator complex normally but cannot bind UBP1, and consequently fails to exert any competitive inhibition on the UBP1 reporter (Fig 3G). This demonstrates that the squelching effect is specifically dependent on the MED16-UBP1 interaction interface. We have now documented the relative protein expression levels for these experiments in the revised manuscript.

      (9) The lack of any documentation of expression levels for the various ectopic proteins in the majority of Figures, renders mechanistic claims meaningless (Figures 3, 4, 5, 6, 7, S2, S3). This is particularly relevant since the model presented for many of the results invokes concentration-dependent competition.

      In the revised manuscript, we have now included Western blot data documenting the expression levels of ectopic proteins for the key experiments. The expression level data confirm that the proteins were expressed at the expected levels across the different conditions.

      Recommendations for the authors:

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      Reviewer #1 (Recommendations for the authors):

      (1) The writing and presentation are generally good, except that some typos and mistakes in conceptual statements (e.g., lines 407-408 "WDR-deleted MED16, which cannot interact with UBP1") often confuse the readers.

      We thank the reviewer for the careful reading. We have tried out best to correct the typos and misstatements, including the error in line 407-408 where "WDR-deleted MED16, which cannot interact with UBP1" should refer to the αβ-domain deletion rather than the WDR deletion. The whole manuscript has been extensively rewritten for better clarity and preciseness.

      (2) Inconsistency in the citation format between numbering and author names should be avoided.

      The citation format has now been unified throughout the manuscript.

      (3) Two important scientific concepts based on the published literature should be mentioned:

      (a) "The position-dependent function of transcription factors (TFs) relative to the TSS" (lines 427-428) is a well-known fact in the transcription field that partly explains gene activation versus gene repression; and

      (b) the statement "JQ1 that is known to reverse HIV-1 latency by antagonizing BRD4's suppression of the Tat-P-TEFb/SEC (Super Elongation Complex) interaction" (lines 384-386) is only partially correct and is a property unique to the BRD4 long (BRD4-L) isoform.

      We thank the reviewer for this suggestion. We have now cited the relevant literature on the position-dependent function of transcription factors relative to the TSS, including the recent study by Duttke et al. (3), which systematically demonstrated that the effect of TF binding on transcription initiation is highly position-dependent. Our findings provide a clear example that UBP1 activates transcription when its binding motif is located upstream of the TSS but represses when the motif overlaps the TSS, which exemplifying this concept.

      We have corrected the statement to specify that JQ1 antagonizes the BRD4 long isoform (BRD4-L)-mediated suppression of Tat-P-TEFb/SEC, and have noted that the BRD4 short isoform (BRD4-S) may also contribute to HIV-1 promoter repression and JQ1-mediated latency reversal.

      (4) Another relevant concept is that BRD4 short (BRD4-S) isoform-inhibited HIV-1 promoter activity could be potentially alleviated by JQ1, leading to latency reversal. Besides the aforementioned points and those comments already provided in the Public Review, the involvement of TFCP2 and YY1 in MED16-UBP1 function has not been clearly established and will require more careful and detailed investigation.

      We thank the reviewer for these suggestions. Regarding BRD4-S, we have added a discussion of the BRD4 short isoform's role in HIV-1 promoter regulation and its potential contribution to JQ1-mediated latency reversal.

      Regarding the involvement of TFCP2 and YY1, we agree that their precise roles within the MED16-UBP1 regulatory axis have not been fully dissected in the current study. Our data indicate that TFCP2 can dimerize with UBP1 to enhance transcriptional activation (Fig 3H), and YY1 is present in the MED16-containing fractions (Fig 1E), consistent with its previously reported role in HIV-1 silencing. However, the molecular details of how TFCP2 and YY1 contribute to MED16-UBP1 function—and whether they act in the same or parallel pathways—remain to be determined. We have acknowledged this as a limitation and a direction for future investigation in the revised Discussion.

      Reviewer #2 (Recommendations for the authors):

      (1) The data from Figure 1F are hard to interpret, the blot quality is poor for Med23, 16

      We acknowledge that the blot quality for MED23 and MED16 in Figure 1F was suboptimal in the original submission. We have now provided better Western blot, which confirms the specific co-immunoprecipitation of MED16 and MED23 with TFCP2 in both HeLa nuclear extract and the gel filtration fractions. Importantly, the reciprocal co-IP (anti-MED16 IP blotted for TFCP2 and UBP1, Fig 1E) independently validates this interaction.

      (2) Figure 2: No MW markers on gels. Also, these types of experiments are ok for pilot studies, but they aren't rigorous. Only a few proteins were tested, and it's easy to get false positives with how the experiments were completed. Finally, the Mediator proteins were tested as single isolated proteins rather than in their native context. As isolated proteins, they likely have large exposed hydrophobic patches (normally bound to other subunits), and this can generate non-specific "sticky" interactions that can lead to false positive results.

      We have now included MW markers on the gel images in the revised Figure 2.

      The reviewer raises a valid general concern about domain mapping experiments. However, we wish to emphasize that the readout in Figure 2B and 2F is the binding of exogenously expressed truncated proteins to their endogenous interaction partners—MED1, TFCP2, MED16, and MED23—which are natively folded and assembled in their physiological environment. This is not a situation where two isolated proteins are mixed in a test tube. Furthermore, the deletion series themselves contain important internal controls against non-specific "stickiness": In Figure 2B, progressive deletion of the WDR domains leads to a gradual reduction in MED1 binding, consistent with the predicted structural role of the β-propeller (WDR domain). The truncation at the αβ-domain specifically abolishes TFCP2 binding but does not affect its association with Mediator complex as indicated by MED1. If the truncated proteins were non-specifically sticky due to exposed hydrophobic patches, one would expect uniform binding across all truncations or random binding patterns, which is not observed. And in Figure 2F, deletion of the 36-amino-acid USP abolishes its MED16 binding, while deletion of the SAM motif does not. This sequence-specific differential effect is inconsistent with non-specific hydrophobic interactions.

      The domain mapping experiments in Figure 2 were not designed as an unbiased screen but as hypothesis-driven tests guided by the IP-MS results identifying UBP1/TFCP2 as MED16 interactors, sequence and structural analysis predicting the roles of the WDR and αβ-domains. The conclusions from these experiments are corroborated by subsequent functional assays: the USP-deleted mutant and the MED16 Δαβ mutant show corresponding functional defects in both UBP1 reporter and HIV-1 reporter assays (Fig 3G, Fig 5G), providing strong support that the mapped interaction interfaces are functionally relevant.

      (3) Figure 3: UBP1 reporter is artificial, 6X UBP1 sites upstream of a TATA box.

      We appreciate this comment and wish to clarify a misunderstanding. The UBP1 reporter used in Figure 3 is not an artificial construct assembled from random elements. Its backbone is derived from the mouse α-globin gene promoter, a well-established natural target of UBP1/TFCP2, which contains two endogenous UBP1/TFCP2 binding sites. In order to increase the reporter's sensitivity to UBP1/TFCP2, we expanded the original two sites to six. Critically, this expansion did not alter the native architecture of the promoter—the relative positions of the transcription factor binding sites, the CCAAT box, the TATA box, and the TSS remain unchanged from the original α-globin promoter. This reporter therefore retains the physiological spacing and topological organization of a genuine UBP1 target promoter, and its low basal activity and high inducibility by UBP1 make it a well-suited tool for modeling UBP1 transcriptional activity. We have further clarified this description in the revised manuscript.

      (4) Figure 3D: Med16 KD not compelling, perhaps 50% knockdown? Hard to tell because no replicates and no quantitation.

      We acknowledge that the shRNA-mediated MED16 knockdown in Figure 3D was partial and that the original presentation lacked replicate quantification. We initially tested three independent shRNAs targeting MED16, of which only one achieved appreciable knockdown. Recognizing this limitation, we turned to a more definitive loss-of-function approach: CRISPR-Cas9-mediated MED16 knockout. Five independent frameshift KO clones were generated in mouse HT cells, each verified by sequencing and Western blotting (Fig 3F), and all five clones consistently showed reduced UBP1 reporter activity compared with wild-type cells (Fig 3E). The consistency between the shRNA knockdown and CRISPR KO reinforce the conclusion that MED16 is required for UBP1 transcriptional activity.

      (5) Figure 3E: Med16 KO will be a different cell line because of genetic adaptation. That is, an RNA-seq experiment is expected to result in many gene expression changes compared to the parental line. This could contribute to differences, or differences could result from altered Mediator composition to compensate for Med16 loss, but this wasn't addressed by the authors. A rapid degron method would be much more rigorous.

      We thank the reviewer for raising this important concern. We agree that prolonged MED16 loss can elicit cellular adaptation and compensatory changes in Mediator composition, as our own new data on tail module dissociation confirm. This makes it challenging to disentangle direct and indirect effects using constitutive KO models alone.

      However, two independent lines of evidence support a direct role for MED16 in UBP1-mediated transcription that cannot be explained by adaptive responses:

      First, acute shRNA-mediated knockdown produces the same phenotypic trend. shRNA operates on a shorter timescale (days) without prolonged selection for compensatory mutations, yet it also reduces UBP1 reporter activity (Fig 3C). The convergence of acute and chronic loss-of-function approaches suggests the phenotype is driven by MED16 loss itself rather than by the secondary adaptations that accumulate over time in KO cells.

      Second, and most critically, the MED16Δαβ mutant experiment completely bypasses the issues of genetic adaptation and Mediator structural disruption (Fig 3G). This experiment is performed in wild-type cells with an intact endogenous Mediator complex, without any MED16 depletion and without any long-term selection. The MED16Δαβ mutant retains full ability to integrate into the Mediator complex (its WDR domain is intact) but cannot bind UBP1. Overexpression of wild-type MED16 competitively inhibits the UBP1 reporter due to the squelching effect, whereas overexpression of MED16Δαβ at comparable levels does not (Fig 3G). The sole molecular difference is that MED16Δαβ doesn`t contain the UBP1-binding interface. Because this experiment involves no MED16 loss and no adaptive period, the result directly demonstrates that MED16-UBP1 interaction is required for UBP1 transcriptional activity, entirely independent of concerns about clonal adaptation.

      We appreciate the reviewer's suggestion of a rapid auxin-inducible degron approach. While acute depletion strategies offer clear advantages in minimizing cellular adaptation, they are not without limitations. First, degron-based depletion rarely achieves 100% elimination of the target protein, and residual protein may confound interpretation. Second, and particularly relevant to our system, degron-mediated degradation may preferentially target free, unassembled subunits while sparing proteins already stably integrated into large macromolecular complexes such as the Mediator—potentially creating a lag or even complete resistance to degradation for the pool of MED16 that is assembled into the complex. Given these considerations, we believe our convergent strategy—combining shRNA knockdown, CRISPR-mediated knockout in multiple independent clones, and the Δαβ mutant that bypasses structural perturbation—provides a robust and complementary set of evidence that does not rely on any single depletion method.

      (6) The title for Figure 3 is "Med16 is required for UBP1 activation of gene transcription", but that's not conclusively shown by these experiments.

      We agree that the original title of Figure 3 may overstate. We have revised the title to " MED16 acts through the Mediator complex to support UBP1-mediated transcriptional activation ", which may better reflect the data. As discussed in our responses above, this conclusion is supported by converging evidence from shRNA knockdown (Fig 3C), CRISPR-mediated KO across multiple clones (Fig 3E-F), and the specific loss of function observed with the MED16Δαβ mutant (Fig 3G).

      (7) Figure 5: Med16 protein levels were never measured, nor were other transfected proteins. Reporter assay data are correlative only; no evidence for direct effects, and only 2 other MED subunits are probed. Again, this is a starting point, but nothing to use for concrete conclusions about Med16.

      We have now provided Western blot data documenting MED16 and other transfected protein levels for the experiments in Figure 5, confirming that the proteins were expressed at the expected levels. We agree that plasmid-based reporter assays alone cannot establish direct transcriptional mechanisms. The HIV-1 reporter experiments in Figure 5 serve as an initial functional readout, and their conclusions are corroborated by more direct approaches: (i) the position-dependent specificity demonstrated by chimeric reporter assays (Fig 6A-C); (ii) the independence of the effect from TAR-mediated elongation and HDAC activity (Fig S2), ruling out alternative mechanisms; and (iii) the immobilized template transcription assays, which directly measure nascent RNA production and PIC assembly on the HIV-1 promoter (Fig 7). The reporter data should be viewed as one component of a larger body of convergent evidence rather than as standalone proof.

      MED23 and MED24 were chosen as the most relevant controls because they, along with MED16, form the core of the tail module submodule that creates the transcription factor interaction platform. They are structurally adjacent to MED16 within the same submodule. The fact that neither MED23 nor MED24 overexpression affects HIV-1 reporter activity (Fig 5C), whereas MED16 overexpression does so in a UBP1-binding-dependent manner (Fig 5C, 5E), demonstrates that the inhibitory effect is specific to MED16 and not a generic consequence of overexpressing a Mediator tail subunit.

      (8) Figure 6: similar concerns to Figure 5. The promoters are artificial, derived from a chicken beta-actin promoter with artificially inserted UBP1 binding sequences.

      We acknowledge that the chimeric promoters in Figure 6 are indeed deliberately designed. To address this specific question: Does the binding position of the UBP1 relative to the TSS dictate whether UBP1-MED16 activates or represses transcription?

      To isolate position as the sole variable, we needed a backbone promoter that (i) is stably and robustly active in the cell lines used, (ii) does not itself contain endogenous UBP1 binding sites that would confound the interpretation, and (iii) allows precise placement of the UBP1 binding motif either upstream of or overlapping the TSS. The chicken β-actin promoter meets all three criteria and is a widely used tool in molecular biology for such controlled promoter engineering experiments. Notably, its high GC content around the promoter region is also a feature shared with the HIV-1 core promoter, making it a particularly relevant backbone for the HIV-1 TSS chimeric constructs.

      The key controls for this experiment are internal: in the same backbone, placing the UBP1 binding site at the TSS results in MED16-UBP1-dependent repression, whereas placing it upstream does not (Fig 6A vs 6B). Furthermore, this position-dependent effect requires the MED16-UBP1 interaction interface, as the MED16 Δαβ mutant and the UBP1 DBD mutant both fail to produce the inhibitory effect (Fig 6A). The differential outcome between the two constructs—which differ only in the placement of the UBP1 binding site—cannot be attributed to the artificial nature of the backbone, because the backbone is identical in both cases.

      (9) Figure 6D: Not rigorous, no statistical analysis, cannot determine whether the modest changes are biologically relevant/meaningful. Also, data were obtained through indirect measurements and long timeframes, so any direct effects from possible UBP1 binding are probably lost.

      The differentially expressed genes in Figure 6D were selected using DESeq2 with a significance threshold of p < 0.05. The top 50 upregulated and top 50 downregulated transcripts were then used for UBP1 motif enrichment analysis around their promoters. We have clarified these selection criteria in the revised figure legend.

      Regarding the concern that indirect measurements over long timeframes, we now have additional evidence that UBP1 binding effects are preserved in our RNA-seq data. Motif analysis of UBP1-upregulated genes using HOMER identified the CP2 family binding motif (CNRG-N6-CNRG) as the most significantly enriched motif. This indicates that UBP1-binding-dependent transcriptional effects are not lost in our dataset.

      Author response image 4.

      Motif enrichment result of the UBP1 up-regulated genes

      Furthermore, when these UBP1-upregulated and UBP1-downregulated genes are analyzed separately for motif position relative to the TSS, a clear pattern emerges: UBP1-upregulated genes show motif enrichment flanking the TSS, whereas UBP1-downregulated genes show enrichment overlapping the TSS (Fig 6D). If the transcriptional changes were non-specific secondary consequences of prolonged UBP1 overexpression, one would not expect this position-specific distribution. Finally, this positional specificity is corroborated by the chimeric reporter experiments (Fig 6A-C), where moving the UBP1 binding site across the TSS in an otherwise identical promoter directly switches the regulatory outcome. The concordance among the de novo motif discovery, the genome-wide positional analysis, and the functional reporter assays supports the biological relevance of the motif enrichment pattern.

      (10) The title for Figure 6 is "UBP1 binding site determines the UBP1-Med16 inhibition in HIV-1 transcription" but the data do not convincingly show this.

      We appreciate this suggestion. We have revised the title to "The position of the UBP1 binding site relative to the TSS dictates whether MED16-UBP1 activates or represses transcription.". For clarification, Figure 6 not only demonstrates that the UBP1 binding position dictates MED16-UBP1-mediated inhibition in the HIV-1 context, but also revealed that this positional effect could be generalized to endogenous genes: UBP1 motifs flanking the TSS are associated with activation, while motifs overlapping with the TSS are associated with repression.

      (11) Figure 7: The data and the experiment do not support the claim in the title that "MED16-UBP1 complex prohibited PIC formation of HIV-1 transcription". I cannot convince myself that there are meaningful differences between the results shown in 7B, but it's hard to judge because there are no replicates and no quantitation.

      We have revised the title from "MED16-UBP1 complex prohibited PIC formation of HIV-1 transcription" to "MED16 interferes with HIV-1 PIC assembly at the TSS via its interaction with UBP1." We have also provided quantification of the Western blot signals from replicate experiments in Figure 7B, which confirms statistically significant differences in CDK8 binding, Pol II pSer5 signals, and TFIIB retention between conditions. We note that the key observations in Figure 7B were consistently observed across multiple pilot experiments prior to the final dataset presented in the manuscript. Thus, the reported differences are reproducible and not based on a single experiment.

      (12) The immobilized template experiments had only -78 upstream and +60 downstream of the TSS. By my understanding, much more upstream DNA is needed to allow a PIC to assemble, because the 5'-end bead attachment will block. I think this was outlined in an article by the Carey lab, but I'm not sure. A point here is that it would be reassuring for the authors to show that PIC assembly was occurring and that transcription was responsive to a transcription factor, similar to what the Carey lab has done with immobilized templates. See, for example, Figure 1 in Lin & Carey Curr Protocol Mol Biol 2012 Ch12 unit 12.14.

      We thank the reviewer for raising this technical consideration. We respectfully offer a different perspective based on current structural data on the human PIC.

      A recent cryo-EM study of the human Mediator-bound preinitiation complex (4) demonstrated that the PIC spans approximately 50 bp upstream and downstream of the TSS. The DNA templates used for structural determination in that study were generally within 105 bp upstream and 63 bp downstream of the TSS. Our HIV-1 template (-78 to +60, i.e., 78 bp upstream and 60 bp downstream of the TSS) falls within this well-characterized range and provides sufficient space for PIC assembly.

      Regarding the choice of the -78 boundary: the HIV-1 core promoter is defined as -78 to +60, and the region from -78 to -105 encompasses the HIV-1 enhancer, which contains additional regulatory elements including NF-κB and NFAT binding sites. We deliberately excluded these enhancer elements to avoid confounding regulatory inputs and to focus specifically on the UBP1 binding site at the TSS, which is the central focus of our model. The robust luciferase mRNA production observed in wild-type nuclear extract (Fig 7C) provides direct functional evidence that PIC assembly and transcription initiation proceed efficiently on this template under our conditions.

      Author response image 5.

      Promoter templates used for human PIC assembly (Chen et al., Science 372, eaba8490, 2021): templates within −105 to +65 bp of the TSS support PIC formation

      (13) Related to the above concern, the qPCR analysis from the extract experiments may be misleading because of contaminating nucleic acids in the extract. More control experiments are needed, such as no NTP controls.

      We thought this concern has been addressed by our experimental design.

      First, the PIC step in our experimental design (Fig 7B, "PIC") is the no-NTP control: nuclear extract is incubated with the immobilized template without NTPs. After washing, the proteins bound at this step are analyzed by Western blot, and importantly, there is no transcription occurred at this step. Transcription is then initiated by adding NTPs (Fig 7B, "Trx."), and the supernatant from this step is collected for qPCR analysis of transcription.

      Second, it is unlikely that the contaminating nucleic acids from the nuclear extract could confound our qPCR results, because the qPCR analysis specifically detects Luciferase mRNA transcribed from the immobilized HIV-1-Luciferase template, which is absent from eukaryotic genomes; and the endogenous genomic DNA or RNA cannot generate a false-positive signal.

      Lastly, after transcription, the immobilized templates are retained on the streptavidin beads and removed, while only the supernatant containing newly synthesized RNA is collected for qPCR. Any residual template DNA or extract-derived nucleic acids bound to the beads are therefore excluded from the qPCR sample.

      (14) Some references are made with the format of (author name, year), but others use numbers.

      The citation format has been unified throughout the manuscript.

      (15) On line 111-115, it is stated that 2,5 HD somehow "does not interfere with weak hydrophobic interaction due to different positions of hydroxyls" with no reference. Both 1,6 HD and 2,5 HD are hydrophobic molecules, and they will disrupt protein structure. The authors make a point about the differential results with 1,6 HD vs. 2,5 HD, but it's not compelling and not justified.

      We have now provided the reference to support this statement. The specificity of the 1,6-HD effect is further clarified as follows.

      1,6-HD and 2,5-HD are structural isomers sharing the same chemical formula and molecular weight. The key difference lies in the positions of their hydroxyl groups (See Author response image 6). The position of the two hydroxyl groups changes both the molecule’s shape and how it interacts with water and hydrophobic surfaces.

      In 1,6-HD, the two -OH groups sit at the ends of the chain, giving a more symmetric, flexible molecule, while in 2,5-hexanediol the -OH groups are closer to the middle, which changes polarity distribution and local geometry (5, 6). The distinctive structures matter because hydroxyl groups strongly hydrogen-bond with water. When they are terminal and well separated in 1,6-HD, the molecule can present a more balanced hydrophilic “cap” at both ends while keeping a relatively long hydrophobic center, which helps 1,6-HD partition into and disturb weakly interacting hydrophobic regions in condensates or hydrophobic interfaces (Author response image 6). By contrast, moving the hydroxyls inward in 2,5-HD does not help with disrupting the hydrophobic interfaces (Author response image 6). That is why 2,5-HD is often used as a comparison compound: it has the similar chemical formula, but the different hydroxyl pattern usually makes it a weaker or at least differently acting perturbant.

      Author response image 6.

      Differential disruption of hydrophobic interfaces by 1,6-HD vs 2,5-HD

      (16) On lines 117-120, the authors claim that Med16 is "integrated into the Mediator complex through weak hydrophobic interaction but not ionic interaction" based upon a few IP results. They then state (lines 130-131) that "the interaction between Mediator and Med16 is an unstable hydrophobic interaction." These statements make no sense based on any data shown, and they ignore the fact that Med16 purifies with Mediator from many different purification protocols. It's safe to say that its interaction with Mediator results from strong, extensive hydrophobic interfaces, which is further supported by actual structural data. The authors should check their reasoning here because they are over-interpreting their results, and this will be misleading to readers.

      We thank the reviewer for this important suggestion. The reviewer is right that our original phrasing—particularly "unstable hydrophobic interaction"—was imprecise and inconsistent with the established structural data. As the reviewer notes, MED16 co-purifies with the Mediator complex through multiple purification protocols, and recent cryo-EM structures (4) reveal that MED16 engages in extensive hydrophobic interfaces with neighboring tail module subunits, particularly MED24 and MED25. We fully agree that this is a strong, hydrophobic interaction in the physiological context.

      Our 1,6-hexanediol experiments demonstrated that the MED16-Mediator interface does have a hydrophobic character, which renders the sensitivity to 1,6-HD disruption. This does not mean the interaction is weak. We have now carefully described the relevant statements in the revised manuscript.

      Reviewer #3 (Recommendations for the authors):

      (1) It is not accurate to claim that "198 (genes) were repressed by shMED16 knockdown (Fig 4B and 4C)". These genes were simply not activated by UBP1 in the knockdown cells.

      We thank the reviewer for this careful distinction. We have revised the text to state that the UBP1-activated genes were “profoundly downregulated upon MED16 depletion” rather than “repressed by shMED16 knockdown”. The biological point remains unchanged: MED16 is required for UBP1 to activate this set of target genes.

      (2) Like the majority of experiments presented, the RNA-seq presented in Figure 4 may reflect indirect effects of MED16 loss. As such, the results do not support the claim that "This result demonstrated that UBP1 and MED16 collaborate to regulate transcription".

      We acknowledge that RNA-seq data from MED16 knockdown cells alone cannot distinguish direct from indirect effects. However, we did not rely on the RNA-seq data alone to support a collaborative role for UBP1 and MED16.

      The key evidence comes from experiments comparing wild-type MED16 with the MED16Δαβ mutant. MED16Δαβ integrates into the Mediator complex normally but cannot bind UBP1 (Fig 2B). When overexpressed in wild-type cells, wild-type MED16 inhibits the UBP1-driven reporter activity through a squelching effect. In contrast, MED16Δαβ has no effect on the UBP1 reporter assay (Fig 3G), and the same is true for the HIV-1 reporter assays (Fig 5G). Thus, MED16 must both integrate into the Mediator complex and directly bind UBP1 to support UBP1-mediated transcription.

      In addition, the genes identified as UBP1-MED16 co-regulated by RNA-seq are enriched for specific biological processes—angiogenesis (Fig 4D-E)—that align with the known physiological functions of UBP1. Such functional coherence would be unexpected if the transcriptional changes were merely non-specific secondary consequences of Mediator disruption.

      Thus, while we agree that RNA-seq alone has limitations, the convergent evidence from the MED16Δαβ mutant and the functional specificity of the co-regulated gene set supports a collaborative role for UBP1 and MED16 in transcriptional regulation.

      (3) The data in Figure 4 are not presented with controls documenting the relative expression levels of MED16 and UBP1. For example, it would be important to understand the level of UBP1 overexpression achieved, relative to endogenous UBP1 levels in HeLa cells.

      We agree with the reviewer that documenting expression levels is important. Author response image 7 are the CPM values for UBP1 from our RNA-seq data, confirming robust overexpression in the UBP1-overexpression condition compared with the control. The MED16 protein levels in the knockdown cells can be found in Fig 3D, as the same MED16-knockdown HeLa cell line was used for the RNA-seq experiment.

      Author response image 7.

      UBP1 expression level derived from RNA-seq

      Regarding the physiological relevance of UBP1 overexpression, several observations support that the transcriptional effects we observe are specific rather than artifacts of overexpression: (i) the UBP1-activated genes are enriched for pathways known to be regulated by UBP1—angiogenesis (Fig 4D-E)—rather than showing random, non-specific activation; (ii) the activation of these genes is attenuated upon MED16 knockdown, demonstrating dependence on the MED16-UBP1 interaction; and (iii) in reporter assays, the MED16 mutant lacking its C-terminal αβ-domain fails to competitively inhibit UBP1 driven reporter transcription even when UBP1 is similarly overexpressed (Fig 3G), indicating that the competitive inhibition of the UBP1 reporter depends specifically on the MED16-UBP1 interaction interface, rather than on the amount of UBP1 protein overexpressed.

      (4) The enhancer-promoter architecture of the luciferase construct used in Figure 3 is not sufficiently described. Are the elements far enough apart that Mediator-dependent looping is required for transcription, for example?

      We appreciate the reviewer's question. The UBP1 reporter construct used in Figure 3 is derived from the mouse α-globin gene promoter, a well-established natural target of UBP1/TFCP2(7, 8). The native α-globin promoter contains two endogenous UBP1/TFCP2 binding sites; to increase the reporter's sensitivity to UBP1, we expanded these to six tandem copies. Critically, this expansion preserved the native architecture of the promoter—the relative positions of the UBP1 binding sites, the CCAAT box, the TATA box, and the TSS were not altered. The entire reporter construct spans only 198 bp in total length. At this scale, all regulatory elements reside within a compact proximal promoter region, with no element separated by a distance that would require DNA looping for functional communication. The reporter therefore measures UBP1-dependent transcriptional activation from the proximal promoter, consistent with the canonical role of Mediator in facilitating PIC assembly rather than mediating long-range enhancer-promoter interactions. We have added a detailed description of the construct architecture to the Methods section.

      (5) The identification of matches to the UBP1 consensus sequence in a promoter does not prove that this is how UBP1 overexpression is impacting their transcription. "highly matched UBP1 motifs were identified in their promoters (Fig. 4F), leading to their activation when UBP1 was overexpressed."

      We agree with the reviewer that the presence of a UBP1 consensus motif in a promoter does not by itself prove direct regulation. We have revised the original statement: the activation of these genes by UBP1 overexpression is now described as "associated with" the presence of UBP1 motifs in their promoters.

      We acknowledge that the ideal experiment to demonstrate direct UBP1 occupancy would be ChIP-seq. However, ChIP-grade antibodies against UBP1 are not commercially available, and no UBP1 ChIP-seq datasets exist in public databases. We attempted to carry out the ChIP-seq ourselves but were unable to obtain sufficiently good-quality data.

      In the absence of ChIP data, three lines of evidence indicate that the identified UBP1 motifs are functionally relevant. First, motif analysis of UBP1-upregulated genes using HOMER identified the CP2 family binding motif (CNRG-N6-CNRG) as the most significantly enriched motif. This demonstrates that UBP1-binding-dependent transcriptional could be preserved in our RNA-seq data. Second, when we examine the positional distribution of these motifs, a clear pattern emerges: UBP1 motifs flank the TSS in activated genes, but overlap the TSS in repressed genes (Fig 6D). Indirect effects would not produce this position-specific pattern. Third, our chimeric reporter experiments (Fig 6A-C) directly test this position effect: moving the UBP1 binding site upstream of the TSS leads to activation, while moving it to overlap the TSS leads to repression. Together, these three independent approaches support the conclusion that UBP1 regulates these genes through the motifs we identified. See Author response image 4.

      (6) Lines 266 and 277 are redundant.

      We have carefully reviewed lines 266 and 277. Line 266 is the topic sentence (caption) of the section ("MED16 collaborates with UBP1 to inhibit HIV-1 transcription"), which introduces the overall conclusion, while line 277 describes the specific experimental result ("MED16 inhibited HIV-luciferase activity in a dose-dependent manner"). These serve distinct rhetorical functions. We have ensured that no other redundant phrasing exists elsewhere in the section.

      (7) The mere correlation of binding site position with activation status does not prove "that UBP1 may act as either a transcriptional activator or repressor, depending on distance of its binding site to TSS."

      We appreciate the reviewer's concern regarding the distinction between correlation and causation. The genome-wide analysis (Fig 6D) reveals an association between UBP1 motif position and transcriptional outcome, which prompts us to postulate that the UBP1 binding position could dictate the transcriptional activity level. The causal evidence comes from the chimeric reporter experiments (Fig 6A-C), in which we directly manipulated the position of the UBP1 binding site relative to the TSS in an otherwise identical promoter background. Placing the UBP1 binding site at the TSS resulted in MED16-UBP1-dependent repression, whereas placing it upstream of the TSS did not. This controlled experiment demonstrates that changing only the position of the UBP1 binding site is sufficient to switch the regulatory outcome, thereby establishing a causal relationship between binding site position and transcriptional effect. We have revised the text to clearly distinguish the correlational genome-wide analysis from the causal reporter experiments.

      (8) In Figure 7, MED16 overexpression results in less CDK8 binding. This undermines the claim that MED16 overexpression lowers transcription by binding to UBP1, and suggests an indirect effect.

      We appreciate the reviewer's careful observation. The reduction in CDK8 binding upon MED16 overexpression is consistent with our model: MED16, through UBP1 occupying the TSS, interferes with PIC assembly, of which CDK8 is a component. Importantly, the input levels of CDK8 and other Mediator subunits remained unchanged across the three conditions (Fig 7B), indicating that the reduced CDK8 signal in the PIC is not due to altered protein expression.

      In support of the specificity of the transcriptional effect, MED16Δαβ—which integrates into the Mediator complex but cannot bind UBP1 (Fig 2B)—fails to competitively inhibit either the UBP1 reporter (Fig 3G) or the HIV-1 reporter (Fig 5G), confirming that the transcriptional outcome depends on the MED16-UBP1 interaction.

      (9) Figure 8F is not referenced in the text or legend.

      Figure 8F has now been referenced in the Results and Discussion section.

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      (8) Chae JH, Kim CG. CP2 binding to the promoter is essential for the enhanced transcription of globin genes in erythroid cells. Mol Cells. 2003;15(1):40-7.

    1. eLife Assessment

      This is a useful study that seeks to elucidate the molecular mechanisms underlying spinal circuit assembly. The authors demonstrate that loss of Onecut transcription factors in spinal motor neurons affects the size and spatial distribution of pre-motor interneurons. The main conclusion that Onecut acts through Neurotrophin-3 to regulate interneuron development in a non-cell autonomous manner is partially supported by the data, rendering the study incomplete. The work will be of broad interest to cell and developmental biologists.

    2. Reviewer #1 (Public review):

      Summary:

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

      Strengths:

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

      Weaknesses:

      The evidence supporting their claims is incomplete. In figure 2 and 4, the control distributions (despite reporting the same populations in the same regions) look different, suggesting large sample-to-sample variance in distribution. The authors explain that this is due to several technical limitations in their study.

      Furthermore, the way the Ntf3 results are framed can be misleading; it reads as though one of the main claims of this study is that OC acts through Ntf3.

    3. Reviewer #2 (Public review):

      The study by Angla et al. proposes a model in which NT-3 produced by motor neurons regulates interneuron numbers and distribution in a non-cell autonomous manner. The authors demonstrate that ablation of motor neurons (MNs) and global and conditional deletion of OC transcription factors lead to changes in interneuron distribution. They identify that NT3 is upregulated after MN-specific OC deletion in RNA-seq experiments and show that olig2-cre mediated NT3 deletion leads to increased ventral interneuron numbers, altered distribution and defects in locomotor behavior. The authors conclude that MN-derived NT3, under OC control, regulates interneuron development. While this is an intriguing hypothesis, the study does not consolidate the results from the different experimental models in a cohesive model to provide any mechanistic insights into how NT3 may accomplish this function.

      (1) The authors utilize different experimental models (OC transcription factor global and conditional knockouts, DTA ablation and NT3 deletion) to show that MNs may influence interneuron numbers and positioning, but there doesn't seem to be a direct link into how each manipulation relates to the others in an informative way. For example, the relationship between OC and NT3 deletion data is not entirely clear, even though NT3 is downstream of OC transcription factors. Both deletions lead to changes in interneuron distribution but there doesn't seem to be any correlation between the two that relates to relative changes in NT3 levels. Although it is understandable that transcription factor manipulations may have additional effects, it is hard to derive any mechanistic insights into how exactly MNs influence interneuron differentiation or migration from the data presented here. Similarly, while the rationale for following up on NT3 seems to be the chick electroporation experiments, only a very minor decrease in Chx10 interneurons is shown in those experiments. Shouldn't NT3 overexpression lead to substantial decreases in IN numbers according to the authors' model?

      (2) The study primarily quantifies interneuron numbers and distribution at different levels of the spinal cord and under different genetic manipulations. In different figures, the values and distributions shown for controls vary quite a lot. For example, in figure 2B vs figure 4B, the number of FoxP2+ V1 neurons at brachial levels is ~350 vs 125. Similarly, the control distributions in 2I and 4I are quite different. While this can be partially explained by technical issues, it remains challenging to determine whether the conclusions regarding the impact of each genetic manipulation on interneuron numbers and distribution are valid.

      (3) It is not clear that the behavioral phenotypes seen in the olig2-cre mediated deletion of NT3 can be attributed to specific changes in interneuron development, connectivity or function. While the behavioral phenotype is in theory consistent with defects in motor circuits, there is a big gap between the embryonic changes shown here and behavior, with no in between circuit level changes in locomotor circuits shown. Thus, the behavioral experiments are not very informative as to how exactly components of motor circuits might be altered with the NT3 deletion.

    4. Reviewer #3 (Public review):

      This manuscript aims to investigate cell extrinsic mechanisms that regulate differentiation and distribution of interneuron types in the spinal cord. The authors demonstrate that the loss of motor neurons leads to changes in number and distribution of different interneuron types, specifically V0v, V1, and V2b (but not V2a). The authors then hypothesize that this phenotype may be controlled by the action of Onecut (OC) transcription factors in motor neurons. Conditional knockout of OC1 + OC2 in motor neurons using Olig2-Cre, however, does not lead to significant changes in numbers of V1, V2a, V2b interneurons, although there is a change in their spatial distribution. While the authors do not check V0v neurons in OC mutants, they do check V2c, which show a reduction in number and change in distribution. Why the same neurons are not checked across experiments is unclear. The authors then analyze existing RNA-seq data to identify factors that could be mediating effects of the OC factors in motor neurons. They identify Ntf3 as a candidate and confirm that it is upregulated in OC mutants. Conditional loss of function of Ntf3 (Olig2-Cre) leads to increases in V1, V2a, and V2b (but not V2c) interneurons and changes in distribution of all four interneuron types. However, the Ntf3 phenotypes do not necessarily suggest that it works in the same pathway as OC to control interneuron development. Finally, the authors demonstrate that these Ntf3 conditional mutants have major defects in motor function.

      The main conclusions of the manuscript are that i) motor neurons non-cell autonomously control interneuron development, ii) that OC activity in Olig2 lineage cells non-cell autonomously controls aspects of interneuron development, and iii) that Ntf3 in motor neurons non-cell autonomously controls aspects of interneuron development. The mechanistic link between OC and Ntf3 activity is not clear. And the interneuron phenotype seen when motor neurons are depleted it not phenocopied by either loss of OC or Ntf3 from motor neurons. While the authors addressed some of my initial comments, the impact of this work on the field remains difficult to assess for the following reasons:

      (1) The manuscript relies heavily on quantifying numbers and spatial distribution of interneuron populations. However, these do not seem to be consistent in control animals across experiments, making it difficult to interpret any changes observed in genetic manipulations. Specifically, in Fig. 2 and 4, the same markers are being used to quantify V1, V2a, V2b, and V2c interneurons in controls vs. OC (Fig. 2) or Ntf3 (Fig. 4) conditional knockouts, but the numbers of neurons and their distribution in control animals are variable between these two figures. For example, there seem to be a mean of >300 V1 neurons in E12.5 brachial sections of Fig. 2 controls, but this number is <150 in Fig. 4 controls. The cell distribution scoring is similarly variable between these controls without any explanation. The same is true for E14.5 controls used in Fig. S2 vs. Fig. S6.<br /> The authors explain in response to the 1st review that technical variability including different antibodies, different researchers performing experiments, and different mouse backgrounds may contribute to this variation, but do not include this explanation clearly in the manuscript where it would be useful for readers. It would have also helped, for example, if authors had labeled datapoints on bar plots based on the animal that they came from to understand inter-animal variability within experiments. Overall, the biological significance of the results remain difficult to interpret due to high technical variability.

      (2) In the initial review, it was noted that there is no data to support the claim that OC1/2 mutants have altered interneuron development because of higher Ntf3 expression. In response to this, the authors note that the manuscript does not claim that OC exerts its non-cell autonomous effects through Ntf3 and add a new paragraph in the discussion which now addresses the fact that it is difficult to compare the phenotypes observed the different genetic manipulations presented in the work. While this paragraph addresses the incomplete mechanistic link between OC and Ntf3, it also decreases the mechanistic impact of the work.

      (3) In Figure 3E-3G, the authors show that some V1, V2a, V2b interneurons express the Ntf3 receptor TrkC. It is not stated (apologies if I missed it) what age this expression is, and from the images, it looks like in addition to medially located newborn neurons being negative, laterally located Gata3 interneurons are also broadly negative for TrkC, and many Foxd3 and Chx10 interneurons are also negative. Separate images of each channel and/or scoring would help clarify this. If many relevant interneurons are indeed TrkC negative, how this affects the conclusions should be addressed in the manuscript.

      (4) Behavioral phenotypes are not necessarily caused by Ntf3's role in premotor interneuron development.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

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

      Strengths:

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

      Weaknesses:

      There are two main concerns that are not fully addressed:

      (1) Based on the effects observed with OC vs. Ntf3 cKO, it is unclear whether OC is indeed exerting its non-cell-autonomous effects via Ntf3. Knocking out both Ntf3 and OC and comparing the effects to those seen with just OC cKO alone could provide more insight on this point.

      In this study, we did not intend to demonstrate that Onecut transcription factors exert their non-cell autonomous action on spinal interneuron development by regulating Ntf3 expression, and we do not state in the manuscript that this is the case. We only show that Onecut factors and Ntf3, the expression of which they regulate, contribute to the non-cell autonomous regulation of spinal interneuron development by the motor neurons. We are convinced that Onecut factors could regulate multiple independent factors and pathways involved in extrinsic regulation of interneuron development. This possibly also includes, as demonstrated in cell culture for multiple homeoproteins including human Onecut factors, the intercellular transfer of the Onecut homeoproteins during spinal cord development, a process that we are currently investigating. Knocking out both OC and Ntf3 in the motor neurons, beyond being technically extremely challenging (1/64 probability to obtain double-mutant embryos), would not enable to address this question, as it will simply results in the addition of two different defects.

      Also, a quantitative summary of the effects of Ntf3 overexpression in motor neurons in the chick is lacking.

      A quantitative summary of the effects of Ntf3 overexpression in the chicken embryonic spinal cord is provided in Figure S2.

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

      The experimental procedure to analyze the spatial distribution of spinal interneurons at different stages of development is described in details in the “Statistical analyses” paragraph of the Materials and Methods section of the manuscript, and has been repeatedly used by ourselves [1,2] and by others (see for example [3-5]) to conduct similar analyses.

      We also noticed that the distribution of the different analyzed interneuron populations in the control embryos showed some differences between the cOc1Oc2<sup>-/-</sup> and in the cNtf3<sup>-/-</sup> lines. Several parameters can account for this observation. First, this study has been conducted over a period of 15 years, different investigators each contributing to different steps of the analysis. Second, the genetic background of these two lines is not identical, impacting both the duration of the gestation (hence, the embryonic stage of the performed analyses, even if the embryos were collected on the same gestation day) and possibly the distribution of some interneuron populations. Third, because of evolutions in the availability of the primary antibodies used to label the interneuron populations of interest, the same antibodies were not used throughout the study, as stated for in the Materials and Methods section for Chx10, Gata3 and Sox1, although the same antibody was used by the same investigator to label the same interneuron population in each mouse line at each developmental stage.

      A detailed description of the number of sections and embryos included in each analysis as well as the whole statistical workflow that was used for the distribution analyses, which takes into account variance within control or mutant samples, is now provided in the revised version of the manuscript.

      Reviewer #2 (Public review):

      The study by Angla et al. proposes a model in which NT-3 produced by motor neurons regulates interneuron numbers and distribution in a non-cell autonomous manner. The authors demonstrate that ablation of motor neurons (MNs) and global and conditional deletion of OC transcription factors lead to changes in interneuron distribution. They identify that NT3 is upregulated after MN-specific OC deletion in RNA-seq experiments and show that olig2-cre mediated NT3 deletion leads to increased ventral interneuron numbers, altered distribution, and defects in locomotor behavior. The authors conclude that MN-derived NT3, under OC control, regulates interneuron development. While this is an intriguing hypothesis, additional experiments are needed to support it and strengthen the link between the different experiments described here.

      (1) The study primarily quantifies interneuron numbers and distribution at different levels of the spinal cord and under different genetic manipulations. Experimental details are lacking, defining how many sections were analyzed (several are noted in the methods) and how the rostrocaudal levels of the spinal cord were precisely aligned.

      A detailed description of the number of sections and embryos included in each analysis as well as the whole statistical workflow that was used for the distribution analyses is now provided in the revised version of the manuscript. The rostrocaudal levels of the spinal cord were precisely aligned using the distribution of Foxp1 in the Lateral Motor Columns (LMCs) at brachial or lumbar levels of the spinal cord, which is also indicated in the revised version.

      In different figures, the values and distributions shown for controls vary quite a lot. For example, in Figure 2B vs Figure 4B, the number of FoxP2+ V1 neurons at brachial levels is ~350 vs 125. Similarly, the control distributions in 2I and 4I are quite different. This makes it challenging to determine whether the conclusions regarding the impact of each genetic manipulation on interneuron numbers and distribution are valid.

      Several parameters can account for these observations. First, this study has been conducted over a period of 15 years, different investigators each contributing to different steps of the analysis. Second, the genetic background of these two lines is not identical, impacting both the duration of the gestation (hence, the embryonic stage of the performed analyses, even if the embryos were collected on the same gestation day) and possibly the distribution of some interneuron populations. Third, because of evolutions in the availability of the primary antibodies used to label the interneuron populations of interest, the same antibodies were not used throughout the study, as stated for in the Materials and Methods section for Chx10, Gata3 and Sox1, although the same antibody was used by the same investigator to label the same interneuron population in each mouse line at each developmental stage.

      (2) The relationship between OC and NT3 deletion data is not entirely clear. Both deletions presumably lead to changes in interneuron distribution, but is there any reverse relationship between the two that relates to relative changes in NT3 levels? The authors do not directly compare NT3 and OC KO IN distributions. Similarly, one might expect a decrease in interneuron numbers in OC mutants, which is only reported for V2c neurons. However, the image presented in Figure 2G shows an equal number of V2c INs in control and mutant.

      In this study, we did not intend to demonstrate that Onecut transcription factors exert their non-cell autonomous action on spinal interneuron development by regulating Ntf3 expression, and we do not state in the manuscript that this is the case. We only show that Onecut factors and Ntf3, the expression of which they regulate, contribute to the non-cell autonomous regulation of spinal interneuron development by the motor neurons. We are convinced that Onecut factors could regulate multiple independent factors and pathways involved in extrinsic regulation of interneuron development. For example, as mentioned above, we observed intercellular transfer of the Onecut homeoproteins during spinal cord development, suggesting different strategies for non-cell autonomous regulation of interneuron development. The two mouse lines studied here consist on the one side in a combination of OC inactivation and Ntf3 increased expression, and on the other side in Ntf3 inactivation. Therefore, a reverse relationship between the changes in interneuron distribution is not expected. Furthermore, gain-of-function and loss-of-function experiments in mouse models frequently generate phenotypes that are not inverse to each other.

      (3) It is not clear that the behavioral phenotypes seen in the olig2-cre mediated deletion of NT3 can be attributed to changes in interneuron development. How about a role of NT3 in oligodendrocytes? There is a big gap between the embryonic changes shown here and behavior, with no in-between circuit-level changes in locomotor circuits shown.

      We agree, the motor behavior changes that we recorded in Ntf3 conditional mutant mice are, as stated, “consistent with the hypothesis that Ntf3 produced by MNs is required to generate locomotor circuits with properly coordinated activity” but do not demonstrate a direct causal relationship. However, investigating the intrinsic activity of the spinal locomotor circuits, independently from, for example, oligodendrocyte contribution may prove to be extremely challenging and was beyond the scope of this study. In addition, to our best knowledge, Ntf3 has not been shown to be expressed in healthy oligodendrocytes in vivo, and TrkC has not been reported to be displayed by these cells in the same conditions.

      A more restricted manipulation would be deleting TrkC from specific interneuron populations. Related to this, although TrkC is shown to be broadly expressed in ventral interneurons, it is not shown specifically to colocalize with any of the interneuron markers. The authors should validate that the receptor is expressed in the subsets that they are investigating.

      We agree, investigating the consequences of inactivating the TrkC receptor in specific interneuron populations would be extremely informative. However, this experiment is also very challenging to perform, as most of the driver lines available to target spinal interneuron populations additionally target multiple neuronal populations outside of the spinal cord that are also involved in the control of movements and could therefore induce confounding effects on motor behavior analyses.

      We thank the reviewer for suggesting to investigate in more details the interneuron populations that display TrkC receptors, which is now included in the revised version of the manuscript (Figure 3E-G).

      (4) The rationale for following up on NT3 seems to be the chick electroporation experiments; however, no changes in distribution are shown in those experiments, and only a very minor decrease in Chx10 interneurons. Shouldn't NT3 overexpression lead to substantial decreases in IN numbers according to the authors' model? The "data not shown", which presumably refers to distribution, would be important to show here, to further support this rationale.

      Chicken spinal cord electroporation only enables to study spinal cord development for a few days, given the high mortality rate observed after longer incubation. At the stage we collected the electroporated embryos for analyses, interneuron migration has barely been initiated, and distribution cannot be studied yet. Consistently, we are not aware of any report of interneuron distribution analysis in electroporated chicken embryonic spinal cord, as compared to mouse embryos [1-5].

      (5) The idea that NT3 downregulation causes an increase in IN numbers is not intuitive. Also, considering the DTA experiments in Figure 1, showing that MN ablation leads to a decrease in several IN subtypes and no changes in V2a neurons. It would be helpful for the reader if the authors could synthesize their results in the discussion and reconcile their experimental findings.

      We agree, this has now been included in the Discussion section of the revised manuscript.

      Reviewer #3 (Public review):

      This manuscript aims to investigate cell extrinsic mechanisms that regulate the differentiation and distribution of interneuron types in the spinal cord. The authors demonstrate that the loss of motor neurons leads to changes in the number and distribution of different interneuron types, specifically V0v, V1, and V2b (but not V2a). The authors then hypothesize that this phenotype may be controlled by the action of Onecut (OC) transcription factors in motor neurons. Conditional knockout of OC1 + OC2 in motor neurons using Olig2-Cre, however, does not lead to significant changes in the numbers of V1, V2a, and V2b interneurons, although there is a change in their spatial distribution. While the authors do not check V0v neurons in OC mutants, they do check V2c, which show a reduction in number and change in distribution. Why the same neurons are not checked across experiments is unclear. The authors then analyze existing RNA-seq data to identify factors that could be mediating the effects of the OC factors in motor neurons. They identify Ntf3 as a candidate and confirm that it is upregulated in OC mutants. Conditional loss of function of Ntf3 (Olig2-Cre) leads to increases in V1, V2a, and V2b (but not V2c) interneurons and changes in the distribution of all four interneuron types. Finally, the authors demonstrate that these Ntf3 conditional mutants have major defects in motor function.

      The conclusions of this manuscript are not well supported by the data for the reasons listed below, making it difficult to assess the impact of this work on the field.

      (1) The manuscript relies heavily on quantifying numbers and the spatial distribution of interneuron populations. However, these do not seem to be consistent in control animals across experiments, making it difficult to interpret any changes observed in genetic manipulations. Specifically, in Figures 2 and 4, the same markers are being used to quantify V1, V2a, V2b, and V2c interneurons in controls vs. OC (Figure 2) or Ntf3 (Figure 4) conditional knockouts, but the numbers of neurons and their distribution in control animals are variable between these two figures. For example, there seems to be a mean of >300 V1 neurons in E12.5 brachial sections of Fig. 2 controls, but this number is <150 in Fig. 4 controls. The cell distribution scoring is similarly variable between these controls without any explanation. The same is true for E14.5 controls used in Figure S1 vs. Figure S3.

      We also noticed that quantifications and distributions of the different analyzed interneuron populations in the control embryos showed some differences between the cOc1Oc2<sup>-/-</sup> and in the cNtf3<sup>-/-</sup> lines. Several parameters can account for this observation. First, this study has been conducted over a period of 15 years, different investigators each contributing to different steps of the analysis. Therefore, the interneuron distribution analyses in these two mouse lines were not conducted by the same researchers. Second, the genetic background of these two lines is not identical, impacting both the duration of the gestation (hence, the embryonic stage of the performed analyses, even if the embryos were collected on the same gestation day) and possibly the distribution of some interneuron populations. Third, because of evolutions in the availability of the primary antibodies used to label the interneuron populations of interest, the same antibodies were not used throughout the study, as stated in the Materials and Methods section for Chx10, Gata3 and Sox1, although the same antibody was used by the same investigator to label the same interneuron population in each mouse line at each developmental stage.

      (2) Neurotrophic factors generally promote neuronal survival. However, in this study, the loss of Ntf3 leads to increased numbers of interneurons. This finding is in disagreement with previous observations in slice cultures of spinal cords, as stated in the discussion. This discrepancy makes it even more important that the cell counts reported in the figures discussed above are robust.

      Considering that neurotrophic factors only support neuronal survival would strongly neglect their important function in neuronal differentiation, which has been broadly demonstrated. Severe immunotoxic ablation of motor neurons or anti-serum blockade of Ntf3 activity severely depleted inhibitory, but not excitatory, interneurons in a highly apoptotic-prone organotypic culture model of embryonic rat spinal cord slices, which was rescued by Ntf3 in the first model [6]. Opposite results were obtained in vivo by other researchers using mouse models lacking almost all MNs due to the elimination of skeletal muscles, where the number of spinal INs remained unaffected [7,8]. Combined to our results, these in vivo observations suggest that Ntf-3 is involved in interneuron differentiation rather in their survival. Consistently, Ntf3 has been shown to promote neuronal differentiation [9].

      (3) The claim that phenotypes are non-cell autonomously driven by motor neurons is not well supported. In Olig2-Cre conditional knockouts of Onecut and Ntf3, there is no confirmation that the loss of these factors is specific to motor neurons. Therefore, it cannot be ruled out that other cell populations may be mediating the phenotypes.

      Combined conditional inactivation of Oc1 and Oc2 has been reported in [10]. Conditional inactivation of Ntf3 only impacts motor neurons as it is the only cell population in the ventral spinal cord wherein this factor is produced (this study and [11-13]). Furthermore, Olig2-Cre has been shown to be active in motor neurons and in V3 interneurons (see for example [14], which, for this reason, have not been studied in the frame of this project as stated in the manuscript.

      (4) The claim that interneuron development is regulated by OC control of Ntf3 expression in motor neurons is not well supported. The authors show that loss of OC1/2 leads to an increase in Ntf3 expression in motor neurons. If this pathway were controlling interneurons, loss of OC function and overexpression of Ntf3 would have the same phenotype, which is not the case. Additionally, it would also be expected that loss of OC function and loss of Ntf3 function would have inverse phenotypes, which is also not the case. The phenotypes from OC loss of function and Ntf3 loss of function seem distinct from one another. The authors state that too little and too much Ntf3 are both bad for interneuron development, but there is no data to support their claim that OC1/2 mutants have altered interneuron development because of higher Ntf3 expression.

      In this study, we did not intend to demonstrate that Onecut transcription factors exert their non-cell autonomous action on spinal interneuron development by regulating Ntf3 expression, and we do not state in the manuscript that this is the case. We only show that Onecut factors and Ntf3, the expression of which they regulate, contribute to the non-cell autonomous regulation of spinal interneuron development by the motor neurons. We are convinced that Onecut factors could regulate multiple independent factors and pathways involved in extrinsic regulation of interneuron development. This possibly also includes, as demonstrated in cell culture for multiple homeoproteins including human Onecut factors, the intercellular transfer of the Onecut homeoproteins during spinal cord development, a process that we are currently investigating.

      (5) It is not clear that interneurons being studied express the Ntf3 receptor TrkC, which makes it difficult to assess whether changes in Ntf3 signaling are directly responsible for the phenotype.

      We thank the reviewer for suggesting to investigate in more details the interneuron populations that display TrkC receptors, which is now included in the revised version of the manuscript (Figure 3E-G).

      (6) While the behavioral phenotypes are consistent with Ntf3 playing a role in motor circuits, there is no evidence to suggest that Ntf3's influence on premotor interneurons being studied is driving or contributing to this phenotype, as discussed by the authors.

      We agree, the motor behavior changes that we recorded in Ntf3 conditional mutant mice are, as stated, “consistent with the hypothesis that Ntf3 produced by MNs is required to generate locomotor circuits with properly coordinated activity” but do not demonstrate a direct causal relationship. However, investigating the intrinsic activity of the spinal locomotor circuits was beyond the scope of this study.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Figure 3: A more comprehensive overview of the RNA-seq results would be helpful to see how much gene expression is shifted as a result of OC loss (e.g., volcano plot, pathway enrichment analysis).

      We agree, this has now been added in Figures 3B and S3 of the revised manuscript.

      (2) Figure 2, 4: How does the spatial distribution of motor neurons change?

      Describing the distribution of motor neurons is highly challenging given that the expression of markers that are collectively required to label all the motor neuron subset, including Foxp1, Hb9 and most significantly Isl1, is altered when Onecut expression is modified. Furthermore, changes in motor neuron distribution could constitute consequences of alterations in pre-motor interneuron numbers or distributions. However, as far as we can observe, motor neuron distribution was not obviously modified in the studied mutants.

      (3) Figure S2: Why is Ntf3 expression so limited compared to Isl1-expressing motor neurons? An explanation would be helpful.

      As stated in the Material and Methods section and in the legend of Figure S4 of the revised manuscript, expression after chicken embryonic spinal cord electroporation is transient and maximal reporter gene expression, including GFP, can usually only be visualized 24h after electroporation, but not at later stages. Forty-height hours after electroporation, only residual expression can still be detected in a few targeted cells.

      Reviewer #3 (Recommendations for the authors):

      (1) Changes in interneuron numbers are attributed to defects in differentiation. However, the possibility that these could be caused by changes in cell death has not been ruled out.

      Absence of increased cell death had been demonstrated previously for Onecut mutant embryos [15] and has been verified now in conditional Ntf3 mutant embryos (Figure S5). Both information have been included in the text.

      (2) In Figures 2 and 4, it is unclear if the images of the spinal cords are from brachial, thoracic, or lumbar levels. As the three levels frequently show different phenotypes, it is difficult to match the immunostaining with the reported quantification.

      All the pictures show thoracic regions of the spinal cord, as now stated in the figure legends of the revised version of the manuscript.

      (3) Statistics are missing in the cell distribution plots in Figure S1.

      This is not the case in our version of the pdf file.

      (4) In experiments where motor neurons are eliminated with Olig2-Cre/DTA, the authors write that progenitors are not affected without showing data. These data should be included because i) Olig2 is expressed in progenitors, so it would be expected that motor neuron progenitors are lost or reduced in this mouse, and ii) showing no changes in progenitors is crucial to the claim that postmitotic motor neurons are affected interneuron populations.

      We agree, these data are now included as Figure S1 of the revised version of the manuscript.

      (1) Harris, A. et al. Onecut factors and Pou2f2 regulate the distribution of V2 interneurons in the mouse developing spinal cord. Front Cell Neurosci 13 (2019). https://doi.org/10.3389/fncel.2019.00184

      (2) Kabayiza, K. U. et al. The Onecut Transcription Factors Regulate Differentiation and Distribution of Dorsal Interneurons during Spinal Cord Development. Front Mol Neurosci 10, 157 (2017). https://doi.org/10.3389/fnmol.2017.00157

      (3) Deska-Gauthier, D. et al. Embryonic temporal-spatial delineation of excitatory spinal V3 interneuron diversity. Cell Rep 43, 113635 (2024). https://doi.org/10.1016/j.celrep.2023.113635

      (4) Bikoff, J. B. et al. Spinal Inhibitory Interneuron Diversity Delineates Variant Motor Microcircuits. Cell 165, 207–219 (2016). https://doi.org/10.1016/j.cell.2016.01.027

      (5) Hayashi, M. et al. Graded Arrays of Spinal and Supraspinal V2a Interneuron Subtypes Underlie Forelimb and Hindlimb Motor Control. Neuron 97, 869–884 e865 (2018). https://doi.org/10.1016/j.neuron.2018.01.023

      (6) Bechade, C., Mallecourt, C., Sedel, F., Vyas, S. & Triller, A. in J Neurosci Vol. 22 8779–8784 (2002).

      (7) Grieshammer, U., Lewandoski, M., Prevette, D., Oppenheim, R. W. & Martin, G. R. Muscle-specific cell ablation conditional upon Cre-mediated DNA recombination in transgenic mice leads to massive spinal and cranial motoneuron loss. Dev Biol 197, 234–247 (1998). https://doi.org/10.1006/dbio.1997.8859

      (8) Kablar, B. & Rudnicki, M. A. Development in the absence of skeletal muscle results in the sequential ablation of motor neurons from the spinal cord to the brain. Dev Biol 208, 93–109 (1999). https://doi.org/10.1006/dbio.1998.9184

      (9) Dutton, R., Yamada, T., Turnley, A., Bartlett, P. F. & Murphy, M. Regulation of spinal motoneuron differentiation by the combined action of Sonic hedgehog and neurotrophin 3. Clin Exp Pharmacol Physiol 26, 746–748 (1999). https://doi.org/10.1046/j.1440-1681.1999.03108.x

      (10) Toch, M. et al. Onecut-dependent Nkx6.2 transcription factor expression is required for proper formation and activity of spinal locomotor circuits. Sci Rep 10, 996 (2020). https://doi.org/10.1038/s41598-020-57945-4

      (11) Buck, C. R., Seburn, K. L. & Cope, T. C. Neurotrophin expression by spinal motoneurons in adult and developing rats. J Comp Neurol 416, 309–318 (2000). https://doi.org/10.1002/(sici)1096-9861(20000117)416:3<309::aid-cne3>3.0.co;2-u

      (12) Henderson, C. E. et al. Neurotrophins promote motor neuron survival and are present in embryonic limb bud. Nature 363, 266–270 (1993). https://doi.org/10.1038/363266a0

      (13) Usui, N. et al. Role of motoneuron-derived neurotrophin 3 in survival and axonal projection of sensory neurons during neural circuit formation. Development 139, 1125–1132 (2012). https://doi.org/10.1242/dev.069997

      (14) Debrulle, S. et al. Vsx1 and Chx10 paralogs sequentially secure V2 interneuron identity during spinal cord development. Cell Mol Life Sci 77, 4117–4131 (2020). https://doi.org/10.1007/s00018-019-03408-7

      (15) Roy, A. et al. Onecut transcription factors act upstream of Isl1 to regulate spinal motoneuron diversification. Development 139, 3109–3119 (2012). https://doi.org/10.1242/dev.078501

    1. eLife Assessment

      This valuable study is a rigorous and comprehensive anatomical description of populations of spinal-projecting neurons in the brain of the larval zebrafish. The compelling data reveal a far more complete and robust visualization of these populations than past work. The scholarship is also notable, featuring thoughtful contextualization of the findings with respect to both non-mammalian and mammalian literature.

    2. Reviewer #2 (Public review):

      Summary:

      Vertebrate spinal cord receives inputs from many supraspinal regions. The authors used optical backfilling to trace neurons in the zebrafish larval brain sending axons to the spinal cord . With two-photon microscopy, they managed to render a comprehensive 3D map of these neurons and drew homologs with mammalian brain structures.

      Strengths:

      The main strength lies in the precise 3D mapping. The fact that most of previously reported neuron groups have been confirmed by their approach is a solid endorsement of their methodology.

      This study provides a comprehensive alternative anatomical reference framework for studying individual groups of supraspinal neurons with projections to the zebrafish spinal cord.

      Weaknesses:

      The backfilling approach does not reveal the full trajectories of axons, which is available to some degree by ZExplorer Atlas.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors have achieved an excellent, thorough anatomical characterization of all spinal projecting neurons in the larval zebrafish. The comprehensive nature of their labeling approach and their quantification will make this work an instant reference benchmark for a wide number of zebrafish researchers. In addition, the scholarly approach in comparisons with other work in and outside of fish makes the manuscript valuable to researchers outside the field who would like to know how to translate between zebrafish and mouse terminologies.

      Strengths:

      The figures are clear and easy to follow. The literature review is impressive. The authors are careful to note the few limitations of their approach (eg the absence of Mauthner cell labeling and associated large neuron weak label). The manuscript does the whole field a major service.

      Weaknesses:

      No weaknesses were identified by this reviewer.

      We deeply thank reviewer #1 for recognizing the importance of our work.

      Reviewer #2 (Public review):

      Summary:

      The vertebrate spinal cord receives inputs from many supraspinal regions. The authors used optical backfilling to trace neurons in the zebrafish larval brain sending axons to the spinal cord. With two-photon microscopy, they managed to render a comprehensive 3D map of these neurons and drew homologs with mammalian brain structures.

      Strengths:

      The main strength lies in the precise 3D mapping. The fact that most of the previously reported neuron groups have been confirmed by their approach is a solid endorsement of their methodology. This study provides a comprehensive alternative anatomical reference framework for studying individual groups of supraspinal neurons with projections to the zebrafish spinal cord.

      Weaknesses:

      The whole approach could be enhanced by counter-staining their preparation to profile brain structures, including many nuclei more precisely.

      Also, the backfilling approach does not reveal the full trajectories of axons, which is already available to some degree by ZExplorer Atlas.

      We thank Reviewer #2 for the attention given to important details. As you will read below, our approach needed to be implemented on live larval zebrafish for the PA GFP to fluoresce only when photactivated. Although counterstains on fixed samples could have been done, it would have been difficult to trace it back in a fixed (and deformed) sample where all neurons appear GFP-positive. We wrote this publication so it can serve the field best before the release of full brain connectome in 2027. Further molecular and histological characterization will be the topic of a subsequent study. Below we will individually address the issues stated by the reviewer.

      Reviewer #2 (Recommendations for the authors):

      (1) The power of 800 nm laser: 1 mW in Figure 1a, 10 mW stated in the method text.

      Thanks for catching this, this is now corrected to 1 mW in the revised manuscript.

      (2) Figure 2c: laterality density (e.g., 90:40 by neuron numbers) is already quite clear. The laterality index is less informative if not causing confusion. Take POA as an example; shouldn't the value be close to 1, rather than -0.8 based on your formula?

      The laterality density plot shows the anatomical spatial distribution of spinal descending neurons relative to the midline, whereas the laterality index is a scalar summary of the relative neuron counts on each side, independent of spatial position. For POA, we observed after pooling across animals 26 ipsilateral versus 1 contralateral neurons, which gives an LI close to -0.8 (after bootstrapping), indicating strong ipsilateral dominance. The density plot additionally shows that these neurons cluster close to the midline. We view the two measures as complementary: one quantifies laterality, the other reflects its spatial organization.

      (3) Line 286, check grammar.

      Corrected to "Using the ZExplorer atlas (Du et al., 2025), which contains thousands of single-neuron tracings in the larval zebrafish brain, we searched for telencephalic neurons projecting to the spinal cord."

      (4) Add discussion on comparison between your approach, conventional backfill tracing method, singlecell tracing by dye or GFP, and, more importantly, ZExplorer Atlas.

      We believe this comparison is sufficiently addressed in the Introduction and Discussion.

      (5) Line 346, add "in bi" after "scale bar".

      Corrected, thank you for spotting this.

      (6) Figure 4, change label ICN to INC in c and d.

      We apologize for this confusion, all of them are now corrected in the revised manuscript.

      (7) Line 389, change to "ventral (top) to dorsal (bottom)". Try to use the same order for all figures, i.e., dorsal images on top and ventral ones at the bottom.

      Corrected for the specific line and now all figures follow the same convention.

      (8) Figure 7b, add stack location diagrams.

      Added. Thank you very much for suggesting an improvement to the figure readability.

      (9) Figure 7c and other figure panels, suggest changing the word "medial" to "middle" since medial/lateral is usually used to describe sagittal locations.

      Good point: now corrected as suggested.

      (10) Why was DAPI counter-staining not carried out? This would help the authors to better draw outlines for different nuclei. It also provides important data on the proportion of SPNs within each nucleus.

      We agree with the reviewer that determining the proportion of SPNs within each nucleus is an important question; however, properly addressing this requires an independent, cytoarchitecture-based definition of nucleus boundaries, a substantial project that we consider outside the scope of the present study.

      DAPI labels cell nuclei generally but carries limited information relevant to classical neuroanatomical delineation of nuclei. Methods such as Nissl staining are better suited to this purpose, as they reveal the cytoarchitectonic characteristics of neurons. As mentioned in the Introduction and Discussion, we instead based our nuclei definitions on the strong evolutionary conservation of spinal-projecting neuron distributions, which we used as a proxy for delineating the different nuclei.

      (11) The photoactivation is restricted to the rostral spinal cord. Therefore, how far the axons project throughout the spinal cord cannot be assessed with the current photo-activation method.

      We agree with the reviewer: because photoactivation was restricted to the rostral spinal cord, our current data cannot resolve how far individual axons project caudally. However, all evolutionary comparisons in this study were performed across vertebrates using similarly rostrally-labeled SPNs, ensuring consistency in our cross-species analysis. We therefore do not believe that this limitation undermines our initial characterization of the SPN populations.

      We agree, however, that extending photoactivation to multiple spinal levels will be valuable for future work characterizing and refining the nuclei proposed in this work. Some of this effort is carried for vsx2+ neurons only by Jia et al., under revision in Nature Communications.

      (12) Line 544, if it is the spinal trigeminal tract, why does there appear to be some feint soma staining?

      We apologize for the confusion. We always meant to say trigeminal nucleus and not its tract. Text and figure legend are now corrected.

      Reviewer #3 (Public review):

      In this study, the authors aim to provide the most comprehensive and detailed topographic map to date of spinal projection neurons in the larval zebrafish brain. They achieve this by retrogradely photoactivating, in the rostral spinal cord, a photoconvertible GFP expressed pan-neuronally, and by constructing a template larval zebrafish brain atlas to regionalize the location of all labeled somata across the brain. The labeling strategy, together with the chosen animal model, provides strong support for the completeness of the dataset. The generation of a standardized anatomical atlas establishes a rigorous framework for analysis. Molecular and anatomical evidence suggesting evolutionary conservation of selected regions of interest appears solid.

      Overall, the authors successfully achieve their aim. By generating this atlas of spinal projection neurons, they provide not only an anatomical framework of the regions involved, but also an important reference for improving the orientation and regionalization of the zebrafish brain, which has historically been difficult to define. This work may serve as a valuable resource for future evolutionary, developmental, and comparative studies of spinally projecting neuronal populations implicated in diverse functions.

      We deeply thank Reviewer #3 for recognizing the impact of our work and benefit for the community at large, which has always been our intention.

    1. eLife Assessment

      This is an important study with implications for signal transduction, breast cancer, and metastasis research. The study uses cell lines from the innovative HER2BOW model mouse to compare and contrast properties of mouse mammary tumors induced by wt-HER2, dex16-HER2, and p95-HER2. The study involves mechanistic approaches that are convincing for distinguishing signaling from dex16-HER2 and p95-HER2; this will be of interest to the cancer field.

    2. Reviewer #1 (Public review):

      Summary:

      The study by Fernandes et al used multiple types of experiments to study the increased metastatic activity conferred by p95-HER2 and presented evidence that biased downstream signaling was engaged by p95-HER2. Further studies demonstrated crucial phosphorylation sites and downstream transcription targets. Overall, this study will contribute to increased understanding of HER2 biology and breast cancer metastasis.

      Strengths:

      Multiple types of experiments to assess the increased invasion property by p95-HER2. Solid evidence was provided that p95-HER2 elicits a different transcription program, enhancing EMT. Convincing results were achieved in knockdown studies on crucial genes such as MRTFA and TGFB1I1.

      Weaknesses:

      To enhance the quality of this study, several points need to be addressed.

      (1) First, as the background to this study, the authors stated in the introduction that different isoforms of HER2 lead to different invasive properties. Is this conclusion based on clinical observation or animal models? This needs to be specified to help readers grasp the importance of the paper.

      (2) Second, this study is mostly performed on three cell lines generated from three tumor models, WT, d16, and p95. Were multiple cell lines generated from the same tumor model (e.g., p95), and how similar are these cell lines if they are from the same tumor model?

      (3) Third, Figure 2 presented data from a very interesting experiment. However, the overall conclusion of this experiment is hard to grasp from the text.

    3. Reviewer #2 (Public review):

      Summary:

      This is a significant study with implications for signal transduction, breast cancer, and metastasis research. The study uses cell lines from the innovative HER2BOW model mouse to compare and contrast properties of mouse mammary tumors induced by wt HER2, dex16 HER2, and p95 HER2. The linkage between p95 Y1139 and TGFB1I1, as well as MRTFA, is very interesting. The study involves mechanistic approaches to distinguish signaling from dex16 HER2 and p95 HER2 that will be well received by the field.

      Strengths:

      The study describes the development of novel cell lines for mouse mammary tumors induced by different forms of HER2. In vitro analysis includes a wide variety of assays, including high-resolution fluorescent imaging and tracing of cell movement/ECM degradation, and single-cell RNA-seq analysis. Several approaches are used to confirm p95 HER2 pY1139-induced signaling through nuclear MRTFA and TGFB1I1. shRNA and CRISPR/Cas9 were used to confirm signaling through this pathway.

      Weaknesses:

      The authors have only described one cell line for each HER2 form, a weakness which they acknowledge. The fact that these lines mimic characteristics that were described in their previous work on tumors induced by wt HER2 vs. dex16 HER2 vs. p95 HER2 is, however, supportive of their conclusions. In addition, the specific connection between p95 and nuclear MRTFA is confirmed in HEK cells. The manuscript could be improved with some discussion on how p95 p-Y1139 is likely to signal (what binds to this site?)

    4. Reviewer #3 (Public review):

      Summary:

      The study aims to explain why different HER2 isoforms produce distinct cancer-cell phenotypes, with a particular focus on the poorly understood mechanisms by which p95-HER2 promotes the invasive and metastatic behaviors of HER2-positive breast cancer. The authors propose that functional selectivity at the HER2 receptor level, particularly signaling through Y1139, preferentially engages an MRTFA-TGFB1I1 transcriptional/cytoskeletal program, thereby shifting p95-HER2-expressing cells toward individual motility, matrix degradation, invasion, and metastasis rather than simply proliferation.

      Strengths:

      A major strength is the multi-level experimental strategy. Rather than relying on a single invasion assay, the authors integrate phenotypic profiling, single-cell RNA-seq, imaging of MRTFA nuclear localization, protein validation, cytoskeletal analysis, matrix-degradation assays, transwell invasion, 3D spheroid/organoid assays, and orthotopic transplantation. This provides relatively coherent evidence that p95-HER2 cells occupy a distinct biological state characterized by individual motility and invasion, accompanied by a myogenic/contractile transcriptional program and increased MRTFA nuclear localization. Another strong aspect is the attempt to connect the downstream phenotype to a specific HER2 phosphorylation site. The authors identify Y1139 as a candidate determinant of the "go" phenotype and show that mutation of Y1139 to phenylalanine eliminates MRTFA translocation as well as the associated motility and invasion phenotypes

      Weaknesses:

      The main limitation is that the evidence for the proposed signaling mechanism is stronger for functional dependence than for direct molecular mechanism. The data establish that Y1139, MRTFA, and TGFB1I1 are required for the p95-HER2 invasive phenotype, but they do not appear to fully establish how phosphorylation at Y1139 mechanistically couples p95-HER2 to MRTFA activation. In particular, a direct biochemical link between Y1139 and the machinery controlling actin polymerization/MRTFA nuclear translocation would make the proposed pathway substantially more convincing.

      A second limitation is the model system used in the study. Much of the mechanistic work relies on three particular engineered HER2 isoform-expressing cell lines, while the in vivo experiments use NOD/SCID mice. This is useful for testing tumor-cell intrinsic invasion and metastasis, but it limits conclusions about the generality of the functional regulation across different cell contexts, and also limits the understanding of how the pathway operates in the intact immune microenvironment. This is especially relevant because the manuscript itself places p95-HER2 in the broader context of immune evasion and notes that MRTFA can regulate PD-L1; however, the current models cannot adequately establish whether the proposed pathway contributes to immune-mediated metastatic progression in vivo.

      Finally, the distinction between "go" and "grow" signaling is conceptually attractive, but the extent to which it represents a general property of HER2-positive human breast cancers remains less certain. The study provides strong experimental evidence for functional selectivity in the models examined, but additional validation in independent patient-derived models and clinically relevant tumors would be needed before generalizing this mechanism across HER2-positive disease.

    5. Author response:

      Reviewer #1 (Public review):

      Summary:

      The study by Fernandes et al used multiple types of experiments to study the increased metastatic activity conferred by p95-HER2 and presented evidence that biased downstream signaling was engaged by p95-HER2. Further studies demonstrated crucial phosphorylation sites and downstream transcription targets. Overall, this study will contribute to increased understanding of HER2 biology and breast cancer metastasis.

      Strengths:

      Multiple types of experiments to assess the increased invasion property by p95-HER2. Solid evidence was provided that p95-HER2 elicits a different transcription program, enhancing EMT. Convincing results were achieved in knockdown studies on crucial genes such as MRTFA and TGFB1I1.

      We thank the reviewer for this positive feedback on our manuscript.

      Weaknesses:

      To enhance the quality of this study, several points need to be addressed.

      (1) First, as the background to this study, the authors stated in the introduction that different isoforms of HER2 lead to different invasive properties. Is this conclusion based on clinical observation or animal models? This needs to be specified to help readers grasp the importance of the paper.

      We thank the reviewer for bringing this to our attention; we will add additional language to emphasize and explain prior findings referenced in the introduction establishing HER2 isoforms’ different invasive properties. The increased invasiveness of both d16 and p95 isoforms of HER2 has been established both in animal models and in clinical studies (1-6). We summarize these studies below:

      (1) Expression of the delta16 splice variant isoform has been shown in human cell lines to be more oncogenic than the WT HER2 isoform (7), and associated with different transcriptional profile including increased expression of cancer metastasis and cancer stem cell-related genes (4). Clinically, patients with d16HER2-positive breast cancer have higher rates of lymph node metastases than those with d16HER2-negative breast cancer (8). Castagnoli et al (9) provides an overview of preclinical and clinical data on d16 in HER2+ cancers.

      (2) The p95 isoform of HER2 is associated with increased metastasis (2) and worse outcomes (1, 10) in HER2+ breast cancer patients. Preclinical models show that p95-HER2 (also known as 611-CTF) activates a transcriptional program that promotes invasion and metastasis (3), part of the basis for our interest in HER2 isoform biology.

      Moreover, our group has previously detailed different tumor growth and dissemination characteristics of WT, d16, and p95 HER2 isoforms in our HER2 Crainbow mouse model which expresses all 3 of these isoforms in the mammary epithelium (5).

      Both d16 and p95 tumors in Her2Crainbow mice have the ability to progress to overt metastasis, unlike WT HER2 in this model (6). During early stages of malignant transformation prior to palpable tumor formation, p95 cells disseminated to the lung were 5x more numerous than d16 cells in the lung (despite more proliferation of d16 cells in the mammary gland at that timepoint). This is the foundation on which we built this current work, to study what could promote these different proliferation, dissemination, and metastasis behaviors in different HER2 isoforms.

      (2) Second, this study is mostly performed on three cell lines generated from three tumor models, WT, d16, and p95. Were multiple cell lines generated from the same tumor model (e.g., p95), and how similar are these cell lines if they are from the same tumor model?

      Our lab has generated a number of tumor cell lines derived from individual tumors developed in different HER2 Crainbow mice. We hypothesize, based on preliminary data in these cell lines as well as our previously reported data examining phenotypes of WT, d16, and p95 tumors (5, 6), that different clones will share core features such as invasive properties. In order to strengthen the generalizability of our conclusions, we intend to perform transwell assays, matrix degradation assays, and western blots to examine expression of Mrtfa and Tgfb1i1 in additional HER2 Crainbow-derived cell lines to validate if our findings hold true across cell lines derived from different tumors.

      (3) Third, Figure 2 presented data from a very interesting experiment. However, the overall conclusion of this experiment is hard to grasp from the text.

      We thank the reviewer for this comment. We will revise the text accompanying Figure 2 to provide further detail on the interpretation of the PCA plots and clarify the overall conclusions by adding the following text:

      Vectors indicate variable loadings, where vector direction corresponds to increasing values of the variable and vector length reflects the magnitude of the variable’s contribution to the displayed principal components. For example, HCR-WT populations cluster toward lower values of PC1 and higher values PC2, notably in the direction of higher self-correlation indicating an association with greater spatial clustering. HCR-16 and HCR-95 cells cluster similarly and are driven by their similarity in speed, MSD, persistence, and individual behavior (low self-correlation and high proportions of cells with zero neighbors), whereas HCR-WT is distinct for its collective cell behavior (high self-correlation) (Figure 2E). This separation indicates potentially distinct kinematic and spatial organization compared to HCR-16 and HCR-95 cells.

      Reviewer #2 (Public review):

      Summary:

      This is a significant study with implications for signal transduction, breast cancer, and metastasis research. The study uses cell lines from the innovative HER2BOW model mouse to compare and contrast properties of mouse mammary tumors induced by wt HER2, dex16 HER2, and p95 HER2. The linkage between p95 Y1139 and TGFB1I1, as well as MRTFA, is very interesting. The study involves mechanistic approaches to distinguish signaling from dex16 HER2 and p95 HER2 that will be well received by the field.

      Strengths:

      The study describes the development of novel cell lines for mouse mammary tumors induced by different forms of HER2. In vitro analysis includes a wide variety of assays, including high-resolution fluorescent imaging and tracing of cell movement/ECM degradation, and single-cell RNA-seq analysis. Several approaches are used to confirm p95 HER2 pY1139-induced signaling through nuclear MRTFA and TGFB1I1. shRNA and CRISPR/Cas9 were used to confirm signaling through this pathway.

      We thank the reviewer for these positive comments.

      Weaknesses:

      The authors have only described one cell line for each HER2 form, a weakness which they acknowledge. The fact that these lines mimic characteristics that were described in their previous work on tumors induced by wt HER2 vs. dex16 HER2 vs. p95 HER2 is, however, supportive of their conclusions. In addition, the specific connection between p95 and nuclear MRTFA is confirmed in HEK cells. The manuscript could be improved with some discussion on how p95 p-Y1139 is likely to signal (what binds to this site?)

      We thank the reviewer for this assessment, and we agree that adding additional cell lines derived from different HER2 Crainbow mouse tumors would strengthen the conclusions drawn in this manuscript. As mentioned in response to Reviewer #1, we intend to perform transwell assays, matrix degradation assays, and western blots to examine expression of Mrtfa and Tgfb1i1 in additional HER2 Crainbow-derived cell lines to validate if our findings hold true across cell lines derived from different tumors.

      With regards to p95 p-Y1139 signaling, previous work by Dankort et al. (11, 12) established that tyrosine autophosphorylation site Y1144 on rat neu (equivalent to Y1139 in human HER2) promoted lung metastasis in animal models, and that this site was necessary and sufficient for direct binding of Grb2.

      We intend to clarify and discuss links between Y1139 on p95 HER2 and the nuclear translocation of MRTFA.

      Reviewer #3 (Public review):

      Summary:

      The study aims to explain why different HER2 isoforms produce distinct cancer-cell phenotypes, with a particular focus on the poorly understood mechanisms by which p95-HER2 promotes the invasive and metastatic behaviors of HER2-positive breast cancer. The authors propose that functional selectivity at the HER2 receptor level, particularly signaling through Y1139, preferentially engages an MRTFA-TGFB1I1 transcriptional/cytoskeletal program, thereby shifting p95-HER2-expressing cells toward individual motility, matrix degradation, invasion, and metastasis rather than simply proliferation.

      Strengths:

      A major strength is the multi-level experimental strategy. Rather than relying on a single invasion assay, the authors integrate phenotypic profiling, single-cell RNA-seq, imaging of MRTFA nuclear localization, protein validation, cytoskeletal analysis, matrix-degradation assays, transwell invasion, 3D spheroid/organoid assays, and orthotopic transplantation. This provides relatively coherent evidence that p95-HER2 cells occupy a distinct biological state characterized by individual motility and invasion, accompanied by a myogenic/contractile transcriptional program and increased MRTFA nuclear localization. Another strong aspect is the attempt to connect the downstream phenotype to a specific HER2 phosphorylation site. The authors identify Y1139 as a candidate determinant of the "go" phenotype and show that mutation of Y1139 to phenylalanine eliminates MRTFA translocation as well as the associated motility and invasion phenotypes

      We thank the reviewer for these positive comments on our manuscript.

      Weaknesses:

      The main limitation is that the evidence for the proposed signaling mechanism is stronger for functional dependence than for direct molecular mechanism. The data establish that Y1139, MRTFA, and TGFB1I1 are required for the p95-HER2 invasive phenotype, but they do not appear to fully establish how phosphorylation at Y1139 mechanistically couples p95-HER2 to MRTFA activation. In particular, a direct biochemical link between Y1139 and the machinery controlling actin polymerization/MRTFA nuclear translocation would make the proposed pathway substantially more convincing.

      We thank the reviewer for these insightful comments. We agree that establishing a mechanistic link between p95 Y1139 and MRTFA nuclear translocation would strengthen the conclusions in this study. As such, we intend to perform Rho activation assays and western blotting for proteins in HER2 signaling cascades to explore the signaling link between p95 Y1139 and MRTFA. Additionally, we plan to treat cells with RhoA/ROCK inhibitors to explore our hypothesis that Y1139 acts through RhoA activation to ultimately alter MRTFA translocation.

      A second limitation is the model system used in the study. Much of the mechanistic work relies on three particular engineered HER2 isoform-expressing cell lines, while the in vivo experiments use NOD/SCID mice. This is useful for testing tumor-cell intrinsic invasion and metastasis, but it limits conclusions about the generality of the functional regulation across different cell contexts, and also limits the understanding of how the pathway operates in the intact immune microenvironment. This is especially relevant because the manuscript itself places p95-HER2 in the broader context of immune evasion and notes that MRTFA can regulate PD-L1; however, the current models cannot adequately establish whether the proposed pathway contributes to immune-mediated metastatic progression in vivo.

      We also acknowledge the limitations of relying on mostly in vitro assays and in vivo experiments in an immunodeficient environment, and will more explicitly address this in our discussion. The interplay between p95-HER2 and the immune system is being explored in a parallel project that will be published separately in the future; the focus of the current manuscript is the cell-intrinsic signaling mechanisms that contribute to p95 invasiveness. Points about immune evasion that were brought up in the introduction will be moved to the discussion section, to make it clear that the current study does not address the immunocompetent environment but that we wish to further investigate the results we report in this manuscript in that context as well.

      Finally, the distinction between "go" and "grow" signaling is conceptually attractive, but the extent to which it represents a general property of HER2-positive human breast cancers remains less certain. The study provides strong experimental evidence for functional selectivity in the models examined, but additional validation in independent patient-derived models and clinically relevant tumors would be needed before generalizing this mechanism across HER2-positive disease.

      With respect to validating findings in human samples, we intend to utilize human HER2+ breast cancer cell lines and/or patient-derived xenografts to investigate the extent to which our described mechanism is relevant in human HER2+ breast cancer.

      References:

      (1) Sáez R, Molina MA, Ramsey EE, Rojo F, Keenan EJ, Albanell J, et al. p95HER-2 Predicts Worse Outcome in Patients with HER-2-Positive Breast Cancer. Clinical Cancer Research. 2006;12:424–31.

      (2) Molina MA, Saez R, Ramsey EE, Garcia-Barchino MJ, Rojo F, Evans AJ, et al. NH(2)-terminal truncated HER-2 protein but not full-length receptor is associated with nodal metastasis in human breast cancer. Clin Cancer Res. 2002;8:347–53.

      (3) Pedersen K, Angelini PD, Laos S, Bach-Faig A, Cunningham MP, Ferrer-Ramon C, et al. A naturally occurring HER2 carboxy-terminal fragment promotes mammary tumor growth and metastasis. Mol Cell Biol. 2009;29:3319–31.

      (4) Turpin J, Ling C, Crosby EJ, Hartman ZC, Simond AM, Chodosh LA, et al. The ErbB2DeltaEx16 splice variant is a major oncogenic driver in breast cancer that promotes a pro-metastatic tumor microenvironment. Oncogene. 2016;35:6053–64.

      (5) Ginzel JD, Acharya CR, Lubkov V, Mori H, Boone PG, Rochelle LK, et al. HER2 Isoforms Uniquely Program Intratumor Heterogeneity and Predetermine Breast Cancer Trajectories During the Occult Tumorigenic Phase. Mol Cancer Res. 2021; 19(10):1699–1711.

      (6) Ginzel JD, Chapman H, Sills JE, Allen EJ, Barak LS, Cardiff RD, et al. Nonlinear progression during the occult transition establishes cancer lethality. Dis Model Mech. 2025;18:dmm052113.

      (7) Kwong KY, Hung M-C. A novel splice variant of HER2 with increased transformation activity. Molecular Carcinogenesis. 1998;23:62–8.

      (8) Mitra D, Brumlik M, Okamgba S, Zhu Y, Duplessis T, Parvani J, et al. An oncogenic isoform of HER2 associated with locally disseminated breast cancer and trastuzumab resistance. Mol Cancer Ther. 2009;8(8):2152-62.

      (9) Castagnoli L, Ladomery M, Tagliabue E, Pupa SM. The d16HER2 Splice Variant: A Friend or Foe of HER2-Positive Cancers? Cancers (Basel). 2019;11.

      (10) Sperinde J, Jin X, Banerjee J, Penuel E, Saha A, Diedrich G, et al. Quantitation of p95HER2 in paraffin sections by using a p95-specific antibody and correlation with outcome in a cohort of trastuzumab-treated breast cancer patients. Clin Cancer Res. 2010;16:4226–35.

      (11) Dankort DL, Wang Z, Blackmore V, Moran MF, Muller WJ. Distinct tyrosine autophosphorylation sites negatively and positively modulate neu-mediated transformation. Mol Cell Biol. 1997;17:5410–25.

      (12) Dankort D, Maslikowski B, Warner N, Kanno N, Kim H, Wang Z, et al. Grb2 and Shc adapter proteins play distinct roles in Neu (ErbB-2)-induced mammary tumorigenesis: implications for human breast cancer. Mol Cell Biol. 2001;21:1540–51.

    1. eLife Assessment

      This is a valuable study that aims to resolve the mechanistic details of the final steps of bacterial lipoprotein trafficking. The evidence supporting the conclusions is mostly solid but support for other claims is incomplete. This work will be of interest to biochemists and microbiologists studying the bacterial cell envelope and those aiming to develop novel antibacterial molecules.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript aims to mechanistically characterize the final step of the Lol lipoprotein trafficking pathway in Gram-negative bacteria, where triacylated lipoproteins are transferred from the periplasmic chaperone protein LolA to the outer membrane receptor LolB for subsequent insertion into the inner leaflet of the outer membrane. The crystal structures of LolA R43L-LolB, LolB L114G-lipoprotein, LolB Q44A (open), and LolB Q44A (closed) are reported. A combination of structural analysis, growth assays, pull-downs, and fluorescence spectroscopy was employed to characterize the behaviour of wild-type and mutant forms of Lol proteins. The authors then used previously published molecular dynamics simulations to propose a six-step model for the transfer and insertion of lipoproteins from LolA to LolB, and finally into the membrane.

      The structural work is mostly solid, but the mechanistic conclusions drawn from it rely too much on interpolation between static states and on functional assays with limited dynamic range.

      Strengths:

      (1) The structure-guided mutagenesis was thorough and well designed. Mutants were tested both in isolation and in combination with one another, utilizing both alanine and charge reversal mutants, to fully tease apart which residues were most important for the interaction.

      (2) The dissection of the Hook interactions allowed the authors to identify the importance of the identity of the hook apex residue, as well as the critical hydrogen bond that maintained the Hook geometry.

      (3) The structure of the lipoprotein-bound LolB is a valuable addition to the body of knowledge around this pathway, and the way that the complex was produced was very clever.

      Weaknesses:

      (1) There is no raw data shown for any of the interactions or lipoprotein transfer experiments. All of the quantitative claims about LolA association and lipoprotein transfer rely on SDS-PAGE densitometry, and not a single gel image from those experiments is provided in the main text or supplemental.

      (2) Pull-down assays are a fairly poor assay to use for protein interactions when there are so many quantitative alternatives that would be amenable to this experimental system, especially when the association and transfer of lipoprotein are the central claims of the manuscript.

      (3) The crystal structures are under-refined for their resolution, with large R-factor gaps and somewhat concerning statistics contained in the wwPDB reports (e.g. >20% RSRZ outliers in 2/4 structures). The data also seem to be cut at strangely high I/sigI for the two Q44A structures with no explanation.

      (4) In the LolB L114G structure, the Hook residue L68 is the most poorly fit in the whole structure. The validation report indicates the entire hook is poorly resolved, but without access to the maps and models, we are unable to assess this independently.

      (5) There is no rubric for what constitutes a significant result for the DAUDA fluorescence spectroscopy. What level of increase vs wild-type is considered significant? I188G is incredibly close to L144G, but one is treated as a non-result and the other as something that should be further investigated.

      (6) The conformational trajectory of the proposed model is on static endpoints.

    3. Reviewer #2 (Public review):

      Summary:

      The manuscript by Jepson and colleagues presents the structural and biochemical study of the outer membrane LolB in complex with LolA in its apo and lipoprotein-bound states. The Lol system (LolABCDE) is essential in the translocation of lipoproteins to the outer membrane. Several studies have looked at the inner membrane complex. This study captured the last step of the process, the translocation of lipoproteins to the outer membrane via the LolB and LolA proteins. The crystal structures of LolB in complex with LolA revealed key interactions via the hook and charged belt motifs. Mutagenesis was not able to fully dissociate the complex or prevent the transfer of lipoprotein. The complex of LolB with LolA and lipoprotein revealed the 'final' step of engaging the complex for lipoprotein insertion into the outer membrane.

      Overall, this is an excellent and exciting study for the Lol field as it captures the events happening at the outer membrane.

      Strengths:

      The authors have determined several high-resolution structures and validated them with excellent mutagenesis experiments. The key structure is that of LolB with LolA and the lipoprotein (even partially resolved).

      Weaknesses:

      The only 'weakness' is that the authors have used mutants of either LolB or LolA to capture the stable states, and the discussion section would benefit from commenting if these states could be dead-end states, as validation of the mutants has been done with a WT background. In addition, can they discuss whether a2 is likely to undergo further conformational changes to open a lateral gate to the OM for the acyl chains of the lipoprotein to insert?

    1. eLife Assessment

      This valuable study carefully assesses the anatomical organization of the adult zebrafish olfactory bulb using a densely reconstructed electron microscopy dataset. The data were generated using a previously acquired state-of-the-art serial block-face scanning electron microscopy dataset, followed by manual classification of cells into distinct anatomical groups. The analysis is a convincing application of careful expert neuroanatomical understanding that reveals key details of the cell class and connectivity in the olfactory bulb, with helpful detailed descriptions of why each cell class was distinguished.

    2. Reviewer #1 (Public review):

      Summary

      The authors set out to address a critical gap in olfactory bulb research: the lack of a comprehensive annotation of neuron types and their connectivity in the adult zebrafish. Using a large serial block-face scanning electron microscopy volume, they reconstructed 459 neurons and performed a detailed morphological and ultrastructural classification. They identified 13 major neuron subclasses, including two projection neuron types (mitral cells and ruffed cells) and 11 interneuron types. A key finding is that despite the diffuse appearance of the lateral olfactory bulb neuropil, it is organized into discrete, segregated glomeruli. The authors also performed targeted synapse annotation, revealing systematic and selective connectivity patterns between projection neurons and specific interneuron subnetworks, and discovered distinct microcircuit motifs involving reciprocal and unidirectional connectivity. This work provides a crucial anatomical framework for understanding the circuit basis of olfactory computations.

      Strengths

      (1) The volumetric EM reconstructions capture fine ultrastructural features (e.g., spine shapes, neurite caliber variations, and varicosity morphology) at nanometer resolution across hundreds of neurons. The descriptive detail and 3D rendering provided for each subclass are remarkably thorough.

      (2) The study goes beyond simple morphological descriptions by integrating ultrastructural features, such as spine shape, neurite diameter, and synaptic arrangements, to define neuron classes. This provides a more functionally relevant taxonomy. The classification was also cross-validated by independent neuroscientists.

      (3) The targeted synapse annotation translates anatomy into testable circuit hypotheses, revealing distinct connectivity motifs (reciprocal vs. unidirectional, MC-selective vs. RC-selective) that provide structural substrates for computational functions such as normalization.

      Weaknesses

      (1) It is important to provide an estimate of the total neuronal population of the adult zebrafish OB and justify that their sample size is sufficient to claim a "comprehensive" neuron type classification.

      (2) The classification was based on morphology and ultrastructure. If feasible, we recommend an unsupervised clustering analysis using only the synaptic connectivity matrix for fully reconstructed neurons to test whether connectivity patterns recapitulate the 13 morphological subclasses. Besides, cross-validation by independent annotators is great. However, when the annotators disagreed on a neuron's subclass, how was it resolved?

      (3) A summarized reference table that includes features for each subclass is highly recommended (the main text cited several tables; however, I did not find them). This would facilitate community adoption of the classification.

      (4) The schematic in Figure 20 could be optimized to reflect synaptic connections among 13 subclasses with connection strength annotated.

    3. Reviewer #2 (Public review):

      Summary:

      The authors have used a previously published SBEM dataset of the adult zebrafish olfactory bulb and carefully reconstructed the fine structure of a large number (459) of neurons. Through manual classification of cells into 13 different anatomical classes, they provide a carefully annotated library of cells. Classifications have been validated by comparing the labeling of 5 human annotators, which revealed a large degree of reproducibility. Synaptic identification enables assignment of connectivity, revealing a major contribution of reciprocal connections (two-way synapses) that may support key computational features during olfactory processing. In general, the work provides a clean descriptive account of the organization of the zebrafish olfactory bulb, which should help to better understand principles of olfactory processing in vertebrates.

      Strengths:

      The authors provide a library of neurons in the olfactory bulb of adult zebrafish that will be highly useful to the broader circuit neuroscience community. Figures of classified cell types have been convincingly arranged to appreciate the high quality of the EM volume and reconstructions. Cells are easily accessible through a web interface to facilitate the assessment of classifications. An important result is that anatomical structures in zebrafish clearly follow the glomerular arrangement known in mammals, which argues against a microglomerulus model previously proposed for the zebrafish olfactory bulb.

      Weaknesses:

      The paper uses Wanner et al. 2016 as a main base dataset to generate a descriptive library of cells, in a limited volume (18%) of the zebrafish olfactory bulb. Annotations of cell types and synapses are largely human-based. Using a multi-human validation strategy helps to assess labeling, but it would benefit from more rigorous statistics, in particular for the synapse annotations. In general, the paper would be easier to read with a glossary of cell types. Some features are quite intriguing, like the cilia structures in some of the somata or the ruffs, but discussion of the potential computational roles of these fine structures is missing.

    4. Reviewer #3 (Public review):

      Summary:

      The authors aimed to build a comprehensive understanding of the cell classes that make up the adult zebrafish olfactory bulb (OB) and their connectivity. Using an EM volume covering the three OB layers and several glomeruli, they aim to reconstruct a sufficient sample of all cell classes in the OB. Based on 459 proofread reconstructions, they identify 13 cell classes and a small number of subclasses and offer detailed expert neuroanatomical arguments, largely qualitative, for why these classes are distinct. While they do not have a synapse classification, they further build a manual wiring diagram of connectivity based on these reconstructions, which together offer a strong baseline understanding of the organization of the OB and useful properties to build into a future quantitative assessment of fish neuroanatomy in general.

      Strengths:

      The paper offers a powerful example of why expert assessment remains a vital tool in neuroanatomy. While quantification is deeply important to scale data, the kind of careful multimodal human investigation of neuronal shape, connectivity, and ultrastructure forms a key basis for discovering and differentiating neuronal cell types. The authors use multi-expert classification to validate qualitative assessments as well, which is a good way to handle cross-individual uncertainty. The descriptions of each type and their relationships are generally quite thorough, and there are enough cells to offer a satisfying baseline categorization. Anyone studying zebrafish OB is likely to get a lot out of this paper.

      Weaknesses:

      While I am a strong believer in expert neuroanatomical intuition alongside quantitative neuroanatomical approaches to cell typing (especially given the manageable number of cells and cell types), I would have liked a section explaining why this approach was taken as opposed to a more quantitative one from the beginning. I think that this is a case study in where the expert qualitative approaches are useful, especially with fascinating details like the unusual hand-shaped spines. A lot of the strength of the paper comes from this philosophy, in my view, and it would be nice to hear it elucidated by the authors. I would also be curious if the authors came to any conclusions about what might have been possible with a more data-driven approach, although this may be beyond the scope of the work or not terribly interesting.

      There were some aspects of the cell typing that I didn't totally understand. Two of the most common issues with cell typing are that (1) it is unclear if cell types are discrete and (2) unusual cells appear that don't have enough peers to classify into an unambiguous type. Did those happen here? For example, it was not obvious to me how it was decided that mitral cells fell into three subclasses: small, medium, and large, and not a continuum from small to large. Similarly, some interneuron classes had subclasses. What anatomical qualities suggested that a split was a subclass-level split vs a class-level split? Were there any cells that were impossible to classify into a group, or was everything actually tidy? How confident are the authors that they have captured the complete diversity of the OB from this sampling?

      I generally appreciated the numerous detailed figures and found them generally clear and informative. However, as someone who is not deeply familiar with zebrafish olfactory bulb organization, I would have liked to have at least some global context for each cell class in terms of how typical cells relate to the layers and glomeruli. Figure 18B does a nice job of this with many cells, but it would be clearer to see this along the way with the individual classes (and subclasses) as well.

      For the connectivity analysis, I commend the authors for doing as much manual synapse annotation as they did, but the lack of completeness makes it less clear how to interpret the connectivity findings. In particular, how complete is the current assessment? For example, when describing connectivity between pairs of cells, how were the pairs selected? Did they necessarily have contact between the meshes, or innervate the same glomerulus, or were they just two cells of the same type in any location? This is useful to understand how to interpret the connectivity fractions measured.

    5. Author response:

      We thank the reviewers for their constructive feedback.

      Reviewer 1:

      (1) We will provide an estimate of total neuron numbers.

      (2) We will explore the possibility to extend cell type classification by unsupervised clustering of connectivity but it may not be possible to obtained a meaningful clustering based on the available connectivity data. The main issue is that connectivity information is available only for subsets of neurons. The underlying reason is that synapses were annotated manually and a complete manual annotation of the entire dataset would be an excessive task. In principle, this problem could be solved by automation but networks for automated annotation of synapses in other brain areas do not produce reliable results in the olfactory bulb, most likely because many neurites are both pre-and postsynaptic (further remarks below).

      (3) We are happy to supply the missing tables and to provide a glossary/table summarizing the features of the different neuron types.

      (4) Thank you; we will consider the suggestion to optimize Fig. 20.

      Reviewer 2:

      We agree that more a more comprehensive analysis of synaptic connectivity would be desired. The underlying problem, however, is that scaling synapse annotation requires automation but networks used for synapse annotation in other brain areas failed to produce reliable results in the olfactory bulb. The underlying reason is most likely that neurites of many olfactory bulb neurons are both pre- and postsynaptic; their ultrastructure is therefore different from classical axons or dendrites, e.g., vesicles are frequently found in close proximity on both sides. We therefore annotated synapses manually, which is the reason why synapse annotation was restricted to subsets of neurons. We will explain this situation in the revisions. Moreover, we will explore further approaches for automated cell type classification using both morphological and synaptic features.

      We shall be happy to include a glossary of cell types.

      We are also happy to expand the discussion of ultrastructural observations. Concerning primary cilia, we do not have a strong hypothesis concerning their function, neither based on our observations nor based on the literature.

      Reviewer 3:

      Following the reviewer’s suggestion we will be happy to include a more in-depth discussion of why our analysis is based primarily on human annotations and observations rather than more automated classification.

      As suggested by the reviewer we are planning to include additional figures and/or tables to show the relations between neuron types and layers, and to provide more summary information about cell types.

      We will also explain in more detail how neurons were chosen for connectivity analysis, with a specific focus on the assessment of connectivity fractions.

    1. eLife Assessment

      This fundamental study delivers a population reference panel for long-read sequencing-based structural variants and demonstrates its utility for disease association analyses in the UK Biobank, expanding genetic discovery beyond conventional SNV-based approaches. The authors provide convincing evidence through extensive benchmarking and systematic analyses that the panel improves structural variant detection and can support fine-mapping of trait associations.

    2. Reviewer #1 (Public review):

      Summary:

      The authors sequenced 888 individuals from the 1000 Genomes Project using the Oxford Nanopore long-read sequencing method to achieve highly sensitive, genome-wide detection of structural variants (SVs) at the population level. They conducted solid benchmarking of SV calling and systematically characterized the identified SVs. While short-read sequencing methods, including those used in the 1000 Genomes Project, have been widely applied, they exhibit high accuracy in detecting single nucleotide variants (SNVs) and small insertions and deletions but have limited sensitivity for SV detection. This study significantly enhances SV detection capabilities, establishing it as a valuable resource for human genetic research. Furthermore, the authors constructed an SV imputation panel using the generated data and imputed SVs in 488,130 individuals from the UK Biobank. They then conducted a proof-of-principle genome-wide association study (GWAS) analysis based on the imputed SVs and selected traits within the UK Biobank. Their findings demonstrate that incorporating SV-GWAS analysis provides additional insights beyond conventional GWAS frameworks focusing on SNVs, particularly in improving fine-mapping.

      Strengths:

      The authors constructed a high-sensitivity reference panel of genome-wide SVs at the population level, addressing a critical gap in the field of human genetics. This resource is expected to significantly advance research in human genetics. They demonstrated the imputation of SVs in individuals from the UK Biobank using this panel and conducted a proof-of-concept SV-based GWAS. Their findings highlight a novel and effective strategy for integrating SVs into GWAS, which will facilitate the analysis of human genetic data from the UK Biobank and other datasets. Their conclusions are supported by comprehensive analyses.

      Weaknesses:

      The authors have addressed many of my previous comments, and I appreciate their efforts. However, I still have two related concerns.

      (1) Shortly after reviewing this manuscript last year, my laboratory obtained access to the UK Biobank (UKB) Tier 3 dataset for an unrelated project. In August 2025, I searched the UKB Research Analysis Platform (UKB-RAP) for the imputed structural variant (SV) dataset described in this manuscript but was unable to locate it. After contacting UKB, I was informed that they were developing the system for releasing the data. To the best of my knowledge, the dataset remains unavailable. A major contribution of this work is the generation of an imputed SV resource for approximately 500,000 UKB participants with extensive phenotypic information. If this resource is not accessible to the research community, even to authorized UKB users, the practical impact and utility of the study are substantially diminished.

      (2) Given that the imputed SV dataset is currently unavailable, it becomes even more important for the authors to provide a detailed, ready-to-run SV imputation pipeline for UKB-RAP, even if the "data processing simply consisted of running standard bioinformatics tools with the parameters exactly as described in the manuscript". In particular, the pipeline should include practical information such as computational requirements (e.g., memory and storage), expected running time, and estimated cost. Anyone with experience using UKB-RAP will agree that reproducing large-scale analyses on the platform can be both technically complex and financially expensive. Such pipeline would greatly improve the reproducibility and accessibility of this work.

      Because my initial assessment of the manuscript was generally positive, I do not wish to change my overall evaluation, summary, or assessment of its strengths. However, I would view the work even more favorably if either (i) the imputed SV dataset became publicly available to authorized UKB users, or (ii) the authors extended their SV-GWAS analyses to the full range of UKB phenotypes and released the resulting summary statistics, analogous to the Pan UKBB ("https://pan.ukbb.broadinstitute.org/") resource. Although this would require considerable additional effort and computational resources, it would substantially enhance the long-term value and impact of the study.

      Finally, I would like to emphasize that these comments are not intended to create unnecessary difficulties for the authors or the editors. Rather, I believe this highlights a broader issue in the use of this kind of large public datasets: reviewers cannot independently verify key results, and readers cannot readily build upon the work if the underlying resources are inaccessible, even after obtaining authorized access to the original dataset. I hope the authors, together with the eLife editors and UK Biobank where appropriate, can help facilitate the timely release of this valuable resource.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Our revision includes:

      (1) The generated data from long-read whole-genome sequencing of 1000 Genomes Project samples, including FASTQ files, SV calls, and the imputation panel, are now openly available via ENA and OpnMe. The imputed structural variant data have been submitted to UK Biobank for release through the UK Biobank Research Analysis Platform, subject to UK Biobank release procedures. SV-WAS summary statistics have been made available via OpnMe.

      (2) Clarification of analyses and methods, addition of two new Supplementary Figures, and correction of minor issues throughout the manuscript.

      (3) A significantly expanded Discussion to address the reviewers’ comments and better contextualise our methods and results.

      eLife Assessment

      This fundamental work significantly enhances our understanding of how structural variants influence human phenotypes. The conclusion is convincingly supported by rigorous analyses of long-read sequencing data. If the raw data are made publicly available, these high-quality datasets and findings will further advance our knowledge of genetic variation in the human population.

      We thank the editors for this positive assessment of our work. The raw long-read sequencing data (FASTQ files) can now be accessed through the European Nucleotide Archive (ENA) under accession number PRJEB89727, as part of a larger collection of 1019 sequenced probands from the 1000 Genomes Project (https://www.ebi.ac.uk/ena/browser/view/PRJEB89727). The generated imputation panel and the structural variant calls, based on the 888 probands used in the present manuscript, remain freely available for download at https://opnme.com/genomiclens. We have now added the summary statistics of 32 SV-wide association studies to the same resource. In addition, we have submitted the imputed SV genotypes for UK Biobank participants to the UK Biobank; once processed by UK Biobank, these genotypes will be released via the UK Biobank Research Analysis Platform (RAP).

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors sequenced 888 individuals from the 1000 Genomes Project using the Oxford Nanopore long-read sequencing method to achieve highly sensitive, genome-wide detection of structural variants (SVs) at the population level. They conducted solid benchmarking of SV calling and systematically characterized the identified SVs. While short-read sequencing methods, including those used in the 1000 Genomes Project, have been widely applied, they exhibit high accuracy in detecting single nucleotide variants (SNVs) and small insertions and deletions but have limited sensitivity for SV detection. This study significantly enhances SV detection capabilities, establishing it as a valuable resource for human genetic research. Furthermore, the authors constructed an SV imputation panel using the generated data and imputed SVs in 488,130 individuals from the UK Biobank. They then conducted a proof-of-principle genome-wide association study (GWAS) analysis based on the imputed SVs and selected traits within the UK Biobank. Their findings demonstrate that incorporating SV-GWAS analysis provides additional insights beyond conventional GWAS frameworks focusing on SNVs, particularly in improving fine mapping.

      The authors constructed a high-sensitivity reference panel of genome-wide SVs at the population level, addressing a critical gap in the field of human genetics. This resource is expected to significantly advance research in human genetics. They demonstrated the imputation of SVs in individuals from the UK Biobank using this panel and conducted a proof-of-concept SV-based GWAS. Their findings highlight a novel and effective strategy for integrating SVs into GWAS, which will facilitate the analysis of human genetic data from the UK Biobank and other datasets. Their conclusions are supported by comprehensive analyses.

      We thank the reviewer for highlighting the value of our SV imputation reference panel.

      Weaknesses:

      (1) Although the authors employ state-of-the-art analytical approaches for the identification of SVs, the overall accuracy remains suboptimal, as indicated by an F1 score of 74.0%, particularly in tandem repeat regions. To enhance accuracy, it would be beneficial to explore alternative SV detection methods or develop novel approaches. Given the value of the reference panel and the fact that improved SV accuracy would lead to more precise SV imputation and GWAS results, investing effort in methodological refinement is highly encouraged.

      Accurate SV calling remains an active area of research and is beyond the scope of the present study. Tandem repeat regions are particularly challenging for standardised SV detection. We believe that achieving a benchmark for NA12878 of F1 = 74% on a genome-wide level and, notably, F1 = 91% when excluding longer tandem repeats, represents strong performance. This result is especially convincing when considering that our benchmarking compared the SV calls to data generated using a different sequencing technology and processed using different bioinformatics pipelines.

      (2) From the Methods section, it appears that the authors employed Beagle for both imputation and the UK Biobank imputation.

      (a) It would be better to explicitly clarify this in the Results section and provide a detailed description of the corresponding procedures and parameters in the Methods section for both analyses, as this represents a key aspect of the study.

      We thank the reviewer for these suggestions. Accordingly, we added the clarification to the Methods section that the leave-one-out imputation used exactly the same pipeline and settings as the UK Biobank imputation (page 14, section “Leave-one-out imputation performance”):

      “We excluded one individual from the panel and imputed SVs for this individual using the panel of the remaining 887 samples, applying exactly the same pipeline and settings as those later used for SV imputation into UK Biobank (see below).”

      (b) Additionally, Beagle is not specifically designed for SV imputation, the imputation quality of SVs is generally lower than that of SNVs. Exploring strategies to improve SV imputation, such as developing a novel method with reference panel data, may enhance performance.

      As stated in our manuscript (page 4), we believe that, in our study, the imputation quality of SVs is lower than of that of SNVs primarily because of a) the greater difficulty of SV calling compared to SNV genotype calling and b) the heterogeneity in SV representation across samples. Once SVs are encoded as bi-allelic markers in the reference panel, they can be imputed using the same LD/haplotype-based framework as any other variants. Accordingly, improving SV imputation is likely to benefit most from more accurate upstream SV calling and genotyping (e.g., through more robust multi-sample calling and harmonised variant representations) and not so much from improved or SV-specific imputation methods. While improved imputation is an important research direction, it is beyond the scope of the present manuscript.

      (c) It is also important to assess how this reduced imputation quality may influence GWAS results. For instance, it would be useful to examine whether associated SVs exhibit higher imputation quality and whether SVs with lower quality are less likely to achieve significant association signals. In addition, the lower imputation quality observed for INV, DUP, and BND variants (Figure 3) may be due to their greater lengths (Figure 2). It is better to investigate the relationship between SV length and imputation quality.

      We agree that imputation quality can influence GWAS results. For example, for the FEV1/FVC phenotype, SVs with INFO > 0.9 are almost twice as likely to reach genome-wide significance (p < 5e-8) compared with SVs with 0.7 < INFO < 0.9 (odds ratio 1.95; Fisher’s exact test p-value 2.5e-5). This is consistent with the intuitive notion (applicable to any variants, not only to SVs) that greater uncertainty in the imputed genotypes dilutes association signals and therefore reduces power. For a detailed discussion of the relationship between allele frequency, imputation accuracy, and GWAS association results, see Zhang et al., Human Molecular Genetics 31(1):146–155 (2022), https://doi.org/10.1093/hmg/ddab203

      We have now investigated the relationship between SV length and imputation quality (the new Supplementary Figure 6). The results suggest that the observed association between imputation quality and SV size is primarily driven by the SV-size–dependent minor allele frequency in the imputation panel.

      (3) All examples presented in the manuscript focus on SVs that overlap with genes. It may also be valuable to investigate SVs that do not overlap with genes but intersect with enhancer regions. SVs can contribute to disease by altering regulatory elements, such as enhancers, which play a crucial role in gene expression. Including such analyses would further demonstrate the utility of SV-GWAS and provide deeper insights into the functional impact of SVs.

      We agree with the reviewer that examining SVs intersecting with enhancer regions could be an interesting direction for future studies, as it would provide additional insights into regulatory mechanisms and disease associations. However, in the present proof-of-principle study, we prefer focusing on SVs overlapping with genes and have now highlighted this in additional detail in the revised manuscript (Discussion, page 7):

      “In the present proof-of-principle study, we focused on SVs overlapping with the coding sequence of genes. In future applications of our SV imputation panel, more refined gene mapping approaches could be employed, e.g., including SVs overlapping enhancer regions or epigenetic marks. Such an enhanced mapping would increase the number of identified associated genes and thus provide additional insights into regulatory mechanisms and disease biology.”

      (4) The data availability link currently provides only a VCF file ("sniffles2_joint_sv_calls.vcf.gz") containing the identified SVs.

      (a) It would be beneficial for the authors to make all raw sequencing data (FASTQ files) and key processed datasets (such as alignment results and merged SV and SNV files) available. Providing these resources would enable other researchers to develop improved SV detection and imputation methods or conduct further genetic analyses.

      Thank you for emphasising the importance of data sharing, which we agree with.

      The Data Availability section of the manuscript already includes a link to https://opnme.com/genomiclens, where we made both the SV calls and the full and reduced SV imputation panel files freely available. We have now added SV summary statistics from 32 SVwide association studies to the same resource. Based on the reviewer’s request, we now also reference the ENA repository project PRJEB89727 (https://www.ebi.ac.uk/ena/browser/view/PRJEB89727), where the raw FASTQ files are available for download, in the manuscript.

      We have appended the Data Availability statement on page 22 of the revised manuscript as follows:

      “Raw SV calls, the long-read sequencing-based SV imputation panel, and the SV summary statistics from 32 SV-wide association studies are available through the OpnMe initiative of Boehringer Ingelheim GmbH (https://opnme.com/genomiclens). The raw long-read sequencing data (FASTQ files) for the 1000 Genomes Project samples included in this study are accessible via the European Nucleotide Archive under accession number PRJEB89727 (https://www.ebi.ac.uk/ena/browser/view/PRJEB89727). The dataset analysed here constitutes a subset of this broader collection.”

      (b) Furthermore, establishing a dedicated website for data access, along with a genome browser for SV visualization, could significantly enhance the impact and accessibility of the study. Additionally, all code, particularly the SV imputation pipeline accompanied by a detailed tutorial, should be deposited in a public repository such as GitHub. This would support researchers in imputing SVs and conducting SV-GWAS on their own datasets.

      The Methods section provides a full and detailed description of the imputation pipeline and parameters in the section “Preprocessing and imputation of SVs into UK Biobank” on page 14 of the revised manuscript. Our data processing simply consisted of running standard bioinformatics tools with the parameters exactly as described in the manuscript.

      Reviewer #1 (Recommendations for the authors):

      (1) In the Results section, Figure 3b is mentioned before 3a, and it is better to switch them in the Figure.

      Thank you for highlighting this fact. We acknowledge that typically the sequence of sections matches exactly between text and figures. However, in this specific case, we would prefer to deviate from the norm: In our opinion, Figure 3 is easier to interpret in its current sequence. At the same time, the text flows more logically in its current sequence, describing 3b before 3a. We would therefore prefer to stick to the current order, even if it means that Fig. 3b is described before 3a in the text.

      (2) Page 10, "Figure 1e" -> "Figure 2e".

      Thank you, we corrected this issue.

      (3) Page 14, "Leave-one out" -> "Leave-one-out".

      Thank you, we corrected this mistake.

      (4) It is better not to use abbreviations in the subheadings, especially "UKB" (page 3).

      Thank you, we changed the acronym ‘UKB’ to ‘UK Biobank’ in all subheadings.

      Reviewer #2 (Public review):

      Summary:

      The authors aimed to develop a novel and efficient method for SV detection, utilizing data from the 1000 Genomes Project (1KGP) for modeling and calibration. This method was subsequently validated using UK population data and applied to identify structural variants associated with specific disease phenotypes.

      Strengths:

      Third-generation single-molecule sequencing data offers several advantages over traditional high-throughput sequencing methods, particularly due to its long-read lengths, which provide valuable insights into significant forms of genomic variation. The authors have developed an efficient method for detecting structural variations and optimizing the utilization of genomic data. We hope that this method will continue to be refined, enabling researchers to more effectively leverage long-read data, high-throughput data, or even a synergistic combination of both.

      Weaknesses:

      Although this research contributes to our ability to more effectively utilize long-length and high-throughput data, there are some key issues that need to be addressed in terms of analyzing the specific results as well as writing the article.

      Reviewer #2 (Recommendations for the authors):

      (1) How to discuss the lower detection rate of structural variations (SVs) in East Asian populations, it is worth considering whether the authors' training dataset, which may have been based on raw data with insufficient representation of East Asian individuals, could have introduced a bias favoring other populations. This potential bias might arise from the relatively limited data available for Asian ancestry. Alternatively, the observed differences could also be influenced by the role of natural selection, which may have shaped the genomic landscape of East Asian populations in distinct ways. Further investigation is needed to clarify these possibilities.

      Thank you for raising this important point. Although an interesting research direction, a detailed investigation of the factors affecting SV detection rates is beyond the scope of the present study. However, we do not think that the lower detection rate in East Asians is due to an underrepresentation of Asian ancestry in our dataset. To explain this to all readers, we have added the following explanation to page 7 of the Discussion:

      “In this context, we observed that the number of SVs detected per individual differed between superpopulations. We identified the highest average number of SVs in individuals of African descent and a slightly lower average in East Asians, compared to the other superpopulations. While we included a higher number of African ancestry individuals, the number of East Asian individuals included in our reference panel was comparable to the number of individuals from other, non-African ancestries. In fact, it was even larger than the number of European ancestry individuals (AFR n=241, SAS n=171; EAS n=168; EUR n=164; AMR n=144). Therefore, we do not expect a major bias from underrepresentation of any superpopulation in the training dataset. It is well established that African ancestry is more diverse than is the case for other superpopulations [32, 33] and previous studies indicate that East Asian populations tend to exhibit slightly lower genetic diversity compared to European populations [34], which is consistent with the lower observed SV counts per genome.”

      (2) The authors did not present the results of the detection of CNV.

      Copy number variations (CNVs) are considered a subclass of structural variants. In our analysis, we detected deletions and duplications, which represent the most common forms of CNVs. However, we did not specifically investigate high copy-number SVs, as these are often larger than what can be reliably detected using long-read sequencing. Large-scale CNVs are typically identified in biobank studies through analysis of intensity data from genotyping microarrays using tools like PennCNV, and there is extensive literature supporting the use of this microarray approach in UK Biobank and other genotyped cohorts, see for example Aguirre et al.: Phenomewide Burden of Copy-Number Variation in the UK Biobank. Am J Hum Genet. 2019, Aug 1;105(2):373-383. doi:10.1016/j.ajhg.2019.07.001.

      (3) Multiple testing correction is essential for ensuring the validity of large-scale structural variation (SV) association analyses. It is strongly recommended that the statistical methods and correction strategies employed, such as Bonferroni correction or false discovery rate (FDR) control, be explicitly detailed to enhance the transparency and reliability of the findings.

      For genome-wide SV association analyses, we applied the commonly used genome-wide significance threshold of 5e-8, which is standard in genome-wide studies. Given that these were exploratory proof-of-principle analyses illustrating use cases for SV analyses, we decided not to correct on top of that for multiple testing for the number of traits (32) tested. For the pQTL analyses, we further adjusted this threshold using a Bonferroni-type correction based on the number of proteins tested (1,463), to account for the increased number of multiple comparisons.

      We have now added a more detailed description of this multiple testing procedure to the Methods subsection “SV-wide association studies in UK Biobank” on page 16 of the revised manuscript:

      “In the exploratory SV-WAS, we used the standard threshold for genome-wide significance of p < 5×10<sup>-8</sup>. For the pQTL analyses, we applied Bonferroni correction for multiple testing on top of that genome-wide threshold, correcting for the number of tested protein levels (n=1463): p < 5×10<sup>-8</sup>/ 1463 = 3.4×10<sup>-11</sup>.”

      (4) The study primarily relied on data from the 1000 Genomes Project (1KGP) and the UK Biobank; however, the UK Biobank cohort is predominantly composed of individuals of European ancestry, which may restrict the generalizability of the research findings to other populations.

      Our reference panel was constructed to cover multiple ancestries, enabling imputation for diverse populations. Thus, our imputation panel can be applied to biobanks around the world and is freely available for this purpose. As a proof of principle, we have demonstrated the feasibility and performance of SV imputation in UK Biobank as an example of a broadly accessible cohort. We are looking forward to biobanks from diverse ancestries downloading our imputation panel and applying it to their populations.

      (5) Although the study employed long-read sequencing technology, the validation of structural variation (SV) detection accuracy predominantly relied on internal data, such as 'leave-one-out' validation. To further strengthen the reliability of the SV detection methods, it is recommended to incorporate additional external independent datasets for validation.

      The leave-one-out procedure in our study was used to validate the imputation performance, not the accuracy of SV detection. To assess SV calling accuracy, we performed extensive benchmarking against external SV call datasets derived from PacBio long-read sequencing and Illumina short-read sequencing. These details are provided under the subheading ‘Structural variant calling and benchmarking’ in the Results section on page 2 of the manuscript.

      (6) Some of the SVs mentioned in the study overlap with disease association loci in the GWAS Catalog, but functional annotation and exploration of the biological mechanisms of these SVs are more limited. It is suggested that LD can be added to analyse whether there are SNP that are highly linked to them to further explore their functions.

      We thank the reviewer for this suggestion. We have actually conducted an analysis addressing exactly this question: We performed conditional association analyses of the SV signals with nearby short variants (SNPs and InDels) at the SV locus. Such a conditional analysis addresses whether the observed SV association is influenced by LD-correlated SNPs or not. The results of this analysis are reported in Supplementary Tables 16 and 17. These tables include both the conditional analysis results and the LD between each SV and the variant at the locus with the second-highest evidence for an association.

      Researchers interested in exploring the biological significance of the SV-WAS results in more detail can now download the full SV-WAS summary statistics from https://opnme.com/genomiclens.

      (7) The discussion section could be further expanded to explore the role of SV in complex diseases and its potential application in precision medicine. For example, it could discuss how SV information can be integrated into existing GWAS frameworks to enhance the accuracy of disease risk prediction.

      Thank you for the suggestion, we have now added the following sentences to the discussion (page 7/8):

      “Structural variants can influence complex disease biology through either the disruption of coding sequence or an altered regulation of gene expression. Such effects may not be well captured by short variants alone. Incorporating SVs into GWAS and follow-up analyses would thus provide more accurate disease risk prediction, uncover underlying pathomechanisms by highlighting actionable pathways and targets, and support precision medicine by providing biomarkers for patient stratification.”

      (8) The geographic labeling of certain samples in Figure 2 appears to contain inaccuracies. For instance, the CDX sample, which represents the Dai population from Xishuangbanna in China's Yunnan Province, is currently mislabeled as originating from China's Inner Mongolia. This discrepancy should be corrected to ensure the accuracy of the data representation.

      We apologise for the misunderstanding. The geographic map in Figure 2a serves as an illustrative mapping of the samples to countries. It is intended to provide readers with an overview of population coverage, rather than to indicate the precise geographic origins of individual populations. The populations CDX, CHB, and CHS are displayed within the outline of China in alphabetical order, without any intention to indicate their exact geographic origin. We changed the respective figure caption to make this clear (page 19 of the revised manuscript):

      “Map of the 888 samples from the 1000 Genomes project, mapping the samples to countries and not indicating detailed geographical origins of populations.”

      Reviewer #3 (Public review):

      This study successfully identified genetic loci associated with various traits by generating large-scale long-read sequencing data from a diverse set of samples. This study is significant because it not only produces large-scale long-read genome sequencing data but also demonstrates its application in actual genetics research. Given its potential utility in various fields, this study is expected to make a valuable contribution to the academic community and to this journal. However, there are several critical aspects that could be improved. Below are specific comments for consideration.

      Strengths:

      Producing high-quality, large-scale variant datasets and imputation datasets

      Weaknesses:

      (1) Data availability

      Currently, it appears that only the Genomic Lens SV Panel is available on the webpage described in the Data Availability section. It is unclear whether the authors intend to release the raw sequencing data. Since the study utilized samples from the 1000 Genomes Project, there should be no restriction on making the data publicly accessible. Given this, would the authors consider making the raw sequencing reads publicly available? If so, NCBI SRA or EBI ENA would be the most appropriate repositories for data deposition. I strongly encourage the authors to consider public data release. Additionally, accessing the Genomic Lens SV Panel data does not seem straightforward. The manuscript should provide a more detailed description of how researchers can access and utilize these data. In my opinion, the best approach would be to upload the variant data (VCF files) to a public database such as the European Variation Archive (EVA) hosted by EBI.

      I strongly request that the authors publicly deposit the variant data. At a minimum:

      (a) The joint genotype data for all 888 samples from the 1000 Genomes Project must be publicly available.

      Thank you for emphasising the importance of data sharing, which we agree with.

      The Data Availability section of the manuscript already includes a link to https://opnme.com/genomiclens, where we make both the SV calls and the full and reduced SV imputation panels (provided as multi-sample VCF files) freely available. Based on the reviewer’s request, we now also reference the ENA repository project PRJEB89727 (https://www.ebi.ac.uk/ena/browser/view/PRJEB89727), where the raw FASTQ files are available for download.

      We have appended the Data Availability statement on page 22 of the revised manuscript as follows:

      “Raw SV calls, the long-read sequencing-based SV imputation panel, and the SV summary statistics from 32 SV-wide association studies are available through the OpnMe initiative of Boehringer Ingelheim GmbH (https://opnme.com/genomiclens). The raw long-read sequencing data (FASTQ files) for the 1000 Genomes Project samples included in this study are accessible via the European Nucleotide Archive under accession number PRJEB89727 (https://www.ebi.ac.uk/ena/browser/view/PRJEB89727). The dataset analysed here constitutes a subset of this broader collection.”

      (b) For the UK Biobank samples, at least allele frequency data should be disclosed.

      Supplementary Table 5 includes the allele frequencies of the SVs imputed into UK Biobank.

      (c) Since eLife has a well-established data-sharing policy, compliance with these guidelines is essential for publication in this journal.

      By sharing the FASTQ files, the SV calls, the SV imputation panels, the SV summary statistics, and (once processed by UK Biobank) the genotypes of SVs imputed into UK Biobank, we are providing all SV data generated in our study.

      (2) Long-read sequencing data quality

      While the manuscript presents N50 read length and mean or median read base quality for each sample in a table, it would be highly beneficial to visualize these data in figures as well. A violin plot or similar visualization summarizing these distributions would significantly improve data presentation.

      Notably, the base quality of ONT long-read sequencing data appears lower than expected. This may be attributed to the use of pore version 9.4.1, but the unexpectedly low base quality still warrants attention. It would be helpful to include a small figure within Figure 2 to illustrate this point. A visual representation of read length distribution and base quality distribution would strengthen the manuscript.

      We thank the reviewer for this suggestion. We have now included two violin plots (the new Supplementary Figure 1) to the revised manuscript, summarising a) the N50 read length per sequencing run and b) the median read quality per sequencing run. These plots provide a clearer visualisation of the underlying distributions. We do not consider the ONT base quality to be low. Importantly, structural variant detection is generally robust to modest variations of per-base quality. Therefore, we do not expect the observed base quality levels to significantly affect SV calling in this study.

      (3) Variant detection precision, recall, and F1 score

      This study focuses on insertions and deletions (indels) {greater than or equal to}50 bp, but it remains unclear how well variants <50 bp are detected. I am particularly interested in the precision, recall, and F1 score for variants between 5-49 bp.

      While ONT base quality is relatively low, single-base variants are challenging to analyze, but variants {greater than or equal to}5 bp should still be detectable as their read accuracy is still approximately 90%, making analysis feasible. Given that Sniffles supports the detection of variants as small as 1 bp, I strongly encourage the authors to conduct an additional analysis.

      A simple two-category classification (e.g., 5-49 bp and {greater than or equal to}50 bp) should suffice. Additionally, a comparative analysis with HiFi and short-read sequencing data would be highly valuable. If possible, I strongly recommend that all detected variants {greater than or equal to}5 bp be made publicly available as VCF files.

      Because short InDels are available from high-coverage Illumina sequencing data generated for the same individuals (i.e., the data referred to as the NYGC dataset in our manuscript), we decided against calling such short variants from our lower coverage Oxford Nanopore data and thus concentrated our efforts on reliably calling longer variants covering at least 50 bp, consistent with the conventional definition of structural variants.

      (4) Assembly-based methods

      Given the low read accuracy and low sequencing depth in this dataset, it is understandable that genome assembly is challenging. However, the latest high-quality human genome datasets-such as those produced by the Human Pangenome Reference Consortium (HPRC)demonstrate that assembly-based approaches provide significant advantages, particularly for resolving complex and long structural variants.

      Since HPRC data also utilize 1000 Genomes Project samples, it would be highly informative to compare the accuracy of ONT sequencing in this study with HPRC's assembly-based genome data. The recent publication on 47 HPRC samples provides a valuable reference for such a comparison. Given its relevance, the authors should consider providing a comparative analysis with HPRC data.

      The aim of the present study was to generate an SV reference panel that enables SV imputation for large biobanks. Detailed assessments of ONT sequencing quality in general and comparisons to other sequencing efforts and technologies are out of scope for the present manuscript. We invite the scientific community to use the FASTQ files provided at ENA for conducting such detailed assessments in follow-up studies.

    1. eLife Assessment

      This important paper introduces a generalisable tissue-clearing method to study human anatomy and the organisation of complex tissues and organs. The evidence for its broad applicability is compelling, based on results from 15 diverse human tissues, including highly heterogeneous specimens, with successful labelling using five antibodies and five fluorescent dyes. The method has broad potential applications across multiple disciplines.

    2. Reviewer #1 (Public review):

      Summary:

      The authors sought to develop a generalisable method for clearing and labelling large, highly complex human organs and tissues while preserving their three-dimensional structure, enabling visualisation at high resolution. The resulting body of work is substantial, encompassing a diverse range of tissues and labelling approaches, and demonstrates an impressive breadth of experimental work.

      Strengths:

      A major strength of the method is its systematic validation across a diverse range of human tissues, including several understudied tissues, in combination with labelling using multiple antibodies and fluorescent dyes. An additional strength is that the skin was retained on the finger throughout the clearing, labelling, and imaging procedures, allowing the tissue to be analysed in an intact state. This is particularly relevant to sensory neuroscience, where preserving the cutaneous sensory structures may be important. The use of tissue from donors aged 85 years and older is also a strength, as it provides a stringent test of the method in tissues with substantial age-related pigment accumulation and autofluorescence.

      Weaknesses:

      A major limitation is the lack of evidence that the method can be applied to formalin-fixed, paraffin-embedded (FFPE) tissue, which represents a substantial proportion of archived human tissue and is an important potential application of tissue-clearing approaches. Furthermore, the claim that the method is cost-effective is not sufficiently supported by the data presented, as there is no comparison of the costs of the proposed approach with those of existing methods.

      Appraisal:

      (1) Overall, I feel that the authors have broadly achieved their stated aims, although several aspects would benefit from further qualification or supporting evidence.

      (2) Although magnetic resonance imaging (MRI) was performed on four tissues, the MRI data do not appear to be fully integrated with the microscopy data, and I did not find evidence of precise spatial correlation of structural information between the two modalities.

      (3) The term "photoclearing" may be useful to describe the approach, but the underlying concept does not appear to be entirely novel, given that increased optical transparency can occur as a consequence of photobleaching. Furthermore, the evidence presented is not sufficient, in my view, to establish that the observed effects are attributable to photodegradation of pigments.

      (4) I question whether the broader claims regarding the scalability of existing tissue-clearing approaches are sufficiently supported. In particular, the claim that aqueous- and hydrogel-based methods have limited scalability may require further evidence, given that perfusion-based approaches can facilitate processing of large tissue volumes. Similarly, the assertion that organic solvent-based approaches are better suited to large tissue volumes does not appear to be fully supported by the data presented, unless this refers specifically to the beneficial effects of tissue shrinkage during dehydration on imaging.

      (5) More generally, a direct comparison of the clearing, labelling, and imaging outcomes for the thyroid samples fixed for 48 hours versus 8 years would be valuable for assessing the extent to which fixation duration influences the performance of the method.

      Discussion:

      The results have potential value for medical education, comparative anatomy, and research requiring the characterisation of human anatomy across spatial scales. The method could have applications across multiple disciplines and may be of interest to researchers who have access to human tissue from sources including archival material, biopsies, surgical resections, tissue banks, autopsy specimens, and donor tissue. However, the length and complexity of the protocol may limit its practical adoption, particularly in settings where throughput is an important consideration.

      Context:

      The results for the inner ear and ring finger are particularly notable because they demonstrate the ability of the method to clear, label, and image anatomically complex, highly calcified, and densely structured tissues. The successful clearing and immunolabelling of tissue fixed for 15 years is also significant, given the potential challenges associated with antibody penetration and preservation of epitopes following prolonged fixation. More broadly, successful imaging of large organs, such as the heart, is technically important given the challenges that their size and geometry present for light-sheet microscopy, and demonstrates the feasibility of applying the approach to intact human organs.

      Technical concern:

      The manuscript should acknowledge the hazardous and potentially toxic nature of several reagents used in the protocol, together with the challenges of safely handling and disposing of the relatively large volumes of chemical waste generated. These factors may have practical implications for implementation of the method.

      Conceptual concerns:

      The role and added value of photogrammetry in the study are not entirely clear. It would be helpful for the authors to clarify how photogrammetry was used beyond its presentation in the figures and what additional information or utility it provides relative to the other imaging modalities employed.

    3. Reviewer #2 (Public review):

      Summary:

      The authors claim to present a generally applicable tissue processing pipeline for optical tissue clearing and labelling of whole human organs. To achieve this, they combine multiple previously published tissue pretreatment steps to enhance tissue permeabilization, and hence label penetration, as well as optical transparency. Most notably, the authors include LED photobleaching, or photoclearing as they call it here, to reduce both autofluorescent background and increase tissue transparency. Extending their previously published method towards a wide variety of large human samples encompassing multiple organs and tissues, they combine their clearing protocol with several small molecule dyes for general overview stains as well as immunofluorescence labelling. For some of the processed samples, they introduce a novel multimodal imaging pipeline, which includes photogrammetry, MRI, and 3D histology/microscopy. Both the multimodal pipeline and the described tissue clearing and labelling protocols seem highly useful for both pathology as well as fundamental research, especially since the modularity of tissue-clearing protocols allows for the integration of individual steps, such as photoclearing, into already established methods.

      Strengths:

      The transparency of the large, archival human samples is very impressive and, to my knowledge, constitutes the best human tissue clearing to date. The positive effects of photobleaching/clearing on autofluorescence reduction as well as transparency are obvious and highly interesting for the field, as this method can easily be implemented into any tissue clearing approach. It is highly appreciated that notes are provided in both the methods and results, on which steps of the pipeline had to be adjusted for different tissue types and sizes. However, this could be presented in a more structured way, by showing a graphical representation or at least a table of all used tissues and all the steps of the pipeline, with indications for which steps were skipped/adjusted per tissue type as well as the incubation times for every step. Table 2 already does that for light exposure and decalcification times, but it would be beneficial to do this for all steps to get a better overview. This would also aid replication and implementation of their method, as other groups can just look up the closest matching tissue type in the table and go from there rather than piecing this information together with the in-text notes.

      The combination of different small molecule dyes, while in itself not new, has the potential to provide 3D alternatives for conventional histological overview stains. Other groups have already shown 3D H&E and Nissl stains, and it would be interesting what other routinely used stains could be mimicked in that way. Here, the combination of Eosin Y and Fast Green seems especially interesting.

      The quality of the immunofluorescence labelling is extremely good, especially given the age of some of the samples (e.g. very high-quality SMA staining in 15-year-old material). It would be interesting to know if the authors encountered antibodies that were not compatible with their archival tissue and, if so, which ones.

      Weaknesses:

      It is not exactly clear to me how some of the image volumes were acquired. For example, the panels 1f-h: The regions these volumes originate from are indicated in the cleared heart (1e), but were these regions blocked out or was the entire heart somehow immersed in the imaging chamber? The latter would be, to the best of my knowledge, a novel scale of light sheet imaging and should be mentioned. If the regions were somehow blocked out, this should be described. In general, a lot of the imaging volumes seemed to have been acquired mainly towards the periphery of the sample, especially the higher magnification inserts. This immediately raises the question of whether this is because imaging was not possible in the centre and why (limitation of the imaging chamber size, light penetration, label penetration, or a combination of those?). While it is understandable that it is currently not possible to image, e.g. entire kidneys in light-sheet set-ups, the limits of feasible sample sizes should be mentioned somewhere to give the reader a better appreciation of the usefulness of the method. The authors demonstrate that it is possible to clear whole organs, but is it also possible to label whole organs? Is that possible with antibodies as well, or only with small-molecule dyes? And how deep can samples be imaged in the best-case scenario? Right now, the paper uses a common scheme in the tissue clearing literature, of showing a very large cleared sample and then imaging a smaller sub-volume while implying that, in principle, it is possible to do that for the entire organ, if the objective working distance and imaging chamber would allow. However, to scale their method up, the authors would first need to show that their labels would penetrate throughout the entire sample, and this is currently not done in an unambiguous manner.

      The biggest limitation in the manuscript for me is the authors' claim: "We demonstrate the voxel-by-voxel alignment between MRI and microscopy datasets, merging cellular-scale detail and protein-specific labelling with macroscopic medical imaging." While technically true, the authors only show actual overlays of microscopy and MRI data for the inner ear sample in Figure 5. No overlays between MRI and the microscopy volumes are shown for the heart and finger data, which is a missed opportunity. Similarly, no overlays between all three acquired modalities (photogrammetry, MRI, 3D microscopy) are shown for any of the samples. This is a pity, as the authors put a lot of emphasis on this novel multimodal pipeline, even though side-by-side demonstrations of MRI and 3D microscopy in a similar manner have been shown already by other groups and a truly multimodal dataset with the different modalities, e.g. as different channels in a volume, would be very exciting.

    1. eLife Assessment

      This valuable study introduces an innovative task and framework for examining whether movement vigor and reward-seeking behavior are governed by common or separate processes. The behavioral findings provide solid evidence that these behaviors can be differentially influenced by effort and delay, but support for the proposed model and its mechanistic claims remains incomplete, requiring more rigorous validation and analyses to address potential confounds. The work will be of interest to researchers studying motor control, decision-making, and effort-based behavior.

    2. Reviewer #1 (Public review):

      Summary:

      The authors introduce a foraging task designed to test whether movement and harvesting durations are governed by a common objective combining reward, effort, and time or by separate objectives. Participants generate wrist torque to move a cursor to a rewarding patch and then hold it within the patch while reward accumulates at a diminishing rate. The task allows reach and harvest effort, as well as delays before or between the two phases, to be manipulated separately.

      Reach effort strongly affected reach duration but had little effect on harvest duration, whereas a delay imposed after reaching altered harvest duration without affecting the completed reach. Manipulation of harvest effort did not affect either duration. The authors also found consistency across individuals within each type of duration but no reliable association between reach and harvest durations. The separate model fit the behavioral results better than the common-utility model, particularly for the post-reach delay condition. The findings therefore support partly dissociable, but coupled optimization of harvest and movement in this task, while not excluding common optimization in other contexts.

      Strengths:

      The task provides a controlled way to manipulate the effort and delays associated with reaching and harvesting while measuring both durations. The control experiments alter the timing of delays, allowing the experimenter to test whether their effects depend on when they occur. These manipulations produce clear behavioral dissociations, particularly the selective effect of a post-reach delay on harvest duration. The delay and effort manipulations were verified, and most participants reported noticing the changes in effort and time.

      The separate model extends a previous cost-of-time framework to a foraging task involving movement and reward harvesting. Its assumptions have a clear relationship to the experimental design: applied delays and actions enter the time offset for each separate cost function. This provides an intuitive account of why a delay occurring after the reach can affect harvesting without affecting the completed movement. The separate model also provides a better fit than the common utility model in both modeled experiments.

      The individual-level behavioral analyses are also useful. Reach durations are correlated across reach conditions, and harvest durations are correlated across harvest conditions, indicating stable individual differences within each measure. The lack of a corresponding relationship between reach and harvest durations raises an important question about whether the two forms of vigor necessarily reflect a common individual tendency in their task.

      Weaknesses:

      The block design and familiarization made the changes in effort and delay learnable, and most participants reported awareness of these manipulations. The observed dissociation may reflect adaptation to this task structure. This does not weaken the behavioral result, but it limits how directly the findings can be generalized to tasks in which changes in reward, effort, or time are less explicit.

      The main modeling limitation is that both models are fitted to ten group-level median observations in each experiment, rather than to individuals' behavior. The separate model has seven fitted parameters compared with three in the common model. Although the reported AIC penalizes additional parameters, the analysis includes no participant-level fitting, resampling, or cross-validation.

    3. Reviewer #2 (Public review):

      Summary:

      The manuscript by Conessa and colleagues tests whether movement time and decision time are co-regulated in humans using a novel harvesting paradigm and computational modelling of the behavioral data. The data contradict the common utility model and are better supported by an alternative model where the costs of the harvesting and movement phases are optimized separately - rather than jointly.

      Strengths:

      The major strength of the study is the development of the novel paradigm where the forces applied during the distinct phases of the task (reaching and harvesting) can be perfectly controlled and measured.

      This elegant manipulation allows precise estimation of subjective effort costs, and later can be used to better understand how these costs shape behavior.

      The novel separate-cost model is also an interesting development.

      Weaknesses:

      In my opinion, the manuscript suffers from too many weaknesses to solidly support the general conclusions:

      (1) The manuscript oversells the significance of the results;

      (2) The novel model appears to be built ad hoc based on the results, and its ingredients are poorly motivated

      (3) There are important methodological shortcomings. I develop all of these points below. The paper also lacks clarity, particularly when presenting the key concepts of the novel model.

      Importantly, I also recommend that the authors follow open science policy and publish their data and code on a public repository.

      (1) Significance

      As mentioned above, the question of the co-regulation of the vigor of different behaviors is a very interesting topic, but I believe the significance of the results is overstated, for different reasons:

      a) This is a harvesting task, which means that decisions are specifically about the duration of the reward period. In other words, more time directly provides more (immediate) reward, whereas in other types of tasks more time allows gathering and processing more information, and so reaching a more informed decision, which is a more indirect way of securing better outcomes - see e.g. Drugowitsch, Moreno-Bote et al. JNeurosci 2012 for optimizing decision time in this most common setting. This major difference is not discussed in the manuscript, and it is not clear whether the results found here would hold for the most common type of decisions. This is also different from the time to initiate a response, as explored by Morvan et al. in their cited preprint.

      b) The title of the study mentions "flexible" co-regulation, but it's hard to see direct evidence for flexibility in the results. This flexibility is extrapolated in the Discussion, but in order to make such a statement, it is necessary to show that the co-regulation differs between different conditions, as predicted from the model.

      c) The lack of impact of the harvesting effort on harvest time is very surprising, and the null value of the sensitivity to effort (d=0) in the model is intellectually uncomfortable. As some value of effort, it SHOULD matter. I am not very satisfied with the explanation provided in the Discussion ("possible hypothesis would be that the perceived effort of harvest was outweighed by the reward value"): this trade-off should be precisely dealt with by the model.

      (2) Separate-Cost Model

      The separate-cost model is poorly motivated and not clearly presented, in particular the indirect coupling between the two phases.

      a) Some key components are motivated by the constraint to obtain a well-behaved optimization problem ('we need to find a cost that increases with time' ) rather than based on cognitive principles. Some other key components seem to have been placed there just to fit the data better, but their plausibility is not even discussed. Notably:<br /> i- The fact that the offset o_h includes the reaching delay is key to the coupling between t_h and t_r, but is not properly motivated. Why should this offset include all preceding intervals within the trial (leading to double counting of the cost of the reaching phase, in both G_r and G_d)? In this cyclic paradigm where participants aim at maximizing cumulative reward, it is not even clear that they parse behavior into different trials starting as they need to reach for a new target.<br /> ii- Why is there a free parameter d for the cost of harvest effort? If time and motor costs are converted into the same currency, and that conversion holds for all types of phases, then I believe we should have d=1. This free parameter seems to have been introduced a posteriori to accommodate the lack of effect of harvest effort on harvest time, but is not motivated.

      b) The two models differ in two fundamental aspects: whether optimization is run separately on the different phases of the trial, or jointly; and how the different costs and rewards are assembled in the to-be-optimized metric. Which aspect is more important to capture behavior is not assessed.

      c) The presentation of the "Separate-cost model" is very obscure. For example, the notion of a `cost of reward` is rather odd, and consequently the formula for R(t_h) appears arbitrary. I understand that this is the difference between the maximum reward that can be collected within a trial and the actual reward that is collected, but it is not even presented as such. The effort cost of harvesting seems to be discarded in the model presentation but does appear in equation 14.

      (3) Methodological Weaknesses

      I believe that there are numerous methodological weaknesses that really limit the inferences that can be drawn from the experimental and modelling results.

      a) Sample sizes are small, especially for control experiments, and it is not clear how they have been selected. This is especially problematic because the only manipulation that shows no significant effect on behavior (harvest duration) is only tested with the smallest sample size (n=10), severely limiting the significance of this negative result.

      b) The rationale for the different manipulations in different experiments is not clear. Notably, why were modulations of the harvest effort not included in the main experiment? Because not a single participant sees a modulation of both reach and harvest efforts, we cannot evaluate whether the model can capture both effects at the same time (i.e., with the same parameter values). The number of data points used for fitting directly depends on the number of conditions performed by the participant, and is very low compared to the number of parameters to be fitted. For example, the model used very different shapes for the costs of time (Figure 8) for different sets of conditions, but this cost of time should be fixed, so it's not clear whether a single cost of time function can accommodate all manipulations.

      c) Best practice in behavioral modelling is to model single-subject data, as fitting the model jointly to all subjects completely obliterates subject heterogeneity. Fitting on single-subject data would also allow investigating whether the models reproduce the consistency of harvest durations and reach durations across conditions, and most importantly the absence of correlation across subjects between reach and harvest duration. At the moment, we're left wondering what the implications of these analyses for the original question are.

      d) What is the rationale for comparing reach duration in one condition to harvest duration in another condition?

      e) The study does not take into account the likely impact of delays on muscular fatigue and perceived effort: the effort associated with maintaining the cursor on target during harvest might be perceived as less effortful after a longer break, partially explaining the impact of delay on harvest duration.

      f) I am confused by the reference to indirect effects in the separate-cost model (L692-7, Table 2). To fit this model, was the experimental value or theoretical value of the reach duration t_d used to determine the optimal harvest value (from Equation 14)? In both cases, the reach duration DOES increase with reach effort, so we should expect an impact on the harvest duration. This is indeed seen in Figure 7A. So why does Table 2 suggest that reach effort should not impact harvest duration in this model?

      g) Could the effect of pre-reach delay on reach time be alternatively explained simply by longer reaction times as reported in Figure S2, e.g., a longer period to reactivate the muscles after a longer break? In other words, as far as I understood, here reaction times are subsumed in the motor time, against the tradition of splitting them into two independent (non-overlapping) behavioral metrics.

      h) The EMG-time integral in the harvest phase should be largely proportional to the harvest duration, given that the applied torque is constrained to remain on target. Is this the case? If so, then how to interpret the significant effect of reach effort on this value, given that the main analyses did not identify an effect of reach effort on harvest duration?

    4. Author response:

      Reviewer #1 (Public review):

      Summary:

      The authors introduce a foraging task designed to test whether movement and harvesting durations are governed by a common objective combining reward, effort, and time or by separate objectives. Participants generate wrist torque to move a cursor to a rewarding patch and then hold it within the patch while reward accumulates at a diminishing rate. The task allows reach and harvest effort, as well as delays before or between the two phases, to be manipulated separately.

      Reach effort strongly affected reach duration but had little effect on harvest duration, whereas a delay imposed after reaching altered harvest duration without affecting the completed reach. Manipulation of harvest effort did not affect either duration. The authors also found consistency across individuals within each type of duration but no reliable association between reach and harvest durations. The separate model fit the behavioral results better than the common-utility model, particularly for the post-reach delay condition. The findings therefore support partly dissociable, but coupled optimization of harvest and movement in this task, while not excluding common optimization in other contexts.

      Strengths:

      The task provides a controlled way to manipulate the effort and delays associated with reaching and harvesting while measuring both durations. The control experiments alter the timing of delays, allowing the experimenter to test whether their effects depend on when they occur. These manipulations produce clear behavioral dissociations, particularly the selective effect of a post-reach delay on harvest duration. The delay and effort manipulations were verified, and most participants reported noticing the changes in effort and time.

      The separate model extends a previous cost-of-time framework to a foraging task involving movement and reward harvesting. Its assumptions have a clear relationship to the experimental design: applied delays and actions enter the time offset for each separate cost function. This provides an intuitive account of why a delay occurring after the reach can affect harvesting without affecting the completed movement. The separate model also provides a better fit than the common utility model in both modeled experiments.

      The individual-level behavioral analyses are also useful. Reach durations are correlated across reach conditions, and harvest durations are correlated across harvest conditions, indicating stable individual differences within each measure. The lack of a corresponding relationship between reach and harvest durations raises an important question about whether the two forms of vigor necessarily reflect a common individual tendency in their task.

      We thank the reviewer for acknowledging the relevance of our study.

      Weaknesses:

      The block design and familiarization made the changes in effort and delay learnable, and most participants reported awareness of these manipulations. The observed dissociation may reflect adaptation to this task structure. This does not weaken the behavioral result, but it limits how directly the findings can be generalized to tasks in which changes in reward, effort, or time are less explicit.

      The results show that, in a controlled environment, the common-utility model did not account for certain behaviors observed in our participants. In variable environments, because the previous history of reward and effort affect the current trial (Yoon et al., 2018; Sukumar et al., 2024), the behavioral regulation could be less stable and apparent. Here, we provided a favorable environment to assess the common-utility model, with stable manipulations of time and effort in a block-wise design allowing clear estimation of the capture rate by the participants. We agree with the reviewer that it is not necessarily generalizable to other types of environments involving uncertainty for instance. However, demonstrating that movement and decision vigor can be dissociated in a specific task is good evidence to suggest that it is not always co-regulated, and should therefore be flexible. This potential adaptation resulting from the development of explicit knowledge is a problem present in most studies of this type, where conditions are introduced in a blocked manner (Saleri Lunazzi et al., 2021; Sukumar et al., 2024). We will discuss this point in more detail in the revised version of the manuscript.

      The main modeling limitation is that both models are fitted to ten group-level median observations in each experiment, rather than to individuals' behavior. The separate model has seven fitted parameters compared with three in the common model. Although the reported AIC penalizes additional parameters, the analysis includes no participant-level fitting, resampling, or cross-validation.

      We acknowledge that fitting the models to participants’ individual behavior could provide better insight into the ability of the evaluated models to accurately capture the effects of time and effort on behavior. We will perform these analyses during the review.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Conessa and colleagues tests whether movement time and decision time are co-regulated in humans using a novel harvesting paradigm and computational modelling of the behavioral data. The data contradict the common utility model and are better supported by an alternative model where the costs of the harvesting and movement phases are optimized separately - rather than jointly.

      Strengths:

      The major strength of the study is the development of the novel paradigm where the forces applied during the distinct phases of the task (reaching and harvesting) can be perfectly controlled and measured.

      This elegant manipulation allows precise estimation of subjective effort costs, and later can be used to better understand how these costs shape behavior.

      The novel separate-cost model is also an interesting development.

      We thank the reviewer for acknowledging the strengths of our study and for the constructive suggestions. Below, we provide preliminary responses with some planned adjustments.

      Weaknesses:

      In my opinion, the manuscript suffers from too many weaknesses to solidly support the general conclusions:

      (1) The manuscript oversells the significance of the results;

      (2) The novel model appears to be built ad hoc based on the results, and its ingredients are poorly motivated

      (3) There are important methodological shortcomings. I develop all of these points below. The paper also lacks clarity, particularly when presenting the key concepts of the novel model.

      The model relies on a large part of the literature that assumes a cost of time affecting movement and decision-making. For instance, the urgency-gating model associate a time-growing signal with the pace of decisions (Cisek et al., 2009; Thura, 2020; Saleri Lunazzi et al., 2021); and a cost of time underlying human reaching vigor has been identified in previous studies (Shadmehr et al., 2010a; Choi et al., 2014; Berret et al., 2018; Berret and Baud-Bovy, 2022; Verdel et al., 2023). Our model is simply grounded on those studies. We will detail further the rationale behind the separate-cost model and address as much as possible the points highlighted by the reviewer.

      Importantly, I also recommend that the authors follow open science policy and publish their data and code on a public repository.

      The authors agree with the reviewer. Data are available in Supplementary Data and code used will be published on a public repository.

      (1) Significance

      As mentioned above, the question of the co-regulation of the vigor of different behaviors is a very interesting topic, but I believe the significance of the results is overstated, for different reasons:

      a) This is a harvesting task, which means that decisions are specifically about the duration of the reward period. In other words, more time directly provides more (immediate) reward, whereas in other types of tasks more time allows gathering and processing more information, and so reaching a more informed decision, which is a more indirect way of securing better outcomes - see e.g. Drugowitsch, Moreno-Bote et al. JNeurosci 2012 for optimizing decision time in this most common setting. This major difference is not discussed in the manuscript, and it is not clear whether the results found here would hold for the most common type of decisions. This is also different from the time to initiate a response, as explored by Morvan et al. in their cited preprint.

      One feature of the present study, consistent with previous work (Yoon et al., 2018; Sukumar et al., 2024), is the use of a harvesting task to study the decision-making process (Hayden, 2018). The context of optimal foraging provides an interesting approach to study decision-making, as it is directly linked to ecological contexts. Remaining in a food patch does indeed result in an immediate increase in reward, but staying there too long risks reducing the capture rate, which, according to the utility model, represents the actual value that matters. The subject has to gather information about the current state of the patch and the global capture rate of the environment to decide how long he should harvest. Here, the aim of the article was not to generalize the results to all types of decisions, but to test the utility model in a very specific and controlled environment to better dissociate the effects of time and effort. In our task, movement and decision vigor is not co-regulated, which can be seen as a counterexample of a systematic co-regulation. However, there are a lot of possible tasks combining movement and decision-making in different forms.  As such, we argue that behavior may benefit from a certain degree of flexibility that allows for the separation of movement and decision-making vigor when relevant to a given task. Depending on the context, optimal behavior may require moving quickly while deciding quickly, moving quickly while deciding slowly, or other combinations (see Thura et al., 2025 for a review). In this respect, the separate-cost model allows flexible regulation (coupled or not), hence capturing which regulation is optimal, as long as the objective costs (defined by the task) and the participant’s subjective costs (specific to the participant) are appropriately combined. The common-utility model is comparatively more constrained because movement and decision costs are intrinsically integrated.  We agree that this distinction warrants further discussion and will clarify it, together with the resulting limits on generalization, in the revised manuscript.

      b) The title of the study mentions "flexible" co-regulation, but it's hard to see direct evidence for flexibility in the results. This flexibility is extrapolated in the Discussion, but in order to make such a statement, it is necessary to show that the co-regulation differs between different conditions, as predicted from the model.

      The term flexible is used to highlight that the rigid co-regulation predicted by the common-utility model does not apply to all manipulations of effort and time in this controlled design, nor to all types of decision-making (harvest vs reaction time). Decisions in the form of harvest durations do not seem to co-vary with reach durations, contrary to decisions performed during reaction times. Accordingly, and based on recent evidences (see Thura et al. 2025 for a review), this co-regulation appears to be flexible, depending on the actual optimal behavior required by the task and the type of decisions involved. In our paper, we found a co-regulation between reaction times and movement times but an independence of harvest times and movement times. Together with existing results from the literature, we conclude about the flexibility of co-regulation. However, we acknowledge that the (co-)regulations have not been directly compared in our paper. We plan to further assess this point.

      c) The lack of impact of the harvesting effort on harvest time is very surprising, and the null value of the sensitivity to effort (d=0) in the model is intellectually uncomfortable. As some value of effort, it SHOULD matter. I am not very satisfied with the explanation provided in the Discussion ("possible hypothesis would be that the perceived effort of harvest was outweighed by the reward value"): this trade-off should be precisely dealt with by the model.

      We agree with the reviewer and the lack of an effect of harvest effort on its duration was surprising to us too. However, the null value of sensitivity to harvest effort should not be interpreted as the effort does not influence the harvest or the decision in general. This effort is indeed likely not significant enough to necessitate a change in behavior. The free weighting parameters capture the relative sensitivity of the costs involved. Although effort was noticeable for the participants and of the same magnitude as variations in movement effort, it is likely they did not find it disruptive enough to adjust their behavioral strategy, demonstrating that it was not a critical factor in these conditions. We note that both the cost of time and the harvesting cost of effort increase with time, which suggests that in this timescale and this level of effort, the cost of time may have had a greater influence on behavior. Yet, our model could have captured effort-related variations of harvest duration if there were observed experimentally. To observe such effects, we should have increased harvesting effort but it might have led to behavioral changes due to muscular fatigue or maximal isometric torque limitations (which are not considered in the current modeling) rather than to the optimization of subjective utility. We will clarify what this sensitivity of harvest effort represents, and examine alternative constraints on the free parameters.

      (2) Separate-Cost Model

      The separate-cost model is poorly motivated and not clearly presented, in particular the indirect coupling between the two phases.

      a) Some key components are motivated by the constraint to obtain a well-behaved optimization problem ('we need to find a cost that increases with time' ) rather than based on cognitive principles. Some other key components seem to have been placed there just to fit the data better, but their plausibility is not even discussed. Notably:

      i) The fact that the offset o_h includes the reaching delay is key to the coupling between t_h and t_r, but is not properly motivated. Why should this offset include all preceding intervals within the trial (leading to double counting of the cost of the reaching phase, in both G_r and G_d)? In this cyclic paradigm where participants aim at maximizing cumulative reward, it is not even clear that they parse behavior into different trials starting as they need to reach for a new target.

      Precisely, as the reviewer pointed out, this offset is key to the coupling of t<sub>h</sub> and t<sub>r</sub>, and it actually constitutes an original extension to previous `cost of time` models. Including t<sub>r</sub> in the offset o<sub>h</sub> allows the separate-cost model to co-regulation to some extent, without leading to double-counting the reach cost. It must be noted that G<sub>r</sub> and G<sub>h</sub> are distinct time cost functions, and the value of G<sub>h</sub> at t<sub>r</sub> can be different from that of G<sub>h</sub> at </>t<sub>r</sub>. The offset allows reach duration to influence harvest duration because it shifts its cost of time. Recent evidence show that subjects rely only to a limited extent on their past experience when assessing the quality of their environment (Sukumar et al., 2024). Hence, our hypothesis is that only recent delays could influence the cost of time, in the form of an offset that creates a bias affecting the vigor. For this reason, we chose to define it as the cumulative time elapsed from the start of each trial. Due to the cyclic nature, the models are then applied to the average behavior within a condition, allowing us to reduce variability. We plan to make the rationale clearer in the revised manuscript.

      ii) Why is there a free parameter d for the cost of harvest effort? If time and motor costs are converted into the same currency, and that conversion holds for all types of phases, then I believe we should have d=1. This free parameter seems to have been introduced a posteriori to accommodate the lack of effect of harvest effort on harvest time, but is not motivated.

      The number of free weighting parameters is defined by the minimization of a linear combination of cost functions. The reaching phase comprises only two costs in this model, leading to the possibility to have only one free weighting parameter that capture the relative sensitivity to time and effort. However, regarding the harvest phase, as there are three different costs, it was necessary to have at least two free weighting parameters to account for relative sensitivity and convert all costs in the same currency. Alternatively, we could indeed have applied a free parameter to the reward and set d = 1. However, the purpose of the study was to assess the contribution of time and effort changes, and ultimately participants’ sensitivity to changes in these factors. As such, it appeared appropriate to evaluate the sensitivity of the harvest effort captured by the models, rather than the reward (which is not manipulated). It was also to keep some consistency with the common utility model, which includes a free weighting parameter for the costs of effort and time (and none for the reward function) as well. We will add more details regarding the motivation behind the separate-cost model and the use/choice of these free parameters. We agree that the rationale for this parameterization was not sufficiently described in the manuscript and will clarify it in the revised version. We will also examine alternative constraints on the free parameters.

      b) The two models differ in two fundamental aspects: whether optimization is run separately on the different phases of the trial, or jointly; and how the different costs and rewards are assembled in the to-be-optimized metric. Which aspect is more important to capture behavior is not assessed.

      We thank the reviewer for raising this interesting point. It is difficult to determine which aspect is most important: the separate versus joint optimization, or how the costs are assembled (additive vs multiplicative). However, the two are closely linked. It is the switch from a multiplicative association of the cost (common-utility model) to an additive one (separate-cost) that makes the separate optimization possible. For example, it is not possible to achieve an optimal movement duration using the costs interaction from the common-utility model, since there would simply be a discounting of the cost of effort by the cost of time . It would be necessary to add another element justifying why the movement should not be infinitely slow, which would therefore require changing the costs or adapting them to capture a logical behavior. This is what led to the design of the separate-cost model, by switching to an additive model allowing an optimal duration to emerge for each phase. We will further examine this issue in the revision, in particular by testing whether the separate-cost model can also account for observed behaviors under a joint optimization of reaching and harvesting durations.

      c) The presentation of the "Separate-cost model" is very obscure. For example, the notion of a `cost of reward` is rather odd, and consequently the formula for R(t_h) appears arbitrary. I understand that this is the difference between the maximum reward that can be collected within a trial and the actual reward that is collected, but it is not even presented as such. The effort cost of harvesting seems to be discarded in the model presentation but does appear in equation 14.

      The terminology `cost of reward` is admittedly odd but is used because it is the counterpart of the harvest function used in the common-utility f<sub>h</sub>(t<sub>h</sub>). Instead of using it in a form subject to maximization, we use it in a form subject to minimization. The purpose of this function is to maintain a certain consistency between the models and the various types of costs implied; as such it was crucial to incorporate a function capturing the harvested reward in the separate-cost model. We will detail further this point in the revised version, as we agree that our previous explanation was insufficient. The effort cost of harvesting was not presented again in the separate-cost model section since it was already identified (as was the effort cost of reaching) in the common-utility section and referenced to the corresponding equation.

      (3) Methodological Weaknesses

      I believe that there are numerous methodological weaknesses that really limit the inferences that can be drawn from the experimental and modelling results.

      a) Sample sizes are small, especially for control experiments, and it is not clear how they have been selected. This is especially problematic because the only manipulation that shows no significant effect on behavior (harvest duration) is only tested with the smallest sample size (n=10), severely limiting the significance of this negative result.

      We acknowledge that some of the sample sizes are small. Importantly, the main goal of the study was to determine the effect of time and effort manipulations on the behavioral vigor. The first main experiment had already provided us with lot of insights to reach this objective, but to go further and explore our subsequent hypotheses, we decided to examine the role of the delay position and another type of effort cost. The change in harvesting effort is not the only manipulation that showed no effect on behavior. In the main experiment, we observed that changes in effort required for reaching does not affect the harvest duration, even though this is predicted by the common-utility model. In the same experiment, we also observed that changes in delays do not affect the reach duration, even though this is also predicted by common-utility model. Nevertheless, we will discuss the limit of the control experiments further in the revised version.

      b) The rationale for the different manipulations in different experiments is not clear. Notably, why were modulations of the harvest effort not included in the main experiment? Because not a single participant sees a modulation of both reach and harvest efforts, we cannot evaluate whether the model can capture both effects at the same time (i.e., with the same parameter values). The number of data points used for fitting directly depends on the number of conditions performed by the participant, and is very low compared to the number of parameters to be fitted. For example, the model used very different shapes for the costs of time (Figure 8) for different sets of conditions, but this cost of time should be fixed, so it's not clear whether a single cost of time function can accommodate all manipulations.

      We agree that the rationale for the different manipulations across experiments was not sufficiently explained. Traditionally, optimal foraging has not been studied in conjunction with movement vigor (Yoon et al., 2018), but a recent framework accounts for both decision-making and motor control (Shadmehr et al., 2016). In the main experiment, we chose to manipulate reaching effort because our primary aim was to test whether variations in the cost involved in motor control influence (i.e., co-regulate) the decision-making process. The manipulations of effort and time in the main experiment were a good basis for evaluating the common-utility model in a controlled environment. Showing that reaching effort would affect harvest duration would have already been great evidence to illustrate a form of co-regulation. Moreover, we had no certainty that the participants would exhibit stable behavior, and adding twice as many conditions risked introducing further biases related to fatigue and attentional focus. We decided to run control experiments upon seeing the results of this initial experiment, when other questions arose regarding the effect of the delay position and of the type of effort. We acknowledge that performing all manipulation in one large experiment would indeed have significantly increased the significance of the models’ predictions. Nonetheless, we made sure that the number of parameters fitted were taken into account by using the AIC to evaluate models’ performance. Regarding the assumption that the shape of the cost of time should be fixed: adding a waiting period changes (slightly) the task, and it is conceivable that a different task should be represented by a different cost of time. Reaching and harvesting are too distinct tasks, and the subjective cost of elapsed time may therefore differ depending on whether participants are moving or remaining stationary. Different cost of times have been proposed for both arm-reaching movements and saccades (Shadmehr et al., 2010b; Berret et al., 2018), in particular because these movements occur over very different timescales. This suggests that how the passage of time is valued may depend on the task at hand. Altogether, this led us to consider letting the cost of time have different shapes for each phase and each experiment (although fixed within the sigmoidal family of function). We will clarify the rationale for the different manipulations and the use of different cost of time for each phase in the revised manuscript.

      c) Best practice in behavioral modelling is to model single-subject data, as fitting the model jointly to all subjects completely obliterates subject heterogeneity. Fitting on single-subject data would also allow investigating whether the models reproduce the consistency of harvest durations and reach durations across conditions, and most importantly the absence of correlation across subjects between reach and harvest duration. At the moment, we're left wondering what the implications of these analyses for the original question are.

      We agree that fitting these models to the individual behavior of the participants would be interesting to assess the models’ ability to capture participants’ variability. We will explore this during the revision.

      d) What is the rationale for comparing reach duration in one condition to harvest duration in another condition?

      If behavioral vigor (either of movement or decision) is a trait-like characteristic supported by shared processes, some degree of consistency between movement and decision vigor is expected. For instance, participants who tend to move faster might also tend to decide faster, if their vigor relates to traits like impulsivity for instance (Choi et al., 2014; Berret et al., 2018; Labaune et al., 2020). As such, we decided to perform all correlations to provide a comprehensive overview of inter-individual consistency of vigor in a foraging-like task.

      e) The study does not take into account the likely impact of delays on muscular fatigue and perceived effort: the effort associated with maintaining the cursor on target during harvest might be perceived as less effortful after a longer break, partially explaining the impact of delay on harvest duration.

      The idea that delays may influence muscular fatigue and perceived effort is intriguing. However, we believe that if perceived effort was the reason leading to changes in harvest duration, changes would also have been elicited by manipulation of harvest effort. This point will be discussed further during the revision.

      f) I am confused by the reference to indirect effects in the separate-cost model (L692-7, Table 2). To fit this model, was the experimental value or theoretical value of the reach duration t_d used to determine the optimal harvest value (from Equation 14)? In both cases, the reach duration DOES increase with reach effort, so we should expect an impact on the harvest duration. This is indeed seen in Figure 7A. So why does Table 2 suggest that reach effort should not impact harvest duration in this model?

      We agree that this point was not sufficiently clear. The purpose of Table 2 was to present the specific effect of each modulation. Thus, the prediction that reach effort does not affect harvest duration in the separate-cost model refers specifically to a change in reach effort at a fixed reach duration. An example with our experimental design would be to display a virtual movement of fixed duration during which participants have to produce a torque (isometric contraction) superior to a certain threshold that could vary between conditions. The inter-dependence of reach and harvest are based on duration changes; manipulation of the magnitude of effort exerted during movement at fixed duration would therefore not affect harvest duration. Table 2 was intended to distinguish these direct and indirect effects rather than to predict that reach effort should have no effect on harvest duration in our experiment. To fit the model to the data, the experimental value of the reach duration was used. However, since Table 2 shows only the directions of the variations, an arbitrary fixed value for reach duration (of the same magnitude) was used to determine the direct effect represented. We will clarify this distinction and the purpose of Table 2 in the revised manuscript.

      g) Could the effect of pre-reach delay on reach time be alternatively explained simply by longer reaction times as reported in Figure S2, e.g., a longer period to reactivate the muscles after a longer break? In other words, as far as I understood, here reaction times are subsumed in the motor time, against the tradition of splitting them into two independent (non-overlapping) behavioral metrics.

      This is an interesting point raised by the reviewer and will be discussed further in the revision. Here, we assume that a shorter reaction times are associated with a less steep cost-of-time function. According to drift-diffusion models, it brings the decision-related neural activity more slowly toward the commitment threshold, thereby delaying movement initiation. Since the cost of time is less steep, the movement duration will also be increased, thus implying this co-regulation between movement and reaction time.

      Here, reaction time was not separated, since the protocol is not appropriately designed to its study, particularly in the main experiment. The detection of reaction time is highly conservative (see text of Time-Related supplementary analyses) and it is not entirely clear whether we can properly refer to a reaction time in the main experiment, since the two phases follow one another without any delay. Hence to avoid any uncertainty, we chose not to separate these presumed reaction times from motor times.

      h) The EMG-time integral in the harvest phase should be largely proportional to the harvest duration, given that the applied torque is constrained to remain on target. Is this the case? If so, then how to interpret the significant effect of reach effort on this value, given that the main analyses did not identify an effect of reach effort on harvest duration?

      We thank the reviewer for pointing this interesting effect. It is possible that a greater muscle activation during the reaching phase was carried over to the harvest phase (due to increased co-contraction for instance), leading to an increase in the EMG-time integral in the harvest despite manipulation of reach effort. We will explore this during the revision. In particular, we will examine how the EMG signals recorded during the reaching relate to those recorded during the harvesting, as well as the relationship between the EMG signals and the torque exerted by the participants.

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    1. eLife Assessment

      This study presents an important study into the molecular function of AT-HOOK MOTIF NUCLEAR LOCALIZED 15 (AHL15), a member of the AHL protein family, identifying it as a potential regulator of three-dimensional gene-loop organization within transcribed gene bodies. The authors support this claim with strong genome-wide evidence, integrating AHL15 binding profiles with transcriptional and chromatin accessibility changes, as well as demonstrating overlap with genes known to form loops across transcribed regions. The evidence supporting the claims of the authors is convincing. Collectively, these findings will be of broad interest to biologists seeking to understand the regulatory mechanisms underlying gene expression.

    2. Reviewer #1 (Public review):

      The study by Luden et al. seeks to elucidate the molecular functions of AHL15, a member of the AT-HOOK MOTIF NUCLEAR LOCALIZED (AHL) protein family, whose overexpression has been shown to extend plant longevity in Arabidopsis. To address this question, the authors conducted genome-wide ChIP-sequencing analyses to identify AHL15 binding sites. They further integrated these data with RNA-sequencing and ATAC-sequencing analyses to compare directly bound AHL15 targets with genes exhibiting altered expression and chromatin accessibility upon ectopic AHL15 overexpression.

      The analyses indicate that AHL15 preferentially associates with regions near transcription start sites (TSS) and transcription end sites (TES). Notably, no clear consensus DNA-binding motif was identified, suggesting that AHL15 binding may be mediated through interactions with other regulatory factors rather than through direct sequence recognition. The authors further show that AHL15 predominantly represses its direct target genes; however, this repression appears to be largely independent of detectable changes in chromatin accessibility.

      In addition to the AHL protein family, the globular H1 domain-containing high-mobility group A (GH1-HMGA) protein family also harbors AT-hook DNA-binding domains. Recent studies have shown that GH1-HMGA proteins repress FLC, a key regulator of flowering time, by interfering with gene-loop formation. The observed enrichment of AHL15 at both TSS and TES regions, therefore, raises the intriguing possibility that AHL15 may also participate in regulating gene-loop architecture. Consistent with this idea, the authors report that several direct AHL15 target genes are known to form gene loops.

      Overall, the conclusions of this study are well supported by the presented data and provide new mechanistic insights into how AHL family proteins may regulate gene expression.

    3. Reviewer #2 (Public review):

      Summary:

      The manuscript by Luden et al. investigates the molecular function and DNA-binding modes of AHL15, a transcription factor with pleiotropic effects on plant development. The results contribute to our understanding of AHL15 function in development specifically and transcriptional regulation in plants more broadly.

      Strengths:

      The authors developed a set of genetic tools for high-resolution profiling of AHL15 DNA binding and provide exploratory analyses of chromatin accessibility changes upon AHL15 overexpression. The generated data (CHiP-Seq, ATAC-Seq and RNA-Seq is a valuable resource for further studies. The data suggest that AHL15 does not operate as a pioneer TF, but is likely involved in gene looping.

      Weaknesses:

      The authors have extended the motif analysis to the top 1,000 shared peaks, addressing part of my previous concern. It did not erase my worries about overclaiming completely, but I think it can be considered as a terminological issue, rather than technical one. Therefore, it could be addressed through minor revision, without further analyses.

      Specifically, the absence of a predominant enriched motif does not establish that AHL15 binds non-specifically or lacks sequence preferences. Low motif prevalence limits the proportion of peaks it could explain but does not exclude a genuine binding preference; a binding motif need not be rare in the genomic background (binding can be supported by other factors). Please revise the interpretation in lines 188-194, and the conclusion in lines 204-206 to state that the present analysis did not identify a strongly enriched motif accounting for a substantial proportion of AHL15-associated regions. Any equivalent claims of non-specific binding elsewhere in the manuscript should be toned down.

      Additional minor points:

      (1) Please provide the exact HOMER background-selection procedure, and definition of the 50-bp search windows.

      (2) Figures 2B-C and the related supplementary figures, add the x axis label.

    4. Reviewer #3 (Public review):

      Summary:

      This study investigated the role of AHL15 in regulation of gene expression using AHL15 overexpression lines. Their results do show that more gene are downregulated when AHL15 is upregulated and its binding is not affecting the chromatin accessibility. Further, they investigated AHL15 binds in regions depleted in histone modifications and other epigenetic signatures. Subsequently, they investigated the presence of AHL15 in the gene chromatin loops. They found overlaps with both upregulated and downregulated genes. The methods are appropriately described, but could be improved to include the analysis of self-looping gene boundaries.

      Strengths:

      Their study clearly showed lack of any specific sequence enrichment in the AHL15 binding sites, other than these being AT-rich, suggesting that AHL proteins do not recognize a specific DNA sequence but are recruited to their AT-rich target sites in another way. The study does suggest significant enrichment of AHL15 binding sites at TSS and TES, and AHL15 sites are depleted of any histone marks. They also identified that AHL15 binding sites overlap with self-looping gene boundaries. The authors have addressed the comments raised in the revised manuscript.

      Comments on revised version.

      The authors have addressed the comments raised, in the revised manuscript.

    5. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The study by Luden et al. seeks to elucidate the molecular functions of AHL15, a member of the AT-HOOK MOTIF NUCLEAR LOCALIZED (AHL) protein family, whose overexpression has been shown to extend plant longevity in Arabidopsis. To address this question, the authors conducted genome-wide ChIP-sequencing analyses to identify AHL15 binding sites. They further integrated these data with RNA-sequencing and ATAC-sequencing analyses to compare directly bound AHL15 targets with genes exhibiting altered expression and chromatin accessibility upon ectopic AHL15 overexpression.

      The analyses indicate that AHL15 preferentially associates with regions near transcription start sites (TSS) and transcription end sites (TES). Notably, no clear consensus DNA-binding motif was identified, suggesting that AHL15 binding may be mediated through interactions with other regulatory factors rather than through direct sequence recognition. The authors further show that AHL15 predominantly represses its direct target genes; however, this repression appears to be largely independent of detectable changes in chromatin accessibility.

      In addition to the AHL protein family, the globular H1 domain-containing high-mobility group A (GH1-HMGA) protein family also harbors AT-hook DNA-binding domains. Recent studies have shown that GH1-HMGA proteins repress FLC, a key regulator of flowering time, by interfering with gene-loop formation. The observed enrichment of AHL15 at both TSS and TES regions, therefore, raises the intriguing possibility that AHL15 may also participate in regulating gene-loop architecture. Consistent with this idea, the authors report that several direct AHL15 target genes are known to form gene loops.

      Overall, the conclusions of this study are well supported by the presented data and provide new mechanistic insights into how AHL family proteins may regulate gene expression.

      However, it is important to note that the genome-wide analyses in this study rely predominantly on ectopic overexpression of AHL15 at developmental stages when the gene is not usually expressed. Moreover, loss-of-function phenotypes for AHL15 have not been reported, leaving unresolved whether AHL15 plays a physiological role in regulating plant longevity under native conditions. It therefore remains possible that longevity control is mediated by other AHL family members rather than by AHL15 itself. In this regard, the manuscript's title would benefit from more accurately reflecting this broader implication.

      The ahl15 loss-of-function phenotype has previously been described in Karami et al., 2020 (Nat. Plants), Rahimi et al., 2022a (New Phyt.), and Rahimi et al., 2022b (Curr. Biol.), showing that ahl15 loss-of-function among others results in accelerated vegetative phase change and flowering, a reduced number of leaves produced by axillary meristems in short day grown plants and reduced secondary growth in the inflorescence stem. The dominant-negative ahl15 delta-G allele, expressing a mutant protein lacking the conserved G motif in the PPC domain, shows these phenotypes more clearly in the heterozygous ahl15 +/- background, and is embryo lethal in the homozygous ahl15 background (Karami et al., 2021, Nature Comm.). In addition, we recently show that leaf senescence is significantly accelerated in the ahl15 loss-of-function mutant (Luden et al., 2025, BioRxiv). These results show that AHL15 is involved in several aspects of ageing in Arabidopsis, and we have adjusted the introduction to discuss these previous findings more explicitlyWe agree with reviewer 1 on the possibility that multiple AHLs could have an effect on longevity, which is partially supported by the delayed flowering time observed in the AHL20, AHL27, or AHL29 overexpression lines (Karami et al., 2020, Street et al., 2008). However, the induction of the AHL15-GR fusion alone by DEX shows a clear delay of developmental phase transitions and the aging process in general, indicating that AHL15 by itself is able to extend longevity as other AHLs are not affected by DEX treatment (proven by the fact that their expression is not significantly changed in our RNA-seq analysis of DEX-treated 35S:AHL15-GR seedlings).

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Luden et al. investigates the molecular function and DNA-binding modes of AHL15, a transcription factor with pleiotropic effects on plant development. The results contribute to our understanding of AHL15 function in development, specifically, and transcriptional regulation in plants, more broadly.

      Strengths:

      The authors developed a set of genetic tools for high-resolution profiling of AHL15 DNA binding and provided exploratory analyses of chromatin accessibility changes upon AHL15 overexpression. The generated data (CHiP-Seq, ATAC-Seq and RNA-Seq is a valuable resource for further studies. The data suggest that AHL15 does not operate as a pioneer TF, but is likely involved in gene looping.

      Weaknesses:

      While the overall message is conveyed clearly and convincingly, I see one major issue concerning motif discovery and interpretation. The authors state that because HOMER detected highly enriched motifs at frequencies below 1%, they conclude that "a true DNA binding motif would be present in a large portion of the AHL15 peaks (targets) and would be rare in other regions of the genome (background)."

      I agree that the frequency below 1% is unexpectedly low; however, this more likely reflects problems in data preprocessing or motif discovery rather than intrinsic biological properties of the transcriptional factor that possesses a DNA-binding domain and is known to bind AT_rich motifs. As it is, Figure 2 cannot serve as a main figure in the manuscript: it rather suggests that the generated CHiP-Seq peakset is dominated by noise (or motif discovery was done improperly) than that AHL15 binds nonspecifically.

      Since key methodological details on the HOMER workflow are missing in the M&M section, it is not possible to determine what went wrong. Looking at other results, i.e. the reasonably structured peak distribution around TSS/TTS and consistent overlap of the peaks between the replicas, I assume that the motif discovery step was done improperly.

      Therefore, I recommend redoing the motif analysis, for example, by restricting the search to the top-ranked peaks (e.g. TOP1000) and by using an appropriate background set (HOMER can generate good backgrounds, but it was not documented in the manuscript how the authors did it). If HOMER remains unsuccessful, the authors should consider complementary methods such as STREME or MEME, similar to the approach used for GH1-HMGA (https://pmc.ncbi.nlm.nih.gov/). If the peakset is of good quality, I would expect the analysis to identify an AT-rich motif with a frequency substantially higher than 1%-more likely in the range of at least 30%. If such a motif is detected, it should be reported clearly, ideally with positional enrichment information relative to TSS or TTS. It would also be informative to compare the recovered motif with known GH1-HMGA motifs.

      If de novo motif discovery remains inconclusive, the authors should, at a minimum, assess enrichment of known AHL binding motifs using available PWMs (e.g. from JASPAR). As it stands, the claim that "our ChIP-seq data show that AHL15 binds to AT-rich DNA throughout the Arabidopsis genome with limited sequence specificity (Figure 2A, Figure S2-S4)" is not convincingly supported.

      Another point concerns the authors' hypothesis regarding the role of AHL15 in gene looping. While I like this hypothesis and it is good to discuss it in the discussion section, the data presented are not sufficient to support the claim, stated in the abstract, that AHL15 "regulates 3D genome organization," as such a conclusion would require additional, dedicated experiments.

      The motifs discovered by HOMER are ranked by their enrichment over background, of which the highest-scoring motifs are very rare in the AHL15-bound targets, but even rarer in the background, which is why they score highly on the percent enrichment score. As expected by reviewer 2, we identified AT-rich motifs that were present in a larger percentage of AHL15 targets (found in 3-18% of targets, depending on the motif, see for example motif #5 in figure S4A), which can be seen at the right tail of the histograms shown in figures 2B-C and figures S2-S4 B-C. However, these motifs were also common in the background and were therefore not considered as significantly enriched in the AHL15-bound regions, with a target:background ratio of <2. As most of these motifs were flagged by HOMER as possible false-positives, and to limit the size of the (supplemental) figures, we did not show each of the motifs identified by HOMER in table form, but the full tables of de novo motifs identified by HOMER, including possible false-positive results are included in Additional file 3.

      Although the identification of AT-rich motifs shows that AHL15 (and very likely most other AHL proteins as well) binds AT-rich regions, it does not sufficiently explain the binding of AHL15 to its target genes, as these motifs are found at almost equal frequencies in non-AHL15-bound regions. In addition, a sequence found at this frequency in the genomic background is, in our view, too unspecific to be considered as a transcription factor binding site. Based on this, we concluded that AHL15 lacks a specific binding motif that can define the genes it binds.

      We have updated the methods section to include more details on the HOMER analysis and have also run the analysis in the top1000 shared peaks as suggested by reviewer 2 for both AHL15 and AHL29 ChIP-seq peaks, which showed that unlike in AHL29, a clear AT-rich motif cannot be found for AHL15 (Additional file 1: Figure S5).

      Reviewer #3 (Public review):

      Summary:

      This study investigated the role of AHL15 in the regulation of gene expression using AHL15 overexpression lines. Their results do show that more genes are downregulated when AHL15 is upregulated, and its binding does not affect the chromatin accessibility. Further, they investigated AHL15 binds in regions depleted in histone modifications and other epigenetic signatures. Subsequently, they investigated the presence of AHL15 in the gene chromatin loops. They found overlaps with both upregulated and downregulated genes. The methods are appropriately described, but could be improved to include the analysis of self-looping gene boundaries.

      Strengths:

      Their study clearly showed a lack of any specific sequence enrichment in the AHL15 binding sites, other than these being AT-rich, suggesting that AHL proteins do not recognize a specific DNA sequence but are recruited to their AT-rich target sites in another way. The study does suggest significant enrichment of AHL15 binding sites at TSS and TES, and AHL15 sites are depleted of any histone marks. They also identified that AHL15 binding sites overlap with self-looping gene boundaries.

      Weaknesses:

      The claim that AHL15 acts as a repressor and genes regulated by it are downregulated needs to be investigated based on AHL15 binding sites, to show enrichment/ depletion of AHL15 binding sites in overexpressing genes and repressed genes. The authors should provide data to support plant longevity with AHL15 overexpression using the DEX-induced system to support the claims in the title. Calculation of the enrichment score of AHL15 peaks in the self-looping genes that are upregulated or downregulated, and discussion about the different effects of AHL15 binding on self-looping regions to regulate gene expression may be helpful to understand the significance of the study. Motif enrichment in upregulated and downregulated genes separately to identify binding sequence preferences may be useful. It is not clear how the overlap of AHL15 peaks with self-looping genes has been carried out.

      A metagenome plot of AHL15 binding around genes that are differentially expressed upon DEX treatment can be found in Figure 3F. This analysis shows that AHL15 binding near differentially expressed genes is more pronounced compared to all AHL15-bound genes, and that AHL15 binding near the TSS is especially enriched for upregulated genes.

      As also suggested by reviewer 2, we ran a motif enrichment analysis on the differentially expressed genes that are bound by AHL15 to see if any motifs are enriched compared to the background and overrepresented in the AHL15-bound genes. Again, this did not reveal an AT-rich motif nor a motif that was conserved between up- and downregulated AHL15-bound genes (Additional file 1: Figure S6).

      Plant longevity in 35S:AHL15-GR Arabidopsis plants treated with DEX has been reported previously.. DEX treatment extended vegetative development after flowering resulting in polycarpy (Karami et al., 2020, Nature Plants), enhanced secondary growth resulting in woody stems (Rahimi et al., 2022, Current Biol.) and recently we showed that it delays leaf senescence in Arabidopsis (Luden et al., 2025, bioRxiv). All these observations have now been incorporated in the results section where the p35S::AHL15-GR plants are first presented. In addition, we show that 35S:AHL15-GR plants treated a single time with DEX at 10 days after germination show a significantly delayed flowering time in figure 4C-D of this manuscript.

      The enrichment of AHL15 ChIP-seq peaks in self-looping genes will be analyzed as suggested and compared to a random set of genes as a control, and the methods section will be updated to clarify how the analyses on self-looping genes were carried out.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The authors need to correct Line 28 on page 10:

      By comparing the AHL15 ChIP-seq data with 1766 previously identified self-looping genes -> By comparing the AHL15 ChIP-seq data with 1792 previously identified self-looping genes (Liu et al., 2016)

      Note: 1792 genes with self-loops were first reported by Liu et al. (2016)

      Liu, C., Wang, C.M., Wang, G., Becker, C., Zaidem, M., and Weigel, D. (2016). Genome-wide analysis of chromatin packing in at single-gene resolution. Genome Res 26, 1057-1068.

      These numbers will be corrected in the text.

      Reviewer #2 (Recommendations for the authors):

      (1) The newly generated datasets have been deposited only as raw sequencing reads. For reusability and reproducibility, the authors should also provide processed data accompanied by detailed metadata.

      We will upload the processed data and corresponding metadata to GEO.

      (2) Figures 4B and 5E are of low quality; can they be improved?

      We have submitted the original high-quality images to the publisher, which should resolve the issue.

      (3) Supplementary Table 5 lacks description (columns do not have names).

      This has been fixed.

      Reviewer #3 (Recommendations for the authors):

      Suggestions:

      (1) Motif enrichment in upregulated and downregulated genes separately to identify binding sequence preferences may be useful.

      This analysis will be performed and included in the revised manuscript as Additional file 1: Figure S6.

      (2) Investigate AHL15 binding sites to show enrichment/ depletion of AHL15 binding sites in overexpressing genes than repressed genes.

      This analysis has been done, please see figure 3F.

      (3) Provide data to support plant longevity with AHL15 overexpression using the DEX-induced system to support the claims in the title.

      For the effect of AHL15-GR induction by DEX on vegetative phase change and flowering time, please see figure 4C-D and Rahimi et al., (2022, New Phytologist). For other phenotypic changes induced by DEX treatment of 35S:AHL15-GR plants, please see Karami et al. (2020; Nature Plants), Rahimi et al., (2022, Current Biology) and Luden et al., (2025; BioRxiv). Text has been added to the results section where the 35S:AHL15-GR line is first introduced to refer to these previous publications.

      (4) Calculation of the enrichment score of AHL15 peaks in the self-looping genes that are upregulated or downregulated, and discussion about the different effects of AHL15 binding on self-looping regions to regulate gene expression, may be helpful to understand the significance of the study.

      This analysis has been done and included in the revised manuscript as Additional file 1: Table S1.

      (5) Describe how the overlap of AHL15 peaks with self-looping genes has been carried out.

      The methods section has been updated with detailed information on this analysis.

    1. eLife Assessment

      This study provides important findings on the composition of glutamate receptor (GluR) subunits across diverse Drosophila neuromuscular junctions, spanning larval and adult stages and multiple muscle types. Most previous work has focused on larval body wall muscles, and this study demonstrates that this model does not capture the full diversity of GluR composition across development and tissues. The revised manuscript provides a convincing account of this diversity and will be of broad interest to researchers across neuroscience.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript by Sustar et al. takes a methodical approach to document the types of glutamate receptor subunits that reside in Drosophila muscles, examining developmental stages spanning from larvae to adults. Prior work thoroughly documented the subunits operating in Drosophila larval body wall muscles. Most subsequent research focused on the glutamate receptor heterotetramers found in the body wall, composed of GluRIIA/C/D/E or GluRIIB/C/D/E subunits, along with auxiliary subunits like isoforms of Neto.

      For the current work, the authors report that the larval muscle glutamate receptor composition is not universal for all Drosophila muscles. They examine the following muscle systems: larval body wall, adult abdomen, adult leg coxa, and adult indirect flight. They also briefly examine adult muscle structures associated with the proboscis, neck, and haltere. The authors find that the receptor subunits in the adult abdomen (mostly) match those in the larval body wall. This makes sense given that the adult abdominal muscles are derived from the larval body wall. Yet not much else matches the larval body wall. For example, all (or most) of the GluRII-type subunits are missing from the adult indirect flight muscles. Leg muscles have GluRII-type subunits, but they do not have all of them expressed prominently, and they are missing GluRIIB. Additionally, leg muscles express a glutamate-gated chloride channel, which could be a source of inhibitory glutamatergic transmission. Interestingly, when it comes to non-abdominal adult muscles, one general theme seems to be an active promoter (GAL4 driver) for the kainate-type glutamate receptor called Clumsy. The authors propose that Clumsy could be key to understanding how functional GluR complexes are assembled in adult insects.

      Strengths:

      (1) Documenting the types of glutamate receptors that operate in diverse insect muscle systems is important because it uncovers fundamental information.

      (2) Much of the prior research focus has been on how the body wall muscle tetramers assemble and operate. It is a strength to demonstrate the other receptor solutions used by adult NMJs.

      (3) The work uses GAL4 drivers and immunohistochemistry (when possible) in combination to draw conclusions.

      (4) The muscle anatomical analyses are high quality. This allows the research group to reach refined conclusions.

      (5) The confocal-level images of synaptic active zones and their apposed glutamate receptor clusters are high quality.

      (6) The dataset adds specific detail and complementary context to bulk transcript analysis collections like FlyAtlas.

      (7) The Discussion section is insightful. It is not simply a recap of the data in the paper. It is also an integration of that data with prior work, and it suggests direct experiments that the research team or others could attempt downstream.

      Weaknesses:

      One can draw expression-level conclusions from these data. But genetic tests (e.g., would clumsy losses of function impair leg muscles?) could help the authors and the field draw stronger conclusions about the roles of some of these glutamate receptor gene products.

      Overall Assessment and Discussion:

      The data in this study are high quality, and the results support the main conclusion: adult muscle glutamate receptor clusters do not recapitulate the "canonical" larval body wall clusters.

      Comments on revised version.

      The authors have done a serious revision of their manuscript, and they have directly addressed context/interpretation-level concerns that I had from the first round.

    3. Reviewer #3 (Public review):

      The Sustar et al. manuscript catalogs glutamate receptor composition across distinct Drosophila NMJs: larval and adult abdominal NMJs, as well as NMJs on adult leg and flight muscles. This work is important and probably overdue. The larval NMJ is the exemplar NMJ in this system, and the identity of "essential" and "alternative" subunits at this stage is assumed by many to hold across developmental stages and NMJ types. Here, the authors show that there is surprising diversification among NMJ types and that the notion of essential/alternative subunits only holds true at larval NMJs.

      The study will generate interest in the Clumsy GluR subunit, which has not been well-characterized at all, but is widely expressed at adult NMJs. They also find striking extrasynaptic expression of glutamate-gated chloride channel GluRClalpha in adult leg and flight muscles, raising questions about its role. The study is interesting, logical, and well-written. The figures are clear, and the discussion was particularly thoughtful.

      Comments on revised version.

      This is a nicely revised manuscript that went a long way toward addressing the concerns in the first round. The response to the reviewers is clear and thoughtful. I think the question is framed very well and the authors do a much better job throughout making clear where the heterologous approaches agree and differ. I also think the Limitations section in the Discussion does a great job putting the work in context.

    4. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment:

      This study provides important findings on the expression of glutamate receptor (GluR) subunits across developmental stages and muscle types in Drosophila. It shows that adult muscle differs in GluR composition from larval body wall muscles, which have been the focus of most past studies. The study, while convincing, could be strengthened by acknowledging that it relies on heterogeneous methods and the absence of positive signals to infer receptor loss, which limits confidence in some of its claims. The findings illuminate how Drosophila excites muscles in diverse tissue types at different life stages, and are of interest to researchers across neuroscience.

      A note on the eLife assessment

      We are grateful for the assessment and for the reviewers' engagement. The assessment notes that the study "could be strengthened by acknowledging that it relies on heterogeneous methods and the absence of positive signals to infer receptor loss." We have taken this seriously and have addressed it in three ways in the revised manuscript:

      (i) We have replaced claims of absence with "not detected" throughout;

      (ii) We have added a paragraph describing the discrepancies between methods and the specific false-negative risks of each; and

      (ii) We have made explicit the internal positive controls that constrain our negative results, including the reciprocal GluRIIB/GluRIIC labeling within a single femur and the detection of Brp, Neto-β, and GluClα in the same flight muscle preparations in which GluRII subunits were not detected.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The manuscript by Sustar et al. takes a methodical approach to document the types of glutamate receptor subunits that reside in Drosophila muscles, examining developmental stages spanning from larvae to adults. Prior work thoroughly documented the subunits operating in Drosophila larval body wall muscles. Most subsequent research focused on the glutamate receptor heterotetramers found in the body wall, composed of GluRIIA/C/D/E or GluRIIB/C/D/E subunits, along with auxiliary subunits like isoforms of Neto.

      For the current work, the authors report that the larval muscle glutamate receptor composition is not universal for all Drosophila muscles. They examine the following muscle systems: larval body wall, adult abdomen, adult leg coxa, and adult indirect flight. They also briefly examine adult muscle structures associated with the proboscis, neck, and haltere. The authors find that the receptor subunits in the adult abdomen (mostly) match those in the larval body wall. This makes sense given that the adult abdominal muscles are derived from the larval body wall. Yet not much else matches the larval body wall. For example, all (or most) of the GluRII-type subunits are missing from the adult indirect flight muscles. Leg muscles have GluRII-type subunits, but they do not have all of them expressed prominently, and they are missing GluRIIB. Additionally, leg muscles express a glutamate-gated chloride channel, which could be a source of inhibitory glutamatergic transmission. Interestingly, when it comes to non-abdominal adult muscles, one general theme seems to be an active promoter (GAL4 driver) for the kainate-type glutamate receptor called Clumsy. The authors propose that Clumsy could be key to understanding how functional GluR complexes are assembled in adult insects.

      Strengths:

      (1) Documenting the types of glutamate receptors that operate in diverse insect muscle systems is important because it uncovers fundamental information.

      (2) Much of the prior research focus has been on how the body wall muscle tetramers assemble and operate. It is a strength to demonstrate the other receptor solutions used by adult NMJs.

      (3) The work uses GAL4 drivers and immunohistochemistry (when possible) in combination to draw conclusions.

      (4) The muscle anatomical analyses are of high quality. This allows the research group to reach refined conclusions.

      (5) The confocal-level images of synaptic active zones and their apposed glutamate receptor clusters are of high quality.

      Weaknesses:

      (1) There is a strawman argument that is used repeatedly to highlight the significance of the work. The argument implies that the field broadly assumes (or “tacitly” assumes) that the larval body wall glutamate receptor composition extrapolates to all muscles of the fly, including the adult. This reviewer cannot find evidence that this assumption or argument has been explicitly promulgated by others. More likely, others have not examined these muscles directly, and thus, they have not speculated one way or the other.

      The reviewer is correct that we could not find a paper stating explicitly that "the adult NMJ is molecularly identical to the larval NMJ," and we have revised the text to avoid implying that such an explicit claim exists. However, we do argue that larval body-wall findings are routinely presented as properties of "the Drosophila NMJ," "the fly NMJ," or "Drosophila muscle". That unqualified generalization is what has led readers (ourselves included) to extrapolate the canonical larval architecture to the adult.

      Many concrete examples can be found in the literature. He and Dickman (2025, Curr Opin Neurobiol) state that "two subtypes of ionotropic glutamate receptors (GluRs) mediate postsynaptic currents in Drosophila muscle, GluRIIA- and GluRIIB-containing," and describe their regulation by Neto as a general feature of "the fly NMJ". The word "adult" does not appear in the review. Chou et al. (2020, Neural Development) similarly assert that "at the Drosophila NMJ, iGluRs are heterometric tetramers composed of three invariant subunits, GluRIII/GluRIIC, GluRIID, and GluRIIE, as well as one of either GluRIIA or GluRIIB". A highly cited review of these receptors is titled "Glutamate receptors at the Drosophila neuromuscular junction" (DiAntonio, 2006) yet describes only the larval body-wall system, and Harris and Littleton (2015, Genetics) introduce the larval NMJ as "a well-established model glutamatergic synapse" before discussing "the fly NMJ" throughout. Notably, we could not find a review paper about Drosophila neuromuscular control that discussed differences in receptor composition between larval and adult muscles.

      This assumption is related to the genesis of this project, which came about due to the failure of pharmacological tools used in the larva (e.g., philanthotoxin) to block synaptic transmission at adult fly muscles. We (the Tuthill Lab) contacted several principal investigators studying adult fly motor control and within the larval NMJ field (including now co-author Dion Dickman) and asked for advice about why these drugs were not effective in paralyzing leg muscles. Nobody suggested that this could be because adult muscles use different glutamate receptors than the larvae. Indeed, it was only years later when we noticed a high level of GluCl expression in the FlyCellAtlas data that we realized it could be due to differences in receptor expression.

      We agree with the reviewer that adult muscle has rarely been examined directly (Rivlin et al., 2004 being the principal exception, which we cite and now discuss at greater length). Our argument is that this gap has been filled by an untested extrapolation from larvae rather than by data. Of the ~50 papers citing Rivlin et al. (2004), we could not find any that compare glutamate‑receptor subunit expression between larval and adult muscle. Follow‑up work pursued NMJ morphology, remodeling, adhesion molecules, and transporters, leaving the receptor question open.

      Overall, we respectfully disagree with the reviewer that this argument is a strawman, because it reflects our lived experiences as active investigators of adult and larval fly motor control. However, we take the reviewer’s point. We have removed language implying that an explicit model was being overturned, and we now frame the assumption as implicit and not universally held. We note only that the assumption was operative in practice: this project began when pharmacological tools that reliably block transmission at the larval NMJ (e.g., philanthotoxin) failed to paralyze adult leg muscles, and when we consulted colleagues who work on the larval NMJ and adult fly motor control, differences in receptor composition were not among the explanations offered. We recognize this is an anecdote rather than evidence, and we have kept it out of the manuscript.

      (2) Related - to the extent that there has been any tacit assumption about GluRIIC/D/E-anchored receptors being ubiquitous among adult muscles, tacit doubt was raised by Rivilin et al., 2004 (cited by the authors but not as a source of doubt) and by RNAseq datasets like FlyAtlas from 2022 (replicated in Figures s11 and s12). To be clear, the current analysis is better than a bulk transcript analysis from adult tissues. But rather than “overturning” a field or being paradigm-shifting, the current data seem confirmatory of FlyAtlas - and confirmatory of Rivlin et al., 2004, which explicitly concluded that larval and adult NMJs were different

      Rivlin et al. did not examine GluRIIC, D and E, or most of the other glutamate receptor subunits. Nevertheless, we revised the beginning of our Discussion (pg 8) to position our work in the context of their important findings:

      In 2004, Rivlin and colleagues conducted a careful survey of NMJs in seven muscles in the adult Drosophila thorax. They established that adult NMJs are morphologically distinct from larval NMJs, and that GluRIIA and GluRIIB are expressed in muscle-specific subsets, with some muscles mysteriously lacking both. Importantly, they speculated that some adult muscles must be using different glutamate receptor subunits (which were unknown at the time) to build synapses. This problem has remained unexplored for the last two decades.

      As the reviewer requested, we have removed "overturning"/"paradigm-shifting" language throughout. But we respectfully disagree that our analysis is “confirmatory” of the Fly Cell Atlas. The Fly Cell Atlas (Li et al., 2022) does not mention glutamate receptors, or the NMJ, anywhere in its analysis. It is a general single-nucleus transcriptomic resource whose adult-muscle glutamate-receptor content was never extracted, curated, or interpreted. Extracting meaning from the dataset required posing the question, curating the relevant cells and genes, and analyzing them, which is itself a meaningful scientific contribution.

      (3) One can draw expression-level conclusions from these data. But genetic tests (e.g., would clumsy losses of function impair leg muscles?) could help the authors and the field draw stronger conclusions about the roles of some of these glutamate receptor gene products. The current dataset falls short of definitively establishing the function of alternate glutamate receptor modules.

      We intend to perform functional and behavioral experiments with clumsy loss-of-function flies. However, this work will require creation of new genetic reagents and behavioral experiments that make it beyond the scope of this manuscript. We intend to address this important topic in a future paper.

      (4) The confocal synaptic images are of high quality. They are good enough that one could analyze how well Brp directly apposes a specific glutamate receptor subunit for all the associated imaging data underlying Figures S1-S8. No such analysis is done, but understanding what components seem to directly oppose the site of release could lead to better conclusions.

      In all tissues where IIA-E are expressed at the NMJ, the protein localization is directly adjacent to Brp (see representative images in Figure 2B and s5-8). This localization is often clear in single z-slices. We attempted to perform more detailed analyses, like measure the distance between Brp and GluR puncta, but found it very challenging due to the 3D orientation of the NMJ structure. We added a sentence to page 4 of the manuscript describing what we see and the overall similarity of IIA-E localization.

      Overall Assessment and Discussion:

      The data in this study are of high quality, and the results support the main conclusion: adult muscle glutamate receptor clusters do not recapitulate the “canonical” larval body wall clusters. This is important, and the data stand on their own. That is the most important part. This reviewer does have suggestions on how to put the current work in proper context; the current draft appears to overstate the novelty of the findings. Additionally, some sentences need editing for accuracy. None of those concerns impeach the excellent foundational data.

      Reviewer #2 (Public review):

      Summary:

      This manuscript presents a broad survey of glutamate receptor composition at the neuromuscular junction in Drosophila across developmental stages and muscle types. The topic is clearly important, and the central observation-that adult muscles differ substantially from the canonical larval NMJ-is interesting and potentially impactful. The dataset is extensive and will likely be of value to the community. However, in my view, there are significant limitations in how the data are generated and interpreted, which at present reduce the strength of the conclusions.

      Strengths:

      The study addresses a relevant and timely question and provides a large and systematic dataset. The finding that adult muscles diverge from larval NMJ organization is compelling and challenges a widely held assumption in the field. The breadth of approaches, including genetic reporters, immunohistochemistry, endogenous tagging, and transcriptomic data, is, in principle, a strong aspect of the work and allows for a broad overview of receptor expression across tissues and developmental stages. Even in its current form, the manuscript provides useful descriptive information that will be of interest to the community.

      Weaknesses:

      A major concern is the reliance on a heterogeneous combination of detection methods (GAL4 reporters, antibody staining, endogenous tagging, and RNA), which are treated largely as equivalent lines of evidence. These approaches differ substantially in what they measure and in their sensitivity and specificity. While convergence across methods can in principle be convincing, here this convergence is often inferred from the shared absence of signal. This is problematic because all methods used are susceptible to false negatives for different reasons. As a result, the repeated conclusion that specific GluR subunits are “absent” from adult muscles, including those previously considered essential, is not fully justified by the data presented.

      This issue is not only theoretical. The manuscript itself seemingly contains examples where methods disagree, demonstrating that detection is incomplete and method-dependent. These discrepancies could be better integrated into the interpretation. Instead, negative results across methods are often taken as strong evidence for absence, which overstates the certainty of the findings.

      In addition, antibody validation appears to rely largely on prior work in larval tissue. Given the structural and biochemical differences in adult muscles, it is not clear that staining performance is equivalent, particularly in cases where the signal is weak or undetected. This further complicates the interpretation of negative results.

      More generally, the manuscript moves in several places from descriptive observations to functional or mechanistic implications that are not directly supported. The suggestion that adult muscles operate with fundamentally different receptor assemblies is intriguing, but remains speculative without functional validation. At a minimum, the distinction between observation and interpretation should be made more explicit.

      I thus think that the current conclusions need to be more carefully constrained. Ideally, the study would be strengthened by at least one functional experiment, such as electrophysiological recordings from adult NMJs or perturbation of candidate receptors like GluClα or Clumsy. This would help to anchor the expression data in synaptic function.

      We thank the reviewer for a careful and constructive critique. We agree that inference from the absence of signal requires caution, and we have revised both the wording and the framing of the manuscript substantially in response. We would also like to draw attention to a feature of our dataset that we did not previously make explicit, and which we believe speaks directly to the core concern: a number of our negative results are internally controlled by positive signal from the same reagent, in the same tissue, in the same preparation.

      (i) In the adult femur, anti-GluRIIB labels NMJs on tibia extensor fibers but not on accessory tibia flexor fibers within the same bisected femur, processed, stained, and imaged together (Figure 3C-D).

      (ii) Anti-GluRIIC shows the reciprocal pattern in the same preparations (Figure 3E-F). The failure to detect GluRIIC in the tibia extensor therefore cannot be attributed to a general failure of that antibody in adult tissue, to fixation, or to reagent penetration, because the same antibody labels NMJs tens of microns away in the same section.

      (iii) In indirect flight muscle, where we detected none of the five GluRII subunits, we reliably detected Brp, Neto-β::sfGFP, and GluClα:V5 at and around NMJs in the same preparations (Figures 2B, 2D, 4C). Reagent access, epitope preservation, and NMJ identification are therefore all demonstrated in the same tissue where our negative results are strongest.

      These controls do not exclude the possibility that receptors are present below our detection threshold, and we have accordingly replaced "absent" with "not detected" throughout the manuscript. They do, however, argue that the muscle-specific differences we report reflect genuine biological variation rather than method failure. We have added a paragraph to the Discussion making this argument explicitly, alongside a new paragraph describing the discrepancies between methods, and we have added a description of antibody validation to the Methods.

      In summary, this is an interesting and potentially important study, but the current manuscript somewhat overinterprets heterogeneous and partly indirect evidence. It will already be useful in its present form, but could be more convincing if the authors more rigorously account for methodological limitations and moderate their claims accordingly.

      Reviewer #3 (Public review):

      The Sustar et al. manuscript catalogs glutamate receptor composition across distinct Drosophila NMJs: larval and adult abdominal NMJs, as well as NMJs on adult leg and flight muscles. This work is important and probably overdue. The larval NMJ is the exemplar NMJ in this system, and the identity of “essential” and “alternative” subunits at this stage is assumed by many to hold across developmental stages and NMJ types. Here, the authors show that there is surprising diversification among NMJ types and that the notion of essential/alternative subunits only holds true at larval NMJs.

      The study will generate interest in the Clumsy GluR subunit, which has not been well-characterized at all, but is widely expressed at adult NMJs. They also find striking extrasynaptic expression of glutamate-gated chloride channel GluRClalpha in adult leg and flight muscles, raising questions about its role. The study is interesting, logical, and well-written. The figures are clear, and the discussion was particularly thoughtful. I have a couple of comments that the authors could consider.

      (1) They cite Rivlin et al., (2004) in the Introduction as the sole previous study to investigate the molecular composition of adult NMJs, but do not mention this work again. In the Discussion, it would be helpful to compare/contrast their finding with those of the earlier work.

      Thank you for the suggestion. We have revised the beginning of our Discussion (pg. 8) to position our work in the context of Rivlin et al.’s important findings.

      (2) Were these analyses done in adults of consistent ages? It seems possible that the GluR subunit composition could be different in very young adults or in aged flies. The age of the animals should be mentioned in the Methods.

      Thank you for noticing this gap. We added age of flies to the Methods section, including the important detail that in cases where we failed to detect any GAL4 signal (e.g., GluRIID-GAL4), we additionally imaged very young and very old flies (0-2 hours old and 20-day-old). It did not change the results.

      (3) The broad expression of GluCl:V5 in adult leg and flight muscles is surprisingly robust and appears to light up the edges of all muscle fibers. Would the authors comment on the controls that were done to ensure that this staining is real and specific to animals carrying that V5 endogenous tag?

      We added a sentence to the results section (pg 8) to highlight the V5 negative staining controls that are shown in Figure s15.

      (4) The snRNAseq data in Figure S12 differ a bit from the IHC/GAL4 data summarized in the table in Figure 2. In particular, the data suggests that Ukar and Grik are widely expressed in adult muscles. Is there a reason not to include an “snRNA seq” column in Figure 2 alongside the data from GAL4 lines and IHC? To my mind, it is about as reliable as GAL4 lines that often capture only a subset of the full expression pattern. In this case, the snRNAseq data suggest that Ukar/Grik are likely at adult flight muscle NMJs, which might be important since NMJ was negative for everything except Neto-beta by IHC.

      We liked your suggestion to put these data together in one figure, and we tried to incorporate this during our revisions. However, because of the difficulty of combining multiple types of data into one table, we ultimately decided to keep them separate to avoid any confusion. The GAL4 analysis is qualitative (no expression, weak expression, strong expression) in percent of muscles. The RNA-seq data is quantitative and measured in fraction of muscle cells. Figure s13 now shows RNA-seq results in a table format that is parallel to the table with GAL4 results in Figure 2.

      We also added a paragraph to the end of the discussion to mull several discrepancies in results (e.g. Grik and Ukar) when using different methods.

      We did observe some discrepancies across the different experimental approaches. For GAL4 reporter lines, we used T2A gene trap lines where possible (for 15 of the 16 GluR genes) to determine receptor expression. However, some of these reporters were extremely weak and may have resulted in false negatives. For example, we did not detect Grik-GAL4 or Ukar-GAL4 expression in any tissue (Figure 2C), despite RNA-seq evidence that these are expressed robustly in adult muscles (Figures s11, s12). We do not have antibodies or protein reporters for these genes to help resolve this. Another discrepancy was with GluRIIE. In this case, antibody staining and RNA-seq did not detect GluRIIE in flight muscles (Figures s8, s11), while the GAL4>GFP reporter showed extremely faint signal in the flight muscles. Because of the marginal signal and the discrepancy with the other methods, we believe this could likely be a false positive. The table in Figure 2 does not effectively capture the nuance of this otherwise confusing result. Overall, however, we found that results from different approaches generally agreed and revealed intriguing differences between different muscle types.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Most of these recommendations are for text edits. Where experiments are noted (#8 and #9 below), they could be helpful, but they are not essential because the study has value as is.

      (1) It would be appropriate to scrub the entire manuscript of statements that say that the work is overturning an incorrect model about larval GluRII receptor tetramers working the same way in adult muscles. First, it is not necessary. But more importantly, by this reviewer’s reading, there is not an explicit model promoted by others that the larval body wall findings extrapolate to other muscles. The better way to frame the work is to state that it clearly shows the field has a lot of work to do to properly characterize glutamate receptor composition and function in Drosophila adults. Places in the text that the authors should edit include:

      - “...challenging assumptions about the uniformity of neuromuscular function” (Abstract)

      We deleted this clause.

      - “...it is often tacitly assumed...This extrapolation overlooks...during metamorphosis.” (Introduction)

      New wording: … “…it has commonly been assumed…” (Abstract) and “…we and others assumed…” (Intro)

      - “(With one notable exception, Rivlin et al., 2004)” (Introduction). This reference deserves more than an obscure parenthetical because it explicitly concluded that larval and adult NMJs differ.

      As described above, we revised the beginning of our Discussion (pg 8) to position our work in the context of Rivlin et al.’s important findings.

      In 2004, Rivlin and colleagues conducted a careful survey of NMJs in seven muscles in the adult Drosophila thorax. They established that adult NMJs are morphologically distinct from larval NMJs, and that GluRIIA and GluRIIB are expressed in muscle-specific subsets, with some muscles mysteriously lacking both. Importantly, they speculated that some adult muscles must be using different glutamate receptor subunits (which were unknown at the time) to build synapses. This problem has remained unexplored for the last two decades.

      - “Collectively, these results overturn the assumption of NMJ uniformity...” (Introduction)

      New wording: Collectively, these results reveal a diversity in NMJ composition”

      - “Our findings challenge the assumption...” (Conclusions)

      We deleted this clause.

      (2) (Related) The authors quibble in a couple of places that the GluRIIC/D/E subunits have been “previously considered essential for viability and NMJ function.” This reviewer’s understanding of those subunits has always been that they are essential for life, and indeed, they are essential because null mutants arrest as embryos. The places where the text seems to combine viability and NMJ function (including the Abstract) as incorrectly assigned roles for these subunits, should be more carefully edited. This reviewer agrees with the “NMJ function” conclusion since the authors are considering so many NMJs. Viability still holds.

      We agree with the reviewer, and we thank them for catching an imprecision that recurred throughout the manuscript. GluRIIC, GluRIID, and GluRIIE are essential for viability, and nothing in our data contradicts that conclusion.

      We think the point we were trying to make is narrower and sharper when stated precisely. Null mutants for these subunits arrest as embryos — that is, they die before the adult leg and flight muscles are formed. Those experiments therefore established a requirement for viability and for larval NMJ function, but they could not, in principle, have tested whether the same subunits are required at NMJs that arise only during pupal development. Our data do not revise the viability conclusion. They show that the subunit requirement established at the larval body-wall NMJ does not generalize to every NMJ in the animal.

      We have decoupled "viability" from "NMJ function" throughout the manuscript (Abstract, Introduction, Discussion) and have added a sentence to the Discussion making the developmental logic explicit:

      Importantly, because these mutants die before metamorphosis, these experiments established a requirement for viability and for larval NMJ function, but could not test whether the same subunits are required at adult NMJs, which are formed during pupal development.

      (3) “...the physiology and molecular architecture of adult fly NMJs remain largely unexplored...” The authors cite the Rivilin paper that examined the prothorax. But physiology has certainly been examined at DLMs as well as proboscis NMJs.

      As the manuscript says on page 2, the topic is largely unexplored compared to the larva. The Rivlin paper is an excellent contribution but was conducted over 20 years ago with more limited genetic reagents. There were also no follow-up studies. In the same amount of time, over 1000 papers have been published regarding the Drosophila larval NMJ.

      The reviewer is correct that physiology has been performed in adult DLMs and proboscis muscles. These studies, however, have focused on other biological questions and did not investigate the makeup of the NMJ or manipulate glutamate receptors with pharmacology or genetic tools. Our study reveals the need for such experiments.

      (4) “Surprisingly, we found that none of the primary GluRII subunits was strongly expressed in adult indirect flight muscles...” This sentence was difficult to parse when simultaneously examining the large dot for GluRIIE-Gal4 in the IFMs (Figure 2C), until the realization that the blank IHC result was driving the conclusion (no expression). The authors may want to delineate Gal4 vs. IHC for each dataset to avoid confusion for readers.

      We agree with the reviewer that this result is confusing. GluRIIE-GAL4 expression was a case in which a dot in the table didn’t capture the subtlety of the result. The GluRIIE-GAL4 was expressed very faintly in the IFM, to a degree that we almost called it negative.

      We added a paragraph to the Discussion in which we reflect on some data discrepancies:

      We did observe some discrepancies across the different experimental approaches. For GAL4 reporter lines, we used T2A gene trap lines where possible (for 15 of the 16 GluR genes) to determine receptor expression. However, some of these reporters were extremely weak and may have resulted in false negatives. For example, we did not detect Grik-GAL4 or Ukar-GAL4 expression in any tissue (Figure 2C), despite RNA-seq evidence that these are expressed robustly in adult muscles (Figures s11, s12). We do not have antibodies or protein reporters for these genes to help resolve this. Another discrepancy was with GluRIIE. In this case, antibody staining and RNA-seq did not detect GluRIIE in flight muscles (Figures s8, s11), while the GAL4>GFP reporter showed extremely faint signal in the flight muscles. Because of the marginal signal and the discrepancy with the other methods, we believe this could likely be a false positive. The table in Figure 2 does not effectively capture the nuance of this otherwise confusing result. Overall, however, we found that results from different approaches generally agreed and revealed intriguing differences between different muscle types.

      (5) “GluClalpha in adult muscles establishes the molecular identity of inhibitory glutamate responses described decades ago in other insects...” This reviewer really likes this idea. But the current data fall far short of the conclusion in this sentence.

      You are correct. We tempered this statement to “The identification of extrasynaptic GluClα in adult muscles establishes a possible mechanism for inhibitory glutamate responses described decades ago […]”

      (6) “One past study (Han et al., 2015) explored many combinations....” Nothing in this sentence is untrue. This reviewer just wanted to point out that one interesting observation from the Han paper was that an A/E combination alone yielded significant function in the heterologous system. To the extent that A and B have been described as competitive for the same C/D/E core, it is not a stretch to imagine that a B/E combination would have significant function, just like A/E. Or B/D/E as well.

      We thank the reviewer for this observation, which we think materially strengthens the plausibility of our interpretation, and we have incorporated it into the Discussion. Han et al. (2015) found that GluRIIA and GluRIIE together were sufficient to reconstitute functional receptors in a heterologous system, without GluRIIC or GluRIID. Because GluRIIA and GluRIIB are thought to compete for the same position within the tetramer, there is no obvious reason a GluRIIB/IIE or GluRIIB/IID/IIE complex should not also be functional. This provides a concrete precedent for exactly the kind of non-canonical combinations our expression data imply — for example, GluRIIB in the absence of detectable GluRIIC in tibia extensor fibers. We have added the following to the Discussion (p. 9):

      Notably, Han et al. (2015) found that GluRIIA and GluRIIE alone were sufficient to reconstitute functional receptors, without GluRIIC or GluRIID. Because GluRIIA and GluRIIB are thought to occupy the same position within the tetramer, this raises the possibility that GluRIIB-containing complexes lacking GluRIIC — such as the GluRIIB/IID/IIE combination implied by our data in the tibia extensor — could likewise be functional. Neto-β, which is required for receptor gating and synaptic clustering (Han et al., 2024; Kim et al., 2012), is expressed at all adult NMJs we examined (Figures 2D, 3B), so the auxiliary subunit requirement for such complexes would be satisfied. Direct tests of these subunit combinations by heterologous reconstitution are an important next step.

      (7) GluClalpha roles: The idea of a negative feedback controller is a good one. An alternative idea can be gleaned from the NMJ anatomy of circuits like the Mauthner Cell Escape Circuit in zebrafish. In that circuit, the fish have excitatory connections on some muscles and simultaneous control of inhibitory connections wired for other muscles to enable fast changes of direction.

      We thank the reviewer for this interesting comparison. We think the Mauthner arrangement is unlikely to apply here, for two reasons. First, GluClα appears to be expressed broadly across the leg and flight muscles we examined, rather than in a subset complementary to the excitatory receptors. Second, and more importantly, GluClα is extrasynaptic: it is distributed along the periphery of the muscle fiber and is not apposed to Brp-positive active zones (Figure 4C-D). Target-specific inhibitory transmission of the kind used in the Mauthner circuit requires inhibitory receptors positioned at synapses, whereas the localization we observe is better suited to tonic or slowly varying modulation of muscle excitability. We have added a sentence to the Discussion making this distinction, since the reviewer's question is one that readers will also have. And add to the manuscript, at the end of the GluClα hypothesis paragraph in the Discussion:

      We note that this arrangement differs from circuits in which excitatory and inhibitory transmission are targeted to different muscles to produce rapid directional control, as in the Mauthner cell escape circuit of larval zebrafish. While most arthropods possess GABAergic motor neurons that directly inhibit leg muscles, holometabolous insects such as Drosophila do not (Witten and Truman, 1998). And because GluClα is extrasynaptic rather than apposed to active zones, it is unlikely to support fast, target-specific inhibitory transmission.

      Two optional experimental suggestions:

      (8) The authors’ findings with Clumsy expression along with Clumsy message expression in the FlyAtlas RNAseq datasets - and finally, the unpublished work on Clumsy - all collectively suggest that it is a great target for adult knockdown. It is not required, but the authors’ conclusions would be bolstered if they were to test RNAi against Clumsy in chosen adult NMJs and check behavior (e.g., walking?) for defects.

      We intend to perform functional and behavioral experiments with clumsy loss-of-function flies. However, this work will require creation of new genetic reagents and behavioral experiments that make it beyond the scope of this manuscript. We intend to address this in a future paper.

      (9) The confocal synaptic images are of high quality. They are good enough that one could analyze how well Brp directly apposes a specific glutamate receptor subunit for all the associated imaging data underlying Figures S1-S8. No such analysis is done, but understanding what components seem to directly oppose the site of release could lead to better conclusions.

      In all tissues where IIA-E are expressed at the NMJ, the protein localization is directly adjacent to Brp (see representative images in Figure 2B and s5-8). This localization is often clear in single z-slices. We attempted to perform more detailed analyses, like measure the distance between Brp and GluR puncta, but found it very challenging due to the 3D orientation of the NMJ structure. We added a sentence to page 4 of the manuscript describing what we see and the overall similarity of IIA-E localization.

      Reviewer #2 (Recommendations for the authors):

      The authors should revise the wording throughout to more clearly distinguish between “absence” and “not detected,” in particular for statements regarding GluRII subunits in adult muscles. In the current form, some claims appear stronger than supported by the methods.

      Thank you for this important suggestion. We replaced “absence” with “not detected” throughout the manuscript. We also note this distinction explicitly in the Discussion.

      It would also improve clarity if the different experimental approaches (GAL4, antibody staining, tagging, RNA-seq) were presented more explicitly separated, rather than combined into a single readout. This would allow a better assessment of the evidence.

      We have kept the Results organized by finding rather than by technique, because the majority of our results were concordant across approaches and a technique-by-technique presentation would fragment each biological conclusion across four sections. However, we agree that the evidence should be separable by the reader, and it now is: Figure 2C reports GAL4 and IHC in separate columns, Figure s13 presents the RNA-seq results in a parallel table format, and the new Discussion paragraph identifies each case in which the methods disagreed and states which result we consider likely to be a false positive or false negative. Where a single method underlies a conclusion, we now say so in the text.

      The discrepancies between methods (e.g., RNA-seq vs GAL4 expression) should be discussed more directly, and the possibility of false negatives, especially for antibody staining in adult tissue, should be acknowledged more clearly.

      Thank you. Other reviewers also noticed this weakness of our manuscript, and as noted above, we added a paragraph in the discussion to highlight several important discrepancies in methods and possibilities of false positives.

      A functional experiment is not strictly required, but would clearly strengthen the manuscript. For example, electrophysiology at adult NMJs or perturbation of candidate receptors (e.g., GluClα or Clumsy) would help to link expression to function.

      We intend to perform functional and behavioral experiments with clumsy loss-of-function flies. However, this work will require creation of new genetic reagents and behavioral experiments that make it beyond the scope of this manuscript. These experiments and the accompanying behavioral analysis will form a separate study, which will be the foundation of a future paper.

    1. eLife Assessment

      Overall, this is an important manuscript that delivers an invaluable community resource. The execution is relatively simple, but the evidence of this resource is solid. The effort invested in generating the knockout lines that for validation experiments is a clear strength of the study, but it doesn't substitute for a careful evaluation of the same reagents in the end-users' specific protocols before making any biological statement.

    2. Reviewer #1 (Public review):

      Summary:

      The authors address the lack of validated tools for detection and quantification of proteins associated with amyotrophic lateral sclerosis (ALS) through an extensive screening of 303 commercially available antibodies to 33 protein targets. Their ALS-Reproducible Antibody Platform (ALS-RAP) delivers a validated antibody toolbox for ALS research, which will provide an advantageous starting point for researchers in this field. Ayoubi R. et al. showcase the characterization workflow, presenting as an example the characterization of antibodies targeting Galectin-1, encoded by the LGALS1 gene. A selection of these antibodies was also used to profile protein levels across human induced pluripotent stem cell (iPSC)-derived and primary neurological cell types, and the fundings support that ALS disease mechanism involves both neuronal and glial cells.

      Strengths:

      The knockout (KO)-based approach is definitively the major strength of this study, providing a high level of confidence in the data collected in human induced pluripotent stem cell (iPSC)-derived and primary neurological cell types. Important is also the focus on renewable reagents (monoclonal and recombinant antibodies). The extensive characterization of this set of antibodies will benefit any scientist interested in any of the 33 targets proteins, even in fields other than neuroscience.

      The authors perform an interesting protein profiling study assessing 27 proteins, comparing RNA and protein expression data, and using two independent WB preparations of the same cell types. The conclusions that can be drawn from this first assessment might not be final, but data are compelling, because they have been collected with reliable and validated antibodies.

      Another strength of this work is the data dissemination strategy, which includes the Only Good Antibodies (OGA) platform, where YCharOS data are curated and presented in an easy and intuitive manner that facilitates antibody selection by the end user for WB, IP and IF applications.

      The authors mentioned the development of single-chain variable fragment (scFv) recombinant antibodies raised by the SGC against the six proteins (ANXA11, OPTN, MATR3, PFN1, UBQLN2 and VCP) that had limited renewable antibodies that are commercially available. The development was optimized to generate antibodies particularly suitable for IP, and the clone selection process was carried out using IP coupled to mass spectrometry. They provide details on the screening process for the SGC-generated scFv antibodies, plus share that scFv sequences.

      Weakness:

      The protein profiling study is limited to WB data, and the authors did not provide any explanation on why there was no integration with IP and IF data, not even for those targets that have validated antibodies. Also, not all the cell types have been screened by chemiluminescence-based detection and by fluorescence-based WB, and the authors do not elaborate on the reason for such choice. Technical and practical considerations for such decisions could have been briefly described.

    3. Reviewer #2 (Public review):

      Overall, this is a solid manuscript that delivers an important community resource. The execution is relatively simple, but the value is real, the work is rigorously performed, and the open dissemination through Zenodo, the F1000Research YCharOS Gateway and OGA is well executed. The effort invested in generating the knockout lines for validation experiments is a clear strength of the study.

      Comments on revised version.

      I thank the authors for their responses. A number of the smaller points I raised are now resolved. However, I am disappointed to see that the more substantial concerns, which relate to the cell-type expression claims running through the Results and the Discussion, remain unaddressed. My original point was that these claims are not quantitatively supported and that the identity of the iPSC-derived cell types used to make them has not been documented, and I do not think this revision changes that. Most of what I asked for was quantification of experiments the authors have already performed, or quality control on the cell types themselves, and this has been answered with scope arguments rather than with data. The other points I originally raised remain.

      I give further details on my two main remaining concerns below:

      (1) The cell-type expression claims across the manuscript are still supported by qualitative Western blot images only. These include statements such as "higher signal in glial populations," "enriched in neuronal lineages," "predominantly detected in microglial populations," and the "convergence on multicellular disease mechanisms" narrative in the Discussion. I understand the authors' decision to keep ALS-RAP as a tool and resource paper rather than expand the experimental work. However, if that is the framing, the biological interpretations of these expression data need to be removed. In their response, the authors themselves acknowledge that "quantification across multiple biological replicates would be required to establish definitive differences in protein abundance between neuronal and glial cell types." As long as this quantification is not provided, every claim of cell-type-specific expression should be removed from the Abstract, Results and Discussion, and the data presented as a descriptive survey.

      (2) Related to the previous point, I do not think that quality control of the non-microglia iPSC lineages is beyond the scope of the study, as argued in the response. For ALS-RAP to be adopted as a resource, other laboratories need to trust that the authors' iPSC-derived neurons, astrocytes and oligodendrocytes are really neurons, astrocytes and oligodendrocytes. The fact that the differentiation protocols have been previously published does not confirm that they have been correctly implemented here, and this is precisely the kind of quality control that should be documented in the paper. In their response, the authors list the single markers already shown in Figure S3 (ISL1, TH, MBP, GFAP, CD44) as the answer to my concern, but a single positive marker per lineage does not establish purity.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      (1) The authors mentioned the development of single-chain variable fragment (scFv) recombinant antibodies raised by the SGC against the six proteins (ANXA11, OPTN, MATR3, PFN1, UBQLN2 and VCP) that had limited renewable antibodies that are commercially available. The development was optimized to generate antibodies particularly suitable for IP, and the clone selection process was carried out using IP coupled to mass spectrometry. Even though the generation of these novel reagents is not the focus of this work, the authors do not provide any data on this aspect.

      The scFv reagents represent an important component of this resource and warranted additional description. In the revised manuscript, we expanded the section describing their origin, selection strategy, and intended use. We also added an updated reference describing the scFv discovery methodology, together with a brief summary of the workflow. In addition, the sequences of all six scFvs (ANXA11, OPTN, MATR3, PFN1, UBQLN2, and VCP) are now provided in Supplementary Table 2. During the revision process, we also identified that two scFv plasmids were missing from Add gene; these have now been deposited, making all six reagents and their sequences publicly available.

      (2) The protein profiling study is limited to WB data, and the authors did not provide any explanation on why there was no integration with IP and IF data, not even for those targets that have validated antibodies. Also, not all the cell types have been screened by chemiluminescence-based detection and by fluorescence-based WB, and the authors do not elaborate on the reason for such a choice.

      ALS-RAP was conceived as an open science resource for the ALS community. Given the breadth of the project (33 ALS-associated proteins), it is unrealistic for a single laboratory to investigate the biochemical and cellular biology of every target. We therefore generated and openly disseminated antibody characterization data across three complementary applications—western blot (WB), immunoprecipitation (IP), and immunofluorescence (IF)—to provide the community with validated tools that can support diverse downstream studies, including protein abundance, protein interactions, and subcellular localization.

      The primary objective of the protein profiling study presented in this manuscript, however, was to compare relative protein abundance across multiple neuronal and glial cell types. Western blotting was selected because it provides a robust and semi-quantitative measurement of protein abundance across biological samples, making it well suited for systematic comparisons between cell types. In contrast, IP and IF were primarily included as antibody validation assays and as community resources for future mechanistic studies rather than for quantitative expression profiling.

      Generating the large quantities of iPSC-derived neuronal and glial cells required to profile 33 ALS-associated proteins is technically demanding and resource-intensive. Consequently, a limited number of protein-cell type combinations are absent from one of the two detection modalities because insufficient lysate remained for repeat analyses. Importantly, every protein was evaluated across the complete panel of neurological cell types in at least one western blot detection modality. In most cases, the omitted samples corresponded to cell types in which the target protein was undetectable or expressed at very low levels in the initial experiment.

      Reviewer #2 (Public review):

      (1) The rationale for the selection of these 33 genes is insufficient. The authors lean on the Nijs & VanDamme classification and on PubMed entry counts, but the number of PubMed entries is not a meaningful criterion for what constitutes an important ALS protein - some of the most disease-relevant genes are precisely those with fewer publications, while heavily cited genes such as CAV1 carry weak ALS-specific evidence. The authors should provide a more transparent and biologically motivated rationale for inclusion and exclusion (ClinGen evidence tier, replicated GWAS signals, large meta-analyses, ALSoD) and explain why specific risk genes outside this list were not part of ALS-RAP.

      We thank the reviewer for this comment and apologize if our rationale for target selection was not sufficiently clear. We agree that the number of PubMed entries should not be interpreted as a criterion for inclusion. Rather, we included this metric as a descriptive measure of how extensively each gene has been studied in the context of ALS. Specifically, comparing the number of publications for Gene versus Gene + ALS provides an indication of the relative attention a target has received within the ALS field, but it was not used to prioritize genes for inclusion.

      The selection of the 33 ALS-RAP targets was instead based on established genetic evidence for disease association. Specifically, we prioritized ALS-associated genes supported by replicated human genetic studies, including rare variants with high, intermediate, or modest effect sizes, following the classification framework proposed by Nijs and Van Damme. As described in the manuscript, 25 of the 33 targets have also been independently curated by the Clinical Genome Resource (ClinGen) ALS Gene Curation Expert Panel and classified as having evidence supporting an association with ALS (Table 1). The remaining genes were selected based on published human genetic studies and emerging evidence supporting their relevance to ALS biology.

      (2) "107 of 231 (46%) demonstrated specific target staining in IF." The criteria used to define "specific target staining" at the IF level are not stated. From the Galectin-1 example, the mosaic WT/KO strategy provides a binary readout, but for proteins with low expression, weak punctate staining or unusual subcellular distributions, a single threshold is unlikely to capture specificity uniformly across 231 antibodies.

      We thank the reviewer for highlighting this omission. We agree that the criteria used to classify antibodies in each application should be explicitly stated. In the revised manuscript, we now define the classification criteria for Western blot (WB), immunoprecipitation (IP), and immunofluorescence (IF) in the Methods section. Briefly, WB antibodies were considered target-selective when the predominant immunoreactive band at the expected molecular weight was substantially reduced or absent in the corresponding knockout (KO) lysate. IP antibodies were considered successful when they specifically enriched the target protein from wild-type lysates, as assessed by immunoblotting of the immunoprecipitated material. For IF, antibodies were considered specific when the mean fluorescence intensity in wild-type cells was at least 1.5-fold higher than that measured in KO cells using the mosaic WT/KO assay. A minimum of 250 WT and 250 KO cells were quantified for each antibody. These criteria were applied uniformly across all antibodies to provide a standardized and objective benchmarking framework. We have also added references to the detailed YCharOS characterization workflows previously described in Ayoubi et al., Nature Protocols (2025).

      (3) Several claims in the manuscript depend on differential protein abundance across cell types. As presented, these claims are supported by qualitative Western blot images only. They should be substantiated by quantification across multiple biological replicates.

      The expression patterns observed were reproducible across two independent batches of iPSC-derived neuronal and glial cell types generated using the same differentiation protocols. In addition, several proteins enriched in iPSC-derived microglia also showed enrichment in primary human microglia and monocyte-derived macrophages, providing independent support for these observations.

      We agree that quantification across multiple biological replicates would be required to establish definitive differences in protein abundance between neuronal and glial cell types. However, this was not the objective of the present study. Rather, our goal was to perform an initial survey of ALS-associated protein expression using rigorously validated antibodies and to identify expression patterns that may warrant further investigation.

      ALS-RAP was conceived as an open science resource for the ALS community. Given the breadth of the project (33 ALS-associated proteins), we do not envision that a single laboratory can comprehensively define the expression, localization, and function of every target across all relevant cell types, tissues, and disease states. Instead, we provide validated reagents together with an initial characterization of protein expression in well-defined iPSC-derived neuronal and glial cell models to enable and accelerate future studies by the broader research community.

      (4) This manuscript represents a unique opportunity to address antibody recognition of splicing variants, which is something of considerable value to the community. For each target, the predicted isoforms in Ensembl could be cross-referenced against the observed bands, and the pattern of bands compared across cell types could be informative about which isoforms each antibody captures. This would convert ambiguous "extra bands" into useful biological information and would substantially increase the value of the resource. I strongly encourage the authors to include this analysis.

      We agree that defining which protein isoforms are recognized by individual antibodies would be valuable to the research community. However, we believe that such an analysis would be difficult to interpret and potentially misleading based on the data generated in the present study.

      All antibodies were initially characterized in a common cancer cell line, and the best-performing reagents were subsequently used to profile ALS-associated proteins in iPSC-derived neuronal and glial cell types. Additional bands observed in neurological cells, but not in the original characterization cell line, may reflect several nonmutually exclusive possibilities, including cross-reactivity with proteins specifically expressed in neurological cells, recognition of cell type-specific splice isoforms, or post-translationally modified forms of the target protein.

      Without knockout validation in each neurological cell type, these possibilities cannot be distinguished confidently.

      In addition, protein migration on SDS-PAGE frequently differs from the theoretical molecular weight predicted from amino acid sequence alone. Consequently, assigning individual immunoreactive bands to Ensembl-predicted isoforms based solely on apparent molecular weight would be speculative and could lead to incorrect conclusions. We therefore believe that a rigorous isoform analysis would require complementary experimental approaches beyond the scope of the present resource study.

      (5) The iPSC-derived microglia receive a comprehensive QC panel (IBA1/PU.1 IF, CD45/CD11b flow, qRT-PCR for nine canonical markers; Figure S4), which allows the reader to assess culture purity. The other iPSC-derived lineages - motor neurons, dopaminergic neurons, oligodendrocytes and astrocytes- are validated by a single marker each in WB (Figure S3) without purity quantification. Given that several conclusions of the manuscript rest on the cell-type-specific detection of ALS-associated proteins, equivalent quality control should be performed for the other lineages so that the reader can evaluate the purity of each preparation.

      We thank the reviewer for this important comment and agree that documenting the identity of iPSC-derived cell populations is essential given their inherent heterogeneity. The differentiation protocols used in this study follow well-established, published methods that have been extensively validated by their developers and the broader stem cell community. We confirmed the identity of each differentiated lineage using established lineage-specific markers, including ISL1 for motor neurons, TH for dopaminergic neurons, SATB2 and CTIP2 for cortical neurons, MBP for oligodendrocytes, GFAP and CD44 for astrocytes, and CD11b and Iba1 for microglia.

      We agree that comprehensive quantitative purity analyses for every differentiated lineage would further strengthen the study. However, we believe that these experiments fall beyond the scope of the present work, whose primary objective was to establish a validated antibody resource rather than to optimize or benchmark differentiation protocols. We also note that our conclusions are intentionally limited to broad protein expression patterns rather than definitive quantitative comparisons between cell types.

      Finally, the inclusion of primary human microglia, fetal astrocytes, and monocyte-derived macrophages provides an independent layer of biological validation. The concordance of expression patterns observed between these primary cells and the corresponding iPSC-derived populations supports the robustness of our principal observations.

      (6) The robustness of the resource would be substantially increased by validating at least a subset of the targets in a second iPSC background, in at least some of the cell types analysed.

      We agree that validating antibody performance and protein expression across multiple iPSC genetic backgrounds would further strengthen the resource. However, we believe that this objective falls beyond the scope of the present study, whose primary aim was to establish a rigorously validated antibody toolbox for ALS-associated proteins and to provide an initial survey of their expression in representative neuronal and glial cell types.

      We envision ALS-RAP as the foundation of a broader community effort. Rather than comprehensively evaluating all 33 proteins across multiple genetic backgrounds and tissues, our goal is to provide validated, renewable reagents that will enable such studies by the broader ALS research community.

      (7) The newly developed SGC scFv antibodies are arguably the most novel reagent contribution of this manuscript, yet they receive a single sentence in the body of the paper. A more thorough description is warranted.

      We agree that the scFv reagents represent an important component of this resource and warranted additional description. In the revised manuscript, we expanded the section describing their origin, selection strategy, and intended use. We also added an updated reference describing the scFv discovery methodology, together with a brief summary of the workflow. In addition, the sequences of all six scFvs (ANXA11, OPTN, MATR3, PFN1, UBQLN2, and VCP) are now provided in Supplementary Table 2. During the revision process, we also identified that two scFv plasmids were missing from Addgene; these have now been deposited, making all six reagents and their sequences publicly available.

      (8) Accessibility of the resource through Zenodo is not straightforward - the reader currently has to navigate to individual antibody characterization reports one by one to extract recommendations for a given target. While the use of an established public repository is important for permanence, a dedicated ALS-RAP website with an interactive, searchable interface - filterable by target, application, host species and clonality - would meaningfully improve uptake. The relationship between such a portal and the existing OGA platform should also be clarified.

      We agree that navigating individual antibody characterization reports on Zenodo can be cumbersome for end users. This challenge was one of the motivations behind the development of the Only Good Antibodies (OGA) platform, which curates YCharOS antibody characterization data, including the ALS-RAP dataset, into an interactive and searchable interface. OGA allows users to identify antibodies by target, application, host species, clonality, and other relevant attributes, while providing direct links to the complete characterization reports archived on Zenodo. In this way, Zenodo serves as the permanent open repository for the complete datasets, whereas OGA provides a user-friendly interface for data exploration and antibody selection.

    1. eLife Assessment

      This study used several approaches (computational modeling, neuronal cultures, rodent epilepsy model, and human intracranial recordings) to address a significant question in epilepsy research, with additional relevance to EEG studies more broadly: Are high frequency oscillations (or "fast ripples", defined as >200 Hz by the authors) distinct from randomly occurring clustering of spikes? The results suggest fast ripples can occur by chance and how this may occur. The significance was considered important and the strength of evidence convincing, with minor revisions suggested to temper conclusions related to stochastic vs. oscillatory fast ripples.

    2. Reviewer #1 (Public review):

      Summary:

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

      Strengths:

      The conceptualization of fast ripples as potentially arising by chance is highly novel and builds effectively on questions raised in prior studies that have never been satisfactorily resolved. Integration across biological scales and models provides a rigorous approach, now improved by addressing theoretical concerns regarding validity of the shuffling approach and state dependence. The discussion has been updated to provide a more nuanced interpretation of the study's findings.

      Weaknesses:

      The authors have satisfactorily and thoughtfully addressed the critiques provided in the first review. However, there remain two points that I would like authors to address prior to publication:

      (1) Synchronized burst firing is a key feature of an epileptic site generating interictal discharges, and one that could generate either oscillatory or stochastic FRs as documented in multiple prior publications cited in the manuscript and/or in the prior review. Paroxysmal depolarization, for example, has been very well described, and consists of strong, disorganized burst firing (resulting in summated postsynaptic potentials strong enough to generate high gamma signal) in a neuronal population coinciding with a large low-frequency deflection. I would like to see the results described in this context, and to avoid blanket dismissal of stochastic FRs without a clear oscillatory component.

      (2) It would be highly useful to add a conclusion paragraph that spells out implications of the study for use of FRs as epileptic biomarkers in clinical invasive EEG recordings.

      Comments on revised version.

      The author's additions to the discussion are appreciated.

    3. Reviewer #2 (Public review):

      Summary:

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

      Strengths:

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

      Comments on revised version.

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

    4. Reviewer #3 (Public review):

      Summary:

      An outstanding question in the field of high frequency oscillations (HFOs) in the context of epilepsy is how these oscillations emerge, considering that they occur at such high frequencies i.e., 250Hz well above the firing ability of single neurons. One hypothesis that has been suggested in the past is that neurons that fire in an out of phase fashion or rather at random intervals may contribute to a spectrum of HFOs ranging from 250-500Hz that observed in epilepsy. However, how possible it is that random action potentials could aggregate to the extent that they could give rise to HFOs in the so-called fast ripple (FRs) frequency range (>200 according to the authors) remains unclear. To test this hypothesis, they used computational modeling to randomly insert action potentials in a signal, and they found that this approach is sufficient to generate FRs. Some of the predictors of whether FRs could occur were neuronal count, firing rate and synchronization. Besides computational modeling, they used different model systems to test whether that would be possible to be observed in neuronal cultures, in epileptic rats (intrahippocampal kainic acid model), and human data. Neuronal cultures treated with picrotoxin did not show evidence that FRs could be generated more than chance aggregation of action potentials. They then asked whether synchronization and firing rate could play a role in the emergence of FRs. They found that changes in neural firing and synchronization, such as those occurring during differences phase of the sleep-wake cycle could affect the number of FRs occurring by chance aggregation, with more FRs seen during periods of wakefulness, a result that they replicated in human data.

      The authors largely achieve their proposed aims of demonstrating that random neuronal firing can, in principle, generate FRs. Results from this study could influence current thinking around mechanisms generating FRs in epilepsy. The use of different computational approaches and model systems could offer new analytical methodologies for the study of FRs in the context of brain disease.

      Strengths:

      (1) The authors used a multi-level approach combining computational modeling with experimental datasets, including neuronal cultures, a rat model of temporal lobe epilepsy and human data.

      (2) Identification of key parameters such as neuronal count, firing rate, synchronization and brain state in observed incidence of FRs generated through random aggregation of neural firing.

      (3) Cross-species validation increases the likelihood of generalizability of the findings.

      Comments on revised version.

      The authors have addressed my comments and have no further suggestions or any changes in my assessment.

    5. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

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

      Strengths:

      The conceptualization of fast ripples as potentially arising by chance is highly novel and builds effectively on questions raised in prior studies that have never been satisfactorily resolved. Integration across biological scales and models provides a rigorous approach, now improved by addressing theoretical concerns regarding validity of the shuffling approach and state dependence. The discussion has been updated to provide a more nuanced interpretation of the study's findings.

      Weaknesses:

      The authors have satisfactorily and thoughtfully addressed the critiques provided in the first review. However, there remain two points that I would like authors to address:

      (1) Synchronized burst firing is a key feature of an epileptic site generating interictal discharges, and one that could generate either oscillatory or stochastic FRs as documented in multiple prior publications cited in the manuscript and/or in the prior review. Paroxysmal depolarization, for example, has been very well described, and consists of strong, disorganized burst firing (resulting in summated postsynaptic potentials strong enough to generate high gamma signal) in a neuronal population coinciding with a large low-frequency deflection. I would like to see the results described in this context, and to avoid blanket dismissal of stochastic FRs without a clear oscillatory component.

      To describe the results in this context, we have updated the Discussion. It reads:

      “Depolarizations that promote burst firing should also increase the likelihood of FRs. Paroxysmal depolarization shifts, which are the cellular events underlying epileptiform abnormalities, would therefore be expected to co-occur with FRs. Accordingly, epileptiform abnormalities should be associated with increased gamma/high-gamma (> 80 Hz) activity at their source as has been observed in epileptogenic parenchyma (Ren et al., 2015; Sheybani et al., 2021; Weiss et al., 2015).”

      (2) It would be highly useful to add a conclusion paragraph that spells out implications of the study for use of FRs as epileptic biomarkers in clinical invasive EEG recordings.

      We fully agree that the main message should be further clarified. We have added this paragraph to this end:

      “Clinically, our findings do not argue for or against the use of FRs as epilepsy biomarkers, as we did not assess their relationship with the epileptogenic zone. However, they do indicate that FRs are not highly specific to epileptic tissue. We would, however, recommend selecting FRs with particularly long duration, which may offer greater specificity and potentially better reliability as a biomarker. The clinical value of FRs as distinct entities or as emergent oscillations remains to be clarified.”

      Please address the above critiques in Discussion, or elsewhere as deemed necessary by the authors.

      Reviewer #2 (Public review):

      Summary:

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

      Strengths:

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

      Comments on revised version.

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

      Reviewer #3 (Public review):

      Summary:

      An outstanding question in the field of high-frequency oscillations (HFOs) in the context of epilepsy is how these oscillations emerge, considering that they occur at such high frequencies i.e., 250Hz well above the firing ability of single neurons. One hypothesis that has been suggested in the past is that neurons that fire in an out-of-phase fashion or rather at random intervals may contribute to a spectrum of HFOs ranging from 250-500Hz that observed in epilepsy. However, how possible it is that random action potentials could aggregate to the extent that they could give rise to HFOs in the so-called fast ripple (FRs) frequency range (>200 according to the authors) remains unclear. To test this hypothesis, they used computational modeling to randomly insert action potentials in a signal, and they found that this approach is sufficient to generate FRs. Some of the predictors of whether FRs could occur were neuronal count, firing rate and synchronization. Besides computational modeling, they used different model systems to test whether that would be possible to be observed in neuronal cultures, in epileptic rats (intrahippocampal kainic acid model), and human data. Neuronal cultures treated with picrotoxin did not show evidence that FRs could be generated more than chance aggregation of action potentials. They then asked whether synchronization and firing rate could play a role in the emergence of FRs. They found that changes in neural firing and synchronization, such as those occurring during different phases of the sleep-wake cycle could affect the number of FRs occurring by chance aggregation, with more FRs seen during periods of wakefulness, a result that they replicated in human data.

      The authors largely achieve their proposed aims of demonstrating that random neuronal firing can, in principle, generate FRs. Results from this study could influence current thinking around mechanisms generating FRs in epilepsy. The use of different computational approaches and model systems could offer new analytical methodologies for the study of FRs in the context of brain disease.

      Strengths:

      (1) The authors used a multi-level approach combining computational modeling with experimental datasets, including neuronal cultures, a rat model of temporal lobe epilepsy and human data.

      (2) Identification of key parameters such as neuronal count, firing rate, synchronization and brain state in observed incidence of FRs generated through random aggregation of neural firing.

      (3) Cross-species validation increases the likelihood of generalizability of the findings.

      Minor weakness:

      (1) The analyses conducted in human data lack direct comparison with sleep data due to no available data, but would encourage future investigations directly comparing HFOs during wakefulness and nocturnal sleep.

      Comments on revised version.

      The authors have addressed my comments and I have no further suggestions.

    1. eLife Assessment

      This landmark study is part of an impressive large-scale effort to assess the consistency of published findings in the field of Drosophila immunity. In this article, the authors report an analysis of claims made in approximately 400 research papers from the Drosophila immunity literature. The authors perform validation experiments to test a subset of the claims that are most inconsistent with current understanding in the field. These experiments do not necessarily aim to reproduce the original experiments, but instead to test the repeatability of the result using updated tools and methods. In some cases, the original claim is validated and in others it is not. The conclusions on the consistency of the claims, as presented in this paper, are based on mostly convincing evidence.

    2. Reviewer #1 (Public Review):

      This work revisits a substantial part of the published literature in the field of Drosophila innate immunity from 1959 to 2011. The strategy has been to restrain the analysis to some 400 articles and then to extract a main claim, two to four major claims and up to four minor claims totaling some 2000 claims overall. The consistency of these claims with the current state-of-the-art has been evaluated and reported on a dedicated Web site known as Reprosci and also in the text as well as in the 28 Supplements that report experimental verification, direct or indirect, e.g., using novel null mutants unavailable at the time, of a selected set of claims made in several articles. Of note, this review is mostly limited to the manuscript and its associated supplements and does not integrally cover the Reprosci website.

      Strengths:<br /> One major strength of this article is that it tackles the issue of reproducibility/consistency on a large-scale. Indeed, while many investigators have some serious doubts on some results found in the literature, few have the courage, or the means and time, to seriously challenge studies, especially if published by leaders in the field. The Discussion adequately states the major limitations of the Reprosci approach, which should be kept in mind by the reader to form its own opinion.

      This study also allows investigators not familiar with the field to have a clearer understanding of the questions at stake and to derive a more coherent global picture that allows them to better frame their own scientific questions. Besides a thorough and up-to-date knowledge of the literature used to assess the consistency of the claims with our current knowledge, a merit of this study is the undertaking of independent experiments to address some puzzling findings and the evidence presented is often convincing, albeit one should keep in mind the inherent limitations as several parameters are difficult to control, especially in the field of infections, as underlined by the authors themselves. Importantly, some work of the lead author has also been re-evaluated (e.g., Supplements S2-S4). Thus, while utmost caution should be exerted, and often is, in challenging claims, even if the challenge eventually proves to be not grounded, it is valuable to point out potential controversial issues to the scientific community.

      While this is not a point of this review, it should be acknowledged that the possibility to post comments on the ReproSci website will allow further readjustments by the community in the appreciation of the literature and also of the Reprosci assessments themselves and of its complementary additional experiments. As science is evolving continuously, this site allows the current state of knowledge to be updated as needed, not necessarily by the authors themselves.

      Comments on revised version:

      The major weaknesses identified in the original version of the manuscript have been adequately addressed by the authors. In a few cases, additional KO mutants have been generated and analyzed thus further strengthening the manuscript.

    3. Reviewer #2 (Public review):

      Summary:

      The authors present an ambitious and large-scale reproducibility analysis of 400 articles on Drosophila immunity published before 2011. They extract major and minor claims from each article, assess their verifiability through literature comparison and, when possible, through targeted experimental re-testing, and synthesize their findings in an openly accessible online database. The goal is to provide clarity to the community regarding claims that have been contradicted, incompletely supported, or insufficiently followed up in the literature, and to foster broader community participation in evaluating historical findings. The manuscript summarizes the major insights emerging from this systematic effort.

      Strengths:

      (1) Novelty and community value: This work represents a rare example of a systematic, transparent, and community-facing reproducibility project in a specific research domain. The creation of a dedicated public platform for disseminating and discussing these assessments is particularly innovative.

      (2) Breadth and depth: The authors analyze an impressive number of publications spanning multiple decades, and they couple literature-based assessments with new experimental data where follow-up is missing.

      (3) Clarity of purpose: The manuscript carefully distinguishes between assessing evidential support for claims and judging the scientific merit of historical work. This helps frame the project as constructive rather than punitive.

      (4) Metascientific relevance: The analysis identifies methodological and contextual factors that commonly underlie irreproducible claims, providing a useful guide for future study design and interpretation.

      (5) Transparency: Supplementary datasets and the public website provide an exceptional degree of openness, which should facilitate community engagement and further refinement.

      Comments on revised version:

      In the revised manuscript the authors addressed in a satisfactory manner the weaknesses I previously highlighted in my initial review.

    4. Reviewer #3 (Public review):

      Summary:

      In this ambitious study the authors set out to analyse the validity of a number of claims, both minor and major from 400 published articles within the field of Drosophila immunity that were published before 2011. The authors were able to determine initially if claims were supported by comparing to other published literature in the field and if required by experimentally testing 'unchallenged' claims that had not been followed up in subsequent published literature. Using this approach the authors identified a number of claims that had contradictory evidence using new methods or taking into account developments within the field post initial publication. They publish their findings on a publicly available website designed to enable the research community to assess published work within the field with greater clarity.

      Strengths:

      The work presented is rigorous and methodical, the data presentation is high quality and importantly the data presented support the conclusions. The discussion is balanced and the study is written considerately and respectfully highlighting that the aim of the study is not to assign merit to individual scientists or publications but rather to improve clarity for scientists across the field. The approach carried out by the researchers focuses on testing the validity of the claims made in the original papers rather than testing whether the original experimental methods produced reproducible results. This is an important point since there are many reasons why the original interpretation of data may have understandably led to the claims made. These potential explanations for irreproducible data or conclusions are discussed in detail by the authors for each claim investigated.

      The authors have generated an accompanying website which provides a valuable tool for the Drosophila Immunity research community that can be used to fact check key claims and encourages community engagement. This will achieve one important goal of this study - to prevent time loss for scientists who base their research on claims that are irreproducible. The authors rightly point out that it is impossible (and indeed undesirable) to avoid publication of irreproducible results within a field since science is 'an exploratory process where progress is made by constant course correction'. This study is however an important piece of work that will make that course correction more efficient.

      Comments on revised version:

      I'm impressed with the revisions carried out by the authors. No further comments.

    5. Reviewer #4 (Public review):

      Westlake, Lemaitre and colleagues have revised their manuscript regarding the reproducibility of studies on the Drosophila immune system. As was emphasized by all reviewers at the original submission of this manuscript, this paper captures a tremendous effort and makes a valuable contribution to the community.

      One strong conclusion to be drawn from the overall effort is that the scientific literature is, for the most part, very good. The vast majority of claims made in the 400 papers assembled for this project were substantiated, either with previously published follow-up work or with new experimentation presented here. Although the authors have chosen not to quantify the overall validation rate of scientific claims in the present study, that information is available on the ReproSci website and in the companion paper.

      In the revised manuscript, the authors make the distinction between repeatability/direct reproducibility and what they term "conceptual replication" and "indirect reproducibility", emphasizing that this study falls in the latter category because in many cases the present authors are not attempting to reproduce or replicate the original study. Instead, they are often using new tools to reinvestigate original claims and determine the accuracy of those claims, and whether they stand up to additional data. The authors have replaced the word "irreproducible" with "irreplicable" in the revised manuscript. I continue to believe the present work can be considered "validation" of the original studies and that the present work is accurately considered a "validation project". The clarification of terminology has strengthened the revised manuscript.

      As with any study, the strength and breadth of conclusions that can be drawn from the present work are a function of the strength and resolution of the experiments performed. This may vary across the supplements so readers should interpret the presented results accordingly. In my opinion, all of the supplements in the revised article are sufficiently convincing to warrant inclusion in the final manuscript.

      There are several instances where the authors have declined to do additional experiments in response to review. I am comfortable with these decisions, recognizing that the extra work required would be prohibitively much. Instead, the authors have adequately revised their presentation of findings. In some instances where the authors have declined to perform additional experiments, they have provided a compelling logic for their decision in the response to reviews, which unfortunately does not appear in the main manuscript. For example, in response to a comment by Reviewer 1, the authors wrote "In response to the suggestion of scanning multiple blots for quantification, we note that no clear enhancement of ModSP autoactivation was observed across different incubation times. We also explored alternative incubation conditions, but none resulted in a detectable increase in activation. Thus, additional quantification would not change the overall conclusion." This is convincing, but no readers will see it unless it appears in the manuscript or the public response to review. At the same time, I recognize the practical limitations in available space in the main article and supplement. There are a handful of instances where reviewers have made suggestions of additional/alternative experiments that would be more rigorous, accurate, or convincing than what is currently presented but the authors have declined to take up those recommendations. In some instances, I strongly agree with the authors' rationale and in other cases it is less so. However, I did not find any instance where the absence of the suggested additional experiment substantially undermined the conclusions being put forward in this manuscript. Therefore, I believe these decisions can be left to the discretion of the authors in accordance with the eLife publication model and interpretations can be left to the discretion of the readers.

      Reviewer 1 and Reviewer 4 both commented on a lack of demonstration of RNAi efficacy in the knockdown experiments and suggested that negative conclusions drawn from RNAi experiments should include controls showing that the RNAi was effective. The authors have declined to include such controls, and I recognize they may be impossible to add now without redoing the entire associated experiment. Instead, the authors point to prior use of the RNA constructs by other authors in the literature. That should be recognized as less than ideal, although it is probably substantially adequate. The authors have added statements to the Discussion that RNAi knockdowns are not equivalent to null mutations and negative results may appear because of hypomorphic effects. This is a positive addition.

      The replacement of the sphinx RNAi with a deletion of Sphinx 1 and Sphinx 2 as part of a general strengthening of the section on sphinx and spheroid is an extremely strong addition to the paper. I could not expect that level of effort for each of the validations and I acknowledge and appreciate that they have made that effort here.

      Reviewer 1 and Reviewer 4 both commented that some figures in the original submission were ambiguous with respect to sample size and distribution of data points, including undefined error bars and inappropriate graphical representation of experiments with low sample size. These concerns have been largely addressed in the revised manuscript, including with addition of statistical analyses in several cases. The authors note that the figures were generated by multiple individuals across several research teams so it would be a large effort unify their style. I agree that this would be an unreasonable effort at this stage, for minimal return that is primarily aesthetic. The authors have now added illustration of all data points in most figures and have defined the error bars. One response to Reviewer 1 "Data points should be shown in the figure" with respect to a figure in S16 was "Unfortunately, we could not address this point to the difficulty to reach the collaborator who provided the data." That response certainly would not be acceptable in the publication of a typical scientific paper. However, I sense a different standard is being applied to the present work and the lack of these data points in this particular figure does not decrease my confidence in the authors' overall conclusion.

      Overall, this is a unique piece of research that is impressive in its scope. Scientific finding is never complete; the process is eternally iterative. Inclusion of the present study in the scientific literature is a testament to collective willingness to engage in that iteration. The authors of this paper are making an exemplar contribution to accuracy in scientific research and should be commended for their effort, dedication, and fairness in what can be a delicate topic.

    6. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This fundamental study is part of an impressive, large-scale effort to assess the reproducibility of published findings in the field of Drosophila immunity. In a companion article, the authors analyze 400 papers published between 1959 and 2011, and assess how many of the claims in these papers have been tested in subsequent publications. In this article, the authors report the results of validation experiments to assess a subset of the claims that, according to the literature, have not been corroborated. While the evidence reported for some of these validation studies is convincing, it remains incomplete for others.

      We thank the reviewers for their careful assessment of our manuscript. We are fully aware that this review process required a substantial investment of time and effort, and we are deeply grateful for their engagement. We also thank the eLife editors for enabling an open and constructive evaluation process of the highest standard.

      We fully agree with the eLife assessment, including the concluding statement highlighting the limitations of our analysis. We also concur with most of the reviewers’ comments, which we have addressed in detail in the revised manuscript. Addressing these comments required extensive additional work, which explains the duration of the revision. This period was also valuable in allowing colleagues from the Drosophila community to provide feedback on our study, including identifying potential issues—for instance regarding SR-C1—that we have carefully re-examined.

      Major revisions include the addition of new experimental data in the sections on Hemese, SR-C1, Sphinx1/2, and Caspar. These results largely confirm our initial conclusions, with the exception of SR-C1. We have therefore revised our interpretation of SR-C1, confirming earlier findings that it promotes bacterial binding, while maintaining that it does not function as a major phagocytic receptor. A table with new list of new reagents has been added at the end of the supplement (Table Supplement S4). We also add a comment on the fact that mutation in the serine protease MP1 (work from B.L.) does not affect wound melanization as initially proposed (Tang et al., 2006).

      One final point: our study assesses replicability using alternative experimental approaches, and we cannot exclude the possibility that our conclusions may be inaccurate. This limitation is inherent to all reproducibility studies. Nonetheless, our work should encourage the community to exercise caution when interpreting certain previous claims, some of which have become widely accepted.

      We thank the reviewers and editors for their constructive input, which has significantly improved the manuscript. At this stage, we are ready to proceed toward a version of record together with the eLife assessment (which may still evolve). We continue to believe that our reproducibility project is unique in its scope and depth. It not only provides quantitative insight into replicability (See companion article)—suggesting that replication rates may be higher in certain fields, consistent with the maturation of knowledge over recent decades—but also contributes to clarifying and refining a number of statements within the field. Although aware of the cost of assessing reproducibility (hard work for articles that are not quoted and statements that are not taking in account), I consider it as one of my most important and original works.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work revisits a substantial part of the published literature in the field of Drosophila innate immunity from 1959 to 2011. The strategy has been to restrain the analysis to some 400 articles and then to extract a main claim, two to four major claims and up to four minor claims totaling some 2000 claims overall. The consistency of these claims with the current state-of the-art has been evaluated and reported on a dedicated Web site known as ReproSci and also in the text as well as in the 28 Supplements that report experimental verification, direct or indirect, e.g., using novel null mutants unavailable at the time, of a selected set of claims made in several articles. Of note, this review is mostly limited to the manuscript and its associated supplements and does not integrally cover the ReproSci website.

      Strengths:

      One major strength of this article is that it tackles the issue of reproducibility/consistency on a large scale. Indeed, while many investigators have some serious doubts about some results found in the literature, few have the courage, or the means and time, to seriously challenge studies, especially if published by leaders in the field. The Discussion adequately states the major limitations of the ReproSci approach, which should be kept in mind by the reader to form their own opinion.

      This study also allows investigators not familiar with the field to have a clearer understanding of the questions at stake and to derive a more coherent global picture that allows them to better frame their own scientific questions. Besides a thorough and up-to-date knowledge of the literature used to assess the consistency of the claims with our current knowledge, a merit of this study is the undertaking of independent experiments to address some puzzling findings and the evidence presented is often convincing, albeit one should keep in mind the inherent limitations as several parameters are difficult to control, especially in the field of infections, as underlined by the authors themselves. Importantly, some work of the lead author has also been re-evaluated (Supplements S2-S4). Thus, while utmost caution should be exerted, and often is, in challenging claims, even if the challenge eventually proves to be not grounded, it is valuable to point out potential controversial issues to the scientific community.

      While this is not a point of this review, it should be acknowledged that the possibility to post comments on the ReproSci website will allow further readjustments by the community in the appreciation of the literature and also of the ReproSci assessments themselves and of its complementary additional experiments.

      We praise the reviewer for this assessment of our project

      Weaknesses:

      Challenging the results from articles is, by its very nature, a highly sensitive issue, and utmost care should be taken when challenging claims. While the authors generally acknowledge the limitations of their approach in the main text and Supplements, there are a few instances where their challenges remain questionable and should be reassessed. This is certainly the case for Supplement S18, for which the ReproSci authors make a claim for a point that was not made in the publication under scrutiny. The authors of that study (Ramet et al., Immunity, 2001) never claimed that scavenger receptor SR-CI is a phagocytosis receptor, but that it is required for optimal binding of S2 cells to bacteria. Westlake et al. here have tested for a role of this scavenger receptor in phagocytosis, which had not been tested by Ramet et al. Thus, even though the ReproSci study brings additional knowledge to our understanding of the function of SR-CI by directly testing its involvement in phagocytosis by larval hemocytes, it did not address the major point of the Ramet et al. study, SR-CI binding to bacteria, and thus inappropriately concludes in Supplement S18 that "Contrary to (Ramet et al., 2001, Saleh et al., 2006), we find that SR-CI is unlikely to be a major Drosophila phagocytic receptor for bacteria in vivo." It follows that the results of Ramet et al. cannot be challenged by ReproSci as it did not address this program. Of note, Saleh et al. (2006) also mistakenly stated that SR-CI impaired phagocytosis in S2 cells and could be used as a positive control to monitor phagocytosis in S2 cells. Their assay appears to have actually not monitored phagocytosis but the association of FITC-labeled bacteria to S2 cells by FACS, as they did not mention quenching the fluorescence of bacteria associated with the surface with Trypan blue.

      We have revised our evaluation of Sr-C1, as we acknowledge that we did not initially read Rämet et al. carefully. We incorrectly assumed that this study demonstrated a role for SR-C1 in phagocytosis, whereas it actually reports a defect in binding only. We have updated our assessment of SR-C1, validating the results from Rämet et al., 2001. We still mention our results on phagocytosis (See below for more information).

      The inference method to assess the consistency of results with current knowledge also has limitations that should be better acknowledged. At times, the argument is made that the gene under scrutiny may not be expressed at the right time according to large-scale data or that the gene product was not detected in the hemolymph by a mass-spectrometry approach. While being in theory strong arguments, some genes, for instance, those encoding proteases at the apex of proteolytic activation cascades, need not necessarily be strongly expressed and might be released by a few cells. In addition, we are often lacking relevant information on the expression of genes of interest upon specific immune challenges such as infections with such and such pathogens.

      The pattern of expression is always used as an element supporting our assessment but never as the central point of decision (See below for more information).

      As regards mass spectrometry, there is always the issue of sensitivity that limits the force of the argument. Our understanding of melanization remains currently limited, and methods are lacking to accurately measure the killing activity associated with the triggering of the proPO activation cascade. In this study, the authors monitor only the blackening reaction of the wound site based on a semi-quantitative measurement. They are not attempting to use other assays, such as monitoring the cleavage of proPOs into active POs or measuring PO enzymatic activity. These techniques are sometimes difficult to implement, and they suffer at times from variability. Thus, caution should be exerted when drawing conclusions from just monitoring the melanization of wounds.

      We employed approaches similar to those used in the original studies under evaluation. However, we acknowledge that our analysis primarily relied on cuticular melanization, which is more straightforward to monitor. We have revised the manuscript to clarify this limitation, particularly in the case of PGRP-LE.

      Likewise, the study of phagocytosis is limited by several factors. As most studies in the field focus on adults, the potential role of phagocytosis in controlling Gram-negative bacterial infections is often masked by the efficiency of the strong IMD-mediated systemic immune response mediated by AMPs (Hanson et al, eLife, 2019). This problem can be bypassed in rare instances of intestinal infections by Gram-negative bacteria such as Serratia marcescens (Nehme et al., PLoS Pathogens, 2007) or Pseudomonas aeruginosa (Limmer et al. PNAS, 2011), which escape from the digestive tract into the hemocoel without triggering, at least initially, the systemic immune response. It is technically feasible to monitor bacterial uptake in adults by injecting fluorescently labeled bacteria and subsequently quenching the signal from noningested bacteria. Nonetheless, many investigators prefer to resort to ex vivo assays starting from hemocytes collected from third-instar wandering larvae as they are easier to collect and then to analyze, e.g., by FACS. However, it should be pointed out that these hemocytes have been strongly exposed to a peak of ecdysone, which may alter their properties. Like for S2 cells, it is thus not clear whether third-instar larval hemocytes faithfully reproduce the situation in adults. The phagocytic assays are often performed with killed bacteria. Evidence with live microorganisms is better, especially with pathogens. Assays with live bacteria require however, an antibody used in a differential permeabilization protocol. Furthermore, the killing method alters the surface of the microorganisms, a key property for phagocytic uptake. Bacterial surface changes are minimal when microorganisms are killed by X-ray or UV light. These limitations should be kept in mind when proceeding to inference analysis of the consistency of claims. Eater illustrates this point well. Westlake et al. state that:" [...] subsequent studies showed that a null mutation of eater does not impact phagocytosis". The authors refer here to Bretscher et al., Biology Open, 2015, in which binding to heat-killed E. coli was assessed in an ex vivo assay in third instar larvae. In contrast, Chung and Kocks (JBC, 2011) tested whether the recombinant extracellular N-terminal ligand-binding domain was able to bind to bacteria. They found that this domain binds to live Gram-positive bacteria but not to live Gram-negative bacteria. For the latter, killing bacteria with ethanol or heating, but not by formaldehyde treatment, allowed binding. More importantly, Chung and Kocks documented a complex picture in which AMPs may be needed to permeabilize the Gram-negative bacterial cell wall that would then allow access of at least the recombinant secreted Eater extracellular domain to peptidoglycan or peptidoglycan-associated molecules. Thus, the systemic Imd-dependent immune response would be required in vivo to allow Eater-dependent uptake of Gram-negative bacteria by adult hemocytes. In ex vivo assays, any AMPs may be diluted too much to effectively attack the bacterial membrane. A prediction is then that there should be an altered phagocytosis of Gramnegative bacteria in IMD-pathway mutants, e.g., an imd null mutant but not the hypomorphic imd[1] allele. This could easily be tested by ReproSci using the adult phagocytosis assay used by Kocks et al, Cell, 2005. At the very least, the part on the role of Eater in phagocytosis should take the Chung &Kocks study into account, and the conclusions modulated.

      We agree that experimental conditions can always influence the outcome and may partly explain discrepancies in reproducibility. However, our approach is based on conceptual replication, with the aim of testing the generalizability of the original findings rather than reproducing them under identical conditions. We often used approaches quite similar to the initial statements.

      In the case of Eater, our results challenge the conclusion from Kocks et al. that phagocytosis of Gram-negative bacteria is impaired in the absence of Eater, as this was not confirmed when using null eater mutants. Nevertheless, our data support the idea that Eater contributes to the phagocytosis of Gram-negative bacteria, likely in cooperation with NimC1.

      Importantly, our findings are fully consistent with those of Chung and Knocks, who showed that the extracellular domain of Eater binds Gram-positive but not Gram-negative bacteria. Therefore, our work refines a specific aspect of the original conclusion, without questioning the overall significance of the initial Eater studies.

      Another point is that some mutant phenotypes may be highly sensitive to the genetic background, for instance, even after isogenization in two different backgrounds. In the framework of a Reproducibility project, there might be no other option for such cases than direct reproduction of the experiment as relying solely on inference may not be reliable enough.

      With respect to the experimental part, some minor weaknesses have been noted. The authors rely on survival to infection experiments, but often do not show any control experiments with mock-challenged or noninfected mutant fly lines. In some cases, monitoring the microbial burden would have strengthened the evidence. For long survival experiments, a check on the health status of the lines (viral microbiota, Wolbachia) would have been welcome. Also, the experimental validation of reagents, RNAi lines, or KO lines is not documented in all cases.

      We thank the reviewer for their careful and constructive assessment. We agree with most of the points raised and have revised the manuscript accordingly. In particular, we have softened the wording throughout to make our evaluation less abrupt. We have also added a dedicated section on limitations at the end of the manuscript, highlighting potential sources of bias and the inherent constraints of our reproducibility approach.

      In addition, we have expanded the description of the tools used and the validation of the RNAi experiments (see below). We consider it unlikely that factors such as Wolbachia or Nora virus significantly affect our conclusions, as our assays rely on short-term survival experiments, whereas these variables are more likely to influence ageing-related phenotypes. That said, we fully acknowledge that unrecognized parameters—such as environmental conditions (e.g., humidity), which we did not systematically control—could contribute to discrepancies. This limitation, however, applies broadly to most attempts at experimental reproduction.

      More generally, many of the claims we challenge have not been revisited for over a decade. Our inability to validate some of them suggests that the underlying biology may be more complex than initially proposed. We recognize that some of our conclusions may be incorrect, and it would be surprising if all were entirely accurate. At the same time, reproducibility studies often face an asymmetry in standards of evidence, whereby contradicting a published claim requires a higher burden of proof than establishing it. We believe it is important to document and share such discrepancies. We also explicitly acknowledge in the revised manuscript that the ReproSci framework itself may contain errors. Even if a significant proportion of our assessments are not correct, we believe this work contributes meaningfully to clarifying ongoing debates in the field for most of them.

      Finally, since the launch of the website, we have received three comments from the community—one addressing a major claim (Sr-C1) and two addressing more minor points— which have led us to refine our conclusions. The limited feedback we received suggest a broad agreement of the community with our assessment. It illustrates the value of maintaining an open, community-driven resource. The public and evolving nature of the project is, in our view, a key strength.

      Reviewer #2 (Public review):

      Summary:

      The authors present an ambitious and large-scale reproducibility analysis of 400 articles on Drosophila immunity published before 2011. They extract major and minor claims from each article, assess their verifiability through literature comparison and, when possible, through targeted experimental re-testing, and synthesize their findings in an openly accessible online database. The goal is to provide clarity to the community regarding claims that have been contradicted, incompletely supported, or insufficiently followed up in the literature, and to foster broader community participation in evaluating historical findings. The manuscript summarizes the major insights emerging from this systematic effort.

      Strengths:

      (1) Novelty and community value: This work represents a rare example of a systematic, transparent, and community-facing reproducibility project in a specific research domain. The creation of a dedicated public platform for disseminating and discussing these assessments is particularly innovative.

      (2) Breadth and depth: The authors analyze an impressive number of publications spanning multiple decades, and they couple literature-based assessments with new experimental data where follow-up is missing.

      (3) Clarity of purpose: The manuscript carefully distinguishes between assessing evidential support for claims and judging the scientific merit of historical work. This helps frame the project as constructive rather than punitive.

      (4) Metascientific relevance: The analysis identifies methodological and contextual factors that commonly underlie irreproducible claims, providing a useful guide for future study design and interpretation.

      (5) Transparency: Supplementary datasets and the public website provide an exceptional degree of openness, which should facilitate community engagement and further refinement.

      We praise the reviewer for this assessment of our project.

      Weaknesses:

      (1) Subjectivity in selection: Despite the authors' efforts, the choice of which papers and claims to highlight cannot be entirely objective. This is an inherent limitation of any retrospective curation effort, but it remains important to acknowledge explicitly.

      We have added a section at the end of the discussion to discuss the limitation of our study.

      (2) Emphasis on irreproducible claims: The manuscript focuses primarily on claims that are challenged or found to be weakly supported. While understandable from the perspective of novelty, this emphasis may risk overshadowing the value of claims that are well supported and reproducible.

      We have added two sentences at the end of the introduction and at the beginning of the discussion that most claims are reproducible.

      (3) Framing and language: Certain passages could benefit from more neutral phrasing and avoidance of binary terms such as "correct" or "incorrect," in keeping with the open-ended and iterative nature of scientific progress.

      We have carefully checked the phrasing of our article to be more nuanced and avoid black-and-white assessments.

      (4) Community interaction with the dataset: While the website is an excellent resource, the manuscript could further clarify how the community is expected to contribute, challenge, or refine the annotations, especially given the large volume of supplementary data.

      This point is already addressed in the revised sentence: “We hope that the community accessible website will encourage researchers to share their perspectives and contribute data from diverse sources, thereby improving objectivity.” In addition, we have proactively engaged with principal investigators to motivate them to contribute to this effort by sharing unpublished results and information. However, it remains unclear to what extent scientists are genuinely interested in reproducibility, as has been noted in other studies on the topic. For example, articles that challenge previous findings are often under-cited, and the presence of contradictory evidence does not necessarily prevent researchers from continuing to cite the original claims. This is clearly a challenge of reproducibility study. Comments, evidence and corrections will not be integrated in the ReproSci website. We have already updated this database when revising this article and will continue to do it.

      (5) Minor inconsistency: The manuscript states that papers from 1959-2011 were included, but the Methods section mentions a range beginning in 1940.

      This should be aligned for clarity.

      We have solved this discrepancy by correcting the method section. This should be 1959-2011. Thank you for spotting this mistake.

      Impact and significance:

      This contribution is likely to have a meaningful impact on both the Drosophila immunity community and the broader scientific ecosystem. It highlights methodological pitfalls, encourages transparent post-publication evaluation, and offers a reusable framework that other fields could adopt. The work also has pedagogical value for early-career researchers entering the field, who often struggle to navigate contradictory or outdated claims. By centralizing and contextualizing these discussions, the manuscript should help accelerate more robust and reproducible research.

      Reviewer #3 (Public review):

      Summary:

      In this ambitious study, the authors set out to analyse the validity of a number of claims, both minor and major, from 400 published articles within the field of Drosophila immunity that were published before 2011. The authors were able to determine initially if claims were supported by comparing them to other published literature in the field and, if required, by experimentally testing 'unchallenged' claims that had not been followed up in subsequent published literature. Using this approach, the authors identified a number of claims that had contradictory evidence using new methods or taking into account developments within the field post-initial publication. They put their findings on a publicly available website designed to enable the research community to assess published work within the field with greater clarity.

      Strengths:

      The work presented is rigorous and methodical, the data presentation is high quality, and importantly, the data presented support the conclusions. The discussion is balanced, and the study is written considerately and respectfully, highlighting that the aim of the study is not to assign merit to individual scientists or publications but rather to improve clarity for scientists across the field. The approach carried out by the researchers focuses on testing the validity of the claims made in the original papers rather than testing whether the original experimental methods produced reproducible results. This is an important point since there are many reasons why the original interpretation of data may have understandably led to the claims made. These potential explanations for irreproducible data or conclusions are discussed in detail by the authors for each claim investigated.

      The authors have generated an accompanying website, which provides a valuable tool for the Drosophila Immunity research community that can be used to fact-check key claims and encourages community engagement. This will achieve one important goal of this study - to prevent time loss for scientists who base their research on claims that are irreproducible. The authors rightly point out that it is impossible (and indeed undesirable) to avoid publication of irreproducible results within a field since science is 'an exploratory process where progress is made by constant course correction'. This study is, however, an important piece of work that will make that course correction more efficient.

      Weaknesses:

      I have little to recommend for the improvement of this manuscript. As outlined in my comments above, I am very supportive of this manuscript and think it is a bold and ambitious body of work that is important for the Drosophila immunity field and beyond.

      We thank the reviewer for this positive assessment. This was indeed a huge amount of work with the hope this would be useful.

      Reviewer #4 (Public review):

      This is an important paper that can do much to set an example for thoughtful and rigorous evaluation of a discipline-wide body of literature. The compiled website of publications in Drosophila immunity is by itself a valuable contribution to the field. There is much to praise in this work, especially including the extensive and careful evaluation of the published literature.

      However, there are also cautions.

      We praise the reviewer for this assessment of our project.

      One notable concern is that the validation experiments are generally done at low sample sizes and low replication rates, and often lack statistical analysis. This is slippery ground for declaring a published study to be untrue. Since the conclusions reported here are nearly all negative, it is essential that the experiments be performed with adequate power to detect the originally described effects. At a minimum, they should be performed with the same sample size and replication structure as the originally reported studies.

      Many of the original claims we are assessing were obtained with lower sample size than our replicability experiments. Furthermore, we are often complementing our data with alternative approaches that are more robust (survival with additional bacterial strains, AMP kinetics with several time points, etc.). Although we can never claim for a total absence of an effect, our experimental designs are largely sufficient to demonstrate that we cannot report the major effects reported in the initial publications.

      The first section of Results should be an overview of the general accuracy of the literature.

      Of all claims made in the 400 evaluated papers, what proportion fell into each category of "verified", "unchallenged", "challenged", "mixed", or "partially verified"? This summary overview would provide a valuable assessment of the field as a whole. A detailed dispute of individual highlighted claims could follow the summary overview.

      We have described the statistic of our analysis on claim validity in a companion article that will be published back-to-back with this article. We have preferred to divide assessment of claims and statistics on replicability in two articles. The present one is to provide an overview of the main claims that we considered to be fragile. Following the reviewer’s comment, we have added at the start of the manuscript a sentence stating that most of the claims were found to be validated, highlighting the solidity of the field with a link to the other article.

      Section headings are phrased as declarative statements, "Gene X is not involved in process Y", which is more definitive phrasing than we typically use in scientific research. It implies proving a negative, which is difficult and rare, and the evidence provided in the present manuscript generally does not reach that threshold. A more common phrasing would be "We find no evidence that gene X contributes to process Y". A good model for this more qualified phrasing is the "We conclude that while Caspar might affect the Imd pathway in certain tissue-specific contexts, it is unlikely to act as a generic negative regulator of the Imd pathway," concluding the section on the role of Caspar. I am sure the authors feel that the softer, more qualified phrasing would undermine their article's goal of cleansing the literature of inaccuracies, but the hard declarative 'never' statements are difficult to justify unless every validation experiment is done with a high degree of rigor under a variety of experimental conditions. This caveat is acknowledged in the 3rd paragraph of the Discussion, but it is not reflected in the writing of the Results. The caveat should also appear in the Introduction.

      We fully agree with the reviewer and we have softened the way we discuss all the claims in the result section.

      The article is clear that "Claims were assessed as verified, unchallenged, challenged, mixed, or partially verified," but the project is called "reproducibility project" in the 7th line of the abstract, and the website is "ReproSci". The fourth line of the abstract and the introduction call some published research "irreproducible". Most of the present manuscript does not describe reproduction or replication. It describes validation, or independent experimental tests for consistency. Published work is considered validated if subsequent studies using distinct approaches yielded consistent results. For work that the authors consider suspicious, or that has not been subsequently tested, the new experiments provided here do not necessarily recreate the published experiment. Instead, the published result is evaluated with experiments that use different tools or methods, again testing for consistency of results. This is an important form of validation, but it is not reproduction, and it should not be referred to as such. I strongly suggest that variations of the words "reproducible" or "replication" be removed from the manuscript and replaced with "validation". This will be more scientifically accurate and will have the additional benefit of reducing the emotional charge that can be associated with declaring published research to be irreproducible.

      We agree on this point. In response to the reviewer’s comment, we have clarified our terminology. Specifically, we now use the term “conceptual replicability” in the abstract and consistently refer to “replicability” and “irreplicability” throughout the manuscript. We hope this revised terminology improves clarity.

      The manuscript includes an explanatory passage in the Results section, "Our project focuses on assessing the strength of the claims themselves (inferential/indirect reproducibility) rather than testing whether the original methods produce repeatable results (results/direct reproducibility). Thus, our conclusions do not directly challenge the initial results leading to a claim, but rather the general applicability of the claim itself." Rather than first appearing in Results, this statement should appear prominently in the abstract and introduction because it is a core element of the premise of the study. This can be combined with the content of the present Disclaimer section into a single paragraph in the Introduction instead of appearing in two redundant passages. I would again encourage the authors to substitute the word validation for reproduction, which would eliminate the need for the invented distinction between indirect versus direct reproduction. It is notable that the authors have chosen to title the relevant Methods section "Experimental Validation" and not "Replication".

      The disclaimer has been moved at the end of the introduction and one sentence has been added in the abstract.

      Experimental data "from various laboratories" in the last paragraph of the Introduction and the first paragraph of the Results are ambiguous. Since these new experiments are part of the central core of the manuscript, the specific laboratories contributing them should be named in the two paragraphs. If experiments are being contributed by all authors on the manuscript, it would suffice to say "the authors' laboratories". The attribution to "various labs" appears to be contradicted by the Discussion paragraph 2, which states "the host laboratory has expertise in" antibacterial and antifungal defense, implying a single lab. The claim of expertise by the lead author's laboratory is unnecessary and can be deleted if the Lemaitre lab is the ultimate source of all validation experiments.

      The authors and laboratories that did the experiments are listed in the author list and are easy to identify for experts. At the same time, we did not want to highlight the specific authors to avoid a negative impact on their career. We were very happy that a small number of labs helped in our project. Since all the supplement were reviewed by Hannah Westlake (who has left the lab and the project) and myself (BL), our expertise was important as we did not want to test articles far from what we are doing in the lab. I rephrase the sentence to make it clear.

      The passage on the controversial role of Duox in the gut is balanced and scholarly, and stands out for its discussion of multiple alternative lines of evidence in the published literature and supplement. This passage may benefit from research by multiple groups following up on the original claims that are not available for other claims, but the tone of the Duox section can be a model for the other sections.

      Comments on other sections and supplements:

      I understand the desire to explain how original results may have been obtained when they are not substantiated by subsequent experiments. However, statements such as "The initial results may have been obtained due to residual impurities in preparations of recombinant GNBP1" and "Non-replicable results on the roles of Spirit, Sphinx and Spheroide in Toll pathway activation may be due to off-target effects common to first-generation RNAi tools" are speculation. No experimental data are presented to support these assertions, so these statements and others like them (currently at the end of most "insights" sections) should not appear in Results. I recognize that the authors are trying to soften their criticism of prior studies by providing explanations for how errors may have occurred innocently. If they wish to do so, the speculative hypotheses should appear in the Discussion.

      The statement in Results that "The initial claim concerning wntD may be explained by a genetic background effect independent of wntD" similarly appears to be a speculation based on the reading of the main text Results. However, the Discussion clarifies that "Here, we obtained the same results as the authors of the claim when using the same mutant lines, but the result does not stand when using an independent mutant of the same gene, indicating the result was likely due to genetic background." That additional explanation in the Discussion greatly increases reader confidence in the Result and should be explained with reference to S5 in the Results. Such complete explanations should be provided everywhere possible without requiring the reader to check the Supplement in each instance.

      In some cases, such as "The results of the initial papers are likely due to the use of ubiquitous overexpression of PGRP-LE, resulting in melanization due to overactivation of the Imd pathway and resulting tissue damage", the claim to explain the original finding would be easy to test. The authors should perform those tests where they can, if they wish to retain the statements in the manuscript. Similarly, the claim "The published data are most consistent with a scenario in which RNAi generated off-target knockdown of a protein related to retinophilin/undertaker, while Undertaker itself is unlikely to have a role in phagocytosis" would be stronger if the authors searched the Drosophila genome for a plausible homolog that might have been impacted by the RNAi construct, and then put forth an argument as to why the off-target gene is more likely to have generated the original phenotype than the nominally targeted gene. There is a brief mention in S19 that junctophilin is the authors' preferred off-target candidate, but no evidence or rationale is presented to support that assertion. If the original RNAi line is still available, it would be easy enough to test whether junctophilin is knocked down as an offtarget, and ideally then to use an independent knockdown of junctophilin to recapitulate the original phenotype. Otherwise, the off-target knockdown hypothesis is idle speculation.

      A good model is the passage on extracellular DNA, which states, "experiments performed for ReproSci using the original DNAse IIlo hypomorph show that elevated Diptericin expression in the hypomorph is eliminated by outcrossing of chromosome II, and does not occur in an independent DNAse II null mutant, indicating that this effect is due to genetic background (Supplementary S11)." In this case, the authors have performed a clear experiment that explains the original finding, and inclusion of that explanation is warranted. Similar background replacement experiments in other validations are equally compelling.

      We believe that offering possible explanations for discrepancies between results is valuable for readers. For this reason, we have generally retained these elements in the Results section, with the exception of PGRP-SD. In that specific case, we removed the sentence stating that we could not identify the expected mutation in the original PGRP-SD mutants (see below). Although these points could have been moved to the Discussion, doing so would have made it difficult to directly associate each explanation with the corresponding claim. We therefore consider that keeping them in the Results section provides greater clarity. Our hypotheses are grounded in well-established considerations. It is known, for example, that early generations of RNAi constructs often exhibited off-target effects, that commercially available LPS preparations (e.g., from Sigma) were sometimes contaminated, and that genetic background can significantly influence immunological outcomes. It is therefore essential to consider and discuss such confounding factors, particularly those that were not fully appreciated at the time the field emerged.

      Identifying motivated collaborators and coordinating experimental validation proved to be extremely time-consuming, making it impractical to systematically provide experimental verification for every discrepancy. As a result, most of our explanations remain speculative, except for the cases of WntD and DNase II, where the observed differences can be attributed to genetic background effects.

      The statement "Analysis of several fly stocks expected to carry the PGRP-SDdS3 mutation used in the initial study revealed the presence of a wild-type copy PGRP-SD, suggesting that either the stock used in this study did not carry the expected mutation, or that the mutation was lost by contamination prior to sharing the stock with other labs" provides a documentable explanation of a potential error in the original two manuscripts, but the subsequent "analysis of several fly stocks" needs citations to published literature or explanation in the supplement. It is unclear from this passage how the wildtype allele in the purportedly mutant stocks could have led to the misattribution of function to PGRP-SD, so that should be explained more clearly in the manuscript.

      We have removed this statement in the revised version from the result section

      The originally claimed anorexia of the Gr28b mutation is explained as having been "likely obtained due to comparison to a wild-type line with unusually high feeding rates". This claim would be stronger if the wildtype line in question were named and data showing a high rate of feeding were presented in the supplement or cited from published literature. Otherwise, this appears to be speculation.

      The wild-type fly stocks we used are named in the supplement.

      In the section "The Toll immune pathway is not negatively regulated by wntD", FlyAtlas is cited as evidence that wntD is not expressed in adult flies. However, the FlyAtlas data is not adequately sensitive to make this claim conclusively. If the present authors wish to state that wntD is not expressed in adults, they should do a thorough test themselves and report it in the Supplement.

      Alternatively, the statement "data from FlyAtlas show that wntD is only expressed at the embryonic stage and not at the adult stage at which the experiments were performed by (Gordon et al., 2005a)" could be rephrased to something like "data from FlyAtlas show strong expression of wntD in the embryo but not the adult" and it should be followed by a direct statement that adult expression was also found to be near-undetectable by qPCR in supplement S5. That data is currently "not shown" in the supplement, but it should be shown because this is a central result that is being used to refute the original claim. This manuscript passage should also describe the expression data described in Gordon et al. (2005), for contrast, which was an experimental demonstration of expression in the embryo and a claim "RT-PCR was used to confirm expression of endogenous wntD RNA in adults (data not shown)."

      We agree on this but the expression pattern provided from fly atlas and other genomic resource is an additional element that reinforce our conclusion. For instance, the observation that the expression pattern of WntD is limited to early embryo is consistent with a role in the regulation of Toll pathway in D/V patterning but not immunity. The use of the expression pattern is not the key element of our assessments that rely on the use of loss-of-function mutation. We have rephrased the text in the manuscript according to Reviewer’s suggestion.

      Inclusion of the section on croquemort is curious because it seems to be focused exclusively on clearance of apoptotic cells in the embryo, not on anything related to immunity. The subsection is titled "Croquemort is not a phagocytic engulfment receptor for apoptotic cells or bacteria", but the text passage contains no mention of phagocytosis of bacteria, and phagocytosis of bacteria is not tested in the S17 supplement. I would suggest deleting this passage entirely if there is not going to be any discussion of the immune-related phenotypes.

      The reviewer is correct and we have in the revised version removed ‘bacteria’ from the title. We have kept the section S17 because this statement ‘Crq recognize and uptake apoptotic cells’ (Science, Immunity)’ is still a source of confusion while the role of crq is more downstream. We agree that efferocytosis might not considered as part of innate immunity but we consider these articles were important in the Drosophila immunity community at that time.

      The claim "Toll is not activated by overexpression of GNBP3 or Grass: Experiments performed for ReproSci find that contrary to previous reports, overexpression of GNBP3 (Goear et al., 2006) or Grass (El Chamy et al., 2008) in the absence of immune challenge does not effectively activate Toll signaling (Supplementaries S6, S7)" is overly strongly stated unless the authors can directly repeat the original published studies with identical experimental conditions. In the absence of that, the claim in the present manuscript needs to be softened to "we find no evidence that..." or something similar. The definitive claim "does not" presumes that the current experiments are more accurate or correct than the published ones, but no explanation is provided as to why that should be the case. In the absence of a clear and compelling argument as to why the current experiment is more accurate, it appears that there is one study (the original) that obtained a certain result and a second study (the present one) that did not. This can be reported as an inconsistency, but the second experiment does not prove that the first was an error.

      We have softened our statement and revised the supplement document. The title of the section is now ‘Toll is not strongly activated by overexpression of GNBP3 or Grass’. However we want to keep these supplements. The comment of the reviewer applies to all our assessment as we could be wrong. We are using experimental validation and not replication, although in this present case, we are using very similar approach to the described in the original study. Of note, the fact that over-expression of GNBP3 does not consistently activate Drs was already stated in Buchon et al 2019 and we spend quite sometimes in attempts to repeat this experiment. Finally, the Ferrandon team that produce this claim can easily repeat the experiment. This does not affect the main conclusion of their landmark article on GNBP3.

      The same comment applies to the refutation of the roles for Edin and IRC. Even though the current experiments are done in the context of a broader validation study, this does not automatically make them more correct. The present work should adhere to the same standards of reporting that we expect in any other piece of science.

      We fully agree with the reviewer but the comments he/she raised apply to all the assessments made in our article and nearly all the reproducibility experiments. Our assessment does not state that we are right but that we fail to conceptually reproduce the claim. This is clearly mentioned in our article. We hope to see clarification in the future. It may be one day shown that i) IRC indeed regulates intestinal immunity, ii) PGRP-LE will be shown to have an extracellular form, iii) 18W could indeed be a PRR for LPS (maybe in a specific tissues), iv) PGRPSD will be shown to function upstream of Toll, v) that Crq indeed binds apoptotic cell to internalize them… Our study highlights statements that are source of debate, but this is far from being clear. The authors of these challenged claims have the opportunity to contradict us.

      The statement "Furthermore, evidence from multiple papers suggests that this result, and other instances where mutations have been found to specifically eliminate Defensin expression, is likely due to segregating polymorphisms within Defensin that disrupt primer binding in some genetic backgrounds and lead to a false negative result (Supplementary S20)" should include citations to the multiple papers being referenced. This passage would benefit from a brief summary of the logic presented in S20 regarding the various means of quantifying Defensin expression.

      We appreciate the value of citing some of the papers underlying this claim. Key papers in question are discussed at length in Supplement S20, and the original papers do not realize the issue we are reporting. We therefore modified the text as follows: "Furthermore, this result, and other instances where mutations have been found to specifically eliminate Defensin expression, is likely due to segregating polymorphisms within Defensin that disrupt primer binding in some genetic backgrounds, leading to false negative results (Brennan 2007,Neyen 2014 and see Supplementary S20)”. The hypothesis we raise, namely that defensin function may involve specific polymorphisms that are absent from the reference Drosophila genome, is an important consideration that should be communicated to the community.

      In S22 Results, the statement "For general characterization of the IrcMB11278 mutant, including developmental and motor defects and survival to septic injury, see additional information on the ReproSci website" is not acceptable. All necessary information associated with the paper needs to be included in the Supplement. There cannot be supporting data relegated to an independent website with no guaranteed stability or version control. The same comment applies to "Our results show that eiger flies do not have reduced feeding compared to appropriate controls (See ReproSci website)" in S25.

      The ReproSci website contains additional data that we could not display in the supplement but may be useful to scientists. We believe that it is worth to mention them. We have however changed the writing stating ‘Experiments reported in the ReproSci website’ rather than ‘See ReproSci website’ to make it clear that the data are not part of the supplement.

      For IRC: We have added a new section that addresses the general characterization of the IrcMB11278 mutant in the revised version (Supplement 22 Figure 2 and 3 with associated text). We do not refer to the ReproSci website anymore.

      Supplement S21 appears to show a difference between the wildtype and hemese mutants in parasitoid encapsulation, which would support the original finding. However, the validation experiment is performed at a small sample size and is not replicated, so there can be no statistical analysis. There is no reported quantification of lamellocytes or total hemocytes. The validation experiment does not support the conclusion that the original study should be refuted. The S21 evaluation of hemese must either be performed rigorously or removed from the Supplement and the main text.

      To address reviewers’ comment, we generated two new Hemese null mutants, Hemese<sup>JP187</sup> and Hemese<sup>JP828</sup>, carrying deletions in the central region of the gene. Using these lines, we show that wild-type and Hemese mutant larvae display comparable levels of lamellocytes and similar numbers of melanized capsules following wasp infestation. We further quantified lamellocyte differentiation and included appropriate statistical analyses. We have removed the previous results obtained with the Hemese<sup>SK2</sup> mutant, which yielded similar observations. By reproducing our previous findings with independent mutant lines and by providing quantitative analyses, we strengthen our conclusion that Hemese does not act as a negative regulator of lamellocyte differentiation upon wasp infestation.

      In S22, the second sentence of the passage "Due to the fact that IrcMB11278 flies always survived at least 24h prior to death after becoming stuck to the substrate by their wings, we do not attribute the increased mortality in Ecc15-fed IrcMB11278 flies primarily to pathogen ingestion, but rather to locomotor defects. The difference in survival between sucrose-fed and Ecc15-fed IrcMB11278 flies may be explained by the increased viscosity of the Ecc15-containing substrate compared to the sucrose-containing substrate" is quite strange. The first sentence is plausible and a reasonable interpretation of the observations. But to then conclude that the difference between the bacterial treatment versus the control is more plausibly due to substrate viscosity than direct action of the bacteria on the fly is surprising. If the authors wish to put forward that interpretation, they need to test substrate viscosity and demonstrate that fly mortality correlates with viscosity. Otherwise, they must conclude that the validation experiment is consistent with the original study.

      The viscosity of a vial can easily be assessed by direct observation. Here, we provide an information that may guide further research but this is by no way a strong point of our article. Thus, we did not do any change to address this comment. We agree that the function of IRC should be entirely re-visited. We have however changed the name viscosity for stickiness to be more neutral.

      In S27, the visualization of eiger expression using a GFP reporter is very non-standard as a quantitative assay. The correct assay is qPCR, as is performed in other validation experiments, and which can easily be done on dissected fat body for a tissue-specific analysis. S27 Figure 1 should be replaced with a proper experiment and quantitative analysis. In S27 Figure 2, the authors should add a panel showing that eiger is successfully knocked down with each driver>construct combination. This is important because the data being reported show no effect of knockdown; it is therefore imperative to show that the knockdown is actually occurring. The same comment applies everywhere there is an RNAi to demonstrate a lack of effect.

      Eiger expression: Previous RNA-seq (Flyseq, Troha et al., 2018) or Affymetrix array using poly (A)- RNA (De Gregorio et al., 2001) have never identified eiger as an immune-induced gene. We have used a previously characterized GFP reporter gene using the same approach done by the author to attempt to see an induction in the fat body without success. Our conclusions have been nuanced.

      Eiger RNAi KD: The two RNAi lines we are using have already been used in many studies assessing the function of eiger ((#108814, reference here, #45253 reference here)

      The Drosomycin expression data in S3 Figure 2A look extremely noisy and are presented without error bars or statistical analysis. The S4 claim that sphinx and spheroid are not regulators of the Toll pathway because quantitative expression levels of these genes do not correlate with Toll target expression levels is an extremely weak inference. The RNAi did not work in S4, so no conclusion should be inferred from those experiments. Although the original claims in dispute may be errors in both cases, the validation data used to refute the original claims must be rigorous and of an acceptable scientific standard.

      S3: The inducibility of antimicrobial peptide genes is high, albeit very variable. Importantly, we do not observe any inhibition of Drs expression at the two time points (24 and 48 h) following infection with two distinct bacteria, M. luteus and E. faecalis. Although each experiment was performed once, this corresponds to four independent tests challenging the claim from my lab that “Spheroide is required for activation of the Toll pathway.” Furthermore, if Spheroide played a significant role in Toll pathway activation, increased susceptibility to E. faecalis, B. subtilis, and B. bassiana would be expected. However, both survival data (three independent experiments) and Drs expression profiles (four conditions) consistently indicate that Spheroide is not required for Toll pathway activation. For clarity, we have removed the data related to S. aureus.

      S4: To address the reviewer’s comments and reinforce our conclusion, we generated a deletion removing both sphinx 1 and sphinx 2 and analyzed Toll pathway activity in the absence of these two serine proteases. We find that flies lacking sphinx 1 and sphinx 2 exhibit wild-type induction of Drosomycin following M. luteus infection, as well as normal survival upon E. faecalis challenge. These new data provide strong evidence supporting our claim that, in contrast to our previous report (Kambris et al., 2006), Sphinx 1 and Sphinx 2 do not regulate the Toll pathway. Accordingly, we have replaced the earlier RNAi-based results with data obtained from loss-of-function mutants.

      In S6 Figure 1, it is inappropriate to plot n=2 data points as a histogram with mean and standard errors. If there are fewer than four independent points, all points should be plotted as a dot plot. This comment applies to many qPCR figures throughout the supplement. In S7 Figure 1, "one representative experiment" out of two performed is shown. This strongly suggests that the two replicates are noisy, and a cynical reader might suspect that the authors are trying to hide the variance. This also applies to S5 Fig 3. Particularly in the context of a validation study, it is imperative to present all data clearly and objectively, especially when these are the specific data that are being used to refute the claim.

      S6: The figure 1 of S6 shows all the points. Collectively, this represents 6 replications using 3 drivers of the claim ‘over-expression of GNBP3 efficiently activate the Toll pathway’. The observation, we are now reporting in S6, was already mentioned in Buchon et al 2009 (PNAS). To address reviewer’s concern, we have removed the standard deviation. Finally, our conclusion is rather soft ‘Overexpression of GNBP3 either ubiquitously or in the fat body does not effectively activate the Toll pathway’. At most, we find similar to (El Chamy et al., 2008) that overexpression may produce ‘weak but detectable’ activation of Toll’.

      S7: We could not address this point. The point stands, there is no condition where overexpression of native Grass corresponds with higher Drosomycin expression

      Other comments:

      In S26, the authors suggest that much of the observed melanization arises from excessive tissue damage associated with abdominal injection contrasted to the lesser damage associated with thoracic injection. I believe there may be a methodological difference here. The Methods of S27 are not entirely clear, but it appears that the validation experiment was done with a pinprick, whereas the original Mabary and Schneider study was done with injection via a pulled capillary. My lab group (and I personally) have extensive experience with both techniques. In our hands, pinpricks to the abdomen do indeed cause substantial injury, and the physically less pliable thorax is more robust to pinpricks. However, capillary injections to the abdomen do virtually no tissue damage - very probably less than thoracic injections - and result in substantially higher survivals of infection even than thoracic injections. Thus, the present manuscript may infer substantial tissue damage in the original study because they are employing a different technique.

      The reviewer is correct that injection and pinprick injury do not induce the same type of injury and there are differences across labs. We still believe that our approach is valuable. The point is that we confirmed a role of eiger in melanization but not due to its expression in the fat body. It is important to underline that our ReproSci is a conceptual replication project and that we never directly challenge the original observation but rather the robustness of the claim. Injection with a pulled capillary was attempted — in our hands, this caused extensive trauma and killed over 80% of flies it was attempted on. The pinprick on the other hand caused very little trauma. The first sentence of the Results/discussion section states this point.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      It is questionable whether all the studies relevant to the field of assessment have been identified, starting with the own publications from the lead authors. Indeed, Buchon et al, Genes & Development, 2009, Buchon et al., Cell Host&Microbe, 2009, Buchon et al., BMC Biology, 2010 have not been included in the 400 articles, even though one of them is cited when discussing the role of Duox in the intestinal host defense. Other missing articles that fulfill the criteria set by the authors include, but may not be limited to, Chung et al. JBC, 2011, Nehme et al., PLoS One, 2011, Cronin et al., Science, 2009, Limmer et al. 2011.

      In our selection, we did not choose articles dealing with intestinal homeostasis and stem cells as well as microbiota/symbionts (Buchon 2009a,b and 2010 Cronin 2009). We also did not analyze articles centered on virulence factor (Limmer 2011). I agree that we missed Chung et al. JBC, 2011 and Nehme et al., PLoS One, 2011 that were published in 2011. Table S1 provide all the information on the article we selected and the ones we removed from the first search.

      One area of the field that would have benefited from further insights of ReproSci is that of innate immunity memory or priming (Pham et al., PLoS Pathogens, 2007, has been included in the 400 articles). That being said, the scope of this article is already considerable, and this is just a suggestion for future scrutiny.

      The reviewer should recognize that this study (Pham et al.) is inherently complex to reproduce. The field of innate immune priming has also matured considerably since the first publications. Importantly, the set of articles containing “non-validated claims” does not fully overlap with the category of studies that might be considered as weak or problematic.

      A major issue in science is the exaggeration of claims, which our approach does not capture. As a result, some articles that are strongly promoted yet make relatively trivial claims— often a source of frustration within the community—may still be classified as valid under our framework. This represents an important limitation of our approach.

      For the Supplements, it would be better to show all the data points, especially with respect to RTqPCR data. The nature of the error bars (SD, SE) is never indicated except for one exception (Figure 5, S28). When introducing or using novel lines affecting gene expression, such as RNAi lines or KO mutants, there are often no indications as to how these lines have been validated.

      This is especially critical for negative results.

      Errors bars: The errors bars were standard deviation. Sorry for this lacks of clarity. This is now indicated in the supplement. Experiments were done in multiple laboratories requiring complex coordination. For this reason, changing all the graphs was too complicated and could not be done for all the figures in the revised version. However data points are shown in many figures of the original draft and all the new figures of the revised version.

      For RNAi validation: Concerning the RNAi validation:

      (a) Sphinx ½: We have extended our work on Sphinx using a newly generated double

      mutant that remove both Sphinx1 and 2. Thus our conclusion does not rely anymore only the use of RNAi.

      (b) S12: Hep RNAi : We are using standard Hep-RNAi and BskDN fly tools that have been validated in many studies.

      Articles validating the HepRNAi (BDSC35210) can be found on this link. This line has also been validated in Figure 1 of the revised version of Supplement S12.

      Articles validating the BskDN (BDSC6409) can be found on this link.

      Of note, the Bloomington number of Hep RNAi was miss-annotated and has been corrected in the revised version.

      (a) S16. Dscam: The Trip RNAi lines has been used in other articles (ex. Bui et al., “Adjacent neuronal fascicle guides motoneuron dendritic branching 2024 (eNeuro)).

      (b) S23. Duox. We are using two RNAi lines including the original RNAi construct used in the article we assessed (Ha et al., 2005).

      (c) Eiger: we are using two distinct UAS-RNAi constructs that have been validated (#108814, reference here, #45253 reference here).

      Loss-of-function mutations:

      For previously described mutants: we used some previously described mutants available in stock centers or from other labs. These include Spheroide<sup>D104</sup>, wntD mutants Irc<sup>MB11278</sup>, NOS<sup>Δall</sup> and Gr28b. See references in the supplement. We have always checked the genotypes of these mutants.

      For newly generated mutants

      spirit<sup>DR32</sup> see Figure 1 in S2

      edin<sup>KO</sup>: we have added a new figure describing the mutant in S14 D

      ΔSphinx1/2 we have added a figure describing the mutant in S4

      DNaseII<sup>sk4</sup>: the exact frameshift mutation is indicated in S11

      SR-CI<sup>SK6</sup> and Δ(Sr-CI,Sr-CIII): we have added a figure describing the mutant in S18

      Hemese<sup>JP187</sup> and HemeseJP<sup>828</sup> we have added a figure describing the mutant in S21

      Listericin<sup>Δ1</sup> and Listericin<sup>Δ2</sup> mutation are described in Figure 1 in S15

      We have now included in the revised supplement a table with all the new tools generated in the framework of the ReproSci project (Supplementary Table 4).

      The only uncharacterized mutant is PGRP-LE<sup>Δ53</sup>, a gift from François Rouyer (Gif-sur-Yvette). But we also did our experiments with an established mutant: PGRP-LE<sup>112</sup>.

      Is the repeated use of "in vitro" to refer to cell culture studies appropriate? In vitro usually refers to experiments performed in the absence of any living organism or cell, e.g., biochemical reactions in a test tube.

      Accordingly, we have replaced in vitro by in cell culture when appropriate

      (A) MAIN TEXT

      (1) Introduction:

      PGRP-LC: Ramet et al, Nature 2002 reference is missing.

      "[...]epithelial repair through stem cell proliferation" is a typical disease tolerance mechanism and therefore unlikely to "[...]contribute to DETERRING ingested pathogens". In addition, is "detterring" really the appropriate word?

      The changes have been made.

      (2) Results:

      GNBP1: "and find no evidence" would read better as "and found no evidence". In addition, for GNBP3, the authors may also want to cite Mishima et al. (JBC, 2009) and possibly one on silkworm ßGRP (Takehasi et al., PNAS, 2009).

      The changes have been made.

      Wnt D: "Dif" and not "dif"=diffuse irregular facets

      The changes have been made.

      cGLR-STING pathway (Cai et al., 2022): subsequent studies indicated that cGLR ligands are RNA molecules, not DNA. Thus, whether such receptors would be able to detect naked DNA remains a remote possibility.

      We agree and have amended the text accordingly

      DSCAM1: the statement "not amenable to genetic studies" might be modulated into "not easily amenable to genetic studies" as exemplified by Dong et al. PLoS Biol, 2025 (which the authors may now add to their reference list as this article was published after submission of the current work) and all the literature on DSCAM and the Drosophila nervous system.

      We agree and have amended the text accordingly

      Psidin: "multiple papers suggest...". References are not given.

      We have amended the text and reported to the supplement. Some references have been added.

      NOS: "this study reports" should be "this study reported"; otherwise, the reader falsely believes that the authors refer to the Reprosci article. NOS expression: what about data from subsequent RNAseq studies and data from FlyCell Atlas?

      We agree with the reviewer and used the past time. For simplicity, we did not discuss Nos expression pattern.

      Eiger: the Kodra et al. reference appears to be missing in the reference list.

      The Kodra reference has been added.

      (3) Discussion:

      Third source of irreproducible claims: besides off-target effects, a limitation of the RNAi approach is that one can never be sure that a null phenotype is obtained. Thus, when obtaining negative results, it may be caused by a hypomorphic effect.

      This additional point has been added with an example that illustrates the point “Besides off-target effects, a limitation of the RNAi approach is that one can never be sure that it mimics a full null phenotype. As an example, the partial silencing of the serine protease gene Grass may explain why its role in the antifungal response was not detected using in vivo RNAi, but was revealed through the use of a null mutation (El Chamy et al., 2008; Kambris et al., 2006b)”.

      (4) Reference list:

      Cuttell et al, Gordon et al, 2005, and Melcarne et al, 2019 are duplicated.

      Thank you for noticing it. Change has been made.

      (5) Supplementary materials:

      Table S2 and S3 appear to have been interchanged.

      This has been corrected.

      Ouyang et al: BioRXiv should be indicated.

      This has been corrected.

      (B) INDIVIDUAL SUPPLEMENTS:

      (1) S1: enzymatic activity of GNBP1.

      The number of independent experiments does not appear to be mentioned. With respect to the western blot shown in Figure 2A, it seems that the intensity of the band corresponding to activated ModSP is lower in lane 1 than in other lanes. The suggestion is to scan multiple blots to quantify the activation of this protein in different conditions. Do the authors know the nature of the high mobility band that appears solely when S. aureus peptidoglycan is added?

      Regarding the lower intensity of the activated ModSP band in lane 1, a similar phenomenon has been reported in An evolutionarily conserved serine protease network mediates melanization and Toll activation in Drosophila (Fig. S2), where ModSP autoactivation was observed following incubation with PGRP-SA, GNBP1, and peptidoglycans. In current experimental design, the primary objective was to determine whether preincubation of peptidoglycans with GNBP1 further enhances ModSP activation. Therefore, our analysis focused on comparisons across different incubation time points rather than direct comparison with lane 1. Within this context, the lower intensity of the activated ModSP band in lane 1 is expected and does not affect the interpretation of the results

      In response to the suggestion of scanning multiple blots for quantification, we note that no clear enhancement of ModSP autoactivation was observed across different incubation times. We also explored alternative incubation conditions, but none resulted in a detectable increase in activation. Thus, additional quantification would not change the overall conclusion.

      (2) S2: Spirit

      It would have been nice to display the spirit mRNA levels in the KO and KD mutants. Is the catalytic domain removed by the deletion and can the authors exclude a potential expression of a truncated Spirit from a cryptic translation initiation site upon leaky Gal4 expression of the transgene, hence the need for RTqPCR data using primers that map 3' to the transposon insertion?

      We are using a deletion that remove all the Spirit isoforms that all start with the same ATG which was removed in the mutant, so it's very unlikely. There is an HSP70T = Terminator after the Gal4, which should exclude an initiation of translation after Gal4. However, we have verified the expected mutation by PCR.

      Figure 2: Crystal cells appear to be present in spirit larvae. They, however, appear to be smaller, which may be difficult to measure. Quantification of the number of crystal cells is necessary.

      We have removed this part in the revised version (Figure 2C and associated text) because it does not concern the assessed claims in Kambris et al et., 2006 that Spirit regulates the Toll pathway.

      Figure 3: It is not clear if pooled data are presented for survival experiments.

      Yes survivals show pooled data. We have updated figure legend to make this clearer.

      (3) S4: Sphinx 1/2

      In the absence of testing null mutant flies, the conclusion may be too strong. Can the authors totally exclude the expression of these proteases in a few cells? If they function as apical proteases, a minimal expression may be sufficient to trigger a proteolytic cascade.

      To address the reviewer’s comments, we generated a deletion removing both sphinx 1 and sphinx 2 and analyzed Toll pathway activity in the absence of these two serine proteases. We find that flies lacking sphinx 1 and sphinx 2 exhibit wild-type induction of Drosomycin following M. luteus infection, as well as normal survival upon E. faecalis challenge. These new data provide strong evidence supporting our claim that, in contrast to our previous report (Kambris et al., 2006), Sphinx 1 and Sphinx 2 do not regulate the Toll pathway. Accordingly, we have replaced the earlier RNAi-based results with data obtained from loss-of-function mutants.

      (4) S5: wntD

      Figure 1, if any, appears to be missing.

      Thank you for noticing this. This is now corrected Figure 2=>Figure 1 and Figure 3=> Figure

      Fig. 3C-C': please, homogenize the color code with other panels (yw in blue and not suddenly in orange)

      We have homogenized the color code in the revised version.

      (5) S6-S7: Activation of Toll by overexpression of GNBP3 or protease genes The point is not framed appropriately: the overexpression studies were not intended to determine whether the overexpression of these genes activates the Toll pathway to levels of activation encountered during infection, but as tools for epistatic analysis, and in the case of S8, to identify novel protease genes that might be involved in Toll pathway activation. The results of the epistatic analysis are not disputed and, in some cases, have been validated by independent approaches (e.g., by hemolymph transfer experiments), and the hits of the protease screen have been validated by other laboratories, namely grass and SPE. In Figure S5B of Gottar et al, Cell, 2006, a level of only 30% of immunized was achieved, whereas it was about 70% in Figure 3A. Thus, the level of induction by overexpression of GNBP3 is variable but nevertheless sufficient for epistatic analysis both for Gottar et al. and for El Chamy et al., Nat. Immunol, 2008. Furthermore, levels of activation sufficient for epistatic analysis of Toll pathway activation or stimulation of the melanization proteolytic activation cascade upon the overexpression of either GNBP3 or GNBP1 together with PGRP-SA have been reached (Gobert et al., 2003; Gottar et al. 2006; El Chamy et al, 2008; Matskevich et al., Eur. J. Immunol., 2010). Thus, the overexpression of GNBP3 functions for genetic analysis purposes in two independent teams, that however, are working in the same research Unit. While one cannot exclude that enough peptidoglycan metabolites, e.g., TCT, are provided by the microbiota, it cannot be excluded that ß-(1-3)glucans might originate from the food or mycobiota, which may be distinct from that in the Reprosci laboratory. This illustrates the difficulty of controlling all parameters.

      We fully agree that the notion that GNBP3 functions upstream of serine protease is validated. The point is that we cannot reproduce the observation that overexpression of GNBP3 trigger a strong Toll activation sufficient for epistatic analysis as shown in Gottar et al., 2006. The point is that ReproSci does not challenged any of the major claims of Gottar et al. 2006 but only a minor claim. The magnitude of gene induction relative to the unchallenged condition is an important consideration in the field of Drosophila immunity and can be misleading. For example, a gene described as being induced fivefold may appear significant, whereas the same gene can be induced up to 1,000-fold following infection. Therefore, absolute expression level compared to challenged level, rather than fold changes alone, should be carefully considered when interpreting immune gene activation.

      We have done our studies with two different constructs. The observation that overexpression of a gene activate an immune pathway is by itself a result suggesting a concentration-dependent mode of activation. For instance, overexpression of ModSP is sufficient to activate the Toll pathway (Buchon 2009). This was not the case for GNBP3, even at low level. We cannot exclude that this could be the case in a specific background but at least not all background. Over-expression of GNBP3 is used in 3 panels of Gottar 2006 and this was a significant piece of data. On my side, my lab has lost significant amount of time to reproduce this without success.

      Now we could be wrong in our assessment but we let the author of this study to show it.

      To take in consideration Reviewer’s comment, we are softened our conclusions : ‘Overexpression of GNBP3 either ubiquitously or in the fat body does not effectively activate the Toll pathway’. At most, we find similar to (El Chamy et al., 2008) that overexpression may produce ‘weak but detectable’ activation of Toll’.

      References:

      Alphabetical order is not respected (Matskevich before Kim), and the Mishima reference is not optimal: it should be the JBC article in the same year that documents also the biochemistry of binding to ß-glucans.

      We have done the changes in the revised version

      (6) S8: PGRP-LE not in hemolymph

      Fink et al., Mucosal Immunol. 2016 used the NP1-Gal4, which is not expressed in enteroendocrine cells, as stated in the text, but in enterocytes (and possibly the nervous system).

      For controls, is the wild-type control the w[iso] Drosdel background?

      Please provide the supplier of the GFP antibody.

      We have addressed all the points in the revised version.

      (7) S9: PGRP-LE and melanization

      Figure 1D-E: A quantification would bolster the claim.

      We agree that quantification of Figure 1D-E would reinforce the claims but we believe that our data are sufficient to state that PGRP-LE does not block the cuticular melanization upon septic injury (which is also consistent with an intracellular role). We have softened our conclusion.

      (8) S10: PGRP-LE and bacillus

      Please provide a description of the Bacillus subtilis strain.

      This is a strain that derived from the Jean Lambert collection of Strasbourg and used in Lemaitre B PNAS 1997 (J.Millet and A. Klier, Pasteur Institute of Paris). We have checked the strain and confirm that it was indeed a B. subtilis strain that we name B subtillis FS.

      It is not clear from the Mat & Meth whether the 2-3 independent replicates per genotype represent biological replicates in one experiment or correspond to several independent experiments. Are the pooled data displayed?

      This information was on the methods. It has been copied to the figure caption.

      (9) S11: DNAse II and regulation of AMP genes

      The exact larval stage is not provided in S11, nor in the original study.

      The experiment was done on wandering third-instar larvae as indicated in the material and methods

      The number of flies for survival experiments is rather low, but this is not a major issue given the outcome of the experiment.

      We agree with the reviewer.

      What is the concentration of the injected DNA? As the solution was likely viscous, was the DNA sheared?

      We have used a concentration of DNA of: 200 ng/ul, which is now indicated in the revised version of the Supplement. DNA was no sheared.

      Figure 1A&D: A positive control of immunization would have been nice. 1D: At which time point was Diptericin expression measured by RTqPCR?

      Unchallenged flies were used in Figure 1A while in Figure 1D Diptericin was measured 6 h after challenged. We agree that a bacterial challenge control would have been nice, but clearly the level of Diptericin in 1A corresponds to levels observed in unchallenged flies. Of note, we have tried several different concentrations, of more or less long "sizes" of DNA (short or very long fragments), and also different DNA sources (E. coli or Drosophila) but we did not see any effect on the activation of the Imd pathway.

      DNAseII-deficient larvae have wild-type hemocyte numbers. His sub-title is not accurate as Figure 3 actually reports an INCREASED number of hemocytes in the DNase II mutant.

      The reviewer is correct and we have adjusted the text.

      (10) S12: JNK pathway and AMP gene expression

      Hep RNAi lines and possibly also the Bsk-DN line should be validated by monitoring puckered mRNA levels after a stress that activates the JNK pathway. The absence of this control weakens the Reprosci conclusions.

      As stated above, we are using standard Hep-RNAi and BskDN fly tools that have been validated in many studies.

      Articles validating the HepRNAi (BDSC35210) can be found on this link. We have also validated the RNAi in new Figure 1 of Supplement S12.

      Articles validating the BskDN (BDSC6409) can be found on this link

      The Bloomington number of Hep RNAi was miss-annotated and has been corrected in the revised version

      (11) S13: Caspar

      There is clearly a trend for improved survival of the Caspar mutant in Figure 1A. Please, show the pooled data and appropriate statistical analysis. The legend to 1A mentions one overexpression strain that is not displayed and has likely not been performed because loft experiments were performed at 25{degree sign}C, whereas OE experiments should be performed at 29{degree sign}C, hence in separate experiments.

      There was a mistake in the original figure legend when we mentioned the impact of overexpression of caspar on survival without showing the data (no effect was observed). The text has been removed in the revised version because it is not related to the claims we assessed from the Caspar article (Kim et al.,2006). To addressed reviewer’s comments, we now present the pooled data in Figure 1 of the revised Supplement S13. The conclusion is that we do not observe any increase host survival in caspar mutant related to wild-type consistent with our observation that the Imd pathway is not strikingly over-activated in absence of infection in caspar loss-of-function mutants.

      Why was the c729-Gal4 driver chosen? Has it been validated as a bona fide fat body driver?

      The use of c729-Gal4 was suggested by, and borrowed from, the laboratory of Prof. Gaiti Hasan, who had characterized and used it in adult fat body experiments (Subramanian M et al., 2013, Dis. Mod. & Mech). The BDSC number for the corresponding stock is BDSC_6983. Based on Flybase data, it is also expressed in somatic cells of male/female reproductive systems in the adult.

      The RTqPCR experiments are not overwhelmingly convincing for a lack of data points: pooled data should be shown, inasmuch as there is a significant difference upon Caspar OE 6h after Ecc15 challenge.

      We now show in the revised version of the Supplement S13 the pooled data from different experiments with individual points shown.

      In conclusion and although our results are variable, they do not reproduce the results produced in the original article by (Kim et al., 2006) According to our results, we observe:

      (1) no increase Dpt expression in absence of challenge both in caspar deficient larvae and adults.

      (2) A possible inhibition of Dpt upon over-expression caspar at 6h in adult.

      - A possible increased Dpt expression in caspar loss of function mutant larvae upon challenge.

      The later may suggest an inhibition role upon overexpression of caspar that required further analysis. The text of the supplement has been amended to take all those points in consideration.

      (12) S15: listericin

      Introduction: The LLO (also referred to as hly) L. monocytogenes mutant does not prevent the entry of Listeria inside cells. The pore-forming toxin listeriolysin is required for pathogen escape from the phagosome.

      There is no reference to LLO in the introduction of the S15 listericin section.

      Has the STAT-92E RNAi line been validated? It is difficult to draw a definitive conclusion on the absence of the role of the JAK-STAT pathway in adults using an RNAi transgene because it may only imperfectly silence the expression of the target gene. "Contrary to Goto et al.2010, in our hands reduction of JAK-STAT activity through RNAi [...] in adult flies": Goto et al. apparently tested the JAK-STAT pathway only in S2 cells. This statement should therefore be modulated accordingly.

      We have validated the STAT92 RNA in the revised version by confirming that Turandot gene expression is reduced in this mutant (Supplement 15 Figure 2 C-D). We have also validated the PGRP-LE<sup>112</sup> null mutant (Supplement 15 Figure 1C).

      We have adjusted the conclusion: “Contrary to (Goto et al., 2010), we find that Listericin expression is not specifically dependent on PGRP-LE, and is not dependent on JAK-STAT signaling in flies. Of note, this could be the case in S2 cells where the initial experiments were done.

      Antibacterial effects of listericin: It would be cautious not to rely solely on survival experiments, but also to monitor the bacterial titer to draw a definitive conclusion (assay used by Goto et al.). A control overexpressing GFP or a non-relevant peptide would have been welcome to control for potential metabolic effects that would decrease the effect of the overexpression. More importantly, Goto et al. used a Cg-Gal4 driver that is also expressed in hemocytes, which is likely not the case for the Lpp driver. Is it known if the c564 driver is also expressed in hemocytes and to the same level as the Cg-Gal4 driver?

      We have used two fat body drivers to overexpress Listerin, Lpp and C564, the latter being expressed in hemocytes indeed. We agree that bacterial counting would provide a sensitive assay but in absence of any protective effect of Listericin overexpression, we do not expect any major titer change in L. monocytogenes growth. To reinforce our conclusion, we have generated Listericin null mutations (Fig 4C) and we have shown that they are not more susceptible than the wild-type to L. monocytogenes.

      (13) S16: DSCAM1

      The authors use an RNAi construct to knock down DSCAM1 expression. Which part of the transcripts is targeted? Did the authors verify that silencing was effective and would affect all DSCAM transcripts?

      The DScam RNAI (BDSC #38945) that we are using has been used in other articles (link) and is validated in Kamiyama et al., Dev cell (Figure S4).

      Data points should be shown in the figure.

      Unfortunately, we could not address this point to the difficulty to reach the collaborator who provided the data.

      Their conclusions would be strengthened by directly testing opsonization using the assay developed by Haller et al., EMBO Reports, 2018. Also, to derive the phagocytic index, the authors must have counted the number of hemocytes retrieved from bled larvae? Was there a decrease when DSCAM1 was silenced in hemocytes? The use of a hml-Gal4-Gal80ts driver would have allowed to bypass the issue of developmental effects.

      We expect an opsonin to be abundant in the hemolymph but we did not find any secreted form of dScam in our proteomics analyses (Rommelaere 2024). 20 years after the publication of this landmark article in Science, the absence of any confirmation is puzzling and we are reluctant to do any additional experiment taking into consideration that phagocytosis assays are often variable.

      (14) S18: SR-CI

      The major issue with this supplement has been dealt with in the Public Review: no phagocytic receptor function was claimed in the Ramet et al, 2001 study.

      We fully agree with the reviewer and with Mika Rämet and Monty Krieger (personal communication), and we apologize for this misunderstanding. In fact, Rämet et al. (2001) did not present data demonstrating a role for Sr-CI in phagocytosis per se in his article, but rather showed that this receptor is involved in bacterial binding. Our confusion arose from a subtitle in the Results section of their article (“SR-CI Binds Both Gram-Positive and Gram-Negative Bacteria and Is Necessary for Optimal Phagocytosis by S2 Cells”) and the article title that claim that Sr-CI is a pattern recognition receptor, which does not reflect the content of the article.

      To clarify this point, we have revisited the original claim. Consistent with Rämet et al. (2001), we now observe a defect in hemocyte binding to bacteria in Sr-CI mutants. Thus, we validate the claim. Because Sr-CI is often assumed to function as a phagocytic receptor, we have also retained the experiments showing that it is not essential for the phagocytosis of E. coli or S. aureus. All experiments have been repeated during the revision process using a null single mutation and a double mutant removing both Sr-CI and Sr-CIII.

      Introduction: Is it really adequate to mention a phagocytic receptor for a ligand, dsRNA, that gets endocytosed?

      In the introduction, we summarized what is known on the Sr-CI, including its implication in the uptake of dsRNA. As suggested by the reviewer, we used the term endocytosis to refer to the ability to uptake dsRNA.

      Methods: Have the authors excluded the potential off-target effect of CRISPR-Cas9 in other scavenger receptor genes? Monitoring their expression levels would also allow the detection of any compensatory effect in the null mutant by overexpression of other scavenger receptors.

      We are now using two mutant lines: one carrying a frameshift mutation (SK6) that specifically affects Sr-CI, and another with a deletion removing both Sr-CI and Sr-CIII. We have carefully validated both mutations by genomic sequencing. As our results are now consistent with the original findings of Rämet et al. (2001), we believe that the analyses presented in the Supplementary Information are genetically well supported.

      Results: Figure 1 is really confusing with so much variability in the data, which raises doubts about the reliability of the method. The reduction of the phagocytic index in B is not "mild". Again, one would like to see the individual data points and to know what the error bars are. For 1G, has any statistical analysis been performed? Finally, Panels A to G lack homogeneity in their presentation. Also, why are the absolute values of the y-axis varying so much from panel to panel (A-B vs C-D; E vs. F)?

      We have repeated our experiments using a new set of mutants, both generated in the isogenic DrosDel background. In the revised version, we include additional experiments assessing the contribution of Sr-CI to hemocyte binding to bacteria. Consistent with the original report by Rämet et al. (2001), we observe similar results. We have revised the text accordingly and moderated our claims. In addition, we performed further biological replicates and now provide appropriate statistical analyses.

      (15) S19: Undertaker

      Undertaker has been identified through a deficiency screen that restricted the genomic zone of interest to eight genes. RNAi was then used to identify the candidate gene as being Undertaker. While an off-target effect of RNAi cannot be excluded, it cannot account for the results of the deficiency screen (junctophilin is on the second chromosome, whereas undertaker is on the third). The deficiency phenotype could be rescued by an Undertaker transgene. While overexpression can indeed provide a rescuing activity due to high levels of ectopic expression, why would it do so in the context of an 82F deficiency, unless this deficiency carries a second site mutation, which is a rather remote possibility.

      Given that the deficiency line deletes at least 14 genes in a cluster, several of which are involved in growth and development, we assume that another gene(s) within the span of this deficiency is responsible for the effect on efferocytosis, or that some other mutation in the uncontrolled genetic background is responsible. The impact of Df(3R)3-4 on efferocytosis remains to be investigated.

      The authors claim that Undertaker is not expressed at the right embryonic stage and hardly in S2 cells, which is correct according to large-scale data. However, Cuttell et al. successfully stained S2 cells with an antibody raised against retinophilin/undertaker by the investigators who studied the role of this gene in the eye (Mecklenburg et al., 2007). Should we trust more largescale datasets based on transcriptomics or a specially developed tool? (Both junctophilin and undertaker are at the limit of detection of the proteomic data available on FlyBase; equivalent signals in hemocytes were measured in FlyCellAtlas). Thus, the argument of the absence of expression is not definitive.

      We agree on this but the expression pattern is an additional element that reinforce our conclusion. This is not the key element that relies on the use of loss-of-function mutation. Another element is the absence of follow-up study in the last 18 years.

      The new phagocytosis data set generated by Reprosci shows an absence of impact of the undertaker loft mutation in larval hemocytes. Whether these data can be extrapolated to S2 cells, embryonic or adult hemocytes, remains debatable, as are the explanations put forth by Reprosci in their conclusion to explain by a possible off-target effect of RNAi on junctophilin. It would be interesting to determine the phenotype of junctophilin mutants in the ex vivo larval hemocyte assay.

      We have analyzed the role of Undertaker using third instar larvae in an ex vivo assay and did not find any role in efferocytosis. I think that all our data and analysis of the literature is consistent with our statement that’ that undertaker is unlikely to have a role in phagocytosis’. Now, we can never exclude that Undertake may play a subtle role in this process. The role of Draper has been observed in larval hemocyte that represent macrophage of flies. We are using a null mutation in undertaker preventing the existence of off-targets. We have nuanced our conclusion in the article and add a sentence in the conclusion of the supplement: ‘We cannot however exclude that Undertaker play a role at a specific stage of development that we did not assess.’

      (16) S20: Psidin

      This supplement is duplicated

      The duplicated supplement has been removed.

      Phagocytosis assay: the authors use E. coli-GFP. How can they discriminate between internalization and simple binding at the surface of hemocytes? It would have been more appropriate to use a phalloidin, stained to another color rather than green. The suggestion here is to first use FITC-labeled bacteria, followed by Trypan blue treatment to quench the fluorescence of noninternalized bacteria. Second, pHrodo bacteria could be used to monitor the acidification of the phagosome in wt and psidin hemocytes.

      We agree with the reviewer that this would be better. Because our observations are consistent with the data reported in this original article, we did not feel inclined to extend our analysis.

      (17) S21: Hemese

      The pie charts in Figure 1 would benefit from a statistical analysis. Why do the encapsulated eggs appear to be much larger in the hemese mutant? Could it be that more lamellocytes are recruited and make a thicker capsule? Should Figure 1C be imperatively quantified?

      To extent our analysis, we generated two new Hemese null mutants, Hemese<sup>JP187</sup> and Hemese<sup>JP828</sup>, carrying deletions in the central region of the gene. Using these lines, we show that wild-type and <sup>Hemese</sup> mutant larvae display comparable levels of lamellocytes and similar numbers of melanized capsules following wasp infestation. We further quantified lamellocyte differentiation and included appropriate statistical analyses. We have removed the previous results obtained with the Hemese<sup>SK2</sup> mutant, which yielded similar observations. By reproducing our previous findings with independent mutant lines and by providing quantitative analyses, we strengthen our conclusion that Hemese does not act as a negative regulator of lamellocyte differentiation upon wasp infestation.

      (18) S22 IRC

      IRC is actually not a catalase but a heme peroxidase. For the experiments on C. albicans, can the authors exclude that the IRC flies succumb because they are more sensitive to ethanol than wt or spz flies (sucrose is fermented by C. albicans): a control would be to use glycerol (nonfermentable) instead of sucrose solution.

      We thank the reviewer and Carolina Barillas (personal communication) to inform us that IRC is not a catalase but an heme peroxidase. This is now included in the article and the supplement. It is clear that the name IRC for immune regulated catalase is misleading. We cannot exclude that the IRC flies succumb because they are more sensitive to ethanol than wt or spz flies but we did not test this interesting idea during the revision.

      Do IRC mutants succumb to the ingestion of killed bacteria?

      No, they get stuck in fly food and die.

      (19) S23 DUOX

      Introduction: When citing Kumar et al., the authors might also want to cite Bai et al. on the Bactrocera dorsalis peritrophic matrix.

      This reference has been added

      Results:

      Figure 1: any statistical analysis? Data points are not shown for the lef panel, and error bars are not defined.

      We have added in the revised version ‘When using the Mann-Whitney U test, we still observed a statistically significant difference in CFU counts at 6 hpi in Figure 2B, as well as at both 6 hpi and 24 hpi in Figure 2F. However, the magnitude of these differences is relatively small’. Error bars have been defined in the general revision of the supplements (see above).

      Figure 2A shows only a limited reduction in Duox expression at the transcriptional level. "successfully" might be a bit of an overstatement.

      We have removed the term ‘successfully’.

      Figure 2E is too small.

      We have increased the size of Figure 2E.

      The authors might want to discuss the role of Duox in enterocytes destined to die as described by Amchelavsky et al., Cell Reports, 2020.

      Good point, we have discussed this reference in the revised version.

      The Sajjadian reference is incomplete.

      The reference has been completed

      (20) S24: NOS

      Maybe the work from the Royet laboratory could be cited along with that of Neyen et al. and Zaidmann-Remy et al. in the Introduction.

      Good points. We have added two references related to the work of the Royet lab (Bosco-Drayon et al., 2012; Charroux et al., 2017).

      It is not clear from Rabinovich et al. (2016) which line should be the relevant wild-type control. Why use w[1118] in Figure 1 and Ore-R in Figure 2? A more definitive conclusion could be reached upon isogenization or testing a second null mutant in homozygous and transheterozygous conditions?

      We agree that ideally, we should have used isogenic wild-type control but in absence of any effect of NOS mutants, we consider that the use of w[1118] and Oregon wild-type control is acceptable, considering that they behave similarly in the assay we have used.

      (21) S25 eiger survival

      Survival experiments: Are pooled data shown? Have the authors monitored the health status of their fly lines? In some of the survivals with late phenotypes, e.g., egr mutants and E. faecalis, this may be an issue, inasmuch as noninfected or mock-infected controls are not shown (have they been performed?).

      The survival analysis showed the pooled of 40 flies and were often done with two sexes. They also include many controls. We cannot never exclude minor effects at late time points, but the critical message is that we cannot reproduced the previously published results.

      The survival graphs are difficult to analyze: the authors should definitely avoid light colors such as yellow that do not show up well in a white background.

      As stated above, these experiments were done by many scientists in different lab and it was difficult to homogenize the data. Therefore, we did not modify our graphs although we agree with the reviewers. We believe that line patterns and contrast differences between groups guarantee that the readers can still assess the data.

      (22) S27: eiger humoral response

      Introduction: Have the authors also checked the Flysickseq data from the Buchon lab?

      There is no induction of eiger in Flysick. This information along with the reference Troha et al., have been added to S27.

      Methods: Have the RNAi lines been validated, and how? Is one more efficient at silencing than the other? This set of data would complement Figure 1 with RTqPCR experiments.

      The two RNAi lines we are using have already been used in many studies assessing the function of eiger ((#108814, reference here, #45253 reference here)

      Figure 3 colors in survival graphs: see S25.

      We did not modify our graphs although we agree with the reviewers. By increasing the magnification, the readers can still assess the data. As stated above, these experiments were done by many scientists in different labs and it was difficult to homogenize the data.

      Would it have been worth to also test the transheterozygous Gr28b mutants to avoid any issue of second-site mutations?

      We could have extended the genetics approach but, given that both us and another group (Sang) found no effect on feeding in an independent mutant, and extremely high feeding in the claimed 'anorexic' mutant, this would not be relevant to the claim. Future study may tell us if Gr28b has clearly a feeding phenotype.

      Reviewer #2 (Recommendations for the authors):

      (1) Adjust the discussion to better contextualize subjectivity: Because the choice of claims and papers inevitably involves subjective decisions, consider expanding the discussion to acknowledge this limitation more explicitly and explain how the companion metascience manuscript complements this work.

      We have added a section at the end of the discussion to discuss the limitation of our study and we establish more link to the companion article.

      (2) Refine language and avoid binary judgments: Terms such as "correct," "incorrect," or similar definitive formulations should be softened. Emphasizing uncertainty and openness to future revision will better reflect the iterative nature of scientific inquiry.

      We are softening our language in several of our assessment to avoid binary judgement.

      (3) Highlight reproducible findings as well: While the focus on irreproducible or challenged claims is understandable, it may be helpful to balance this by more clearly acknowledging wellsupported claims or areas of strong reproducibility to reinforce the positive impact of robust findings.

      We have added two sentences at the end of the introduction and at the beginning of the discussion that most claims are reproducible and that we have focused here on nonreproducible claim.

      (4) Clarify how the community can engage with the dataset: Because you provide extensive supplementary material and a large online resource, consider adding more explicit guidance on how researchers can contribute comments, evidence, or corrections, and how these contributions will be integrated.

      This point is already addressed in the revised sentence: “We hope that the community-accessible website will encourage researchers to share their perspectives and contribute data from diverse sources, thereby improving objectivity.” In addition, we have proactively engaged with principal investigators to motivate them to contribute to this effort by sharing unpublished results and information. However, it remains unclear to what extent scientists are genuinely interested in reproducibility, as has been noted in other studies on the topic. For example, articles that challenge previous findings are often under-cited, and the presence of contradictory evidence does not necessarily prevent researchers from continuing to cite the original claims. This is clearly a challenge of reproducibility study. Comments, evidence and corrections will be integrated in the ReproSci website. We have already updated this database when revising this article and will continue to do it.

      (5) Resolve the date-range inconsistency: The Introduction mentions analysis of papers from 1959-2011, whereas the Methods section cites 1940-2011. This discrepancy should be corrected for consistency.

      We have solved this discrepancy by correcting the method section. This should be 1959-2011. Thank you for spotting this mistake.

      Reviewer #4 (Recommendations for the authors):

      (1) The "disclaimer" subsection should be incorporated into the Introduction, as it is central to the premise of the study.

      The disclaimer has been moved at the end of the introduction and one sentence has been added in the abstract.

      (2) The inconsistency of graphical representation and statistical analysis of survival and gene expression data across different supplements is distracting and makes the project feel cobbled together. A consistent and unified framework for analysis and presentation would feel more cohesive and rigorous.

      The experiments were conducted across multiple laboratories and required substantial coordination. As a result, modifying the graphs at this stage would be technically complex and would require monumental effort for a tiny return. Therefore, we regret that we were unable to implement this change. The most important is that data and analyses are presented transparently, allowing readers to evaluate and interpret the results independently.

      (3) The purported roles of eiger and Gr28b in immunity have nothing to do with each other, aside from both having been published by David Schneider's group. I would suggest not combining them into a single section unless there is to be a 'miscellaneous' section that contains work by multiple groups.

      We have done two separate sections.

      (4) It would be easier to comment on specific passages in the manuscript if line numbers had been included.

      Sorry for this. We have included number of lines in the revised version.

      (5) The use of the word "elegant" to describe melanization reactions on page 3 is a bit odd and feels out of place.

      It has been removed

      (6) The sentence "We conclude that PGRP-LE is an exclusively intracellular sensor, with a prominent role regulating the gut immune response (Bosco-Drayon et al., 2012; Neyen et al., 2012)" should be edited to read "as reported in (citations)".

      We have edited the sentence.

      (7) The sentence "Experiments performed for ReproSci and review of the literature suggest that JNK is unlikely to have a strong direct or systematic role in the regulation of Drosophila AMPs in vivo, and is not required in the adult fat body for AMP expression in response to infection (Supplementary S12)" lacks detail. I realize that the experiments are described in S12, but I would suggest a brief summary in the main text of the article as well.

      We have provided more information in the article.

      (8) The phrasing of "While many of the challenged claims discussed in this paper are known to be controversial by experts, or have fallen quietly out of current research interests, some are still a source of confusion. This is particularly true for young scientists new to the field of Drosophila immunity or scientists with primary interests outside of Drosophila immunity that use the field as a reference." could be edited for clarity. A more direct phrasing could help, such as "Even in the absence of published refutation, many of the challenged claims were already considered controversial by experts in the field. Others have quietly fallen out of the general current research interest. These may nevertheless still be a source of confusion for young scientists new to the field of Drosophila immunity or scientists with primary interests outside of Drosophila immunity that use the field as a reference."

      We have changed the sentence accordingly.

      (9) On Discussion page 15, the comma should be removed from "In this article we have not discussed a number of claims, that had no clear follow-up studies and remain unchallenged."

      The coma has been removed.

      (10) In Methods, the authors "acknowledge that our verification experiments could also be erroneous". It would be appropriate to also include such a disclaimer in the Introduction and/or Discussion.

      The disclaimer has been moved at the end of the introduction and one sentence has been added in the abstract.

    1. eLife Assessment

      This important study presents an impressive large-scale effort to assess the reproducibility of published findings in the field of Drosophila immunity. The authors analyse 400 papers published between 1959 and 2011 and assess how many of the claims in these papers have been tested in subsequent publications. In a companion article they report the results of experiments to test a subset of the claims that, according to the literature, have not been tested. The evidence presented is convincing, and the limitations inherent to evaluating reproducibility primarily on the basis of the published literature - as opposed to repeating experiments - are acknowledged. The authors also explore if various factors related to authors, institutions and journals influence reproducibility in this field: in general, most correlations between these factors and reproducibility are weak, and some are dependent on analysis choices.

    2. Reviewer #1 (Public review):

      Summary:

      The authors set out on the ambitious task of establishing the reproducibility of claims from the Drosophila immunity literature. Starting out from a corpus of 400 articles from 1959 and 2011, the authors sought to determine whether their claims were confirmed or contradicted by previous or subsequent publications. Additionally, they actively sought to replicate a subset of the claims for which no previous replications were available (although this set was not necessarily representative of the whole sample, as the authors focused on suspicious and/or easily testable claims). The focus of the article is on inferential reproducibility; thus, methods don't necessarily map exactly to the original ones.

      The article is a large-scale analysis of the individual replication findings, which are presented in a companion article (Westlake et al. doi.org/10.1101/2025.07.07.663442). In their retrospective analysis, the authors find that 61% of the original claims were verified by the literature, 7.5% were partially verified, and only 6.8% was challenged, with 23.8% having no replication available. This is in stark contrast with the result of their prospective replications, in which only 16% of claims were successfully reproduced.

      The authors proceed to investigate correlates of replicability, with the most consistent finding being that findings stemming from higher-ranked universities were more likely to be challenged - a finding that is observed in a multivariable model and multiple sensitivity analyses. Other trends observed in the initial descriptive analysis, such as less replicability in findings from "trophy" journals and duration of engagement with the field, seem less consistent and fail to reach significance in the multivariable model, and should thus be considered tentative.

      Although this is a major contribution to the field, it is important to note that the high replication rates are mostly based on a retrospective survey of the literature, while prospective replication rates in the subset of articles directly replicated by the authors were much lower. Thus, the possibility of biases relating both to the field's decisions about what to replicate (and what to publish) and to the authors' criteria in evaluating claims should be considered when comparing these results with prospective replicability estimates obtained in other fields of science.

      Strengths:

      The work presents a large-scale, in-depth analysis of a particular field of science that includes authors with deep domain expertise of the field. This is a rare endeavour to establish the reproducibility of a particular subfield of science, and I'd argue that we need many more of these in different areas.

      The project was built on a collaborative basis (https://ReproSci.epfl.ch/), using an online database (https://ReproSci.epfl.ch/), which was used to organize the annotations and comments of the community about the claims. The website remains online and can be a valuable resource to the Drosophila immunity community. A companion article details the individual replication findings (Westlake et al. doi.org/10.1101/2025.07.07.663442).

      The revised version of the preprint includes a number of sensitivity analyses as supplementary material, which help to evaluate how the main findings change according to analytical decisions.

      Main concerns:

      (1) Although a number sensitivity analyses have now been conducted in order to address reviewer comments, these have been relegated to the supplementary material. Their existence is acknowledged in the multivariable model section, but the actual results of the main analysis are not mentioned at all in the main text and not taken into account in the discussion. Thus, the casual reader is not made aware of how particular results are dependent on analysis choices.<br /> Although the main finding of the correlation between replicability and university ranking holds over nearly all the analyses conducted, others are not as consistent. The association of irreplicability with trophy journals, for instance, is not statistically significant in the multivariate model (suggesting that it may have been partially due to confounding of journal tier with institutional ranking). It becomes even weaker when unchallenged claims are removed from the analysis (see point 2 below). Similarly, the association of irreplicability with "exploratory" career style is already weak in the univariate analysis and becomes even more marginal in the multivariable model. The effect of the lead author having been a first author in the field, on the other hand, is stronger than that of journal impact factor or that of the continuity/exploratory style (even though it fails to reach formal significance), but seems to receive less attention in the discussion.<br /> These differences in strength of evidence could be made more clear both in the abstract and in the discussion, which seem to prioritize some trends over others when discussing results. Once more, presenting a brief description of how results are affected by the sensitivity analyses in the results would be important to make these nuances clearer to the reader.

      (2) I still do not understand the authors' choice of including unchallenged claims along with verified ones in the multivariate model used to investigate predictors of replicability. In particular, the justification given for this choice in the supplementary material (i.e. the fact that a finding remaining unchallenged is not a random event and might be influenced by variables such as journal tier and institutional ranking) actually seems like an additional reason for not including unchallenged claims along with verified ones: if findings from low-tier journals or less prestigious institutions are more likely to remain unchallenged, this could lead to a spurious claim of "less irreplicability" in these journals and institutions, which could be purely due to receiving less attention. Predictably, both associations are reduced when unchallenged claims are removed (although the reduction is much more marked in the journal case) in the sensitivity analysis (whose results, again, are only mentioned in the supplementary material).<br /> Moreover, multiple other statements by the authors themselves concerning unchallenged claims seem to contradict their own decision. In multiple points in the text, they reiterate that remaining unchallenged is associated with less replicability (as suggested by the fact that replicability rates were lower in prospective replications than in the literature, even for non-suspicious claims). The decision to include unchallenged claims along with verified ones thus seems unjustified - if anything, I'd have expected the opposite approach after reading the discussion. That said, I'd argue that the best decision would be to exclude them from the analysis, as done in Figure S4, and presenting this model as the main one.

    3. Reviewer #3 (Public review):

      Summary:

      The authors of this paper were trying to identify how reproducible, or not, their subfield (Drosophila immunity) was since its inception over 50 years ago. This required identifying not only the papers, but the specific claims made in the paper, assessing if these claims were followed up in the literature, and if so whether they supported or refuted the original claim. In addition to this large manually curated effort, the authors further investigated some claims that were left unchallenged in the literature by conducting replications themselves. This provided a rich corpus of the subfield that could be investigated into what characteristics influence reproducibility.

      A major strength of this study is the focus on a subfield, the detailing of identifying the main, major, and minor claims - which is a very challenging manual task - and then cataloging not only their assessment of if these claims were followed up in the literature, but also what characteristics might be contributing to reproducibility, which also included more manual effort to supplement the data that they were able to extract from the published papers. While this provides a rich dataset for analysis, there is a major weakness with this approach, which is not unique to this study.

      The main weakness is relying heavily on the published literature as the source of whether a claim was determined to be verified or not. Nonetheless it is understandable why the authors took this approach - it is the only way to get at a breadth of the literature. However, there are many documented issues with this stemming from every field of research - such as publication bias, selective reporting, all the way to fraud. Importantly, this limitation is highlighted throughout the paper. At the same time, it is not reasonable to expect this study to have conducted independent experimental replications for all 400 papers identified as this would have been a laborious and costly effort. To overcome this weakness, the authors leveraged their own expertise, and crowdsourcing within the Drosophila immunity community, to assess the validity of the claims. While this helps mitigate some of this weakness, it does introduce new challenges in interpretation, which is acknowledged in the limitations section of the discussion. Overall, this situates this study more as an expert assessment of the replicability of the published literature opposed to an assessment of independent experimental replications.

      The authors should be applauded for the monumental effort they put into this project, which does a wonderful job of having experts within a subfield engage their community to understand the connectiveness of the literature and attempt to understand how reliable specific results are and what factors might contribute to them. This project provides a nice blueprint for others to build from as well as leverage the data generated from this subfield and thus should have an impact in the broader discussion on reproducibility and reliability of research evidence.

    4. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment:

      This important study presents an impressive large-scale effort to assess the reproducibility of published findings in the field of Drosophila immunity. The authors analyse 400 papers published between 1959 and 2011, and assess how many of the claims in these papers have been tested in subsequent publications. In a companion article they report the results of experiments to test a subset of the claims that, according to the literature, have not been tested. The present article also explores if various factors related to authors, institutions and journals influence reproducibility in this field. The evidence supporting the claims is solid, but there is considerable scope for strengthening and extending the analysis. The limitations inherent to evaluating reproducibility based on the published literature should also be acknowledged.

      We have included in the abstract the limitation of evaluating replicability based on the published literature

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors set out on the ambitious task of establishing the reproducibility of claims from the Drosophila immunity literature. Starting out from a corpus of 400 articles from 1959 and 2011, the authors sought to determine whether their claims were confirmed or contradicted by previous or subsequent publications. Additionally, they actively sought to replicate a subset of the claims for which no previous replications were available (although this set was not representative of the whole sample, as the authors focused on suspicious and/or easily testable claims). The focus of the article is on inferential reproducibility; thus, methods don't necessarily map exactly to the original ones.

      The authors present a large-scale analysis of the individual replication findings, which are presented in a companion article (Westlake et al., 2025. DOI 10.1101/2025.07.07.663442). In their retrospective analysis of reproducibility, the authors find that 61% of the original claims were verified by the literature, 7.5% were partialy verified, and only 6.8% were challenged, with 23.8% having no replication available. This is in stark contrast with the result of their prospective replications, in which only 16% of claims were successfully reproduced. The authors proceed to investigate correlates of replicability, with the most consistent finding being that findings stemming from higher-ranked universities (and possibly from very high impact journals) were more likely to be challenged.

      Strengths:

      (1) The work presents a large-scale, in-depth analysis of a particular field of science that includes authors with deep domain expertise of the field. This is a rare endeavour to establish the reproducibility of a particular subfield of science, and I'd argue that we need many more of these in different areas.

      (2) The project was built on a collaborative basis (https://ReproSci.epfl.ch/), using an online database (https://ReproSci.epfl.ch/), which was used to organize the annotations and comments of the community about the claims. The website remains online and can be a valuable resource to the Drosophila immunity community.

      (3) Data and code are shared in the authors' GitHub repository, with a Jupyter notebook available to reproduce the results.

      We thank the reviewer for their positive comments.

      Main concerns:

      (1) Although the authors claim that "Drosophila immunity claims are mostly replicable", this conclusion is strictly based on the retrospective analysis - in which around 84% of the claims for which a published verification attempt was found. This is in very stark contrast with the findings that the authors replicate prospectively, of which only 16% are verified. Although this large discrepancy may be explained by the fact that the authors focused on unchallenged and suspicious claims (which seems to be their preferred explanation), an alternative hypothesis is that there is a large amount of confirmation bias in the Drosophila immunity literature, either because attempts to replicate previous findings tend to reach similar results due to researcher bias, or because results that validate previous findings are more likely to be published.

      Both explanations are plausible (and, not being an expert in the field, I'd have a hard time estimating their relative probability), and in the absence of prospective replication of a systematic sample of claims - which could determine whether the replication rate for a random sample of claims is as high as that observed in the literature -, both should be considered in the manuscript.

      I still believe that most of ‘verified’ claims are indeed solid, consistent with the feeling of increased knowledge in the Drosophila immunity. Of note, when I started in the field in the early 90, nearly all the articles in Drosophila developmental biology, the dominant field at that time, were solid. I agree that this may represent a past period. Still, perceived irreproducibility is high because affecting claims published in high-impact articles. To address the reviewer’s comments, we have included the possibility of confirmation bias in the discussion of the article.

      (2) The fact that the analysis of factors correlating with reproducibility includes both prospective and retrospective replications also leads to the possibility of confusion bias in this analysis. If most of the challenged claims come from the authors' prospective replications, while most of the verified ones come from those that were replicated by the literature, it becomes unclear whether the identified factors are correlated with actual reproducibility of the claims or with the likelihood that a given claim will be tested by other authors and that this replication will be published.

      We agree that the ReproSci is a human biased endeavor and may be subjected to bias, notably the one mentioned. Some verified claims might be considered as challenged and some challenged claims as verified. But this is likely not affecting most of our results. Our papers have been posted on a public archive and that the community could scrutinize our data (most authors have checked their claims!). Based on community feedback, we have had to change the status of three claims only: one verified claim became challenged, one challenged claim became unchallenged and one unchallenged became verified. We therefore did not expect major changes in our conclusions. We have provided an estimation of irreproducibility ranging from 10-20% and we believe this is a rather safe interval. This is still significant. The fact that the ReproSci project is a human endeavor, and therefore subject to inherent limitations, is already emphasized in the last section of the discussion.

      (3) The methods are very brief for a project of this size, and many of the aspects in determining whether claims were conceptually replicated and how replications were set up are missing.

      Some of these - such as the PubMed search string for the publications and a better description of the annotation process - are described in the companion article, but this could be more explicitly stated. Others, however, remain obscure. Statements such as "Claims were cross-checked with evidence from previous, contemporary and subsequent publications and assigned a verification category" summarize a very complex process for which more detail should be given - in particular because what constitutes inferential reproducibility is not a self-evident concept. And although I appreciate that what constitutes a replication is ultimately a case-by-case decision, a general description of the guidelines used by the authors to determine this should be provided. As these processes were done by one author and reviewed by another, it would also be useful to know the agreement rates between them to have a general sense of how reproducible the annotation process might be.

      The same gap in methods descriptions holds for the prospective replications. How were labs selected, how were experimental protocols developed, and how was the validity of the experiments as a conceptual replication assessed? I understand that providing the methods for each individual replication is beyond the scope of the article, but a general description of how they were developed would be important.

      We have extended the first section of the Result to mention that an extended Material and Methods can be found in the complementary article Westlake et al., eLife 2026 and on the ReproSci website indicated the resources described in the companion articles. Disagreement between the two authors were limited and mostly dealt with textual interpretation (the definition of the major claims, which can affect if they are verified or not). As mentioned above, our articles are now public since more 6 months and we got little request to change the status of a claim (only 3 over more than 1000). Of note many claims from some ancient articles are part of the common knowledge, are verified routinely by many labs in the field, and therefore were easy to categorize.

      We also added a sentence to also underline that the scientific value of an article does not fully correlate with its status. A number of problematic articles filled with exaggeration or confused claims were still categorized as verified. Thus, we screen for replicability but they are many other publication issues we could not capture in our study. This point is now mentioned in the discussion.

      (4) As far as I could tell, the large-scale analysis of the replication results was not preregistered, and many decisions seem somewhat ad hoc. In particular, the categorization of journals (e.g. low impact, high impact, "trophy") and universities (e.g. top 50, 51-100, 101+) relies on arbitrary thresholds, and it is unclear how much the results are dependent on these decisions, as no sensitivity analyses are provided.

      Particularly, for analyses that correlate reproducibility with continuous variable (such as year of publication, impact factor or university ranking, I'd strongly favor using these variables as continuous variables in the analysis (e.g. using logistic regression) rather than performing pairwise comparisons between categories determined by arbitrary cutoffs. This would not only reduce the impact of arbitrary thresholds in the analysis, but would also increase statistical power in the univariate analyses (as the whole sample can be used in at once) and reduce the number of parameters in the multivariate model (as they will be included as a single variable rather than multiple dummy variables when there are more than two categories).

      We did not pre-registered our study, which is quite different from other reproducibility projects. We did not know at the start how the project would unfold. Of note, I believe that ‘cleaning’ an extensive field by experts was by itself an achievement and represent another way to assess replicability, with its own limitation.

      We agree that the cut-off can be seen as arbitrary. However, publication year has been modeled as a continuous variable through a spline (to allow for non-linearity) in the multivariate analysis. For journal and institutional ranking, we would like to defend and keep the categorical specifications in the main text for two reasons:

      - Institution ranking: For institutional ranking, the largest institutional category is “Not Ranked” which we cannot assign a rank and ranks institutions individually only through the top 100. Below that it publishes bands (101-150, 151-200, 201-300, 301400, 401-500) of variable width, which are not straightforward to use continuously without making strong assumptions (e.g midpoint) unsupported by the data.

      - Prestige: As mentioned by another reviewer there are career consequences attached to Nature, Science and Cell that are different from other journal, and the effect of moving from a journal from an IF 3 to 8 is different than from 30 to 35. We provide a sensitivity analysis in the supplementary information where we refit the multivariate model with log-transformed impact factor continuous predictor. We obtain very similar results than these in the main text, with the exception that the impact factor of the journal becomes significant. We describe and discuss this results in the supplementary information (section S5), while justifying why we keep the categorical model in the main text.

      We are grateful to the reviewer for prompting these additions, which we agree strengthen the paper.

      The text added in the SI states:

      “As a sensitivity analysis, we refitted the multivariable hierarchical logistic model by replacing journal-impact-factor categories with log₂-transformed continuous impact factor, while retaining all other covariates and random intercepts from the primary analysis. University ranking was retained as a categorical variable because the 2010 Shanghai Academic Ranking reports individual ranks only for institutions ranked 1– 100 and otherwise reports rank bands (101–150, 151–200, 201–300, 301–400, and 401–500); moreover, 441 of 1,006 claims (43.8%) were from unranked institutions, which constitute a distinct group.

      Each doubling of journal impact factor was associated with 1.51-fold higher odds that a claim was challenged (OR 1.51, 94% HDI 1.13–2.08). At the mean impact factors of the low-impact and trophy-journal tiers (4.4 and 61, respectively; approximately 3.8 doublings), the continuous model implies a trophy-versus-low-impact odds ratio of approximately 4.8. By contrast, the primary model directly estimated the trophy-versus-low-impact odds ratio as 1.75 (94% HDI 0.59–4.91). Although this wide interval includes 4.8, the point estimates differ between the two models. We interpret the larger implied trophy-journal effect in the continuous model cautiously because it assumes that the log-linear slope estimated predominantly from the many journals with lower impact factors (around 2–12) applies unchanged to the sparsely represented trophy journals (62 claims across Science, Nature, and Cell). We therefore retained the categorical journal analysis as the primary analysis because it directly estimates the trophy-journal comparison using claims published in those journals. Estimates for all other covariates were broadly similar to those in the primary model. In particular, the association with Top 50 university affiliation remained evident (OR 4.04, 94% HDI 1.57–10.08, compared with OR 3.68, 94% HDI 1.50–9.28 in the primary model)”.

      [This response is referred later as “Response to categorical variable encoding”]

      (5) The multivariate model used to investigate predictors of replicability includes unchallenged claims along with verified ones in the outcome, which seems like an odd decision. If the intention is to analyze which factors are correlated with reproducibility, it would make more sense to remove the unchallenged findings, as these are likely uninformative in this sense. In fact, based on the authors' own replications of unchallenged findings, they may be more likely to belong the "challenged" category than to the "unchallenged" one if they were to be verified.

      We thank the reviewer for raising this comment, we agree that the classification of the unchallenged claims as not challenged is difficult given their nature. We refitted the multivariable analysis excluding unchallenged claims, with a classification of Challenged and Unchallenged (Verified, Partially Verified, Mixed). The results are shown in the supplementary section S4 and are extremely similar to the main model.

      The text added in the SI states:

      “We fit the multivariate model from the main text while excluding unchallenged claims from the analysis. The methods are similar and the convergence and diagnosis checks are good and not reported here for brevity. The model simultaneously adjusts for author demographics, laboratory attributes and journal or institutional prestige and predict a binary outcome for each claim: challenged or not challenged (including Verified, Partially Verified, Mixed). The re-analysis included 659 claims, of which 60 (9.1%) were challenged. The non-challenged reference group comprised of Verified (n=525), Partially Verified (n=64), Mixed (n=10). The Adjusted odds ratios and 94% highest-density intervals are reported in Figure S4. The results are very similar to the analysis with the unchallenged claims in the main text, with only university ranking (Top 50) being a significant predictor (OR 3.34 (1.28–9.01) vs the main text OR 3.68 (94% HDI 1.50– 9.28). The other variable with the biggest changes shows only modest differences:

      - High-impact journal OR 1.12 (vs 1.31)

      - Trophy journal OR 1.47 (vs 1.75)

      - Post-doc OR 1.44 (vs 1.16) and their intervals still include 1. Note that the excluding unchallenged decision condition on follow-up, which is not random and might be influenced by our key variable (journal tier, institutional ranking) and bias results for this model.”

      However, we did not replace the main model of the article by this particular model because when you chose only claim that are either verified or challenged (so you exclude unchallenged), the condition that made these claims included in this analysis could be influenced by a cofactor, so our odds ratios might be wrong.

      [This response is referred later as “Response to unchallenged exclusion”]

      Reviewer #2 (Public review):

      Summary:

      Lemaitre et al. conducted an analysis of 400 publications in the Drosophila immunity field (1959-2011), performing both univariable and multivariable analyses to identify factors that correlate with or influence the irreproducibility of scientific claims. Some of the findings are unexpected, for instance, neither the career stage of the PI nor that of the first author appears to matter that much, while others, such as the influence of institutional prestige or publication in "trophy journals," are more predictable. The results provide valuable insight into patterns of irreproducibility in academia and may help inform policies to improve research reproducibility in the field.

      Strengths:

      This study is based on a large, manually curated dataset, complemented by a companion paper (Westlake et al., 2025. DOI 10.1101/2025.07.07.663442) that provides additional details on experimentally documented cases. The statistical methods are appropriate, and the findings are both important and informative. The results are clearly presented and supported by accessible documentation through the ReproSci project.

      We thank the reviewer for their assessment.

      Weaknesses:

      The analysis is limited to a specific field (immunity) and model system (Drosophila). Since biological context may influence reproducibility -- for example, depending on whether mechanisms are more hardwired or variable -- and the model system itself may contribute to these effects (as the authors note), it remains unclear to what extent these findings generalize to other fields or organisms. The authors could expand the discussion to address the potential scope and limitations of the study's generalizability.

      We have added a limitation section at the end of the manuscript to discuss these points. We wrote:

      “Finally, the findings we report may be specific to a particular field, model organism, and time period, and may be strongly influenced by factors idiosyncratic to their development. Because this analysis is limited to a single field (immunity) and model system (Drosophila), it remains unclear to what extent these findings generalize to other fields, organisms, or more recent studies.”

      However, we believe that our study adds on the discussion on reproducibility by providing another approach. Our study also aligns with the feeling that knowledge of Drosophila innate immunity has increased so much in the last decades, and that science is still quite robust in many fields.

      Reviewer #3 (Public review):

      Summary:

      The authors of this paper were trying to identify how reproducible, or not, their subfield (Drosophila immunity) was since its inception over 50 years ago. This required identifying not only the papers, but the specific claims made in the paper, assessing if these claims were followed up in the literature, and if so whether the subsequent papers supported or refuted the original claim. In addition to this large manually curated effort, the authors further investigated some claims that were left unchallenged in the literature by conducting replications themselves. This provided a rich corpus of the subfield that could be investigated into what characteristics influence reproducibility.

      Strengths:

      A major strength of this study is the focus on a subfield, the detailing of identifying the main, major, and minor claims - which is a very challenging manual task - and then cataloging not only their assessment of if these claims were followed up in the literature, but also what characteristics might be contributing to reproducibility, which also included more manual effort to supplement the data that they were able to extract from the published papers. While this provides a rich dataset for analysis, there is a major weakness with this approach, which is not unique to this study.

      Weaknesses:

      The main weakness is relying heavily on the published literature as the source for if a claim was determined to be verified or not. There are many documented issues with this stemming from every field of research - such as publication bias, selective reporting, all the way to fraud. It's understandable why the authors took this approach - it is the only way to get at a breadth of the literature - however the flaw with this approach is it takes the literature as a solid ground truth, which it is not. At the same time, it is not reasonable to expect the authors to have conducted independent replications for all of the 400 papers they identified.

      However, there is a big difference trying to assess the reproducibility of the literature by using the literature as the 'ground truth' vs doing this independently like other large-scale replication projects have attempted to do. This means the interpretation of the data is a bit challenging.

      We acknowledge that our analysis necessarily relies on the published literature; however, two important points should be considered. First, we assessed replicability more than a decade after the original studies, allowing us to evaluate whether their claims have withstood subsequent scrutiny. Second, the evaluation draws on the authors’ strong expertise in the field. Accordingly, the validity of published claims was not accepted uncritically but subjected to informed expert assessment. This is exemplified by the case of the proposed microbicidal role of Duox, which we classified as challenged despite the original article accumulating more than 600 citations.

      Overall, our replicability analysis, like similar efforts, relies not only on literature but also on expert judgment. Of course it has inherent limitations, which are now more clearly articulated in the discussion.

      Below are suggestions for the authors and readers to consider:

      (1) I understand why the authors prefer to mention claims as their primary means of reporting what they found, but it is nested within paper, and that makes it very hard to understand how to interpret these results at times. I also cannot understand at the high-level the relationship between claims and papers. The methods suggest there are 3-4 major claims per paper, but at 400 papers and 1,006 claims, this averages to ~2.5 claims per paper. Can the authors consider describing this relationship better (e.g., distribution of claims and papers) and/or considering presenting the data two ways (primary figures as claims and complimentary supplementary figures with papers as the unit). This will help the reader interpret the data both ways without confusion. I am also curious how the results look when presented both ways (e.g., does shifting to the paper as the unit of analysis shift the figures and interpretation?). This is especially true since the first and last author analysis shows there is varying distribution of papers and claims by authors (and thus the relationship between these is important for the reader).

      Articles can contain claims that we found replicable and other that we did not. Thus, we believe than an evaluation by claim is more precise. The extraction of the different claims relies on the reading of the article, but is a human enterprise. There are clearly some unavoidable biases at this step. Of note, the articles analyzed in this study are very different one from another (size, novelty, amount of data…).

      However, we agree that the analysis at the article level is interesting, and we followed the reviewer suggestion and provided in Supplementary Section S6 a sensitivity analysis of using article-level effects for the multivariate analysis. We note that because only 43 articles (12.5%) contained at least one challenged claim, this analysis is less robust than the claim analysis. The text added in the SI states:

      “Two claims in the same paper might not be independent, so we might want to incorporate an article random effect to the primary Bayesian formulation. However, due to the low number of claim per article (mostly one to four) and only 43 articles (12.5%) contained at least one challenged claim (and most articles contains only one to four claims), we could not manage to fit this model properly (we observe MCMC divergences). As a work-around sensitivity analysis, we repeated the analysis with the article as the unit. Each of 345 articles with complete covariate data (representing 869 major claims, 60 of the 69 challenged claims in the full dataset; the remaining 9 fell in articles excluded for missing covariates) contributed as outcome the proportion of its major claims classified as challenged. The model was a (frequentist) binomial-link fractional-response generalized linear model with HC3 standard errors (the intervals reported here are 94% confidence intervals rather than posterior credible intervals).

      Each article contributed equally regardless of its number of claims. The Top-50 institutional effect remained significant under both analysis (OR 5.15, 94% CI 2.18– 12.15; versus a claim-level OR 3.68, 94% HDI 1.50–9.28). In this new model Trophy journals also showed had higher odds of challenged claims than low-impact journals (OR 2.90, 94% CI 1.24–6.78 vs a claim-level OR 1.75, 94% HDI 0.59–4.91). This impact was not significant in the primary model but became significant at using article-level analysis. Other estimates are consistent in direction and magnitude with the primary claim-level model. Model diagnostics were satisfactory.”

      Moreover, we added Figure S6 which display visually the relationship between the claims and the article.

      [This response is referred later as “Response to claim vs article level analysis”]

      (2) As mentioned above, I think the biggest weakness is that the authors are taking the literature at face value when assigning if a claim was validated or challenged vs gathering new independent evidence. This means the paper leans more on papers, making it more like a citation analysis vs an independent effort like other large-scale replication projects. I highly recommend the authors state this in their limitations section.

      We have now explicitly acknowledged this limitation in a dedicated section at the end of the revised manuscript. However, as noted above, we do not take the literature at face value. Notably, some of the claims we identify as challenged originate from highly cited articles, which could lead a naïve reader to assume their validity.

      Ultimately, this replicabilty project remains a human, expert-driven effort aimed at determining which claims withstand scrutiny within a field. The broader community was invited to engage with and respond to these assessments and we did get some feedbacks. Importantly, claims were not evaluated in isolation but in the context of the entire body of knowledge, and all evaluations were made transparent and explicitly justified in the ReproSci database.

      On top of that, I have questions that I could not figure out (though I acknowledge I did not dig super deep into the data to try). The main comment I have is How was verified (and challenged) determined? It seems from the methods it was determined by "Claims were cross-checked with evidence from previous, contemporary and subsequent publications and assigned a verification category". If this is true, and all claims were done this way - are verified claims double counted then? (e.g., an original claim is found by a future claim to be verified - and thus that future claim is also considered to be verified because of the original claim).

      When two articles reported the same main claim and this claim was judged as “verified,” each instance was counted in the analysis. However, when validation came from studies published after 2011, the claim was counted only once. Thus, our study specifically evaluates the replicability of claims originating from a defined period (pre-2011).

      We note that, in principle, repeated publication of the same finding across many articles could artificially inflate estimates of replicability. However, such cases appeared to be rare in practice, and studies reporting similar claims typically provided complementary evidence—for example, elucidating gene function through either genetic or biochemical approaches.

      Related, did the authors look at the strength of validation or challenged claims? That is, if there is a relationship mapping the authors did for original claims and follow-up claims, I would imagine some claims have deeper (i.e., more) claims that followed up on them vs others. This might be interested to look at as well.

      We did not perform a systematic mapping between original claims and subsequent follow-up studies. While this is an important and interesting aspect, it was considered beyond the scope of the present work. The current study already represents a substantial investment of time and effort, and the resulting database provides a valuable resource for future analyses. In particular, it could support a dedicated bibliometric study at a later stage.

      (3) I recommend the authors add sample sizes when not present (e.g., Fig 4C).

      The cited panel appears to correspond to Figure 5C in the revised numbering, as the current Figure 4 contains only panels A and B. We systematically checked the denominators of all figures and revised the captions to report the numbers of claims, authors or laboratories represented, together with the reasons for exclusions. We also added the numbers of authors and claims directly within the author-level scatter plots, with the observational unit explicitly identified.

      I also find that the sample sizes are a bit confusing, and I recommend the authors check them and add more explanation when not complete, like they did for Fig 4A. For example, Fig 7B equals to 178 labs (how did more than 156 labs get determined here?), and yet the total number of claims is 996 (opposed to 1,006).

      There are 156 labs total, 23 labs contributed publications during both junior and senior stages, and 11 claims lacked a junior/senior classification. This has been explicited in the caption.

      Another example, is why does Fig 8B not have all 156 labs accounted for?

      The panel does not include all 156 laboratories because we could obtain information about first author training for only 146 PI and laboratories while prior first-author status was unavailable or indeterminate for ten PI. This has been added to the caption.

      (Related to Fig 8B, I caution on reporting a p value and drawing strong conclusions from this very small sample size - 22 authors).

      The 22 authors are not the total sample size; they constitute the subgroup of PIs who had previously published a first-author paper on Drosophila immunity in another laboratory. In the submitted panel, they were compared with 117 PIs without this training history, giving 139 classified laboratories and 774 claims.

      As a last example, Fig 8C has al 156 labs and 1,006 claims - is that expected? I guess it means authors who published before 1995 (as shown in Figure 8A continued to publish after 1995?) in that case, it's all authors? But the text says when they 'set up their lab' after 1995, but how can that be?

      Thank you for identifying this inconsistency. Figure 8C intentionally includes the full cohort of 156 PIs and all 1,006 claims. Panel C compares research styles and does not impose a restriction based on year of entry into the field or year of claim publication. Thus, PIs who entered the field before 1995 are included together with all claims attributed to them; the panel is not restricted to claims published after 1995.

      The statement that only PIs who established their laboratories after 1995 were included was an erroneous carryover from an earlier version of the analysis. We removed this restriction from the Results and revised the caption to state that exploratory PIs (n = 91) contributed 366 claims, whereas continuity PIs (n = 65) contributed 640 claims.

      We also replaced references to the date when PIs “established their laboratory” with “year of entry into the field,” defined as the year of their first Drosophila-immunity publication as either first or last author. During the same check, we revised Panel B to include all 146 PIs eligible for the pre-PI training comparison, contributing 793 claims.

      (4) Finally, I think it would help if the authors expanded on the limitations generally and potential alternative explanations and/or driving factors. For example, the line "though likely underestimated' is indicated in the discussion about the low rate of challenged claims, it might be useful to call out how publication bias is likely the driver here and thus it needs to be carefully considered in the interpretation of this. Related, I caution the authors on overinterpreting their suggestive evidence. The abstract for example, states claims of what was found in their analysis, when these are suggestive at best, which the authors acknowledge in the paper. But since most people start with the abstract, I worry this is indicating stronger evidence than what the authors actually have.

      We have added a dedicated section on limitations at the end of the Discussion to explicitly acknowledge key constraints, and we also address these points throughout the Discussion where relevant. In addition, these limitations are now in the abstract.

      The authors should be applauded for the monumental effort they put into this project, which does a wonderful job of having experts within a subfield engage their community to understand the connectiveness of the literature and attempt to understand how reliable specific results are and what factors might contribute to them. This project provides a nice blueprint for others to build from as well as leverage the data generated from this subfield, and thus should have an impact in the broader discussion on reproducibility and reliability of research evidence.

      Thank you for this positive assessment!

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Comments and recommendations are provided in the order that they appear in the manuscript.

      Introduction:

      - In the second paragraph of the introduction, I don't think all the references refer to "molecular life sciences" (e.g. one of them is from ecology and evolution, for example). I'd also argue that Macleod et al. 2014 refers to risk of bias rather than to reproducibility per se.

      We have corrected this in the revised version.

      - The author use both "reproducibility" and "replicability", apparently as synonyms, but as definitions for these words vary across sources (e.g. https://arxiv.org/abs/1802.03311, https://www.nationalacademies.org/our-work/reproducibility-and-replicability-in-science, https://osf.io/br9sp/) it may be worth stating their definitions upfront.

      Thank you for the references. We agree that the literature contains multiple, sometimes conflicting definitions of these terms, which can lead to confusion. In response to the reviewer’s comment, we have clarified our terminology. Specifically, we now use the term “conceptual replicability” in the abstract and consistently refer to “replicability” and “irreplicability” throughout the manuscript. We hope this revised terminology improves clarity.

      - Why are analysis described as "exploratory" and "multivariate"? If it has not been preregistered, shouldn't the multivariate analysis be considered exploratory as well?

      Thanks, we agree. The word “exploratory” is confusing as we use it for its structural meaning and not its epistemological meaning. We have replaced it throughout by “descriptive”, as to not imply that the multivariate analysis is preregistered by contrast.

      Results:

      Methodology and claim assessment

      - The methods for the selection, annotation and classification of studies are described very cursorily here, and the Methods section doesn't add that much, but I'll keep these observations to the methods. Still, it's worth mentioning that it's hard to know how reproducible the annotation and classification process is from the information provided.

      We have provided more information on the method section on the way to annotate and class claims. Of note, there is more information on the methods on the associated publication Westlake et al., 2025 and on the ReproSci website.

      - "Then, experimental work was performed in several laboratories...". Once again, a much better description of how replications were set up and performed is needed.

      We have provided information on the number of laboratories (nine) involved in the reproduction of 45 major claims. The laboratories involved in replication are listed in the author list. All the files associated with experimental replication can be found in the supplementary materials of Westlake et al 2025 or the ReproSci website.

      - "These claims subjected to experimental validation were selected according to criteria such as appearing suspicious due to the absence of direct follow up or being straightforward to test experimentally". This is rather vague, but if no explicit criteria were set upfront this may be inevitable. Still, it would be useful to know who made this decision.

      A first list of claims to be replicated was made by Hannah Westlake and Bruno Lemaitre. The claims that were selected to be replicated were tested by the labs and experimental workers that had the expertise required to test them. The choice of the claims to be tested was not random. Claims that were suspicious are claims that were already tested in the host laboratory without success, claims whose validity are debated at meeting, claims from highranked journal with no follow-up. This is now indicated in the method of the revised version.

      Drosophila immunity claims are mostly reproducible

      - The percentages given in the initial description of the results pool the results of the retrospective analysis of the literature and the prospective replications performed. As mentioned in the Public Review, I think these are rather distinct ways to assess reproducibility, and would recommend that these two analyses are described separately.

      We agree with the reviewer but we believe that we have already well separated the results before and after experimental work in the revised version. This is clearly shown in Table 1. Our text says ‘Importantly, 6.8% of claims (69 out of 1006) were challenged. Among these, 44.9% (31 out of 69) were contested by published articles including 7.2% by the same authors, and 55.1% (38 out of 69) were challenged experimentally as part of the ReproSci project.’

      - "These results may reflect the robust scientific standards and methodological rigor of Drosophila research...". Once again, as mentioned in the Public Review, I think publication bias is a plausible explanation, so this should be mentioned as an alternative.

      We have mentioned publication bias in the revised version in the section on limitation. But an important point to underline is that this analysis did not take publication results as face fact. This is a critical analysis by experts in the field. As already mentioned, some claims like ‘Duox produces microbicidal ROS” ‘NO is a signaling molecule…’that have been mentioned in multiple articles were considered as challenged.

      A second point is that it is likely that publications in some fields are more replicable than in other. Actually, we did not discuss ‘reproducibility issues’ when I started in the field 30 year ago because this was not perceived as an issue. It is normal that reproducibility rate varies according to fields. Nevertheless, we do find important claims with high visibility that are not replicable in our sample. Thus, our findings nuance the ‘reproducibility crisis narrative’ but we still believe that there is concern with reproducibility in life science. Last point, the criterium we have used in our study did not map all the problematic papers, notably some claims might be fundamentally verified but exaggerated in the original paper.

      - It would be useful to break down the "partially verified" category somewhere. Illustrative examples of what is meant by "insightful data were accompanied by incomplete interpretations" or "incomplete data were paired with insightful interpretations" would be useful as well.

      All the claims are listed in the public database with an explanation as to why they got affected to a category. The categories mixed and partially verified refer to claims that are more complex to categorize.

      Here are are two examples from the ReproSci website:

      Claim: Relish Rel mutants are very susceptible to bacterial and fungal infection. Partially verified

      Assessment: Relish mutants are primarily susceptible to Gram-negative bacterial infections, and do not typically have increased susceptibility to Gram-positive bacteria and fungi which are primarily rebuffed by the Toll pathway, although Imd signaling may provide a minor contribution to defense against these. The high susceptibility of Relish mutant to fungi observed in this article is likely due to the presence of the ebony marker (Lemaitre et al., 1996) [here only one part of the statement has been verified]

      Claim: PGRP-SC1a is required for Lys-type peptidoglycan recognition and Toll activation. Mixed

      Assessment: A possible role for PGRP-SCs in Toll activity has been strongly debated. Challenged by (Bischoff et al., 2006) who found no effect of PGRP-SC RNAi on activation of Toll (Drs expression) in adult flies (although note that their data show a minor reduction of Drs expression in flies in response to E. faecalis). Supported by (Costechareyre et al., 2016) who used single -SC1 and -SC2 mutants to show that Toll activation (Drs expression) was reduced in -SC1 but particularly -SC2 mutants (~50%) in response to E. faecalis (although survival was not affected). The supplementary data of (Paredes et al., 2011) similarly show that Drs expression was reduced (50%) in response to M. luteus septic infection in PGRP-SCdelta flies (PGRP-SC1A/B -SC2 triple mutant), but concluded that this was due to a secondary effect of Imd overactivation. Note that the phenotype found by (Garver et al., 2006) was much stronger (complete ablation of Drs expression), which is not consistent with any subsequent results and argues for a secondary mutation in this line, although this is not consistent with the successful rescue of Drs expression by transgenic replacement of PGRP-SC1a. Downregulation of Toll in these mutants could be explained if the Toll pathway requires amidase activity of PGRP-SC for immunogenicity (e.g. the sugar backbone without stem peptides is a stronger elicitor than whole peptidoglycan), whereas similar cleavage reduces recognition by the Imd pathway (consistent with the demonstrated requirement of stem peptides for full stimulation of the Imd pathway (Chang et al., 2005; Stenbak et al., 2004)). See annotation for (Mellroth et al., 2003). [here the claim is mostly challenged but there are observations that go in the same directions]

      As shown by these examples, category assessment was complex process and relied on human expertise. However, there was very little contestation from the community after the submission of our articles and the opening of the ReproSci website.

      A significant fraction of unchallenged claims is non-reproducible

      - Excluding the 45 unchallenged major claims that were experimentally tested, we categorized the remaining 240 unchallenged claims into three groups". No information is provided on that classification process (either here or in the methods). Who made these assessments (which seem quite subjective and dependent on field expertise), and how do you know if such judgments are reproducible across different evaluators?

      This categorization was done by Hannah Westlake and reviewed by Bruno Lemaitre with few disagreements. This is now indicated in the revised version. The category ‘unchallenged logically consistent” and ‘‘unchallenged logically consistent’ refer to claims that although not directly verified are corroborated or in with current literature. See ReproSci for justifications.

      Example from ReproSci website:

      Claim: The minimal structure needed to activate the Toll pathway is a muropeptide dimer.

      Assesment: Unchallenged logically consistent This is consistent with (Park et al., 2007) who show that on a linearized strand of peptidoglycan, the minimal motif is at least 3 dimers. But it is expected that a non-linearized dimer linked by the peptide bridge could serve as the minimal motif by clustering peptidoglycan.

      Claim: eater null flies are susceptible to oral infection with Serratia marcescens Unchallenged logically consistent Inconsistent with the observation that Eater mutants successfully phagocytose Serratia marcescens (Bretscher et al., 2015). This may indicate that another factor affected by eater mutation is required for defense against S. marcescens, such as formation of lamellopodia and filopodia or adhesion of hemocytes to the body wall, or that Eater contributes to binding of Gram-negative bacteria but does not trigger phagocytosis in response to them as it does for Gram-positive bacteria.

      Higher representation of challenged claims in trophy journals and from top universities

      - Both impact factor and the Shanghai university ranking are continuous variables, but the authors opt to use them as categorical variables in the analysis (i.e. "low-impact", "highimpact", "trophy"; "Top 50", "51-100", "101+"). While there may be legitimate reasons to do this if they feel that these categories are a better descriptor of the underlying reality (e.g. perhaps Cell, Science and Nature are indeed in a category by themselves), it is an unusual decision that leads the comparison to ignore the distinctions between journals/universities within a category. Moreover, it also opens up the opportunity to analysis bias as categories can be set up in many different ways. Thus, if the analysis was not preregistered, I'd recommend that analyses using impact factor and ranking as quantitative variables are added as sensitivity analyses, as these seem to me to be the most natural/less ad hoc way to look at the issue.

      See “Response to categorical variable encoding” above.

      - As mentioned in the Public Review, the analysis here pools the results from the retrospective analysis based on the literature and the prospective one based on the performed replications. Although this may be justified from a sample size perspective (as the number of challenged claims is not that high), it would be useful to perform this analysis separately on the two sets of results as well, as different trends may be noticed. There are many biases that might come into play here (e.g. results from top institutions being more or less likely to have challenged/unchallenged claims published) and looking at the results separately could help in tearing apart these hypotheses.

      The ReproSci project analyzes all the papers of a community during a period of time. As such, the total number of claims is limited and we believe that pooling the data was justified. We provide as sensitivity analysis in Supplement S7, a multivariate model with claims in their pre-experimental classification state:

      “As a sensitivity analysis, we refitted the multivariable hierarchical logistic model using only the claim classifications available before experimental validation by the ReproSci project, as the choice of which claims to further validate could have been influenced by covariate (impact factor, university status). The 45 claims tested prospectively were therefore restored to their original “Unchallenged” classification, while all covariates and author-level random intercepts were retained from the primary analysis. The complete-case analysis included 869 claims, of which 28 (3.2%) were challenged. No predictor had a 94% highest-density interval excluding 1, including publication in trophy journals (OR 1.11, 94% HDI 0.33–3.77) and affiliation with a Top50 institution (OR 1.44, 94% HDI 0.48–3.94). Thus, the associations observed in the pooled analysis were not evident when using classifications based exclusively on the retrospective literature review, although estimates were imprecise because of the small number of challenged claims”

      The irreproducibility rate has increased over time as the field has grown in popularity

      - Again, why not use year as a continuous variable rather than using 5-year windows (as this would effectively include more information)? I think the categorization here is less ad hoc than in the journal/university case, but it is still an unusual decision. More important than this, however, is the fact that pairwise comparisons between periods is probably not an appropriate strategy to look for a time trend, as it excludes all information not related to the pair in each analysis. I'd strongly suggest substituting this for a straightforward regression with publication year as a continuous variable.

      We thank the reviewer for this important suggestion. We agree that categorizing publication year into five-year periods may obscure temporal trends.

      We therefore modelled publication year continuously using a spline in the multivariable analysis, thereby retaining the full temporal information while allowing for non-linearity. A straightforward linear regression would impose a linear trend, which may not adequately capture changes over time. We retained the five-year groupings solely for visualization.

      - "The subsequent increase in unchallenged claims may reflect the rapid conceptual expansion of the field, which likely outpaced the growth in the number of researchers". Do we have data on the growth in the number of researchers in the field? If so, it might be interesting to cite this.

      We do not have the number of researchers to assess the growth of the field, however we can see in Figure 8A an increase in the number of laboratories (as identified by last author name) after 1995. This together with the increase in the number of articles (Figure 3A) clearly show the expansion on of the field.

      - Isn't a "rise in unchallenged claims" over time expected by chance, as older findings will have more time to have been verified by someone else? I understand that the 14-year window probably mitigates this effect, but it still probably exists in some degree and should be mentioned.

      The reviewer is correct that the % unchallenged claims should increase over time, but this does not explain the very low number of unchallenged claims between 1992-2001.

      First-author patterns of irreproducibility

      - With 69 challenged claims and 289 first authors, it would be impossible to achieve "perfect equality" (e.g. a Gini index of 0.88). To verify how far the observed coefficient deviates from chance, it would be useful to obtain (perhaps via simulations) the Gini coefficient expected by chance given the number of challenged claims/authors, and perhaps derive a p value as well (e.g. the proportion of random permutations in which the index exceeds the observed value).

      Very good observation, thank you, we provide this analysis for both first and last author, in the main text and in a supplementary table.

      Added first author text:

      “The distribution of challenged claims among first authors was highly unequal with a Gini coefficient of 0.881 (inequality index ranging from 0 -perfect equality- to 1 extreme inequality-, Figure 4B): the top 10% of first authors accounted for 73.9% of challenged claims, and the top 20% accounted for all challenged claims. This inequality exceeded that expected by chance (reassigning randomly the 69 challenged labels across individual claims, while preserving each author’s number of claims, p = 0.000001). It also remained greater than expected when claims from the same paper were kept together (p = 0.015; Supplementary Table Sx). The concentration of challenged claims among first authors cannot be explained solely by differences in the number of claims or by clustering within papers.”

      Added last author text:

      “Challenged claims were also unevenly distributed among leading authors (Gini coefficient = 0.856): the top 10% accounted for 71.0% of challenged claims and the top 20% accounted for 94.2%. The observed inequality exceeded that expected when challenged labels were reassigned across individual claims (p = 0.00081), but not when claims from the same paper were kept together (p = 0.127; Supplementary Table S9). The apparent concentration among leading authors could be explained by multiple challenged claims arising from the same papers.”

      Added method text:

      “We compared the observed Gini coefficients with two permutation analyses, each based on 1,000,000 permutations. First, we randomly reassigned the challenged labels across individual claims while keeping every claim attached to its original author. This preserved the number of claims contributed by each author. Second, we kept the challenged-claim pattern of each paper together and reassigned these patterns among papers containing the same number of claims. This additionally accounted for the possibility that claims from the same paper were challenged because of a shared problem. For each permutation, we recalculated the Gini coefficient across authors and calculated the p-value as the proportion of simulated coefficients that equalled or exceeded the observed coefficient. Full results are reported in Supplementary.”

      - Claims in a single paper may not be fully independent from each other (as both could be irreproducible due to the same error), so it could be worth adding the article as a random variable here.

      See answer above: [“Response to claim vs article level analysis”]

      - In Fig. 4B, what defines the order of the dots between 0 and 250 (i.e. authors that have no challenged claims)? Is this merely arbitrary? This should be stated more clearly.

      Thank you, the order was arbitrary, it has now been fixed by using a secondary sorting (verified claim proportion), and Authors tied on challenged-claim proportion are ordered by verified-claim proportion has been added the legend.

      - In Fig. 5C, there are clearly less dots than there are authors. This is likely due to superposition (i.e. there are probably multiple circles with 1 article and 0% verified claims). Nevertheless, it means that the graph conveys an erroneous message. As the x axis is a discrete variable, it may be worth turning it into five categories (e.g. 1 to 5) and adding some jitter to each of them in order to let the reader know how many dots are in each category/%.

      Thank you, this was indeed due to superposition. After trying to add a jitter, the graph was still confusing, so we changed the representation to show circle whose size depends on the number of authors. The graph now conveys the size of each block properly.

      Lead-author patterns of irreproducibility

      - The same comment concerning the Gini index made for the first authors also holds here.

      We added a text, see answer above.

      - In Fig. 6B, the same comment made for figure 4B also holds.

      Thank you, the order was arbitrary, it has now been fixed by using a secondary sorting (verified claim proportion), and Authors tied on challenged-claim proportion are ordered by verified-claim proportion has been added the legend.

      - The division between "senior" and "junior" PIs is rather ad hoc here. Once more, why not use "years from first last-author publication" as a continuous variable as a more neutral way to analyze this (as done in Fig. 8)? Also note that it is not clear whether "published a lastauthor article at least five years prior to the considered publication" means any article or one about Drosophila immunity (as in Fig. 8A), and which database was used to examine this (PubMed? Other?).

      Senior and junior researchers were classified based on whether they had published a last-author article five years or more using Pubmed (for seniors). We did not specify articles in Drosophila immunity when considering seniority but only having a last author article five year before the publication.

      - In Fig. 7C, the same comment made for Fig. 5C also holds, although the solution here is less obvious as there are more possible numbers for "number of articles".

      Thank you. As in Fig. 5C, superposition obscured multiple authors occupying the same coordinates. We therefore revised Fig. 7C so that authors with the same number of articles and proportion of challenged claims are represented by a single circle. Circle size and the number shown inside indicate how many authors are represented.

      - As far as I could tell, the data on Fig. 8A refers to when authors published their first first/last author papers on Drosophila immunity (which makes it different from Fig. 7, which refers to any article, but I could be mistaken). If this is the case, I'm not sure it's correct to talk about "the period when principal investigators established their laboratories" as mentioned in the text, as (a) they could have published a first author paper in somebody else's lab or (b) they could have started a lab and only later published a paper on Drosophila immunity. "Year of entry in the field" as in the figure legend seems more appropriate.

      We have changed for ‘year of entry in the field’ as suggested by the reviewer

      - In the same figure, the 1995 cutoff seems completely arbitrary. Why not analyze this as a regression with year of entry as a continuous variable?

      The 1995 boundary is historical as it marks the expansion of Drosophila immunity from a marginal subject into a popular field. The expansion of the field at this specific time was probably driven by a combination of factors: major advances in Drosophila genetics and genomic resources and growing knowledge of antimicrobial peptides in the early 1990, a strong interest on innate immunity that drew attention on the power of Drosophila to answer key questions in this new area. These developments attracted new researchers to insect immunity, and the later discoveries of the IMD and Toll pathways in 1995 and 1996 gave the field an additional (and probably even stronger) boost. Our corpus holds 29 articles for 1959– 1994 (0.8 per year) against 371 for 1995–2011 (21.8 per year), and only 13 of the 156 PIs entered before 1995. The comparison is therefore a cohort contrast more than a contrast or search for a cutting point. Moreover, a regression would impose a linear relationship between the different years, which we believe is too constrained on regard of the observed data.

      - "We hypothesized that these authors, having gained prior hands-on experience on Drosophila immunity, would be less prone to publishing irreproducible claims.". This is a possibility, but given that most of the sample is retrospective, it's also possible that the field is more prone to publicly challenging findings from newcomers.

      Since we do not observe major difference in replicability between senior and junior PI, we still believe that training as first author in a traditional immunity laboratory compared to no training is significant.

      Irreproducibility according to research styles

      - The objective definition of "continuity" and "exploratory" PIs should be stated here for this to be interpretable (note that this is not clear in the Methods either).

      We have better defined how we separate "continuity" and "exploratory" PIs in the methods and in the result section.

      Multivariable analysis of predictors of claim irreproducibility

      - As mentioned in the Public Review, does it make sense to include "unchallenged" along with "verified" in the outcome? If the objective is to reduce the analysis to a "verified/not verified" claim, wouldn't it make more sense to remove the unchallenged findings (as these are likely uninformative, and based on the authors' own replications may be more related to the "challenged" category than to the "unchallenged" one?

      See above, “Response to unchallenged exclusion”

      - As stated previously, why not include journal impact factor, university ranking, year of first paper and year of first Drosophila immunity paper, as continuous variables rather than categorical ones (which would effectively lead to a model with less parameters when there are more than 2 categories). Particularly, there is more information to be gained from adding a single variable than from performing individual comparisons between categories when these have a natural order.

      See above, “Response to categorical variable encoding”

      Discussion:

      - As "conceptual reproducibility" is somewhat of a vague concept, it seems important to discuss (both in the Methods and Discussion) how this was operationalized. Although it's obvious some decisions of what constitutes a direct replication will vary on a case-by-case basis, general guidelines on how these criteria were set are needed.

      We have defined in the methods used to categorize article claims as well as the link with the other companion article. In the ReproSci database, all the assessment are justified allowing to see how we classified claims.

      - "Contrary to the more dramatic narratives...". Here, it is important to emphasize the key differences between this and the cited references: (a) the fact that the majority of the replicability of the sample was found on the basis of a retrospective sample and (b) the fact that the study is dealing with conceptual rather than direct replications. Also, the possibility of confirmation bias should be mentioned as an alternative hypothesis in the last sentence of this paragraph.

      This is a good point and we have highlighted that methodological differences between our study and other studies could explain differences in replicability rates.

      - "findings that, despite being published, have never been independently tested". A more accurate description may be "have never been independently confirmed or refuted in the published literature", as many (and perhaps most - see Baker 2016) replication attempts may go unpublished, as the following sentences themselves indicate.

      We have changed the text according to the reviewer’s suggestion.

      - The description of how findings came to be regarded as "suspicious" here is interesting - and an important part of how the sample was determined. In this sense, I'd consider this as part of the methods. Even though defining what makes something "suspicious" may not be completely systematic, a general description of the method that led the researchers to arrive at this list (e.g. the process that seems to be hinted at in this paragraph) deserves a thorough description.

      The description of how findings came to be regarded as "suspicious" is now detailed in the methods.

      - "Our results suggest the status of a claim being "unchallenged" is not a reliable proxy for its validity". I agree, but this is statement is in direct contradiction with the authors' decision to include unchallenged claims along with validated ones in the binary outcome of the multivariate model.

      See above, “Response to unchallenged exclusion”

      - "Trophy journals are more likely to publish articles with challenged claims than high or lowimpact, although the difference was not significant." Again, a single analysis using a continuous measure of impact should provide more statistical evidence than the pairwise comparisons.

      See above, “Response to categorical variable encoding”

      - "However, this higher rate of follow-up work cannot fully explain by itself the higher proportion of challenged claims in trophy journals." Why not, exactly? I don't remember seeing an objective analysis of it.

      If we remove the unchallenged claims, we still observe higher rate of irreplicability in trophy journal: 15.85% in trophy journal versus 9% in high-impact and 8.1% in low-impact. So we believe that the higher rate of irreplicability in trophy journals cannot be explained by a lower level of unchallenged.

      - The very large paragraph on pages 21-22 could be split into two, one about journals and the other about universities.

      This has been done.

      - "Our data align with the broader narrative of increasing rates of non-reproducible science.". Does it? The time trend did not seem very clear to me (and I would argue that it was not analyzed properly).

      As indicated in the text, we observed an increase in the rate of irreplicable claims over time; however, this trend did not reach statistical significance. We are confident in the robustness of our analysis and therefore did not pursue this question further. While our findings align with concerns about non-replicable science, they also offer a more nuanced perspective, showing that in some fields, replicability remains significantly high.

      - "A similar but more acute pattern was observed during the SARS-CoV-2 pandemic." References should be provided here.

      We have added this reference to suggest that articles published on SARS-CoV-2 pandemic are overall less reliable than others: An alarming retraction rate for scientific publications on Coronavirus Disease 2019 (COVID-19) Nicole Shu Ling Yeo-The https://doi.org/10.1080/08989621.2020.1782203

      - Some of the trends discussed here (e.g. time, continuity vs. exploratory style are not supported (even as a trend) by the multivariate model, and this caveat should be mentioned. An odds ratio of 0.89 with a very wide confidence interval may be too weak in terms of evidence strength to merit a whole paragraph in the introduction discussing it.

      We did not mention the question of time horizon or the distinction between continuity and exploratory research styles in the Introduction; however, we believe these issues merit consideration in the Discussion. In particular, the contrast between continuity-driven and exploratory approaches is noteworthy. Although we found no significant difference in the rate of challenged claims between these approaches, there was a significant difference in the rate of unchallenged claims, which are subsequently more likely to be challenged. This finding is important in the current funding landscape, where many agencies prioritize short-term, exploratory projects. Such incentives may inadvertently contribute to issues of irreproducibility.

      - There are many other limitations beyond those mentioned, in particular the fact that much of the analysis is retrospective and based on a potentially biased literature. The fact that the analysis does not seem to be preregistered and is dependent on a lot of ad hoc decisions also merits discussion. This should definitely be explored in more detail in the Limitations section.

      These limitations are now discussed in the limitation section at the end of the discussion. We agree on the fact that our analysis was not preregistered and that some of our conclusions are raised after analyzing the data. This article is part of a more global project including the databases with all the information. However, full objectivity when analyzing literature is not unachievable, and these critics are inherent to all reproducibility projects.

      Methods:

      Selected articles, annotation and experimental validation:

      - "In brief, a list of 400 publications published before 2011 was generated using a curated search string on the publicly available PubMed database.". Please state that the search string is available in the companion article (e.g. https://doi.org/10.1101/2025.07.07.663442) or include it here.

      This is now stated.

      - "Selected primary articles were annotated by a single researcher".

      By "annotation", do the authors mean the extraction of claims? This is not self-evident. Also, what does the "review" process entails? Do the authors have any data on agreement?

      We do not have data on agreement but overall, there were few discrepancies. We agree that the way to section article in separate claims could affect the conclusion in a number of cases. All the data are public and we did not have any request from the community. The fact that this project depends from arbitrage from the two annotators is mentioned in the limitations.

      - "Claims were cross-checked with evidence from previous, contemporary and subsequent publications and assigned a verification category."

      How was this process performed? This seems to be quite complex and there's hardly any information about it. And once again, do the authors have any data on how reproducible this process would be when performed by different people?

      All the data, notably claim assessment and justifications, are available on the website. Findings cross-checking the claim could be identified by knowledge of the authors, checking articles that quotes the articles. This was an enormous amount of work with human arbitrage. We expected more feedback from the community. The reaction of the community will be described in a companion article later.

      - The authors mentioned that annotations and verifications were made available for comments on the community, but how were these comments incorporated if different opinions were voiced? Can the authors provide data on how frequent these comments were, and how often they were incorporated?

      All data is available on the website, where members of the community can publicly comment on our assessments. The authors can see these comments on the ReproSci website

      Table 1:

      - Again, the three "partially verified" categories in Table 1 seem to refer to very different situations and it would be interesting to break these down somewhere.

      We agree that categories ‘mixed’ and ‘partially verified’ are complex. They represent situations where a decision was not easy. We prefer not to break down those categories in multiple sub-categories.

      First and last author classification:

      - What does "status of first author" mean?

      Status means their position: technician, PhD student, Post-doc, PI….

      - "PIs were manually classified as..." - there seems to be something missing in this sentence (e.g. "as senior or junior on the basis of whether they have...")

      We have added PIs: PIs were manually classified as senior or junior PIs on the basis on having published an article in Pubmed as last author more than five year ago.

      - The distinction between continuity and exploratory is not explained in objective terms. Is there any objective definition of what it means to "continue to work in the field". Publishing a paper in the last X years? And what counts as a "transient" incursion?

      We have better explained in the methods this distinction.

      Statistical analysis:

      - Can the authors define exactly what they mean by "weakly informative priors"?

      Weakly informative prior are Bayesian prior distribution that are meant to be vague as to let the data dominate the results (vs the prior belief of the scientist). They can be seen as very close to flat priors, which are not used here because they cause numerical convergence issues.

      - The authors mention a lot of variables included in the multivariable analysis, but little information is provided on the categorization. What are "low, high and trophy journals"? What university rankings are used?

      We used Shanghai Ranking’s 2010 Academic Ranking of World Universities as our ranking of university. Our binning of ranking in categories make the analysis less susceptible to changes in ranking system. We selected the Shanghai ranking as it primarily evaluates research output and awards (where Times also evaluate teaching, and QS also employer reputation), which we believe represent better the variable than may affect irreplaceability. For the categorization, please see answer above: “Response to categorical variable encoding”

      - "Exact formulas (...) are available in the public repository". What repository do authors mean? The project website? The GitHub repository? Please specify and provide a direct link if possible.

      We added a link to the main text (it was only in the supplementary document). The Supplementary document contains information on how to reproduce the results.

      Reviewer #2 (Recommendations for the authors):

      Specific comments:

      Some methodological details related to the main conclusions of the paper are missing.

      We have extended the methodological section and also better link this article to the companion article.

      - Impact factor: It is unclear which year(s) of journal impact factors were used. Trophy journals are defined as those with an impact factor >50 (Science, Nature, and Cell), but according to Figure 2B, there are only three such journals. Are "trophy journals" limited to these three, or are others included that meet the >50 impact factor threshold?

      These are the 2022 impact factor, we made that clear in the main text. Moreover, only 3 journals in our datasets had an impact factor >50: Nature, Cell, and Science (so the Trophy Journal category is limited to these 3, but not by definition). This is explicit in Table S1: List of journals with impact factor and claim assessment.

      - University ranking: Similarly, please clarify which year(s) of university ranking data were used. Since rankings vary depending on the system (e.g., QS, Times, Shanghai), were the conclusions consistent across multiple ranking sources?

      We used Shanghai Ranking’s 2010 Academic Ranking of World Universities as our ranking of university. Our binning of ranking in categories make the analysis less susceptible to changes in ranking system. We selected the Shanghai ranking as it primarily evaluate research output and awards (where Times also evaluate teaching, and QS also employer reputation), which we believe represent better the variable than may affect irreplaceability.

      Reviewer #3 (Recommendations for the authors):

      Table S4 - for leading author there is a variable of 'historical lab after 1998 continuity' - should that be 1995?

      Correct, we changed this label to Trained in Historical laboratory (Comparison restricted to claims published after 1995) to make it more explicit.

    1. eLife Assessment

      This useful study shows that neural activity before a conversational response contains information about the duration and timing of the subsequent turn. The evidence that this information originates from speech-production planning is incomplete. Because the analyzed EEG is recorded during processing of the partner's speech, and because that speech causally determines the subsequent response, production planning and comprehension/context are confounded in the current analyses. It would strengthen the production interpretation considerably if the authors could show that the relationship with forthcoming duration survives richer controls for the preceding turn, or ideally after accounting for EEG variance driven by the incoming speech.

    2. Reviewer #1 (Public review):

      This manuscript examines EEG activity during face-to-face Diapix conversations and asks whether neural activity preceding a participant's next speaking turn predicts the latency and duration of that turn. The authors report sustained ERP and alpha/beta effects, particularly associated with upcoming response duration, together with temporal-generalization decoding beginning approximately 1-1.3 s before speech onset. They interpret these results as evidence for two stages of conversational speech planning: an early process that activates and maintains the forthcoming response, and a later process related to commitment and motor execution. The naturalistic dyadic EEG setting, relatively large number of conversational turns, trial-level analyses, and convergence across ERP, oscillatory, and multivariate approaches are important strengths.

      I nevertheless have substantial concerns about the interpretation. Most importantly, the EEG analyzed during this period is recorded while participants are processing their partner's speech. Therefore, neural activity necessarily includes auditory, linguistic, semantic, pragmatic, and turn-boundary processing, in addition to any potential response-planning activity. Because properties of the incoming utterance are themselves strongly related to what the listener subsequently says, predicting subsequent response duration or latency does not by itself isolate production planning. The authors acknowledge some of these limitations, but the current title, theoretical framing, Figure 8, and conclusions remain considerably stronger than the evidence allows. Indeed, the Discussion itself acknowledges that the signals cannot be determined to reflect linguistic content, general planning demands, or motor preparation.

      I therefore think the study could become valuable, but substantial additional analyses and conceptual reframing are required.

      Major concerns:

      (1) The central problem is dissociating speech planning from listening/comprehension

      This is my main concern with the manuscript and also with the claims in the Discussion. The authors repeatedly infer that because EEG during the partner's turn predicts the duration or latency of the participant's subsequent response, this activity reflects speech planning. For example, the manuscript states that examining the period before self-speech onset "isolat[es] preparatory neural activity before articulation." I do not think this inference is justified.

      During precisely this interval, the participant is also listening to and comprehending the partner. The incoming utterance is not independent of the forthcoming response; it is what determines the response. A complex statement, several questions, a request for clarification, or an information-rich description will produce different auditory/linguistic/comprehension activity and may also systematically change the length and timing of the subsequent response. Consequently, both partner speech → listening/comprehension EEG and partner speech → subsequent response properties can generate an EEG-response relationship without requiring that the measured EEG activity itself reflect production planning.

      The authors control partner-turn duration, which is useful, and they show that self-duration, self-latency, and partner duration are behaviorally related. However, partner duration is only a very coarse property of the input and cannot control for what participants are actually processing. The analysis should consider, at minimum, partner word count/speech rate, acoustic properties, information density or lexical/syntactic complexity, dialogue act, question versus statement, and preferably semantic/discourse content. For example, the number of questions or propositions in the partner's turn may directly determine both comprehension demands and how much the listener subsequently needs to say.

      Given that transcripts and word-level timing are already available, the authors should be able to substantially improve this analysis. One particularly useful approach would be to construct an encoding model of the EEG driven by acoustic and linguistic features of the partner's speech and then test whether the residual neural activity continues to predict subsequent response properties. At minimum, richer input-related covariates are necessary.

      Without such analyses, I think the main conclusion should be reframed from "neural signatures of speech planning" to something closer to "pre-response neural activity during conversational listening predicts properties of the subsequent turn." The latter is supported by the data; the former requires stronger dissociation.

      (2) It is not clear that the analyzed period is actually a "listening interval".

      Relatedly, the definition of the analyzed interval needs careful reconsideration. Epochs are aligned to the participant's own speech onset and extend back 2.2 s, whereas a turn transition is accepted whenever self-onset falls between −1 and +1 s relative to partner offset.

      Therefore, the interval from −2 to 0 s relative to self-onset is not necessarily an interval during which the partner is continuously speaking. For positive response latencies, part of this interval contains silence following partner offset. If the preceding partner IPU is relatively short, earlier portions of the epoch could potentially contain a different conversational state altogether. Yet the manuscript describes the whole pre-onset period as being "while they are listening to their partner."

      This matters particularly for the comparison between the early and late periods. Different response-latency conditions will necessarily place the partner offset at systematically different positions within an epoch aligned to self-onset. The authors already demonstrate this problem for the anterior latency ERP, where its inflection tracks the distribution of partner offsets. This is an important observation, but I do not think the problem is restricted to that single component.

      The authors should quantify, separately for TW1 and TW2, how much time is occupied by: active partner speech, silence after partner offset, overlapping speech, and potentially other conversational states.

      I would strongly recommend repeating the critical analyses for periods/trials during which the partner is actually speaking, and/or explicitly modeling partner-speech presence and the temporal distance to partner offset. Analyses aligned with both partner offset and self-onset would also help separate responses to the conversational boundary from activity specifically preceding self-production.

      (3) The Early-versus-Late Planning hypotheses are not sufficiently differentiated

      I was confused by the theoretical contrast (e.g., Lines 53-62). The Early Planning account is described as proposing that response preparation begins once sufficient information is available, whereas the Late Planning account explicitly allows "some aspects of conceptual preparation" during listening but proposes that full articulatory planning is delayed until near the turn boundary.

      Under these definitions, both hypotheses allow for early planning. They differ mainly in the level of production planning that occurs early, that is, conceptual/content preparation versus articulatory/motor preparation. This is quite different from a simple early-versus-late timing hypothesis.

      This is problematic because the current EEG measures do not determine whether the early activity reflects conceptual planning, lexical formulation, articulatory planning, comprehension, or general cognitive demands. Indeed, the authors explicitly acknowledge this later. Consequently, observing EEG-behavior relationships 1 s before speech cannot adjudicate between the two hypotheses as currently formulated.

      The theoretical framework should therefore distinguish explicitly among at least: conceptual/message planning → linguistic formulation → articulatory/motor preparation → initiation.

      If the "Late Planning" hypothesis specifically concerns late articulatory planning, then the authors need evidence that distinguishes articulatory planning from earlier conceptual processes. Otherwise, I would describe the findings more conservatively as showing early behavior-related neural activity followed by stronger onset-proximal sensorimotor activity, rather than claiming that Early and Late Planning accounts have been reconciled.

      (4) Response duration is not equivalent to the amount or content of a response that has already been planned

      The strongest empirical finding concerns upcoming speaking duration. I agree that this is interesting. However, throughout the Discussion, duration is gradually interpreted as the "extent," "scale," or even the amount of content already prepared. For example, the authors argue that longer responses involve maintaining a larger set of propositions, lexical items, or planned sequences. Figure 8 goes even further and explicitly contrasts "High Content" and "Low Content," although no measure of speech content has been analyzed.

      I do not think response duration alone supports this inference.

      A 2-s utterance is not necessarily twice as much planned content as a 1-s utterance. Duration can vary because of speech rate, hesitations, lexical retrieval, syntactic formulation, repetitions, interactive feedback, or decisions made after speech has already begun. An eventual IPU duration is therefore not necessarily specified before articulation.

      This issue is especially important because the manuscript explicitly acknowledges that the analyses cannot establish whether the signals encode lexical or semantic content. It is then inconsistent to conclude later that early activity supports "what and how much to say."

      Since transcripts are available, the authors could directly test several alternatives: word count, content-word count, number of propositions, speech rate, syntactic complexity, semantic similarity between partner and self turns, and possibly dialogue-act categories. If the early signal predicts eventual linguistic content/amount rather than merely duration, the claim of maintained response specification would become considerably stronger. If not, the interpretation should remain at the level of response duration.

      (5) The ROI/time-window linear mixed-model (LMM) analysis appears circular

      I have a methodological concern about the relationship between the cluster analyses and subsequent LMMs. The authors first identify electrodes and time ranges showing significant differences between long versus short responses or fast versus slow responses. They then use those same data-driven clusters to define anterior/posterior ROIs and TW1/TW2 and extract EEG values from the same trials, after which the extracted EEG measures are tested as predictors of the same behavioral variables.

      This creates a potentially serious double-dipping/selection bias problem. The continuous LMM outcome is not statistically independent of the median-split outcome used to select the neural features. FDR correction at the LMM stage does not correct for this feature-selection procedure.

      I think the confirmatory analyses need independent feature definition, for example, predefined electrodes/time windows from previous literature, split-half analysis, leave-one-participant-out feature selection, or another cross-validated procedure in which the data used to identify the ROI/window are independent from those used to estimate and test the EEG-behavior relationship.

      This is particularly important because the LMM results are used as evidence that the effect survives behavioral covariates. That conclusion should come from statistically independent tests.

      (6) Several statistical and validation issues need additional attention

      There are several related concerns here. First, the trial-level LMMs include only (1 | participant), despite the fact that the observations come from dyadic interactions, multiple 4-min conversations, and temporally adjacent conversational turns. Participants within the same dyad do not generate independent observations because the speech of one participant directly determines the context for the other participant. I would expect the hierarchy to include at least a dyad/conversation block, and random slopes for critical within-participant predictors should be considered. Robustness to conversational autocorrelation should also be examined.

      Second, the authors describe response duration as approximately log-normal, but the model description indicates only centering/scaling rather than transformation. The authors should report residual diagnostics and consider modeling log-duration or using an appropriate non-Gaussian mixed model.

      Third, the decoding uses stratified 10-fold cross-validation, but it is unclear how conversational dependence is handled. Randomly assigning turns from the same 4-min conversation to training and testing sets may permit classifiers to exploit slow neural states, discourse context, or block-specific characteristics shared by temporally adjacent turns. A more convincing analysis would use blocked cross-validation, such as leave-one-conversation/run-out. The authors should also clarify whether decoding is performed independently within participants and whether median thresholds are participant-specific or global.

      These analyses are particularly important because temporal generalisation establishes stable predictive information, but not the functional identity of that information. A stable partner-speech/context signal could also produce a broad temporal-generalisation pattern.

      (7) ERP baseline correction and potential speech/muscle contamination require clarification

      The ERP preprocessing raises another concern. ERPs were baseline corrected using the entire −2200 to +500 ms epoch. This interval includes the first 500 ms after self-speech onset, the period most vulnerable to speech-related muscular activity. Because the main grouping variable is upcoming response duration, post-onset activity may itself systematically differ between long and short responses. Subtracting a baseline that includes this activity could introduce condition-dependent shifts in the pre-speech ERP.

      I therefore think the critical ERP results should be demonstrated without using post-speech activity in baseline estimation, for example, by using an appropriate pre-speech/regression-based baseline strategy, or by analyses demonstrating that the result is insensitive to baseline choice.

      There is also an intriguing inconsistency concerning EMG. The participant section states that facial hair was an exclusion criterion to ensure electrode contact for EMG recordings, whereas the Limitations section suggests that combining EEG with EMG would be useful in future work. If EMG was in fact recorded, it should be described and, ideally, analyzed to establish when peripheral speech preparation begins and whether the onset-proximal alpha/beta results remain significant after controlling for muscle activity. This would be particularly valuable for the proposed late "motor commitment" interpretation.

    3. Reviewer #2 (Public review):

      This is an excellent paper, well researched, correctly referenced and very well presented. The main addition to the existing literature, apart from supporting earlier results (see also Roberts et al. 2015), is the ability to detect the length of the upcoming conversational contribution from the neural signal. This is interesting because it shows very early and sustained planning of response during listening to the incoming turn from the interlocutor. Why is that interesting? Because this simultaneous listening and response planning is utilising the very same mental machinery - a kind of dual-tasking that humans generally find very hard, given extreme capacity constraints on short-term and working memory. This then talks to the issue of whether speech production and comprehension are more independent than current theory holds. Clearly, human language is one of the elite skills of the species, but this dual-task ability in just this domain may be a specialization evolved over deep evolutionary time. I would like to see this background sketched more clearly in the introduction - it would make the paper of more interest to the general reader.

      The other main contribution is methodological, namely the ability to extract meaningful EEG out of unconstrained verbal interaction, using, e.g. machine learning to filter out movement artefacts. Earlier work was held back by scepticism that this was possible, but this paper points clearly to one way to do this. A further contribution is the suggestion that Beta modulation is associated with the duration of the turn under planning, while Alpha modulation was in addition associated with preparation for execution. This needs to be tested in further studies, but could be a valuable addition to the analytical armoury.

      In the final discussion, a conceptual model is outlined in Figure 8. This looks very similar to that given in Levinson & Torreira 2015 and Levinson 2016 - at least if there is a difference, it would be good to see it clarified. Note that in that model we hypothesized that preparation of the upcoming turn was continued right up to completion and then held if necessary for ending cues from the interlocutor. Note that quite late delivery preparations have been noted both in Torreira et al. 2015 (breathing signal) and in Bögels & Levinson 2023; in the latter, there is evidence of tongue preparation sometimes occurring not tied time-wise to delivery (suggesting inhibition till incoming turn is ending). So our model already had an early and late component - the news here is that the early component can be robustly discerned in naturalistic verbal interaction.

      Also, in the discussion, the Pickering & Garrod IAM model is favourably discussed, even though that model imagines the production machinery is used to predict the unfolding comprehension of the incoming turn: in that case, the production machinery would have to be doing two things at once - doing comprehension and planning one's own turn. In a way, the main lesson of the findings of the submission is precisely that this is implausible.

      In general, I have no hesitation recommending publication of an important contribution to the neuroscience of human performance in ecologically more valid settings.

      References:

      Bögels S, Levinson SC. Ultrasound measurements of interactive turn-taking in question-answer sequences: Articulatory preparation is delayed but not tied to the response. PLoS One. 2023 Jul 5;18(7):e0276470. doi: 10.1371/journal.pone.0276470. PMID: 37405982; PMCID: PMC10321606.

      Griffin, Z.M. and Bock, K. (2000) What the eyes say about speaking. Psychol. Sci. 4, 274-279.

      Levinson SC. Turn-taking in Human Communication--Origins and Implications for Language Processing. Trends Cogn Sci. 2016 Jan;20(1):6-14. doi: 10.1016/j.tics.2015.10.010. Epub 2015 Dec 1. PMID: 26651245.

      Levinson SC, Torreira F. Timing in turn-taking and its implications for processing models of language. Front Psychol. 2015 Jun 12;6:731. doi: 10.3389/fpsyg.2015.00731. PMID: 26124727; PMCID: PMC4464110.

      Roberts SG, Torreira F, Levinson SC. The effects of processing and sequence organization on the timing of turn taking: a corpus study. Front Psychol. 2015 May 13;6:509. doi: 10.3389/fpsyg.2015.00509. PMID: 26029125; PMCID: PMC4429583.

    1. eLife Assessment

      This important study provides theoretical and experimental evidence that aliphatic-to-threonine substitutions in transmembrane domains improve the translocation properties of mitochondrial proteins from the cytosol to mitochondria. The authors provide convincing evidence that substitutions within hydrophobic transmembrane regions of mitochondrial proteins may have contributed to their evolutionary adaptation following the transfer of their genes from the mitochondria to the nucleus. This study will be of interest to biologists and biochemists working on mitochondrial protein import and mitochondrial evolution.

    2. Reviewer #1 (Public review):

      In this manuscript, Yadav and coworkers investigate hydrophobicity governing membrane insertion of mitochondria-encoded polypeptides. The basis of their considerations is the question of why mitochondria maintain a co-translational insertion mechanism and what rules allow allotopic expression. The issue if mitochondrial-encoded proteins can be expressed in the cytoplasm and imported into mitochondria is a long-standing topic in the field with broad implications for the treatment of mitochondrial disorders. In the manuscript, Yadav et al. propose that aliphatic-to-threonine substitution (ATS) are a recurrent and convergent adaptation in the transmembrane helices (TMH) of the inner mitochondrial membrane (MIM) proteins, solving the conflict between hydrophobicity required for membrane insertion and the need for soluble cytosolic transit. The study combines structural comparative genomics, MD simulations, and cellular experiments.

      This is a well-written and highly interesting study that provides a solid basis for considerations regarding mitochondrial protein insertion. I am sure that the study will represent an important asset for future studies on allotopic protein expression and potentially the development of genetic tools. The study provides solid evidence, from evolutionary correlation to experimental manipulation and molecular-level explanation. The study provides abundant and well-thought-out controls.

      Specific points

      (1) There are multiple comparisons throughout the manuscript, and not all of them appear to have been explicitly corrected. Numerous Wilcoxon tests, t-tests, and ANOVAs are reported (Figures 2, 3, 4), without a systematic correction for multiple testing or clear mention of FDR/Bonferroni corrections, except for the directional analysis of substitutions, for which the Benjamini-Hochberg procedure was applied. Given the large number of pairwise comparisons in Figures 3 A-C; and Figure 3 Supplement 1, Supplement 2, Supplement 4, and Supplement 5, with dozens of p-values per panel, it would be relevant to clarify whether a multiple-testing correction was also applied in these analyses. If not, the authors should explain the rationale for not applying one.

      (2) The authors used experimental structures as one criterion but, in other cases, AlphaFold2 to compare evolutionarily distant pairs. Using non-experimental structures as a base for structural alignment among very divergent proteins is understandable but also prone to errors, especially for membrane proteins. It might wrongly identify which helices are considered "related". To address this, the authors need to clearly indicate which data are based on experimental or computational data - this is not always the case.

      (3) The Kyte-Doolittle hydrophobicity scale is standard but relatively raw in comparison with scales based on experimental insertion energy (like Hessa et al., cited in the text or Bothelo et al.). It might be useful to explain why the authors preferred K-D over an experimental membrane insertion scale for the analysis. Particularly, since the Hessa et al. scale was later used in Figure 8G for the thermodynamic analysis.

      (4) The phylogenetic tree of ATP6 (Figure 5A) presents low bootstrap values in some internal nodes (43, 40, 55) on which the interpretation of "independent events" of genetic transfer relies. This must be discussed, since a bootstrap of 40-50% provides not too much confidence in the deep topology of the tree, even though the point that the nuclear sequences don't form a monophyletic group with the mitochondrion seems robust (supported by distinct MTS in each clade).

      (5) Definition of "core" vs "peripheric" in TMH (11 central positions) seems arbitrary; it would be worth clarifying why that cut was chosen and whether the results are robust when they vary.

      (6) There is a notable gap in the literature when describing some of the findings in the Results and Discussion section:

      Biophysics of threonine/serine in TMH:<br /> Senes et al., 2004, Dawson et al., 2002. The result described in Figure 8 (Threonine rotamers) is essentially a biophysical refinement of a body of work already established by the Engelman and DeGrado labs about how Ser-Thr, in comparison with other polar residues, promotes specifically the packing and stability of TMH through intra-inter helical hydrogen bonds, without compromising the membrane insertion. Chamberlain & Bowie, 2002 was included (C-H bonds), and Hessa et al. 2005 (energy of insertion), but no reference was included about why Ser/Thr are polar amino acids "tolerated" at the hydrophobic core of TMH, which is the state the authors describe.

      In the argument about why TMH hydrophobicity matters for targeting (not only for membrane insertion), there is much more direct and recent mechanistic work that should be included: Botelho et al., 2011 established experimentally the hydrophobicity threshold for the "stop-transfer" pathway of TIM23 with quantitative data in an analogous way as reported in this work. Lee et al., 2020 and Yang et al., 2025 described structurally how hydrophobicity of the substrate modulates the Mgr2-Tim17 interaction to allow precursors to be embedded within the membrane.

      It may be helpful to consider previous published work as an important precedent for the discussion of TMH hydrophobicity and TIM23 in allotopic expression. The final observations of incomplete mitochondrial localization despite reduced TMH hydrophobicity could also be discussed in light of previous published studies. These studies suggest that hydrophobicity is an important factor, but not the only determinant of efficient mitochondrial targeting. The role of TMH hydrophobicity in mitochondrial import had already been explored in this context. This would help clarify how the current findings build upon the existing literature.

    3. Reviewer #2 (Public review):

      Summary:

      The paper by Yadav et al. provides compelling theoretical and experimental evidence for an aliphatic-to-threonine substitution (ATS) in the transmembrane helices (TMs) of membrane proteins residing in the inner mitochondrial membrane (IMM) across several species. Furthermore, using ATS, they designed variants of the human Atp6 subunit and of Atp6 subunits from other species that seem to be internalized into mitochondria.

      Strengths:

      In general, the results support the conclusions. The findings are particularly relevant to the field of allotopic expression and could have significant implications for the future development of gene therapies for mitochondrial disorders.

      Weaknesses:

      The manuscript could be substantially strengthened by the addition of further experimental evidence and by some minor revisions and adjustments to the interpretation and presentation of the results.

      Major Points

      (1) The protein product of atp6 is an essential component of the proton channel of ATP synthase, and more than a dozen pathogenic mutations in this gene have been associated with mitochondrial diseases. Accordingly, several studies have reported attempts to achieve allotopic expression of Atp6 in human cells. I would dedicate a pair of paragraphs in the Discussion to acknowledging the most relevant of these previous efforts. Here, in the work of Yadav et al., several Atp6 constructs were detected within mitochondria by fluorescence microscopy. However, no direct evidence was provided that the allotopically expressed Atp6 protein was incorporated into complex V. Localization within mitochondria, by itself, does not demonstrate successful allotopic expression in the functional sense, as the protein could remain unassembled, inserted in an incorrect topology, or in a process of degradation. In my view, this is the main weakness of the present study. A convincing demonstration of allotopic ATP6 expression should ideally be supported by biochemical evidence showing that the imported subunit is stably incorporated into ATP synthase and, most importantly, contributes to its function. This would require demonstrating at least partial restoration of the respiratory or OXPHOS defect.

      I therefore encourage the authors to test their different constructs in cybrid cells carrying a pathogenic mutation in the mitochondrial atp6 gene and determine whether they can restore OXPHOS function or, at a minimum, respiratory capacity. Such an experiment would provide substantially stronger evidence that the imported Atp6 protein is not merely localized to mitochondria but is functionally incorporated into ATP synthase. Alternatively, Blue Native PAGE of mitochondria isolated after allotopic expression of the different constructs, followed by second-dimensional SDS-PAGE, could provide direct evidence that a tagged Atp6 subunit has been incorporated into the ATP synthase complex. Furthermore, this experiment could provide a valuable functional comparison of the three ATS constructs (T1, T2, and T3) and identify which one is most effective in restoring function. Importantly, the T3 construct, despite carrying the highest level of ATS, may not necessarily be the most effective in increasing the levels of assembled ATP synthase or in restoring ATP synthase activity.

      (2) I do not agree with the interpretation of the results presented in Figure 7 - Figure Supplement 3, "Targeting tracks with hydrophobicity, not expression." I am concerned that the experimental design may be biased. The two most highly hydrophobic proteins (WT and T1), which are expected to have greater difficulty being imported into mitochondria, were selected for low-level expression. In contrast, the two constructs with extensive ATS (T2 and T3) were expressed at high levels. The hydrophobicity effect and the expression levels are confounded in this experiment, making it difficult to determine whether the observed targeting differences are attributable to one of these parameters. To disentangle these effects, this experiment should be repeated using both low and high expression levels for all four constructs, resulting in eight experimental conditions (four constructs x two expression levels).

    4. Reviewer #3 (Public review):

      Yadav et al. revisit the old conundrum of why certain genes encoding inner mitochondrial membrane (IMM) proteins are retained in the mitochondrial genome (mtIMMs; 13 genes in humans) while genes encoding other IMM proteins (nuIMMs) have been relocated to the nuclear genome during evolution. It was noted already in 1986 that nuIMMs have transmembrane helices (TMHs) of lower hydrophobicity than mtIMMs (FEBS Lett 198:1). A number of subsequent studies have shown that some mtIMMs can be converted to nuIMMs by appending a mitochondrial targeting sequence (MTS) and, in most cases, reducing the hydrophobicity of one or more of the TMHs (reviewed in Genes & Dis 7:578). Conversely, increasing the hydrophobicity of a TMH in a nuIMM protein can prevent its import into mitochondria (e.g., PNAS 99:10510). Whether the reduction in TMH hydrophobicity in nuIMMs is mainly driven by the need to bypass cotranslational targeting of the protein to the ER and trafficking along the secretory pathway, or to prevent the protein from being prematurely stuck in the IMM during mitochondrial import, is still, to some extent and open question, and experimental support for both possibilities exists (e.g., FEBS Lett 591:65).

      Yadav et al. present an extensive bioinformatics study which nicely confirms the reduced hydrophobicity of nuIMM TMHs compared to mtIMM TMHs (and also compared to TMHs in proteins that traffic along the secretory pathway). The study is based on a lot more sequence data and many more kinds of comparisons than previous publications, and leaves little doubt that the reduction in hydrophobicity in nuIMM TMHs is real.

      Based on their bioinformatics analysis, the authors also propose that aliphatic -> Thr substitutions (ATS) are the main driver of hydrophobicity reduction in nuIMMs, TMHs, and that Thr has been enriched also in mtIMMs. TMHs in organisms that depend only on Oxa1-type IMM insertases, possibly in order to reduce the aggregation propensity of these proteins when produced in the mitochondrial matrix. This is a novel hypothesis that has not been proposed in earlier work.

      The authors additionally show that a mitochondrially encoded ATP6 protein can be converted into a nuclearly encoded, import-competent protein using ATS to reduce the hydrophobicity of its TMHs and, conversely, that import of a nuclearly encoded ATP6 protein is blocked when Thr residues in its TMHs are mutated to aliphatic residues. Similar findings have been published before; what is new is the use of aliphatic -> Thr substitutions (rather than other aliphatic -> polar substitutions).

      Detailed comments:

      While previous bioinformatics studies of how gene relocation from the mitochondrial to the nuclear genome impacts protein sequence have focused on TMH hydrophobicity, Yadav et al. expand this to include individual amino acid frequencies. The authors present four types of analyses:

      (1) The authors identify E. coli homologs of the 13 genes retained in human mitochondrial DNA, do structure-based sequence alignments of the homologous pairs, and then compare the individual aligned TMH pairs in terms of hydrophobicity and Thr content. The authors also calculate an amino acid substitution matrix from the aligned TMH pairs. These data are presented in Figure 2 (and supplements).

      Hydrophobicity is calculated based using the GRAVY algorithm and the Kyte-Doolittle scale, and by calculating the fraction of ALIVF residues. Both nuIMM and mtIMM TMHs are found to be significantly less hydrophobic than the E. coli TMHs (p ≈ 0.002). Thr is reported to be enriched in nuIMM and mtIMM TMHs relative to E. coli TMHs (as are Asn and Pro, while Val and Phe are reduced relative to E. coli TMHs, panel C). Panels F and G show an enrichment of Thr residues in nuIMMs of marginal significance (p = 0.02), and an enrichment in mtIMMs of higher significance (but the statistics are skewed by one mtIMM TMH that apparently contains almost 40% Thr residues). The substitution matrix in panel H indicates that aliphatic I, L, A, and G residues in the E. coli TMHs are often substituted by Thr and Val in the mitochondrial proteins. F does not show this substitution pattern, questioning the use of ALIVF counts in the analysis.

      (2) The authors calculate hydrophobicities and amino acid frequencies for all human TMHs found in AlphaFold2 structures and grouped according to their cellular location: ER, Golgi, plasma membrane, nucleus, outer mitochondrial membrane (OMM), nuclearly encoded proteins in the inner mitochondrial membrane (nuIMM), and mitochondrially encoded proteins in the inner mitochondrial membrane (mtIMM). These data are presented in Figure 3 (and supplements).

      OMM and nuIMM TMHs are significantly less hydrophobic than the secretory pathway TMHs, whereas mtIMM TMHs seem to have similar hydrophobicities as the secretory ones. nuIMM TMHs may have a slightly increased Thr content compared to secretory pathway proteins, but only one p-value is significant (Golgi vs nuIMM). mtIMM TMHs appear to have a higher Thr content than nuIMM proteins, but the statistics is skewed by a single point at a Thr fraction of 0.25. The Thr enrichment is less pronounced than for the E. coli comparison in Figure 2, Figure 3 Supplement. Fig. 8 shows that TMHs in chloroplast and mitochondrial plant proteins both have reduced hydrophobicity compared to ER-targeted proteins.

      (3) Starting from the 66 mitochondrially encoded proteins in Jakobids, the authors identify homologous proteins that have become nuclearly encoded and carry an N-terminal mitochondrial targeting sequence (MTS) in organisms with smaller mitochondrial genomes. As in 1, the authors then do structure-based sequence alignments of the 63 homologous nuIMM and mtIMM TMH pairs found, and compare the individual aligned TMH pairs in terms of hydrophobicity and Thr content. These data are presented in Figure 4 (and supplements).

      The reduction in hydrophobicity in nuIMM vs mtIMM TMHs upon relocation to the nucleus is robust and confirms earlier publications (panel C). The increase in Thr content in nuIMM vs mtIMM TMHs is less convincing (p = 0.012, panel G) and is opposite to what is seen in Figure 3C.

      (4) By phylogenetic analysis, the authors identify three independent relocations of the ATP6 gene to the nuclear genome, do structure-based sequence alignments of nuIMM and mtIMM pairs representing these three events, and illustrate the reduced hydrophobicity of the TMHs in the nuclearly encoded proteins by hydrophobicity plots. These data are presented in Figure 5 (and supplements).

      In panel C, it is not only aliphatic-to-Thr substitutions that reduce the hydrophobicity of the nuATP6 TMHs. How much is the hydrophobicity reduced if the Thr mutations are reversed (i.e., how much does the hydrophobicity reduction depend on ATS specifically and not on other kinds of substitutions)?

      In a short experimental section, the authors go on to show that a mitochondrially encoded ATP6 protein can be converted into a nuclearly encoded, import-competent protein using ATS to reduce the hydrophobicity of its TMHs (but functionality is not tested) and, conversely, that import of a nuclearly encoded ATP6 protein is blocked when Thr residues in its TMHs are mutated to aliphatic residues (Figures 6, 7).

      Finally, the authors carry out all-atom MD simulations to study the energetics of H-bond formation between Thr side chains and TMH backbone atoms.

      In summary, the reduction in TMH hydrophobicity in nuIMMs compared to mtIMMs (and to secretory pathway membrane proteins) is robust and nicely confirms earlier studies. The purported enrichment in Thr content in both nuIMMs and mtIMMs has weaker statistical support in the data: p-values are typically > 0.01 (or may be skewed by an outlier) and appear not to have been corrected for multiple testing in all cases. As is clear from the ATP6 data in Figure 5, aliphatic -> Thr substitutions explain only a fraction of the overall hydrophobicity reduction in nuIMM TMHs, and the role of other hydrophobicity-reducing mutations in gene relocation could be better defined.

    5. Author response:

      We thank the reviewers for their careful reading of the manuscript and for their insightful comments. Several suggestions relate to a lack of clarity in our presentation, and others propose analyses that will strengthen the impact and robustness of the work. We outline our planned revisions below.

      Reviewer 1:

      We are grateful to the reviewer for appreciating the significance of our work for the treatment of mitochondrial disorders and its future implications in the development of genetic tools and protein design.

      (1) Multiple testing

      Figure 3A–C, Figure 3—figure supplements 1, 2, 4 and 5, Figure 6G and Figure 7F used one-way ANOVA with Tukey's post-hoc test, in which correction is intrinsic; the p-values shown are already adjusted. Our legends did not state this, and we will include this information in the revised manuscript.

      We will apply Benjamini-Hochberg correction across all 400 comparisons, and as individual cells are likely underpowered, will also test aliphatic-to-threonine substitution at the level of residue classes, analogous to the directional analysis in Figure 4.

      (2) Experimental versus predicted structures

      We thank the reviewer for pointing this out. Our description here was not adequately clear. All structural alignments and compositional analyses used AlphaFold2 models; experimental structure availability was used only as an inclusion criterion for the compartment comparisons. We chose predicted models because experimental structures are frequently incomplete, which would bias residue composition. We will state this more prominently and add the source of the structure to the relevant legends.

      (3) Hydrophobicity scale

      We used Kyte-Doolittle for comparability with the prior literature on TMH hydrophobicity in relocated mitochondrial genes, and because it is additive by construction, which the mean-hydrophobicity analyses require. The Hessa scale is defined for insertion of a complete segment and carries substantial position dependence (PMID: 15674282). It is used in Figure 8G because that analysis plots single-residue transfer free energies, which is the scale's intended use and which Kyte-Doolittle, being a hydropathy index does not provide. We will state this rationale in the Methods and in the Figure 8G legend.

      (4) Bootstrap support in the ATP6 tree

      We agree that the low-support nodes warrant discussion, and we should have addressed it in the original text. These nodes (43, 55, 40) concern relationships among Porifera, Ctenophora and Cnidaria, which remain contested in analyses of far larger datasets (PMID: 41686647). The inference of independent relocation does not depend on them: the apicomplexan, chlorophyte and ctenophore sequences are separated by backbone nodes with 100% support. We thank the reviewer for noting that the distinct MTSs in each clade provide independent support, and we will state this explicitly along with an acknowledgement that values below 70 indicate unresolved relationships.

      (5) Core versus peripheral definition

      We sincerely thank the reviewer for pointing this out as our text was unclear regarding this. This was set using the ER TMHs, which provide the largest and best-powered set, and applied unchanged to all other groups. The window was chosen to give comparable numbers of positions in each bin, and so that core positions remain membrane-embedded despite helix tilt and uncertainty in the inferred helix midpoint, both of which are greatest near the termini. We will make the rationale explicit and add a robustness analysis at alternative window sizes, as recommended by the reviewer.

      (6) Literature

      We thank the reviewer for these references and agree they should be discussed. We will incorporate the role of Ser and Thr -containing motifs in TM helix association into the discussion of the biophysical properties of these residues. We will review relevant literature regarding hydrophobicity preferences of the TIM translocon in the context of competition between Sec61 and TIM/TOM insertion, together with the differences between insertion of proteins encoded in the mitochondrial and nuclear genomes. Further, we will also make a version of Figure 8G using the Bothelo and the Kyte-Doolittle scale.

      Reviewer 2:

      We sincerely appreciate the reviewer for their insightful comments, and for suggesting experiments to further strengthen the manuscript.

      (1) Assembly and function of allotopically expressed ATP6

      We agree that mitochondrial localization does not establish functional allotopic expression, and will revise the text so this is unambiguous in our manuscript. Our figures 6 and 7 test whether reduced TMH hydrophobicity allows a mitochondrially encoded protein to reach mitochondria when expressed from the nucleus. Assembly into complex V requires further constraints — correct topology, coordinated assembly with the c-ring, and stoichiometry with nuclear-encoded subunits — for which ATS may not be sufficient.

      Following the reviewer's thoughtful suggestion, we will perform Blue Native PAGE to test whether the imported subunit is incorporated into ATP synthase and to compare the T1, T2 and T3 constructs. We will also add a Discussion section on topology as a distinct barrier and acknowledge the previous efforts at ATP6 allotopic expression.  As the reviewer anticipates, we expect that it can be challenging for the current constructs to be fully functional, but we will test this experimentally following the BN-PAGE assays. If we do not get successful incorporation, we will discuss this in the text as to what caveats may have caused this.

      (2) Expression level confound

      We appreciate with the reviewer’s concern regarding this panel. For context, we would like to point out that the conditions originally shown were chosen so that the comparison runs against our conclusion: the most hydrophobic constructs were expressed at low levels and the ATS constructs at high levels, so expression would be expected to work against the observed difference rather than produce it. We recognize that this rationale was not apparent from the figure.

      To address this concern further, we have the remaining conditions in hand and will show all four constructs at both expression levels, as requested.

      Reviewer 3:

      We are very grateful to the reviewer for their thorough review of the manuscript and for providing constructive feedback.

      (1) Statistical support for threonine enrichment

      We take this point. The strength of statistical support varies across these comparisons, and we will report the p-values with the outliers removed, together with the corrections described under Reviewer 1 point 1. We note that the comparisons are constrained by the number of proteins available there are 13 mtDNA-encoded human IMM proteins, and the E. coli-human comparisons are limited to pairs with detectable homology.

      (2) Enrichment of threonine relative to other residues

      We will report the absolute residue compositions for the E. coli and human sets in a supplementary figure, and examine the relative enrichment of threonine, asparagine and proline more closely.

      (3) Threonine content in Figure 3

      The values in Figure 3C are Tukey-adjusted, and all pairwise comparisons are shown in Figure 3—figure supplement 1. The reviewer is correct that only the Golgi comparison reaches significance. Figure 2 compares each human TMH to its aligned E. coli counterpart, so each protein serves as its own control; Figure 3 compares means across unrelated proteins, where between-protein variation is large relative to the compositional difference. We will make this distinction clearer in the text.

      (4) Hydrophobicity of mtDNA-encoded TMHs in Figure 3

      The reviewer is correct that mtDNA-encoded IMM TMHs do not differ significantly in hydrophobicity from secretory-pathway TMHs. The compartment comparisons test proteins of shared nuclear origin with different destinations; the mtDNA-encoded group is shown for reference, as it differs in both genomic origin and biogenesis route. We will revise the text to specify which mitochondrial class the differences apply to.

      (5) Direction of the Figure 3C and Figure 4G comparisons

      We thank the reviewer for bringing this up. Our current text did not make it clear that these comparisons involved different organisms. The mtDNA-encoded proteins in Figure 4 are drawn largely from jakobids with large mitochondrial genomes, which retain extensive IMM biogenesis machinery and may face weaker constraints on TMH composition. Those in Figure 3C are the 13 human mitochondrially encoded proteins, which do face these constraints and are correspondingly threonine-rich. The apparent difference in direction reflects this rather than an inconsistency in the underlying trend, and we will make it explicit in the text.

      (6) Contribution of ATS to hydrophobicity reduction

      We are grateful to the reviewer for their insightful reading of potential evolutionary solutions to the targeting problem. We agree that ATS accounts for part rather than all of the reduction, and that the role of other hydrophobicity-reducing substitutions should be better defined. We will quantify the share attributable to aliphatic-to-threonine substitution, and for the ATP6 examples, report how much of the reduction remains when the threonine substitutions are reverted.

      (7) ALIVF grouping

      The reviewer is right for noting that phenylalanine does not show substitution towards threonine in the way the other aliphatic residues do in Figure 2H. Phenylalanine is nonetheless depleted in human mitochondrial TMHs alongside isoleucine, valine and alanine (Figure 2C), so the grouping is consistent at the level of composition even though the substitution routes differ. We will also discuss aliphatic-to-valine substitutions, which are largely hydrophobicity-neutral and so do not contribute to the reduction in the same way.

      (8) Literature

      We thank the reviewer for pointing out early attempts at understanding mitochondrial protein biogenesis and allotopic expression. We will incorporate these references into our discussion, specifically in the context of considering factors which could favor TIM/TOM over Sec61 insertion.

    1. eLife Assessment

      This study investigates the temporal dynamics of neural activity preceding self-initiated movements and makes a valuable contribution to this field. To overcome methodological and analytical limitations in previous work the authors introduce a matched control condition in which no response is required to assess when neural activity is predictive of self-initiated movement (using electro- and magnetoencephalography). Applying solid methods and machine-learning analyses, the authors show that a late-stage neural event is most predictive of upcoming movements (~100 ms before the response), whereas earlier neural activity is less informative. This finding contradicts previous work that suggested that earlier build-up of neural activity (e.g., starting 500 ms before the response) reflects the planning and preparation of movement.

    2. Reviewer #2 (Public review):

      Summary:

      The authors set out to investigate how well the onset of a self-initiated movement could be predicted at different times prior to action onset. To do so, they collected EEG and MEG data across 15 human participants who watched natural landscape images on a screen. These participants performed active self-initiated movements, or observed passive actions, to have a new image appear. By comparing the neural activity prior to active and time-matched passive actions, the authors found that even though a build-up of neural activity is visible close to 1s prior to action, action onset could only be reliably predicted around 100ms prior to action. These results confirm what was already suggested in previous literature: the commitment to action is only clear from the late stages in the visible neural ramp-up to action onset.

      Strengths:

      (1) The paper presents a well thought out methodology to assess the predictive value of neural activity prior to a self-initiated movement and passively observed action, while keeping all other experimental factors identical. This methodology can be applied outside the specific scope of this paper as well, in efforts to assess the correspondence of a neural signature with an observed behavior.

      (2) The results are a strong confirmation of what was suggested less clearly in previous research (Trevena & Miller, 2010, Consciousness & Cognition; Schmidt et al., 2016, Neuroscience & Biobehavioral Reviews; Travers et al., 2020, NeuroImage).

      Comments on revised version.

      I thank the authors for addressing my concerns in the Introduction and Discussion of their paper. I don't require any further edits at this point.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1:

      Summary:

      Jeay-Bizot and colleagues investigate the neural correlates of the preparation of, and commitment to, a self-initiated motor action. In their introduction, they differentiate between theoretical proposals relating to the timing of such neural correlates relative to the time of a recorded motor action (e.g., a keypress). These are categorised into 'early' and 'late' timing accounts. The authors advocate for 'late' accounts based on several arguments that align well with contemporary models of decision-making in other domains (for example, evidence accumulation models applied to perceptual decisions). They also clearly describe prevalent methodological issues related to the measurement of event-related potentials (ERPs) and time-frequency power to gauge the timing of the commitment to making a motor action. These methodological insights are communicated clearly and denote potentially important limitations on the inferences that can be drawn from a large body of existing work.

      To attempt to account for such methodological concerns, the authors devise an innovative experiment that includes an experimental condition whereby participants make a motor action (a right-hand keypress) to make an image disappear. They also include a condition whereby the stimulus presentation program automatically proceeds at a set time that is matched to the response timing in a previous trial. In this latter condition, no motor action is required by the participant. The authors then attempt to determine the times at which they can differentiate between these two conditions (motor action vs no motor action) based on EEG and MEG data, using event-related potential analyses, time-frequency analyses, and multivariate classifiers. They also apply analysis techniques based on comparing M/EEG amplitudes at different time windows (as used in previous work) to compare these results to those of their key analyses.

      When using multivariate classifiers to discriminate between conditions, they observed very high classification performance at around -100ms from the time of the motor response or computer-initiated image transition, but lower classification performance and a lack of statistically significant effects across analyses for earlier time points. Based on this, they make the key claim that measured M/EEG responses at the earlier time points (i.e., earlier than around -100ms from the motor action) do not reliably correlate with the execution of a motor action (as opposed to no such action being prepared or made). This is argued to favour 'late' accounts of motor action commitment, aligning with the well-made theoretical arguments in favour of these accounts in the introduction. Although the exact time window related to 'late' accounts is not concretely specified, an effect that occurs around -100ms from response onset is assumed here to fall within that window.

      Importantly, this claim relies on accepting the null hypothesis of zero effect for the time points preceding around -100ms based on a somewhat small sample of n=15 and some additional analyses of individual participant datasets. Although the authors argue that their classifiers are sensitive to detecting relevant effects, and the study appears well-powered to detect the (likely to be large magnitude) M/EEG signal differences occurring around the time of the response or computer-initiated image transition, there is no guarantee that the study is adequately sensitive to detect earlier differences in M/EEG signals. These earlier effects are likely to be more subtle and exhibit lower signal-to-noise ratios, but would still be relevant to the 'early' vs 'late' debate framed in the manuscript. This, along with some observed patterns in the data, may substantially reduce the confidence one may have in the key claim about the onset timing of M/EEG signal differences.

      We thank the reviewer for this thorough summary and for providing the opportunity to clarify our claims in light of the concerns identified by this review. Our key claim we make is not that there are no early signals. Rather it is that the well-known and reliable signals (e.g. the RP) that could be construed as evidence for early commitments do not have the characteristics of early commitment signals (i.e. they are not predictive). Based on our main results (Figure 3) we can classify the presence of those early signals using traditional approaches rather well (time-based approach), but when we control for autocorrelation in the data (task-based approach), this classification performance goes away – indicating that those particular signals might not contain movement or intention specific information. Further, we do have evidence that our paradigm was sensitive enough to detect small differences between the two conditions as is evidenced in Appendix 1, Figure 12 with above chance AUC right after trial start, a period during which participants likely recall the trial type (Active vs. passive). We have now added individual peak AUC to the figure to illustrate that our approach was sensitive to these differences (See Figure S12).

      We do however fully agree that absence of evidence is not evidence of absence. We have therefore effected the following changes to mitigate misinterpretations of our results: Abstract: “…it was not useful in predicting movement.” “ “Our results substantially constrain the interpretation that early EEG antecedents, including the RP, reflect a commitment to act, instead indicating that EEG-detectable evidence consistent with such a commitment emerges closer to movement onset, in line with the timing of conscious intention and the capacity for late movement inhibition.

      Introduction:

      “We show that, once appropriate controls are introduced, there is little evidence that cortical EEG contains reliable predictive information much earlier than ~100 ms before movement. Those results better align with late-decision accounts, which posit that the final neural commitment to initiate movement arises very close to the time of movement onset, in the context of self-initiated movements that are not pre-planned.”

      “[…] and at odds with the presence of predictive early signals indicative of a commitment to move – from the vantage point of cortical activity.”

      Discussion:

      “Instead, the data better align with the more recent late-decision accounts”

      “We show here that the data, once properly controlled for, do not support this early neural commitment.”

      “Importantly, the significance of these findings lies not in whether premovement neural activity exists, but in what that activity can legitimately be interpreted to reflect. Our results suggest that although early ramping activity precedes self-initiated movement, this ramping activity may not reflect a true commitment to initiate movement. Instead, they may reflect broader preparatory neural dynamics that bias action likelihood without determining action outcome. Importantly, our findings do not establish whether the relationship between premovement activity, intention, and action is causal or merely correlational. Rather, they constrain interpretations that equate the presence of early neural antecedents with evidence that a specific voluntary decision has already been unconsciously determined one or more seconds before movement onset.”

      Figure S12

      Added individual peak AUCs post slide appearance

      Caption: “Individual peaks after the appearance of the slide are displayed as black circular dots in the figure.”

      Notably, there is some indication of above-chance (above 0.5 AUC) classification performance at time points earlier than -100ms from the response, as visible in Figure 3A for the task-based EEG analyses (EEG OC dataset, blue line). While this was not statistically significantly above chance for their n=15 sample, these results do not appear to be clear evidence in favour of a zero-effect null-hypothesis. In Figures 2A-B, there are also visible differences in the ERPs across conditions, from around the time that motor action-related components have been previously observed (around -500ms from the response). The plotted standard errors in the data are large enough to indicate that the study may not have been adequately powered to differentiate between the conditions.

      We agree with the reviewer that our data does not support the strong claim that there are no early signals. In particular, we agree that the absence of statistically significant decoding does not demonstrate the absence of any underlying signal, especially for potentially subtle effects. However, our central claim is more specific. We do not argue that no early signals exist, rather, we argue that signals previously taken to be evidence of early commitment to move (like the RP) do not exhibit the characteristics expected of a cortical EEG signature of commitment: namely being predictive.

      The slow ramping that we observed, despite its early onset, was not reliably predictive of upcoming movement. It is precisely the contrast between the early presence of ERPs without reliable early decoding that supports our main claim that those signals do not reflect commitment-like processes.

      Classifiers specifically designed to pick up the clearly observable slow ramping activity (e.g. basic-slope LDA in Figure 5) or trained on a large search space of features (e.g. Haar-Adaboost) failed to produce strong or reliable early decoding.

      Importantly, Haar-AdaBoost was not restricted to slow ramping activity or the RP. Its feature space included spatial, temporal, and time-frequency information, allowing it to detect other potentially informative EEG features beyond slow ramping activity, including changes in spectral power. Our key claim is not that no early neural signals exist. Rather, we found little reliable early predictive information across a broad range of EEG features. Our classifiers were sensitive to slow ramping activity (like the RP), spatial patterns, and changes in spectral power, yet robust discrimination emerged only close to movement onset. Thus, any earlier commitment signal would need either to differ from the RP and the other features accessible to our analyses, or to be largely invisible to scalp EEG and MEG. We have revised the abstract and selected sections to clarify that our findings concern early predictive information detectable by our classifiers in EEG more broadly, including but not limited to the RP. These revisions do not rule out an early neural commitment represented in signals inaccessible to EEG or MEG or to our classifiers.

      Abstract:

      “Although early ramping activity was present, our classifiers found little reliable predictive information in the EEG until about 100 ms before movement, when classification accuracy rose abruptly.”

      “Our results substantially constrain the interpretation that early EEG antecedents, including the RP, reflect a commitment to act, instead indicating that EEG-detectable evidence consistent with such a commitment emerges closer to movement onset, in line with the timing of conscious intention and the capacity for late movement inhibition.”

      Introduction:

      “Here, we introduce a paradigm that tests whether early antecedents detectable in EEG—including the RP, but not limited to slow ramping activity—distinguish epochs culminating in self-initiated movement from well-matched epochs not culminating in movement, thereby assessing whether early EEG activity contains reliable outcome-specific information consistent with a commitment to act.”

      “Thus, the analysis was not restricted to the RP nor to other slow potentials, but could exploit temporal, spatial, and spectral differences throughout the EEG signal.”

      Results:

      “Our results show that the slow buildup observed in movement-locked averages was not reliably predictive under more carefully controlled conditions (i.e. when classified against a control condition using the task-based approach). More broadly, classifiers sensitive to diverse temporal, spatial, and spectral features found little reliable early predictive information in the EEG.”

      Discussion:

      “Our results cannot rule out the possibility of an early neural commitment that is undetectable in EEG or MEG. Rather, neither the RP nor slow ramping nor other early EEG/MEG features accessible to our classifiers exhibited reliable commitment-like predictability. Any earlier commitment signal would therefore need to be distinct from the RP and either largely invisible to EEG/MEG or too weak, distributed, or otherwise inaccessible for the present decoding methods to recover.”

      Further, we agree that our study may be underpowered to detect extremely small early effects. However, several observations suggest that any such effects are weak and unlikely to reflect a robust commitment. First, the slight early above-chance decoding visible in Figure 3A is primarily driven by a small number of participants and disappears following outlier exclusion (Figure S7). Second, even when present, early decoding remained extremely weak (approximately AUC ≈ 0.52–0.55), far below the level expected from a stable neural commitment to a specific action outcome. Third, when examining the earliest time points at which single trials became consistently and continuously classifiable until movement onset (EDT analysis), decoding emerged only very close to movement onset (~100 ms on average, with the most extreme participant remaining below ~200 ms). This pattern was additionally replicated in the now-expanded Libet dataset (N = 8; Figure S4), despite early onset of low-decoding performance at the group level, single trials EDTs or non-outlier group onsets were also very close to movement onset.

      Importantly, these conclusions were robust across multiple classifier architectures, parameter choices, and preprocessing pipelines. We additionally reprocessed the EEG data using a 0.05–80 Hz band-pass filter, a 58–62 Hz notch filter, and down-sampling to 512 Hz, following the reviewer’s recommendations (below). Although this preprocessing marginally increased early above-chance decoding, the effect again depended primarily on a single outlier participant (participant 6), and single-trial EDTs remained below ~200 ms even for the outliers (Figure S14). Excluding this participant eliminated the apparent early decoding.

      We therefore agree that early signals may exist. However, our findings suggest that the classical premovement ramping previously interpreted as evidence for an early decision does not behave like a commitment signal. At best, the early decoding observed in outliers could reflect weak biases or task compliance rather than early commitments to a specific course of action. Our conclusions do not depend on proving the complete absence of early information. Rather, they depend on showing that the early signals traditionally invoked in support of early-decision accounts lack the reliability and predictive strength that would be expected if a neural commitment to act were represented by a stable EEG signal.

      In addition to the changes presented earlier, we have made the following changes to the manuscript to accommodate the above:

      Introduction:

      “Although very weak sustained above-chance classification was observed at early time points (Figure 3), these effects were driven by outliers (Figure S7). Crucially, this above-chance classification does not provide evidence for a commitment to initiate movement. Instead, it indicates that upcoming movements can be weakly discriminated from a matched control condition only in a small subset of participants and trials. Moreover, classifiers trained exclusively on slow ramping activity performed comparably—but not better—during the pre-movement period (Figure 5).”

      Results:

      “…we could decode only slightly better than chance…”

      “…could only be classified reliably very close to movement onset…”

      “Indeed, once outliers were excluded, classification remained at chance until very close to movement onset (Figure S7).”

      “This single-trial EDT suggests that commitment, defined as sticking to a course of action, arises only late in time, close to movement onset.”

      “This single-trial EDT suggests that commitment, defined as sticking to a course of action, arises only late in time, close to movement onset.”

      “Additionally, while ramping activity is observable in multiple participants (Figure S3) it disappears when a high-pass filter with a 0.1 Hz cutoff is applied (Figure S14). This suggests that the effect may be driven by very slow fluctuations, slower than typical anticipatory signals, and may therefore reflect non-neural or artifactual processes.”

      Discussion:

      “, when using a classifier based solely on these slow ramping signals (basic slope LDA), performance also hovered around chance levels before movement, indicating that early ramping activity is not predictive of upcoming movements (Figure 5).”

      “Some readers may note a slight above-chance performance in early time windows (Fig. 3). However, this effect was driven by outliers (Fig. S7). Even when present, such weak performance (e.g., AUC ≈ 0.53) falls far short of what would be expected from a reliable neural signature of commitment. Moreover, analyses of EDTs derived from correct classifications at the single-trial level show that correct predictions emerge and are sustained only very close to movement onset. This indicates that the underlying predictive signal itself arises only late. Together, these results suggest that the signal supporting early classification is not commitment-like.”

      “In our primary analyses, we deliberately avoided filtering to preserve even the slowest fluctuations in the signal. However, this choice may have allowed low-frequency noise to remain, potentially obscuring smaller effects. To address this possibility, we re-analyzed the data using a 0.05–80 Hz band-pass filter, applied a band-stop filter to attenuate line noise, and down-sampled the data to 512 Hz (rather than 500 Hz, to minimize loss of frequency information). The filtered analysis (Fig. S14) revealed broadly similar patterns, with two notable differences: the slow drift in the passive ERP was no longer present, and group-level AUC onsets (both by-eye and 90\% criteria) appeared earlier. However, these early onsets were driven by a single outlier (participant 6; figure S14). When considering mean EDTs at the participant level, or when excluding this outlier, onset estimates remained late (figure S14) and consistent with our main findings (Figure 3).”

      New figure in the appendix Figure S14

      New figure with filtered data

      Caption: “Decoding performance and MRCPs from filtered EEG data. EEG data were sampled at 512 Hz to avoid loss of frequency content during downsampling. Line noise was attenuated using a 58–62 Hz band-stop filter, and signals were further filtered using a 0.05–80 Hz one-pass zero-phase FIR filter (FieldTrip, firws) to reduce artifactual activity. (A) Grand-average movement-related cortical potentials (MRCPs) at electrode C3 for active (blue) and passive (red) trials, aligned to slide transition (t = 0; dotted gray line). Shaded areas indicate the standard error of the mean. MRCP onsets are marked by dashed gray lines. (B) Grand-average time course of validation AUC (10-fold cross-validation) for the task-based decoding approach (blue line). The x-axis reflects the leading edge of the sliding window relative to slide transition (t = 0; dotted gray line). Shaded areas indicate the standard error of the mean. AUC onsets are marked by dashed gray lines. Despite early apparent onsets at the group level (AUC onset-by-eye and 90% method > 1 s before movement), classification performance remains near chance and does not exceed ~0.55 until close to movement onset. At the participant level, both AUC onsets and single-trial EDTs average -0.09 s. Excluding participant 6 shifts group-level AUC onset estimates to -0.44 s (by eye) and -0.32 s (90% onset method), indicating that early onsets are driven by outliers.”

      Although the authors acknowledge this limitation in the discussion section of their manuscript, their counter-argument is that the classifiers could reliably differentiate between conditions at time points very close to the motor response, and in the time-based analyses where substantive confounds are likely to be present, as demonstrated in a set of analyses. Based on this data, the authors imply that the study is sufficiently powered to detect effects across the range of time points used in the analyses. While it's commendable that these extra analyses were run, they do not provide convincing evidence that the study is necessarily sensitive to detecting more subtle effects that may occur at earlier time points. In other words, the ability of classifiers (or other analysis methods) to detect what are likely to be very prominent, large effects around the time of the motor response does not guarantee that such analyses will detect smaller magnitude effects at other time points.

      In summary, the authors develop some very important lines of argument for why existing work may have misestimated the timing of neural signals that precede motor actions. This in itself is an important contribution to the field. However, their attempt to better estimate the timing of such signals is limited by a reliance on accepting the null hypothesis based on non-statistically significant results, and arguably a limited degree of sensitivity to detect subtle but meaningful effects.

      Strengths:

      This manuscript provides compelling reasons why existing studies may have misestimated the timing of the neural correlates of motor action preparation and execution. They provide additional analyses as evidence of the relevant confounds and provide simulations to back up their claims. This will be important to consider for many in the field. They also endeavoured to collect large numbers of trials per participant to also examine effects in individuals, which is commendable and arguably better aligned with contemporary theory (which pertains to how individuals make decisions to act, rather than groups of people).

      The innovative control condition in their experiment may also be very useful for providing complementary evidence that can better characterise the neural correlates of motor action preparation and commitment. The method for matching image durations across active and passive conditions is particularly well thought-out and provides a nice control for a range of potential confounding factors.

      Weaknesses:

      There is a mismatch between the stated theoretical phenomenon of interest (commitment to making a motor action) and what is actually tested in the study (differences in neural responses when an action is prepared and made compared to when no action is required). The assumed link between these concepts could be made more explicit for readers, particularly because it is argued in the manuscript that neural correlates of motor action preparation are not necessarily correlates of motor action commitment.

      We thank the reviewer for highlighting this important conceptual distinction. When proposing a matched control condition, the primary requirement was that the two conditions differ in the presence versus absence of a commitment to act. We argue that our two conditions (active vs. passive) satisfy this criterion. In the active condition, participants are instructed to execute a self-initiated motor action to advance from one slide to the next. In the passive condition, participants are not preparing or committing to any action during the slideshow.

      We agree, however, that the two conditions are not matched in all other respects. Most notably, as pointed out by the reviewer, they also differ in the presence versus absence of movement itself as well as of accompanying movement preparation. As such, any differences observed between the conditions could in principle reflect motor-related processes, intention-related processes, or both. Importantly however, this ambiguity does not weaken our main conclusion. If early commitment-related signals were present and robust, they should still contribute to reliable discrimination between conditions. Yet despite the presence of visible premovement ramping activity in the averaged ERPs, classification remained weak and unreliable until very close to movement onset.

      We have made the following changes to the manuscript accordingly:

      Introduction:

      “While the two conditions differ in the presence or absence of movement, they also differ in the presence or absence of an intention to move. As such, any early differences, if present, could reflect either motor-related or intention-related processes. However, this ambiguity is not a concern here, as we only observe differences at later time points.”

      Discussion:

      “A recent study (Derchi et al., 2023) contrasted volitional blinks with spontaneous blinks, thereby keeping the occurrence of movement constant while varying the presence of intention. Such an approach would avoid the movement-versus-no-movement contrast inherent to our paradigm. Importantly however, matching motor outcomes across conditions would likely make classification substantially more difficult, since motor-related information could no longer contribute to decoding performance.”

      As mentioned in the summary, the main issue is the strong reliance on accepting the null hypothesis of no differences between motor action and computer initiation conditions based on a lack of statistically significant results from the modest (n=15) sample. Although a larger sample will increase measurement precision at the group level, there are some EEG data processing changes that could increase the signal-to-noise ratio of the analysed data and produce more precise estimates of effects, which may improve the ability to detect more subtle effects, or at least provide more confidence in the claims of null effects.

      See our responses above regarding accepting the null. Also, we included an additional 5 participants to the Libet task (See updated Figure S4) to slightly improve our sample size. Although as previously stated in our manuscript, the number of trials matter more than the number of participants for a decoding study and with 1400 trials representing about 4 hours of data collection, should such early signals exist yet remain undetectable even with extensive trial counts and multiple decoding approaches, our results would suggest that EEG and MEG may have limited utility for studying them.

      We have emphasized this point in our revised version of the manuscript:

      Discussion:

      “Some of our participants contributed up to 1400 trials, totaling up to 4 hours of data collection over 2 to 4 sessions in the EEG or MEG. Should such early signals exist yet remain undetectable even with extensive trial counts and multiple decoding approaches, our results would suggest that EEG and MEG may have limited utility for studying them.”

      “[…] in eight additional participants and we found more or less identical results […]”

      Figure S4:

      Figure was updated to reflect an additional 5 participants

      Caption updated to “AUC and ERP of Libet-task participants. Machine learning and ERP results for eight participants that we ran with a spontaneous movement initiation task based on Libet et al. (1983). (A) Readiness potential at Cz for the eight participants. In blue are the averages of the press trials and in red the averages of the no-press trials. Time 0 is the time of the trial end (dotted black line). Shaded areas are the standard errors of the mean. RP onsets using the 3 methods on press trials are reported and accordingly labeled (dashed grey lines) (B) Validation AUC for the eight participants using the task-based approach. Time 0 is the time of slide transition (dotted gray line). The AUC is aligned to the leading edge of the sliding window. Shaded areas are the standard errors of the AUC. AUC onsets using the three onset detection methods are reported and labeled accordingly (dashed grey lines). Our slideshow paradigm, while providing a more ecologically valid task, is agnostic as to the spontaneity of the participants’ movement decisions. To ensure that our task would generalize to more standard paradigms in the field of self-initiated action, we performed our analyses on data from a spontaneous voluntary movement paradigm based on (Libet et al., 1983). Here we collected eight further participants (4 female, 4 males, age M=24, 1 left-handed, 7 right-handed). They performed 350 trials of a task similar to the task performed by participants at OC except for the fact that there were no pictures, simply a fixation cross. Furthermore, the instructions for the manual trials were for participants to wait for a minimum of 3 seconds then start monitoring inwards for an urge to move. Whenever they detected such urge they were instructed to press as abruptly and spontaneously as possible, ending the trial. In the automatic trials, participants were instructed to do the same (monitor introspectively for an urge), except that they should not act on the urge if/when they felt it, but rather to wait for the next urge passively, and repeat such process until the trial ended automatically. This paradigm is much closer to the seminal studies on self-initiated actions (Libet et al., 1983; Kornhuber & Deecke., 1965). Here movements are performed spontaneously, and the matched condition (automatic) is identical in most regards apart from it not containing or terminating in a movement. All preprocessing and data analysis applied were the same. We found no strong qualitative difference with our main result (Figures 2A and 3A), suggesting that our task would generalize to other types of self-initiated actions.”

      First, it is stated in the EEG acquisition and preprocessing section that the 64-channel Biosemi EEG data were recorded with a common average reference applied. Unless some non-standard acquisition software was used (of which we are not aware exists), Biosemi systems do not actually apply this reference at recording (it is for display purposes only, but often mistaken to be the actual reference applied). As stated in the Biosemi online documentation, a reference should be subsequently applied offline; otherwise, there is a substantial decrease in the signal-to-noise ratio of the EEG data, and a large portion of ambient alternating current noise is retained in the recordings. This can be easily fixed by applying a referencing scheme (e.g., the common average reference) offline as one of the first steps of data processing. If this was, in fact, done offline, it should be clearly communicated in the manuscript.

      We did perform common average reference during the data preprocessing (lines 200-204 Slideshow2025/preprocess.m at main · lucasjeaybizot/Slideshow2025 · GitHub). We however clarified this description in the methods to clarify that this was performed offline:

      Methods:

      “We recorded EEG using a 64-channel BioSemi system keeping electrode offsets below 10 mV.”

      The CAR was moved down for clarity.

      “Data was re-referenced to the common average offline.”

      In addition, the data is downsampled using a non-integer divisor of the original sampling rate (a 2,048 Hz dataset is downsampled to 500 Hz rather than 512 Hz). Downsampling using a non-integer divisor is not recommended and can lead to substantial artefacts in raw data as a result, as personally observed by this Reviewer in Biosemi data.

      We had initially downsampled to 500 Hz to keep our analyses consistent from PF to OC. However, this is a very valid point, we have therefore re-analyzed the data using a downsampling to 512 Hz. Results did not change qualitatively nor quantitatively. Descriptions can now be found in the new supplementary Figure S14 and as referenced in the main manuscript (see changes below).

      Discussion:

      “In our primary analyses, we deliberately avoided filtering to preserve even the slowest fluctuations in the signal. However, this choice may have allowed low-frequency noise to remain, potentially obscuring smaller effects. To address this possibility, we re-analyzed the data using a 0.05–80 Hz band-pass filter, applied a band-stop filter to attenuate line noise, and downsampled the data to 512 Hz (rather than 500 Hz, to minimize loss of frequency information). The filtered analysis (Fig. S14) revealed broadly similar patterns, with two notable differences: the slow drift in the passive ERP was no longer present, and group-level AUC onsets (both by-eye and 90% criteria) appeared earlier. However, these early onsets were driven by a single outlier (participant 6; figure S14). When considering mean EDTs at the participant level, or when excluding this outlier, onset estimates remained late (figure S14) and consistent with our main findings (Figure 3).”

      Finally, although a 30 Hz low-pass filter is applied for visualisation purposes of ERPs, no such filter is applied prior to analyses, and no method is used to account for alternating current noise that is likely to be in the data. As noted above, much of the alternating current noise will be retained when an offline reference is not applied, and this is likely to further degrade the quality of the data and reduce one's ability to identify subtle patterns in EEG signals. Changes in data processing to address these issues would likely lead to more precise estimates of EEG signals (and by extension differences across conditions).

      In order to assess whether line noise or the absence of filtering in our main analyses could explain the absence of strong early decoding, we re-processed our data with a low pass filter at 80 Hz, a high pass filter at 0.01 Hz and a notch filter from 58 to 62 Hz. We further downsampled our data from 2048 Hz to 512 Hz to maximally retain spectral features. We then ran this preprocessed data through our algorithm. These changes are all comprised in new figure S14 and described above (see response to previous comment).

      With regard to possible effects extending hundreds of milliseconds before the response, it would be helpful for the authors to more precisely clarify the time windows associated with 'early' and 'late' theories in this case.

      We agree this is a very important clarification for the readers and as it stands is only implied in the literature. We now formalized it as follows, an early signal is a signal that would precede the time participants become aware of their intentions to move. In the case of self-initiated action, a recent meta-analysis suggests the lower bound of the 95% predictability intervals lands at -257 ms (Braun et al., 2021).

      We have made the following modifications to the manuscript to reflect this definition:

      Introduction:

      “According to early-decision accounts, decisions (i.e., commitments to a course of action) occur early at the neural level, before participants typically report becoming aware of them. The lower bound of meta-analytic estimates of awareness, at ~250 ms before movement onset (Braun et al., 2021), can be used as a pragmatic threshold to classify neural signals as “early” (more than 250 ms before movement onset) or “late” (less than 250 ms before movement onset).”

      “[…] well in advance, prior to the typical range during which participants report becoming aware of having decided (Braun et al., 2021).”

      “[…] in time to movement onset, during the typical range during which participants report becoming aware of having decided (Braun et al., 2021).”

      Discussion:

      “Early-decision accounts posit that there is an early neural commitment to a decision that occurs prior to the subjective awareness of the decision.”

      The EEG data that would be required to support 'early' theories is also not made sufficiently clear. For example, even quite early neural correlates of motor actions in this task (e.g., around -500ms from the response, or earlier) could still be taken as evidence for the 'late' theories if these correlates simply reflect the accumulation of evidence toward making a decision and associated motor action, as implied by the Leaky Stochastic Accumulator model described by the authors. In other words, even observations of neural correlates of motor action preparation that occur much earlier than the response would not constitute clear evidence against the 'late' account if this neural activity represents an antecedent to a decision and action (rather than commitment to the action), as the authors point out in the introduction.

      We thank the reviewers for pointing out a set of two very pivotal assumptions in our work: early models predict that there are early commitment signals and that those signals both, (1) precede the subjective experience of the decision and, (2) are highly specific to the decisions’ outcome.

      Assumption (1) rests on a set of findings that participants report being first aware of their decision up until very close to movement onset (Libet et al., 1983) which, according to a recent meta-analysis occurs up to around ~250 ms before movement onset (Braun et al., 2021). It is additionally supported by a large corpus of literature on movement inhibition: participants can inhibit their movement up until within ~225 ms from executing them (Vergbruggen et al, 2019). A similar time is also reported specifically for self-paced arbitrary movements (Schultze-Kraft et al., 2016). As such we take it that early-decision accounts, to satisfy (1), predict the early signal to be present more than ~250 ms before movement onset.

      Assumption (2) relates to the conceptualization of decisions. Early-decision accounts posit that decisions are made early. A decision can be viewed as an act of settling on a plan of action. That is, unless new information interferes, the plan of action that was settled on by the act of deciding will be carried out. Maintaining this settled plan of action until it is carried out implies that once a decision has occurred, its accompanying plan of action must be maintained somewhere in the brain. In other words, there should be a brain signal that could in theory be used to predict with high accuracy an upcoming action. While ‘high accuracy’ is a vague concept, another prediction is that it should be equally useful over time to predict the decision, in other words, once a trial begins being correctly classified, it should remain so until movement onset.

      In our paradigm, evidence for early theories would therefore be EDTs (Figure 3B) that occur more than 250 ms before the slide transition.

      We have added the following section to clarify this in our manuscript:

      Discussion:

      “Evidence for early accounts would require EDTs that occur earlier than the ~250 ms lower boundary of meta-analytic estimates of the subjective awareness of the decision (Braun et al., 2021). In our data, no participants' EDTs (Figure 3B) occurred earlier than 250 ms before slide transition. While AUC onsets computed on the group data did occur earlier than 250 ms (Figure 3A), this was driven by outliers (Figure S7) and not present at the individual level.”

      In addition, there is some discrepancy regarding the data that is used by the classifiers to differentiate between the conditions in the EEG data and the claims about the timing of neural responses that differentiate between conditions. Unless we reviewers are mistaken, the Sliding Window section of the methods states that the AUC scores in Figure 3 are based on windows of EEG data that extend from the plotted time point until 0.5 seconds into the past. In other words, an AUC value at -100ms from the response is based on classifiers applied to data ranging from -600 to -100 milliseconds relative to the response. In this case, the range of data used by the classifiers extends much earlier than the time points indicated by Figure 3, and it is difficult to know whether the data at these earlier time points may have contributed (even in subtle ways) to the success of the classifiers. This may undermine the claim that neural responses only become differentiable from around -100ms from response onset. The spans of these windows used for classification could be made more explicit in Figure 3, and classification windows that are narrower could be included in a subset of analyses to ensure that classifiers only using data in a narrow window around the response show the high degree of classification performance in the dataset. If we are mistaken, then perhaps these details could be clarified in the method and results sections.

      The reviewer correctly describes the plotted data. However, it is important to note that because of two reasons (1 and 2 below), the concern of the onset of the AUC of the leading edge of the sliding window not reflecting the onset of the outcome-specific brain activity, can be substantially reduced. (1) The analysis we conducted is a sliding window, which means that an onset detected at -100 ms is above chance performance when classifying data taken from -600 to -100 ms but no such classification for the previous window from -620 to -120 ms. While this could mean that the entire window of activity was necessary for classification, it also suggests that only when incorporating data from the -120 ms to -100 ms window can the classifier perform above chance. (2) We used Haar features of different sub-window length combined with AdaBoost. AdaBoost selects the most informative features given the data, under this circumstance an increase in performance occurring only when including data from -120 ms to 100 ms indicates that data from that window was necessary for this increase in performance.

      Additionally, to ensure more precision we now tested window of 250 ms and 100 ms (updated figure S11), the results of which align with what we previously report.

      Discussion:

      “Another potential concern is that predictive performance observed in wide sliding windows (e.g., -600 to -100 ms) could reflect information present as early as the beginning of the window. However, this interpretation is inconsistent with the temporal profile of decoding performance. For example, if the -620 to -120 ms window is non-predictive whereas the -600 to -100 ms window becomes predictive, the most parsimonious interpretation is that predictive information emerges near the newly included time points close to -100 ms, rather than being present throughout the entire window. This interpretation is further supported by the comparable performance observed across different window widths (figure S11), suggesting that the classifier was able to localize informative features within broader windows.”

      Figure S11

      Figure was updated to include analysis run with 250 ms window and 100 ms window Figure caption: “Evaluation of the sliding window widths. (A) Average validation AUC of Haar-Adaboost of the three participants of the PF EEG dataset using a sliding window (light blue) or growing window approach (pink) with the task-based approach. Time 0 is the time of slide transition (dotted gray line). The AUC is aligned to the leading edge of the sliding window. Shaded area is the standard error of the mean. A growing window analysis is similar to the sliding window analysis except that at each iteration, instead of sliding the window, the window width is increased by 0.02 s. This means that for an epoch beginning 3 s before movement, the window of analysis for the leading edge aligned to the slide transition would be 3 s wide from -3 s to 0 s (while for the sliding window method it would be 0.5 s wide from -0.5 s to 0 s). While it enables the capture of more patterns, it is a computation heavy analysis. To ensure that we were not missing out on some substantial longer-term patterns with sliding window, we ran a growing window on our PF EEG data. We found no improvement of using growing window over sliding window. (B) Average validation AUC of Haar-AdaBoost on the main OC EEG dataset using sliding windows of 100 ms (light blue), 250 ms (medium blue), and 500 ms (dark blue). Shaded areas represent the standard error of the mean. While clear classification differences emerged following movement onset, no substantial differences were observed prior to movement onset. This indicates that the 500 ms window was not too large to obscure finer temporal patterns. Furthermore, these results suggest that Haar-AdaBoost can identify localized informative features within larger windows, supporting the interpretation that the leading edge of the sliding window approximates the earliest time point at which predictive information becomes available.”

      Reviewer #2:

      Summary:

      The authors set out to investigate how well the onset of a self-initiated movement could be predicted at different times prior to action onset. To do so, they collected EEG and MEG data across 15 human participants who watched natural landscape images on a screen. These participants performed active self-initiated movements or observed passive actions to have a new image appear. By comparing the neural activity prior to active and time-matched passive actions, the authors found that even though a build-up of neural activity is visible close to 1s prior to action, action onset could only be reliably predicted around 100ms prior to action. These results confirm what was already suggested in previous literature: the commitment to action is only clear from the late stages in the visible neural ramp-up to action onset.

      Strengths:

      (1) The paper presents a well-thought-out methodology to assess the predictive value of neural activity prior to a self-initiated movement and passively observed action, while keeping all other experimental factors identical. This methodology can be applied outside the specific scope of this paper as well, in efforts to assess the correspondence of a neural signature with an observed behavior.

      (2) The results are a strong confirmation of what was suggested less clearly in previous research (Trevena & Miller, 2010, Consciousness & Cognition; Schmidt et al., 2016, Neuroscience & Biobehavioral Reviews; Travers et al., 2020, NeuroImage).

      Weaknesses:

      (1) Although the authors conducted a solid confirmatory study, the importance of this confirmation is less clear to me. How do the current results change our interpretation of the relation between conscious intention and neural preparation for action? Do these results affect our interpretation of free will? Why does it matter at all whether we see neural preparatory activity prior to the report of a conscious intention to act, or prior to action observation? This study does not clarify the relationship between the observed neural phenomenon, the action or the experienced intention. It does not explain whether this relation is causal, correlational or something else.

      We thank the reviewer for their review. While it was previously suggested that the commitment to action comes late (Schultze-Kraft et al. 2016) and mechanisms were proposed to explain the early ramp-up in light of this late commitment (Schurger et al., 2012; Schmidt et al., 2016). Our main contribution is not merely confirmatory. Rather, our study introduces a matched control condition together with a classifier-based framework specifically designed to test whether early premovement activity is sufficiently predictive to qualify as a commitment-like signal.

      Trevena & Miller 2010 differ largely from our approach, their control condition being matched for ‘decision’ but differing only in motor output. Their results are therefore still consistent with the RP being a decision-following signal. In contrast, our paradigm directly tests whether the early ramping activity reliably distinguishes epochs culminating in self-initiated movement from well-matched epochs not culminating in movement. Travers et al. 2020, while more directly testing the question of the specificity of the RP to self-initiated actions, provided ambiguous and contradictory interpretations, stating both that “[RP-like events] occur to the same degree in control data” and that “these events do not support the hypothesis that Readiness Potentials happen in the absence of action” with results allowing for multiple interpretations. Our study addresses this ambiguity directly by testing whether these early signals contain reliable outcome-specific information predictive of an upcoming action.

      More broadly, while our findings are relevant to debates surrounding free will, our primary goal is neuroscientific rather than philosophical. Specifically, we address whether the signals traditionally interpreted as evidence for early decisions actually possess the properties expected of an early commitment signal. Our findings suggest that they do not.

      Our data therefore invites a reinterpretation of the signals previously taken to challenge views of free will that require decisions to be made consciously. Specifically, the slow ramping signals traditionally interpreted as markers of decisions occurring very early in the brain do not exhibit the characteristics expected of a commitment-like signal. Rather than reflecting a stable and outcome-specific commitment to act, these early ramping signals may instead reflect broader preparatory dynamics that bias action likelihood without determining action outcome.

      Our findings do not establish whether the relationship between premovement neural activity, intention, and action is causal or merely correlational. Addressing that question would require causal interventions beyond the scope of the present study. However, if early premovement activity were causally related to a settled commitment to act, one would expect it to contain strong and reliable predictive information regarding the upcoming action outcome. Our results do not support this account.

      To clarify these points further, we have revised the manuscript accordingly and added the following text to the Introduction and Discussion sections.

      Intoduction:

      “Previous work showed that these early ramping signals were not specific to the outcome of a motor decision (Trevena & Miller, 2010). However, because their analyses remained time-locked to decision outcomes, they could not directly test whether the observed slow buildup specifically reflected a commitment-like process. Related work further suggested that RP-like patterns can emerge elsewhere in ongoing neural activity as features of spontaneous low-frequency fluctuations (Travers et al., 2020). Here, we introduce a paradigm that explicitly tests whether these early ramping signals distinguish epochs culminating in self-initiated movement, where a motor intention is present, from well-matched epochs not culminating in movement, where no motor intentions were present, thereby assessing whether they reliably reflect a commitment to act.”

      Discussion:

      “Our data therefore invites a reinterpretation of the signals previously taken to challenge views of free will that require decisions to be made consciously. Specifically, the slow ramping signals traditionally interpreted as markers of decisions occurring very early in the brain do not exhibit the characteristics expected of a commitment-like signal. Rather than reflecting a stable and outcome-specific commitment to act, this early antecedent ramp-like buildup may instead reflect broader preparatory dynamics that bias action likelihood without determining action outcome.”

      (2) Whereas Derchi et al. (2023, Scientific Reports) were able to keep the entire experimental context similar across intended and unintended conditions, Jeay-Bizot et al. have one big difference between their passive and active conditions: the presence of a movement. Therefore, the present results explain the presence or absence of a movement rather than the presence or absence of an intention to act.

      We thank the reviewer for pointing us to this very relevant and elegant paradigm introduced in Derchi et al., 2023. We agree that our active and passive conditions are not matched for movement itself, and therefore that any differences observed between these conditions could in principle reflect motor-related processes, intention-related processes, or both.

      However, our primary goal was not to isolate a pure neural correlate of intention independent of movement, but rather to test whether the early premovement signals traditionally interpreted as markers of commitment contain reliable outcome-specific information predictive of an upcoming action. In that respect, the presence of an additional movement-related difference between conditions would be expected to increase, rather than decrease, decodability. Consequently, if reliable early commitment-like signals were present, they should still have contributed to reliable early classification performance in our paradigm.

      More closely matching the motor output across conditions, as in Derchi et al. (2023), would likely make classification substantially more difficult by removing movement-related information from the decoding problem altogether. Importantly, despite movement differing between our two conditions, reliable decoding still emerged only very close to movement onset.

      In addition to our modifications made with respect to a similar comment by reviewer #1 (see above), we have added the following paragraph:

      Discussion:

      “Our goal was not to isolate intention, but to test whether the signals traditionally interpreted as commitment signals actually possess commitment-like predictive properties. A recent study (Derchi et al., 2023) contrasted volitional eyeblinks with spontaneous eyeblinks, thereby keeping the occurrence of movement constant while varying the presence of intention. Such an approach would avoid the movement-versus-no-movement contrast inherent to our paradigm. However, matching motor outcomes across conditions would likely make classification substantially more difficult, since motor-related information could no longer contribute to decoding performance. More importantly, our paradigm is focused on the neural antecedents that are specific to whether or not a movement is initiated. This is a fundamentally different question from Derchi et al, which is focused on whether or not an intention is present. The two questions are complementary, however, and taken together, our study and the Derchi study contribute significantly to our understanding of spontaneous voluntary action.”

    1. eLife Assessment

      This study introduces a valuable simulation-based inference (SBI) framework to identify degenerate compensatory mechanisms that stabilize network activity despite neuronal hyperexcitability, a feature common to many brain disorders. By estimating posterior distributions of network parameters, the authors highlight factors such as threshold potential and interneuron-to-principal cell connectivity as key compensators for increased intrinsic excitability and interneuron loss. The approach is promising and can become a convincing tool for probing network degeneracy.

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

      Strengths:

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

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

      (3) Code and data are available.

    3. Author response:

      The following is the authors’ response to the previous reviews.

      Joint Public Reviews:

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

      We agree and have therefore added the simulation-based calibration result as a supplementary figure.

      (2) The authors present two independently trained estimators and show strong agreement between them. This is a useful robustness analysis. However, the rebuttal occasionally presents this as addressing concerns regarding cross-validation and generalization. The new analysis does not constitute cross-validation in the usual sense and does not directly assess generalization to held-out targets or posterior accuracy.

      I recommend that the authors explicitly describe Figure 4 as a reproducibility analysis and avoid presenting it as a substitute for validation.

      We are sorry for causing confusion with our response in the rebuttal. But we hope that the manuscript text is clearer than our rebuttal. We refer to the results as a “replication of the posterior estimator”. Regarding cross-validation, Figure 1 D shows additional simulations of posterior parameters that were not part of the training dataset.

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

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

      The language throughout the manuscript should therefore be softened.

      We agree and have added hedging phrases to communicate the caveats of the results. For example, we now refer to “putative compensatory mechanisms” and adjusted the language in many other parts of the manuscript to highlight that our results provide estimates of mechanisms rather than the mechanisms themselves.

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

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

      You are correct. We are currently working to use our approach on patient data, but the present manuscript does not test the translational potential of the method and therefore some of our previous claims are unsupported. We have therefore deleted references to clinical potential in large parts of the manuscript and add qualifying phrases where we kept discussions of translational potential.

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

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

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

      We agree that the main function of the discussion is to put results into a broader context. But we also believe that repeating results at different levels of detail, different wording, and different interpretation can be to the benefit of some readers. We have therefore left the discussion largely intact except for the changes relating to above points 3 & 4.

      (6) The discussion around lines 271-282 overstates what can be concluded from the inferred posteriors.

      The statement that correlations "discover broadly applicable mechanisms" whereas conditionals "identify specific mechanisms" is stronger than the presented evidence supports. Likewise, the conclusion that conditional distributions are more useful for precision treatments is speculative and not directly demonstrated.

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

      We have deleted the reference to personalized treatment and refer to the mechanisms as “hypotheses” in other parts of the discussion.

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

      We have added definitions of theta and x. We hope that q is sufficiently defined when we call it the “posterior estimate”.

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

      We hope that the changes described above make it clear that we view the KS distance as an estimate of putative compensatory mechanisms in the simulator, rather than direct measures of biological efficacy.

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

      Non-identifiability is indeed an important aspect of simulation-based inference. We allude to it in the introduction where we mention degeneracy. But since we do not present results directly quantifying non-identifiability, we believe an explicit discussion goes beyond the scope of our manuscript.

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

      We thank you for the comments that led to the improved revision and for your constructive feedback on this revised version.

    1. eLife Assessment

      This is a very helpful study that presents an easy to use and reliable alternative to conventional and UV-based tetramers for the identification of antigen-specific CD8 T cells. The authors demonstrate that tetramers for a number of HLA alleles can be subjected to specific temperatures that facilitate peptide exchange whilst maintaining structural integrity, thereby generating detection reagents. They provide convincing evidence for this method, and the approach is likely to provide a useful alternative to generating reagents for examining T cell specificities.

    2. Reviewer #1 (Public review):

      Summary:

      A fundamental technique for the identification of peptide-specific CD8 T cells is the use of fluorophore-conjugated and peptide loaded MHC tetramers. Classically, refolding of specific peptides with MHC monomers can be labour intensive, and not optimal for screening large numbers of different peptides. Hence, UV-exchanged tetramers have been developed to upscale this, however, still has some associated challenges such as UV-mediated damage to peptide complexes. Here, Pothast, C.R. et al demonstrate the efficacy of using temperature exchanged tetramers for the prevalent alleles HLA-A*03:01, A*11:01, B*07:02, and C*07:02. Building upon their previous work with HLA-A*02:01, H-2Kb, and HLA-E. They first demonstrate the complex stability of tetramers with different affinity peptides at high temperature, showing complex destabilisation can be rescued with higher affinity peptides. This is followed by an optimisation of peptide exchange temperatures, tailored for each allele. The authors then demonstrate successful binding to clonal T cell lines, and then a step further with viral peptides against PBMCs from individuals with confirmed infection history. For the latter they compare to conventional tetramers and demonstrate comparable signal.

      Due to the prevalence of these 4 alleles, the ease-of-handling, and short time requirements, these tetramers are likely to show high utility.

      Strengths:

      The manuscript is well-written and results are solid, although more detail may add clarity to some of the results, in particular figure 1 and 2. Other than the points reported below, the study uses accurate controls to demonstrate the specificity of the tetramers, and the data is convincing.

      Overall, their interpretation of the results is accurate and the discussion thorough, additional comments may be included to cover potential tetramer batch variability, and differences in the stability of different alleles. Specifically, whether certain alleles require higher affinity peptides to be stable, compared to others.

      Comments on revised version.

      The authors addressed the weaknesses highlighted in the previous version, specifically addressing that they do not conclude that thermal peptide exchange is not superior to existing technologies, but may be more feasible, as well as including a section on the stability of the different alleles and template peptides. The authors also updated the labelling and description of figures, making them much more interpretable. Addition of the LILRB1 reporter cell line to demonstrate successful peptide exchange and stability adds extra validity to the results.

    3. Reviewer #2 (Public review):

      Summary:

      This work highlights a new approach to build an array of highly stable peptide MHC-I using temperature exchange, which can be used to identify antigen specific CD8+ T cells.

      Strengths:

      In this work, the authors have proposed an alternative method to reload the peptide MHC-I molecule. Their temperature-exchange approach is distinct from current reloadable peptide MHC technologies involving photolabile peptide, empty MHC-I (Nat Commun 11, 1314 (2020). https://doi.org/10.1038/s41467-020-14862-4), tapasin/TAPBPR chaperone-assisted (eLife 7:e40126.), enzyme exchangeable (https://pubmed.ncbi.nlm.nih.gov/31201791/) and small alcohol (Curr Res Immunol. 2022 Aug 18;3:167-174. doi: 10.1016/j.crimmu.2022.08.002) approaches

      Weaknesses:

      The temperature-exchange approach identifies antigen-specific T cells at the same frequency of the photolabile peptide MHC-I molecules. The time saved using temperature-exchange protocol may not be a pull factor as the photolabile peptide MHC-I approach is not unreasonably laborious.

      As with other exchange systems, performance can vary by HLA allele and complex stability, so each new allele needs empirical optimization.

      In short, temperature-based pMHC exchange is a practical and fast loading strategy with good utility for multimer generation, but its main limitation is dependence on thermal stability and careful template selection.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      A fundamental technique for the identification of peptide-specific CD8 T cells is the use of fluorophore-conjugated and peptide loaded MHC tetramers. Classically, refolding of specific peptides with MHC monomers can be labour intensive, and not optimal for screening large numbers of different peptides. Hence, UV-exchanged tetramers have been developed to upscale this, however, still has some associated challenges such as UV-mediated damage to peptide complexes. Here, Pothast, C.R. et al demonstrate the efficacy of using temperature exchanged tetramers for the prevalent alleles HLA-A*03:01, A*11:01, B*07:02, and C*07:02. Building upon their previous work with HLA-A*02:01, H-2Kb, and HLA-E. They first demonstrate the complex stability of tetramers with different affinity peptides at high temperature, showing complex destabilisation can be rescued with higher affinity peptides. This is followed by an optimisation of peptide exchange temperatures, tailored for each allele. The authors then demonstrate successful binding to clonal T cell lines, and then a step further with viral peptides against PBMCs from individuals with confirmed infection history. For the latter they compare to conventional tetramers and demonstrate comparable signal.

      Due to the prevalence of these 4 alleles, the ease-of-handling, and short time requirements, these tetramers are likely to show high utility.

      Strengths:

      The manuscript is well-written and the results are solid, although more detail may add clarity to some of the results, in particular Figures 1 and 2. Other than the points reported below, the study uses accurate controls to demonstrate the specificity of the tetramers, and the data are convincing.

      Overall, the interpretation of the results is accurate, and the discussion is thorough. Additional comments may be included to cover potential tetramer batch variability and differences in the stability of different alleles. Specifically, whether certain alleles require higher-affinity peptides to be stable, compared to others.

      Weaknesses:

      The authors demonstrate the equivalence of temperature-exchanged tetramers to conventional ones, however, as they are an advancement on UV-exchange, it would be useful to show data on how their stability, exchange efficacy, and binding to T cell lines compare to UV-based tetramers. It would be supportive to show that temperature does not impact fluorophore intensity as well.

      Reviewer #2 (Public review):

      Summary:

      The majority of CD8+ T cell responses rely on the proper presentation of antigens through stable MHC-I (but not requiring a stable immunological synapse). This work highlights a new approach to build an array of stable peptide MHC-I using temperature exchange, which can be used to identify antigen-specific CD8+ T cells.

      Strengths:

      In this work, the authors have proposed an alternative method to reload the peptide MHC-I molecule. Their temperature-exchange approach is distinct from current reloadable peptide MHC technologies involving photolabile peptide, empty MHC-I (Nat Commun 11, 1314 (2020). https://doi.org/10.1038/s41467-020-14862-4), tapasin/TAPBPR chaperone-assisted (eLife 7:e40126.), enzyme exchangeable (WO2020226570) and small alcohol (Curr Res Immunol. 2022 Aug 18;3:167-174. doi: 10.1016/j.crimmu.2022.08.002) approaches.

      Weaknesses:

      However, the proposed temperature-exchange approach does not substantially improve the quality of antigen-specific T cells that can be identified using the photolabile peptide MHC-I molecules.

      The time saved using the temperature-exchange protocol may not be a pull factor as the photolabile peptide MHC-I approach is not unreasonably laborious.

      Reviewer #3 (Public review):

      Summary:

      The study by Pothast and colleagues outlines an extension of their previously described temperature-based MHC-I peptide exchange method on 4 common HLA alleles, to enable the generation of peptide/MCH-I tetramers for characterization of antigen-specific T cells by flow cytometry.

      Strengths:

      This work outlines a protocol for generating MHC-I tetramers on 4 common HLA allotypes, which can then be applied to monitor T cell responses by flow cytometry studies. The work provides conditional ligands for exchange on each HLA and demonstrates proof of concept studies using clonotypic T cells and CD8+ PBMCs.

      The results support that the temperature-exchanged tetramers can perform similarly to conventional tetramers in some settings.

      Weaknesses:

      Given that there are several proposed methodologies addressing the same task (including UV-mediated, disulfide-bond based stabilization of empty MHC-I conformers, and chaperone-based methods), the relevance of the proposed temperature-mediated technology is questionable.

      More specifically, important limitations of the study include:

      (1) A lack of quantification of exchanged molecules relative to molecules that retain the original placeholder peptides, or completely empty molecules present in the same sample.

      (2) A lack of validation that peptide exchange has occurred in the absence of a reporter T cell line appears to be a significant limitation of the methodology for antigen / T cell discovery.

      (3) The sub-optimal exchange efficiency relative to conventional prepared pMHC-I molecules, shown in Figure 4, is a significant limitation of the approach.

      (4) There are no data to support that exchange proceeds through the generation of empty molecules during the temperature cycle, or by peptide binding on empty molecules that are already present in the sample. Understanding the mechanism of exchange is important for the necessary improvements to the methodology.

      (5) It is possible that the temperature cycle causes protein aggregation or other irreversible changes to the sample - this should be explicitly quantified and addressed in the paper, since misfolded MHC-I molecules can lead to high levels of background staining.

      (6) These potential limitations should limit detection of low-affinity/low-avidity interactions between TCRs and their cognate pMHC antigens - this should be addressed explicitly in a model antigen setting.

      (7) The approach appears to be limited to the HLAs showing high thermal stability, which have been explored in this study. However, a large fraction of HLAs show sub-optimal thermal stabilities. It seems that explicit validation of peptide exchange would be required for any new HLA allele introduced into this process.

      (8) Whether the approach can be used to load suboptimal peptides with lower thermal stabilities that are emerging immunotherapy targets is not addressed in the present study.

      Because of these limitations, the present manuscript does not conclusively support the claim that temperature-based exchange can be used as a robust methodology to generate pMHC-I tetramers with desired peptide specificities.

      As a result, the scope of applications using these suboptimal exchanged pHLA tetramers is limited, and should be addressed with further improvements of the methodology, including better characterization of exchange efficiency, demonstration of functionality across a broader range of HLA allotypes with varying thermal stability profiles, and validation with clinically relevant low-affinity peptides that would strengthen the potential utility of this approach in immunotherapy development and basic T cell biology research.

      We are submittng a revised version that addresses the major points raised. We believe that the manuscript in its present form clearly communicates the development of a robust and versatile temperature-based peptide exchange platform applicable across multiple HLA class I alleles, with relevance for both fundamental and clinical immunology. While we appreciate the constructive feedback provided, we feel that the core findings and conclusions are already well supported and suitable for publication.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The main recommendation here is to include a stability and staining comparison to UV-exchanged tetramers, as this will directly address why temperature-exchanged tetramers are superior. It could also be discussed that research groups may have better access to temperature-regulating equipment than UV. Other minor corrections may include:

      We thank the reviewer for this suggestion. We have previously demonstrated that HLA‑A*02:01 and H‑2Kb remain stable as frozen monomers, and similarly showed the stability of thermal peptide exchange for HLA‑E, both at the monomer stage and after multimerization into thermal‑exchange HLA‑E tetramers. In addition, we have directly compared these reagents with conventional tetramers (see Figure 5). Conventional monomers and tetramers are routinely stored at −80 °C for several months without loss of staining performance, and we applied the same storage conditions to thermal‑exchange HLA‑E tetramers. Consistent with conventional tetramers, thermal‑exchange tetramers maintained their ability to stain TCR‑expressing cells for up to six months, with optimal staining preserved at both −20 °C and −80 °C. These findings have been published previously and are discussed in the current manuscript.

      We would like to emphasize that we do not conclude that thermal peptide exchange is superior to existing technologies. Our intention is to demonstrate that this approach is feasible and to present its advantages and limitations, which are discussed in detail in the manuscript.

      In the discussion we have included a sentence about the accessibility of heat exchange in relation to the equipment needed for UV exchange.

      “Moreover, the broader availability and simplicity of equipment needed for thermal exchange compared to UV sources enables wider adoption of this approach.”

      (1) Greater explanation of the different coloured curves in Figures 1 and 2, and whether all curves represent samples that are incubated at 50 degrees Centigrade.

      We have now clarified in the figure legends that the different coloured curves represent the distinct peptide–HLA complexes analysed, and we explicitly state which curves correspond to samples incubated at 50 °C.

      (2) Addition of X-axis title for Figures 1 and 2.

      An X‑axis title was already present in Figures 1 and 2, but it was minimal; we have now revised it to make the labeling clearer and more informative.

      (3) Clarify whether the background signal (negative) increases with the addition of more tetramer in Supplementary Figure 3 (non-specific binding).

      The background (negative) signal did not appreciably increase with the addition of higher tetramer concentrations, indicating minimal non-specific binding under these conditions. This is consistent with observations in the MHC class I multimer field, where non-specific binding is generally limited under optimized staining conditions, and MHC class I multimers can be reliably used to detect antigen-specific T cells.

      (4) Address stability of different alleles, or whether they are similar.

      Peptide stability varies for each peptide–HLA combination rather than being strictly allele‑specific. We have therefore clarified this point in the manuscript. In addition, as requested by the reviewer, we have included a dedicated section describing the stability of the thermal‑exchange template peptides.

      “The peptides that formed stable complexes at 4°C but unstable complexes at 20–32°C and 50°C showed affinities ranging from 4 to 11 µM. Such low affinities are uncommon for endogenous peptides and are unlikely to have physiological relevance, given that the complexes are highly unstable at 37°C.”.

      (5) Measure fluorescence signal of tetramer before and after temperature exchange, compared to UV, to show fluorophore stability.

      Fluorescent streptavidin forms a highly stable complex. During UV-mediated peptide exchange, reagents are exposed to heat generated by the UV lamp, which can partially denature MHC-I proteins. For this reason, it is necessary to centrifuge the complexes after UV exchange to remove denatured protein aggregates. Importantly, the fluorescence of streptavidin itself is not affected by this process. In contrast, thermal exchange is performed at substantially lower temperatures than UV-mediated exchange and does not compromise MHC-I protein integrity. Therefore, in our view, it does not pose a significant risk to fluorophore stability. In addition, UV may destroy fluorescence by simple bleaching, which cannot be an issue with temperature-dependent peptide exchange.

      (6) Use HPLC to show stability of tetramer following temperature exchange versus UV.

      HPLC analysis of tetramers is not possible and this is also not performed by others. The reason is that the tetramers are very heterogeneous (clustering of streptavidin together and the amount of MHCI monomers attached to streptavidin). As discussed above we do not claim to be better or worse compared to UV-exchange. This technology has a some additional benefits compared to UV-exchange, such as the tetramerization can be done before the peptide exchange. Additional it does not need a calibrated UV lamp but just an heatblock is needed for the peptide exchange making the exchange less complicated and in our opinion easier to implement. The UV exposure may oxidize amino acids, which is unlike to occur under temperature dependent conditions.

      Reviewer #2 (Recommendations for the authors):

      Minor in figure:

      The authors should explain what "inputs" are in the figure legend.

      We have now clarified in the figure legends that the different coloured curves represent the distinct peptide–HLA complexes analysed, and we explicitly state the nature of the “inputs”.

      Minor in text:

      The authors should also state two important limitations when designing temperature-sensitive peptide MHC-I, which are peptide binding affinity and subsequent T cell recognition. Poorly designed peptides can result in unexchanged peptide MHC-I complexes that may be recognized by the CD8 T cells, leading to false positives.

      We have adjusted this in the text (discussion) although we did not make poorly designed peptides. The peptide presented in this manuscript are only stable at 4°C and unstable from 20°C and higher. As the T cell staining are performed at room temperature, there will be no peptide MHC-I complex of the original thermal-exchange peptide present as this is disintegrated. Only the stable, higher-affinity peptide-exchanged MHC-I is in complex. This does pose a problem for peptide exchange with low-affinity peptides, which is mentioned in the current version. However, this does not differ from the other loading technologies as in each case the mother peptide has to be exchanged for a proper peptide and a low-affinity peptide will be lost in time in each other case as well

      The authors should include other recent reloadable peptide MHC technologies, e.g. chaperone-assisted, enzyme-exchangeble, and small alcohol treatment.

      We apologize for not including these technologies and in the current manuscript they are included in the introduction. (see text below)

      “Several technologies have since been developed to allow the parallel production of multiple MHC-I multimers, facilitating high-throughput analysis. For instance, dipeptides have been used as catalysts for peptide exchange[7-10][FS1] , UV-sensitive conditional MHC ligands have been a widely adopted approach [11, 12], in which a photocleavable peptide is cleaved upon UV exposure. Additional peptide exchange technologies include: chaperone-mediated peptide exchange[13, 14] enzymatic cleavage of low-affinity peptides that are covalently linked [15] and small alcohol peptide exchange [16].”

      Major in data:

      The authors should show if any T cell was detected using the original unexchanged peptide MHC-I in cytometry, as the interpreted use of an irrelevant peptide also depends on the existence of antigen-specific T cells in circulation.

      The unexchanged peptide–MHC‑I complex cannot be used for T‑cell staining under our standard conditions because the template peptide dissociates at room temperature, leading to disintegration of the complex. Since our T‑cell staining protocol is performed at room temperature, unexchanged complexes would not remain intact long enough to allow reliable detection. Performing this experiment would require modifying the standard protocol and carrying out all staining steps strictly at 4°C. We choose not to change the staining protocol to perform this experiment.

      In addition, the thermal‑exchanged peptide–MHC‑I complexes do not produce elevated background staining. As shown in Figure 5, background staining would appear as single‑positive events (PE or APC only), which we do not observe. This indicates that the thermal exchange approach does not introduce nonspecific tetramer binding.

      Reviewer #3 (Recommendations for the authors):

      (1) The proportion of exchanged molecules should be quantified relative to those retaining the original placeholder peptides and completely empty molecules present in the sample, using quantitative mass-spectrometry analysis. This would provide critical information about exchange efficiency and help assess the method's reliability.

      We thank the reviewer for this point. We have performed this experiment and quantified the efficiencies. This is in the current version in figure 2b.

      (2) Validation studies demonstrating peptide exchange in the absence of reporter T cell lines should be included. This would strengthen the methodology's applicability for antigen/T cell discovery applications where such reporter lines may not be available.

      We thank the reviewer for raising this important point. Indeed, incomplete or unstable peptide exchange is a general concern inherent to all MHC-I peptide exchange protocols. To address this, we implemented a T cell–independent validation strategy based on LILRB1 binding. The innate immune receptor LILRB1 selectively recognizes properly folded peptide–MHC-I complexes by interacting with β2-microglobulin (β2M) and the α3 domain of the MHC heavy chain in a peptide-independent manner.

      Critically, in our system the conditional template peptides are thermally unstable and dissociate during the exchange step. As a result, only successfully exchanged complexes containing a stabilizing peptide remain intact. Consequently, LILRB1 binding serves as a functional readout of successful peptide exchange and pMHC stability.

      To assess this, we used K562 cells retrovirally transduced with LILRB1 and stained them with the corresponding fluorophore-conjugated pMHC multimers. Flow cytometric analysis enables clear discrimination between stable (successfully exchanged) and unstable complexes.

      This assay is fully independent of TCR-based reporter systems and can therefore be broadly applied in antigen discovery settings where such reagents are not available. Importantly, it complements our HPLC-based quantification of peptide exchange efficiency (Figure 2B) by providing a scalable quality-control readout of structural integrity (Figure 2C).

      We have now included this analysis in the Results section and updated the Discussion and Materials and Methods accordingly.

      (3) Optimization strategies to improve exchange efficiency should be explored, along with direct comparisons with established methods.

      In the revised manuscript, we have included the quantified exchange efficiencies of the thermal peptide exchange reaction, as determined by HPLC analysis on monomeric complexes. In addition, we now present a novel assay that enables routine quality control of each peptide exchange. This analysis is performed using LILRB1<sup>+</sup> cells and can be readily applied to any peptide–MHC complex using standard flow cytometry.

      Our data demonstrate that the exchange efficiencies obtained with our method are robust, and for the HLA alleles included in this study no further optimization was required. With respect to comparisons with established technologies, we chose to benchmark our approach against conventionally folded tetramers (Fig. 5). As stated above, we do not claim superiority over other peptide-exchange platforms; rather, we aim to present an alternative workflow with distinct practical advantages.

      (4) Potential protein aggregation or other irreversible structural changes resulting from temperature cycling should be quantified and addressed. Since misfolded MHC-I molecules can contribute to high background staining, this characterization is essential for assessing the method's reliability. This can be done using DLS studies, which should report on the fraction of misfolded, high MW species present in the sample upon temperature treatment.

      We thank the reviewer for raising this important point regarding potential aggregation or irreversible structural changes during temperature cycling. We agree that the presence of misfolded or aggregated MHC-I complexes could contribute to nonspecific staining and affect the reliability of the method.

      Although we did not perform dynamic light scattering (DLS), we addressed this concern using complementary biochemical and functional approaches. First, monomeric peptide–MHC complexes were analyzed by size-exclusion HPLC throughout the study. These analyses consistently showed well-defined peaks corresponding to properly folded complexes, without detectable high–molecular weight species that would indicate aggregation (Figures 1 and 2).

      Second, and importantly, we assessed the functional consequences of any potential misfolding or aggregation at the multimer level. If aggregated or misfolded MHC-I complexes were present, this would be expected to result in increased nonspecific binding and elevated background staining. However, across all experiments using both clonal T cell lines and primary PBMCs, we consistently observed low background staining and clear antigen-specific populations (Figures 3–5), arguing against the presence of aggregation-prone or sticky multimer species.

      Third, we incorporated an additional quality control step based on LILRB1 binding (Figure 2C). Because LILRB1 selectively recognizes properly folded peptide–MHC-I complexes, this assay provides a sensitive readout for structural integrity. Unstable or improperly folded complexes do not bind LILRB1, allowing discrimination between functional and non-functional pMHC complexes.

      Taken together, these complementary analyses indicate that temperature-based peptide exchange does not induce detectable aggregation or irreversible structural damage under our experimental conditions, and that the resulting multimer reagents are structurally intact and suitable for downstream applications. We have clarified this in the revised manuscript.

      (5) The method's capacity to detect low-affinity/low-avidity TCR-pMHC interactions in a well-characterized model antigen system should be explicitly addressed. This would define the sensitivity limits of the approach compared to established methods.

      The reviewer raises a valid and broadly relevant point that applies to all MHC-based technologies. However, we consider a full discussion of this topic to be outside the scope of the current manuscript. We do address this limitation for low-affinity peptides in the Discussion, where we write: “This technology is most likely not optimal for low-affinity peptides, as the template peptide itself must also have low affinity.” However, this problem may be universal since low-affinity peptides will simply release all MHC class I-peptide exchanged complexes during the staining process.

      (6) Since the current study focuses on HLAs with high thermal stability, the approach should be tested with HLA alleles exhibiting lower thermal stability profiles. A validation framework could be developed that could be applied when introducing new HLA alleles to this process.

      We thank the reviewer for this insightful comment. To our knowledge, there is no established classification of HLA alleles strictly based on “high” versus “low” intrinsic thermal stability; rather, stability is largely determined by the peptide–HLA combination.

      Regarding the development of a validation framework for additional HLA alleles, our data indicate that suitable template peptide affinities generally fall within a range of approximately 4–11 µM. We anticipate that this range may vary between alleles, and therefore each HLA allele will require empirical identification of an optimal template peptide to achieve appropriate thermal stability and efficient peptide exchange.

      This can be accomplished using the workflow presented in our study: first, assessing complex stability at 50 °C in the absence or presence of a high-affinity peptide (Fig. 1), followed by stepwise temperature reduction to determine optimal exchange conditions and incubation times (Fig. 2). In addition, LILRB1-based quality control can be incorporated to confirm the formation of properly folded and stable peptide–MHC complexes.

      (7) It is encouraged that the approach be explored for its ability to effectively load suboptimal peptides with lower thermal stabilities. Demonstrating this capability would significantly enhance the method's clinical relevance.

      We agree with the reviewer that evaluating the performance of the approach on suboptimal, low‑affinity peptides would be highly valuable and would further strengthen the clinical relevance of the method. In the current study, however, this falls outside our experimental scope. We are working on this in a follow-up project. We have now acknowledged the limitation of low-affinity peptides in the revised manuscript, noting that low‑affinity peptides are less suitable to this technology because the template peptide itself must also possess low affinity. Future work will be needed to systematically explore template design and exchange conditions optimized for such peptides.

    1. eLife Assessment

      This important study reports a novel phenomenon of maternal growth during pregnancy that is independent of growth hormone (GH), adding a new dimension to maternal biology of reproduction. The evidence is convincing and supported by state-of-the-art methodologies conducted in mice and persuasive, interesting and unique observations in humans with hereditary isolated GH deficiency.

    2. Reviewer #1 (Public review):

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

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

    3. Reviewer #2 (Public review):

      This manuscript describes the fascinating phenomenon of growth hormone (GH)-independent growth occurring in the mother during pregnancy. This growth was most pronounced in dwarf mice that are lacking the receptor for growth hormone-releasing hormone (GHRH) and therefore showing isolated GH deficiency. However, the pregnancy-induced growth could also be observed in wild-type mice, suggesting that it is a normal part of the maternal adaptation to pregnancy. The study falls short of identifying the mechanism(s) driving this pregnancy-induced growth response, but it certainly reveals a novel insight into maternal physiology. The authors have completed a range of experiments in mice to prove that, as well as being GH independent, the pregnancy-induced growth also did not require GH signaling in the liver (i.e., not another pregnancy-specific ligand operating through the GHR to promote IGF). They also provided complementary data from a population of humans with untreated isolated GH deficiency that are broadly consistent with the hypothesis. While it is important to consider the significant species differences between rodents and humans, both in terms of growth physiology and also in terms of evolution of placental somato-mammotrophic hormones, this unique population is a valuable resource and adds credence to the study. Overall, I find this a compelling research story, but somewhat unfinished. There are some areas where additional information could improve the ability to interpret the data, and some additional concepts that could be considered in the discussion. There are also areas where additional experiments might provide important insights. However, I think that such suggestions can be considered as appropriate for future research, rather than delaying the dissemination of the findings in the current manuscript.

    4. Reviewer #3 (Public review):

      Summary:

      The study describes an increase in body growth and body composition in both mice and women. In mice, the impact on growth is mainly seen during the first pregnancy, and the changes postpartum on body composition are also different during the first and second pregnancies. The study has used various knockout models in the growth hormone axis to understand these changes as well as some gene expression analysis related to GH, IGF-1 and estrogen signalling pathways.

      Strengths:

      (1) The inclusion of various knock-out mouse models that allow for exploration of mechanisms related to the above-mentioned changes.

      (2) The investigation of gene expression of GHR, IGF-1R and ER pathways.

    5. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment:

      This important study reports a novel phenomenon of maternal growth during pregnancy that is independent of growth hormone (GH), adding a new dimension to maternal biology of reproduction. The evidence is convincing and supported by state-of-the-art methodologies conducted in mice and persuasive observations in humans with hereditary isolated GH deficiency. Revised discussion should focus on possible mechanisms, including the role of IGF2, and on directions for future research.

      We thank the editors for these valuable suggestions. In response, we assessed hepatic Igf2 mRNA expression. As shown in Author response image 1, hepatic Igf2 mRNA expression was higher in Ghrhr<sup>lit/lit</sup> mice but was not significantly affected by pregnancy. However, we recognize that IGF-2 is produced by multiple tissues, particularly at the maternal-fetal interface, as well as by other maternal tissues, exerting endocrine or paracrine effects. Therefore, we believe that hepatic Igf2 expression alone provides only a limited view of the potential contribution of IGF-2 to the maternal adaptations induced by pregnancy. For this reason, we chose not to include these data in the revised manuscript. Instead, we expanded the Discussion to address the potential role of IGF-2 in the observed phenotypes and to highlight important directions for future research, as suggested.

      Author response image 1.

      Public Reviews:

      Reviewer #1 (Public review):

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

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

      We sincerely thank the reviewer for this thoughtful and highly positive evaluation of our work and for recognizing its novelty, rigor, and potential physiological and clinical significance.

      Reviewer #2 (Public review):

      This manuscript describes the fascinating phenomenon of growth hormone (GH)-independent growth occurring in the mother during pregnancy. This growth was most pronounced in dwarf mice that are lacking the receptor for growth hormone-releasing hormone (GHRH) and therefore showing isolated GH deficiency. However, the pregnancy-induced growth could also be observed in wild-type mice, suggesting that it is a normal part of the maternal adaptation to pregnancy. The study falls short of identifying the mechanism(s) driving this pregnancy-induced growth response, but it certainly reveals a novel insight into maternal physiology. The authors have completed a range of experiments in mice to prove that, as well as being GH independent, the pregnancy-induced growth also did not require GH signaling in the liver (i.e. not another pregnancy-specific ligand operating through the GHR to promote IGF). They also provided complementary data from a population of humans with untreated isolated GH deficiency that are broadly consistent with the hypothesis. While it is important to consider the significant species differences between rodents and humans, both in terms of growth physiology and also in terms of evolution of placental somato-mammotrophic hormones, this unique population are a valuable resource and adds credence to the study. Overall, I find this a compelling research story, but disappointingly unfinished. There are some areas where additional information could improve the ability to interpret the data, and some additional concepts that could be considered in the discussion. There are also areas where additional experiments might provide important insights. However, I think that such suggestions can be considered as appropriate for future research, rather than delaying consideration of the current manuscript.

      Main comments:

      (1) Data in Figure 1 are remarkable - not so much the growth in pregnancy in the wildtype mice, because while elevated GH is well known in pregnancy, but growth in the dwarf mice is indicative of GH-independent growth. From these data, it seems that there is good evidence that growth in pregnancy is an adaptive function. However, it is possible that growth is achieved in dwarf mice and that in wildtype mice may have been mediated through different mechanisms. The dwarf mice showed an increase in liver and plasma IGF1, suggestive of an additional ligand driving IGF in pregnancy. One could hypothesize that such an effect could be mediated by an additional pregnancy-specific ligand activating the GH receptor. In humans, placental growth hormone could be such a ligand, but as far as we know, there is no placental GH in mice. In contrast, the wildtype animals showed suppression of liver and circulating IGF1, and low levels of pSTAT5 in the liver during pregnancy. These data (in Figure 5) are very surprising. Given the high circulating GH in pregnancy, as well as high placental lactogen (which would be expected to activate STAT5 in the liver through the Prlr), the low levels of pSTAT5 are unexpected and would seem to indicate some sort of acquired insensitivity to GH. Is this entirely driven by down-regulation of STAT5b protein, or could there be activation of other, negative regulators of STAT signalling, such as SOCS? What is causing such a profound suppression of STAT5? Regardless of the mechanism, this suggests that pregnancy-induced growth in wildtype mice is independent of circulating IGF1 (potentially a different mechanism or in addition to that seen in IGHD mice).

      We thank the reviewer for this insightful comment. The mechanisms underlying the reduced hepatic STAT5b protein expression observed during pregnancy remain unknown. Previous studies have also reported desensitization of hepatic JAK2/STAT5 GH signaling during pregnancy, associated with increased expression of negative regulators of STAT signaling, including CIS (Miquet et al., Am J Physiol Endocrinol Metab 289–E607, 2005). In the current manuscript, we also observed increased Cish mRNA expression in the liver of pregnant mice. We have incorporated this information into the revised manuscript. In addition, we expanded the Discussion to acknowledge the possibility that pregnancy-induced growth may occur through distinct mechanisms in wild-type and GH-deficient mice and that, in wild-type mice, this response may involve liver-derived IGF-1-independent mechanisms.

      The data shown in Figure 6 are a major strength of the study, showing that the pregnancy-induced changes are not specific to one particular transgenic model, but still occur in a variety of models affecting GH through different approaches. Given the pregnancy-specific nature of the changes, however, it seems an oversight not to have evaluated the role of placental lactogens. Prlr is highly expressed in the liver, but the function of this hormone in the liver is not well established. Could the extremely high levels of PL be mediating this growth response? Given the low expression of STAT5 in the liver and the fact that plasma IGF1 is not markedly elevated, it seems more likely that this growth response may be mediated by locally produced IGF1 in target tissues.

      We thank the reviewer for this important suggestion. Although we were not able to investigate the role of prolactin receptor signaling using mouse models in the present study, we agree that prolactin may contribute to pregnancy-induced growth. Accordingly, we revised the Discussion to acknowledge this possibility and to include the potential roles of placental lactogens and locally produced IGF-1 as additional mechanisms that may underlie pregnancy-induced body growth.

      I think these possibilities could be addressed by an expanded discussion of species variation in placental hormones, to highlight that humans have expansion of the GH locus, but rodents have expansion of the prolactin axis (see Soares, M. J. The prolactin and growth hormone families: pregnancy-specific hormones/cytokines at the maternal-fetal interface. Reprod Biol Endocrinol 2, 51, 2004). Importantly, placental GH and chorionic somatomammotropins (CSM) in humans are all variants of the GH gene, but CSM have preferential activity at Prlr. This seems to be a fundamental species difference in pregnancy biology, but has been interpreted as an example of convergent evolution, with conservation of prolactin and GH-like functions at the maternal-fetal interface, mediated by different mechanisms, likely contributing to the metabolic adaptations of the mother (see Newbern D, Freemark M. Placental hormones and the control of maternal metabolism and fetal growth. Curr Opin Endocrinol Diabetes Obes. 2011; 18: 409-416). While the preceding function has focused on explaining the evolution of placental lactogens (either prolactin or GH variants), the present data suggest that there are also mechanisms to maintain growth in pregnancy, independent of GH (even in the absence of a placental GH).

      Thank you very much for this observation and suggestion. In the Introduction, we succinctly mention species-specific differences in the GH locus (see Introduction). Since our study focused primarily on mice, we believe expanding on this information would detract from the study's focus, especially given that such details are available in other publications. However, we have added a sentence to the discussion noting that, despite significant species-specific differences in the GH locus, there has been evolutionary conservation in the maintenance of pregnancy-induced growth, potentially involving distinct mechanisms in rodents and humans, though both appear to be GH-independent.

      “However, important differences are observed between humans and rodents. Humans, but not rodents, possess the GH2 gene, which encodes the placental growth hormone (GH) variant, whose production progressively replaces pituitary GH (derived from the GH1 gene) secretion during pregnancy (Liao et al., 2018). In contrast, rodents maintain pituitary GH secretion throughout gestation (Gatford et al., 2017; Liao et al., 2018). Additionally, human GH, both pituitary and placental variants, as well as chorionic somatomammotropins, can activate both the GH receptor (GHR) and the prolactin receptor (PrlR), explaining their lactogenic effects. In contrast, murine GH activates only the GHR (Bartke and Kopchick, 2015).”

      (2) The human data are very interesting, and my initial impression was that it seemed unlikely to be the same phenomenon. Was there any real evidence for "growth" in pregnancy? Pubertal maturation of long bone growth might be expected to prevent further growth in adulthood. However, these issues were appropriately discussed, and it seems well justified to evaluate this unique population of women with IGHD who underwent pregnancy. It would be very interesting to know if these women experienced elevated IGF1 during pregnancy, indicative of placental GH contributing to growth. Mechanistically, this might be more like the dwarf mouse situation of IGHD, that the situation in wildtype mice (associated with liver insensitivity to GH and low IGF1).

      We thank the reviewer for this comment. Unfortunately, the pregnancies of the women with IGHD occurred with limited medical assistance, and no additional clinical information is available, particularly regarding changes in circulating IGF-1 levels during pregnancy. Therefore, the evidence presented and discussed in the current manuscript represents the only information currently available on pregnancy-induced growth in this unique population.

      (3) It would be useful to include investigations that isolate the effects of pregnancy and the placental hormones. Such studies could include evaluating growth in pseudopregnant mice with IGHD (pregnancy-like changes in hormones but lacking the placental contribution) and in IGHD animals that experience pregnancy but not lactation (pups removed at birth). I accept that this might be too large an additional study to add for the present manuscript.

      We thank the reviewer for this valuable suggestion and agree that this experiment could provide valuable information regarding the role of placental hormones in pregnancy-induced growth. Interestingly, previous work has shown that female mice housed with vasectomized males exhibit increased body weight compared with non-reproductive females, although this increase is substantially smaller than that observed in females with successive litters (Garratt et al., Proc Natl Acad Sci U S A 117:15748–15754, 2020). We discuss this study in the current manuscript. However, this study did not provide additional information on whether this increase reflects greater adiposity, enhanced body growth, or both. Unfortunately, we are unable to perform these time-intensive experiments. We appreciate the reviewer's understanding and agree that this important question should be addressed in future studies.

      (4) It is an important and translationally relevant observation that pregnancy increased the risk of long-term weight gain, and that after the first pregnancy, the pregnancy-induced growth response was more directed to promoting fat deposition. Does this provide any mechanistic insight? Could a metabolic adaptation result in growth?

      We thank the reviewer for this thoughtful comment. At present, we do not have additional data to explain the mechanisms underlying the pregnancy-induced increases in body growth and adiposity or to determine whether these processes are mechanistically linked. In the revised manuscript, we have expanded the Discussion to address potential mechanisms that may contribute to the observed post-pregnancy growth, including hormonal and metabolic factors, while acknowledging that these possibilities remain speculative and require further investigation.

      Reviewer #3 (Public review):

      Summary:

      The study describes an increase in body growth and body composition in both mice and women. In mice, the impact on growth is mainly seen during the first pregnancy, and the changes postpartum on body composition are also different during the first and second pregnancies. The study has used various knock-out models in the growth hormone axis to understand these changes as well as some gene expression analysis related to GH, IGF-1 and estrogen signalling pathways.

      Strengths:

      (1) The inclusion of various knock-out mouse models that allow for exploration of mechanisms related to the above-mentioned changes.

      (2) The investigation of gene expression of GHR, IGF-1R and ER pathways.

      Weaknesses:

      The human findings are dependent on the patient's recollection of bodily changes after their pregnancies.

      Conclusion:

      The authors have partly achieved their aim of describing changes in growth and body composition that remain after pregnancy and the mechanisms behind these changes. This study may have importance for a wide variety of research areas as well as in the clinical setting. The study is also unique in its attempt to bridge findings in mice to a unique human model of congenital GH deficiency.

      We sincerely thank the reviewer for the thoughtful evaluation of our study, for recognizing its novelty and potential significance, and for highlighting the strengths of our experimental approach. We have revised the manuscript to further acknowledge the limitations of the human data, as suggested.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The findings presented in this study are novel, exciting and important. I believe it would help the readers to include a diagram showing the timing of the various measurements and tissue collection in relation to conception, parturition and weaning. It would also be helpful to expand the discussion to include the author's thoughts on the possible role of various placental hormones, pituitary prolactin, metabolic changes and/or other factors in the observed growth. Finally, humans from whom the data were obtained should probably be referred to as individuals or subjects rather than patients.

      We thank the reviewer for these helpful suggestions. We included Figure 7 in the revised manuscript, which summarizes the study's experimental timeline. Additionally, we expanded the Discussion as recommended and replaced the term "patients" with "subjects" throughout the manuscript.

      Reviewer #2 (Recommendations for the authors):

      In addition to my comment above about addressing the different evolution of placental hormones, the following minor suggestions are provided:

      (1) Abstract line 23 "most" could be "some" (because lots of evidence that pregnancy-induced adaptations are long-term - e.g. brain structure, maternal behavior, endocrine changes) - see below.

      Done.

      (2) Is "reproductive experience" the best term? Or is it "pregnancy"? (likely specifically due to the presence of placental hormones and/or maternal patterns of hormones, rather than other aspects of "reproductive experience) e.g. mating, parturition or lactation?

      We appreciate the reviewer's point. However, because we cannot exclude potential contributions of mating, parturition, or lactation to the observed increases in body growth and adiposity, we prefer to retain the term "reproductive experience," as the mice in the present study underwent all of these processes rather than pregnancy alone.

      (3) Potentially, the introduction could add references to brain changes in humans in pregnancy (this is a particularly topical area of research where long-term changes have been reported; e.g. Pritschet L, Taylor CM, Cossio D, Faskowitz J, Santander T, Handwerker DA, Grotzinger H, Layher E, Chrastil ER, Jacobs EG. Neuroanatomical changes observed over the course of a human pregnancy. Nat Neurosci. 2024 Nov;27(11):2253-2260; Martínez-García M, Paternina-Die M, Barba-Müller E, Martín de Blas D, Beumala L, Cortizo R, Pozzobon C, Marcos-Vidal L, Fernández-Pena A, Picado M, Belmonte-Padilla E, Massó-Rodriguez A, Ballesteros A, Desco M, Vilarroya Ó, Hoekzema E, Carmona S. Do Pregnancy-Induced Brain Changes Reverse? The Brain of a Mother Six Years after Parturition. Brain Sci. 2021 Jan 28;11(2):168. There is also a significant literature on long-term effects of reproductive experience (Bridges RS. Long-term alterations in neural and endocrine processes induced by motherhood in mammals. Horm Behav. 2016 Jan;77:193-203).

      We thank the reviewer for this helpful suggestion. Accordingly, we incorporated these references into the Introduction to further emphasize the long-term effects of reproductive experience.

      (4) Figure 1: Why show body weight as a "delta" for primiparous animals, and "absolute "values for multiparous? Similarly, it seems that body length is an informative readout for these experiments, but this is only reported for one model, and most models are only shown as body weight and lean mass.

      We thank the reviewer for this comment. For the primiparous mice, we chose to present the change (Δ) in body weight and lean mass because it more directly illustrates the effects of the first reproductive experience relative to each animal's baseline while avoiding redundant presentation of both absolute and Δ values, as both analyses yielded the same statistical conclusions. For the multiparous mice, absolute values were presented to facilitate comparison with the corresponding images and to allow direct comparison of body size between wild-type and Ghrhr<sup>lit/lit</sup> mice. Regarding body length, these measurements were collected only in the multiparous cohort and were not obtained after the first reproductive experience. Therefore, we are unable to provide body length data for the primiparous groups.

      (5) It seems it would be better to present all of the mouse data first, then the supporting human data.

      Done.

      (6) The manuscript includes a high-quality investigation of GH secretion - particularly the pulsatile secretion of GH. Is it possible to have matching IGF data (at least multiple time points in pregnancy)?

      We agree with the reviewer that serial measurements of circulating IGF-1 during pregnancy would provide valuable additional information. Unfortunately, this analysis is not feasible because the limited blood volume collected during the study did not allow sufficient sample storage for additional measurements.

      (7) Line 221 - "GHRH signaling is required for pregnancy-induced increases in GH secretion...". Can you actually make that conclusion, or is it possible that the lack of GHRH developmentally has permanently altered the pituitary gland, so it cannot respond to other GH secretagogues?

      We thank the reviewer for this important comment. We agree that the congenital absence of GHRH signaling may cause developmental alterations in the pituitary that impair the response to other GH secretagogues. Therefore, we cannot distinguish whether the lack of pregnancy-induced increases in GH secretion reflects the absence of GHRH signaling per se or the developmental defects resulting from GHRH deficiency. We removed the aforementioned phrase from Results and have revised the Discussion to acknowledge this important limitation and to clarify that the observed phenotype may be secondary to impaired pituitary development.

      (8) Line 238 - "GHR signaling stimulates hepatic insulin-like growth factor 1 (IGF-1) secretion, particularly by activating the signal transducer and activator of transcription 5b (STAT5b) pathway" for this point, should probably cite: Davey HW, Xie T, McLachlan MJ, Wilkins RJ, Waxman DJ, Grattan DR. STAT5b is required for GH-induced liver IGF-I gene expression. Endocrinology. 2001 Sep;142(9):3836-41.

      Thank you for this suggestion; we added this reference.

      (9) Line 240 - "GH action in the liver is not only controlled by the pattern of GH secretion...". should probably cite Waxman's work regarding patterns of GH secretion (e.g reviewed in Waxman DJ, O'Connor C. Growth hormone regulation of sex-dependent liver gene expression. Mol Endocrinol. 2006 Nov;20(11):2613-29.).

      This reference was included in this sentence.

      (10) Methods: Check statistics - some of the analysis should have been repeated measures?

      We thank the reviewer for this comment. We carefully re-evaluated all statistical analyses and did not identify any experiment for which a repeated-measures analysis would have been appropriate. Therefore, no changes to the statistical analyses were necessary.

      Reviewer #3 (Recommendations for the authors):

      (1) Introduction: The context of the sentence "Furthermore, historically, females have been underrepresented in studies" is unclear.

      We thank the reviewer for this observation. Our intention was to emphasize that, because pregnancy is unique to females and females have historically been underrepresented in biomedical research, pregnancy-related physiology has received comparatively less attention. However, we agree that this statement was not essential in this context. Thus, we have removed it from the revised manuscript.

      (2) Introduction: Based on the paper, the main aim seems to be to study the lasting effects of pregnancy on the mother and not primarily to study the hormonal changes during pregnancy.

      We thank the reviewer for this helpful observation. We agree that our study primarily focuses on the long-term effects of pregnancy on the maternal organism rather than the hormonal changes that occur during pregnancy. Accordingly, we revised this sentence to better reflect the main objective of the study: “Therefore, the goal of this study is to investigate the lasting effects of pregnancy on the maternal organism and the possible involvement of the somatotrophic axis in these changes.”

      (3) Other endocrine factors could affect the changes seen after pregnancy in mice and humans, such as the insulin level and activation of the IR.

      We thank the reviewer for this valuable suggestion. In the revised manuscript, we expanded the Discussion to acknowledge the potential contribution of several endocrine factors, including insulin signaling, to pregnancy-induced growth.

      (4) The receptor activation in tissues other than the liver has not been studied, such as in the growth plate, adipose tissue and muscle.

      We thank the reviewer for this important comment. We agree with this point and have addressed this possibility in the revised Discussion.

      (5) Information on the age of the patient at the time of pregnancy could be included as well as the patient's height and weight before pregnancy and at follow-up.

      This information was added as Table 1.

    1. eLife Assessment

      By integrating RelB proximity labeling with targeted CRISPR screening, the authors identify several potential regulators of HIV latency and provide evidence implicating RelB, KAT7, and p300 in AZD5582-mediated HIV reactivation. While the study is important, the current work is incomplete, as it needs to establish physiological relevance. The findings require validation in primary latency models, and the unusually high basal RelB expression in the experimental system raises concerns about whether the observed effects accurately reflect endogenous mechanisms regulating HIV latency.

    2. Reviewer #1 (Public review):

      Summary:

      Bussey-Sutton, Gray, Wu et al. combine RelB proximity-labeling proteomics (BioID) with a custom, BioID-informed HIV-CRISPR screen to map the chromatin/transcriptional network that links non-canonical NF-κB (RelB/p52) activation by the SMAC mimetic AZD5582 to HIV-1 latency reversal. They identify a broad basal RelB interactome (chromatin remodelers, RNA-binding proteins, transcription machinery) that is partially reshaped, rather than replaced, by AZD5582 treatment. A targeted CRISPR screen against the BioID hit list then functionally separates these interactors into pro-reactivation factors (KAT7/HBO1, NSD2, SIN3A) and restrictive factors (p300, CHD4, USP7, UHRF1, DNMT1).

      Pharmacological inhibition of the two acetyltransferase hits (KAT7i and p300i) validates opposing phenotypes predicted by the screen, and a comparative TNF-α (canonical NF-κB) screen argues that at least the p300 and, more selectively, the KAT7 pharmacology are biased toward the ncNF-κB axis. The dataset is a useful resource, and the BioID, CRISPR, pharmacology pipeline is a nice template for functionally triaging proximity-proteomics hits rather than treating them as a static interactome.

      Strengths:

      This study takes an integrative approach in which proximity proteomics is used to define candidate RelB-associated regulators, a purpose-built targeted CRISPR library tests these candidates directly for a functional phenotype, and pharmacological inhibition cross-validates the two acetyltransferase hits with an orthogonal, catalytic-activity-specific perturbation. This funneling from unbiased proteomic discovery to targeted functional testing to mechanistic pharmacology is an efficient and logical pipeline for converting a large interactome into a smaller set of high-confidence, functionally validated regulators.

      The comparison of basal versus AZD5582-stimulated RelB interactomes is conceptually useful, as it shows that RelB engages a largely pre-existing chromatin and transcriptional regulatory scaffold upon ncNF-κB activation, rather than recruiting an entirely new complex. This is a finding with potential implications for non-canonical NF-κB signaling beyond the HIV latency context specifically.

      The inclusion of a parallel HIV-CRISPR screen using TNF-α to activate canonical NF-κB signaling is a valuable control, allowing the authors to distinguish regulators that are broadly required for NF-κB-dependent HIV expression from those more selectively linked to AZD5582-driven ncNF-κB signaling. This comparison strengthens the authors' claims of pathway specificity for hits such as KAT7 and p300.

      Finally, pharmacological validation of the top acetyltransferase hits was performed across two independent latency models (J-Lat 10.6 and N6), which helps address, at least in part, the concern that phenotypes are specific to a single clonal integration site. Together with the deposited proteomics and screen datasets, this provides a genuinely useful resource for the field.

      Weaknesses:

      (1) BioID inherently reports both direct and indirect (proximal) interactions, and no individual hits are validated by an orthogonal method (e.g., co-IP, PLA). As a transcription factor, RelB would be expected to reside near transcriptional/chromatin machinery, so this enrichment alone isn't surprising, making it hard to distinguish functionally relevant RelB complexes from bystander proteins in the same active chromatin neighborhood. At least a subset of top hits should be validated independently.

      (2) BioID interactome was defined in a single clonal cell line, limiting generalizability across latent cell types. The RelB BioID proteomics, which forms the entire basis for the downstream targeted CRISPR library generation, was performed in one engineered Jurkat N6 clone. Both RelA/RelB pathway dependency and the baseline expression of the chromatin regulators identified as RelB interactors (e.g., DNMT1, UHRF1, SWI/SNF subunits, NSD2) can vary considerably across cell lines, latency clones, and primary CD4+ T cells. Since the CRISPR screen, and thus all functional hits in the paper, is entirely constrained by this single-cell-line interactome, any RelB interactor that is absent or differently regulated elsewhere would never have been tested, regardless of its true biological importance. To support claims of a broadly relevant RelB/ncNF-κB regulatory network, the authors should at least partially validate the basal and AZD5582-induced interactome (e.g., by co-IP/western for key hits) in a second cell line or primary CD4+ T cells, to distinguish clone-specific interactions from generalizable ones.

      (3) Is KAT7/HBO1's contribution to reactivation a direct interaction/synergy with AZD5582, or an independent parallel requirement? The data show that KAT7/HBO1 knockout and catalytic inhibition (WM-3835) both reduce AZD5582-induced reactivation, and that KAT7 was detected as AZD5582-enriched in the RelB BioID. However, it remains unclear from the current data whether KAT7/HBO1 acts as a direct downstream effector in the ncNF-κB/RelB signaling axis (i.e., is recruited by or acts together with RelB/p52 at the LTR specifically upon AZD5582 stimulation), or whether it represents a more general/parallel chromatin requirement for HIV reactivation that is simply necessary regardless of the activating stimulus (and happens to also associate with RelB). Distinguishing these possibilities matters for interpretation: the former would support a model in which AZD5582 and KAT7 act synergistically through the same pathway (i.e., acetylation is mechanistically coupled to ncNF-κB activation), whereas the latter would suggest KAT7-mediated acetylation and RelB/p52 signaling are two independent, simultaneously required mechanisms that converge on the same locus without being mechanistically linked. The comparative TNF-α data (Figure 3C), where the genetic screen shows a shared dependency but the pharmacological inhibitor does not phenocopy this for TNF-α, adds to this ambiguity rather than resolving it. Clarifying whether KAT7/HBO1 functions upstream, downstream, or in parallel with RelB/p52 (e.g., via RelB ChIP after KAT7 knockdown, or KAT7 recruitment kinetics relative to RelB/p52 at the LTR) would strengthen the mechanistic claims in this section.

      (4) A major limitation of the study is that screen results were validated in only two cell line models, and neither is a primary cell system. Different latency models are known to have distinct dominant transcriptional blocks, not just different genetic backgrounds. J-Lat clones, U1, ACH2, and other transformed lines differ substantially in why the provirus is silent; some are more Tat/P-TEFb-limited, some more chromatin/Polycomb-restricted, some more dependent on transcription factor availability (NF-κB, NFAT, AP-1). A regulator that is rate-limiting in J-Lat 10.6 specifically (a single integration site, clonally selected for a particular chromatin environment around the LTR) may therefore be irrelevant, or could even act in the opposite direction, in a model where the block is elsewhere.

      (5) HIV latency and LRA responsiveness are well known to be strongly integration-site and clone-dependent. Pharmacological validation of CRISPR screen hits extends to N6, but the primary screen hits themselves are not confirmed in a second genetic background. This is an important caveat that should be discussed explicitly, and ideally at least the top hits (KAT7, NSD2, SIN3A, p300, CHD4) should be individually knocked out (not just inhibited) in a second model to confirm the genetic phenotype generalizability, not just the pharmacology.

      (6) More importantly, no validation was performed in primary CD4+ T cells or ex vivo resting CD4+ T cells from people with HIV, which remain the field's benchmark for confirming that a candidate regulator is relevant to authentic latency rather than to a transformed cell line. Basal chromatin state, NF-κB pathway, and expression of the chromatin regulators identified here (e.g., DNMT1, UHRF1, SWI/SNF subunits) can differ substantially between transformed lines and primary resting T cells. Given this, the current claims of a broadly applicable "chromatin regulator" network should be tempered, and at least the top hits (e.g., KAT7/HBO1, NSD2, SIN3A, p300) should ideally be tested in a primary cell model before being generalized as regulators of HIV latency and reactivation more broadly.

      (7) The finding that p300 restricts AZD5582-driven reactivation is framed as consistent with Horvath & Sadowski (ref 36), but p300/CBP is also a well-established Tat/LTR coactivator in many contexts (Kiernan et al., Marzio et al). The Discussion would benefit from a slightly more developed treatment of how these findings can be reconciled rather than citing a single concordant paper.

    3. Reviewer #2 (Public review):

      Summary:

      Bussey-Sutton et al. investigate the RelB interactome in the presence and absence of the SMAC mimetic AZD5582. They combined RelB BioID with CRISPR screening and highlighted multiple potential transcriptional regulators that may contribute to the maintenance or reversal of HIV latency. However, there are several technical limitations and gaps in validation in the current version of the work.

      Strengths:

      The overall topic is of clear interest; the RelB interactome and CRISPR screens provide interesting hits.

      Weaknesses:

      From a technical standpoint, RelB is expressed using a doxycycline-inducible system, yet the fusion protein (Figure S1A) appears to be expressed at high levels even in the absence of doxycycline (and does not seem to respond to doxycycline supplementation). As this is the key model system used in the paper to identify RelB interactors, this limitation should be discussed and potentially addressed.

      It would also be important to include some orthogonal validation (e.g., co-immunoprecipitation) of top novel RelB interactors identified by BioID.

      In Figure 2C, two non-targeting controls appear among the most highly enriched hits. This raises the question that other top hits might reflect nonspecific effects or assay noise. Consistent with this concern, the effects observed following siRNA-mediated depletion of the top CRISPR-screen candidates in Figure 2D and Figure 3 appear relatively modest.

      More broadly, the absence of validation in primary-cell models is a further limitation, as the chromatin environment and transcriptional regulation of HIV latency in the two cell-line models used here are likely to differ substantially from those in primary CD4+ T cells.

      Minor point:

      Endogenous RelB remains present in the RelB-BioID cells and could compete with the miniTurbo-RelB fusion protein for physiological binding partners. Moreover, the effect of two sources of constitutive RelB expression (see comment above) on the physiologic RelB interactome is not obvious. Controlling for this is not trivial, but this potential confounding factor should be acknowledged (and further reinforces the need for orthogonal targeted validations of interactors in cells with endogenous RelB).

    4. Reviewer #3 (Public review):

      The study is built on the premise that the SMAC mimetic AZD5582 can reverse HIV latency via the RelB-mediated non-canonical NF-kB pathway activation; however, its molecular regulators remain undefined. The authors use an inducible BioID RelB reporter system in Jurkat N6 cells containing an integrated HIV-1 provirus and identify both the basal and the AZD5582-activated chromatin and transcriptional regulatory network of the non-canonical NF-kB pathway. To further explore the role of the identified regulators, an HIV-CRISPR latency screening approach was used, and based on enrichment or depletion of guide RNAs in the supernatant, genes that inhibited or activated virus reactivation, respectively, were identified. Genes like LRPPRC (RNA-binding protein), KAT7 (acetyltransferase), NSD2 (H3L36 methyltransferase), SIN3A (part of histone deacetylase complex), and others were identified to be essential for HIV reactivation. Nearly half of the identified factors were found to be negative regulators of reactivation, notably CBP/p300 (acetyltransferase), CHD4, USP7, UHRF1, and DNMT1. The roles of KAT7 and p300 were validated through the use of specific inhibitors. To narrow down regulators that are specific for nc NF-kB HIV reactivation vs NF-kB dependent HIV expression, a parallel CRISPR screen was performed with TNF-alpha, which reconfirmed the role of RelB, KAT7, and p300 in AZD5582-mediated HIV reactivation.

      Overall, this is a solid and well-designed study that combines complementary approaches to deliver a valuable resource.

      The study's strength lies in integrating two high-throughput approaches of RelB proximity labelling and targeted CSIPR screening to identify molecular regulators of the HIV reactivation pathway. A parallel CSIPR screen with TNF-alpha provides key insights into pathway-specific vs shared regulators of latency reversal.

      The limitation of the study, as indicated by the authors, is that the assays are based only on HIV output. Additionally, some of the identified interactors may be indirect or context-dependent and may not represent all HIV reservoirs in vivo.

    5. Author response:

      We greatly appreciate the editors and reviewers for their time and careful evaluation of our manuscript. It was clear that the reviewers valued our study that integrated RelB proximity proteomics with targeted HIV-CRISPR screening to identify chromatin and transcriptional regulators of AZD5582-mediated HIV reactivation. We appreciate all of the constructive comments and agree that the manuscript can be further strengthened by clarifying several technical points, correcting text and figure issues, and more clearly defining the scope of the study.

      A recurring theme of the reviews is the need to better define which RelB-associated factors are functionally meaningful regulators of HIV reactivation. We agree with the reviewers’ point that BioID/miniTurbo proximity labeling does not by itself establish direct physical interaction. That said, the method provides an in vivo cell measurement of protein proximity and can capture core complex interactions as well as transient, weak, or chromatin-associated interactions. Notably, such transient interactions are often lost during cell lysis and extraction, as well as co-IP. In the revised manuscript, we will clarify this distinction and emphasize that the BioID dataset identifies RelB-proximal candidate regulators rather than exclusively direct binding partners. We will also consider orthogonal validation of key hits using either co-IP or an orthogonal in cell proximity assay like PLA, which would preserve the cellular context of RelB-associated regulatory complexes.

      The reviewers also raised a fair concern regarding physiological relevance and the use of transformed latency models. We agree that primary cell validation would be an important next step for determining how broadly these findings apply to HIV regulation. At the same time, the present study was intended to be an initial resource of RelB-associated factors that contribute to HIV transcription in tractable latency models. Nonetheless, we will temper any language that implies broad generalization across all latency contexts and further discuss the limitations of clonal and transformed latency models. Additionally, and as the reviewers suggested, we will also test whether the KAT7/HBO1 and p300 inhibitor phenotypes extend to a primary CD4+ T-cell latency model.

      We also appreciate comments regarding the RelB-miniTurbo system; notably concerns with the basal expression of the fusion protein and presence of endogenous RelB. We will revise the manuscript to better describe these limitations and discuss how they may affect interpretation of the BioID data. In particular, we will emphasize that the dataset should be viewed as a resource for candidate RelB-associated regulatory factors that require functional and orthogonal follow-up.

      Several reviewers identified figure and text issues that will be addressed in the revised manuscript. Specifically, we will correct the Figure 1 legend and reference errors, the duplicated sentence, and other typos mentioned. We will also clarify the number of biological replicates and statistical thresholds used for the BioID comparisons, improve the Supplementary Table 1 annotation, and expand the discussion of p300/CBP to better reconcile our findings with prior literature.

      Overall, the revised manuscript will more clearly separate what this study establishes from what remains to be tested. Our goal for revision will be to enhance the value of the study while more clearly highlighting the limitations and what this resource provides for future directions.