Reviewer #1 (Public review):

Summary:

The authors addressed how viral-mediated expression of amyloid in medial septum (MS) cholinergic neurons, or broadband amyloid expression, affects the integrity of MS cholinergic neurons in aging mice, as well as cognition, sleep, and hyperexcitability. Using fiber photometry and viral tracing, they show that MS cholinergic neurons are active during wakefulness and REM sleep and that they also project to many different areas. Next, they show that when they express a viral vector carrying APP to encode amyloid beta in MS cholinergic neurons, these neurons express amyloid as they do in a globally expressing APP model (APP-NLGF). They find that amyloid may spread largely following MS projections and that MS die over time presumably due to amyloid expression. They also describe the emergence of memory deficits and reduced REM sleep attributable to loss of MS cholinergic neurons. Lastly, they report a higher burden of epileptiform activity in mice with broadband amyloid expression and the emergence of neuroinflammation in MS, which may be contributing to cell loss and network dysfunction.

Strengths:

(1) New insights on a potential role of MS cholinergic neurons in spreading amyloid.

(2) Use of several different methods to address effects of MS dysfunction in aging mice (AAV, global, lesioning).

(3) Combination of activity-related readouts including fiber photometry, EEG coupled to histological, behavioral, tracing, and neuropathology measures.

(4) Consideration of potential confounds to behavioral measures using proxies of anxiety-related behavior.

Weaknesses:

(1) The authors aim to model the prodromal phase of Alzheimer's disease (AD) neuropathology, which is a very promising area to target therapeutic intervention. While reduction in basal forebrain volume has been reported early in AD, presumably functional changes may be happening much earlier, i.e., even before MS start to degenerate or before REM sleep is reduced. This view has been proposed by human studies showing increased ChAT reactivity in MCI (PMID: 11835370) and evidence in mouse models showing that MS cholinergic neurons may be hyperactive early and degenerate late with distinct implications for memory (PMID: 41717904). Thus, functional changes could be considered before structural changes could be discussed, as earlier ages in this model could reveal such early changes.

(2) One limitation of the tracing methodology (Figure 1) that could be improved is sample size, as only 2 mice have been used. Moreover, it would be interesting to conduct the same tracing experiments in APP mice to see how these projections are affected by amyloid pathology.

(3) Figure 3 measurements included the whole hippocampal formation, but a region-specific analysis would be warranted as the authors discuss specific accumulation areas.

(4) Figure 5 novel object recognition comparisons use a group of 10 sec exploration, which is unclear why. Novel vs familiar comparisons and reporting of discrimination indexes are considered more robust measurements to report.

(5) Interictal spike detection would benefit from more methodological detail and examples of spikes detected. Reference 72 does not seem to detail interictal spike detection. Moreover, when during sleep do these spikes happen? It has been shown that they occur primarily during REM sleep when mice show cholinergic hyperactivity (PMID: 37714307). From panel 7B, it seems they occur during NREM, which may be explained by a diminished drive of cholinergic circuits to drive spikes in these mice (vs REM in younger mice). Thus, a NREM vs REM vs Wake analysis will be insightful.

Reviewer #2 (Public review):

Summary:

In this study, Nollet and colleagues sought to determine whether selective amyloid pathology confined to medial septal (MS) cholinergic neurons is sufficient to recapitulate the prodromal Alzheimer's disease-like phenotypes observed in global AppNL-G-F knock-in mice. To this end, the authors employed a cell-type-specific AAV-mediated approach to selectively express the familial AppNL-G-F allele in MS-ChAT neurons, and subsequently characterized sleep-wake architecture, EEG spectral features, cognitive function, emotional behavior, and histological changes over 13-14 months. By comparing these mice with global AppNL-G-F knock-in mice and with mice in which MS-ChAT neurons were selectively ablated via caspase expression, the authors found that cholinergic cell lesioning recapitulated most disease phenotypes, suggesting that cholinergic loss, rather than amyloid deposition, is a likely driver of these phenotypes.

Strengths:

The study has several notable strengths. First, the experimental design is rigorous and well-controlled, employing three complementary mouse models that enable elegant causal inference. The use of cell-type-specific APP expression is a powerful approach for distinguishing the contributions of MS-ChAT neurons and amyloid deposition. Second, the combination of multiple behavioral assessments, EEG spectral analysis using FOOOF parameterization, and detailed histological quantification strengthens the validity of the conclusions. Third, the finding that caspase-induced cholinergic lesions largely recapitulate the cognitive and REM sleep phenotypes, while amyloid pathology contributes additional features such as epileptiform spikes and astrogliosis, represents an important mechanistic dissection.

Weaknesses:

Despite the overall strength of the study, several limitations warrant consideration. First, the mechanism by which amyloid is "broadcast" from MS-ChAT terminals to distant brain regions remains unclear. The authors do not definitively determine whether the amyloid detected in hippocampal and cortical regions represents released soluble Aβ, transported APP fragments, or amyloid derived from degenerating axons. Second, while the authors demonstrate that MS-ChAT cell loss correlates with cognitive, emotional, and REMS deficits, the causal relationship among these phenomena and the specific circuits involved remains unresolved.

Reviewer #3 (Public review):

Summary:

The central idea of the study is strong and potentially important: that the vulnerability of the cholinergic medial-septal population can account for a substantial fraction of prodromal-like AD phenotypes, thereby shifting part of the mechanistic focus from cortex-centered pathology to subcortical neuromodulatory circuit failure. The work has several notable strengths. The authors combine circuit mapping, calcium photometry, longitudinal EEG/EMG sleep phenotyping, histology, behavior, and a caspase-based lesion comparison to build a multi-level case for medial septal cholinergic involvement in REM Sleep and memory phenotypes. The inclusion of both a focal amyloid model and a partial cholinergic ablation model is especially valuable because it attempts to separate effects of Ch-neuronal loss from effects of amyloid itself.

However, the manuscript has several issues, from manuscript formatting to experimental design, overarching statements, insufficient exclusion of alternative explanations, incomplete quantification details for key histological results, a discussion that often moves beyond the actual data into speculative translational framing, and a discussion that completely ignores the early presence of p-tau in human AD patients and even lacks supplementary materials.

Strengths:

(1) The conceptual premise is compelling: cholinergic basal forebrain vulnerability is a real and important feature of AD, and testing whether selective medial septal cholinergic pathology can drive REM sleep and cognitive phenotypes is mechanistically interesting and clinically relevant.

(2) The experimental framework is broad and generally thoughtful, spanning anatomy, function, sleep architecture, EEG spectral parameterization, behavior, and histopathology.

(3) The projection mapping and photometry provide a useful systems-level introduction, establishing that MSChAT neurons are Wake/REM sleep-active and project strongly to hippocampal and cortical targets before the disease manipulations are introduced.

(4) The MSΔChAT comparison group is valuable because it allows the authors to argue that some phenotypes track with cholinergic loss rather than amyloid per se.

(5) The longitudinal sleep analysis is one of the strongest parts of the study, especially the emphasis on REM sleep quantity and bout architecture over time rather than relying only on an endpoint comparison.

Weaknesses:

(1) The title overreaches in its use of "prodromal phase." In the clinic, "prodromal AD" denotes a biomarker‑positive, pre‑dementia phase with subtle, progressive cognitive decline before widespread neurodegeneration, whereas here the authors demonstrate substantial cholinergic degeneration alongside cognitive impairment, which corresponds to advanced pathology within these models rather than a clinically prodromal stage. Moreover, APP knock‑in mice are amyloid‑centric, lack tau pathology, and don't recapitulate human disease staging; therefore, it would be better to avoid terms used for AD staging in the clinic. A more accurate framing of the title would be "Modeling the prodromal-like phase in an Alzheimer's disease mouse model".

(2) The opening statement in the abstract (line no 22) is overstated. Current evidence supports that changes in REM sleep, slow‑wave sleep disruption, and excessive daytime sleepiness are associated with a higher risk of AD and reflect early involvement of brain regions vulnerable to AD proteinopathy. No study indicates that REM sleep changes per se are a strong predictor on their own. For example, Jin et 2025 studied REM latency in AD and concluded that prolonged REM latency may be a marker of early neurodegeneration (PMID: 39868572). Thus, the opening statements need to be modified.

(3) Line 63: The current phrasing of neuromodulators being also essential for orchestrating sleep/wake states is very simplistic. Sleep/wake regulation is a highly complex process involving several interacting neurotransmitters and neuromodulatory systems. I recommend revising this sentence to reflect the broader, multi‑system nature of sleep/wake control.

(4) Line 64: "ACh is required for the generation of REMS" is incomplete. The sentence implies REM sleep generation depends exclusively on ACh. Instead, the sentence must emphasize that ACh is a crucial component of a broader REM sleep circuitry and explain why it is critical for REM sleep.

(5) Line 65: The sentence "Importantly, reductions and alterations in REMS have emerged as strong predictors of clinical AD onset" (Reference 37) is an overstatement of the evidence; Peas et al. 2017 analyzed a dementia cohort that included AD cases and concluded: "Despite contemporary interest in slow-wave sleep and dementia pathology, our findings implicate REM sleep mechanisms as predictors of clinical dementia." The authors should rephrase this to reflect that the study examined REM sleep changes in a mixed dementia population with AD, rather than to establish REM alterations as strong, standalone predictors of AD onset.

(6) Lines 73-75 address human Alzheimer's studies and state that basal BF-Ch neurons are vulnerable to Aβ but largely omit the well-established contribution of early tau pathology. In human AD patients, p-tau accumulation in BF is an early event (Braak I-II) and is closely associated with BF-Ch neuronal loss and BF atrophy and has been documented extensively. By relying almost exclusively on Aβ-centric framing, the current text risks implying that BF-Ch degeneration is solely amyloid-driven, which is not accurate. Even though the mouse model used here is "amyloid-heavy" and lacks tau pathology, the introduction should acknowledge the role of p-tau (especially when the paragraph contextualizes human studies) and clarify that in humans, BF-Ch vulnerability reflects converging amyloid and tau insults, so that readers do not infer a purely amyloid-dependent mechanism from the way the background is presented.

(7) Line 92: and elsewhere in the manuscript, I recommend avoiding the term "prodromal phase" and instead using the phrase "prodromal-like phase in an AD mouse model". The authors should be more precise in describing the disease stage in animal models that don't recapitulate human disease staging and ensure that clinical staging terminology is specific to human studies.

(8) Age and duration of pathology are major concerns. The different models are not adequately matched for amyloid exposure duration and age at testing. Age is the strongest risk factor for AD, and varying both chronological age and time under pathology across groups is a major design flaw. In MSChAT-AppNL-G-F/GFP mice, AAV injection was delivered at 11-13 weeks of age, and animals were sacrificed at 13-14 months post-injection (roughly 15-16 months old), whereas AppNL-G-F/NL-G-F knock-in mice and APPWT were 13-14 months old at the time of termination. Thereby, there is a difference in the duration of Aβ exposure across models. This mismatch directly weakens comparisons such as the lower epileptiform spike counts in MSChAT-AppNL-G-F versus AppNL-G-F/NL-G-F mice, because differences could simply reflect shorter cumulative pathology exposure rather than a genuinely weaker circuit-specific effect.

The same issue affects the internal control logic of the MSΔChAT model, which is intended to isolate cholinergic neuron loss from amyloid aggregation. For this comparison to be clean, ages and exposure durations should be aligned as closely as possible. Instead, MSΔChAT mice are tested earlier than the AppNL-G-F/NL-G-F and MSChAT-AppNL-G-F/MSChAT-GFP cohorts, introducing a 4 to 7-month age gap that complicates attribution of phenotypic differences solely to cholinergic loss versus amyloid pathology.

Finally, the absence of sham-operated controls is a concern, as it prevents separating the effects of the surgical procedure and AAV delivery from those of amyloid expression or cholinergic ablation.

(9) Line 115 through 117: The text cites Figure 2D, but does not refer to Figure 2C for the statement "their phenotypes were then compared in detail with MSChAT-AppNL-G-F and AppNL-G-F/NL-G-F global knock-in mice that were aged at the same time". Figure 2C depicts D54D2 amyloid staining in MSChAT-GFP vs MSChAT-AppNL-G-F mice. For clarity and consistency, I suggest adding a Figure 2C notation to this sentence (e.g., "Figures 2A, 2C").

(10) In Figure 1C-D, the authors map MSChAT projection targets across a wide range of brain areas, including hippocampal subfields, mPFC, primary cortices, entorhinal cortex, olfactory bulb, thalamus, anterior hypothalamus, amygdala, and medial habenula, and identify several of these as substrates through which MSChAT activity could influence REM sleep and cognition. However, the lateral hypothalamic area (LHA) is conspicuously absent from both the listed projection targets and the tracing panels shown in Figure 1D, despite the anterior hypothalamus being reported as an innervated region.

This omission is notable given that LHA-MCH neurons are among the best-established REM-sleep-promoting neurons, and the authors themselves cite prior work implicating LHA-MCH neurons in the AppNL-G-F REM sleep phenotype (ref. 49, 107; line 403) as an alternative cell-circuit candidate, a claim they explicitly try to weigh against their own MSChAT-centered model in the discussion.

a) The MSChAT neurons are reported to be REM sleep- and wake-active (Figure 1A-B), the same vigilance-state profile as LHA-MCH neurons,<br /> b) The Discussion directly engages with LHA-MCH neurons as a competing/complementary REM sleep-generating mechanism, and<br /> c) The reported anterior hypothalamus innervation (Figure 3C) raises the question of whether MSChAT axons specifically innervate LHA, and whether any projections specifically to LHA or LHA-specific amyloid deposition were examined. Clarifying this would help position the proposed MSChAT-hippocampal circuit mechanism relative to the well-established LHA-MCH REM sleep node.

(11) Line 125: "13- to 14-month-old MSChAT-AppNL-G-F mice immunohistochemical analyses employing the amyloid-specific antibodies....", in the methods section (Line 652) the authors mention MSChAT-AppNL-G-F and MSChAT-GFP mice were perfused 13-14 months after AAV injection (age at the time of injection was 11-13 weeks of age). This leaves the question of how they have 13- to 14-month-old MSChAT-AppNL-G-F mice available to study Amyloid-β load.

(12) Line 174-175: As currently written, the sentence could be read as both wild-type and homozygous AppNL-G-F/NL-G-F mice received AAV injections and were then aged 13-14 months post‑injection. In fact, the Methods clearly state that knock‑in mice are simply aged from birth without any AAV manipulation. The sentence should be rephrased to avoid suggesting that global APP knock‑in animals are part of the AAV‑injected cohorts.

(13) Lines 182-183, 196-197, and 209 refer to "Supplementary information" and imply that detailed behavioral data and analyses are provided in that section. However, in the current submission, the supplementary material consists only of Figures S1-S7 (Amyloid marker and cerebral vasculature, Aβ in hippocampus, GABA and glutamatergic neurotransmission, and sleep/wake parameters) and does not include supplementary figures or tables for the behavioral assays described in the main text. This discrepancy makes it impossible to verify the full behavioral dataset and the analyses referred to in the results section. The authors should carefully check the submission package and ensure that all referenced supplementary figures, tables, and detailed behavioral results are included and appropriately labeled.

(14) The lack of details for histological quantification is a major concern for a manuscript in which major conclusions hinge on Aβ load and MS-Ch neuronal counts. The histological quantification section is severely under-specified. The authors describe a 23% MSChAT loss, differences in regional Aβ burden, and a vascular association; however, the methods section is strangely silent about the quantification pipeline. For Aβ quantification, it is not clear whether "load" reflects percent positive area, plaque counts, or another metric; which Fiji thresholding algorithm(s) were used; how ROIs were defined; how staining batch effects were controlled; and how autofluorescence was normalized. For neuronal counts, the strategy for identifying and counting ChAT-positive neurons, normalization, and blinding are not described. There are no details on section spacing, axis of counting, the number of sections counted per animal, or whether both hemispheres were analyzed. Given that the reported differences are modest and central to the main claims, a more detailed and rigorous description of the image-analysis pipeline is essential.

(15) Statistical annotations in figures: There is inconsistency in how statistical significance is indicated across the figures. For example, in Figure 5C, the significance between MSΔChAT and AAV‑Aβ⁻ is indicated by a connecting bracket (**), whereas the comparison between AAV‑Aβ⁻ and AAV‑Aβ⁺ is marked by asterisks (***) placed above AAV‑Aβ⁺. In addition, the single asterisk above KI-Aβ⁺ does not clearly specify which pairwise comparison it refers to (e.g., AAV‑Aβ⁻ vs WT‑Aβ⁻ or another contrast). This heterogeneity makes it difficult to decipher exactly which group comparisons have been tested and found significant. The notation should be standardized and explicitly linked to the corresponding pairwise comparisons (for example, by using consistent brackets/lines and specifying all contrasts in the figure legend). Figures must be self-explanatory.

(16) Figure 5D statistical notation and group comparisons: The statistical markings in Figure 5D do not seem to match the results text and are difficult to interpret. The authors state that both MSΔChAT and AAV‑Aβ⁺ mice lack a preference for the novel object compared with AAV‑Aβ⁻ controls, yet the figure does not clearly indicate significance for MSΔChAT versus AAV‑Aβ⁻, and the notation over AAV‑Aβ⁺ is ambiguous. As a result, it is unclear which group differences are being tested and reported. It would be preferable to use the standard convention of placing significance annotations directly over the experimental groups (e.g., AAV‑Aβ⁺, KI-Aβ⁺⁺, MSΔChAT) or use notation above brackets to ensure that the figure labels are fully consistent with the statistical statements in the results.

(17) The discussion contains many compelling ideas, but it needs pruning and recalibration. The best discussion points are those linking the lesion comparison to REM sleep/cognitive outcomes and those situating MS cholinergic neurons within broader REM sleep circuitry. The least convincing sections are those implying disease-stage equivalence, prion-like spread, and direct therapeutic implications without sufficient evidentiary support.

(18) Line 448: The authors discussing reduced anxiety-like behavior in their model corroborates with the 3xTg mouse model (Ref: 116). Interestingly, they don't consider or include reports of anxiety-like disorders from human cohort studies that indicate the prevalence of higher anxiety and its association with preclinical and prodromal AD stages (SCD, MCI) and progression of AD. This apparent contradiction with the human literature is not discussed in the discussion section. The authors should explicitly address how their anxiolytic-like phenotype fits with clinical data (e.g., species differences, task specificity, disease stage, or model limitations) and clarify whether they view this as a limitation of the model or as evidence for a more complex relationship between amyloid, cholinergic dysfunction, and emotional behavior.

(19) Line 654 states, "Comparable durations of amyloid pathology," but this is not fully substantiated, as the onset and progression of amyloid in the AAV-driven MSChAT-AppNL-G-F model versus the global AppNL-G-F knock-in model are not described. The data support comparison at a similar late-stage amyloid burden, but not necessarily equal duration of pathology.

Reviewer #1 (Public review):

Summary:

This paper from Bardossy et al. explores whether viral macrodomains in dual-host viruses contribute to infection in the mosquito vector. Using the CHIKV Caribbean strain, the authors generated nsP3 macrodomain catalytic site mutants (N24A or N24D) and identified a compensatory mutation site at position 31 during virus propagation in Vero cells. They then assessed the impact of these mutations on viral growth kinetics in A549 (human) and U4.4 (Ae albopictus cells), as well as on infectivity and dissemination in vivo in Ae. aegypti and Ae. albopictus. Biochemical and structural analyses of recombinant macrodomain proteins (alone or in combination) revealed effects on stability, catalytic activity, and ADP-ribose binding. Overall, the study demonstrates that CHIKV macrodomain catalytic activity plays an important role in virus infectivity and dissemination within the mosquito vector.

Strengths:

A complete set of experimental approaches spanning generation of recombinant viruses, in vitro characterization, in vivo studies in mosquitoes, and detailed biochemical and structural characterization.

Weaknesses:

(1) The sequence analysis of the generated stocks revealed the emergence of a second-site mutation at position 31 of the nsP3 macrodomain when (N24A or N24D) CHIKV mutants were generated on Vero cells. However, it is not clear from the text or the experimental design how many independent replicates were performed. Based on the current description, it appears this was done only once, which raises the question of whether mutations at position 31 represent a reproducible outcome of infection. This is particularly important because experiments in A549 cells did not reveal emergence of mutations at position 31. To strengthen this finding, the experiment should be performed at least three independent times.

(2) Based on the primer information used to generate amplicons for sequencing, the amplicons evaluated do not span the full nsP3 gene as stated in the text (Line 105). Instead, they cover only the first 119 amino acids of the macrodomain (160 aa long). Thus, the current data do not rule out the emergence of other compensatory mutations elsewhere in the nsP3 macrodomain or in the full-length protein. Additional sequencing is recommended, or the text should clearly state that only a portion of the macrodomain was sequenced.

(3) Another key question is whether this is a specific feature of the Caribbean strain or a feature conserved across different CHIKV lineages.

(4) The use of A549 cells (interferon-competent) to study CHIKV infection is somewhat surprising, as the current literature indicates that this cell line is not efficiently infected by Asian or ECSA lineages of CHIKV (PMID: 17604450) unless the Mxra8 receptor is overexpressed (PMID: 29769725) or IFN signaling is inhibited (PMID: 31682641). The data presented here are compelling and suggest specific features of the Caribbean strain that enable efficient infection of this cell line (Do the authors observe detectable cytopathic effect (CPE) in CHIKV-infected A549 cells?).

However, to further support the authors' claim related to human immunocompetent cells, it would be important to demonstrate the phenotype in an additional interferon-competent cell line that is well-established as highly permissive to CHIKV, such as human fibroblasts.

(5) To fully support the conclusion stated in lines 234- 237, the authors should fully sequence the virus stock used to demonstrate that no additional mutations (beyond N24D-D31H/N) are present that could contribute to the enhanced dissemination phenotype. This is especially important if the experiment was performed with only one stock of virus, given justified gain-of-function concerns.

(6) The authors did not assess transmission but transmission potential (only viral dissemination to heads was measured). The sentence at line 360 should be modified to accurately reflect the data-supported conclusion.

Reviewer #2 (Public review):

Summary:

To address how the CHIKV macrodomain contributes to replication dynamics in mammalian and insect hosts, the authors initially created two separate mutations in the highly conserved N24 residue, which is known to be critical for the CHIKV macrodomain's ability to erase ADP-ribose from target proteins. Interestingly, they could not produce a virus with a mutation in this residue without second-site mutations in an aspartic acid residue nearby (D31). However, when tested biochemically, these second-site mutations did not enhance the enzymatic activity of the protein, indicating that other enzyme dynamics, such as substrate binding, may be impacting these mutations. Mutations at this residue allowed the CHIKV to replicate in Vero cells and in mosquito cells, but they replicated poorly in IFN-competent human cells, indicating clear IFN-specific impacts on these viruses. Interestingly, they found unique impacts on virus dissemination and replication in live mosquitoes. While the N24A/D31N virus did poorly in vivo in all accounts, the N24D/D31H/N virus tended to infect both the bodies and heads of the mosquitoes better than the WT virus, though titers were reduced. The authors claimed, based on a DSF assay, that there were no real differences in ADP-ribose binding and thus suggested that these differences could be due to changes in substrate specificity, as the D31 residue resides in the substrate exit path, potentially tuning the virus to unique substrates in different species. The authors also produced crystal structures of the mutants to demonstrate the changes in the binding pocket caused by these mutations.

Strengths:

The authors have done a rigorous job of evaluating CHIKV macrodomain mutant viruses and the proteins' biochemical activities. The use of live mosquitoes is highly unique and provides important insights into the importance of the macrodomain in different species.

Weaknesses:

It is not clear if the interpretation of the ADP-ribose binding data is correct. It appears there are notable differences that could explain the results, though the authors chose to minimize the impact that these differences had on the results. The N24D-D31H/N proteins had at least a 1C degree difference in the thermal shift assay when compared to the N24A/D31N, single D31 mutants, and WT proteins, which is likely significant and could explain the dichotomous results between the two viruses in mosquito cells. Even the single N24D mutant had enhanced binding compared to the WT protein. Furthermore, as this virus has no enzymatic activity, one could hypothesize that enhanced binding to a substrate that is normally cleaved by the protein could certainly lead to alterations in phenotypic effects, whether good or bad. The authors should test the binding activity in a separate assay, such as an ITC assay, to determine if there are, in fact, binding differences or not. Having said this, it is likely that the impacts of these mutations on replication and transmission in human and mosquito cells are multi-factorial and could include both enhanced binding with altered substrate specificity amongst other activities.

Additionally, as both mutants had no detectable enzymatic activity but had quite different phenotypes in mosquitoes, I don't agree with the title stating that catalytic activity modulates dissemination and transmission potential in mosquitoes. It seems more likely that alterations in binding activity or substrate recognition (even suggested by the authors) impact these phenotypes in mosquitoes.

Reviewer #3 (Public review):

Summary:

The authors investigated the role of the nsP3 macrodomain catalytic activity in the replication and transmission of CHIKV in mosquito vectors. The conserved dual-host alphavirus catalytic site N24 has previously been shown to be essential for ADP-ribosylhydrolase activity. Despite this, mosquito-specific alphaviruses do not share this catalytic site. To assess whether the macrodomain catalytic activity of a dual-host virus was essential in insect hosts, the authors targeted the N24 site to abolish catalysis while maintaining binding capacity. The loss of ADP-ribosylation led to the emergence of compensatory mutations at site D31 that impact viral infectivity, dissemination, and transmission in Aedes sp. mosquitoes in vivo. The conclusions are well supported by the results and provide insight into the importance of nsP3 macrodomain activity in the mosquito vector, which hasn't been explored before.

Strengths:

The main strength of this study is the use of Aedes sp. mosquito models to investigate the selective pressure of macrodomain mutations in vivo. The functional characterization as well as the structural analysis of the mutants provide supporting evidence of a potential role of the compensatory mutations at site D31 in substrate recognition.

Weaknesses:

A considerable part of this study relies on the use of N24 mutant viral stocks generated in Vero cells, which yields an additional mutation at site 31 and consequently doesn't allow the authors to properly dissect the effect of mutation of N24 and D31 independently. It would be recommended to generate stocks with individual mutations in both A549 and U4.4 cells, pooling and concentrating them if needed. Replication of the N24A mutant in A549 cells does not lead to mutation at residue 31. Yet surprisingly, there is no reversion from N back to D at site 31 when the double mutant Vero stocks are passaged in A549. Since they are double mutants, it isn't possible to assess whether the defects in the growth of mutants N24A/T-D31N and N24D-D31H/N compared to WT are due to site 24 or 31, or both (Figure 2, panel c). Even though the authors emphasize that the compensatory mutation could have additional roles that impact viral infectivity and transmission in mosquito cells, it would strengthen the work to show that these mutations would spontaneously appear in stocks generated directly in mosquito cells. As a corollary, is it known whether insect-specific alphaviruses that lack macrodomain catalytic activity have corresponding mutations at site 31?

Additionally, there is a lack of consistency in the prevalence of WT virus at days 5 and 7 in in vivo experiments with Ae. albopictus and Ae. aegypti (Figure 3 and Supplementary Figure 2). This raises concern about the reproducibility of these experiments.

The inability to tease apart the roles of N24 and D31 in mosquito hosts partially prevented the authors from fully achieving their aims, but the work is nonetheless of interest to the field and suggests that more work is necessary to fully understand the role of the nsP3 macrodomain and its catalytic activity in the two disparate but obligate hosts for CHIKV and other dual-host alphaviruses.

Reviewer #1 (Public review):

Summary:

The study identifies and characterizes a set of amino acid states that make the protein robust to other mutations, to the point of being able to compensate mutations that render wildtype proteins entirely non-functional. The study uses a previously published dataset and uses it to find and study such super-compensators. It then analyzes the biophysics and fitness landscape structure of what may be behind the compensation, identifying stability as an important parameter that, nevertheless, is not sufficient to explain all of the compensatory effect. These findings have important implications for our understanding of protein evolution, with these super-compensators possibly acting in a role of "permissive mutations" and opening up evolutionary trajectories that may be closed without them. Perhaps the identification of such super-compensator substitutions can be incorporated into various protein design approaches.

Strengths:

The paper presents a compelling case with a rigorous analysis of the expected error rates of observation. While not unique, the current state-of-the-art in the field typically does include experimental error rate estimation like this work. The paper also does a good job in exploring the issue, including looking at plausible biophysical basis of super-compensators.

Weaknesses:

The paper lacks rigor in talking about evolutionary-related issues of the state of the fitness landscape. As an example, the paper mentions that these super-compensators flatten the landscape. While I understand where this is coming from, I think that the fitness landscape in this context is a static entity and cannot be flattened or otherwise altered. A much more accurate description is that a sequence with a super-compensator is located in a flatter-than-expected segment of the fitness landscape, or on a flat fitness ridge. These issues are more semantic in nature, and while the manuscript would benefit from it being shown to an expert in molecular evolution or fitness landscapes, this issue does not take away from the importance of the results.

Reviewer #2 (Public review):

Summary:

This manuscript presents an interesting and conceptually valuable analysis of compensatory evolution using a large combinatorial deep-mutational-scanning dataset for yeast His3p.

Strengths:

I particularly like the identification of "super compensatory" substitutions that improve fitness across diverse genetic backgrounds and apparently reduce the sensitivity of the local fitness landscape to subsequent mutations. The work connects epistasis, protein stability, mutational robustness, and evolvability in a clear and potentially broadly relevant manner.<br /> The authors provide several complementary lines of evidence in support of this central conclusion. In particular, the new experimental validation of S189A is an important strength because it directly demonstrates that a predicted super compensator can buffer the effects of diverse deleterious substitutions, while analyses of additional DMS datasets from other proteins and assay systems suggest that the phenomenon is not restricted to the original His3p landscape.

Weaknesses:

The structural analysis currently relies primarily on correlations with RSA, weighted contact number, conservation, and Rosetta-predicted changes in folding or binding energy. For super compensators, the mechanistic evidence is largely limited to predicted stabilization and individual examples, such as the proposed salt bridge between 110D and R112. I believe that the newly developed structure-aware deep-learning approaches could provide useful information on the mechanism of super compensators. For example, an inverse-folding model such as ESM-IF1 could score complete multi-mutant sequences conditioned on the His3p backbone and test whether adding a super compensator restores sequence-structure compatibility across backgrounds. More recent multimodal mutation-effect or stability models could similarly be used to cross-check the Rosetta results, including models that explicitly support combinatorial mutations. I would not recommend simply comparing AlphaFold confidence scores between mutants, because current structure predictors are not necessarily sensitive to subtle mutation-induced energetic or conformational changes.

The manuscript states that the pipeline was applied to 217 ProteinGym datasets and concludes that super compensators are broadly distributed across proteins and assays. However, this central generalization is described in only a few sentences and is largely relegated to Figure S7. The Methods do not explain which datasets contained sufficient combinatorial mutants to calculate compensatory ability or buffering, how many genotype pairs or quadruplets were available per substitution, or how differences in assay scale and library design were handled. This point requires clarification because supercompensation is inherently a background-dependent property and cannot be established from single-mutant measurements alone. ProteinGym is widely used as a substitution-effect benchmark, and many of its constituent assays primarily contain single substitutions; for example, an analysis of an earlier ProteinGym collection reported that 76 of 87 assays contained only single substitutions. It is therefore unclear how the same compensatory-interaction pipeline could be applied uniformly to all 217 datasets.

The analysis of 335 His3p orthologs in Discussion is potentially very interesting, but co-occurrence between super compensators and putatively deleterious amino-acid states does not by itself demonstrate evolutionary compensation. Closely related species share substitutions through common ancestry, and both states could be associated with a particular lineage or ecological context. A tree-aware analysis would considerably strengthen this result. The authors could reconstruct ancestral states and ask whether acquisition of a super compensator tends to precede or accompany otherwise deleterious substitutions. Alternatively, they could use phylogenetically informed permutations that preserve substitution frequencies and shared ancestry.

Reviewer #3 (Public review):

The manuscript by Jiang and co-authors presents an analysis of experimental measurements (about 400k variants) from a deep mutational scan of the HIS3 enzyme. The authors assess the ability of a genotype to be "rescued" and show that this depends on mutation sites (in particular their solvent accessibility) and mutation effects (should be mild on folding stability or binding affinity). They further identify a set of super-compensatory mutations, and their results suggest that these mutations flatten the fitness landscape.

This finding is interesting and likely of interest to a broad community. The analysis seems sound.

However, I have a number of major concerns regarding the presentation and positioning of the work.

(1) It would improve the manuscript to clarify the present contribution with respect to a previous study by the same authors, namely Pokusaeva et al. 2019. Did the authors apply the same protocol to generate a new library of mutants, or did they re-analyse an already published library? If the library is not new, ambiguous sentences like "Nevertheless, to our knowledge, the His3p library remains one of the largest and most comprehensive resources that contains multi-site mutants" should be reformulated.

(2) Pokusaeva et al. 2019 is cited for the library and also for the deep neural network. It would be beneficial to briefly describe the architecture, the inputs and outputs, and the training procedure. Was the network trained on the current library? What is the purpose of this network? It looks more like an additive linear model (except for the global sigmoid) than a deep neural network. How does it relate to global epistasis models? The sigmoid function is designed to capture plateauing effects; doesn't that introduce some circularity issue in the reasoning?

(3) Are the super-compensatory mutations observed (conserved) across evolution? Beyond the fact that they are accompanied by mildly deleterious mutations in natural sequences. Can we predict them with variant effect predictors?

(4) The AAindex mention should be accompanied by a citation.

(5) Equations should be numbered. WCN formula seems to contain misformatting issues.

(6) A more explicit description of the structural data analysed (which PDB entry?) should be provided.

(7) I believe the citation Van Cleve and Weissman 2015 for the ProteinGym benchmark is incorrect. Additionally, is the Rosetta citation adequate?

(8) How is the definition of rescueability sensitive to the threshold choice?

Reviewer #1 (Public review):

A previous study from the same team (McDougle & Taylor, 2019) demonstrated that explicit strategies during visuomotor adaptation can be dissociated into retrieval-based and algorithmic strategies. However, whether these distinct forms of explicit processing differentially influence implicit recalibration has remained unresolved, with previous studies providing evidence both for relatively independent explicit and implicit processes and for interactions between them. This study addresses this question through a series of experiments that used Critical and Non-Critical targets to induce distinct strategic modes while maintaining comparable adaptation at the Critical target.

Experiment 1 replicated previous findings showing broader implicit generalization under algorithmic strategies. However, this broader generalization could be explained by spillover effects arising from adaptation at the Non-Critical targets. Experiment 2 was designed to reduce such spillover effects by increasing the spatial separation between the Critical and Non-Critical targets. Although broader generalization was still observed in the algorithmic condition, this effect was interpreted as reflecting greater variability in reaching behavior at the Critical target. Finally, Experiment 3 introduced additional controls using an error-clamp paradigm, and the difference in generalization width between the two strategies largely disappeared.

Together, these findings led the authors to conclude that implicit recalibration is relatively insensitive to the type of explicit strategy employed and is primarily shaped by the statistics of the movement plans on which learning occurs.

The experimental design using Critical and Non-Critical targets is particularly interesting and represents a creative approach to manipulating strategy use. Reaction times were generally longer in the algorithmic group, even at the Critical target, suggesting that the manipulation was at least partially successful in biasing participants toward algorithmic versus retrieval-based strategies. The results that the implicit recalibration is independent of the explicit strategy (how you aim) but depends on the aiming point by the explicit strategies (where you aim) are basically reasonable.

I would like the authors to clarify two points.

First, how reasonable is it to infer the use of distinct explicit strategies primarily from reaction time differences? While longer reaction times in the algorithmic group are consistent with greater computational demands, it remains unclear whether the longer reaction times observed at the Critical target necessarily reflect different strategy implementations at that location. In particular, could the increased cognitive demands associated with the Non-Critical targets in the algorithmic condition have carried over to the Critical target, thereby prolonging reaction times without implying qualitatively different strategies at the Critical target itself?

Second, the interpretation of Experiment 3 is not entirely clear to me. The manuscript argues that the algorithmic group continued to exhibit greater reaching variability than the retrieval group. If this variability indeed reflects greater variability in movement plans, one might expect a broader implicit generalization function in the algorithmic group. However, the generalization widths were comparable between groups. Could this result instead suggest that the implicit recalibration process itself generalized more narrowly in the algorithmic group, thereby offsetting the broader distribution of movement plans? More generally, I would appreciate further clarification regarding the relationship between reaching variability, movement-plan variability, and the resulting width of the implicit generalization function.

Reviewer #2 (Public review):

This study addresses an important question in motor learning: whether algorithmic versus retrieval-based explicit strategies differentially shape implicit recalibration. The progressive experimental logic across three experiments is commendable, and the plan-based generalization account is a plausible and interesting interpretation. However, several methodological concerns limit the strength of the conclusions. I recommend the authors temper their claims accordingly, in the results/discussion section.

Concerns

(1) The retrieval group received 5 pre-exposure trials before main training began, which the algorithmic group did not. Faster RTs in the retrieval group could therefore reflect task familiarity from extra practice rather than efficient memory retrieval per se. I might have missed this, but I did not see performance data from these pre-exposure trials. The early training advantage in the retrieval group might be confounded with the 5 pre-exposure trials they received. Unless there is a direct comparison between the pre-exposure trials for the caching group and the first 5 trials of the algorithmic group, the claim that "storing and retrieving a memory from a short-term memory cache confers more rapid performance improvements than executing an algorithmic strategy" seems somewhat unwarranted.

The algorithmic group also visited the critical target approximately 40% of trials across 356 trials (about 140 trials?). McDougle & Taylor (2019) showed that 300 trials of practice with 2 targets is enough transition from algorithmic to caching strategies. It seems likely that the number of visits to the critical target here was sufficient for caching to develop in the algorithmic condition. This concern about caching in the algorithmic group has implications for the implicit recalibration measurements. As I understand it, the 7 exclusion blocks were distributed throughout training, and so, implicit recalibration was measured across both early and late practice. If caching emerged in the algorithmic group during late practice, then the generalization functions - averaged across all 7 exclusion blocks - conflate early algorithmic strategy and later caching. The broader generalization function observed in the algorithmic group may therefore be driven primarily by early exclusion blocks, while later exclusion blocks may increasingly resemble the retrieval group as caching develops. This is testable in the data: if generalization breadth in the algorithmic group narrows across the 7 exclusion blocks while remaining stable in the retrieval group, that would be consistent with a strategy transition occurring during training. The authors should either report exclusion block-by-block generalization functions separately for each group, or acknowledge that the averaged generalization functions may obscure a strategy transition in the algorithmic group.

(2) The error-clamp paradigm in Experiment 3 introduces two problems. First, it breaks the relationship between planned movement direction and feedback of movement direction, likely reducing the sense of agency over movement feedback (indeed, typical error clamp study instructions tell participants to ignore the movement feedback).

Reduced agency may itself suppress differences between algorithmic and caching conditions. First, if strategy type exerts its influence on implicit recalibration via the explicit plan - as the plan-based generalization account predicts - then severing the link between intended movement and feedback might close off the channel through which strategy could shape the implicit system, regardless of which strategy is used. Second, reduced agency could modify the explicit strategies themselves. For caching, the stimulus-response association might be reinforced by a consistent relationship between intended movement and observed outcome; the clamped feedback may make it more difficult to reinforce the cached response, weakening the stimulus-response association. For the algorithmic strategy, effortful mental rotation may depend on the perception that the computation meaningfully determines the outcome; as participants understand that clamped feedback does not depend on their behavior (although yes, the text-based "Excellent/Good Move feedback) does depend on their behavior, they may engage in somewhat less complete mental rotation. Both possibilities could contribute to convergence between groups in generalization. It is noted that the preserved RT difference between groups in Experiment 3 partially argues against a loss of effort under the algorithmic condition, but it does not rule out weakened formation of stimulation-response associations during caching.

Reviewer #3 (Public review):

Summary:

This manuscript asks whether two forms of explicit strategy use in visuomotor adaptation, i.e., algorithmic mental rotation and retrieval of a cached aiming solution, differentially influence implicit recalibration. The question is relevant because much prior work treats explicit strategy as a unitary process, whereas the algorithmic/retrieval distinction is theoretically meaningful and grounded in cognitive theory. Across three experiments, the authors report that algorithmic strategy conditions initially produced broader fitted implicit generalization functions than retrieval conditions, but that this difference was reduced or eliminated when reach variability and sensory prediction errors were more tightly controlled.

Strengths:

The paper is clearly written, theoretically well-motivated, and employs a commendably transparent and progressive experimental logic. The three-experiment structure, in which confounds are systematically identified and addressed, represents a strong model of cumulative experimental design (I will certainly use it in teaching courses on experimental methods):

Experiment 1 establishes an apparent difference in implicit generalization breadth. Experiment 2 attempts to reduce error spillover from Non-Critical targets by increasing angular separation and using delayed endpoint feedback. Experiment 3 uses an error-clamp design to decouple variable reaching from error feedback. This sequence is appropriate for testing whether the initial difference reflects a strategy-dependent change in implicit recalibration or instead follows from the distribution of movement plans and error exposure. The authors also provide reaction-time and performance data that are broadly consistent with the intended distinction between algorithmic and retrieval-like task performance.

Weaknesses:

The evidence does not support the strongest claims made in the manuscript, namely that algorithmic and retrieval strategies generally do not reshape implicit recalibration.

In general, I am skeptical of the authors' interpretation of null results. Several central conclusions depend on non-significant group differences, especially in Experiment 3. Non-significant tests are repeatedly treated as evidence that groups are equivalent or that confounds are absent (e.g., implicit recalibration magnitude (Algorithmic: 11.43 {plus minus} 6.43{degree sign}; Retrieval: 15.49 {plus minus} 8.99{degree sign}; t(38) = −1.65, p = .11), adaptation level before Exclusion probes (F(1,256) = 3.04, p = .08) and Exclusion RT differences (F(1,266) = 3.15, p = .08), whereas a modest model-dependent breadth effect (bootstrap p = .02) is treated as meaningful (for more on the model-dependent breadth effect, see below).

Without confidence intervals, equivalence tests, or Bayesian analyses, I think that the authors' interpretations comprise an inferential gap. A failure to find a significant difference is not equivalent to evidence of equivalence, particularly given that the implicit recalibration signal gets progressively attenuated across experiments (Experiment 1: ~16-17{degree sign}; Experiment 2: ~11-15{degree sign}; Experiment 3: ~7-8{degree sign}). With a substantially diminished signal in Experiment 3, the null result could partly reflect reduced statistical sensitivity rather than true equivalence.

My main technical concern is the analysis of generalization breadth already alluded to. The central claims rely on group-level Gaussian fits to only seven Exclusion probe locations spanning −45{degree sign} to +45{degree sign} around the Critical target. In several cases, the fitted centers and widths are poorly constrained by the sampled range. For example, in Experiment 2 the algorithmic group's fitted center is shifted to approximately 29{degree sign}, meaning that the probe range samples the function asymmetrically relative to its own peak. In Experiment 3, fitted centers are near or outside the sampled range, while estimated widths are very broad. Under these conditions, the width parameter may partly reflect extrapolation or parameter trade-offs between center, amplitude, and width rather than a genuine difference in generalization breadth.

Lastly, I think that the authors' use of an error-clamp paradigm is, from an experimental point of view, quite elegant. By controlling the sensory prediction error independently of reach direction, they can isolate implicit recalibration from the confounds identified in Experiments 1 and 2. However, I see a fundamental problem or question concerning construct validity here: In Experiments 1 and 2, the algorithmic strategy was operationalized as participants computing a counterrotated aiming direction in response to a visible cursor rotation. This is a naturalistic context where mental rotation is both required and meaningfully connected to task success. In Experiment 3, however, there is no visuomotor rotation to compensate for. The error-clamp renders the cursor feedback task-irrelevant. Instead, participants are instructed via text commands (e.g., "move towards 45{degree sign}") to reach invisible locations, rendering the "algorithmic strategy" in this context essentially an instructed spatial navigation toward arbitrary angular locations, not genuine visuomotor mental rotation driven by an error signal.

To put it differently, are we sure that the cognitive process engaged by the algorithmic group in Experiment 3 is the same as the algorithmic mental rotation strategy in Experiments 1 and 2? If not, then the null result in Experiment 3 may not speak to the original question about how algorithmic strategies interact with implicit recalibration after all. Instead, it may reflect the absence of a genuine strategy manipulation.

To their credit, the authors report a compelling RT dissociation that mirrors Experiments 1 and 2: The algorithmic group shows slower RT, which is decreasing over training (0.98s → 0.76s), whereas the retrieval group exhibits faster, stable RT (0.52s → 0.45s). While this pattern is consistent with genuine strategy differences persisting in Experiment 3, it could also reflect the greater spatial precision demands of reaching to invisible targets from text instructions, rather than genuine mental rotation per se. Reaching to an invisible location defined by a verbal angular label is inherently more demanding than reaching to a visible target, regardless of strategy type, and this demand is asymmetrically present in the two groups, since Non-Critical targets are invisible for the algorithmic group but visible for the retrieval group.

Thus, from my point of view, experiment 3 should not be used as definitive evidence that algorithmic and retrieval strategies during standard visuomotor adaptation cannot differentially influence implicit recalibration.

Overall, the manuscript addresses a meaningful question and the multi-experiment structure is useful. The evidence is incomplete for the broad claim that implicit recalibration is insensitive to strategy type. The study would make a clearer contribution if the authors narrowed the claims, strengthened the generalization analyses, and treated null effects with appropriate inferential tools.

Reviewer #1 (Public review):

Summary:

The authors characterize the phospholipid scramblase Xkr in Drosophila. They generate null mutants in both S2 cells and flies and find that phosphatidylserine (PS) exposure is reduced during apoptosis; they show reduced engulfment of apoptotic cells, and that the protein is localized partially within the cytoplasm, overlapping with the ER. They go on to identify Xkr binding partners and show that they overlap with plasma membrane-ER contact sites, suggesting that Xkr facilitates PS transfer from the ER to PM. Overall, this reveals a new role for Xkr and identifies new binding partners, which are valuable contributions to the field.

Strengths:

(1) The generation of new Xkr reagents in both S2 cells and flies to analyze its function. Tools are used to quantify both PS exposure and efferocytosis, and the effects of Xkr knockout are significant.

(2) The discovery of new binding partners of Xkr which also affect PS exposure and efferocytosis.

(3) The authors demonstrate that the binding partners are conserved in mammalian cells.

Weaknesses:

(1) Throughout the manuscript (e.g, lines 105, 165, 274 and discussion), the authors describe Xkr as being activated in a caspase-independent manner, and use this as the rationale for identifying binding partners. However, this is never shown in the manuscript or clearly referenced. Interestingly, there is a TEVDA sequence in the fly ortholog at the same location as the caspase cleavage site in C. elegans Ced-8 (Figure S1), suggesting the caspase cleavage site is conserved. This should be further investigated, or the statements regarding caspase independence should be modified. I don't think the N- and C-terminal GFP fusions indicate caspase independence, especially since apoptosis was not induced in Figure 1A, B. If cleavage occurred at the TEVDA site in Figure S1A, it would not lead to a noticeable change on the Western blot, although the size does look a bit smaller in Figure S2B at the 8 h time point.

(2) The authors examine overlap between tagged Xkr and cellular compartment markers and find substantial overlap with Lamp (and other vesicle markers to a lesser extent) (Figure S2). This is not addressed in the paper and could indicate engulfment of other cells since S2 cells are macrophages. To test this, the staining could be tested on the mixed cells (vesicle-GFP tagged S2 + apoptotic xkr-mcherry). Similarly, calreticulin is an eatme signal that gets translocated to the PM of apoptotic cells. This could affect interpretation of colocalization (Figure 2J), and ideally another ER marker should be used.

(3) There are some places where there is over- or incorrect interpretation, and these instances should be corrected.

Specific examples:

a) Line 342 "Relative expression analysis by RT-qPCR showed that all three mutants were likely null alleles." This does not make sense since there is still mRNA present. In Figure S7A, the tm9sf4 allele is expressed at 75% of the control. The others show a greater reduction, but this is not proof of a null allele.

b) Figure S3I - It looks like mCherry-Lact:C2 does get localized to the PM with AcD treatment in the xkr[ko], although the authors conclude "this disrupted PS localization to the PM could not be restored by apoptosis induction". However, the PM localization does look disrupted in the tm9sf4 and sac1 knockdowns.

c) Figure 3I. The control Lact:C2 staining looks very different from the staining in Figure 2J, with abundant Lact:C2 outside the cell. Given the variability in the staining, were the contact sites quantified? On lines 287-288, it is stated that "fewer ER-PM MCSs were detected in xkrko cells than in WT", but no quantification is provided.

d) Line 299-300 - "the interaction between Xkr and dORP9 was enhanced after apoptosis induction". The interaction does not look enhanced in Figure S5F, so this statement should be removed or data supporting the statement should be provided. The interaction between Xkr and dORP2 looks enhanced upon apoptosis induction, but also paradoxically looks even more enhanced when apoptosis is blocked.

e) The data in Figure S6 are highlighted in the abstract. If this is a major conclusion, it would be best to move it to the main text and provide quantification.

f) Lines 392-4. The concluding statement seems overstated given that there was only a modest inhibition of PS exposure in the osbpl5 knockdown (Figure 6A) and no defects in efferocytosis (Figure 6C). The osbpl8 showed a stronger effect on PS exposure but still a very modest effect on efferocytosis.

Reviewer #2 (Public review):

In this study, the authors investigate the mechanisms underlying phosphatidylserine (PS) exposure during efferocytosis in Drosophila. They first show that Xkr promotes PS exposure and apoptotic cell clearance in both S2 cells and Drosophila embryos. As Drosophila Xkr lacks the canonical caspase cleavage site found in mammalian XKR proteins, the authors further explore the underlying mechanism by which Xkr regulates PS externalization. Through protein interaction studies, they identify TM9SF4 as an interacting partner of Xkr that regulates PS distribution and show that non-vesicular PS transport contributes to apoptotic PS exposure and efferocytosis. Using protein interaction studies, they further demonstrate that Xkr interacts with the lipid transfer protein dORP9 at ER-PM contact sites to facilitate non-vesicular PS transport to the plasma membrane. Loss of these proteins affects PS externalization and efferocytosis in Drosophila. Finally, using human cells, they demonstrate that human OSBPL8 interacts with XKR8 to regulate apoptotic PS exposure. Overall, the study supports a model in which Xkr promotes efferocytosis by facilitating lipid transport in addition to its role as a phospholipid scramblase.

Reviewer #3 (Public review):

Summary:

The manuscript investigates the function of the Drosophila Xkr protein, a homolog of mammalian Xkr8 that lacks the canonical caspase-cleavage motif. The authors show that apoptotic stimuli increase Xkr protein abundance through a post-transcriptional mechanism and that Xkr promotes phosphatidylserine (PS) exposure during apoptosis. Using immunoprecipitation coupled with mass spectrometry, they identify TM9SF4 as an Xkr-interacting protein and further implicate TM9SF4, Sac1, dORP2, dORP9, and Vap33 in regulating apoptotic PS exposure and efferocytosis. Based on these findings, the authors propose that Xkr regulates PS transport at ER-PM contact sites. Similar observations are also presented in human cells.

Strengths:

Overall, this is an interesting study. The authors provide convincing evidence that Drosophila Xkr participates in apoptotic PS exposure and employ multiple complementary approaches to support the involvement of several proteins in this pathway. The identification of TM9SF4 as a potential regulator of Xkr-mediated PS exposure is likely to be of broad interest.

Weaknesses:

I am less convinced by the evidence supporting the proposed role of ER-PM contact sites, and several mechanistic conclusions appear to extend beyond the data presented. Addressing the following points would substantially strengthen the manuscript.

Major concerns:

(1) In Figure 2A and related text, it is unclear whether the mass spectrometry analysis was performed using untreated cells or AcD-treated cells. If the objective was to identify apoptosis-associated Xkr interactors, it would be helpful to clarify the experimental condition and explain whether apoptosis-specific interactors were analyzed separately.

(2) In Figure 2B, 2E, and several other co-IP results, a negative control of Flag tag only is required to exclude experimental errors like insufficient washing, etc.

(3) In Figure S3B, S3F, and several other BiFC results, an mVC-only negative control would be important to exclude nonspecific fluorescence complementation.

(4) In Figure 2G, the quantitative values appear inconsistent with the flow cytometry histograms. The peak shift following Sac1 knockdown appears smaller than that of TM9SF4 knockdown, whereas the quantified values suggest the opposite. Please clarify this apparent discrepancy.

(5) I find the interpretation in Lines 223-227 difficult to reconcile with the data. Knockdown of both tm9sf4 and sac1 impaired apoptotic PS exposure to a similar extent as xkr knockout. However, while xkr deficiency significantly reduced efferocytosis, sac1 knockdown produced only a modest, statistically insignificant effect. These observations suggest that impaired PS exposure alone may not fully account for the efferocytosis phenotype observed in xkr-deficient cells. These results appear difficult to reconcile with the proposed model, which needs careful discussion.

(6) In Lines 274-275, the authors state that 'increased Xkr may accelerate non-vesicular PS transport for efficient apoptotic PS exposure'. However, Xkr protein levels increase only ~8 h after AcD treatment, whereas PS exposure occurs much earlier. Thus, alternative explanations like Xkr relocalization (Figure S5C), rather than increased abundance, may also explain how Xkr mediates PS transport. An Xkr overexpression experiment could be helpful to support this statement.

(7) The interpretation of the MAPPER experiments requires further clarification. In Line 283, the authors refer to "the intracellular proportion of the signal for each protein overlapping with MAPPER." Since MAPPER is designed to label ER-PM contact sites, which are located on the plasma membrane, intracellular MAPPER fluorescence likely represents the ER network rather than bona fide ER-PM contacts. Throughout the manuscript (including Figure S6, etc.), intracellular MAPPER puncta appear to be interpreted as ER-PM contacts, which may not be appropriate. In contrast, the peripheral MAPPER puncta observed along the cell cortex (e.g., Figure S5C after AcD treatment) are more consistent with authentic ER-PM contact sites. It is also not obvious that these cortical MAPPER signals colocalize with Xkr(Figure S5C). Thus, while the data support a role for the ER, they do not yet convincingly demonstrate Xkr clustering at ER-PM contact sites.

(8) In the Xkr knockout cells, all fluorescence signals appear substantially low in intensity. Differences in protein distribution are difficult to interpret when overall probe expression also appears altered. It would be helpful to demonstrate that probe expression levels are comparable between conditions. Furthermore, as noted above, intracellular MAPPER signal may primarily represent ER rather than ER-PM contacts. Finally, despite the reduced signal intensity, the remaining MAPPER and PS signals still appear well colocalized in the knockout cells, similar to the observations in Figure 2J. The interpretation in Lines 285-288 should therefore be reconsidered.

Reviewer #1 (Public review):

Summary and Strengths:

Shin et al deepen our understanding of high frequency oscillations in the frontal cortex during REM in a manner that sheds important light on the roles of these events. In particular, they reveal that cortical HFOs are modulated by theta oscillations, occur in chains and recruit cortical neuronal activation patterns in a manner that is distinct from other high frequency events during nonREM or in hippocampus. They also show that these events occur during increased oscillatory cross-talk between hippocampus and cortex and may protect cortical neurons from down regulation of firing during sleep. Overall, this is important work with several novel observations pointing towards an important role for these events that will open become increasingly understood over time.

I also wanted to comment that 2D is a beautiful illustration of separate and essentially exclusive communication channels used during HF events in NREM vs REM. They almost perfectly complement each other's frequencies.

Weaknesses:

I have only one major scientific critique, I believe we need to see quantification of how phasic REM theta waves with versus without HFOs differ. What do REM HFOs add to the "normal" theta oscillation? Without this, comparison it is more difficult to interpret the meaning of these events. Given that HFO chains have IEIs around the time of a theta cycle duration, are the repeating spiking activities stronger during HFO repeats than during adjacent theta waves without HFOs? What percentage of theta waves contain HFOs and what is the firing rate during those theta waves with vs without HFOs? Is there differential firing rate modulation? The authors may even consider that all REM-HFO-specific quantifications should be shown as differential from phasic theta cycles without HFOs.

As a non-scientific comment on the manuscript itself: unfortunately, the paper is difficult to read and understand at times, requiring great effort by the reader. This is to an extent that communication is hindered. The paper is dense with changing methods often from panel to panel. Unfortunately, the panel quantifications are not explained in the results section in a manner that readers can understand without going to read the methods for often each individual panel. These measures should be explained in a way that lets readers understand the conclusions of each panel and grossly what calculations were used to reach those. Instead, too much jargon is used rather than clear descriptions of overall calculations being done for each panel.

The authors mention in discussion that they see increased functional connectivity between mPFC and CA1, but most data suggesting that seems to be based on LFP rather than spiking. Functional connectivity is defined best by spiking-spiking relationships. And these authors have spiking data. So I believe either the descriptive language should be pulled back to something like "oscillatory coupling" or more analyses should be dedicated to showing spike-spike coordination across regions. 


Comments on revised version.

Previously raised concerns are addressed.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate high-frequency oscillations (HFOs) in the prefrontal cortex during REM sleep. They identify a specific pattern where these HFOs occur in "chains" that are phase-locked to theta oscillations, primarily during the "phasic" periods of REM. The study contrasts these events with isolated HFOs and NREM ripples, suggesting a unique role for these chains in coordinating activity between the prefrontal cortex and the hippocampus. Most notably, the authors report that a specific subset of hippocampal cells-those that co-fire with the prefrontal cortex during these HFOs-increase their firing rates over the course of sleep, suggesting a potential mechanism for selective memory consolidation.

Strengths:

The study addresses an under-explored area of sleep physiology: the fine-grained temporal coordination between the cortex and hippocampus during REM sleep. The identification of HFO "chains" and their association with higher theta power provides an interesting framework for understanding how the brain might organize information transfer outside of NREM sleep. The observation that specific hippocampal populations show differential firing rate changes based on their participation in these HFO events is a striking finding that warrants further investigation.

Comments on revised version.

I do have one remaining concern, which is about their continued use of the term "reactivation" during REM sleep, whereas it still seems "activation" is more appropriate. The only place they show more Post vs. Pre activation is in Figure 6F/6G which includes NREM sleep where indeed reactivation is robust (but not the main focus of this paper). There is no evidence offered that the REM ensembles are not already "pre-configured" and active at similar levels (with similar activation patterns) during Pre sleep. Notably Louie and Wilson 2001 found greater "replay" during Pre than Post during REM. Also, the first half vs. second half comparisons (e.g. Fig 6C) could be more effectively performed in Figure 6A, showing that the same ordering persists across the periods. If this point were addressed, the significance of the findings could potentially increase.

Reviewer #3 (Public review):

Summary:

Shin et al. examine hippocampal-prefrontal interactions during sleep using simultaneous CA1 and prefrontal cortex recordings in rats performing a spatial memory task. They identify high-frequency oscillation (HFO) events in PFC during REM sleep that occur in theta-modulated chains and are associated with increased CA1-PFC coherence and sequential, sparse reactivation of cortical ensembles. This pattern contrasts with the synchronous reactivation observed during NREM cortical ripples. Together with a simple cholinergic network model, the authors propose that REM HFO chains represent a distinct mechanism for hippocampal-cortical coordination that complements NREM ripple-mediated processing during sleep.

Strengths:

A major strength of the work is the extensive electrophysiological dataset, which includes simultaneous recordings of large neuronal populations in both hippocampus and prefrontal cortex across behaviour and subsequent sleep. The analyses linking high-frequency events to population dynamics, interregional coherence, and ensemble reactivation are technically sophisticated and provide an incredibly detailed description of REM-associated cortical activity patterns. In particular, the demonstration that REM HFOs occur in chains aligned to theta phase and organise sequential activation of cortical assemblies represents a potentially important advance in understanding the neural structure of REM sleep activity. The integration of experimental data with a computational model further provides a useful framework for interpreting the observed differences between REM and NREM network states in terms of neuromodulatory influences.

Weaknesses:

While overall this study provides a highly valuable body of work, there are two primary limitations, which if overcome, would provide substantially more significance to the overall characterisation of REM HFOs. Specifically:

Distinction from wake HFOs<br /> The results largely support the authors' claim that REM HFO chains represent a distinct pattern of neural coordination compared to NREM cortical ripples. The analyses consistently show differences between REM and NREM events in terms of neuronal modulation, ensemble structure, and interregional coupling. However, similar high-frequency events during wake are not examined. Since REM sleep shares several network features with wakefulness, including strong theta oscillations, evaluating whether comparable PFC HFOs occur during wake would provide clarity on whether these events are specific to REM sleep (and its associated functions) or represent more general theta-associated phenomenon.

Link to memory consolidation<br /> The manuscript proposes throughout that REM HFO chains may contribute to memory consolidation by coordinating hippocampal-cortical reactivation, but the evidence for this functional role remains indirect. The authors do highlight this as a limitation of the study - the inability to link their findings to learning - but it is not clear why. Further details of the behaviour results should be included. If no learning occurred across the eight behavioural sessions, this should be reported. If learning did occur, but could not be linked to HFO events, this should also be reported.

Comments on revised version.

The authors have since addressed these weaknesses. In supplementary figure S11 the authors now show that while HFOs were detectable during wake, they were not associated with gamma/theta oscillations or theta modulation of unit activity. This suggests that HFOs during REM are a distinct feature of REM sleep and not comparable to HFOs during NREM or wake. It would be interesting for future work to identify the significance of wake PFC HFOs, whether there are differences between HFOs during running compared to stationary behaviour, and their relationship to hippocampal sharp-wave ripples and memory consolidation.

Regarding the link between REM HFOs and memory consolidation, the authors have further acknowledged this as a limitation of the study and requirement for a more specific experimental design to test related hypotheses. Nevertheless, they do show a clear trajectory of learning in the rats and corresponding increase in reactivation of task-related activity which could be associated with REM sleep HFOs. This study paves the way for future experiments to more directly test this link.

Reviewer #1 (Public review):

Summary:

The manuscript investigates value-based decision-making under risk and ambiguity using a combination of behavioral, pupillometric, and EEG data. Participants are stratified into three "decision styles" (ideal, aggressive, conservative) based on how their choices under known risk align with expected-value optimality. The central claim is that ambiguity aversion is not a uniform bias but reflects heterogeneous internal belief models, and that physiology tracks subjective belief rather than objective task structure. While this is an interesting conceptual question, the evidence is underwhelming given that differences between groups are not tested statistically (but just described), there are clear problems with how the computational models are implemented, and there are serious issues with sampling of participants.

Strengths:

The multimodal design (behavior, pupillometry, EEG) and the attempt to link a latent belief parameter to physiological signatures address a question of clear interest.

Weaknesses:

Framing and motivation

(1) The framing conflates two questions that appear distinct. The motivation centers on "ambiguity aversion," but the study's actual aim - how individuals internally represent ambiguous outcomes - seems like a different question. The relationship between these two framings needs to be made explicit, because as written the motivating phenomenon and the studied phenomenon are not obviously the same thing.

(2) Several of the contrasts the paper sets up against prior literature read as strawmen. The claim that ambiguity aversion is treated as "a single bias or fixed trait that applies uniformly" is presented as the view being overturned, but it is not clear this is a position the field actually holds - it reads as a strawman. Relatedly, the central objective-versus-subjective valuation distinction that the results are built around also reads as a strawman dichotomy rather than a genuine competing account.

(3) The motivation for the physiological measures is overly broad. The statement linking EEG to control, attention, valuation, uncertainty, conflict, effort, and engagement is so general as to be uninformative - EEG signals have been linked to essentially everything, so this does not constrain the hypotheses or predictions. A more specific, falsifiable rationale is needed.<br /> Design, sample, and grouping.

(4) The inclusion of the collaborative spacecraft/Apollo task is unclear. It is not explained why this task is included, and its role relative to the core ambiguity question needs justification (this also bears on the leadership analyses; see below).

(5) The participant numbers do not add up and must be reconciled. The text reports 57 participants, yet the analyses describe three groups of roughly 32 + 32 + 31. The relationship between participants, sessions, and group n's needs to be stated clearly and consistently, because at present the sample description is internally contradictory.

(6) The rationale for categorizing participants into three discrete groups is not established, and the approach is statistically questionable. Decision tendency appears to be a continuous variable; dichotomizing/trichotomizing a continuous measure is generally discouraged and can manufacture or distort group differences. The authors should justify why discrete groups are needed at all, and ideally show whether there are genuine group differences (e.g., evidence of discontinuity/clustering) rather than an arbitrary split of a continuum.

Statistics

(7) Key claims about how ambiguity affects groups differently are made without the appropriate test. To support a claim that the effect of ambiguity differs across groups, the interaction (group × ambiguity) must be shown - group-wise effects reported separately are not sufficient. This is really a key limitation of the current work.

(8) The methods mentioned that some participants performed multiple sessions, but their data were treated as if coming from separate participants. This is incorrect for several reasons, particularly given the focus on individual differences.

Belief parameter and terminology

(9) The term "ideal" is not justified. It is unclear why this group is labelled "ideal" - are they Bayes-optimal, or optimal in some defined sense? If the label implies normativity, that needs to be demonstrated; otherwise it should be renamed.

Drift-diffusion modelling

(10) The boundary parameter is fixed (a detail which is hidden in the methods), but this is highly problematic. By enforcing the same boundary value for all participants, the model is forced to capture any variation as drift rate effects. As such, all conclusions about drift rate are not interpretable as they might reflect boundary effects in disguise.

(11) The DDMs are fit separately per group of participants, which again precludes testing interactions. As with the behavioral analyses, fitting separate models means group differences cannot be properly compared within a single statistical framework, and interactions cannot be assessed. The paper does mention some comparison between groups, but comparing DDM parameter estimates across separately fit models is not valid.

(12) Overall, the DDM is very complex, and the manuscript does not yet provide enough validation to make the model trustworthy. Given the number of trial-wise covariates entering the drift rate and the per-participant fitting, stronger evidence that the model is identifiable and that its parameters are recoverable/reliable is needed before the conclusions drawn from it can be accepted.

Methods - EEG and analysis details

(13) The high-pass filter setting appears very aggressive. The authors should confirm whether this risks removing genuine low-frequency signal of interest, particularly given that delta-band effects are later interpreted.

(14) There is an apparent inconsistency in the epoching/time-locking. The time-frequency analysis appears to be computed on choice-locked data, yet elsewhere the epochs are described as stimulus-locked. This needs to be clarified and made consistent, as it affects interpretation of the pre- versus post-decision EEG clusters.

(15) The mixed-effects modelling appears to omit random slopes. The authors should justify the random-effects structure (e.g., why only random intercepts), as this affects the validity of the inference.

Reviewer #2 (Public review):

Summary:

The manuscript by Qin and colleagues entitled "Pupil and Neural Dynamics Reveal Belief-Dependent Decision Making Under Ambiguity" examines decision-making under risk and ambiguity using pupillometry and EEG. The study employs a lottery choice task with three levels of ambiguity (zero, low, high). Participants were classified into three groups based on their choice behavior in a condition with risk and no ambiguity: ideal (choosing in line with objective expected values), aggressive (preference for investments), and conservative (preference against investments). The authors then compared behavior, pupil, and EEG results across these groups. The study concludes that individual beliefs about ambiguity are reflected in different behavioral strategies and neural correlates.

Strengths:

The combination of behavior, computational modeling, pupillometry, and EEG.

Weaknesses:

(1) It is unclear whether group definition is theoretically justified.

One general concern is that the strategy to form three distinct groups is not clearly motivated. The authors created the three groups, "aggressive", "ideal", and "conservative", based on the zero-ambiguity trials. However, as the authors state: "Ambiguity differs fundamentally from risk at both the physiological level (34; 6) and the behavioral level" (page 4). Under this assumption, it is questionable whether forming groups based on risk preferences is a useful strategy for studying ambiguity. What do we learn about ambiguity processing when group differences are primarily based on risk preferences? Might the present results partly be driven by risk preferences rather than ambiguity preferences? I recommend the following two points: (a) Clearly justify the reasoning behind the group approach; (b) Add an additional continuous analysis approach indicating whether the key results hold independent of the group definition based on risky decision-making.

(2) k-parameter.

The authors use the k-parameter that infers the expected high-payoff probability (e.g., page 11). On page 22, this is explained as: "the subjective value term K was assigned according to each participant's internal belief of the high-payoff rate under ambiguity, yielding a participant-specific estimate of expected value under uncertainty." I hope I have not missed anything, but I neither understood the role of this parameter nor how it was computed.

(3) How were individual beliefs and models computed?

A related but more general point is that it remained unclear how the authors computed internal beliefs and internal models in the study. The study contains many statements suggesting that the authors measured internal beliefs. For example:

a) Abstract: "We show that individuals adopt distinct decision strategies that reflect different internal beliefs about unknown outcomes."<br /> b) Page 3: "We then inferred subjective belief parameters that captured how individuals internally interpreted the ambiguous probability mass and examined how these beliefs related to choice behavior, arousal dynamics, and neural activity."<br /> c) Page 16: "Together, these findings show that ambiguity does not evoke a uniform behavioral or physiological response across participants with different decision-making styles; instead, individuals rely on distinct internal models and computational strategies when forming decisions under ambiguity."<br /> d) Page 16: "Taken together, these results show that ambiguity aversion is not a uniform psychological bias, but a set of heterogeneous belief-driven strategies that shape how ambiguity is represented and acted upon."<br /> e) Page 18: "Ambiguity processing, therefore, reflects distinct belief-driven pathways rather than a single canonical mechanism."

Based on the present data, analyses, and results, I don't think that the authors can draw these conclusions. Which analyses in the manuscript identify these internal beliefs, models, or strategies? How can we dissociate a unified strategy from a heterogeneous set of strategies based on the present results? My feeling is that the k-parameter might be related to this, but as explained above, I did not understand how it was computed and what it is supposed to reflect. The DDM analyses might also be targeted at this. However, it remains elusive how the DDM captures internal beliefs about ambiguity itself. My recommendation is that the authors more clearly explain (a) why the DDM is a useful model to study ambiguity, (b) what the different parameters exactly reflect about ambiguity processing, and (c) how the DDM captures internal beliefs and distinct belief-driven strategies in this context.

(4) Statistical tests.

4.1. Figure 2B: The authors summarize the number of participants with significant effects of ambiguity on choice behavior for each group. I recommend a statistical test at the second level that properly assesses the effects of ambiguity and group within a common statistical model. In my opinion, it is not enough to simply count the number of significant tests (from the first level) for each group.

4.2. Figure 2C: For the analysis of response times, the authors might want to consider reporting the main effects of group and ambiguity.

4.3. Figure 2D: The text on page 8 states that Figure 2D indicates that "aggressive investors showed no significant pupil modulation by ambiguity...". However, the figure and its caption indicate significant differences between ambiguous and non-ambiguous trials across all groups. Moreover, if the authors want to compare the groups, it is necessary to compare the groups to each other; a test against zero within each group would not be enough to demonstrate any group differences. In my mind, this would also be important for analyses in Figure 3C and D.

4.4. Strictly speaking, for the statistical tests, it would be necessary to take into account that participants completed multiple sessions (within-subject variance is different from between-subject variance). Currently, each session is treated independently (page 19: "Each individual completed one to three experimental sessions. For data analysis, each session was treated as an independent participant, yielding a total of 108 sessions.")

(5) Necessary quality control for pupillometry and EEG data.

The task was performed in a virtual reality environment with a head-mounted display. The task was not isoluminant, and, to the best of my knowledge, participants were not instructed to avoid eye movements. The authors applied a GLM to control for luminance effects in the pupil data. For EEG, they used ICA to remove ocular and muscular artifacts. While these methods are established, they are usually applied to more controlled paradigms optimized for EEG and pupillometry. To demonstrate high data quality despite these issues, it is necessary to present quality-control analyses. Can the authors please indicate how many blinks had to be removed from the data? Could the authors please indicate how many blinks were removed from the data? Can the authors please show trial-level data (after preprocessing) for a few subjects?

(6) Quality control for the DDM.

The manuscript lacks systematic posterior predictive checks and parameter recovery for the DDM results. It is important to validate that the model accurately captures the data. Currently, we only see the model parameters, but it remains unclear whether the model performs well on the current data set. Moreover, if the authors aimed to test different strategies using the DDM, it might be useful to perform systematic model comparison.

(7) Implications of the second experiment with collaborative task remain unclear.

To me, the link between the main study and the second experiment on leadership and team performance is not obvious. In my opinion, this topic is beyond the scope of the present paper. Linking the two studies more comprehensively based on deeper theoretical grounds would likely be better suited for an independent manuscript.

Reviewer #1 (Public review):

Summary:

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

Strengths and Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #1 (Public review):

Summary:

This study investigated the formation of mitochondrial-derived compartments (MDCs) under metabolic adaptations. They hypothesized that MDCs may play a role in regulating the mitochondrial proteome under these conditions by removing excess and superfluous membrane proteins that may challenge mitochondrial proteostasis. They found that glucose restriction, carbon-source switching, and osmotic stress can stimulate MDC formation. Underlying these stressors is a common signaling pathway that involves Snf1-dependent derepression of mitochondrial biogenesis and rapid synthesis and trafficking of nuclear-encoded proteins into mitochondria. They then showed that MDC formation is attenuated in tom70/tom71 mutants, suggesting that the delivery of these proteins to mitochondria is critical. Data also suggested that HAP4-stimulated mitochondrial biogenesis promotes MDC formation, which is further enhanced by glucose restriction and is suppressed after prolonged adaptation.

Strengths:

The genetically amenable yeast system allowed the authors to generate convincing data showing the rapid formation of MDCs under physiologically relevant conditions where the mitochondrial proteome needs to be expanded to accommodate increasing metabolic function. MDCs therefore function to buffer spillovers of outer membrane proteins upon an abrupt protein influx. Overall, the data presented are of high quality. The conclusion is strongly supported by the data.

I think this is a significant study as (1) it supported MDCs as a physiologically relevant mechanism of mitochondrial proteostasis; and (2) it offers a common mechanistic framework explaining the MDC phenomenon under many other conditions such as TOR inhibition and hydrophobic protein overloading previously published by this group. Although the precise mechanism of MDC formation and how MDC formation contributes to the overall proteostasis of mitochondria remain unknown, as the authors stated in the manuscript, the current work is a clearly identifiable milestone in this specific area of investigation.

Weaknesses:

Although the data are overall strong, weaknesses are mainly related to potential misinterpretation of the data.

(1) I have reservations regarding the interpretation of some results. First, the authors concluded that MDC biogenesis is activated when glycolytic metabolism is altered. I disagree with this. The authors should distinguish between "loss of glycolysis" and "loss of glucose repression". The yeast S288C strains are GAL2 and can ferment galactose. Likewise, glycolysis is also supported by raffinose and sucrose. In a broad sense, these carbon sources do support glycolysis as long as sugar influx is maintained at a high level. However, these alternative carbons do not repress mitochondrial respiration like glucose. It is likely the derepression of mitochondrial respiration (which is stated in some sections of the manuscript) instead of loss of glycolytic metabolism that stimulates MDC formation. This needs to be made clear throughout the manuscript. As such, the statement that "Carbon-source switching" stimulates MDCs is not accurate and needs to be re-interpreted.

(2) The explanation for the requirement of low glucose levels could be misleading. A complete lack of carbon sources and high concentrations of 2-DG may shut down global protein synthesis, cell cycle progression, and many other processes, including mitochondrial biogenesis. Glucose at 0.02% is not sufficient to cause glucose repression, as only the high-affinity but low-influx transporters are functioning. Under the low glucose conditions, mitochondrial respiration is also derepressed. In this scenario, low glucose simply plays a role in supporting cell growth without causing the repression of mitochondrial biogenesis.

(3) The idea that MDCs are formed when protein load exceeds the capacity the organelle can accommodate is attractive. Early studies have shown that the mitochondrial compartment is expanded by several folds in volume when yeast cells are switched from fermentative to oxidative metabolism. Perhaps, space expansion takes longer than protein influx increase. It would be interesting to see whether there is a correlation between MDC frequencies and the delay in volume expansion. Long-term adaptation would solve this challenge, as it allows the cell to complete volume expansion.

(4) HAP4 may primarily activate OXPHOS genes but not some MDC cargo proteins. The requirement for "metabolic remodeling" for full induction of MDC formation may be an overstatement. The authors should either reexamine the proteomic data to see whether known MDC cargos are not subject to HAP4 activation or have this discussed in the manuscript.

Reviewer #2 (Public review):

Summary:

Price et al. present new work providing insight into the function and mechanisms of mitochondrial-derived compartments (MDCs) in yeast. The Hughes lab previously established that these large ~micron-sized structures are formed under a variety of conditions including amino acid stress (rapamycin, conA, cycloheximide), alterations in mitochondrial metabolites and lipids, or acute expression of specific outer membrane proteins. These stressors lead to the sequestration of outer membrane proteins (that can include mistargeted inner membrane proteins) that extend or tubulate into large multilamellar structures that ultimately target the vacuole in an ATG5/Dnm1 dependent autophagy related pathway for degradation. Initially reported in aging yeast a decade ago, it has now been accepted as a mechanism to remove excess mitochondrial proteins as a pathway distinct from mitophagy or the extraction of stalled precursors from the import translocon.

In this study, the authors examined additional metabolic transitions they suspected would drive increased mitochondrial protein expression and promote MDC formation. Indeed, they show that glucose-restricted conditions (or a switch to galactose or incubation with 2DG) induced MDCs within 2 hours. This correlated with increased transcription/translation of mitochondrial precursors porin and OM45. Similar results were seen with osmotic shock, a process previously shown to induce mitochondrial gene expression. The metabolic or osmotic shift was shown to activate a yeast AMPK-type kinase called Snf1, which phosphorylates a key substrate Mig1 - an established repressor of mitochondrial gene expression. Loss of these pathways abolished the generation of MDCs under these conditions. As the key novel finding in the study, the authors explored the relationship/requirement for Snf1 and Mig1 using multiple approaches in different backgrounds and employing auxin-inducible degron tools for acute depletion. These data further support the hypothesis that excess mitochondrial outer membrane proteins result in MDC formation to facilitate their removal, at least transiently until the import machinery can adapt to the increased import demand. To test this more directly, they generated an inducible yeast strain to express a canonical transcription factor Hap4 that induces mitochondrial gene expression. In this system, induction of Hap4 expression also resulted in MDC formation. While not all previously reported MDC inducers act through Snf1/Mig1, the common feature is the transcriptional induction of mitochondrial protein expression.

Strengths:

The important aspect of this work is that the authors dissected the transcriptional signaling pathway that induces MDCs in a much more physiological metabolic transition, which complements the more common use of chemical compounds. They had previously shown that overexpression of individual outer membrane proteins could lead to MDCs, but here the Hap4 expression offers a new condition to show that the canonical induction of mitochondrial biogenesis leads to MDC shedding. Overall, the data are of high quality, the findings are clear, and the work provides important new insights into the regulation of MDC formation.

Weaknesses:

There are a few points that should be addressed.

(1) MDCs are almost exclusively monitored through GFP-tagged TOM70, and the authors do not show the inclusion of any endogenous cargo. The evidence for their fate in the vacuole is through the appearance of cleaved, free GFP after 6 hours that is dependent on ATG5, Dnm1, Pep4, etc. Can the authors demonstrate the appearance of MDCs without expressing any GFP tags and instead monitor known outer membrane cargoes? In the case of Hap4 expression, the proteomics identifies some very highly induced mitochondrial proteins, and there surely must be some with antibodies that can detect the protein by IF and Western blot.

(2) There is a very unexpected ~10X increased in a sporulation factor SPO21 upon induction of Hap4. I see no evidence of sporulation, and it's not long enough for stationary phase. Is the increased mitochondrial biogenesis driving a specific metabolic state of these cells that is signaling to other biology?

(3) It is important to understand the kinetics and stoichiometry of outer membrane loading that drives MDCs, and their transit to the vacuole. This is why it would be highly informative to monitor some endogenous cargoes (previous point). In the review the authors cite (NRMBC, Pfanner lab 2019), it was stated that the import machinery is not generally increased upon metabolic induction of mitochondrial gene expression. Therefore, (pre-MDCs) the field concluded that the import machinery has a very high capacity for the rapid biogenesis of newly synthesized proteins, along with regulation through the phosphorylation of import receptors (ie; the work of Meisenger). Consistent with this, the Hap1 proteomics did not show any increases in the core import machinery, while ETC subunits and a large swath of mitochondrial proteins were elevated over 2-fold (I looked carefully through the Excel sheet). Since MDCs are induced transiently about 2 hours after glucose deprivation, and fully dependent on de-repression of Mig1, the authors are right to imply that this is coupled to the import of newly synthesized proteins.

However, it seems to me that MDCs are being formed at very early stages of mitochondrial protein expression, not after they have necessarily "overloaded" the outer membrane. The Hap4 proteomics after 3.5hr of induction would suggest that the bulk of the mitochondrial proteins have been successfully inserted (no import failure) and are likely already functional (metabolizing). I'm trying to understand the percentage of the proteins that would be incorporated within MDCs, as the mitochondria appear to handle the bulk of their newly inserted proteins without issue. How can the authors adapt their "free GFP" assay to understand the stoichiometry of the transport of endogenous, newly imported outer membrane proteins to the vacuole?

(4) As a last theoretical point for discussion: Can the authors exclude that MDCs are not functional or play a signaling role? Given the emerging work on SPOTs (Lena Pernas), and from the new evidence from Craig Thompson's lab that there can be very specific functional mitochondria (oxidizing vs reducing), it is possible that MDCs are not simply there to be degraded. They last at least 3 hours, which is a long time for yeast (budding cycle 90 min, 3 hours in glucose deprivation). Taking the data presented here very objectively, there is no direct evidence that the cargoes within MDVs reflect any failure to import, or that they are damaged in any way. The deletions of Tom70/71 have way too many pleotropic effects and essentially demonstrate only that the MDC cargoes came from the mitochondria. It could be helpful if the discussion also positioned these MDC mechanisms within the context of other aspects of selective mitochondrial-related compartments that have been emerging in the literature.

Reviewer #3 (Public review):

Summary:

In this manuscript, Price et al. report the physiological conditions and proteins involved in the formation of mitochondria-derived compartments (MDCs), specialized domains exclusively containing outer mitochondrial membrane (OMM) proteins, in budding yeast. Hughes and his colleagues have previously established MDCs as unique multilamellar membrane structures derived from the OMM that arise with both mitochondrial metabolic perturbation and hydrophobic OMM protein load. Whether cells undergo MDC formation in response to physiological changes in mitochondrial biogenesis remains to be explored. In this study, the authors sought to test if glucose restriction, carbon-source switching, and salt stress can induce MDC formation, and found that these situations, which naturally promote acute mitochondrial biogenesis concomitantly with metabolic transitions, trigger MDC induction. Under these conditions, loss of Snf1, an AMP-activated protein kinase that facilitates mitochondrial biogenesis under metabolic stress, almost completely abolished MDC formation. Snf1 induces MDC induction under metabolic stress via phosphorylating (suppressing) Mig1, a transcriptional repressor of mitochondrial biogenesis. Consistent with this idea, loss of Mig1 mostly rescues MDC formation under glucose restriction or salt stress in cells lacking Snf1. The authors further found that acute induction of Hap4, a core activator of mitochondrial biogenesis, is sufficient to trigger MDC formation even without metabolic stress. Finally, cells lacking Tom70 and Tom71, protein receptors of the TOM (translocase of the outer membrane) complex that mediate targeting of hydrophobic mitochondrial proteins, almost failed to form MDCs under glucose restriction. Correctively, the authors propose that MDCs act in the reduction of OMM protein load upon metabolic stress-induced acute mitochondrial biogenesis.

Strengths:

The experiments for this study are well-designed, and the resulting data are mostly convincing, with proper controls and significant statistics to support their conclusions. The paper potentially provides new insights into the physiology of MDC formation.

Weaknesses:

There are only a few new mechanistic advancements in this paper.

Reviewer #1 (Public review):

Summary:

Escalante et al. employ super-resolution microscopy to achieve a clearer, nanoscale view of how trans-sialidases and mucins are organized on the Trypanosoma cruzi parasite membrane. Comparing the experimental data using clustering analysis with model-based simulations, they report two kinds of organizational states describing the non-uniform distribution of these two proteins: a segregated state where mucins and trans-sialidases form spatially distinct nano-clusters, and a non-clustered state where they share a proposed fibrillar network with more ordered, shorter-than-random separation distances. They also look at the oligomerization states of the two proteins to try and propose a mechanistic basis for the observed distributions.

Strengths:

The in-depth analysis of the distributions of both proteins coupled with model-based simulations brings out new insights into organizational principles underlying protein distribution on the membrane surface. The ability to resolve shorter-than-random separation distances even in the non-clustered state is to be highlighted and is a key take-away from this manuscript.

Weaknesses:

The authors propose the oligomeric state of mucins compared to the non-oligomeric trans-sialidases as a basis for explaining the distinct organization of these proteins. Although this hints at how segregation may occur, it does not inform us of how the more ordered non-clustered state could co-exist with the clustered segregated state and warrants further investigation.

Overall, the analytical framework applied in this study to elucidate organizational principles for the non-uniform distribution of proteins can potentially be used in a wide-range of contexts across different organisms and systems. This study also lays the groundwork to understand mechanisms that spatially regulate how trans-sialidases act on their substrates. Going forward, it could be very interesting to look at how different kinds of mucins and trans-sialidases are organized with respect to one-another and amongst themselves. Also, the development of tools to observe the dynamics of these proteins live will likely provide further insights into the mechanism.

Reviewer #2 (Public review):

The manuscript describes a numerical analysis of the domains of the T. cruzi cell surface containing different proteins. It has the potential to be of great interest.

I do not have the expertise necessary to comment on the image collection or analysis.

I have one concern: the amount of manipulation of the cells prior to fixation; these were clearly stated in the methods, which is good.

My concern is whether these manipulations prior to fixation alter the observations. The 'Labelling sialic acid acceptors' involves >6 centrifugations and >90 minutes incubation in PBS prior to fixation, and the 'immunostaining' protocol involves cells 'extensively washed with PBS' prior to fixation. I would like to suggest that the authors do controls in which they compare the pattern of anti-SAPA staining under four conditions.

(1) Cells fixed in culture by the addition of paraformaldehyde to 4%, followed by blocking and PBS washes.

(2) Cells fixed in culture by the addition of paraformaldehyde to 4% and glutaraldehyde to 0.2% followed by blocking and PBS washes.

(3) Cells fixed by the 'labelling sialic acid acceptors' protocol.

(4) Cells fixed by the 'immunostaining protocol'.

Reviewer #3 (Public review):

Summary:

The authors present an innovative approach to tackle the lateral organization of mucins and trans-sialidases (TS) on the cell membrane of the organism Trypanosoma cruzi. By applying dual-color super-resolution microscopy (STORM), the authors report on a differential nanoscale distribution between mucins and TS on the cell membrane. Moreover, they find that 60% of mucins and TS are organized in nanoclusters with an inter-nanocluster distance following a random distribution. The remaining 40% of both proteins are organized in a non-random manner, and, using simulations, the authors claim that they are organized in rectilinear fibers.

Strengths:

The authors use dual-color STORM microscopy to unravel the protein nanoscale organization of mucins and TS on the cell membrane of Trypanosoma cruzi for the first time. They perform a dedicated analysis of the localizations and clustering of both proteins. Moreover, they perform, for every type of analysis on real data, simulations to compare their results for random organization. They also use an analysis approach together with simulations to propose that the lateral organization of both non-clustered proteins are within rectilinear fibers. They also complement their microscopy findings with BN-PAGE. Overall, the use of advanced microscopy techniques, corresponding data analysis and simulations is very solid and remarkable.

Weaknesses:

As the authors point out, they do not provide a molecular/biophysical mechanism explaining the non-random lateral organization of mucins and TS (both clustered and individual proteins).

Reviewer #1 (Public review):

Summary:

This report seeks to understand the mechanisms whereby the ferroptosis inducers ML162 and erastin cause cell death in several tumor cell lines. They present evidence that caspase-5 is activated and required for ferroptosis, but other caspases, including caspase-1 and -4, are not important. Surprisingly, caspase-5 cleaved and activated GSDME, instead of the expected gasdermin target GSDMD

Strengths:

The magnitude of effect for triggering ferroptosis by ML162 and erastin is strong, and the strength of inhibition by YVAD is also very strong, making these effects convincing. The lack of effect of DEVD, which inhibits apoptotic caspases, is also convincing. Also, the lack of effect of necrostatin is convincing. These negative results strengthen the positive results seen with YVAD.

Inhibition by disulfiram is convincing.

Caspase-5 knockout single-cell clones and the ability to complement these with caspase-5, but not catalytically inactive caspase-5 in Figure 5, is strong data.

Weaknesses:

(1) Prior publications have asserted that ferroptosis is caspase-independent. Can the authors repeat some of these experiments directly to reveal whether there was an error in the published work that resulted in missing the phenotype for a caspase in ferroptosis? In my experience, caspase inhibitors sometimes only delay cell death because they are not 100% effective, especially over hours of time. Can the authors repeat the prior experiments to reveal whether this caveat affected previously published data? At the least, the authors should use the z-VAD-fmk and Boc-D-FMK inhibitors to determine whether they give the same effects as YVAD to rule out a very unlikely possibility that these "pan-caspase" inhibitors do not inhibit caspase-5.

a) The original report describing ferroptosis by Dixon and Stockwell, doi: 10.1016/j.cell.2012.03.042, shows that erastin treatment-induced ferroptosis is not affected by z-VAD-fmk in 3 cell lines.<br /> b) A later report from Dr. Stockwell states in data not shown that a different pan-caspase inhibitor (Boc-D-Fmk) does not block erastin-driven ferroptosis. Doi 10.1016/S1535-6108(03)00050-3<br /> c) An earlier 2008 report from Dr. Stockwell shows that z-VAD-fmk and Boc-D-fmk do not rescue cells treated with RSL-3 or RSL-5 treated cell lines derived from BJ cells. doi 10.1016/j.chembiol.2008.02.010<br /> d) A recent paper shows a delay of ferroptosis after RSL3 treatment by pan-caspase inhibitor Q-VD-OPh. Doi 10.1038/s41418-025-01514-7. The delay was about 8 hours in time, so cells were still dying.<br /> e) Gpx4 knockout cells or erastin or RSL3 treatment are unaffected by z-VAD-FMK. Doi 10.1038/ncb3064<br /> f) I encourage the authors to do more thorough searching of the literature to find more publications that have used caspase inhibitors.

(2) The authors should discuss how mouse cells can undergo ferroptosis while they do not encode caspase-5, and the evolutionary conservation of caspase-5 in general. If caspase-5 is not encoded by an animal (as is the case with mice), can their cells undergo ferroptosis?

(3) Disulfiram is not a specific inhibitor. It is a nonspecific inhibitor that modifies cysteine residues of many proteins. This should be described in more detail so the reader can appreciate the strengths and weaknesses of the inhibitor.

(4) I encourage the authors to assess IL-1β processing by Western blot and show that this is inhibited by YVAD. Because ELISA can detect release of the pro form after lytic cell death by other mechanisms.

(5) ASC knockdown in Supplementary Figure 3a for two cell lines is not sufficient to draw any conclusions in Figure 3a.

(6) Caspase-5 can be more specifically inhibited by LEVD inhibitors. Can the authors show that these work as well?

(7) I would like to see a positive control in Figure 5a to show what a strong caspase signal activity looks like.

(8) Since caspase-3 is known to cleave GSDME, the authors need to assess whether caspase-3 is also activated, and whether other caspase-3 target proteins are also cleaved. There are many to choose from. Caspase-3 western blots, including with the cleaved caspase-3-specific antibody, are critical. This is in addition to the blot shown in Supplementary Figure 10. Positive controls should be included. It is important to continue to add controls to rule out caspase-3, with more than just negative data with DEVD inhibitors and the western blot in Figure S10.

(9) The data in Figure 6c are not strong.

(10) One would expect that any mode of activation of caspase-5 should lead to its proteolytic activity upon its preferred substrates, so LPS should cause caspase-5 to cleave GSDME and not GSDMD. Additional data to strongly activate caspase-5 with LPS should be investigated to see if this leads to GSDME cleavage and pyroptosis via GSDME and not GSDMD.

Reviewer #2 (Public review):

Summary:

In the submitted manuscript, Akter et al use a series of ferroptosis inhibitors in mesenchymal-like ovarian cancer cells and discover that the ferroptosis inducers induce cell death that is inhibited by pyroptosis inhibitors, namely YVAD-fmk and disulfiram, which inhibit pore formation by gasdermin D (GSDMD). Remarkably, the authors also saw the release of IL-1β in response to ferroptosis inducers. Unexpectedly, they did not observe the involvement of caspase-1 but rather observed that caspase-5 was activated in response to the ferroptosis inducers. Moreover, they found that caspase-5 directly cleaves GSDME in response to the ferroptosis inducers, establishing CASP5/GSDME as downstream executors of ferroptosis.

Strengths:

These findings are interesting because only CASP1 is known to induce IL-1β maturation, and their data suggest that CASP5 rather than CASP1, is responsible for IL-1β activation in the context of ferroptosis inducers. Notably, CASP3 is the only caspase reported to be able to cleave GSDME, so the identification of CASP5 as a driver of ferroptosis in this context is a significant finding. They genetically show that loss of CASP5 and GSDME knockdown inhibits cell death in response to the ferroptosis inducers ML162 and Erastin, which is evidence that they play a role in this context.

Weaknesses:

The major findings in this paper are interesting, but the data presented do not robustly support the claims made in this paper. For example, they claim that CASP5 is responsible for the activation of GSDME by cleaving it directly to induce cell death. They try to rule out the involvement of CASP1, ASC, and CASP4 using siRNA targeting these genes, but the knockdowns are incomplete, and the loading controls are inconsistent. They also claim they do not see GSDMD or CASP3 cleavage and activation but use negative data to make that claim. It is unclear if the antibodies used can detect cleaved GSDMD or CASP3 as they do not include a positive control to show that they can indeed detect these activation events if they were occurring. This needs to happen in the same experiment - they need to show in the same experiment with the same lysates that they can detect CASP5, GSDME and IL-1β activation but not CASP1, GSDMD, CASP4, or CASP3 activation. Of course, they should include agonists for positive controls of CASP1, CASP4 and GSDMD activation, which are lacking in the current manuscript.

Notably, the major evidence supporting a direct role for CASP5 cleavage of GSDME is one Coomassie gel using recombinant CASP5 and GSDME, but there were too many non-specific bands, and the full-length uncleaved protein could not be detected even in the untreated lanes. The authors need to show a gel where the protein can easily be identified and should also include a positive control protein like GSDMD to show the relative cleavage efficiency of GSDME compared to a known substrate. It would also be great to compare this to CASP3-mediated cleavage of GSDME. With recombinant proteins, calculating the catalytic efficiencies would be the best way to ascertain if this is biologically similar to other known substrates.

The way that ferroptosis is defined, it is caspase-independent, and pyroptosis is defined as gasdermin-mediated cell death. Given that these agents lead to activation of CASP5/GSDME, it would be more accurate to say that these ferroptosis inducers also induce CASP5/GSDME-dependent pyroptosis, as opposed to them being the executors of ferroptosis. This can be a distinct mechanism/pathway from the ferroptosis pathway, as multiple cell death pathways can be initiated in cells. Consistent with this, ferrostatin-1 also inhibited cell death, likely due to inhibition of the ferroptosis signaling cascade. It is unclear if this pathway is upstream of the caspases. How these ferroptosis triggers selectively activate CASP5 and not CASP4 to induce GSDME cleavage is a major unresolved question. Notably, it is also unclear if this biology is specific to the mesenchymal-like cells used in this study or if it expands to other cells.

Reviewer #3 (Public review):

Summary:

Akter et al. identify caspase 5 activation and Gasdermin E cleavage as a novel downstream executioner of ferroptotic cell lysis induced by erastin and ML162. These data are novel and very interesting to the wider cell death community.

Strengths:

Strengths of the study include the use and validation of findings in several mesenchymal ovarian cancer cell lines, the rigorous validation using small molecule approaches, siRNA-mediated silencing and CRISPR/Cas9-mediated knockouts with re-expression.

Weaknesses:

A weakness of the study is the fact that ferroptosis was not induced genetically (GPX4 ko) and, hence, off-targets of the mode of induction cannot be ruled out at this point (e.g. ML162 also targets TrxR1). Moreover, it would be vital to understand at which point in ferroptosis execution caspase 5 is activated in a time-resolved kinetic together with lipid ROS tracing to also obtain hints as to its possible activation.

Conclusion:

Despite the weaknesses described, this is a very interesting, timely, and well-executed study with the described limitations. The work provides important mechanistic insights into the interplay between ferroptosis and pyroptosis with possible consequences for inflammatory responses.

Reviewer #1 (Public review):

Summary:

The authors conducted a comparative acoustic analysis of primate vocal repertoires, focusing on the assumption that speech and language evolution required and involved an expansion in the acoustic space of voiced vocalizations from non-human primates to humans. Results challenge this idea. The study compiles and analyzes a large dataset of calls to quantify differences in vocal production space.

Strengths:

The study is technically sound, with a solid implementation of acoustic measurements and a valuable new dataset that brings empirical rigor to test a dominant, yet hitherto strictly theoretical, notion about what speech and language evolution entailed. It provides concrete comparative acoustic data across species to disprove that speech and language required an increase in the range of voiced calls, and thus, by extension, of vowels. The approach is methodologically rigorous and directly engages with the relevant data, rather than relying on untested presumptions of what great apes "ought" to be able to do or not.

Weaknesses:

The theoretical contextualization should be strengthened and updated, as several aspects contain inaccuracies, most notably by equating voiced calls or vocalizations with speech (overlooking the critical role of consonants, as human languages typically show vowel:consonant ratios of 1:4 or greater) and misrepresenting the premises and current status of the neural (Kuypers-Jürgens) hypothesis.

The discussion drifts into speculative territory on features like syntax and co-articulation that fall outside the paper's scope and data, and it does not sufficiently engage recent evidence on vocal learning and consonant-like capacities in great apes.

Minor issues include incomplete sampling justifications, imprecise terminology, and reliance on references that have been critiqued in more recent work.

Reviewer #2 (Public review):

This study examines the evolutionary context of the emergence of human speech. The authors address the widely held hypothesis that the expansion of the human vocal space, resulting from modifications of the vocal tract, was a key prerequisite for the evolution of spoken language.

To test this hypothesis, the authors quantified the acoustic space of human speech, non-linguistic vocalizations, and musical vocalizations and compared it with that of nonhuman primates, chimpanzees, bonobos, and chacma baboons.

The authors found that speech and song occupied significantly less volume in the acoustic space than human non-linguistic vocalizations. In addition, the acoustic-feature volume of speech and song was not statistically distinct from that of non-human primates. Accordingly, the authors conclude that the evolution of human speech did not depend on an expansion of the human vocal acoustic space.

I find the analysis presented in this manuscript highly convincing. It is conducted at a contemporary scientific standard, and the results provide strong support for the authors' conclusions. I particularly appreciate that the authors explicitly discuss the limitations of their approach. For example, they acknowledge that MFCCs cannot capture all aspects of acoustic structure.

I have only three minor comments:

First, the authors may wish to briefly summarize the main findings of the study by Anikin et al., as it represents the central reference for the present work. A concise summary in two or three sentences would help readers who are not familiar with that study.

Second, I would appreciate a brief explanation of why the authors chose this particular statistical approach.

Third, the authors could briefly mention that the Chacma baboon dataset provides a very comprehensive representation of the vocal repertoire of this species, although a small number of rare vocalizations are not included. I am not sure whether a similar limitation also applies to the chimpanzee and bonobo datasets, but if so, it would be useful to mention this as well.

Reviewer #1 (Public review):

Summary:

The authors develop a GFP-LC3-RFP autophagy reporter under the control of the Rosa26 locus to measure autophagic flux in mouse embryos as well as adult tissues. While image quantification is consistently used, the authors also develop a semi-high-throughput assay for measuring autophagic flux using a microplate reader. Additionally, the authors cross these mice with a Cre-inducible Atg5-deletion mouse model, allowing the investigation of how autophagy flux is affected upon loss of Atg5. With this model, they demonstrate that loss of Atg5 leads to an increased ratio of GFP/RFP intensity in multiple tissues, including the brain, revealing that the brain undergoes basal autophagy. They further go on to show that the increase in GFP/RFP intensity upon Atg5 loss is greater in adult tissues compared to their embryonic counterparts. The development of an animal model, along with quantitative tools to measure the model, will have a high impact on the field. However, the analyses from the data presented do not fully justify the conclusions.

Strengths:

(1) A mouse model to better measure autophagy.

(2) The plate-reader-based method to quantify autophagy across tissues.

(3) Assessment of autophagy in many different tissues.

(4) Crossing the reporter mouse with the Atg5f/f mouse to assess basal autophagy.

Weaknesses:

(1) While the tool is of high impact, there is little new biological or mechanistic insight provided in these studies.

(2) The quantification and normalization method is unclear, making it difficult to compare across tissues accurately.

(3) Differential expression across cell types is not well documented or taken into account for comparisons.

(4) There is no consideration for sex as a biological variable.

Reviewer #2 (Public review):

Summary:

The aim of the authors was to measure starvation-induced and basal autophagy in vivo across several tissues and developmental stages. For this, they developed a novel mouse model expressing the GFP-LC3-RFP reporter. They also aimed to provide a more high-throughput method for autophagy flux measurements than assessment by imaging and developed an assay based on a microplate reader.

Strengths:

(1) Good validation of the mouse model. The knock-in strategy is well explained and illustrated.

(2) The model has potential to be applied to a wide range of research questions. The Cre-dependent expression allows for customization of KO timing, which will be beneficial in developmental studies.

(3) The authors presented consistent findings using two different methods to quantify autophagy, strengthening the robustness of their results.

(4) The authors demonstrated the validity of the high-throughput method (microplate reader).

Weaknesses:

(1) The comparison of neuronal populations in different areas of the brain is not ideal. In the cerebellum, Purkinje cells were chosen, which are rare and not representative of this tissue, as well as functionally very different from the neurons in the hippocampus and cortex that they were compared to.

(2) The explanation of the GFP-LC3-RFP construct and specifically if/how autophagosome formation can be measured and distinguished from flux could be clearer.

Conclusion:

The work presented is thorough, and the authors achieved their goals for this study. The effort used to further investigate unexpectedly high basal levels of autophagy in the brain is well appreciated and adds value to this paper. The conclusions of the authors are mostly very well supported by the data provided. The well-structured description of the results, along with clear figures, allows the reader to comprehend the authors' reasoning in reaching their conclusions.

The presented mouse model has great potential for a lasting positive impact on the research field of in vivo study of autophagy. The method of utilizing a microplate reader will also benefit future research where semi-high throughput is an advantage. Together, the information provided in this study not only presents new methodology that will allow the investigation of new research questions, but also provides novel information about in vivo autophagy flux at the selected developmental stages that opens up new follow-up research questions.

Reviewer #1 (Public review):

We appreciate the authors have provided answers to many of the points we raised, and the changes made to their manuscript, which we think strengthen the overall evidence presented. However, we find that some important controls are still missing across experiments.

Major comments:

(1) Shortcomings in Immunofluorescence experiments:

a. Antibody cross-reactivity was only tested against CK1ɛ, but should also be tested against CK1α, which is abundant in U2OS cells, and is also known to be involved in cell-cycle regulation.

b. Fig. 1: Statistical analyses are missing from the analysis.

c. Fig. 2: No colocalisation analysis shown for figure 2, only some arrowheads pointing to puncta. Appropriate colocalisation statistics are important since for practical reasons, only a few representative images can be shown on the figure.

d. Fig. 6: Even if the figure is illustrative, it is important to show centrosome staining to visualise CK1ẟ's recruitment to the centrosome in G2/prophase, especially since this information is used to propose the model in figure 7.

e. For all figures: Please mention the number of independent biological replicates in the figure legends (1, 2, 6). For figure 1, if there are 3 independent biological replicates, the quantification should take all of them into account (as opposed to the data points corresponding to 10 cells), and statistics must be done appropriately, taking those independent replicates into account. Same for the colocalisation analysis in figure 2 once you include it.

(2) Shortcomings in biochemistry experiments:

On CalA control, this is not a matter of confirming that CalA treatment works in principle, but rather to confirm that CalA treatment worked in this specific replicate. Aliquots may lose potency (e.g. with freeze-thaw cycles / exposure to light), hence checking for enrichment of phospho-proteins is essential to confirm the treatment was successful in this particular instance. In the worst-case scenario, the company may have sent the wrong compound altogether! A positive and a negative control is the basis for every experiment to make meaningful interpretation. On a separate note, many experiments have control and siRNA or compound treatments on two different gels - this should be rectified as they are meaningless if different exposures have been selected for different immunoblots.

(4) As the authors mention, the kinase is not fully inactive when tail phosphorylated. Recent research has also suggested that tail-phosphorylated CK1ẟ may show increased catalytic activity for a few select, specific substrates, in the co-occurrence of pT220 (Cullati et al., 2022; Cullati et al., 2024). It is thus tricky to directly infer that phosphorylated CK1ẟ is inhibited, when no positive control for CK1ẟ inhibition was shown in the evidence presented. It would be necessary to either nuance your claim or include a positive control for CK1ẟ inhibition. Please revise statements in the manuscript accordingly.

(5) It would be important to include statistical analyses for the immunofluorescence data in Fig. 1 and 2.

(9) The authors mentioned "In the eLife study, we show that inhibition of kinase activity by PF670462 stabilizes CK1δ and that the overexpressed kinase-dead mutant CK1δ-K38R is stable." Unfortunately, the data from biochemical analyses presented in the eLife publication is uninterpretable due to a lack of loading controls.

(10) While the data presented in Penas et al. strongly suggests a link between CK1ẟ stabilisation and the APC/C-Cdh1 complex, it is the only study to have shown it. Given that (1) science relies on data reproducibility and (2) your proposed model relies heavily on the relationship between CK1ẟ stabilisation and the APC/CCdh1 complex, it would be appropriate to include the investigations mentioned in our original comment.

Reviewer #2 (Public review):

In this study, Serrano et al. employed a combination of cell biological and molecular approaches to investigate the localization and regulation of Casein Kinase CK1 during the cell cycle using U20S cells. They show that CK1 dynamically localizes between the centrosomes and the nucleus but can be sequestered away from the centrosomes upon overexpression of its binding partner PER2. They provide evidence that CK1 WT but not a phospho-null mutant strongly accumulates in a hyperphosphorylated form upon inhibition of phosphatases (using Calyculin and Okadaic Acid) and thus conclude that CK1 tail phosphorylation protects the kinase from degradation. Using synchronized cells, they show that CK1 accumulates unphosphorylated in S-phase (APC/Cdh1 inactive) but phosphorylated at the G2-M transition. Immunostaining shows that CK1 localizes to the centrosomes during mitosis.

The manuscript has improved overall, but some sections are still inconclusive and require clarification.

Major comments:

Figure 1 is inconclusive. CK1 nuclear staining is highly similar in untreated cells and in cells treated with CHX + PF670462. The reduction in centrosomal staining in these cells is barely significant. However, the authors draw very strong conclusions from these data sets. In panel B, the cells appear to have been fixed incorrectly, and the anti-PCNT shows a strong background signal. Not convinced that immunofluorescence is the best approach to look at protein dynamics in vivo.

In Figure 2, panel B, the authors should co-stain the centrosomes of cells that co-express CRY1 and CK1, as some of these dots may represent the centrosomes.

Figures 3B, please provide information on the non-phosphorylable CK1a mutant (it is mentioned as a variant in which all serine and threonine residues in the C-terminal tail were replaced by alanine). Specify the number of sites mutated and their exact position. Is this non-phosphorylable CK1a version catalytically active? Treatment of samples with inactivated PPase should be used as a control.

Strengths:

The authors reveal that the activity and abundance of dephosphorylated and phosphorylated CK1δ are regulated in a cell cycle-dependent manner. This suggests that these different pools are associated with distinct physiological functions.

Weaknesses:

Unfortunately, some of the data are inconclusive, and there is no data/information linking the cell cycle regulation of CK1δ to its function during the cell cycle.

Reviewer #1 (Public review):

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

This study provides evidence that the apicoplast-locaized isoform of acyl-carrier protein (ACP) has acquired important non-enzymatic functions in the malaria parasite. Previous studies have shown that the apicoplast-located FASII-dependent pathway of fatty acid synthesis is not essential in Plasmodium blood stages. In contrast, genome-wide knockout studies suggested that ACP, a key protein in this pathway, is essential in these stages, indicating that it may have additional non-canonical functions. In this study, the authors confirm that ACP is essential in Pf blood stages (using both apicoplast IPP rescue and conditional knockdown); show that this essential function requires modification with 4-phosphopantetheine and use proximity biotinylation and complementary immunoprecipitation pull-down approaches to provide compelling evidence that ACP binds to and stabilizes the apicoplast-located isoform of pyruvate kinase II. Notably, these interactions appear to differ from those associated with the binding of mitochondrial isoforms of ACP to proteins involved in Fe-S biosynthesis. Loss of ACP was shown to lead to a decrease in PKII levels and apicoplast DNA/RNA synthesis, consistent with loss of NTP synthesis in this organelle. The data are clear and very well described, and the findings represent a significant advance in our understanding of metabolic regulatory mechanisms in apicomplexan apicoplast studies.

Strengths:

The study uses a variety of complementary genetic approaches to demonstrate the essentiality of ACP and the enzyme involved in its activation with 4-PP in Pf blood stages, demonstrating that the ascribed non-enzymatic function is mediated by holo-ACP. Similarly, a number of complementary biochemical approaches, including proximity biotinylation, immunoprecipitation, and co-expression of PfACP and PK-II in a heterologous bacterial expression system, are used to confirm the physiological significance of the PfACP and PK-II interaction. The study also reports additional findings, such as the independence of P. faciparum blood stages on exogenous (media) fatty acids, indicating that intracellular stages can salvage all of their requirements from the red blood cell.

Weaknesses:

Overall, this is a very strong study. While questions remain around the function of other apicoplast ACP-interacting proteins detected in this study, I don't have any suggestions for significant improvements.

Reviewer #2 (Public review):

This study focuses on revealing the essential divergent function of the Acyl Carrier protein (ACP) in the deadliest human malaria parasite, Plasmodium falciparum. More precisely, using inducible KO, cellular and biochemical approaches, the authors determined that instead of a canonical role for ACP allowing the de novo synthesis of fatty acids in the apicoplast (essential relict plastid) of the parasite, the enzyme couples with pyruvate kinase II to generate nucleoside triphosphate to maintain parasite survival during blood stages. The study is novel, well-designed, providing interesting new data on Plasmodium and apicomplexa biology. The results convincingly support the major claim of the study. However, it is currently incomplete to support some claims on the essentiality of some apicoplast pathways.

In this study, Geher et al. focused on deciphering the role of the Acyl Carrier Protein (ACP) present in the relict non-photosynthetic plastid, i.e. the apicoplast of the most lethal human malaria parasite, Plasmodium falciparum. More particularly, they determined an essential function of ACP independent of its usual/typical function as the central protein for the normal function of the apicoplast Type II fatty acid synthesis (FASII) pathway. Rather, the protein seems to associate with the apicoplast Pyruvate Kinase II, together generating an essential nucleoside triphosphate (NTPs) source to fuel the apicoplast and parasite survival instead.

By generating a TetR-DOZY-based inducible KD line for ACP, they confirmed that the protein is indeed essential to maintain apicoplast integrity and parasite survival during asexual blood stages, as previously predicted and experimentally shown. They showed that ACP requires a biochemical modification, typically activating the protein for its function in the FASII pathway, i.e. binding of the 4-PP group by holoACP synthase. Then, they showed that the other enzymes of the FASII pathway are likely dispensable during the blood stage, as they were able to generate a KO line of the first enzyme of the pathway, FabD (which was predicted to be essential in P. falciparum). Based on a cell culture approach in a controlled culture medium, they further claimed that, unlike current evidence-based hypotheses, the FASII pathway (and thus a potentially FASII-linked ACP) has no role/activity during blood stages. Using a proximity biotinylation approach, they determined that ACP associates with the apicoplast pyruvate Kinase II (PKII), previously shown to generate NTPs in the apicoplast for energy and DNA/RNA maintenance (Xia et al. 2019), and not to fuel the FASII pathway as its main function in blood stages. Finally, they showed that the disruption of ACP induces the reduction of the presence/content in PKII in the parasite, as well as the drastic reduction of the apicoplast DNA and RNA content. Together, they concluded that the main function of ACP is indeed the NTP formation via its association with PKII, rather than its canonical role for the generation of fatty acids in the apicoplast.

This study is novel and focuses on a topic of particular interest in malaria biology, but also for most of the apicomplexa-related diseases, and beyond for plastid bearing orgnaisms and this unusual role for ACP. The study is well thought out with proper biochemical approaches that convincingly point to this association of ACP with PKII for NTP synthesis as a major function during P. falciparum blood stages.

Reviewer #1 (Public review):

Summary:

This manuscript addresses an important question in cardiac biology: whether distinct cardiomyocyte (CM) subpopulations play specialized roles during heart development and regeneration. Using single-cell RNA sequencing and newly generated genetic tools, the authors identify phlda2 as a specific marker of primordial cardiomyocytes in the adult zebrafish heart. They further show that these primordial CMs function are essential for myocardial morphogenesis and coronary vascularization but are dispensable for myocardial regeneration or revascularization after injury. These findings indicate that heart regeneration doesn't simply recapitulate developmental processes.

Strengths:

A major strength of the study is the generation of a phlda2 BAC reporter, which provides a specific and reliable marker for primordial cardiomyocytes. The lack of genetic tools has previously limited functional analysis of this CM population. By using phlda2 regulatory elements to generate reporter and NTR-based ablation lines, the authors can visualize and selectively manipulate primordial CMs in vivo. This enables a direct functional interrogation rather than relying on lineage tracing or correlative evidence. Through genetic ablation, the authors convincingly demonstrate that primordial CMs are essential for myocardial morphogenesis and coronary vascular organization during development but are not necessary for heart regeneration.

Weaknesses:

(1) The manuscript would benefit from clarifying whether the primordial cardiomyocytes ablation affects epicardial cell behaviors during heart development, given that the well-established role of the epicardium in supporting coronary vessel growth, it is possible that the vascular phenotypes observed after primordial CM ablation may be affected, at least in part, by altered epicardial cells.

(2) Because primordial cardiomyocytes form a dense, single-cell-thick layer covering the ventricular surface, it would be informative to determine whether their loss alters the spatial distribution or inward migration of coronary endothelial cells or epicardial cells.

(3) The manuscript carefully examines the relationship between primordial CMs and gata4⁺ cardiomyocytes during regeneration. However, their relationship during heart development should be more fully addressed.

(4) As loss of cardiomyocytes is known to induce gata4:GFP activation during regeneration, it would be important to determine whether ablation of primordial cardiomyocytes alone triggers gata4:GFP expression in neighboring cardiomyocytes. This analysis would further support the conclusion that primordial cardiomyocytes are not required for regenerative responses.

Reviewer #2 (Public review):

Summary:

In the manuscript "Primordial Cardiomyocytes orchestrate myocardial morphogenesis and vascularization but are dispensable for regeneration", Sun et al. identify a novel marker of primordial cardiomyocytes and use it to visualize and ablate the population during development and regeneration. The role of the primordial layer has not been investigated because the tools to manipulate this population have not existed. The manuscript is straightforward, easy to understand, and addresses an important question that has not been explored.

While the manuscript provides important insights into the role of primordial CMs, backed by a convincing methodology, the authors should clarify their requirements for heart development and maturation. Specifically, is the primordial layer required for the fish to survive? Do primordial CMs regenerate when ablated during development, and do the defects observed (in trabecular and compact CMs and coronary vessels) resolve after 10 days post-treatment when they were detected?

Strengths:

The major strengths are the identification of a marker that enables manipulation of primordial cardiomyocytes and the tools generated by the team.

Weaknesses:

The major weakness is not considering the longer-term consequences of primordial layer ablation during development, as it is unclear whether the animals succumb to the acute cardiac defects observed or fully recover.

Reviewer #3 (Public review):

Summary:

The authors performed single-cell RNA sequencing of adult zebrafish hearts and identified markers for distinct cardiomyocyte subpopulations. One marker, phlda2, marks primordial cardiomyocytes. They generated transgenic reporter lines to characterize phlda2 expression patterns and a phlda2-NTR ablation line to determine the functional requirement of primordial cardiomyocytes during heart regeneration. They found that phlda2+ primordial cardiomyocytes are essential for myocardial morphogenesis and coronary vessel development. Interestingly, when phlda2+ primordial cardiomyocytes are ablated during heart regeneration, gata4+ cortical cardiomyocytes, coronary vessel revascularization, and scar tissue formation are not affected.

Strengths:

The authors identified a new primordial cardiomyocyte marker, phlda2. They further demonstrated that primordial cardiomyocytes are important for heart morphogenesis but dispensable for heart regeneration. Their findings reveal a potential difference between heart development and regeneration programs.

Weakness:

Despite the interesting findings, the authors did not provide supplemental data for their scRNAseq to demonstrate the data quality and support their conclusions, and some results are not well described.

Reviewer #1 (Public review):

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

Summary:

The manuscript by Lu and colleagues demonstrate convincingly that PRRT2 interacts with brain voltage-gated sodium channels to enhance slow inactivation in vitro and in vivo. The work is interesting and rigorously conducted. The relevance to normal physiology and disease pathophysiology (e.g., PRRT2-related genetic neurodevelopmental disorders) seems high. Some simple additional experiments could elevate the impact and make the study more complete.

Strengths:

Experiments are conducted rigorously including experimenter blinding and appropriate controls. Data presentation is excellent and logical. The paper is well written for a general scientific audience.

Comments on revised version.

The manuscript by Lu and colleagues has been revised sufficiently to address all my prior concerns.

Experiments are conducted rigorously including experimenter blinding and appropriate controls. Data presentation is excellent and logical. The paper is well written for a general scientific audience.

Reviewer #2 (Public review):

Summary:

As a member of DspB subfamily, PRRT2 is predominantly expressed in CNS and has been associated with various paroxysmal neurological disorders. Previous studies have shown that PRRT2 interacts with Nav and Cav channels, modulating channel properties and neuronal excitability.

In this manuscript, Lu et al. demonstrate that PRRT2 is a potent regulator of Nav channel slow inactivation, promoting the development of Nav slow inactivation and impeding the recovery from slow inactivation. This effect is highly conserved in PRRT2s across species as well as among DspB family members (TRARG1 and TMEM233). The authors further confirmed the interaction between Nav channels and PRRT2 in heterologous expression systems as well as in Prrt2-V5 knock-in mice. Prrt2-mutant mice, which lack PRRT2 expression, require lower stimulation thresholds for evoking after-discharges when compared with WT mice.

Overall, this is a well-executed and methodologically comprehensive study. This work offers valuable insight into the physiological functions of PRRT2 and reveals a potential pathogenic mechanism underlying PRRT2-associated neurological disorders.

The revised manuscript has addressed most of the concerns raised by the reviewers and has been substantially strengthened, although I still have several concerns regarding the discussion section.

Strengths:

(1) Overall, this is a well-executed and methodologically comprehensive study. The electrophysiological data strongly support the conclusion that PRRT2 is a potent regulator of Nav channel slow inactivation. The observation that this regulation is conserved in PRRT2 across species and among DspB family members raises the possibility that altered regulation of Nav channels may also contribute to the pathogenesis of TRARG1- or TMEM233-associated disorders.

(2) Co-immunoprecipitation assay performed using brain tissue from genetically modified Prrt2-V5 knock-in mice provides convincing in vivo evidence for the interaction between PRRT2 and Nav1.2 channels.

(3) Prrt2-V5 KI mice show markedly reduced PRRT2 protein expression and display phenotypes similar to those observed in Prrt2-mutant mice, supporting an important role of PRRT2 in regulating neuronal and network excitability.

Weaknesses:

(1) Nav1.6 is also highly expressed in cortical neurons and is widely regarded as a major contributor to action potential initiation and sustained high-frequency firing. Given that PRRT2 similarly regulates the fast and slow inactivation of Nav1.6 and Nav1.2 channels, the potential contribution of Nav1.6 regulation to neuronal and network excitability should be discussed.

(2) Slow inactivation is generally considered to develop over timescales ranging from hundreds of milliseconds to seconds or longer. Therefore, the statement in Discussion (Page 13, line 381-382) that "slow inactivation develops on a timescale of tens of milliseconds to seconds" may not accurately reflect the conventional kinetic definition of slow inactivation and should be clarified.

(3) Page 14, line 417-430: "question about how Nav channel slow inactivation is regulated in cells that do not express PRRT2".<br /> PRRT2 is unlikely to be the sole regulator of Nav channel slow inactivation. Other molecules and signaling pathways may regulate Nav channel and contribute to neuronal excitability. In addition, neuronal excitability can also be regulated through modulating other Nav properties, such as long-term inactivation or slow recovery from inactivation, as well as through modulating the activity of other ion channels, for example, Kv7.2 and Kv7.3 channels. Therefore, PRRT2-negative cells may utilize alternative mechanisms to fine-tune neuronal excitability. In its current form, this paragraph somewhat overstates the role of PRRT2 and would benefit from a more balanced discussion.

(4) Page 50, Figure 7-figure supplement 2: It would be helpful to include representative traces of the 1st and the last (20th) compound APs in panels B and C.

Reviewer #3 (Public review):

This paper reveals that the neuronal protein PRRT2, previously known for its association with paroxysmal dyskinesia and infantile seizures, modulates the slow inactivation of voltage-gated sodium ion (Nav) channels, a gating process that limits excitability during prolonged activity. Using electrophysiology, molecular biology, and mouse models, the authors show that PRRT2 accelerates entry of Nav channels into the slow-inactivated state and slows their recovery, effectively dampening excessive excitability. The effect seems evolutionarily conserved, requires the C-terminal region of PRRT2, and is recapitulated in cortical neurons, where PRRT2 deficiency leads to hyper-responsiveness and reduced cortical resilience in vivo. These findings extend the functional repertoire of PRRT2, identifying it as a physiological brake on neuronal excitability. The work provides a mechanistic link between PRRT2 mutations and episodic neurological phenotypes.

Comments:

(1) The precise structural interface and the molecular basis of gating modulation remain inferred rather than demonstrated.

(2) The in vivo phenotype reflects a complex circuit outcome and does not isolate slow-inactivation defects per se.

(3) Expression of PRRT2 in muscle or heart is low, so the cross-isoform claims are likely of limited physiological significance.

(4) The mechanistic separation between trafficking of PRRT2 and its gating effects is not clearly resolved.

(5) Additional studies with Nav1.6 should be carried out.

Comments on revised version.

These comments have been addressed in the revised version.

Reviewer #1 (Public review):

Summary:

The study investigates the role of asymptomatic pertussis carriage in transmission between mothers and their infants in particular. The authors use a longitudinal cohort study that involved 1,315 mother-infant dyads in Lusaka Zambia and they utilized qPCR based detection of IS481 to track Bordetella pertussis transmission over time. Insights from the study suggest that minimally symptomatic or asymptomatic mothers may act as a reservoir for B. pertussis transmission in the infants thus challenging the traditional surveillance methods that focus on symptomatic cases. Additionally, the study also identified a subgroup of persistently colonized individuals where mothers were majorly asymptomatic despite sustained bacterial presence.

The authors aimed to improve comprehension of pertussis transmission dynamics in high burden low resource settings and they advocated for an enhanced molecular surveillance strategies to capture full pertussis infection including those that might have gone undetected.

Strengths:

The strength are the use of innovative study design especially the longitudinal approach and routine sampling rather than symptom driven testing that minimizes bias in the study. The methodology were also rigorous and transparent by evaluating IS481 signal strength to classify pertussis detection and conducts retesting to assess qPCR reliability. There was also important epidemiological insights and the findings challenge the traditional wisdom by suggesting that pertussis transmission may frequently occur outside of symptomatic cases. The findings also showed its relevance to global health and policy by arguing for the incorporation of molecular tools like qPCR for surveillance of pertussis in low resource setting.

Weaknesses:

These includes reliability on qPCR based detection without additional validation measures like confirmatory culture or serology. There are also potential alternate explanation for transmission patterns observed in the study such as shared environmental exposure or household transmission. Additionally, there are limited generalizability as the study was done in a single urban site in Zambia. There is also lack of functional immune data.

Reviewer #2 (Public review):

Summary:

In this paper, the authors describe the results of a longitudinal study of pertussis infection in mother/infant dyads in Lusaka, Zambia. Unlike many past studies, the authors assessed the infection status of individuals independently of whether they were symptomatic for a respiratory infection. As a result, this work represents one of the first studies specifically designed to assess asymptomatic transmission of pertussis. Using qPCR, the authors find strong evidence for the role of asymptomatic transmission from mothers to infants and also evidence for long-term bacterial carriage. This work represents an important contribution to our understanding of the global burden of pertussis. Also, it highlights the still under-appreciated role of asymptomatic transmission across many infectious diseases (including vaccine-preventable ones).

Strengths:

Unlike many past studies, the authors assessed the infection status of individuals independently of whether they were symptomatic for a respiratory infection. As a result, this work represents one of the first studies specifically designed to assess asymptomatic transmission of pertussis. Using qPCR, the authors find strong evidence for the role of asymptomatic transmission from mothers to infants and also evidence for long-term bacterial carriage.

Comments on revised version:

I appreciate the authors' attention to my comments during the revision process and still believe that their work represents an important contribution to our understanding of pertussis epidemiology. In most cases, the authors have done a thorough job of either addressing or responding to my comments. However, I do not believe the authors engaged sufficiently with two of the queries raised in my previous round of comments. The two queries were about the vaccination status of the mothers and engagement with literature on asymptomatic transmission. I still think they matter and ask the authors to consider them again.

I do not think the authors can rule out two alternative explanations: (1) recent introduction of pertussis and low vaccination coverage amongst study mothers, or (2) recent introduction of a breakthrough strain (either w.r.t. the vaccine or prior infection) and higher vaccination coverage/infection-derived immunity amongst study mothers. Depending on which mechanism was mostly driving the observed patterns in Zambia, i.e.,

a. long-running, widespread, unreported transmission;<br /> b. transmission started recently, and vaccination was low amongst study mothers;<br /> c. a breakthrough strain is causing the current rise (here we'd still want to know about vaccination status); and<br /> d. something else that I have not considered

would have implications for how the results are interpreted, and potentially far-reaching implications for the broader pertussis community. All of that is to say, I think the authors were too quick to dismiss these concerns (even if they disagree with my assertions).

In their reply, the authors largely dismissed concerns about not knowing the mother's vaccination status, stating in their reply that, "our findings strongly suggest ongoing pertussis transmission in this population. Based on this, we expect that mothers in our study who were not vaccinated would likely have some degree of infection-derived immunity."

However, they also stated that, "Zambia offers an evocative example of pertussis surveillance, where no cases have appeared in official WHO reports since 2009" and "As we noted above (and now address in our Discussion), widespread genomic surveillance and microbiological characterization of pertussis are sorely lacking across Africa."

I don't disagree with the authors' conclusion that pertussis is clearly spreading in Zambia. I also don't disagree that there's clearly evidence for minimally symptomatic, infectious mothers spreading infections to children. Both of these findings matter for Zambia and for our broader understanding of pertussis. However, I don't see how the authors can so confidently conclude that low vaccination rates, coupled with a recent introduction, high vaccination rates, coupled with a breakthrough strain, or high infection-derived immunity, coupled with a breakthrough strain, couldn't be what's driving the increase. The authors do hedge in places and also state in the discussion that their findings don't line up with expectations related to WP/infection-derived immunity, "This corresponds to a mean return frequency of one infection per 14.8 years, which is much shorter than the presumed duration of immunity from natural infection or the whole-cell pertussis vaccination used in Zambia (70, 71)." But, my read of the paper is that the authors are pushing way to ward for a preferred hypothesis that is not more favored than other alternatives.

Secondly, I asked about placing this work in the context of other studies on asymptomatic transmission, but realize that I did not list any specific papers. Two worth considering are Warfel et al. 2014 and Althouse and Scarpino 2015. Restating for the editor, the Warfel study found that WP facilitated rapid clearance in a non-human primate experimental infection study (admittedly with small sample sizes and many other caveats). Many took that as evidence that WP would also block transmission (admittedly experiments Warfel did not run). If the mechanism underlying the results in Zambia is that either WP or natural infection does not block transmission (in the absence of a breakthrough strain), that would upend many of the assumptions in pertussis research. While not incompatible with the Warfel et al. results, it would negate most of the importance of their finding that WP blocked transmission. From what I can see, the authors do not even cite Warfel et al. 2014, which is a serious gap regardless of whether the authors agree or disagree with the findings. A quick sidebar, the authors seem to duplicate Craig et al. 2020 10.1093/cid/ciz531, listing it as both citation 9 and 38.

In Althouse and Scarpino, they found evidence of a rise in asymptomatic/underreported/subclinical transmission following the switch from WP to AP. While not as directly relevant to the current study as the Warfel paper (so I leave it to the authors to decide whether citing this paper is important), Althouse and Scarpino discuss asymptomatic transmission at length and also assume that WP conferred strong protection against transmission, so their results (along with dozens and dozens of other studies assuming similar WP/infection-induced immunity protection and durability) would also need to be reinterpreted in the context of this study. The authors should engage with the implication of their results in the context of past modeling studies and what we think we know about vaccine-/infection-derived immunity.

Going back to my earlier points, unvaccinated mothers and the recent introduction of pertussis, or vaccinated/infection-induced immune mothers with a breakthrough strain, would both explain the current results and be compatible with Warfel et al., Althouse and Scarpino, and a sizable number of other studies. Instead, if transmission from WP- or naturally infected mothers is common (in the absence of a breakthrough strain), that would really change the landscape of pertussis epidemiology. The authors have not convinced me that they can make this conclusion. Hence, why I think it's important that the authors engage more actively with those hypotheses and with relevant debates in the pertussis literature on asymptomatic transmission. I think it's appropriate for the authors to present their preferred hypothesis, but, absent other data, they should also present plausible alternatives that are consistent with past publications.

References:

Althouse, B. M., & Scarpino, S. V. (2015). Asymptomatic transmission and the resurgence of Bordetella pertussis. BMC medicine, 13, 1-12.

Warfel, J. M., Zimmerman, L. I., & Merkel, T. J. (2014). Acellular pertussis vaccines protect against disease but fail to prevent infection and transmission in a nonhuman primate model. Proceedings of the National Academy of Sciences, 111(2), 787-792.

Reviewer #1 (Public review):

Summary:

The manuscript further explores the single-cell atlas of Clytia hemisphaerica by incorporating the planula larva. It compares the cell clusters with the previously established atlas of the medusa. It identifies similarities and differences between the two life stages.

Strengths:

The manuscript provides an important set of single-cell data that have not been assessed previously: the Clytia planula. The data is further supplemented with high-quality EM-based histology and an extensive in situ hybridisation of selected genes.

Weaknesses:

The detailed analysis does not go deep into the comparison between stages, nor does it provide an analysis of genes within the clusters; it could be described as remaining overall rather superficial.

Reviewer #2 (Public review):

Summary:

The generation of alternate stages in the life cycle of a single species requires vast remodeling of the cellular complement of the individual during metamorphosis from one stage to another. In this paper, the authors provide a detailed description of single-cell RNA-seq data derived from the planula stage of the hydrozoan model Clytia hemispherica and compare this to an expanded dataset from the medusa stage to assess changes in transcriptomic identity of cell types between these two phases of the life cycle. The paper further includes valuable TEM data illustrating fine anatomy of cell types present at both stages investigated, and documentation of the retention of epithelial polarity from the planula through to the polyp stage, using a reporter line.

Strengths:

The study provides a solid and convincing transcriptomic characterization of planula cell types (including in situ validations and a planula-to-polyp mapping of epithelial polarity), and introduces a potentially valuable method for evaluating cluster similarity.

Weaknesses:

The work suffers from insufficient documentation of methodological approaches and missing code, lack of clarity regarding clustering resolution and nomenclature (thereby hindering cross-referencing with prior papers), and unclear plans for public, fully annotated data release.

Full Review:

The single-cell transcriptomic data analyzed include both previously published and newly generated data: two additional medusa libraries and two additional planula libraries were generated and integrated with the data from https://doi.org/10.1126/sciadv.abh1683, and https://doi.org/10.1126/sciadv.adv1159. The original release of the planula dataset in their 2025 Science Advances paper did not include analyses of all cell types. Here the authors provide this analysis for the planula stage. However, as both the number of clusters and the nomenclature of the clusters changed, this leads to some confusion and inability to cross-reference the two papers. There is no explanation given for the re-processing of the planula dataset in the current paper, and the fact that only some of the data is new is buried in the supplement, which is not referenced in the main document, while the text within the main article suggests that the entire dataset is new. The fact that the dataset in the current analyses contains fewer cells than presented in their Science Advances paper further adds to this confusion. The current paper would benefit from greater transparency in the origin of the data analyzed.

The authors do try to apply the same nomenclature for the updated medusa dataset that is present in their 2021 Science paper. For example, the previously identified 'bioluminescent cells' are identified as 'gas-m8'. A look-up table that has all of the cluster id's cross-referenced would be useful (i.e. new: 8 = gas-m8 = previous: 28 = BC = "Tentacle GFP cells"). The inability to easily cross-compare with the published data is a major weakness of the current work and would benefit greatly from consistency between the three papers. Indeed, the clustering resolution is quite different across all three papers, and the current work does not adequately address how the clustering resolution was selected here. As an updated atlas, one would expect the entire transcriptomic diversity to be included here, so that the previous work can be transferred to the updated genomic mapping resource used in the current work. Nonetheless, presenting a unified nomenclature for moving forward would benefit the community as a whole and would increase the impact of the current work substantially.

The paper also includes new TEM data of the planula cell types. The authors attempt to correlate transcriptomic profiles with these anatomical data through in situ hybridizations that provide spatial distribution of the profiles. While the TEM data are valuable to catalog the presence of cells with different morphologies within the planula, the association with the transcriptomic profiles is somewhat speculative. These valuable anatomical data should be provided at a high enough resolution to zoom in and see the details, and further description could be provided. For example, the paper states that vacuolated cells are characteristic of the basal gastrodermal cells adjacent to the mesoglea; please identify the vacuoles in Figure 4f/h for the reader.

A novel method for reconstructing cluster similarity relationships is applied to grouping clusters into cell categories within the same life cycle stage, and also for matching cell types between stages. This is a valuable contribution to the field that is worthy of further evaluation. This is, however, difficult, as the methods for which DESeq2 was applied ("see code for details") are not present in the provided code, nor is it adequately described how the "binary matrix of marker gene presence/absence" was constructed. Similarly, there are additional details of other parts of the data analysis that are missing from the provided code, and the provided supplementary material is not referenced in the main document. More rigorous documentation of the methods is warranted.

The description of the transcriptomic profiles present in the planula is solid, and the attempt to associate these profiles with anatomic locations and putative morphology provides a foundation onto which further studies can be developed. Mapping of the planula ectoderm through to the polyp stage is also an important step forward in characterizing the life cycle, and the evidence for the retention of the oral/aboral ectodermal axis is convincing. The paper falls short in describing the updated medusa dataset and could benefit from a minor restructuring of the paper. Introducing the new medusa data only after the planula dataset is fully described would mediate the shallower treatment of the updated medusa dataset, where only 22 of the original 36 transcriptomic states are recovered. In this way, the focus will shift onto the cross-life cycle stage comparisons, and it could be argued that the lower resolution of the medusa dataset is justified in order to simplify the comparisons.

It will be essential that the datasets that are presented in this work be made available for public exploration in a fully annotated format. It is currently unclear how the authors intend to do this; however, there are many repositories available for this. The UCSC Cell Browser hosted at cells.ucsc.edu is one very good option if the authors do not wish to develop an interactive tool themselves. It is imperative that the gene annotations which correspond to the dataset, and the cluster annotations that are presented in this paper, are available and easily connected to the released dataset.

Reviewer #1 (Public review):

Summary:

This manuscript presents a genome-wide investigation of the genetic architecture underlying adaptation to prolonged starvation in Drosophila melanogaster, using an E&R experimental design maintained across 60 generations. Four starvation-selected (SS) and four matched control (C) populations were whole-genome resequenced, and two complementary analytical frameworks, selective sweep inference combined with low-heterozygosity mapping, and a diffusion-based drift-filtering approach, were applied to identify genomic regions under selection. As a result, the authors report (1) 62 high-confidence sweep-low-heterozygosity regions encompassing 255 genes, and (2) 3,578 SNPs with allele-frequency shifts exceeding neutral drift expectations shared across all four SS replicates, mapping to 578 genes. Mitochondrial pathways are identified as prominent targets, with a 13.9-fold enrichment of nuclear-encoded mitochondrial genes among candidates and differentiation at the mitochondrial origin of replication. Finally, the authors demonstrate that human orthologs of starvation-responsive fly genes are enriched for highly differentiated variants in four human populations from the 1000 Genomes Project.

Strengths:

(1) The experimental design with four evolution replicates provides proper control for false discovery.

(2) The phenotypic characterisation is thorough. The approximately 3-fold increase in starvation survival and 1.5-fold increase in TAG content provide a clear physiological basis for interpreting the genomic findings, and the observation of increased adult longevity adds a meaningful life-history dimension to the results.

(3) The mito-nuclear analysis is one of the more novel contributions of this paper. The implicated picture of coordinated mito-nuclear remodelling under sustained nutrient deprivation is compelling.

(4) The comparative analysis connecting fly selection candidates to human population differentiation is ambitious and adds evolutionary breadth to the study.

Weaknesses:

(1) Ne estimation is derived from controls only, not from selected populations

The entire drift-filtering framework rests on estimates of effective population size obtained from allele-frequency variance among the four control replicates (Ne = 530 for autosomes, Ne = 461 for the X chromosome). This is justified by assuming that divergence among control populations reflects neutral drift alone, a reasonable assumption for C populations maintained on standard food.

However, the starvation-selected populations experienced 75-80% mortality per generation as an explicit design feature of the selection regime. This severe, recurrent demographic bottleneck would substantially reduce the effective population size within SS lines relative to controls. The authors do not acknowledge this discrepancy, nor do they attempt to estimate Ne within SS replicates or assess the sensitivity of their drift thresholds to plausible reductions in Ne. If Ne in SS populations is appreciably lower than in controls, the drift thresholds derived from the control-based Ne will underestimate the amount of neutral drift occurring in SS lines. Consequently, some allele-frequency shifts that are driven by the repeated bottleneck could be misclassified as candidate loci, inflating the apparent number of selection targets. This is the most consequential methodological concern in the paper. The authors should either estimate Ne separately for SS populations, implement a sensitivity analysis varying Ne over a biologically plausible range, or, at a minimum, provide a thorough discussion of how downward bias in SS Ne would affect their results and conclusions.

(2) Lack of consideration about binomial sampling noise due to the pool size in the modeling

With only 100 individuals pooled per population, binomial sampling from the pool contributes a non-trivial additional source of variance to allele-frequency estimates, on top of genetic drift and sequencing error. This is a well-documented issue in Pool-seq data. Critically, the Kimura diffusion framework used for drift modeling does not appear to explicitly incorporate this binomial sampling noise component, an omission that could affect the calibration of drift thresholds, particularly for low-frequency alleles. The authors should discuss whether and how pool-size-induced sampling variance is accounted for in their drift model.

(3) No benchmarking against established Pool-seq analysis tools

The authors use Pool-HMM for sweep detection and a custom diffusion-based drift framework for allele-frequency analysis, with PoPoolation (v1) used only for Tajima's D calculations. However, the study does not benchmark its candidate SNP sets or sweep regions against well-established Pool-seq analysis frameworks such as PoPoolation2, which provides CMH tests and FST estimation specifically designed for replicated Pool-seq E&R data, or R/poolSeq, which implements drift-aware testing purpose-built for this experimental design. The authors should either benchmark their approach against at least one established alternative or provide explicit justification for why their custom framework is preferable and how it compares in sensitivity and specificity.

(4) Absence of negative controls in the human PBS comparative analysis

A critical missing element in this comparative analysis is a negative control: the authors do not test whether equivalent enrichment is observed in populations with no particular history of famine or nutritional stress, such as European or East Asian populations from the 1000 Genomes Project. The inclusion of at least one negative-control population triplet is necessary to support the cross-species interpretation as stated.

Reviewer #2 (Public review):

Summary:

The authors use an Evolve-and-Resequence approach in Drosophila to study the genomic basis of adaptation to long-term starvation. Replicated selection lines and control populations are sequenced and analyzed to identify signals of selection, which are then related to starvation-related phenotypes. The general experimental design is appropriate, and the combination of genomic and phenotypic data is a clear strength of the study.

Strengths:

The strongest aspect of the work is the experimental evolution framework combined with population genomic inference across replicate populations. The observed parallelism across replicates supports the robustness of at least a subset of the detected selection signals. However, several key methodological details are either unclear or insufficiently justified. In particular, both the maintenance of control populations and demographic assumptions are not fully described, and the treatment of structural variation (e.g., segregating inversions) is not sufficiently addressed. The phenotypic analyses are broadly appropriate and replicated but would benefit from access to raw data.

Weaknesses:

The human ortholog enrichment analysis is an interesting component of the study, but it should be interpreted more cautiously. As currently presented, it is based on correlational signals of differentiation and is therefore sensitive to potential confounding factors. While the analysis may point to intriguing patterns consistent with conserved genetic architecture, the evidence is not sufficient to support strong claims of conserved starvation/malnutrition-related polygenic adaptation in humans. Framing this component more explicitly as exploratory would strengthen the manuscript. In its current form, this analysis is somewhat less conclusive than the experimental evolution results in flies.

Overall, the study provides a useful dataset and a reasonably solid analysis of starvation adaptation in experimental Drosophila populations, but several methodological clarifications and a more balanced framing of the cross-species comparisons would strengthen the manuscript.

Reviewer #3 (Public review):

Summary:

This study tries to identify the genetic signatures of adaptation to starvation conditions. For this, outbred populations of Drosophila melanogaster were selected for starvation resistance by using the 20% surviving adults after starvation to start the next generation. This was done for 60 generations while parallel populations were kept under control conditions. At the end of the experiment, starvation-selected flies showed increased survival, longevity, and TGA storage. DNA poolseq data from control and starvation populations were compared to identify genomic regions with low heterozygosity and signatures of selective sweeps, and SNPs with differences in allele frequency. The candidate regions point to mitochondrial and metabolic pathways as the targets of selection for starvation resistance.

The authors replicate the experimental design, selection approach, data collection, and analyses from Hardy et al 2018 (https://doi.org/10.1093/molbev/msx254), which also investigated adaptation to starvation conditions but used a different Drosophila melanogaster population. In this sense, the current study recapitulates most of the findings from Hardy et al. (2018). The analyses of the mitochondrial results, including the overlap with human data, are the novelty of this paper. However, those analyses are not very well justified. The fact that this study is almost identical to Hardy et al is not clearly stated nor discussed in the manuscript.

Strengths:

The authors made use of an experimental evolution approach to identify the genetic basis underlying adaptation. This is a powerful approach that has proven very successful in the past. They used a good number of replicates (four per condition), an appropriate depth of sequencing, and quantified higher-order phenotypes to validate the claim that the populations had evolved increased starvation resistance.

Weaknesses :

Although the findings of this study seem credible based on the known biology of starvation resistance, there are several aspects of the experimental design that weaken my confidence in the results. The points below should be clarified, and the limitations of the experimental design and analyses need to be included in the discussion.

(1) Pooled genomic data were collected for the four replicates at the end of 60 generations of selection, and four replicates were kept under control conditions. No data were collected at the beginning of the experiment, which is the current standard in Evolve and Resequence experiments. To infer the genomic regions underlying adaptation to starvation, evolved control and starved cages are compared. Although this will identify regions that are possibly truly caused by adaptation to starvation stress, the available data doesn't allow to determine, for example: a) whether the differences between control and starvation regimes are due to changes in control cages relative to the starting population, combined with no changes in starvation cages relative to the starting population; b) whether the differences across replicates are due to different genomic composition at the start of the experiment that could have been amplified by drift.

(2) Selection was applied by starving flies until ~80% of the population died. The 20% surviving flies were used to seed the next generation. The control populations, on the other hand, were propagated using the whole population. Given that only the starvation populations were subject to such a strong bottleneck, it is not possible to disentangle whether the genomic signatures at the end of the experiment are due to this, and not necessarily to starvation resistance. For example, the low heterozygosity blocks and the very great changes in allele frequency could be a natural result of such a bottleneck. A proper comparison would have been to select a random 20% of the control individuals to seed every generation.

(3) The analyses that involve human populations are poorly justified, and the enrichment tests are not clearly explained. There is no evidence of signatures of selection for starvation resistance in human datasets (as mentioned in the text, line 112), and yet the authors claim that their analyses that identify branch-specific alleles for a set of four human populations serve as a dataset for it. I don't think the results of this analysis and further overlap with candidate genes identified in the Drosophila experiment support the conclusion that polygenic adaptation of metabolic pathways is conserved across species (line 303).

(4) The conclusion that adaptation to starvation conditions is repeatable is not justified by the data. The overlap across replicates is very low in every metric.

(5) The methods are poorly described. In most of the sections, there is not enough information to be able to replicate the experiments or the analyses. Several of the analyses presented in the results are not described in the methods. Without this information, it is very difficult to assess whether the analyses were correctly done or whether the results are robust.

Reviewer #1 (Public review):

Summary:

This is an interesting paper on an important topic, the taxonomic and conservation status of some unusual salmonid populations in Taiwan.

Strengths:

The first part of the manuscript is quite strong: the authors sequence and build a reference genome and conduct a phylogenomic analysis. They examine chromosome structure and rearrangements, test for loss-of-function mutations, and do a proteomic analysis. As a stand-alone, this could serve as its own manuscript, perhaps for a more specialized journal.

Weaknesses:

I find this manuscript rather disjointed. The first part of the manuscript is related to phylogenomics of the taxon in question, compared to other nearby species from Japan. The authors go on to describe chromosomal rearrangements, sex-chromosome location, and proteomics. All of these fit within a paper about taxon-level issues. I do find the proteomic analysis perhaps unnecessary. I'm not sure we learn much of substance through this analysis, which is highly speculative.

PSMC analysis seems highly questionable for taxa with such strong genetic structure. If historical Ne and past changes in structure are confounded, what does this analysis provide? I recommend deletion of the analysis included in Figure 1e.

The second portion of the manuscript deals with population structure of three O. formosanus populations, based on RADSeq data. This part reads as a separate manuscript, in my opinion. I think the authors are trying to squeeze too much into one manuscript.

For the second part on population genomics, not enough detail is provided to evaluate the methods, results, and interpretations. For example, not enough detail is provided about each of the three Taiwan populations, the stocking history, and the demographic data collection. The only information available is a brief paragraph in the introduction. Was the Luoyewei (L) population stocked from a brook derived from this population or from Qijiawan (Q)? Why do three L fish have such different levels of MLH? Are these stocked from somewhere else? Are the rest of the fish from one pool, and maybe one family (this would also explain the extremely low contemporary Ne)? Only 17 fish were examined from L, and apparently from one site in the stream; more detail is needed. Are L, Q, and H currently isolated? What is the stocking history? The authors conclude that the Hehuan (H) population has more genetic variation and is likely the result of an unknown native population that bred with stocked fish (which arise from Q). This story does align with the genetic results, but again, more detail is needed. Are there alternative explanations? A more careful treatment would be helpful.

The demographic modeling is not convincing. Not enough detail is provided, and the lack of individual identification of fish makes it so the modeling is very general. It is hard to place too much stock in these vital rate estimates. The methods were fishing, snorkeling, and some electrofishing. Scales were used for ageing, and catch curve analysis was employed. Overall, this is an underdeveloped portion of the paper that is important, but not convincing as written.

Reviewer #2 (Public review):

Summary:

Lee et al. is a comprehensive conservation genomics study that combines a chromosome-level genome assembly (sex-specific, too), population resequencing, coalescent species delimitation, and simulations to reassess the evolutionary status and conservation outlook of the Formosan landlocked salmon, Oncorhynchus formosanus. The authors showed a distinctive genome structure, replete with chromosome fusions and an unusual placement of the sex-determining gene sdY. Across sampling sites, they observed variable levels of genetic diversity, but in a way that was surprising given previous census numbers and conservation history. In particular, the authors report a previously unrecognised native population in Hehuan Creek, and conclude that Hehuan is more resilient to typhoon disturbance than the long-protected Qijiawan population - motivating stream-specific rather than range-wide conservation.

Overall, this is a well-written paper that combines a number of elements that are timely and relevant. It uses state-of-the-art techniques to reach its conclusions and is generally performed to a high standard. It describes a critically endangered species that poses its unique conservation challenges. There are a number of things to like, as well as some substantial shortcomings in this paper.

Strengths:

The genomic resource is excellent. The assembly is well validated (97.3% anchored to 25 scaffolds, 95.7% BUSCO), and the authors generated a separate male assembly specifically to resolve the sex-determining region, allowing XY-shared and Y-specific contigs to be distinguished on coverage rather than inference. This is truly well done, and at a high standard. The synteny evidence for telomere-to-telomere fusions involving at least 14 ancestral chromosomes, against two in O. m. masou, is convincing.

The Hehuan Creek result is the paper's most valuable contribution. Elevated heterozygosity, short and infrequent runs of homozygosity, and private alleles absent from the Qijiawan broodstock are difficult to reconcile with a purely reintroduced origin. The contrast with Luoyewei is a clean and useful cautionary case for hatchery supplementation.

Weaknesses:

(1) The species-rank claim is featured in the abstract, but it is made with any level of rigour in the paper. "New species" appears once, in the abstract (l. 32). The Results conclude only that O. formosanus is a distinct evolutionarily significant unit (ll. 188-191), which itself can be well-justified, but it's far from a taxonomic rank (see author's own ref 10). No species concept is explicitly named anywhere, and the taxon is referred to across the manuscript as a subspecies (l. 68), a "new species" (l. 32), and an ESU (l. 189) in turn.

(2) Gene flow is asserted, not tested, and two divergence estimates disagree twentyfold. The abstract reports "no detectable gene flow for ~50,000 years." That figure is a divergence time from BPP under the A00 model, which contains no migration parameter; a model that cannot fit gene flow cannot report its absence. Separately, Figure 1b shows a split at 1.15-5.09 Mya (Figure 1b), while the ddRAD coalescent places the same split at ~50 kya (Figure 1d). The explanation offered (ll. 417-421, "differing temporal sensitivity of genomic markers") is not a mechanism.

(3) The placement of sdY is unresolved, and the paper's own figures conflict with its text. Figure S10 and Table S6 both make O. formosanus chr13 homologous to O. m. masou chr32, whereas reference 28 - on which the authors rely - places the sdY contig on O. m. masou chr7, whose O. formosanus homologue is chr5 (Table S6). These cannot both be correct, and Figure S10's caption compounds the confusion by attributing chr13 to masou and omitting the chr32 track entirely.

(4) The population-viability model's stated mechanisms are contradicted by the authors' own supplementary tables. The Discussion attributes Qijiawan's vulnerability to "lower juvenile survival, decreased fecundity, and narrower terminal age class representation" (ll. 522-525). Table S10 gives Qijiawan higher age-0 survival (0.202/0.616 vs 0.184/0.615); Table S11 gives it higher fecundity at every reproductive age (7.68/19.27/7.86 vs 6.10/8.95/4.14); Table S9 gives it a broader terminal age class (5.0% vs 0.8% age-3 in November). The only parameter favouring Hehuan is age-1 survival - 0.087 (95% CI 0.000-0.180) versus 0.131 (0.093-0.187) under typhoon, and 0.054 (0.000-0.167) versus 0.087 (0.047-0.149) at baseline. Both Qijiawan intervals include zero and overlap Hehuan's, yet a reported extinction odds ratio of 4.48 rests on this difference.

(5) The two streams were not measured equivalently, and every asymmetry favours the conclusion.

Reviewer #1 (Public review):

The manuscript by Fisher et al describes the molecular mechanism underlying how G beta gamma subunits engage with the beta 3 isoform of PLC. The paper used a combination of cryo EM, BRET assays, and biochemical assays of PLC beta activity. A key discovery is that G beta gamma is not sufficient to drive membrane binding by itself and instead promotes G alpha activation. The work is important, but suffers slightly from some ambiguity in the actual interface that is present in their cryo EM model, as crosslinkers could stabilise a transient and non-native complex. This is somewhat abrogated by the careful mutational analysis, which shows that mutation of any of these three sites does somewhat block PLC beta G beta gamma activation. However, there could be some improvement in the presentation of this data, as well as possible mutant selection. Overall, this paper is a nice complement to the Falzone et al paper showing the membrane bound complex of PLCB3 on membranes, with this work building on this work, highlighting the importance this will have in our full understanding of PLC beta activation.

Major concerns

My most major concern is the potential that this interface is artefactual based on the crosslinking strategy utilised. Here are thoughts on how this could be better validated, presented in a more convincing way.

(1) The authors main claim is that there is a degree of plasticity of G beta gamma binding to the PLC beta 3 isoform, with three possible binding sites. The main complication of this is of course the possibility that the crosslinking stabilises a non-native complex, driven by a mutated cysteine.

Because of this any other additional details about this interface are going to be critical for the scientific audience to judge if this is accurate.

What would greatly help figure 1, is an evolutionarily conservation analysis of the novel Gbg interface in PLC, to see how well this is conserved, and compare this to the conservation of the previously annotated sites. Conservation of these sites on both the G beta gamma and PLC side would help justify this as a native complex.

This also will help orient the reader to the identity of the mutated residues assayed in figure 3.

(2) The g beta gamma orientation is also different than what I have observed in previous g beta gamma effector structures. Is there any precedent for this as an effector interface? A supplemental figure comparing this structure to other g beta gamma interfaces from other enzymes, for example recent tesmer structure with PI3K.

(3) The mutational analysis in Figure 2D-G seems to give some strange results, and I have some question why certain residues were chosen rather than others. Mutation of the Gbg side will be more complicated as of course that can effect any of the three surfaces. My main question is that from the way fig 2A is oriented that the main salt bridge in their novel interface to me looks like R199-D228, with K183 being in the wrong orientation to E226, and D167 being far from any charged residues. Why did the authors not make the corresponding R199 to D or E mutation?

(4) To help reader interpretation of Figure 2A, I would recommend a supplemental figure showing the density for interfacial residues, as that also would increase confidence in the interface.

Comment on revised version.

After revision the authors have addressed all of my concerns.

Reviewer #2 (Public review):

In this manuscript, the authors dissect how Gβγ potentiates PLCβ3 signaling in cells. Using engineered crosslinking to stabilize a Gβγ-PLCβ3 complex, single particle cryo-EM, and cell-based functional assays, they identify map multiple putative Gβγ interaction surfaces on PLCβ3, including a previously unrecognized binding mode. Structure-guided mutagenesis supports the functional relevance of these interactions and suggests that Gβγ potentiation is not primarily mediated by PLCβ3 membrane recruitment, but instead enhances PLCβ3 activity after the lipase is already at the membrane.

Previous reconstitution work on membrane surface (Falzone & MacKinnon, 2023) proposed a recruitment/partitioning-centric model in which Gβγ increases PLCβ3 output largely by elevating its membrane surface concentration, whereas Gαq primarily increases catalytic turnover; under those reconstitution conditions, the two inputs can combine approximately multiplicatively. In receptor-driven cellular signaling, however, PLCβ3 is robustly recruited to the plasma membrane upon Gαq activation, which raises the question of whether Gβγ contributes mainly through additional recruitment or through a post-recruitment mechanism once PLCβ3 is already at the membrane.

This manuscript helps address that gap by using membrane-anchored PLCβ3 and complementary cellular readouts to separate "getting PLCβ3 to the membrane" from "boosting activity once PLCβ3 is already there." Their results argue that, in cells, membrane recruitment is largely dominated by Gαq·GTP, while Gβγ can further potentiate PIP2 hydrolysis after membrane association, consistent with a modulatory role at the membrane rather than primary recruitment.

Overall, the work provides a structural and mechanistic framework for Gβγ-PLCβ3 cooperation and helps clarify the basis of Gq pathway amplification.

Comments on revised version.

The authors have reasonably addressed my comments.

Reviewer #3 (Public review):

Summary:

PLCβ3 is activated by both Gαq and Gβγ subunits. This paper follows previous solution and cryoEM studies of the PLCβ3 / Gβγ complex to delineate the molecular details of activation using cellular BRET assays and cryoEM.

Strengths:

The authors find evidence for multiple binding sites on PLCβ3 for Gβγ and suggest that Gβγ is not bone fide activator per se but enhances Gαq activation by positioning the catalytic site towards substrate. The authors also find that this activation is not through recruitment of the enzyme to the membrane by Gβγ released upon G protein activation in accord with other PLCβ enzymes.

Weaknesses:

(1) The main issue is that the author's mechanism does not fully explain how Gβγ activation occurs for PLCβ2 in reconstituted systems in the absence of Gαq subunits but will be investigating this in future studies.

Reviewer #1 (Public review):

Summary:

This study reports a novel and potentially impactful role for NINJ2 in maintaining lysosomal integrity and regulating cellular susceptibility to ferroptosis. The authors demonstrate that NINJ2 localizes to lysosomes and interacts with LAMP1, a key lysosomal membrane glycoprotein involved in sensing lysosomal stress. Loss of NINJ2 increases lysosomal membrane permeabilization (LMP), resulting in selective leakage of lysosomal contents, including labile iron, into the cytosol. The authors further show that NINJ2 deficiency reduces the expression of ferritin storage proteins, thereby sensitizing cells to ferroptosis induced by RSL3 and erastin. Collectively, the work proposes a mechanistic link between NINJ2-mediated control of LMP, iron homeostasis, and ferroptotic vulnerability, with potential relevance to cancer biology.

Strengths:

This study identifies a novel role for NINJ2 in regulating lysosomal integrity and ferroptosis and establishes a mechanistic link between lysosomal membrane permeabilization, iron homeostasis, and ferroptotic sensitivity, with potential translational relevance in cancer.

Weaknesses:

The results overall support the authors' conclusions and provide a plausible mechanistic framework; however, additional quantification of western blot data and further discussion of mechanistic questions would strengthen the study.

The findings are likely to have broad impact by linking lysosomal integrity to ferroptosis and iron homeostasis, both of which are relevant to cancer biology and therapeutic targeting.

Comments on revised version.

The authors have addressed all of my comments and questions. I have no further concerns.

Reviewer #2 (Public review):

This manuscript, "Nerve Injury-Induced Protein 2 preserves lysosomal membrane integrity to suppress ferroptosis", identifies a previously unrecognized function of NINJ2 as a regulator of lysosomal membrane integrity and iron homeostasis, thereby suppressing ferroptosis. The authors demonstrate that NINJ2 localizes to lysosomes, interacts with LAMP1, limits lysosomal membrane permeabilization (LMP), stabilizes ferritin, and protects cells from ferroptotic cell death. They further extend these mechanistic findings to human cancer datasets, showing co overexpression and positive correlation of NINJ2 with ferritin genes in iron addicted cancers.

Overall, the study is conceptually interesting, technically solid, and integrates cell biology, iron metabolism, and ferroptosis in a coherent framework. The work expands the functional repertoire of the Ninjurin family beyond plasma membrane rupture and inflammation, which will be of interest to researchers in cell death, lysosome biology, and cancer metabolism.

Strengths:

(1) The identification of NINJ2 as a lysosome-associated protein that suppresses ferroptosis represents a meaningful advance beyond its previously described roles in inflammation, pyroptosis, and tumorigenesis.

(2) The work distinguishes NINJ2 functionally from NINJ1, reinforcing the idea that structurally related Ninjurins have divergent membrane-related roles.

(3) The study presents a logically connected pathway:<br /> NINJ2 loss → LMP → labile iron increase → ferritin degradation → ferroptosis sensitization, which is well supported by the data.

(4) The link between LAMP1, ferritin turnover, and ferroptosis is particularly compelling and timely given recent interest in lysosomal contributions to ferroptotic signaling.

(5) The authors use confocal microscopy, proximity ligation assays, biochemical IPs, iron measurements, protein half-life analyses, ferroptosis assays, and TCGA-based analyses, providing convergent evidence for their model.

(6) Use of two distinct cell lines (MCF7 and Molt4) strengthens generalizability.

(7) The integration of cancer expression datasets linking NINJ2 with ferritin expression in hepatocellular and breast carcinomas enhances translational relevance.

(8) Assigning NINJ2 a lysosomal protective function, distinct from NINJ1-mediated plasma membrane rupture is novel.

(9) Linking NINJ2 to ferroptosis regulation via lysosomal iron handling, rather than canonical GPX4 or system Xc⁻ pathways is also novel, along with proposing a NINJ2-LAMP1-ferritin axis as a buffering mechanism against iron-driven lipid peroxidation.

(10) These insights are not incremental; they reframe how NINJ2 may function at the intersection of membrane biology, iron metabolism, and regulated cell death.

Reviewer #1 (Public review):

Summary:

The authors investigated the function of a Drosophila chemosensory receptor, IR20a, using genetics, neuronal histology, calcium imaging (in vivo and in cultured cells), and behavioral approaches. They provide evidence that this receptor functions in the detection of the amino acid arginine and of low salt (NaCl) concentrations, functioning in different combinations with "co-receptor" IRs, IR25a and IR76b.

Strengths:

The experiments are generally very well-performed and clearly presented, using established methodology. While, unsurprisingly, some puzzles remain (mentioned below), the work provides one of the clearest lines of evidence for the combinatorial coding of sensory information at the periphery through the combined action of distinct sets of chemosensory IRs.

As taste neurons have long been recognized to express many different combinations of IRs and Gustatory Receptors (GRs), this study will be of interest to chemosensory biologists in general, particularly those studying invertebrate model systems (though co-expression of different families of taste receptors is a feature of mammalian taste cells).

The precise molecular mechanisms remain unclear: there is no direct evidence here for protein complex formation (though this is likely), the stoichiometry of such complexes, or how subunits interact to confer or suppress sensory sensitivity. Nevertheless, these receptors, and the authors' success in reconstituting functionality in cultured cells, might make these a powerful model to explore such questions in the future.

Weaknesses:

Given the particular interest of the data from the heterologous reconstitution in cultured cells, the authors should be quite explicit about the nature of the quantification of the S2 cell responses. It is unclear whether the cited "n" refers to numbers of cells or something else, and whether all or only a fraction of (transfected) cells gave responses.

There has been some prior work on the context-specific role of IR76b in amino acid-sensing and salt sensing by Ganguly and colleagues (Cell Reports 2017), who also implicated (weakly) a contribution of IR20a in contributing to the amino acid-sensing role. In that work, the authors focussed principally on the labellum and used electrophysiology rather than calcium imaging. The present manuscript appears rather dismissive of the earlier results (only mentioning them in the Discussion), and the authors could be a bit more generous about what was previously determined, where they have confirmed previous findings, where their results diverge, and why this might be. Similarly, the original functional analysis of IR76b (Zhang Science 2013) argued this was a low-salt sensor by itself, which is at least partially corroborated here; it remains unclear how this role relates to the low-salt detecting function of a potential complex of IR20a/IR25a/IR76b. It would be useful to have a summary model of the possible variety of complexes of IRs in different types of sensory neurons, as supported by the results in this and previous studies.

The discord between the lack of requirement for IR20a for physiological responses to arginine in tarsi versus the necessity for behavioral responses is puzzling (though might reflect a labellar role for IR20a). There appears to be a trend of a decrease in calcium signal in tarsi to 100 mM arginine, which is the highest concentration tested (Figure 2A, C). Would a statistically significant decrease be observed with lower arginine concentrations? (A more substantial experiment would be to perform calcium imaging in the labellar IR20a neurons, or their axonal projections in the SEZ; this is not necessary, but the authors should at least acknowledge that their imaging of tarsal responses, while convenient, only examines a tiny fraction of the entire IR20a neuron population.

The authors argue for synergistic responses to arginine and NaCl mediated by IR20a/IR25a. It's not clear to me to what extent there is synergism. In Figure 5A, 10 mM arginine or 10 mM NaCl individually lead to c.30-40% PER, and then when both are presented together in the "Mix" (presumably both compounds at 10 mM?), PER rises to c.60%. Is this really synergism, or rather simple additivity of behavioral responses to two attractive compounds? The authors could discuss this more thoroughly. Similarly, in Figure 5G the authors show that 50 mM arginine does not evoke a significant response in S2 cells expressing IR25a/IR20a, but in Figure 4 it would seem likely that a 50 mM dose would produce a significant response (the response to 25 mM arginine in Figure 4F is already elevated above the control, albeit not statistically significant). Is this just a batch effect of the experiments performed at different times (so they are not directly comparable)?

The legend title to Figure 6 implies cooperation between tonic and state-modulated pathways, but I don't see specific evidence for "cooperation". Rather, as in the results text, they seem to work in parallel, so this analysis seems slightly peripheral to the main focus of the manuscript. It's ultimately unclear how the IR20a/IR76b/IR25a low salt sensor and the sensor containing IR56b functionally interact at the behavioral level. Here, a graphical summary, as mentioned above, of the different salt sensing neurons, the receptors they use, and the behaviors they control could be useful to establish the current knowledge and highlight open questions for the future.

Reviewer #2 (Public review):

Summary:

This study identifies IR20a-expressing gustatory neurons in Drosophila as a multimodal sensory population integrating amino acid (arginine) and low-salt signals through combinatorial IR20a/IR25a/IR76b receptor assemblies. The proposed model of peripheral-level signal integration and synergistic enhancement of feeding preference is potentially significant, as it expands current understanding of gustatory coding beyond single-modality labeled lines.

Strengths:

Overall, the findings are conceptually interesting and suggest a novel framework for multimodal taste integration, but some mechanistic interpretations remain incompletely supported by direct evidence.

Weaknesses:

(1) Although the authors demonstrate co-expression of IR20a, IR25a, and IR76b in the same GRN population, this evidence is insufficient to support the proposed model of distinct receptors coexisting within individual neurons. Additional molecular or structural data would be required to distinguish whether these subunits assemble into complexes.

(2) Given that IR76b has already been established as a sodium/salt sensing channel, the novelty of this study relies on the proposed role of IR20a in conferring multimodal integration and synergy. However, it remains unclear whether this represents a fundamentally new sensory mechanism or a re-interpretation of known IR76b-dependent salt responses in a different neuronal context.

(3) Line 127:<br /> -The statement that there is no overlap between IR20a-GAL4 and GR64f-LexA or GR66a-LexA is not sufficiently supported by the presented imaging data. In particular, the resolution and clarity of the confocal images in Figure 1 appear suboptimal, making it difficult to confidently assess co-localization. The authors are encouraged to provide higher-resolution images or additional quantitative co-localization analysis to substantiate this conclusion.<br /> -In addition, the images shown in Figure 1 F1-F2 suggest possible partial overlap between IR20a and GR66a signals, which appears inconsistent with the authors' statement of no co-expression. This discrepancy should be clarified.

(4) Lines 138-141:<br /> There appears to be a discrepancy between imaging and behavioral data: IR20a is reported as dispensable for arginine-evoked neural responses, yet IR20a mutants show significantly reduced attraction to arginine in behavioral assays. The authors should clarify how behavioral deficits arise in the absence of detectable changes in calcium imaging,

(5) The manuscript proposes that IR20a functions in combination with IR25a to mediate multimodal detection of arginine and low NaCl. However, the specific role of IR25a in this context remains unclear.

(6) The authors report that co-expression of IR20a and IR25a confers synergistic responses to combined arginine and NaCl stimulation, whereas the inclusion of IR76b abolishes this response (Figures 5E-K). This is an intriguing and potentially important finding; however, the mechanistic basis for this suppression is not clearly explained.

(7) The authors propose that IR56b mediates state-dependent modulation of low-salt preference. However, the current data do not clearly distinguish whether IR56b acts as a real nutrient state sensor or just functions as a downstream modulatory component within a broader feeding circuit. Additional evidence linking IR56b activity changes to upstream metabolic state signals would be necessary to support the interpretation that IR56b functions as a primary state sensor.

(8) The manuscript suggests that IR20a and IR56b define two parallel and functionally independent pathways mediating nutrient detection and state-dependent preference, respectively. However, this conclusion is not fully supported by the current dataset. While the two receptors are shown to be expressed in distinct neuronal populations, the possibility of indirect interactions or convergence at downstream circuit nodes has not been excluded. Given that both pathways ultimately influence feeding behavior, it remains possible that they converge at higher-order interneurons or shared neuromodulatory circuits.

(9) In the state-dependent feeding assays (Figure 6), using H2O as a control introduces a severe masking effect. Salt-deprived flies actively suppress pure water intake to avoid osmotic shock, which artificially inflates the Preference Index (P.I.) for salt due to the denominator effect. To cleanly isolate salt preference from the thirst/osmotic drive, the authors will need to utilize an "isosmotic sucrose vs. isosmotic sucrose + salt" paradigm (Jaeger et al., 2018, eLife; Puri et al., 2026, PNAS).

Reviewer #3 (Public review):

Summary:

Drosophila, like other animals, use sophisticated taste systems with specialized chemoreceptors to identify gustatory cues in their environment. Multiple gustatory cues associated with a food source are often encountered simultaneously, but our understanding of how this sensory information is detected and integrated remains incompletely understood. This valuable study investigates how salt, amino acids, or their combination are detected by specific combinations of peripheral Ionotropic Receptors, leading to behavioral attraction. The authors show that distinct combinations of IR76b, IR25a, and IR20a confer sensitivity to salt, arginine, or both. They also show striking evidence that cells co-expressing IR25a/IR20a display a synergistic response to a mixture of sub-activating concentrations of these tastants. Together, these experiments lead to the conclusion that combinatorial expression of different subunits and synergistic responses to taste mixtures facilitates integration of taste cues beginning in the periphery. However, in its current form, key methodological details are missing or inadequately described, which complicates interpretation. Additionally, characterization is heavily focused on the population of IR20a+ neurons in the tarsi, while the response properties of the newly-identified, functionally distinct population in the labellum are investigated only through behavioral analysis, limiting the description of potentially additional IR20a complexes. Ultimately, more in-depth biochemical characterization of the IR complexes described will be required to fully support the conclusion that combinatorial assembly of distinct IR20a receptors enables peripheral integration of taste mixtures.

Strengths:

The authors characterize the expression pattern of IR20a in the tarsi as well as in the labellum, a tissue for which IR20a expression has been a point of debate. Multiple levels of analysis, including behavioral assays, physiological recordings, as well as ectopic and heterologous expression systems, are used to characterize the response properties of different combinations of IR subunits, demonstrating remarkably consistent behavior of the IR-complexes across cell types. Well-controlled genetic analysis and the use of multiple behavioral assays provide additional support for their results, including the surprising demonstration of synergistic responses to mixtures of tastants that supports the idea of peripheral integration of gustatory inputs. This report also identifies a distinct IR, IR56b, required for starvation-enhanced responses to salt.

Weaknesses:

(1) The title states that IR20a integrates L-arginine and salt signals via distinct subunit assemblies, though the paper lacks direct evidence that IR20a serves as a multimodal tuning receptor in distinct functional assemblies. Heterologous expression shows that co-expression of IR20a/IR25a confers sensitivity to Arg, IR76b confers sensitivity to NaCl, and IR20a/IR25a/IR76b co-expression confers sensitivity to both Arg and NaCl. This seems to be interpreted to mean that all three subunits are assembling into a single complex. However, current results do not show any difference in salt response when IR76b is expressed alone compared to alongside IR25a+IR20a. Without more direct evidence for co-assembly of all three subunits, it is equally plausible that the responses observed represent activity of distinct IR25a/IR20a and IR76b receptors for Arg and salt, respectively. In this model, genetic disruption resulting in expression of either IR25a or IR20a alone with IR76b could disrupt its activity or membrane trafficking (as seen here and in previous studies) while co-expression of both IR20a and IR25a relieves this inhibition by sequestering IR20a/IR25a into a distinct complex from IR76b. Direct biochemical characterization, for instance in the form of co-immunoprecipitation or FRET, will be required to differentiate between these possibilities.

(2) Key methodological details are missing throughout the manuscript. For instance, incomplete genotype and staining information is provided for images in Figure 1, making it difficult to interpret what is being shown. Additionally, for the calcium imaging methods, what is the imaging speed? How are max values calculated (is this the average of several images or just a single maximum)? How are ligands diluted and delivered to cells, and were they applied in a manner that allowed for subsequent washout?

(3) The composition of the S2 imaging bath buffer requires clarification. As described, the bath buffer appears to lack any Ca2+ or other IR-permeable cations. If this is indeed the case, more detail should be provided about why this bath buffer was selected and what this means for the source and mechanism of calcium responses observed, since it would not reflect direct IR-mediated transduction. It is also notable that addition of water gives such a detectable change in the tarsal preps.

(4) Visualization of IR20a driver activity in the labellum is interesting. Previous descriptions of labellar expression of IR20a range from no expression to expression in bitter neurons, so the current data linking IR20a to a different population of IR76b+ neurons warrants careful analysis in light of this discrepancy. However, some of the strongest presented evidence for expression is found in Figure 1, where the images are quite small, making it difficult to distinguish the morphology and sensillar innervation pattern of the cells labeled by the IR20a driver. In Figure 1A, several of the arrows do not appear to be associated with any visible fluorescence. It is similarly difficult to assess overlap. Including higher-resolution images and/or validating labellar expression, using antibodies, in situ hybridization, RT-PCR, or transcriptomics would strengthen these claims.

(5) Similarly, Figure 2 shows that IR20a is not required for Ca2+ responses to AAs or KCl in the legs, but is required for behavioral preferences and PER responses in the labellum. This suggests that IR20a receptors may function differently in different tissues, though direct evidence is lacking. Calcium imaging from a weakly expressed driver may be difficult, but electrophysiological recordings from relevant labellar sensilla or ectopic/heterologous reconstitution of the molecular receptors found there would give important insights into the response properties of these other IR20a receptor type(s) and could provide evidence for additional IR20a-containing complexes. The current paper focuses exclusively on IR25a/IR20a/IR76b, which do seem to reliably reproduce the Arg/NaCl responses observed in the tarsi, but even for the tarsal neurons it is unclear that this represents an exhaustive list of all the relevant IR20a-interacting subunits coexpressed in these cells. For instance, Koh et al., 2014 (PMID: 25123314) found several additional IR driver lines, including IR56b, were active in the 5v/s tarsal sensilla.

Reviewer #1 (Public review):

Summary:

This Perspective proposes a conceptual model in which incomplete age-related lobular involution (ARLI) in the breast reflects an actively maintained senescent-immune "reserve niche," rather than simply passive failure of lobular regression after menopause. The authors aim to integrate breast cancer epidemiology, mammary gland biology, cellular senescence, immune surveillance, and comparative reserve-tissue systems to explain why persistent postmenopausal lobules are associated with increased breast cancer risk. The manuscript is ambitious, creative, and potentially useful in shifting attention from residual epithelial quantity alone toward the microenvironmental state of persistent lobules.

Strengths:

A major strength of the manuscript is its forward-looking synthesis. The authors bring together several areas that are often considered separately: ARLI as a tissue-level risk marker, inflammatory features of incompletely involuted breast tissue, senescence biology, macrophage-mediated remodeling, and the menopausal transition as a potential window of biological plasticity. The model is conceptually interesting and, if supported by future evidence, could stimulate new approaches to risk stratification and prevention focused on the perimenopausal period.

Weaknesses:

However, the current manuscript often presents the proposed model with more certainty than the available evidence supports. The evidence clearly supports associations among incomplete ARLI, inflammatory or immune features, and breast cancer risk, but it does not yet demonstrate that senescent cells maintain persistent lobules, that immune clearance failure causes incomplete involution, or that a self-sustaining senescent-immune "niche lock" exists in human breast tissue. Much of the mechanistic framework is extrapolated from other tissues, postpartum involution, or general senescence biology. These are reasonable sources for hypothesis generation, but the manuscript would be stronger if it more clearly distinguished established observations from inference and speculation.

The senescence component of the model requires stronger and more direct support. Several claims about senescent burden in the aging breast appear to rely on general senescence literature or mammary aging studies that do not directly demonstrate senescence in persistent human TDLUs. This distinction is important because the manuscript's central model depends on senescent cells being spatially and functionally linked to incomplete ARLI.

The epidemiologic evidence also requires a more balanced treatment. Although several studies support incomplete ARLI as a breast cancer risk-associated phenotype, other cohorts and quantitative approaches have reported attenuated or null associations. This mixed evidence is acknowledged, but it is treated largely as a caveat rather than incorporated into the central argument. For readers, this uncertainty is important for interpreting the strength and generalizability of the proposed model.

Reviewer #2 (Public review):

Summary:

This review constructs a novel theoretical framework to elucidate incomplete postmenopausal age-related lobular involution (ARLI) in the breast. Differing from the conventional view of persistent lobules as passive residual structures, the work innovatively defines them as an actively maintained senescence-immune reserve niche. It comprehensively integrates multidisciplinary evidence from breast epidemiology, stromal biology, cellular senescence and immune surveillance, as well as cross-tissue research findings, and identifies menopause as a core biological turning point regulating ARLI and relevant breast cancer risk, providing a new theoretical perspective for subsequent breast cancer risk assessment and preventive intervention research.

Strengths:

This study presents an original, logically rigorous, and well-organized research hypothesis. It innovatively breaks through the traditional cognitive perspective of ARLI and adopts a multidisciplinary and cross-tissue analytical approach to sort out relevant biological mechanisms systematically. The proposed theoretical framework is insightful, with good theoretical innovation and potential translational value for guiding breast cancer risk evaluation and targeted prevention strategies.

Weaknesses:

The manuscript currently serves primarily as a conceptual framework rather than a rigorously evidenced synthesis. Its central argument relies heavily on cross-sectional correlations and theoretical analogies to other organ systems, lacking operational definitions for the reserve state in human breast tissue.

Reviewer #3 (Public review):

Summary:

Here the authors investigate the role of the Trypanosoma brucei polo-like kinase TbPLK in the function of flagellum-associated cellular structures in trypanosomes. They set out to test the hypothesis that a key substrate of TbPLK is the kinesin protein KIN-G, and that TbPLK phosphorylation of KIN-G regulates its functions in cells.

Strengths:

Using in vitro biochemistry with purified proteins, the authors convincingly demonstrate that TbPLK phosphorylates KIN-G at 29 sites. Moreover, they convincingly show that phosphorylation at one site, T301, impairs the binding of purified KIN-G to purified microtubules. They further confirm that inhibition of TbPLK in cells reduces KIN-G phosphorylation at T301 (and S569). Using immunofluorescence-based imaging approaches, they also show that TbPLK colocalizes with KIN-G at centrin arms during early S-phase of the cell cycle. Centin arms are structures that are located near the basal body and flagellum and are important for new flagellum biogenesis, Golgi positioning, and cell division. To evaluate the function of KIN-G phosphorylation in cells, they depleted KIN-G by RNAi, simultaneously expressed phospho-mimetic (T301D) and phospho-ablative mutant proteins, and used immunofluorescene to examine the impact on flagellum-associated cellular structures. They show that expression of the phospho-mimetic mutant KIN-G-T301D causes the following defects: reduced cell proliferation, disruption of centrin arm and Golgi biogenesis, impairment of FAZ elongation and flagellum positioning, and misplacement of the cell division plane. The data convincingly support the conclusion that KIN-G phosphorylation on T301 plays an important role in regulating the cellular functions of this kinesin motor protein.

Reviewer #2 (Public review):

Summary:

The authors identify KIN-G as an in vitro substrate for phosphorylation by TbPLK and show that several of the in vitro P-ated sites, including T310, overlap with P-ation sites seen in live cells. The authors further show that PLK-mediated P-ation inhibits KIN-G binding to microtubules in vitro, as does a KIN-G-T301D mutant, and that expression of a KIN-G-T301D Phospho-mimic in T. brucei phenocopies KIN-G RNAi knockdowns, producing defects in cell division, morphogenesis of the centrin arm, FAZ and other cellular structures, as well as misplaced cytokinesis furrow.

Understanding cytoskeletal rearrangements that drive cell division in T. brucei is an important and unresolved problem, so the work addresses important questions that are of great interest. PLK and KIN-G have previously been shown to be important for cell division and morphogenesis of cytoskeletal structures that drive cell division in T. brucei. The current work advances our understanding by suggesting a potential mechanism by which PLK and KIN-G might participate, namely through PLK-dependent P-ation to control KIN-G MT binding activity.

Strengths:

The authors use a rigorous combination of biochemistry, phosphoproteomics, cell biology, and mutant analysis to support their conclusion that PLK-mediated P-ation of KIN-G negatively regulates KIN-G microtubule binding and this may explain the observation that a KIN-G T301 phosphomimic mutant blocks cell division and perturbs biogenesis of cytoskeletal structures that drive cell division and morphogenesis. Combining rigorous and informative in vitro studies with mutant analysis in live cells is a great strength. The work is solid and important, though a few pieces are needed to fully connect the in vitro findings with the in vivo observations.

Reviewer #1 (Public review):

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

I think the main points raised in the review have now been addressed. In particular, the new experiment with TbPLK inhibition and mass spectrometry is an important addition, as it provides direct evidence that phosphorylation of KIN-G at Thr301 and Ser569 depends on TbPLK activity in cells.

I also appreciate that the authors have toned down the interpretation of the Golgi phenotype. The revised text now makes clear that the fluorescence data show altered Golgi/ERES organization or duplication, but do not prove a structural defect in Golgi biogenesis.

The added discussion of the T301A result is also helpful. The finding that only a small fraction of KIN-G is phosphorylated at Thr301 in asynchronous cells makes the lack of a strong T301A phenotype more understandable.

Overall, I am happy with the revision of the beautiful manuscript.

Reviewer #1 (Public review):

Summary:

In this study, the authors identified transcription factor combinations capable of inducing retinal neuronal programs in cultured fetal human retinal pigment epithelial (RPE) cells. Using a pooled screening strategy, single-cell RNA sequencing, lineage barcoding, and immunohistochemical analyses, they identified ASCL1 and NEUROD1 as an effective combination for inducing retinal neuron-associated transcriptional states. This work aims to advance the development of therapeutic approaches for retinal regeneration by exploring the plasticity of RPE cells.

Strengths:

A major strength of the study is the comprehensive experimental design. The combination of transcription factor screening, lineage tracing, single-cell transcriptomics, and molecular validation provides a detailed characterization of the cellular responses to reprogramming factor expression.

Weaknesses:

All experiments were performed using fetal human RPE cells. Because fetal RPE remains relatively immature and retains proliferative capacity, it remains unclear to what extent the observed responses reflect true reprogramming of differentiated RPE cells versus activation of developmental plasticity already present in fetal tissue. The absence of adult human RPE controls limits assessment of the generality and translational relevance of the findings.

Reviewer #2 (Public review):

Summary:

This is an interesting study that explores how human RPE could be used as a source for new retinal neurons. This is a welcome addition to the field of retinal regeneration, which is currently focused almost exclusively on the regenerative capacity of Müller glia cells. The line of inquiry is firmly rooted in findings from amphibian and embryonic chick model systems and advances a fetal human retina RPE-based screening system as a rich resource for insights into human RPE biology, including as a potential stem cell source.

The authors investigate the potential of fetal human RPE cells to be reprogrammed into retinal neurons using overexpression of pro-neural factors. While this is a critical knowledge gap in the field of retinal regeneration with significant promise for developing regenerative therapies, several methodological concerns impact the interpretation of results. Firstly, while the authors sought to evaluate factors that enhance RPE reprogramming when co-expressed with ASCL1, nearly all co-expression constructs tested failed to achieve appreciable expression of ASCL1, leaving a central hypothesis of this study largely untested (Major concern 1). Second, although the authors were able to detect a cluster of photoreceptor-like cells in their screen, they were unable to identify which reprogramming construct generated this cluster (Major concern 2). Finally, an essential control that definitively demonstrates the value of combinatorial transcription factor reprogramming is missing (Major concern 3).

In summary, the authors establish a valuable new paradigm for culturing and reprogramming fetal human RPE, and even more importantly, demonstrate successful reprogramming to neural fates. However, the discussion and interpretation of results needs to be modified significantly to make it clear that (i) the outcome of many co-expression paradigms remains effectively unknown/untested due to failed over-expression of ASCL1, and that (ii) the reprogramming construct giving rise to photoreceptor-like cells could not be conclusively identified from their initial screen.

Strengths:

(1) Powerful new screening system advanced for exploring the regenerative potential of human fetal RPE cells.

(2) Co-expression vector system for testing additive effects of proneural transcription factors.

Weaknesses:

Major concerns:

(1) The authors executed a screen for combinations of factors that can enhance ASCL1-mediated reprogramming of RPE into retinal neurons. However, the expression level of ASCL1 was remarkably low in virtually all co-expression paradigms (see Figure 3C). Notably, the reprogramming combination with the highest potency (ASCL1 + NEUROD1) was also the one exhibiting the highest level of ASCL1 expression. The "failed" reprogramming of most of the co-expression constructs (ASCL1+LMO1, ASCL1+EZH2, and ASCL1+RAX2) is potentially a false negative resulting from low transgenic expression of ASCL1.

(2) The authors' interpretation is that the overexpression of NeuroD1 and Ascl1 generated a new cluster that expressed markers of photoreceptors such as RXRG and RCVRN (see Figure 3D). However, there does not actually appear to be any overlap between the ASCL1+NEUROD1 cluster (orange dots, left panel) and the cells expressing markers of photoreceptors (yellow/green/purple?/black? dots, right panel; yellow being ASCL1-EZH2, green being ASCL1, purple being ASCL1-and black being control - though color coding here is admittedly somewhat confusing). Thus, the photoreceptor-like cluster of interest actually seems to correspond to gray cells that were unmapped/exposed to an unknown programming cocktail. So, it remains completely unknown which reprogramming construct generated this cluster.

(3) To conclusively establish the additive role of NEUROD1 in reprogramming, it would be prudent to compare ASCL1 + FA directly to ASCL1+NeuroD1+FA. This control was not included but is needed for a more complete interpretation of results.

Reviewer #1 (Public review):

Summary:

Using cryofixation and serial block-face electron microscopy (SBEM), P. Vijayakumar and K. Cauwenberghs characterize extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs) within native Drosophila olfactory sensilla. The study provides a unique and valuable dataset comprising approximately 7,800 extracellular particles, systematically describing their morphology, size, density, and distribution. The ultrastructural analysis across different sensillum classes offers insights into the potential biogenesis and functions of these extracellular particles.

Strengths:

Cryofixation preserves EVs and NVEPs within native tissue conditions. The detailed quantification of a very large dataset provides a unique source of information on extracellular particle number, categories, and distribution. The expertise of the group in the method and the tissue explored, as well as their detailed quantification, provide confidence in the dataset and observations.

Weaknesses:

Major comments

(1) As the authors state, the rare observation of EV budding or MVB release events suggests that these are transient processes, whereas EVs and NVEPs are retained for relatively long periods within the sensillum lumen. The current analyses may overinterpret steady-state vesicle abundance as differences in vesicle production.

(2) Given the above conclusion, differences between sensillum classes may be somewhat overstated.<br /> a) The absolute number of EVs and NVEPs per sensillum is highly variable, even within the same sensillum class (Figure 3D). For example, a substantial proportion of coeloconic sensilla have an empty lumen (Figure 3C). Consequently, expressing the data as ratios or proportions (Figures 3B, 4C, and 4E) may exaggerate differences between sensillum classes and should therefore be interpreted with caution.<br /> b) The rate of EV/NVEP production is unknown. For a similar rate of production across sensillum classes, Figure 3E suggests that the differences in lumen morphology and size may largely explain variation in EV density and distribution.

That said, I agree that ab1 sensilla display a striking enrichment of large cargo-filled EVs compared with the other sensillum classes, while coeloconic sensilla display enrichment in small dense filled EVs (Figure 4C). Together, large and cargo-filled EV observation provides strong support for differences in EV biogenesis between ab1 sensilla and the other sensillum classes. In that context, I also think the EV size distribution shown in Figure 4 - Figure Supplement 1 should be moved into the main figure, as it demonstrates that the majority of EVs in the ab1 lumen are relatively large and are therefore likely to represent microvesicles. Could you clarify why ab1 sensilla are only included in Figure 4 and not analysed in Figure 3?

(3) Approximately 10% of ORNs appear to be degenerating in 6-8-day-old flies, which seems unexpectedly high. This contrasts with the relatively infrequent occurrence of auxiliary cell apoptosis or complete sensillum degeneration. In these "degenerating ORNs", the authors state that the hallmarks of ORN apoptosis are restricted to the dendrites. As hallmarks, they state dendrite truncation, fragmentation and blebbing. Rather than apoptosis, I wonder whether these observations might instead represent ciliary truncation and ectosome shedding, followed by degradation of the shed ciliary membrane into EVs. Ciliary truncation and ectosome shedding, followed by ciliary regrowth, are dynamic processes that have been described across multiple species. This interpretation could explain large EVs that remain in the lumen long after the cilium has regenerated. It would reconcile this article with the general agreement that cilia are a prime site for the budding of EVs across species. Additional evidence supporting apoptosis of the ORNs would help distinguish between these possibilities. Otherwise, I believe the author should reconsider their interpretation.

Reviewer #2 (Public review):

Summary:

This paper presents a large structural survey of extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs) in the olfactory sensilla of Drosophila melanogaster. Using high-pressure freezing and serial block-face SEM, the authors avoid many of the artifacts associated with conventional fixation and analyze more than 7,800 particles across 352 sensilla. The manuscript maps the distribution of these particles, describes their morphological heterogeneity, and examines their likely origins across different sensillum classes in both normal and degenerating tissue.

Strengths:

The strongest aspect of the paper is the imaging. Preservation is painstakingly controlled. The cryofixation appears to preserve the sensillum lymph in a more convincing native state than standard preparation methods, giving this work gravitas. Further, the authors characterized thousands of particles, further making this data strong.

The figures are strong. They are clear, easy to read, and generally well designed; I think people will use this paper as a model for how to present complex data in a concise and straightforward manner. The manuscript is careful in how it presents the dataset and does not overinterpret the descriptive observations. As an ultrastructural resource, this paper will be useful to the field. The identification of auxiliary support cells as major secretory sites, together with the striking accumulation of EVs in degenerating tissue, will provide a useful starting point for future work.

Weaknesses:

The main point that could use more clarification is the vesicle categorization. In particular, the distinction between "dense," "cargo-filled," and "double EVs" is not always easy to follow from a biological perspective. Some additional discussion of how the authors think these categories relate to one another, and whether they are intended as purely morphological groupings or as distinct biological classes, would strengthen the manuscript.

Reviewer #3 (Public review):

Using cryofixation-based serial block face electron microscopy of several subregions of the Drosophila antenna, the authors segment and assemble a high-resolution atlas of the anatomical structure, density, and spatial distribution of extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs) in different Drosophila olfactory sensilla types. This systematic and thorough description is an important prerequisite to understanding the function of extracellular particles in intercellular signaling in the nervous system. Additionally, they describe examples of putative biogenesis events (budding/fusion), as well as neuronal and axonal cell degeneration events and measure the changes in particle accumulation in these altered microenvironments.

Overall, this is a significant and comprehensive analysis and represents an invaluable resource to this burgeoning field. The authors assemble an important dataset and their claims match the level of evidence provided.

Strengths:

(1) The authors use segmentations from four different patches of the antenna to provide a systematic ultrastructural survey of extracellular particles in native insect sensilla. The dataset captures the diversity of sensilla types and reconstructs ~7800 particles.

(2) We commend the authors for making the EM volumes available in the public Cell Image Library with accession numbers. It would be helpful to the community to also make the segmentations for this great resource easily accessible.

(3) The sample preparation technique appears to minimize typical artifacts associated with chemical fixation, as evidenced by the high reported sphericity of EVs.

Specific points:

(1) In Figure 3C, the authors should include a continuous measure of particle distribution in the sensilla. Currently, the authors define three categories of particle localization. In the five examples shown in Figure 3A, the spatial distribution of these particles appears quite distinct across classes. For example, EVs/NVEPs in large and small basoconic sensilla are largely restricted to the area proximal to the base, with a limited number located more distally. In contrast, intermediate sensilla show a marked concentration of particles more distally.

(2) The conclusion of different EV ratios across sensillum classes stems from a Kruskal-Wallis of p = 0.0476, with none surviving pairwise comparisons. This is not a strongly supported conclusion and is probably better characterized as a trend.

(3) The statement "selective enrichment of large, cargo-filled vesicles within the ab1 lumen suggests specialized EV-mediated communication adapted to the coordination demands of this neuronal population" seems speculative for a Results section without supporting functional evidence. It would seem better suited for the Discussion.

(4) Figure 4: Criteria for defining the classes of EVs.<br /> a) The authors should explain the rationale for classifying EVs using relative density rather than absolute density? We would expect EVs with similar contents to have similar electron density (similar darkness in the images). Would classifying them relative to the background, which itself might vary across sensilla or regions, create a possible confound, especially when comparing across sensillum classes?<br /> b) The two example images (in Figure 4A) of the cargo-filled EVs appear to have different densities themselves. Do the cargo-filled ones also display systematic differences in density and, if so, why is this another class instead of being a subcategory within the dense and lucent classes (i.e. dense with/without cargo, lucent with/without cargo)? The dense and lucent classes are defined by their density, whereas this is a more structural property.<br /> c) Regarding "Double" and "Ball-and-Socket" EVs, does the density vary between the two particles involved (e.g., does the inner structure consistently differ in density from the outer)?

(5) What was the rationale for the 200μm and 1000μm size cutoffs? A continuous distribution of maximum particle sizes would provide a clearer understanding of the data.

Reviewer #1 (Public review):

Summary:

The authors investigate whether EEG neurofeedback (NFB) can be used to increase spontaneous parieto-occipital gamma oscillations and thereby reduce experimentally induced pain. Healthy participants were randomly assigned to active or sham neurofeedback and completed three consecutive neurofeedback blocks with concurrent EEG measurements and phasic painful stimulation. The study addresses a relevant question regarding the causal role of spontaneous gamma oscillations in pain perception and the potential of neurofeedback as a non-pharmacological pain intervention. While the reported findings appear consistent with an association between increased gamma power and reduced pain in a subset of participants, the current analyses do not provide sufficient support for the strong causal conclusions drawn by the authors.

Strengths:

(1) The study addresses an important and timely research question with potential implications for EEG-based neurofeedback approaches to pain modulation.

(2) The sample size is relatively large for an experimental EEG neurofeedback study and includes a sham-control condition.

(3) The manuscript is generally well written and clearly organized.

(3) The authors address an important methodological concern regarding EMG contamination of gamma-band activity by including additional EMG recordings in a subset of participants.

Weaknesses:

(1) The manuscript frequently presents the relationship between spontaneous gamma oscillations and pain perception as established fact. Given the continuing debate regarding the functional significance of EEG gamma oscillations in pain processing, these statements should be moderated.

(2) The responder analysis is the most serious methodological concern. Participants in the active group were retrospectively classified as "responders" based on increased gamma power after neurofeedback, and only these participants appear to have been included in the primary analyses and matched to sham participants. As only 23 of 44 participants (52%) met this criterion, the responder rate alone does not demonstrate successful neurofeedback-induced gamma modulation. More importantly, selecting participants based on the outcome variable and subsequently testing that same outcome constitutes circular analysis (double dipping), invalidating the statistical inference. Consequently, the reported effects should be interpreted as an association within a post hoc selected subgroup rather than evidence that neurofeedback increased gamma activity and reduced pain.

(3) The criterion for successful neurofeedback-induced gamma modulation was not prespecified. It is therefore unclear whether successful modulation was defined by the responder classification, the main effect of session, the group × session interaction, or one of the post hoc comparisons.

(4) Several methodological details reduce the reproducibility and replicability of the study. The spectral analysis does not clearly describe how trial-wise power estimates were aggregated within participants before group-level analyses, and the preprocessing pipeline includes manual ICA-based artifact rejection without specifying the criteria used for component selection. In addition, the analysis pipeline and custom neurofeedback software should be made publicly available to enable independent reproduction and verification of the reported findings.

(5) The neurofeedback implementation also raises questions. Updating the feedback only once per second using a 2-s sliding window results in discontinuous visual feedback that may reduce feedback quality and could introduce visually evoked activity. In addition, the viewing distance of approximately 30 cm likely required substantial eye movements while following the moving feedback object.

(6) The muscle-confound analysis is insufficiently documented. EMG recordings were acquired only in the second cohort, but the manuscript does not clearly state how many participants contributed to this analysis or whether responder selection was performed before or after restricting the sample. These details should be explicitly reported.

Reviewer #2 (Public review):

Summary:

The authors investigated whether neurofeedback (NFB) training targeting spontaneous gamma oscillations (30-60 Hz) at the parieto-occipital region (Pz electrode) could reduce experimental pain perception. They randomized 88 healthy participants to active or sham NFB groups across two cohorts (44 each). Active NFB consisted of real-time feedback based on participants' own gamma power; sham NFB consisted of the preceding participant's gamma power. Participants completed three ~16-min sessions, and approximately 52% of active NFB participants showed increased gamma power in session 3 and were considered responders. Analyses restricted to these 23 responders (matched with 23 sham controls) showed reduced pain intensity, unpleasantness, and laser-evoked potential (LEP) amplitudes, with a significant negative correlation between gamma power and pain intensity after session 3.

Strengths:

(1) The distinction between spontaneous and stimulus-evoked gamma oscillations in pain processing is theoretically important.

(2) The rationale for targeting spontaneous gamma via NFB is clearly articulated.

(3) The study was sham-controlled, and the blinding was adequate.

(4) The authors commendably ran a second cohort (n=44) with simultaneous posterior neck EMG recording to address the critical concern of muscle artifact contamination of gamma, in response to a previous review

Weaknesses:

(1) The most critical issue is about the exclusion of non-responders from the analysis. I find this problematic, as the reasoning becomes circular (selecting the participants who managed to increased gamma and then asking whether gamma NFB influenced pain), effect sizes are inflated, and the selection itself may introduce biases. For example, the responders may differ from the non-responders with respect to other characteristics (better attention skills, better self-regulation, etc). It would be more principled to present the results for the entire sample and only present the responder analysis as a secondary analysis. In the preregistration, the responder-only analysis was not mentioned.

(2) Another critical point is about the causal claims made in the abstract, introduction, and discussion. Given that the current results provide only correlational evidence in a subsample, the language should be revised to avoid overinterpretation. If the authors can demonstrate a significant mediation effect (NFB group -> gamma change -> pain change), they may be able to argue that increases in gamma activity mediate the observed reduction in pain.

Minor points:

(1) For the sham procedure, the authors used the preceding participant's gamma data for feedback. This raises two questions: How was this handled for the first participant? Did the authors check the discrepancy between actual gamma and presented gamma in the sham NFB group?

(2) Was baseline gamma power comparable between groups?

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether spontaneous gamma-band oscillations over the parieto-occipital region can be volitionally upregulated using EEG neurofeedback, and whether this upregulation reduces subsequent pain perception and nociceptive-evoked brain responses. Gamma-band activity has been repeatedly associated with pain processing, but most available evidence remains correlational, and previous attempts to modulate pain-related gamma activity using non-invasive stimulation have not produced robust analgesic effects. The present study therefore addresses an important question: whether real-time neurofeedback may provide a more effective way to train endogenous gamma activity and thereby influence pain.

Strengths:

A major strength of the study is the use of an active/sham neurofeedback design. The authors also combine subjective pain ratings with laser-evoked potentials, which provides converging behavioural and neurophysiological outcome measures. The manuscript is clearly written overall, and the study addresses a question of broad interest for pain neuroscience and neurofeedback research.

Weaknesses:

A number of aspects limit the strength of the conclusions. The first and most important issue concerns the interpretation of scalp gamma-band activity. Gamma-band oscillations recorded with scalp EEG are difficult to measure reliably, are not observable in all participants, and can be strongly affected by muscle activity. The authors acknowledge this issue and include posterior neck EMG, but the control remains limited. A lack of correlation between one posterior neck EMG channel and Pz gamma power is not sufficient to exclude muscle contamination, especially because gamma-band artifacts can arise from multiple muscle groups and may not be well captured by a single EMG channel. This is particularly important because changes in posture, facial tension, breathing, and arousal could all influence high-frequency scalp activity.

Second, the evidence for a causal relationship between parieto-occipital gamma activity and pain perception should be interpreted cautiously. The authors show that gamma power increased in approximately half of the active neurofeedback participants and that these responders showed reduced pain ratings and laser-evoked potentials. However, because the main analgesic effect is tied to responder classification, it remains difficult to separate the specific effect of gamma upregulation from broader individual differences in task engagement, suggestibility, relaxation ability, attentional state, or neurofeedback learning capacity.

A third limitation concerns the control condition and blinding. Participants were reportedly blinded to group allocation, and the credibility ratings appear similar between groups, which is reassuring. However, it is not clear whether the experimenters were also blinded during data collection and interaction with participants. This matters because neurofeedback studies are particularly vulnerable to expectancy.

The choice of the two neurofeedback scenarios requires clearer justification. The manuscript describes a deep ocean scene followed by a seaside scene with relaxation instructions, but it is not clear why these two scenarios were selected, and whether they were matched for attentional engagement and affective content. This is not a minor point, because both groups showed reductions in pain ratings after the entire neurofeedback procedure.

The comparison with tACS is interesting but currently underdeveloped. The authors suggest that neurofeedback may succeed where gamma-frequency tACS failed because it allows real-time, personalized, self-regulatory modulation of ongoing activity. This is plausible, but the manuscript should discuss this distinction more deeply. Neurofeedback may not simply be a different way of modulating gamma; it may recruit volitional control, attentional engagement, immersion, expectation, etc. These mechanisms could be central to the observed pain reduction and may partly explain why neurofeedback effects differ from those of externally applied stimulation.

Overall, this is an interesting study that introduces a promising neurofeedback approach for experimental pain modulation. The findings are encouraging, especially the convergence between subjective ratings and laser-evoked potentials in responders. However, the conclusions should be tempered. The current evidence supports the feasibility of training gamma-band activity in a subset of participants and suggests that successful training is associated with reduced experimental pain.

Reviewer #1 (Public review):

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

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

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

Reviewer #2 (Public review):

Many insects possess extremely sensitive olfactory systems that can detect chemical signals from distances of several kilometers. For decades, the arms race between bats and insects has served as a prime example of acoustic co-evolution. The auditory adaptations of insects to echolocation have been well documented. Cricket has a multi-sensory predator recognition system with keen olfactory, tactile, and auditory senses. However, whether crickets can use the scent of bats to avoid them remains unknown at present. The authors hypothesized that cricket prey (Loxoblemmus equestris) might eavesdrop on predator bat (Scotophilus kuhlii) VOCs as an early warning. L. equestris is one of the prey species of S. kuhlii, and the authors demonstrated that the body odor of the insectivorous bat S. kuhlii triggers robust avoidance and electrophysiological responses in the cricket L. equestris, and that a single compound, (-)-limonene, is sufficient to elicit this avoidance in the laboratory and suppress calling in the field. Overall, this paper has a complete chain of evidence and should be a highly praised study.

Reviewer #1 (Public review):

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

Summary:

Overall, this study is an excellent and systematic investigation of the expansion of repeat sequences in Arabidopsis thaliana, and the genetic mechanisms underlying these expansions. Many of the key findings here confirm smaller studies of both repeat sequence variation and the individual genes associated with the expansion of various repeat classes. The authors present a highly effective and practical approach that requires datasets that are far more readily available than the multiple reference genomes used to annotate repeat variation in recent works. Therefore, they provide an approach that shows significant promise in non-model systems in which far less is known of repeat variation and its underlying drivers.

Strengths:

This is a very methodologically sound study that extends the relatively well-studied Arabidopsis thaliana repeat landscape with more systematic sampling, highlights the loci associated with repeat expansions (many of which were previously identified in a piecemeal manner), and provides some evolutionary inference on these.

Weaknesses:

Regarding cis-QTLs: I foresee at least two causes of these associations: non-repetitive cis-acting sequences that promote or permit the expansion of local repeats, and variation in repeat sequences themselves that directly tag the expanding sequence itself. It's arguable whether these are truly two distinct classes, but an attempt to discriminate between them may provide some insight as to the local factors that allow for repeat expansion, beyond the mere presence of a repeat sequence. One way to discriminate these could be to map the ~1300 12-mer frequency profiles on the reference genome, and filter any SNPs with elevated 12-mer frequency from the GWAS (or to categorize them independently).

I also have a question regarding the choice of k=12 in kmer profile analyses. Did the authors perform any GWAS with other values of K? If so, how did the results change? I would expect that as K is increased, the associations would become more specific to individual repeat families, possibly to the point where only cis-acting loci are detected. The authors show convincing evidence that k=12 is appropriate; however, I would be interested to see if/how GWAS results vary among e.g. k=10, 12, 15, 18.

Reviewer #2 (Public review):

Summary:

The authors introduce a K-mer-based method for profiling repeat content within a species, applied here to 1,142 A. thaliana genomes sequenced with short reads. This approach allowed them to bypass the challenges of genome assembly, particularly for repetitive regions, while still quantifying copy number variation. Their analysis identified >50 trans-acting loci regulating repeat abundance, enriched for genes involved in DNA repair, replication, and methylation. They also speculate on the role of selection in shaping genome repeat content, arguing that purifying selection tends to suppress alleles that promote repeat expansion.

The work presents a scalable way to extract meaningful insights from the large quantities of short-read datasets available. However, I have several concerns regarding the methodology, scope of claims, and interpretation of results.

Strengths:

The authors leverage a large dataset, >1100 samples, of A. thaliana. The scale of the study is impressive and clearly bolsters their findings. Additionally, this provides a framework for future, large-scale studies and offers a solid foundation for hypothesis generation. The k-mer-based method is generally practical for large-scale analysis and should be transferable to other datasets. Finally, the authors are commendably upfront about many of the project's limitations.

Weaknesses:

The decision to use k=12 is loosely justified. While the authors performed a sweep of k-mer lengths (from 5-20) and noted computational constraints, the choice is highly dataset-specific. Benchmarking across different k values with additional datasets (especially including other species) would strengthen confidence in the robustness of the method.

All analyses rely exclusively on the TAIR10 reference genome, which is incomplete and known to collapse certain repetitive regions. This dependence raises concerns that some repeats (especially recently expanded or highly variable ones) are systematically undercounted. With improved A. thaliana assemblies now available, testing the method against a more complete reference would alleviate these concerns.

The manuscript's conclusions are framed in very broad terms (e.g., "shaping genome evolution in plants"). However, the study is restricted to a single species, A. thaliana, which may not represent other plants. While the findings may suggest general principles, the claims in the abstract and conclusion should be moderated to reflect the study system more accurately.

The identification of >50 trans-acting loci enriched for DNA repair and replication genes is compelling, but the conclusions remain correlational.

Reviewer #2 (Public review):

Summary:

The authors studied the resistance against octanoic acid, a compound of the noni fruit in D. simulans, using experimental evolution and resistance/susceptibility in D. melanogaster cells. They identified novel candidate genes and performed functional tests.

Strengths:

The idea of using experimental evolution of a non-resistant species to develop resistance is interesting and the idea of narrowing down a large list of candidate loci by CRISPR based gene knockout in cell culture is innovative. The reviewer also liked the (easy) follow up experiments to validate the results.

Comments on revised version.

Weaknesses:

- The experiments to validate the effect of candidate genes did not match the experimental evolution conditions.

This point has been confirmed by the authors.

- The statistical analysis suffers from some problems and insufficient description of the analyses performed.

Has not been addressed in their response.

- Although D. simulans GWAS data are available, the authors did not make an attempt to estimate the effect of selected variants in the candidate genes in the GWAS data set.

This has now been included in the discussion. I would recommend that they make the distinction between genetic and adaptive architecture, as this matches their verbal description.

- The reviewer would have liked to see more connection between the experimental evolution and GWAS data. As some D. simulans genotypes have similar resistance as D. sechellia, it would have been interesting to test whether this genotype contributed to the observed resistance.

The reviewer is happy with the response.

- At several places the authors discuss the challenge of studying a polygenic trait, but at the same time they claim to have detected and validated candidate genes. It would be helpful if the authors could discuss why they consider that their assays could really detect the contribution of single loci to the polygenic trait. In particular, when GWAS did not detect their candidate genes.

The reviewer is not satisfied with the arm waving explanation of the authors. The important question is how much of the phenotypic variation is explained by the two candidate genes? The reviewer is inclined that based on the weak selection response, the variation is too little to be detected experimentally. Nevertheless, the overexpression of alkbh7 alone was sufficient to generate resistance levels similar to the ones in d. Melanogaster. Hence, it is not adequate to speak of small effects. This discrepancy requires more discussion.

- It is not clear to the reviewer why the authors did not pay more attention to the highly significant peaks emerging from the experimental evolution study. Their functional validation would have been biologically more plausible.

This point remains valid, in particular in the light of the discrepancy between the very limited selection response of alkbh7 and its large phenotypic effect after overexpression.

Impact:

- Given the obvious challenges of functional testing of polygenic traits and the clear limitations of the interpretation of the results, the study will be helpful for future studies aiming to characterize polygenic traits. Unfortunately, the results are just another piece of controversial results regarding resistance against octanoic acid-a trait that is rather easy to evaluate.

The reviewer did not find the reply satisfactory.

Reviewer #1 (Public review):

In this manuscript, the authors investigate the functional consequences of nuclear envelope rupture caused by the depletion of the nucleoporin NPP-3.

They observe that loss of NPP-3 causes condensed chromosomes to localize to the nuclear periphery. This anchoring is independent of the pathway required to anchor heterochromatin and telomeres, but it depends on spindle assembly checkpoint proteins as well as centromere and kinetochore proteins. While the authors propose that relocalization of chromosomes to the nuclear periphery protects genome stability, they do not demonstrate this.

Overall, some of the observations are interesting, but several points should be addressed. Furthermore, the manuscript could be much clearer if certain sections were shortened, simplified, or removed.

Major points:

(1) The title is misleading because the authors provide no experimental evidence that chromosome relocalisation protects genome stability. They are more cautious in the abstract, where they state that it 'may serve a protective role'. If they could provide stronger experimental evidence that chromosome relocalization protects genome stability, this would significantly strengthen the manuscript.

(2) Here, the authors use acute inactivation of npp-3. Do chromosomes also localize to the periphery upon partial npp-3 inactivation? What are the minimal levels of nuclear envelope rupture that cause chromosomes to localize to the periphery? Given that NPP-3 and NPCs have pleiotropic functions, it would be important to analyze conditions where only a few nuclear envelope ruptures are induced. In such conditions, they might be able to explore the link between chromosome localization and genome stability.

(3) The authors primarily examined P1 cells. Is the behaviour of the chromosome different between cells of different lineages?

(4) The authors mentioned that defective chromosomal localisation does not occur upon npp-2 or npp-4 depletion. How do they explain this? Did they attempt to inactivate other NPPs in the Y complexes, and can they be certain that NPP-2 depletion is complete?

(5) The section on AIR-1 (line 147) is confusing and could be removed. To my knowledge, air-1 depletion does not cause the appearance of multiple centrosomes, except maybe in a very few embryos. air-1 depletion causes major defects, so it is difficult to draw a parallel with npp-3 depletion.

(6) The authors show that condensed chromosomes tend to localize to the nuclear envelope upon NPP-3 depletion. Do they condense at the nuclear envelope (NE), or do they condense first and then move to the periphery? This is unclear from the data presented in Figure 1D. Also, why do chromosomes condense earlier? This point could be discussed.

(7) The authors evaluated the consequences of NPP-3 depletion on transcription using RNA sequencing. The relevance of this experiment is questionable, however, as npp-3(RNAi) embryos have significant general defects and not only mislocalised chromosomes.

(8) In the co-depletion experiment npp-3(RNAi), X(RNAi) presented in Figure 3B, the levels of NPP-3 depletion seem highly variable. All the images shown are not similarly exposed, so it is difficult to evaluate these data.

(9) Inactivation of mdf-1/2 suppresses the mislocalization of the chromosomes observed upon npp-3 inactivation. Does it also suppress the premature chromosome condensation phenotype?

(10) Figure 5B: The delay induced by npp-3 depletion is not severe, based on the micrographs presented. The authors should show more representative images. The graph shows the elapsed time between NEBD and NER, and not NER to NEBD, as indicated.

(11) The authors observed that depleting mdf-1 slightly enhanced the lethality associated with npp-3 inactivation. Based on this observation, they conclude that loss of chromosome anchoring exacerbates genomic instability and severely impairs embryonic survival. However, the genetic interaction is not strong, as npp-3(RNAi) embryos already present more than 95% embryonic lethality and have defects other than just mislocalized chromosomes (e.g., defects in kinetochore and spindle assembly).

Reviewer #2 (Public review):

Summary:

The authors aimed to determine the molecular mechanisms by which nuclear pore component NPP-3/NUP205 regulates chromosome localization in C. elegans embryos. Previous studies had shown that depletion of NPP-3 caused premature chromosome condensation and movement of chromosomes to the nuclear periphery. Peripheral location of chromosomes is also observed under respiratory stress conditions, suggesting that peripheral chromosome positioning could act as a protective response to stress conditions. How NPP-3 affects chromosome positioning was unknown. Here, the authors conduct a screen to identify factors that promote chromosome relocation to the periphery in npp-3-depleted embryos, identifying an important role for spindle assembly checkpoint components in this process.

Strengths:

Using cytological tools to visualise chromosomes and nuclear envelope markers, the authors show that, in addition to the peripheral location of chromosomes, NPP-3 depletion causes partial rupture of the nuclear envelope and premature chromosome condensation. By systematically co-depleting NPP-3 and factors required for heterochromatin association with nuclear lamina (CEC-4), telomere binding to nuclear envelope (SUN-1 and POT-1), proteins required for the nuclear rupture repair machinery (BAF-1 and LEM-2), kinetochore proteins and components of the spindle assembly checkpoint (SAC) (MDF-1 and MDF-2), the authors convincingly show that SAC components are required for peripheral relocation of chromosomes in absence of NPP-3. The study also provides convincing evidence that peripheral relocation of chromosomes in the absence of NPP-3 has functional implications as it causes transcriptional deregulation and premature relocation of SAC components from the nuclear envelope to chromosomes. Co-depletion of NPP-3 and SAC components accelerates progression through miotic prophase and increases the incidence of defects in chromosome segregation during mitosis. These findings demonstrate that SAC proteins play an important role in regulating chromosome positioning during prophase (at least in the absence of NPP-3) and that they can regulate cell cycle progression at earlier stages than previously thought.

Weaknesses:

The authors also propose that NPP-3 depletion causes DNA damage; however, the evidence presented to support this claim is not as strong as that presented for the effects mentioned above. Also, the premature condensation of chromosomes appears as a clear consequence of NPP-3 depletion, but this intriguing phenotype remains unexplored.

Reviewer #3 (Public review):

Summary:

This manuscript reports that RNAi depletion of the inner-ring nucleoporin NPP-3/NUP205 in Caenorhabditis elegans embryos causes nuclear envelope rupture, premature chromatin condensation, and relocalization of condensed prophase chromosomes to the nuclear periphery. Through a candidate epistasis screen, the authors argue that this relocalization requires spindle assembly checkpoint (SAC) components (MDF-1, MDF-2, SAN-1), inner kinetochore proteins (HCP-3, HCP-4, and partially KNL-1), and NE rupture-repair factors (BAF-1, LEM-2), but not the CEC-4 heterochromatin- or SUN-1/POT-1 telomere-anchoring pathways. They further show that NPP-3 loss extends prophase and the NEBD-to-anaphase interval in a SAC-dependent manner, redistributes MDF-1/MDF-2, and reduces import of KNL-1/BUB-1/HCP-1. Co-depletion of NPP-3 with MDF-1 abolishes both the arrest and the peripheral localization while increasing lagging chromosomes, HUS-1 foci, micronuclei, and lethality, which the authors interpret as evidence that peripheral positioning is protective.

Weaknesses:

(1) The "protective" conclusion is largely correlative. The protective claim rests on the observation that co-depleting MDF-1 (or MDF-2) with NPP-3 removes the peripheral localization and simultaneously increases DNA damage, micronuclei, and lethality. However, depleting a SAC component removes at least three things at once: the peripheral localization, the prophase extension, and the NEBD-to-anaphase arrest. Because loss of the SAC independently causes premature anaphase and genomic instability through well-established mechanisms unrelated to chromosome positioning, the current design cannot separate damage caused by loss of a protective peripheral location from damage caused by checkpoint bypass. As presented, the increased damage is at least as consistent with simple SAC bypass. To support the protective model, the authors should provide a manipulation that disrupts peripheral positioning without abrogating the SAC-dependent arrest (for example, via the BAF-1/LEM-2 or kinetochore depletion) and show that damage still increases. The LEM-2 co-depletion, which partially suppresses positioning, is a natural place to test whether micronuclei and HUS-1 foci also rise.

(2) Knockdown efficiency of the partner gene in double RNAi is not verified. The double depletions are performed by cloning both gene fragments into a single vector. This risks reducing the effective dose of each dsRNA, so an apparent suppression in an npp-3; gene X (RNAi) condition could reflect weaker NPP-3 knockdown rather than a true epistatic relationship. The authors partially address this by showing that NPP-3::mCherry is still reduced in npp-3;mdf-1 (Figure S4A/B), which is helpful, but they do not demonstrate efficient knockdown of the partner genes in any double condition. For the key epistasis conclusions (MDF-1, MDF-2, HCP-3, HCP-4 suppressions), the knockdown of the second gene should be independently validated with a reporter strain for the second protein.

(3) Alternative explanations for the transcriptomic and H3K9me3 data are not excluded. NPP-3 depletion blocks nuclear import of molecules smaller than ~70 kDa and arrests development at early gastrulation. Both the RNA-seq changes (30% of genes downregulated) and the increased H3K9me3 signal could therefore be secondary consequences of nucleocytoplasmic transport failure and developmental arrest rather than evidence of position-dependent transcriptional repression. Notably, the authors' own finding that up- and down-regulated genes show no chromosomal positional bias (Figure S2C/D) argues against a model in which peripheral repositioning drives silencing of specific chromatin domains. This section should be reframed more cautiously, with the transport/arrest confound explicitly discussed, and RNA-seq replicate number and differential-expression thresholds reported.

(4) Evidence for SAC "activation in prophase" is indirect, and the effect is small. The claim of a novel prophase role for the SAC rests on MDF-1/MDF-2 intensity changes that are repeatedly described as "modest," "slight," or "mild," measured with small n and Student's t-tests, together with phenotypic suppression of prophase extension. There is no direct readout of SAC catalytic activity (for example, MCC assembly). The prophase-extension suppression by MDF-1 is the strongest evidence; the intensity data are weak support. I recommend tempering "the SAC is activated in prophase" to a hypothesis, and strengthening it with a more direct assay if feasible.

(5) The BAF-1 arm of the model is inferred rather than demonstrated. The authors state that baf-1(RNAi) and npp-3;baf-1 produced clustering too severe for epistasis, so BAF-1's requirement for peripheral localization is not actually established genetically; it rests on increased BAF-1 accumulation (correlative) plus the LEM-2 partial suppression. The proposed BAF-1/CENP-C bridge is extrapolated from Drosophila (ref. 71). This is reasonable as a discussion hypothesis but should not be presented in the abstract or summary model as an established dependency.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Major Concerns

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

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

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

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

Reviewer #2 (Public review):

Summary:

STX is a non-steroidal, CNS-selective estrogenic compound with neuroprotective effects in stroke and Alzheimer's disease models, but its molecular target has remained unknown for nearly 20 years. In this study, the authors identify VDAC proteins as the direct mitochondrial targets of STX using chemoproteomics, single-cell qPCR, electrophysiology, and metabolic flux analyses. They further show that VDAC2 is the primary functional target in female POMC neurons, linking STX-mediated VDAC modulation to enhanced mitochondrial bioenergetics and neuroprotection.

Strengths:

This study is strengthened by its innovative chemoproteomic approach, in which the authors developed a novel bifunctional STX probe (BF-STX) containing a photo-crosslinkable diazirine group and an alkyne handle to capture transient STX-protein interactions in living cells. The experimental design is further reinforced by rigorous controls, including no-UV negative controls and competition assays with excess unlabeled STX, which provide convincing evidence that VDAC1, VDAC2, and VDAC3 are genuine STX-binding targets rather than nonspecific artifacts. Finally, the authors validate the STX-VDAC interaction using multiple complementary approaches, including chemoproteomics, single-cell qPCR, planar lipid membrane electrophysiology, and Seahorse metabolic flux analyses, providing strong mechanistic support for their conclusions.

Weaknesses:

While the study provides convincing evidence that STX directly modulates VDAC function, several limitations remain. Most experiments were performed in immortalized cell lines rather than primary neurons or in vivo models, limiting their physiological relevance. In addition, the exact structural binding site of STX on VDAC remains unresolved, and no loss-of-function experiments (e.g., VDAC2 knockdown) were performed to establish a direct causal link between VDAC2 and STX's bioenergetic and neuroprotective effects. The non-linear dose-response at higher STX concentrations also requires further investigation.

Reviewer #1 (Public review):

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

This revised manuscript represents a partial response to the concerns raised in the first round of review. The authors have made one genuine mechanistic addition in the form of the semi-permeabilized cell reconstitution assay, removed the most overreaching conclusions regarding the contribution of cytoplasmic TDP-43 aggregation to disease, and made several minor presentational improvements. However, the central weaknesses of the original submission remain substantially unaddressed. The exclusive reliance on non-physiological TDP-43 variants, the incompletely resolved mechanism linking XPO1 to TDP-43 phase behavior, and the limited organoid validation continue to limit confidence in the major claims. The authors have, in several instances, responded by removing contested data rather than by providing the additional evidence that was requested.

(1) The justification for the 2KQ acetylation-mimetic system remains inadequate.<br /> The authors respond to the concern about the non-physiological nature of the 2KQ mutant by citing published evidence that TDP-43 acetylation occurs in ALS patient spinal cord and is upregulated under oxidative and proteotoxic stress conditions. While these references are real and support the relevance of acetylation as a pathological post-translational modification, they do not resolve the central concern: there is no quantification of how much endogenous TDP-43 is acetylated at the specific lysine residues mimicked by 2KQ in degenerating human neurons, and no evidence that the degree of RNA-binding disruption imposed by the double glutamine substitution is ever achieved by endogenous acetylation in vivo. The 2KQ mutant eliminates RNA binding essentially completely, whereas physiological acetylation events are graded, reversible, and likely partial. The response conflates the existence of TDP-43 acetylation as a phenomenon with validation that 2KQ is a physiologically accurate model of that phenomenon. None of the new experiments address the request to test whether wild-type TDP-43 expressed at near-physiological levels, or a bona fide heterozygous ALS-linked TARDBP mutant in iPSC-derived neurons, responds to XPO1 modulation in a qualitatively similar fashion. Until this is shown, the mechanistic conclusions of this paper remain constrained to a highly artificial overexpression system and cannot be extrapolated to physiological or pathological TDP-43 biology with confidence.

(2) The homozygous K181E organoid model is still not adequately justified, and no heterozygous comparison has been provided.<br /> The authors acknowledge that the homozygous background is "more sensitive for detecting phospho-TDP-43" and argue that homozygous conditions are commonly used in experimental TDP-43 research. However, the critical issue is not whether homozygous models are used in general, but whether the homozygous background specifically alters the relative contribution of cytoplasmic aggregation versus nuclear RNA-processing dysfunction in this study. In a homozygous K181E model, both alleles produce an RNA-binding-defective TDP-43, meaning that every molecule of endogenous TDP-43 in the cell is dysfunctional. This is categorically different from the patient situation in which one wild-type allele is present, and it may substantially exaggerate nuclear loss-of-function relative to cytoplasmic gain-of-function phenotypes. The authors have not performed the requested comparison with heterozygous K181E/+ organoids, nor have they acknowledged that the organoid genotype itself could bias the interpretation of what KPT-276 treatment rescues. Given that the organoid section is now the sole in-disease-model validation of the XPO1 mechanism, this limitation is more consequential than it was in the original submission.

(3) The new semi-permeabilized cell data is a genuine contribution, but the mechanistic interpretation remains insufficiently constrained.<br /> The development of the streptolysin O semi-permeabilized cell reconstitution system is the most substantive new addition to this revision. The finding that LMB-stabilized anisosomes resist cytosol washout but dissolve upon RNase T1 treatment is interesting and provides a plausible indirect mechanism: XPO1 inhibition retains nuclear RNA, and this elevated nuclear RNA availability contributes to maintaining the liquid LLPS state of the TDP-43 2KQ condensate. This is a meaningful mechanistic advance and deserves credit. However, several important limitations of this new data are not adequately discussed. First, RNase T1 degrades single-stranded RNA globally during permeabilization, so the experiment does not identify which specific RNA species stabilize the anisosome, nor whether these are pre-mRNA splicing intermediates, mature mRNA, non-coding RNA, or another class. Second, the same nuclear export blockade that retains RNA will also retain the nuclear concentrations of many RNA-binding proteins, splicing factors, and other XPO1-dependent cargos. The RNase T1 experiment does not exclude the possibility that the relevant effect is mediated by an RNA-binding protein whose nuclear concentration increases upon LMB treatment and which, upon RNase digestion, can no longer engage TDP-43 or the anisosome shell. Third, the permeabilized cell system is by definition not intact and has lost cytosolic factors; whether the RNA-dependent stabilization of anisosomes operates in the same way in intact cells during physiological or pathological nuclear export perturbation is an assumption, not a demonstrated fact. The authors should more carefully frame these data as hypothesis-generating and explicitly note these alternative interpretations in the Discussion.

(4) The conceptual asymmetry between XPO1 inhibition and XPO1 overexpression phenotypes is not resolved by the new mechanism.<br /> The paper continues to present two XPO1 perturbation phenotypes that are difficult to reconcile within a single mechanistic model. XPO1 inhibition enlarges anisosomes, maintains their liquid character by FRAP, and retains them in the nucleus. XPO1 overexpression also enlarges TDP-43 puncta, but these are FRAP-impaired, gel-like, and appear in the cytoplasm. The RNA-retention model proposed by the new semi-permeabilized data explains why XPO1 inhibition stabilizes the liquid state, but it does not explain why XPO1 overexpression drives the opposite outcome: gel-like hardening and cytoplasmic redistribution. If increased nuclear RNA availability is the key variable downstream of XPO1 inhibition, then XPO1 overexpression would be expected to decrease nuclear RNA and thereby destabilize anisosomes toward dissolution or hardening. The paper does not test whether nuclear RNA levels are indeed altered by XPO1 overexpression, nor whether the cytoplasmic gel-like puncta seen in XPO1-overexpressing cells are RNA-poor relative to control anisosomes. The revised Discussion does not engage with this asymmetry in a satisfying way, and the figure model remains qualitative. A quantitative or at least semi-quantitative model that accounts for both arms of the XPO1 perturbation is needed.

(5) The removal of RNA-seq data weakens rather than strengthens the organoid section.<br /> The authors have removed the bulk RNA-seq analysis from the revised manuscript in response to concerns that the modest transcriptional rescue was being over-interpreted. While the decision to remove over-interpretation is appropriate, the result is that the organoid section now rests entirely on pTDP-43 immunostaining as its sole readout. The revised paper thus uses reduction in immunofluorescent pTDP-43 puncta in homozygous K181E organoids as the only evidence that nuclear export inhibition mitigates TDP-43 proteinopathy in a disease-relevant context. This is a weaker evidentiary base than before the revision, not an improvement. The originally requested more sensitive orthogonal readouts, including biochemical fractionation for SDS-insoluble TDP-43, filter-trap assays, or RNA aptamer-based detection of TDP-43 aggregates, remain absent. Without at least one additional independent measure confirming that cytoplasmic TDP-43 aggregation is genuinely reduced rather than simply rendered antigenically undetectable, the organoid conclusion is not adequately supported. At minimum, the authors should provide total and cytoplasmic TDP-43 fractionation data from organoid lysates to corroborate the immunostaining result.

(6) No functional neuronal readout has been provided for the organoid model.<br /> The organoid section now makes the claim that "nuclear export is required for the formation of p-TDP-43-containing aggregates in a disease-relevant organoid model," but no measure of neuronal health, integrity, or function is reported in association with this. Even a simple assessment of neuron survival by TUJ1 or MAP2 quantification, neurite complexity, or cleaved caspase-3 staining before and after KPT-276 treatment would substantially strengthen the biological significance of the pTDP-43 reduction. The current data establish a pharmacological effect on a pathological marker but do not demonstrate that this has any consequence for neuronal biology in the organoid, which is what the disease-relevance framing implies.

(7) The abstract and title continue to overstate the mechanistic conclusions.<br /> Despite the stated intent to reframe the study as a screening study and to temper the conclusions, the revised abstract retains the language: "These findings establish nuclear export as a key regulator of TDP-43 phase transitions and define a mechanistic framework that links altered nuclear transport and phase dynamics to TDP-43 aggregation potential." Similarly, the Discussion still states: "a particularly compelling aspect of our study is the discovery that the nuclear export receptor XPO1 governs TDP-43 liquid-to-solid transitions and subcellular localization." The word "governs" and the phrase "establish nuclear export as a key regulator" are not warranted by data that derive entirely from an overexpressed acetylation-mimetic mutant in a colon cancer cell line and a homozygous K181E organoid model. A more accurate framing would describe these findings as identifying nuclear export as one of several cellular processes that modulate TDP-43 phase behavior in a sensitized model system, with an indirect RNA-mediated mechanism that remains to be defined at the molecular level. The title change from "governs" to "modulates" is appreciated but does not extend into the abstract and Discussion, where the strong causal language persists.

(8) Individual siRNA knockdown validation for XPO1 has not been provided.<br /> The authors argue that validation with 6 independent siRNAs across two rounds of screening, combined with convergent pharmacological data, is sufficient to establish XPO1 as a genuine hit. While the convergence of chemical and genetic evidence is reassuring, the specific request was for protein-level confirmation of XPO1 knockdown efficiency in the DLD1 TDP-43 2KQ cells used for mechanistic follow-up, together with demonstration that the anisosome phenotype is specifically caused by loss of XPO1 and not by off-target effects. This is a straightforward experiment, and its absence is particularly notable given that the entire mechanistic XPO1 narrative hinges on this specificity. At minimum, an immunoblot confirming XPO1 protein depletion in cells treated with the siRNA pool identified in the screen, in the same cell background and induction conditions as the follow-up experiments, should be provided.

(9) The identity of XPO1-dependent cargos that regulate anisosome dynamics remains entirely unknown.<br /> The authors acknowledge that XPO1 does not directly bind TDP-43 and that the mechanism is likely indirect. The new RNA data provides one plausible indirect pathway. However, the possibility that one or more specific RNA-binding proteins or splicing factors, whose nuclear levels rise upon XPO1 inhibition, are the proximate drivers of anisosome stabilization has not been addressed. This matters because if the relevant mechanism operates through a specific cargo rather than bulk RNA retention, the model for how nuclear export connects to TDP-43 aggregation in disease would be fundamentally different. The authors decline to pursue adaptor identification on grounds of scope, which is a defensible position for future work. However, the framing should explicitly state that the current data cannot distinguish between bulk RNA retention and cargo-specific effects, and that the conclusion that nuclear export modulates TDP-43 phase behavior via RNA accumulation is a working hypothesis supported by but not proven by the RNase T1 experiment.

Minor remaining issues.

The number of independent iPSC clones and organoid batches used for the KPT-276 treatment experiment is now stated as two batches per condition, which is minimal for a 3D organoid study and does not fully address the concern about clone-level variability. Ideally, organoids from at least two independently derived isogenic clones per genotype would be used. The mCherry overexpression control added in Supplemental Figure 4 is a useful addition and is acknowledged. The immunoblotting confirmation that drug treatments do not alter total TDP-43 levels addresses a prior concern adequately. The addition of the sentence noting that anisosomes have not been validated in human patient samples is appreciated and appropriate. Statistical detail has been improved in figure legends. These minor improvements are noted positively but do not compensate for the major unresolved concerns above.

Reviewer #2 (Public review):

This manuscript addresses an important and timely question in TDP-43 biology by systematically identifying regulators of TDP-43 anisosome formation, with a particular focus on nuclear export via XPO1. Using a combination of unbiased chemical screening, genetic perturbation, and advanced imaging approaches, the authors propose that inhibition of nuclear export modulates the abundance and biophysical properties of TDP-43 anisosomes. They further strengthen their findings by introducing an additional model system, a semi-permeabilized in vitro assay, which provides mechanistic evidence that XPO1 activity prevents anisosome dissolution by retaining nuclear RNAs. The study is conceptually innovative and has potential relevance for neurodegenerative diseases characterized by TDP-43 pathology. Some minor concerns remain, mostly about experimental design of the newly added data.

Strengths:

(1) The study employs an unbiased, hypothesis-free compound screen to identify regulators of TDP-43 anisosome formation, which is a major strength and reduces confirmation bias.

(2) The authors combine chemical and genetic screening approaches, providing orthogonal validation of key pathways and increasing confidence in the biological relevance of top hits.

(3) The focus on biophysical properties of TDP-43 assemblies, assessed through imaging and FRAP, moves beyond simple presence/absence of aggregates and provides mechanistic insight into the biophysical states of TDP-43.

(4) The use of multiple experimental modalities, including live-cell imaging, FRAP, pharmacological perturbation, and transcriptomic analysis, reflects a technically sophisticated and ambitious study design.

(5) The authors attempt to extend findings beyond immortalized cancer cell lines by incorporating organoid models, demonstrating awareness of disease relevance and translational importance.

(6) The authors extend their study by incorporating a semi-permeabilized in vitro system, which provides compelling evidence that inhibition of nuclear export promotes the retention of nuclear anisosomes, an effect driven by the accumulation of nuclear RNAs.

Overall, the manuscript is clearly written and logically structured, making complex experimental workflows accessible and the central hypotheses easy to follow.

Weaknesses:

(1) The manuscript has significantly improved with the revisions. Some experimental procedures and method details, as well has statements remain incompletely described:<br /> a) What is the smear in Figure S1 after VLX treatment?<br /> b) The authors state that "The reduction in TDP-43 signal was not due to protein elimination.", however no data is provided to support that statement.<br /> c) The authors state that "TDP-43 shifts from phase-separated state to a soluble state ...", however no data is provided to support that statement.<br /> d) Why did the authors choose cow lover cytosol for this study?<br /> e) The experimental setup for supplementing with cytosol/ATP/GTP is unclear. A more detailed schematic would be helpful to understand at what stage in the experiment these factors were added. Which step of the protocol was performed at 37 {degree sign}C, which is indicated in the figure schematic but not described in the methods.<br /> f) In the organoid model, the authors mention that they observe similar levels of total TDP-43, however they do not provide quantification. Instead, they provide a graph that shows highly significant changes in nuclear TDP-43, which was not addressed in the text.

Additionally, some questions remain unclear:

(1) The anisosomes induced by ATP/GTP or cytosol are insufficiently characterized. It remains unclear whether these structures correspond to canonical ring-shaped anisosomes, and whether they exhibit dynamic (liquid-like) or more static (gel-like) properties.

(2) The contribution of the cytosol and ATP/GTP supplementation experiments to the overall narrative is unclear. While the findings are intriguing, their interpretation within the context of the study is not well articulated. In particular, the rationale for including cytosol is not sufficiently justified, given that ATP/GTP alone induces a pronounced effect, whereas cytosol alone does not.

(3) The authors should address why endogenous XPO1 does not co-localize with anisosomes, whereas overexpressed XPO1 does. This raises the possibility that the observed co-localization may be an artifact of non-physiological protein levels, which should be discussed.

(4) The iPSC-based model remains insufficiently characterized. While the authors propose that this system recapitulates the accumulation of liquid and solid aggregates resembling anisosomes, it is unclear whether this phenotype is robustly observed and whether KPT treatment effectively modulates it.

(5) The rationale for the selected treatment durations is unclear, and the timing appears inconsistent across experiments (ranging from 3 to 16 hours), including within experiments involving the same compound. This variability should be justified or standardized.

(6) Several figure legends require clarification:<br /> a) In the section stating "Collectively, our results suggest that the stability and dynamics of anisosomes are modulated by XPO1-mediated nuclear export ...", the cited figure appears to be incorrect. This should refer to Figure 5L rather than Figure 5J.<br /> b) Figure 1B: Please specify the number of replicates per concentration, the number of cells analyzed, and the model used for regression analysis. Additionally, the legend indicates a treatment duration of 15 hours, whereas Figure 1A states 24 hours.<br /> c) Figure 2G: The authors state "7 anisosomes per condition," but the graph displays only 4-6 data points. Please clarify what each data point represents.<br /> d) Figures 3B and 3G: Please clarify whether a defined threshold was used to determine a "reduction in anisosome number."<br /> e) Figure 4B: These do not represent biological replicates, as all samples derive from a single cell line; rather, they constitute independent experimental replicates.<br /> f) Figures 5B and 5H: The legend states "n = 3 biological repeats," but the number of data points shown appears higher. Please clarify.<br /> g) Figures 5K, 6C, and 6E: "Mean Fluorescence Intensity (MPI)" should be corrected to "MFI."<br /> h) Figure 6C: Please include the number of cells analyzed and provide relevant statistical measures (e.g., R², p-value).<br /> i) Figure 6D: The experimental timeline is unclear. Please specify the duration of incubation and the timing of each step.<br /> j) Figure 7B: Improved labeling is needed (e.g., clarification of "mean spot volume") to better align with the figure legend.

Reviewer #3 (Public review):

Summary:

TDP-43 proteinopathy is broadly found in neurodegenerative diseases. This manuscript investigates how nuclear export influences the biophysical properties of TDP-43. The authors use a combination of chemical screening and genome-wide siRNA screening to identify pathways that modulate TDP-43 liquid-to-solid transitions. Overall, the study employs a broad array of approaches and addresses an important question in TDP-43 pathobiology. The identification of nuclear export as a central regulator is compelling and conceptually aligns with the emerging view that TDP-43 nucleocytoplasmic trafficking is a major defect in neurodegeneration.

Strengths:

This work integrates chemical and genetic screening to identify novel modifiers. The candidates were validated in both reporter cell lines and iPS-differentiated organoids. The findings support the nucleocytoplasmic transport is important for the biophysical properties of TDP-43.

Comments on revised version.

The manuscript has been improved with more data and clarification. The RNase T1 treatment experiment suggests that RNA is required for anisosome integrity. However, this does not directly demonstrate LMB increases nuclear RNA availability as changes in protein composition or other RNA-dependent mechanisms may also contribute. The conclusion and discussion need to be edited to consider these alternative scenarios. Overall, as most of the evidence remains indirect, the manuscript should avoid overinterpretation regarding the mechanisms underlying TDP-43 phase transition and aggregation.

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

Strengths:

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

Reviewer #2 (Public review):

Summary:

The authors have made a convincing argument that the current system of in vitro translation using E. coli extracts can be significantly optimized to work with much lesser components, while maintaining activity. They have showcased their improved activity using not only physical but also functional readouts.

Strengths:

The experiments are designed in a very logical and easy to understand manner, which makes it easier not only to follow the paper, but also reproduce the results. Functional assays with the synthesized proteins are a good way to demonstrate functionality and applicability of the system. They also benchmark their system against a commercial kit to show superior performance of their system.

Weaknesses:

The production of the lysate requires special instrumentation, limiting accessibility.

Comments on previous version:

Thank you to the authors for addressing the concerns both textually and experimentally. This work has significant value.

Reviewer #3 (Public review):

Summary:

The authors aimed to overcome the challenges associated with complex, conventional prokaryotic cell-free protein synthesis (CFPS) systems, which require up to thirty-five components, by developing a streamlined and efficient E. coli CFPS platform to encourage broader adoption. The main objective was to reduce the number of reaction components from thirty-five to seven, while also developing an accessible 'fast lysate' preparation protocol that eliminates time-consuming runoff and dialysis steps. The authors also sought to demonstrate the robustness and translational quality of this streamlined system by efficiently synthesising challenging functional proteins, including the cytotoxic restriction endonuclease BsaI and the self-assembling intermediate filament protein vimentin.

Strengths:

This study presents several key strengths of the optimised E. coli cell-free protein synthesis system in terms of its design, performance and accessibility.<br /> - The reaction mixture has been dramatically simplified, with the number of essential core components successfully reduced from up to thirty-five in conventional systems to just seven.<br /> - The "fast lysate" protocol is a significant advance in terms of procedure.<br /> - The system's ability to synthesise challenging, functional proteins is evidence of its robustness.

Comments on previous version.

The authors have adequately addressed my previous concerns.

Reviewer #1 (Public review):

Summary:

This is an important study that describes the consequences of the DNMT3A mutation in human neuronal development for the first time. The selective impact of DNMT3A function on GABAergic interneurons is interesting and an important feature of future therapeutics. The claims made in that manuscript are supported by strong evidence for the most part. And the data are of high quality in general and presented well.

Strengths:

The strengths of the work include 1. Characterization of multiple DNMT3A loss-of-function alleles, including two misense variants, R882H, P904L, and a deletion allele. The missense mutation lines both include an ideal control with the same genetic background. The CRISPRi-mediated DNMT3A knockdown has also been included. The study identifies the mTOR-PI3K pathway as a factor of overgrowth issues found in the mutant organoid. In bulk mRNA sequencing and whole-genome bisulfite sequencing, identify hypomethylated genomic regions associated with gene expression repression. Again, this is more pronounced in the ventral organoid compared to the dorsal organoid. In addition, the extensive electrophysiological characterizations with a high-density microelectrode array support the more mature status of mutant interneurons.

Weaknesses:

Although a strong study overall, some weaknesses are noted. These include:

(1) The lack of validation data for the generated iPSCs and hESCs, such as the chromosomal contents, ploidy, and pluripotency states

(2) Other weaknesses relate to data interpretation and insufficient discussion of related matters, as detailed in the recommendations to the authors.

(3) Also, some errors are noted and detailed in the recommendation section.

Comments on the latest version:

I have reviewed the revised manuscript and the authors' responses to the reviewers' comments. They addressed the comments adequately.

Reviewer #2 (Public review):

Summary:

Chapman, Determan et al. investigate how pathogenic mutations in DNMT3A which cause of Tatton-Brown-Rahman Syndrome (TBRS) disrupt human cortical developmental processes using a comprehensive panel of human pluripotent stem cell models spanning DNMT3A loss-of-function severity. The authors aim to identify the cellular and molecular mechanisms underlying TBRS-associated brain overgrowth and intellectual disability, and to test whether mechanistic convergence exists between TBRS and other overgrowth-intellectual disability disorders (OGIDs) caused by mutations in EZH2 (Weaver syndrome) or PIK3CA pathway components. Their central conclusion is that GABAergic interneuron development is selectively vulnerable to DNMT3A mutation where reduced DNA methylation causes premature de-repression of neuronal and synaptic genes, driving precocious neuronal maturation and hyperactivity sufficient to disrupt neuronal network synchrony. This report adds to a growing literature supporting the vulnerability of GABAergic interneurons in NDDs and further provides a mechanistic view of this vulnerability potentially convergent across OGIDs. The mechanistic claims around H3K27me3 compensation and mTOR-based therapeutic convergence, while promising, rest on more preliminary evidence and would benefit from the distinction between correlation and mechanism being made more explicit in the text. Overall, this is a compelling study with rigorous experimental design and novel findings with potential impact across better understanding OGID pathophysiology.

Strengths:

(1) A major strength of this work is the breadth and rigor of the disease modeling approach. Four independent TBRS model systems are used in tandem: a patient-derived iPSC line with isogenic CRISPR-corrected control (R882H), a knock-in hESC model (P904L) with its wild-type isogenic, patient deletion iPSC lines (Del1/2), and CRISPRi knockdown models (G1/G2), collectively spanning a range of DNMT3A loss-of-function that correlates with phenotypic severity. This allelic series design substantially strengthens causal inference beyond what any single isogenic pair could provide.

(2) The multi-omic integration across matched developmental stages provides a strong mechanistic foundation for the cellular phenotyping and provides significantly enhanced novelty. RNA-seq, whole-genome bisulfite sequencing, and H3K27me3 CUT&Tag are combined in the same cell types and timepoints show that DNMT3A loss reduces CG methylation at neuronal and synaptic gene loci, leading to premature transcriptional activation.

(3) The selective vulnerability of ventral (GABAergic) versus dorsal (glutamatergic) progenitors is one of the study's most important findings. This lineage specificity is consistently observed across all model systems and in both 2D and organoid formats, where ventral NPCs show increased proliferation, premature neuronal gene expression, and increased neurogenesis, while dorsal NPCs are largely unaffected at the transcriptomic and cellular level despite exhibiting comparable DNA methylation changes. This adds to a body of emerging work showing GABAergic interneuron vulnerability in NDDs where ubiquitously expressed genes such as chromatin modifiers are perturbed and provides additional molecular insights into potential mechanisms of "resilience" of dorsal populations.

(4) The functional characterization follows a logical progression from single-neuron electrophysiology (demonstrating GABAergic hyperactivity with increased action potential amplitude and firing rate) to network-level analysis using high-density multi-electrode arrays. The HD-MEA experimental design - pairing TBRS or control GABAergic neurons with a constant background of control iGlut neurons - cleanly isolates GABAergic dysfunction as the driver of network hypersynchrony.

Weaknesses:

(1) The concomitant induction of proliferation and differentiation in TBRS V-NPCs is conceptually striking, since these are generally considered antagonistic developmental programs. The authors clarify that neuronal and synaptic gene de-repression is the more prominent direct consequence of mCG loss, while PIK3/AKT/mTOR pathway upregulation is not itself directly linked to differentially methylated regions, suggesting an indirect relationship between DNMT3A LOF and increased proliferative signaling. This framing is reasonable, but the mechanism linking DNMT3A mutation to mTOR activation remains unresolved, and the manuscript would benefit from being explicit about this gap. Relatedly, the rapamycin rescue, while demonstrated across multiple models including 904 and Del1 (Supplementary Fig. S3e-f), remains limited to proliferation readouts. Whether mTOR inhibition also rescues the downstream neurogenesis, maturation, or network phenotypes is an important open question that the authors appropriately frame as motivation for future work.

(2) The claim that H3K27me3 compensates for mCG loss is supported by prior work (Lii et al. 2022), which demonstrated increased PRC2 component expression and H3K27me3 gain at sites of DNA methylation loss in Dnmt3a knockout mouse neurons, and by data showing that PRC2 subunits (SUZ12, EED, EZH2) are significantly more highly expressed in D-NPCs than V-NPCs. Together, these findings provide a plausible molecular basis for why dorsal progenitors may be better equipped to maintain repression when DNA methylation is lost, and they make the EZH2 overexpression rescue in V-NPCs more interpretable. Yet, a formal distinction related to two competing, potentially underlying mechanisms, between active compensation, in which EZH2 is recruited to specific loci in response to methylation loss, and functional redundancy, in which higher baseline Polycomb occupancy in dorsal cells simply becomes the dominant repressive mark once mCG is reduced, has not been resolved.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors investigated TBRS etiology by using new human pluripotent stem cell models, modeling varying levels of TBRS-associated loss of DNMT3A function. They identified increased lineage-specific proliferation of precursors in TBRS ventral MGE-like progenitors, which they propose was related to increased signaling through the PIK3/AKT/mTOR pathway. Furthermore, they show that reduced DNA methylation during MGE-like progenitor differentiation into GABAergic interneurons can cause a premature expression of neuronal and synaptic genes, triggering precocious neuronal maturation. In conclusion, they propose that TBRS-derived GABAergic neurons exhibit hyperactivity that can alters the development and structure of neuronal networks.

Strengths:

Overall, the data presented is convincing, from an early developmental point of view, given that the iPSC-derived 2D cultures or organoids used do not get to reach a mature state. Nonetheless, the data clearly show the effects that deleterious mutations in TBRS can cause during the period of neurogenesis, which was missing in the field.

Comments on revised version.

The authors have responded to the reviewer's comments satisfactorily, and the manuscript has been much improved.

Reviewer #1 (Public review):

I thank the authors for their thoughtful and thorough responses, which address my concerns. Their two methodological changes: (1) the switch to Poisson stimulation and (2) the new LFP estimation pipeline, together with the expanded parameter-grid sweep and Kuramoto synchrony analysis, substantially strengthen the manuscript. The Poisson spike train better approximates the stochastic subcortical drive cortex receives in vivo and removes the artificiality of the original protocol (Point 1.2). The LFP pipeline directly resolves my concern about the disconnect between simulated voltages and experimental signals; showing that the macroscopic wave structure persists in the LFP-like proxy clarifies the framework's practical relevance (Point 1.6). The expanded per-band sweep addresses my worry that the Allen-connectivity advantage was confined to a narrow regime, and acknowledging the small delta-band difference is a more convincing presentation (Point 1.5). The Kuramoto analysis connects dynamics across scales and gives a clear, quantitative account of the non-monotonic coupling dependence (Points 1.4, 1.7). Finally, I appreciate that the remaining connectivity-realism issues (Points 1.3, 1.8) are now stated explicitly as limitations with concrete future directions. I agree that incorporating them is beyond the scope of the present study, and their upfront acknowledgement is appropriate.

Reviewer #2 (Public review):

Summary:

This work presents a spiking network model of traveling waves at the whole-brain scale in mouse neocortex. The authors use data from the Allen Institute to re-construct connectivity between different neocortical sites. They then quantify macroscopic traveling waves following stimulation of all layer 4 neurons in neocortex.

Strengths:

Overall, the results are interesting and shed new light on the dynamic organization of activity across neocortex of the mouse. The paper uses realistic neuron models specifically fit to intracellular recordings, demonstrating that traveling waves occur in the mouse neocortex with both realistic connectivity and realistic single-neuron dynamics. The paper is also well-written in general. For these reasons, the authors have generally achieved their aims in this work.

Weaknesses:

(1) Description of Algorithm 1: While the Methods section clearly explains the density parameter \rho, the statement on line 358 concerning the "ideal" average number of connections is a little unclear. The authors should explicitly clarify that \rho is a free parameter that can be adjusted to balance computational feasibility (for a given set of computational resources) and biological fidelity.

(2) Lines 102-103: The \rho parameter used here results in approximately 300 connections per neuron on average. The authors should state clearly that the number of connections per cell is the key determinant of computational feasibility (cf. Morrison et al., Neural Computation, 2005). The authors should also review neuronal density and synaptic connectivity in mouse neocortex and clearly reference density and connectivity in their model to the biological scales found in the mouse.

(3) Line 131: From the plots in Figure 2, it is not clear that the stimulus response is necessarily a rhythmic oscillation, in the sense of a single narrowband frequency.

(4) Line 217: Can the authors clarify how these findings relate to the results from Mohajerani et al. (Nature Neuroscience, 2013), or differ from them?

(5) Line 230: Because higher temporal frequency activity also tends to be more spatially localized, a correlation between PGD and temporal frequency could be an inherent consequence of this relationship, rather than a meaningful result.

(6) Line 247-248: It is not clear that the algorithm for generating connections between neurons presented here really relates to those for community detections. For example, in the case of the Allen Institute data, the communities are essentially in the data already.

(7) Line 284-285: The relationship between conduction delay is more direct than this sentence suggests. Conduction delay is fundamentally determined by the time required for action potentials to propagate along axons, making it intrinsically linked to anatomical distance.

(8) Line 287-288: The authors suggest at this point that they do not have enough information to estimate time delays due to axonal conduction along white matter fibers. However, experimental data from white matter connections typically includes information about fiber length, which does enable estimating conduction delays. These estimations have been previously implemented for Allen Institute connectome data in the mouse (Choi and Mihalas, PLoS Comput Biology, 2019) and human connectome data (Budzinski et al., Physical Review Research, 2023).

(9) Lines 294-295: Several methods do exist for detecting and characterizing wave dynamics in three-dimensional data (Budzinski et al., Physical Review Research, 2023).

Comments on revised version.

In this response and revised manuscript, the authors have addressed all points raised in the first round of review. In response to Point 2.7, however, is it not the case that the Allen dataset has the axonal lengths?

Reviewer #2 (Public review):

Summary:

The inability of the mammalian retina to regenerate poses a major clinical challenge. Much has been learned about the regenerative potential of the retina from teleost fish, where Müller glia (MG) are able proliferate and produce new neurons after injury. However, MG do not retain this potential in the mammalian retina. The authors showed previously that that forcing MG to re-enter the cell cycle by downregulating p27 and upregulating cyclin D1 could induce MG to dedifferentiate, but the results were transient, and these cells eventually reverted back to MG and did not form neurons. Here they expand on this to show that in MG, coupling forced cell cycle re-entry with deletion of Rbpj, which inhibits of the transcriptional effects of Notch signaling, induces some MG to proliferate and take on features of multiple cell types, including MG precursor cells, amacrine-like cells, and bipolar-like cells. This work lends valuable insight into the regenerative potential of mammalian MG, particularly when Notch signaling is manipulated.

Strengths:

The major claims of the authors are well-supported. They show convincingly and through multiple methods, including immunostaining, single nucleus RNA sequencing, and in situ hybridization, that coupling notch inhibition with cell cycle re-activation induces the expression of neuronal markers in mammalian MG. The sn-RNA-seq data is particularly valuable in demonstrating the induction of bipolar-cell subtypes. Edu labeling is effective in demonstrating the induction of proliferation, and the long-term viability of the generated neuron-like cells is intriguing.

Comments on revised version:

The authors sufficiently addressed all concerns. I particularly appreciate the additional experiments to demonstrate retinal function, and the edits to the text regarding retinal and cell function and retinal organization.

Reviewer #1 (Public review):

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

The manuscript by Ho and Schock investigates the role of the Z-disc protein Zasp52 during Drosophila flight muscle development. It was known before, mainly by findings from this group, that Zasp52 is required for normal sarcomere morphogenesis, specifically Z-disc morphogenesis in indirect flight muscles. But the exact molecular mechanism by which Zasp52 contributes, apart from the fact that it is localised there and is somehow involved in multimerization/cross-linking, was not clear. This paper proposes that an intrinsically disordered region (IDR) in Zasp52 is needed for some of its functions, by stabilising Zasp52 localisation at the Z-disc. Specifically, the IDR in Zasp52 is proposed to be required for Z-disc maintenance during the mechanical challenges of flight, while being dispensable for the initial morphogenesis during development. This hypothesis is supported by strong genetic evidence and behavioural tests, deleting Zasp's IDR impairs flight from mid-age onwards, while a block in flight activity lifts the phenotype.

Strengths:

(1) The linker in the alternatively spliced exon 15 of Zasp52 was deleted with a state-of-the-art genetic editing strategy. Surprisingly, flies are homozygous viable, showing that this long part of the Zasp52 protein is not essential for animal survival or sarcomere morphogenesis.

(2) The observed sarcomere phenotypes with age, especially the bending Z-discs, are new and exciting.

(3) The displayed EM images document interesting phenotypes.

(4) Most of the observed phenotypes can be rescued by re-expression of the long Zasp52 isoform, which does contain the IDR region, but not by a shorter one without it, suggesting that IDR is important.

(5) FRAP data measure the local turnover of a short-ZaspGFP and show that this increased in the Zasp mutant lacking the IDR domain, suggesting that Zasp-IDR might stabilise Zasp at the Z-disc.

(6) Interestingly, flight and sarcomere morphology phenotypes can be rescued by preventing the flies from flying, suggesting that they are mechanically induced.

Reviewer #2 (Public review):

Summary and Strengths:

This in-depth genetic analysis of Zasp52 function in Drosophila indirect flight muscle (IFM) provides an interesting perspective regarding the role of a partially disordered region (IDR) in exon 15e. This exon seems to be exclusively present in IFM and contributes to the prevention of myofibril disintegration during aging, likely due to interactions of this region with Z-disc insertion and/or stability. The addition of an isoform (PR) that lacks exon 15e serves as a nice control to illustrate the necessity of exon 15e in muscle structure and function. Overall, the manuscript is exceptionally well-written, logical, with nicely controlled experiments and detailed statistical analysis that largely support the conclusions drawn by the authors. While exon 15e is clearly involved in preventing muscle degeneration, a solid role for thin filament stability is not clearly shown (as mentioned in the abstract). In addition, which regions/how the proteins of the IDR may contribute are unclear.

Reviewer #1 (Public review):

This manuscript investigates how people use sequential social information when deciding how much to donate to charity. Across four preregistered experiments, participants first made baseline donations to a set of charities, then observed a sequence of donations from five other people whose mean and variability were experimentally manipulated, and finally made a second donation to the same charities. The authors ask whether the mean and variability of others' donations affect the mean and variability of participants' own donations, and whether individual differences in psychopathy and empathy are associated with responsiveness to social information.

The main behavioral finding is that participants shifted their second donations toward the mean of the donations they observed: generous social information increased donations, whereas stingy social information decreased donations. In contrast, the variability of observed donations had little effect on the mean donation shift, but did affect the variability of participants' subsequent donations, with more consistent social information producing stronger reductions in variability. The authors also fit several computational models and conclude that a hybrid model, in which second donations reflect both participants' initial donations and learned predictions of others' donations, best accounts for the data. Finally, they report that psychopathic traits are positively associated with donation change and with model-derived social-information use, and that this association generalizes to a perceptual social-influence task in Experiment 4.

The paper addresses an interesting question and has several strengths, especially the repeated experimental design, the direct manipulation of social-information statistics, and the attempt to connect descriptive behavior with computational modeling and individual-difference measures. However, several aspects of the design and analysis currently block some of the major conclusions. The behavioral results provide convincing evidence that observed donation levels affect later donation decisions. The current evidence is less decisive for the stronger claims that the winning computational model identifies the underlying mechanism, that individual-level model parameters are robust phenotypes, and that psychopathy specifically increases susceptibility to social information.

Strengths:

A major strength of the manuscript is that it investigates social influence in charitable giving across four preregistered experiments with relatively large samples. The core mean-effect result is replicated across different donation scales, across hypothetical and incentivized settings, and across student and more general online samples. This gives the descriptive behavioral finding substantially more credibility than would be available from a single experiment.

The experimental manipulation is also valuable. Rather than presenting only a single prior donation or a simple group average, the authors expose participants to sequences of donations and independently manipulate the mean and variability of this social information. This design allows the authors to ask not only whether social information changes donation levels, but also whether the distributional structure of that information changes the variability of participants' own responses.

Another strength is the combination of traditional statistical analyses with computational modeling. The hybrid model is a reasonable descriptive candidate because it formalizes the intuitive idea that second donations may depend both on participants' initial preferences and on learned expectations about others' donations. This modeling approach has the potential to clarify mechanisms of social-information use, especially if the validation of the model and its individual-level parameters is strengthened.

Experiment 4 is a sensible extension because it uses an incentivized design, includes a more diverse sample, examines transfer to novel charities, and adds a perceptual social-influence task. These features broaden the empirical scope of the manuscript and make the psychopathy-related findings more interesting, although the perceptual-task result should still be treated as requiring replication.

Weaknesses

The first limitation concerns causal interpretation of the phase effects. Participants always make baseline donations first, then observe social information, and then make second donations to the same charities. There is no non-social repeated-donation control condition. This type of design does support the conclusion that donation changes differ as a function of the observed social-information condition, especially the mean of others' donations. However, it does not by itself fully isolate social influence from other processes that could also occur between a first and second donation to the same item, such as repeated exposure to the charities, slider familiarity, memory of the first donation, regression to the mean, reduced uncertainty, fatigue, or "the experiment clearly wants me to update" demand effects. This issue is especially relevant for the claim that observing others' donations generally reduces the variability of individual donations. The variability effect may well be socially driven, but the absence of a non-social or irrelevant-information repeated-donation control means that this cannot be decisively demonstrated.

The second limitation concerns the trial-level mixed models. The primary mixed-effects models include random intercepts for participants and items, but do not appear to include random slopes for within-participant or within-item phase effects. Since phase is repeatedly manipulated within participants and items, random-intercept-only models may underestimate uncertainty for some phase interactions, resulting in anti-conservative p-values. The convergent participant-level ANOVA analyses are reassuring, but the trial-level inferential claims would be stronger if the authors reported additional analyses using fuller random-effects structures or other methods that better reflect the repeated-measures structure.

The third limitation concerns model comparison and model validation. The computational models are fit separately to each participant, and model comparison is based on summed information criteria and protected exceedance probabilities derived from those participant-level fits. This is informative about relative conditional fit within the tested sample and model set. However, the manuscript uses the winning model to support broader claims about latent computational mechanisms, individual computational phenotypes, psychopathy-related susceptibility, and potential intervention relevance. For these claims, the relevant prediction target is generalization to new participants, whose individual parameters are not known in advance. The current model-comparison approach is not well aligned with that target. Additionally, the loss appears to combine prediction trials and donation outcomes, so the selected model may more strongly reflect performance at predicting participants' guesses about others rather than specifically predicting their own donation decisions.

The fourth limitation concerns the model adequacy checks and recovery analyses. The analyses described as posterior predictive checks do not appear to be posterior predictive checks, because the models are not Bayesian and there consequently isn't a posterior to check. Instead, the analyses appear closer to some sort of in-sample fitted-value reconstruction checks. Such checks provide limited evidence of model adequacy, especially because the same second-donation data used to estimate individual parameters are then used to assess whether the fitted model reproduces the main behavioral patterns. In addition, the reported model and parameter recovery analyses use extremely favorable response-noise assumptions that are not expected to be met in real data. The analyses establish that the models and parameters are mathematically distinguishable in principle, but they do not establish that the individual-level parameters are reliably recoverable under realistic empirical noise levels to the extent required for the analyses performed in the manuscript.

The fifth limitation concerns the interpretation of the psychopathy results. The association between psychopathic traits and donation change is interesting and appears directionally consistent across experiments. However, the interpretation that psychopathy increases susceptibility to social information is vulnerable to biasing by baseline-distance. The manuscript reports that psychopathy is negatively associated with baseline donations in Experiments 1-3. Participants with lower baseline donations have more room to move toward generous social information, and absolute donation change is partly a function of the distance between the initial donation and the observed social mean for mechanical reasons. Thus, an association between psychopathy and absolute donation change could theoretically arise even if psychopathy does not directly increase social susceptibility.

A sixth limitation is that we could not find the links to the preregistration. The authors state when preregistered hypotheses were or were not supported, but it is unclear how these hypotheses were phrased. Most notably, it is unclear how variance in the observed donation choices was supposed to influence participants. As a side note, it was not quite clear if the variance in the observations was higher or lower across charities, across observed persons, or across both.

Several more minor suggestions can also be made regarding the modelling and the presentation of the task, etc.

Reviewer #2 (Public review):

Summary:

This manuscript examines how the statistical properties of others' charitable donations shape subsequent giving using four preregistered experiments and computational modelling. The authors find that both the average level and variability of observed donations influence donation behaviour, and that individual differences in social information use are associated with psychopathic traits.

Strengths:

This is a well-executed paper on the important question of how social information shapes charitable giving. In my view, the combination of preregistered experiments, large sample sizes, computational modelling, and a multi-paradigm approach makes for convincing evidence. The progression across experiments, the use of real donation data rather than deception, the incentivized experiment 4, and the generalization to a second paradigm are all notable strengths. The introduction is clearly written and well-motivated - an enjoyable read. The experimental paradigm is thoughtfully designed, and the methods and supplementary materials are described in considerable detail. The computational modelling provides useful additional insights beyond the behavioural analyses.

As far as I could tell, the manuscript also adheres closely to the preregistrations. The primary hypotheses, experimental designs, exclusion criteria, and key analyses are all consistent with the preregistered plans. Deviations seem to consist of methodological improvements (e.g., mixed-effects models replacing ANOVAs), additional computational and robustness analyses, and therefore strengthen rather than weaken the manuscript. (NB: for transparency, I would appreciate a clearer distinction between preregistered and post hoc analyses, as well as a brief explanation for why some preregistered secondary analyses are no longer reported; see minor comments below).

Overall, I enjoyed reading this paper. I believe it will make a valuable contribution. My comments below are intended to further strengthen an already solid manuscript.

Weaknesses:

(1) The rationale for the social-information phase could be clarified further. Given the research question, I wondered why participants observed the five donations sequentially (and only briefly) rather than simultaneously. In particular, variance is arguably more difficult than the mean to encode and remember, and a sequential presentation may both obscure distributional differences and introduce primacy or recency effects. It would be helpful if the authors could better motivate this design choice, and indicate whether they examined possible order effects.

Relatedly, I felt somewhat uncertain about the purpose of asking participants to predict each donation before observing it. The prediction phase appears to play an important role in the computational model, but its theoretical role is not clearly introduced. Is it intended as a measure of participants' evolving beliefs about the descriptive norm, or primarily as a modelling device? Finally, were these predictions incentivized (e.g., for accuracy), and if not, how should readers interpret them?

(2) I would appreciate having the full experimental materials reproduced in the Supplementary Information. This would make it easier to understand what participants experienced during the task, including what they were told about the "other participants" whose donations they observed.

Minor points:

(1) The interpretations around domain-generality would be strengthened by reporting the association between social information use in the charitable giving task and in the BEAST. Currently, both measures are shown to correlate with psychopathy, but it remains unclear whether individuals who rely strongly on social information in one task also do so in the other. Reporting this correlation (or explaining why it cannot be meaningfully computed) would provide a nice and direct test of a domain-general tendency to use social information.

(2) It would help to explain more explicitly why the standard deviation of donations is theoretically interesting in its own right. The motivation for studying the mean seems immediately intuitive, whereas the motivation for focusing on variability could be elaborated on further in the Introduction.

(3) As I said above, I think the manuscript follows the preregistrations closely. Maybe I missed it, but it seems that prediction accuracy and reaction-time analyses were omitted. It would improve transparency further if the authors would briefly mention the preregistered secondary analyses that are no longer reported (and explain why they were omitted).

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors aimed to assess the mechanisms of social influence on charitable giving, particularly by separating the role of donation magnitude and variability in others' donations, and by examining the role of incremental social information in a learning framework. They additionally investigated individual differences in the magnitude effects in relation to self-reported psychopathy and empathy. The main findings suggest that magnitude and variability of others' donation impacted the magnitude and variability of the participants' donations, respectively, and that the weight of social information on individual decisions correlates positively with psychopathy, but not with empathy.

Strengths:

(1) The findings extend previous evidence for social influence on charitable giving to contexts where social information is provided incrementally, and to effects on the variability in social information (in addition to the mean).

(2) Individual differences suggest a role for psychopathy, but not empathy.

(3) Findings are replicated across all 4 (or for some findings 3 out of the 4) experiments, which helps strengthen the claims.

(4) Multiple experiments are a strength, especially Experiment 4, which helped address concerns/potential confounds in the previous experiments, increase representativeness of the sample, add incentive compatibility, and generalize to another task domain (perceptual).

(5) For modelling, strong model and parameter recovery was obtained, thus validating the modelling pipelines.

(6) The experiments were pre-registered, though it's unclear whether only planned analyses were pre-registered, or specific directional hypotheses. It would help if the manuscript took the reader through the pre-registration (and any deviation from it), instead of expecting the reader to do the comparison between the pre-registrations and actual manuscripts.

(7) The studies are appropriately powered, and power analyses are provided.

Weaknesses

(1) Lack of rationale and justification for the between-subjects design.

While this design may be appropriate in some cases (for example, for the generalization of donation to new charities or as a potential "intervention"), it would have been great to know if the findings related to social influence extend to a within-subjects design, especially given the weak results related to the effects of standard deviation in others' donations. It is possible that variability in others' responses would have a stronger effect if manipulated within individuals, since the same individual exposed to both high-SD and low-SD social information may weight low-SD information more, but this effect may lack when individuals are only exposed to the same variability across trials.

(2) Motivation for the RL framework.

The use of reinforcement learning (RL) isn't very well motivated, both in the introduction and methods/results (given the task). In particular, why is RL relevant to studying the problem of social influence, which isn't inherently a learning problem? This should be better motivated in the introduction. Second, when taking the task into account, it's unclear why RL is an appropriate model, given that from the perspective of the participant, the 5 others are different individuals, so the model shouldn't assume that predicting an individual's donation should be related to the previous individual's donation. Unless participants are informed that there is some dependency between the 5 donors they observe on each trial? If so, this should be made clear.

(3) Specifics of modelling analyses, and separability between prediction and second donation data.

Does the RL-based model (either prediction-only or hybrid) explain more variance in second donations than a simple linear regression model predicting second donation from initial donation and the mean of others' donations (or each individual other's donation)? It could be helpful to add some models that include social influence (i.e., integration of social and individual information) but no learning mechanisms per se. If this is not done, I do not believe that current results show that participants combine "their initial self-donation tendencies with their predictions of observed others' giving to guide their second individual donations". While participants may update their predictions, the authors should test multiple models of prediction update (fit only on the prediction data to understand the specific mechanisms of prediction update independently of second donation - for example, is it RL, or could it just be a running average, or some other heuristic? In parallel, it would be helpful to test whether it's the learned predictions (or whatever other prediction update mechanism was found to best explain the prediction data) or the actual others' donation information that best explains second donation - when combined with initial donation. These latter models would be fit on second donation data only in order to be comparable. If it's not possible to separate people's predictions from the actual social information (others' donations) then this should be acknowledged as a limitation. Ultimately, separating the modelling by data type (prediction only vs second donation data only) would help provide more insights into the learning mechanisms (if any) and whether it's learned prediction, or just social information, which influences second donation.

(4) Missing statistics in generalization to novel donation results.

On page 13, in the generalization effect, the authors mention that "Compared with participants exposed to High-SD social information, those exposed to Low-SD social information exhibited less variability in their novel donations, with this effect being especially pronounced in the Low-Mean condition." Was this supported by a significant interaction between SD and Mean condition? If so, please report the statistics of the interaction; if not, it's probably better to refrain from making this claim.

(5) Behavioral index of social influence individual differences.

For the first analysis reported on the association with psychopathy (Figure S9), as well as empathy (Figure S10), the absolute change between first and second donation does not seem like the appropriate marker of social influence. While I understand from Figure 2 that most participants changed their donation in a direction consistent with the social information, it would appear more appropriate to calculate an index of donation change consistent with influence, so calculated as D2 - D1 for the high mean groups and D1 - D2 for the low mean groups. This would be a better measure to interpret high values as an index of social influence.

(6) Interpretation of psychopathy effects.

a) The general idea that high psychopathy would be associated with increased social influence seems counterintuitive. While I appreciate that the authors controlled for additional variables such as age, gender, condition, and other model parameters, is it possible that this effect could be instead explained by the availability heuristic (the social information is more readily available to participants than their individual choice from the baseline trials), lower memory for their own choice, or lower IQ/cognitive abilities? These appear to be important confounds to address to be able to interpret the findings.

b) Related to this, and given that psychopathy/empathy were negatively/positively related to baseline donation amounts, it would be good to account for baseline mean donation amount in the individual difference analyses.

c) Finally, the authors interpret this association in line with other studies that have shown strategic social blending in psychopathy - while this seems possible in contexts where others are present, it doesn't really seem to be the case in this task. Did participants believe the other donors were watching them somehow? It also appears contradictory for the incentivized experiment, whereby if high psychopathy participants would no longer be able to "maintain a favorable social image while still pursuing their own self-interests" (p.23), since as soon as incentivization is added, participants' own self-interests are directly in conflict with the social image. Was participants' understanding of the incentive compatibility tested in Experiment 4?

(7) Asymmetry between generous vs stingy social influence and link with psychopathy.

a) Was such an asymmetry present - in other words, were people more strongly influenced by generous others or stingy others, or were the two comparable? I believe some analyses could be added to test this, and this is also where a within-subject design could help (e.g., different parameters for the two directions of social influence at the individual levels).

b) Related to that, does the correlation with psychopathy vary between conditions? It appears important to test if the increased social susceptibility is general or specific to increases (~high mean group, generous social influence) or decreases (~low mean group, stingy social influence) in donation. I understand that the main effect of psychopathy survived controlling for conditions, but it would still be interesting to test for an interaction between psychopathy and condition in predicting donation changes (calculated as suggested in point 5 above) or social influence weight.

(8) Perceptual task in Experiment 4.

a) While it is good to show that there was no correlation between psychopathy and initial estimate in the perceptual task, were there differences in initial estimate accuracy (i.e., difference between initial estimate and correct answer) along psychopathology? If so, this should be controlled for in the analyses. Given that social influence is always in the direction of the true value, the proportional deviations between initial estimate and social information could yield larger numerical differences and induce larger changes in estimate.

b) Even if previous studies have excluded rounds in which participants update their estimate in the opposite direction of the social information or move beyond it, I believe analyses that include those rounds should be included, especially in the context of individual difference analyses. Could it be that individuals who are high in psychopathy or low in empathy have a higher proportion of rounds where they go against the social influence? The same question applies to the main 4 experiments (in case this criterion was applied to) as well as the perceptual task.

c) Because the perceptual task was completed by the same participants as Experiment 4, were the two social influence measures correlated across tasks? Was psychopathy better predicted by a combination of predictors across the two tasks?

(9) Were individual difference measures examined in relation to the variability effect?

(10) Discussion.

The authors argue against a role for opportunistic conformity. While I tend to agree with their interpretation, I believe that it could be strengthened as follows:

a) First, it relies on a null result (the absence of a difference in decreases between low-mean low-SD and low-mean high-SD groups), which I do not believe was explicitly tested; and even if it was, it should ideally be corroborated by Bayesian statistics to provide strength of evidence for the null effect.

b) Second, this could be a great opportunity to dive into the mechanisms of social influence in the model, by testing the theory that only the lowest (or highest) donation from the group (rather than the mean, or the learned prediction) influences donation. Could a subset of participants be better fitted by such a model?

(11) Methods. Maybe I missed it, but it's unclear what participants were told about the other donors they are observing. It is mentioned that they were fully debriefed after the experiment, but what they were told in the instructions appears important. Was believability tested (this also relates to my comment #1 about the rationale for a between-subjects design, which creates fairly biased sets of social information from the perspective of a single participant)? And related to my comment #2, what participants were told about the donors could help justify the rationale for the RL framework.

Reviewer #1 (Public review):

Summary:

This manuscript investigates how IRF4 and BLIMP1 coordinate human plasma cell differentiation. Using a stepwise in vitro culture system starting from primary human naïve B cells, the authors define a developmental window enriched for plasma cell precursors and use stage-specific CRISPR/Cas9 perturbation to examine the roles of IRF4 and PRDM1/BLIMP1 during the transition from plasmablast-like precursors to plasma cells. Single-cell transcriptomic analyses suggest that IRF4 acts early to license plasma cell differentiation, whereas BLIMP1 contributes more prominently to consolidation of the terminal plasma cell program. The authors further combine multiome profiling, CUT&RUN, motif modeling, and EMSA assays to propose the sublet nucleotide variation within ISRE/EICE-like motifs contributes to differential or shared binding by IRF4 and BLIMP1.

Overall, this is a carefully performed and conceptually interesting study. It provides a useful experimental platform for dissecting human plasma cell differentiation and offers a mechanistic model for how two closely connected transcription factors can exert distinct and coordinated genomic functions during terminal B cell differentiation.

Strengths:

A major strength of the study is the establishment and detailed characterization of a human in vitro plasma cell differentiation system. The authors combine phenotypic, functional, and single-cell transcriptomic analyses to define the transition from activated B cells to plasmablst/plasma cell precursor-like cells and then to more mature plasma cells. This system is very useful for future perturbation studies of human plasma cell differentiation.

A second strength is the stage-specific perturbation strategy. By targeting IRF4 or PRDM1 at the precursor-enriched stage, the authors avoid some of the interpretive limitations associated with earlier perturbations that would affect B cell activation, proliferation, and plasma cell commitment simultaneously. The distinct phenotypes observed after IRF4 versus PRDM1 perturbation provide support for a model in which these two factors act in a temporally ordered manner.

A third strength is the integration of multiple genomic and biochemical approaches. The combination of single-cell RNA-seq, chromatin accessibility profiling, CUT&RUN, computational motif analysis, and EMSA assays provides a rich dataset and supports the idea that ISRE/EICE sequence variation contributes to differential IRF4 and BLIMP1 occupancy.

Weaknesses:

While the multi-omic approach and computational modeling are highly impressive, several major assumptions regarding the cellular differentiation model and genomic linkages require more rigorous validation.

First, because CRISPR editing was performed on heterogeneous bulk Day 7 cells rather than purified precursor populations, it remains ambiguous whether the observed developmental blocks are truly specific to the prePC window.

Second, given that IRF4 and BLIMP1 operate within a mutually reinforcing positive feedback loop, the phenotypic divergence between IRF4 KO and PRDM1 KO may reflect differences in protein degradation kinetics or hierarchical dominance rather than a strictly ordered "sequential function".

Lastly, the motif-lexicon model is elegant and supported by biochemical DNA-binding assays, but the link between motif variation and gene regulation in cells remains partly correlative. Direct testing of selected regulatory elements would make the causal claim stronger. Alternatively, the authors should temper the language and present the motif lexicon as a predictive model for differential occupancy rather than as a fully demonstrated mechanism of gene regulation.

Reviewer #2 (Public review):

Summary:

The manuscript by Lau et al. investigates the mechanisms underlying IRF4 and BLIMP1 transcriptional activities during antibody-secreting cell fate decision. Both master regulators of plasma cell differentiation, these two transcription factors have distinct targets and non-overlapping roles. The authors used an in vitro culture system to generate antibody-secreting cells from human naïve B cells, and scRNA-seq, Crispr Cas9 editing, and Cut&Run to dissect the molecular mechanisms defining their specificity.

Strengths:

The experiments are overall well executed, and the manuscript is well written. The in vitro culture model appears to generate genuine human antibody-secreting cells. The identification of non-conserved nucleotides within the binding motifs that induce the specific binding of IRF4 or BLIMP1 is convincing, novel, and exciting.

Weaknesses:

The authors need to correct some overstatements and flaws to improve the manuscript.

In Figure 1f, the authors aimed to determine whether in their culture system the plasma cells emerged from the plasmablasts or directly from the activated B cells. First, it is noticeable that the distinction between plasmablasts and plasma cells relies here only on the expression of CD138. It does not include a higher capacity to secrete antibody or their proliferative state. In Figure 1e, the authors could have strengthened their distinction by showing the Ki67 staining at day 21 for both subpopulations. Second, this question does not seem to be related to IRF4 or Blimp1 activity, and thus one could wonder if it is relevant to this study. Finally, and most importantly, the design of the experiment appears flawed to me. The authors sorted cells at day 7 of culture based on their expression of CD20 and put the two subpopulations back for 14 more days. This culture system is a stepwise system, and it is not specified if the CD20+ cells were put back in the day 7 condition or the day 0 condition with the CD40L stimulation. Have both conditions been tested? This experiment also assumes that all B cells have equal potential to differentiate into antibody-secreting cells. What if it is not the case and some are anergic or have committed to the memory B cell fate during the first 7 days? Then the day 7 CD20+ fraction would be enriched in these cells. Moreover, this experiment didn't show that the plasma cell derived from the plasmablasts in the strict sense of the term, as the CD138+CD20- cells could be a mix of proliferative plasmablasts and immature plasma cells.

In Figure 3a and thereafter, the authors claimed that IRF4 acted earlier than BLIMP1, but both deletions strongly affected differentiation at day 7. IRF4 might have a stronger effect, but it does not mean that it had an earlier effect. To substantiate their claim, the authors would need to demonstrate that, at an earlier time point, deletion of IRF4, but not BLIMP1, results in defective differentiation.

In Figure 3b, the authors stated that in each individual KO the expression of the other transcription factor was lower. Given that there were no cells in the gate, it is puzzling to figure out how these expressions were compared.

In Figure 3c, on the UMAP the bottom right part of the activated B cell cluster does not appear to be attributed to any condition. How can it be? Besides, it is highly surprising that at D9 we cannot see any plasmablast on these UMAP, even in the control. Based on the G1/S and G2/M scores, none of the ASC represented were proliferating. Could the authors explain this strong discrepancy with Figure 1?

Another discrepancy exists between Figure 3b and c: Figure 3b depicted no IRF4- or BLIMP1-expressing cells in either KO, so what were the stunted PC and the BLIMP-KO PC reported in Figure 3c? What are the signature genes defining pre-PC and the score depicted in Supplementary Figure 3d, as the materials and methods only state that they are intermediate between PC and B cells? Could the authors show IRF4, BLIMP1 and some of their known target expression in these populations?

The authors claim that BLIMP1 is not needed to initiate the transition from pre-PC to PC, but in Figure 1, the intracellular staining showed that at day 7 the antibody secreting cells already expressed BLIMP1. This would rather suggest that BLIMP1, unlike IRF4, does not need to be maintained once the cell reaches a certain point.

Reviewer #1 (Public review):

In their submitted manuscript, Harkinish-Murray and colleagues from the Kozol lab present convincing evidence for a genetically encoded shift in the odor perception of cavefish compared to their surface ancestors. Surface Astyanax, just as zebrafish, are attracted to food odors and are repelled by death odors and the alarm substance Schreckstoff (released from damaged skin by specialized club cells). Based on the experimental evidence in this manuscript, however, their cavefish counterparts are attracted to these odors as well. This would make sense, in an evolutionary framework, as predation is less likely in cave settings and decaying fish are a valuable source of nutrients for their living counterparts.

Using an F2 hybrid cross scheme between surface fish and cavefish, authors also provide compelling evidence that genetic factors are behind this behavioral shift. Furthermore, they also show that this behavior (i.e., attraction to skin and decay extracts) can be observed in surface fish given long enough food deprivation. This latter observation also makes sense in the light of evolution and is genuinely interesting as it also provides a plausible roadmap to the shift in behavior through Waddingtonian genetic assimilation.

The manuscript is generally well written and clear, we have identified only few weaknesses, some regarding the presentation of the data.

(1) For Figure 3, on the x-axis of panels b, e, and h, supposedly we see surface fish vs. different cavefish populations. This is currently missing and makes the figure harder to interpret. Also, two populations (panel e) show a bimodal distribution upon indirect white light exposure, suggesting that some fish still acted as if they were exposed to direct light, while others acted as if they were in darkness (infrared light). We believe this warrants more consideration as it could tell us something about the existing (and relevant) genetic variance within this population. It is also notable that the third cavefish population also showed increased odor indices under indirect white light and infrared light conditions, suggesting that increasing the number of observations could have yielded a statistically significant result.

(2) Some extra details about the methods could also be provided to enhance the reproducibility of the experiments.

(3) A more serious concern is about the anatomical designation of particular brain regions in Figure 7d and consequently Figure 7f. Whereas we would agree with the positioning of the medial pallium (Dm), we think the region depicting the thalamus is in fact still part of the telencephalon, and the real thalamus should be more posteriorly. On the other hand, we think that the preoptic areas should be under the pallium and not posterior to it (see PMID: 22586363 for corresponding zebrafish anatomy). We would suggest, therefore, that the authors revisit this issue (a minor one, considering the depth of the results presented in the manuscript), and provide a better anatomical annotation - e.g., the identity of particular brain regions could be backed up by Hybridization Chain Reaction experiments for region-specific transcripts. (Disclaimer: we do not consider ourselves experts in adult cavefish neuroanatomy; therefore, we consulted in this case a colleague with much more knowledge on this topic.)

(4) It would also be useful to expand the brain imaging data displaying results for similar tests in surface fish, to see if skin and decay extracts trigger different or similar brain activity in those fish.

Further work will surely be able to discern the more precise genetic changes that made the shift in behavior possible. Once these causative variants (or at least linked markers) are determined, it will be quite revealing to see if these variants are indeed already present in the surface population (as hinted by the authors), and also, if besides the Surface x Tinaja F2 hybrids, crosses between other cave populations and surface fish can be performed, we could also see how much evolutionary convergence happened in the parallel evolution of different cave morphs. Were there multiple possible pathways for similar behaviors in different cave populations, or - as in freshwater stickleback populations - do we see broadly the same genetic playbook repeated each time?

Another outstanding question, also demonstrated and discussed, albeit briefly, in this paper relates to the behavior-modulating effect of light in cavefish. What is the physiological relevance for a dark-dwelling animal to have this capacity? Is this just the chance result of occasional gene flow from surface populations, or does it have a genuine evolutionary significance?

Reviewer #2 (Public review):

Summary:

The authors tested whether the olfactory cues that drive attraction or avoidance behavior have diverged between surface‑dwelling and cave‑adapted strains of the Mexican cavefish Astyanax mexicanus. They use high‑throughput odor‑discrimination assays between known attractants and repellents by calculating an "odor index" per fish (=the difference in time spent in an odor zone versus a control zone). Further, hybrid crosses to probe heritability, starvation experiments to assess plasticity of odor perception, and whole‑brain pERK detection/mapping to link behavioral changes with known localized neural activity. The results support the hypothesis that the extreme cave environment has selected for an approach response to stimuli that are ancestrally aversive (like alarm or death odors) but in harsh environments can be used as guidance to the rare food sources in this ecosystem.

Strengths:

The odor index analysis is convincing, and the experiments for odor attraction/avoidance are robustly performed. The light-to-darkness shift reflected by avoidance to attraction in cavefish towards skin odors is compelling and carefully analyzed. The analysis of odor indices of three cave-dwelling populations in comparison to surface fish highlights a similar regime, yet with differences among the different populations, suggesting population-specific genetic variation.

Another strength of the paper is exactly this genetic inheritance study by generating F2 hybrids of cave-dwelling and surface-living individuals. The hybrids displayed a continuous range of odor indices for social, alarm, and death odors, indicating that these traits are heritable and likely based on additive genetic markers. Further, the authors uncovered a sexual dimorphism: only female cavefish exhibited approach behavior to social odors, whereas males remained neutral. This result aligns with known differences in olfactory organ morphology between sexes of other species from harsh environments.

Although limited in number, the neurophysiological correlation using whole‑brain pERK mapping after 10 min of odor exposure is convincing. The data revealed overlapping activation in the thalamus and pre‑optic region for food and decay odors, suggesting that these brain areas mediate the evolved attraction response to previously repellent stimuli.

Overall, the manuscript presents a concise story: cavefish have evolved attraction to alarm and death odors as a result of shifting from ancestral avoidance-driven to attraction by genetic changes and physiologically similar activation of specific neural circuits. The evidence is robust, with multiple independent experiments (behavioral assays, hybrid genetics, starvation experiments, and brain mapping) that collectively support the conclusions.

Furthermore, exposure to unpleasant odors can not only be tolerated but can even serve as a trigger for foraging. This plasticity demonstrates that genetic predispositions can be put into practice through active changes in physiology in species or organisms confronted with (drastically) changing environmental conditions.

Weaknesses:

I value that the authors are critical of their own data, indicating low numbers in the pERK/brain experiments. Yet this is a weak point as the statistical power is thus limited. However, their reasoning is careful, based on the results and not over-interpreting.

The layout/design of the ethograms (bout category plots) for both individual and population-wise are not easy to follow. Reworking these display items to convey the information is necessary.

Taken together, the manuscript uses odor perception and attraction/avoidance behavior studies to show that environmental changes (light-to-darkness) have an immediate impact on smell perception and behavior. Attraction to otherwise repellent odors is used by cavefish to likely adapt to harsh environments with low food sources. The manuscript convincingly demonstrates this plasticity, which is an interesting idea to follow up for other traits spreading among a population. This also underlines that a genome may be fixed and the blueprint for behavioral traits, but extrinsic cues can readily be adapted to change wired behavior even to the extreme as reported here: changing avoidance to attraction.

Reviewer #1 (Public review):

Summary:

This manuscript uses sci-L3-Strand-seq to map sister chromatid exchange events following CRISPR/Cas9-induced DNA damage. Because exchanges between identical sister chromatids are largely invisible to conventional sequencing, the study addresses an important blind spot in the assessment of genome editing outcomes. The authors compare single-locus Cas9 cleavage, simultaneous targeting of 237 repetitive genomic sites, and Cas9 nickase variants. They further use reciprocal daughter-cell pair analysis to ask whether Cas9-associated SCEs are copy-neutral or linked to larger structural alterations. Overall, this is a valuable study that introduces an important additional layer to the analysis of CRISPR/Cas9 repair outcomes. The central finding that Cas9-induced DSBs can trigger frequent local SCE is well supported and likely to be of broad interest. The evidence for structural complexity associated with some induced SCEs is intriguing, but the mechanistic interpretation should either be tested directly or presented more cautiously.

Strengths:

The major strength of the manuscript is the application of a strand-resolved, single-cell method to a question that is difficult to address with standard genome sequencing. The evidence that a single Cas9-induced DSB can trigger strong local SCE is compelling in concept and supported by multiple guide RNAs targeting distinct loci. The reported on-target SCE frequencies, reaching up to 41%, suggest that inter-sister exchange is a substantial and underappreciated outcome of Cas9 cleavage.

Of particular interest is the comparison between single-site and multi-site targeting. The finding that 237 programmed Cas9 targets produce only mild bulk enrichment of on-target SCE but stronger enrichment in a subpopulation of cells with elevated SCE burden is interesting and may have wider biological implications, particularly if the findings extend beyond Cas9-induced SCE to spontaneous SCEs. Given that potential, the current manuscript would benefit greatly from any experiments characterizing this sub-population: are these cells in a particular cell cycle state, experiencing changes in gene expression, or do they have other unique biological properties?

The reciprocal daughter-cell pair analysis is another notable feature of the study. The observation that some Cas9-associated SCEs are accompanied by structural alterations could challenge the assumption that SCE after a programmed break reflects error-free homologous recombination.

Weaknesses:

The number of informative RDCPs is limited, and the mechanistic interpretation of the "WWC-or-WCC/deletion" signature is more suggestive than definitive. In particular, the manuscript invokes (even though only in the Discussion section) URR or replication-termination-zone resolution and discusses TRAIP-dependent CMG unloading, nuclease cleavage, and polymerase theta-mediated joining, but these pathway components are not directly tested herein. A more conservative conclusion that some Cas9-associated SCEs coincide with structural alterations is more appropriate, particularly in the Discussion and Conclusion. For example, the statement that this work provides "direct genetic evidence" for a URR-type mechanism is overstated unless supported by additional experiments or a more extensive analysis of alternative models. Similarly, while the authors explain the limitations of acute Cas9 disruption of LIG3, LIG4, XRCC1, and XRCC4, the manuscript should clarify what biological questions this experiment can and cannot answer.

Reviewer #2 (Public review):

Summary:

In this short paper, a clever single-cell Strand-seq method was used to study the number and location of sister chromatid exchange events (SCEs) in cells after CRISPR/Cas9-induced DNA double-strand breaks (DSBs). Unique as well as multiple genomic loci were targeted. Cas9-induced cuts at unique genomic locations led to statistical enrichment of SCEs at the target site, whereas Cas9 targeted at repetitive targets revealed only mild enrichment of on-target SCEs unless analysis was restricted to a subset of cells with >8 SCEs per cell. Interestingly, reciprocal daughter-cell pair analysis revealed large-scale structural alterations on some chromosomes. Whereas disruption of DNA repair genes, including LIG3, LIG4, XRCC1, and XRCC4, did not measurably alter SCE frequency per cell within 24 hrs, consistent with delayed functional loss following editing and selection against essential genes. Together, these findings demonstrate that Cas9-induced DSBs are potent local triggers of SCE at unique loci and can be associated with structural alterations, highlighting the influence of lesion type and genomic context on recombination outcomes during genome editing.

Strengths:

The data in this paper represent a very rich resource of how parental DNA template strands are distributed in paired daughter cells after various treatments. Abnormalities observed in only one of such paired daughter cells provide a novel and exciting approach to study mechanisms of DNA instability and DNA repair at a genome-wide level in general and following Cas9-induced DSB in particular.

Weaknesses:

The effect of Cas9-induced DSBs in the cells that are used will depend on the cell cycle stage of the cells that are targeted, as well as the number of times cuts are made. The latter could happen before, during, and after DNA repair reactions on one or both alleles in a diploid cell. As a result, it is very difficult to extrapolate the mechanisms of DNA instability and DNA repair from the observed genomic rearrangements. Novel approaches are needed to limit the number and timing of Cas9-induced breaks to overcome some of these limitations. The language and logic in the paper can be improved, and some of the claims seem incorrect. For example, the abstract reads "A single Cas9 cut at a unique genomic locus led to strong local enrichment of SCE at the break site, reaching up to 41% in the same cell cycle and 17% in the subsequent division, indicating that DSB repair frequently engages non-local inter-sister repair." The evidence that only a single Cas9 cut was made is lacking (see my earlier comment); it is not clear how local enrichment or non-local inter-sister repair are defined.

Reviewer #3 (Public review):

Summary:

Chovanec and Yin used their newly developed sci-L3-Strand-seq powerful method to characterize SCE after Cas9 cleavage in a human cell line, using either a single target site or an element repeated 237 times in the genome. SCE are often neglected in DNA repair analyses since they are “genetically silent”. Interestingly, the authors found enrichment of SCE at unique Cas9 sites, but only a modest enrichment of SCE when Cas9 targets 237 sites in the genome. The genetic control of SCE formation at Cas9 sites is not deliberately addressed in this paper. However, the authors found that targeted SCE seem to be enriched in a subpopulation of cells, particularly “permissive” for SCE, but the determinants of such a population are unknown. Finally, the power of the sci-L3-Strand-seq allowed the authors to characterize a specific type of SCE based on the analysis of reciprocal daughter-cell pairs' genomes that is associated with a specific type of chromosomal rearrangement compatible with the ones observed in HR defective BRCA1/2 deficient cells.

Strengths:

This is an interesting paper that molecularly explores sister chromatid exchanges, which represent an important challenge in molecular biology since they are genetically silent.

Weaknesses:

A complexity of the current paper is that it heavily relies on a recently published paper (Chovanec et al 2026, NAR) describing the powerful but complex technique sci-L3-Strand-seq. Knowledge of this paper is a prerequisite to understanding the current manuscript because no reminder is provided. In addition, the current manuscript presents the use of the sci-L3-Strand-seq technique in the study of SCE after Cas9-induced DSBs, while a companion study is referred to several times for containing results about SCE in XRCC1 KO. At some point, one questions the relevance of splitting the use of sci-L3-Strand-seq in different papers instead of making a single integrated one.

Joint Public Review

Summary:

In this study, the authors investigated the developmental and molecular basis of the unusual metamorphic program of the black soldier fly, Hermetia illucens, which differs from the canonical holometabolous life cycle by inserting a distinct, non-feeding prepupal instar between the final larval stage and pupation. Most insects that undergo complete metamorphosis molt to the final instar and then develop into the prepupal stage without molting. H. illucens, however, undergoes a molt before entering a non-feeding prepupal stage. Thus, it is an unusual, novel developmental strategy, and its regulation has remained a mystery. Through an integrated approach combining detailed morphological characterization, developmental gene expression profiling, and RNAi-mediated functional analyses of the core components of the Metamorphic Gene Network (MGN), the authors examine the developmental identity of this prepupal stage and how the temporal deployment of conserved metamorphic regulators has been reorganized to accommodate this atypical developmental program. In particular, they show that the prepupal stage expresses a distinct combination of the key genes known to regulate life history transitions, including unusually high levels of Br-C expression.

Strengths:

The study represents a valuable contribution to insect developmental biology. A major strength is the comprehensive characterization of postembryonic development, which establishes a robust developmental framework for H. illucens. This is complemented by detailed expression profiling and RNAi-based functional analyses of the Metamorphic Gene Network (MGN), comprising the temporal specifier factors, Kr-h1, chinmo, Br-C, and E93. The results show that these conserved regulators are deployed in a modified temporal sequence that accommodates the distinctive prepupal stage while largely preserving their canonical developmental functions. Together, the morphological, molecular, and functional data support the conclusion that the prepupal stage of H. illucens is a distinct developmental transition associated with a characteristic configuration of the metamorphic gene network. The results are supported by solid methodology and approaches and will serve as valuable resources for future investigations into insect development, the evolution of metamorphosis, and the diversification of insect life-history strategies.

Weaknesses:

While the study successfully establishes the developmental identity of the prepupal stage and its association with a modified temporal deployment of the MGN, some aspects of the proposed regulatory model are less directly supported by the experimental evidence.

(1) Several regulatory interactions within the MGN remain inferential rather than experimentally demonstrated in H. illucens. In particular, the proposed relationship between juvenile hormone (JH), Kr-h1, and chinmo is based primarily on expression dynamics and RNAi-induced transcriptional changes. Although these observations are consistent with the proposed model, they do not directly demonstrate that JH induces chinmo expression or establish the regulatory relationship between Kr-h1 and chinmo in this species. As a result, the corresponding regulatory interactions presented in the final model should be regarded as plausible hypotheses rather than experimentally validated mechanisms.

(2) A second limitation concerns the developmental role assigned to Br-C and E93 during the larval-to-prepupal transition. The authors conclude that sustained Br-C expression is a defining molecular feature of the prepupal stage and discuss its potential role in prepupal specification. However, the functional analyses of both Br-C and E93 were initiated only after larvae had already entered the prepupal stage. Consequently, while the RNAi experiments convincingly demonstrate essential roles for Br-C during the prepupal-to-pupal transition and for E93 during adult differentiation, they do not directly address whether either factor is required to trigger the formation of the prepupal stage itself. Therefore, the molecular mechanisms governing the initiation of this distinctive developmental transition remain unresolved. In particular, the proposed lack of repression of E93 by Br-C is only weakly supported, yet may be an essential feature of the prepupal stage of Hermetia illucens.

(3) Although knockdowns of Kr-h1 and chinmo knockdowns look superficially similar, it would be good to confirm this with higher-magnification views of the cuticles for all three treatments (control, Kr-h1 RNAi, and chinmo RNAi). In other species, Kr-h1 knockdown leads to premature adult cuticle development, whereas chinmo knockdown typically leads to premature appearance of pupal characteristics. Similarly, in Fig. 4A and 4D, higher-magnification images of the cuticle would be helpful.

(4) (Relating to Line 336 and Figure 7): "This low but persistent prepupal Kr-h1 expression, together with modest chinmo expression from PPD0 to PPD8, may be correlated to a JH-dependent antimetamorphic effect that maintains the prepupal stage." However, we are not aware of a function of JH in extending the prepupal stage. In addition, in most insects, the prepupal stage expresses high Kr-h1 expression; this peak likely prevents the animal from turning into an adult instead of the pupa. We presume the same holds true for H. illucens (although the lower expression of Kr-h1 during that stage is curious). As a result, we suggest that Fig. 7D be revised as it may be difficult to distinguish between pupal formation and prepupal maintenance given the experimental set-up. Fig. 7E may also need to be modified since the development of the pupa may require Kr-h1. It is worth noting that at the prepupal stage, JH and Br-C are co-expressed in many insects. If the authors think that Kr-h1 expression needs to be low at this time, this would imply a novel interaction between Kr-h1 and Br-C, and should be discussed.

Reviewer #1 (Public review):

Summary

The authors build a "digital sphinx" by stitching together two neural network models: (i) a recurrent network with fixed parameters derived from the C. elegans connectome and imputed physiological (e.g. neural input/output) functions, and (ii) a feedforward encoder-decoder model with learnable parameters intended to represent a central brain - to - motor interface, then harnessing the combined model to a Drosophila biomechanical model situated in a physics simulator, and finally using deep reinforcement learning (DRL) training to optimize the parameters of the encoder-decoder model to reproduce a set of spatiotemporal patterns of jointed limb activations that together produce the overall organismal behavior of walking, within the physics simulator.

The primary intent of this paper is to dispel the recent grandiose claims made in the mainstream press by a private company, Eon Systems, to have achieved a major advance in biologically based brain simulation of the production of a set of ethologically relevant motor behaviors by the fly. Representatives of the company referred to this modeling and training process euphemistically and deceptively as "brain uploading". The authors proceed with a reduction-to-triviality exercise by constructing their own high-parameter dynamical brain-plus-body model situated in a physical simulation that produces, after training by reinforcement learning, satisfying ethological behavioral imitation in the same vein as the private company claim, but based on a clearly absurd and biologically unrealistic set of model assumptions.

Secondarily, the paper provides two overall admonitions that they assert their computational demonstration illustrates: that training high parameter network models to imitate behavior, even if they possess some biological detail, will deliver little or no biological insight, and that models of behavioral generation must be built from detailed biological data and, crucially, developed in a hypothesis generation/falsification loop with experimental validation, in order to be scientifically useful.

Appraisal

The authors are well justified in challenging the non-rigorous claims of "uploading" or even the delivery of a neurobehavioral simulation with potential scientific utility, in unison with the vocal criticisms of many other researchers in the fields of AI and neuroscience, and it is an important message to deliver to the world. However, the authors' own modeling counter-exercise, while clever and vivid in imagery, suffers from its own lack of rigor, both in disclosure of implementation and in scientific case-making. Some sacrifice of clarity and thoroughness in the interest of brevity is inevitable under the brief format of this manuscript; however, we suggest that crucial additions and modifications should be made to avoid falling into a similar trap of non-rigorous sensationalism.

Because the private company claims were not accompanied by a scientific paper, preprint, code repository, or much methodological disclosure of any kind, the authors have the particular challenge of building a refutation case against an undefined target. As a consequence, the authors chose their own task, model structure, and training paradigm.

The authors argue that brain models need to be built from biological data to be useful for yielding biological insight. We agree with the overall principle; however, in practice, this procedure is fraught with epistemological difficulty. Biological modeling suffers from a unique challenge within the larger endeavor of scientific/physical modeling, which is that it is generally unclear as to precisely what biological quantities should be measured and at what level of detail they should be measured. Additionally, biological data will by necessity be incomplete and noisy, and thus decisions of coarse-graining must be made at the outset of large-scale data collection projects, and some, possibly a substantial, level of data imputation will have to be performed in order to build testable models in our lifetimes. Despite the astonishing success of scaling (in both parameter count and corpus size) in engineered neural networks for certain human-like tasks, it is not at all clear that simply adding more detail to biological models will produce deeper scientific insight, or whether cataloging parameters from snapshot data will yield functional simulations. The failed Blue Brain mega-project should provide a lesson, as well as Marder's longstanding work on parameter variation in neural systems. The coupled, pernicious questions of choosing measurement detail and modeling detail represent a deep, unsolved challenge area for the field, and this context should be raised in the text.

The message about overinterpreting models trained with deep reinforcement learning, while valid and important, should be broadened to be a message about overinterpreting trained high-parameter models in general, in their ability to fit data or reproduce simple behavior. Other parameter optimization/learning procedures for building underdetermined and/or high-parameter models risk the same misinterpretation. The prescription of building models in conjunction with experimental prediction and validation is an important point.

The authors leave out an additional important and underappreciated challenge of brain-model-building, which is that imitating a time segment of behavior is a computational task of unspecified, and possibly low complexity. Successful recapitulation of behavioral time series may simply not be considered cognitively interesting, even if the model is built entirely on biological data. While quantifying task complexity is another open area of computational and neuroscientific research, the authors should, at a minimum, describe their particular task data in explicit mathematical terms and preferentially provide some complexity analysis. In the absence of task complexity analysis, at a minimum, computational controls should be applied to demonstrate the necessity of whatever structure or data is being asserted in the model. This epistemological practice is glaringly absent in much, if not most, of the neurobehavioral modeling literature. This paper would be a good opportunity to set an example of rigor.

Finally, the authors' description of prior work in the field of whole-organism neurobiological simulation feels incomplete and skewed toward work in Drosophila versus other model organisms. An internet search reveals many published efforts to build neurobehavioral models at varying levels of detail in C. elegans, of which only two are referenced.

We do feel this work constitutes an illustrative scientific exercise and important counterpoint to the sensationalism building around efforts in neurobiological simulation. It should inspire further work in defining a rigorous and scientifically productive epistemological framework for these kinds of brain modeling efforts.

Further Comments

(1) The authors oversell the completeness and quality of connectome datasets and what they lack.

Language such as "complete wiring diagrams," "nearly comprehensive connectomes" neglects the well-appreciated gaps in biological data that most practitioners believe necessary for useful, detailed models to be built. There is a brief mention that biological parameters "remain unknown" and that interfaces are "incompletely characterized", but beyond that, the authors do not explain which parameters are missing, why these parameters might matter, and what still needs to be addressed in order to make any plausible whole-brain emulation claims. This may also inadvertently bolster the sensationalist claims that the manuscript is trying to deflate by giving the impression that neurobiological and physiological data collection is a near-complete exercise.

(2) Prior work in C. elegans neurobehavioral modeling should be more acknowledged, if nothing else, for why it has been largely unsatisfying.

C. elegans is rarely discussed, while Drosophila is primarily focused on. The status of C. elegans connectomics, physiological mapping, biomechanics, and neurobehavioral modeling is worth more treatment.

(3) Critiques of Eon Systems announcements also, by and large, apply to more detailed and disclosed efforts in neurobehavioral modeling using RL for parameter imputation, and this should be recognized.

By way of reference to a tweet in the first paragraph, the authors are responding to a recent claim made by a startup that they have fully "uploaded" a fly brain, a significant advance vis-à-vis prior work in neurobehavioral modeling in Drosophila, such as references [3 and 9], which are mentioned as background in the paper but left out of the methodological critique. But one of the central warnings of the paper is around the challenge of interpretability when using reinforcement learning to optimize model parameters. The authors also should acknowledge that the use of RL has been justified by building neurobehavioral model builders as a proxy for the learning and tuning processes thought to occur during animal development.

(4) Substantiate the reservoir computing explanatory claim with appropriate computational controls.

The reservoir computing idea is the only piece of hypothesizing a necessary function for the central brain component model in the paper. This claim could be substantiated with some basic computational controls rather than just hypothesized. We suggest the following possibilities as additions to the model: (a) replace the connectome with an RRNN, (b) shuffle the connectome, or (c) use other simple dynamical systems in place of the worm brain model.

Specific Manuscript Comments

(1) Abstract

"New connectome datasets and musculoskeletal models now enable integrated, closed-loop simulations of the neural and biomechanical systems of the fruit fly Drosophila, an ideal model organism to investigate embodied intelligence."<br /> This sentence could mislead non-specialists into thinking all current simulations are novel because the connectome datasets are new. In fact, FlyWire (2024), NeuroMechFly (2022), and other connectomes have already been available for some years now. We believe that this sentence is a chance to make the opposite point that these resources have existed for a while, and that many simulations have been built before.

"However, many biological parameters of the nervous system and the body, as well as how they interface, remain unknown."<br /> Some examples of specific parameter/physiological data types that are missing and thought to be critical, such as neuronal input/output functions, are warranted. See below for a comment on the confusing construct of "interface" as a distinct entity from the neural network.

(2) Introduction

"Among animals that walk, the integration of brain wiring and body models is perhaps closest to fruition in Drosophila, due to the recent completion of multiple complete wiring diagrams (known as connectomes) of the fly nervous system." ...and... "The fly is the only animal with legs for which nearly comprehensive connectomes of its brain and nerve cord exist."<br /> The walking qualifier allows the authors to skirt around the substantial and decades-long work on connectomes in C. elegans, which crawls and does not walk. Yet sinusoidal crawling is a multidimensional, adaptive behavior, so it seems this exclusion was for narrative convenience rather than contextual accuracy.

"Despite this progress, closed-loop integration of biomechanical and neural models remains far from straightforward."<br /> Work (and shortcomings) in C. elegans neurobehavioral modeling should also be stated here alongside the fly.

"Where interfaces between brains and body models are missing or only partially characterized, one approach is to train an artificial neural network (ANN) to approximate these interfaces with deep reinforcement learning (DRL)."<br /> The choice of "interface" as a distinct, well-defined neurobiological entity is somewhat confusing and may mislead non-practitioner readers. If neuronal and muscular (and their interactions) physiology are incorporated into a neurobehavioral model, then in principle there is nothing left to call an "interface". It would be clearer to explain that prior neurobehavioral models have often inserted a trainable multilayer feedforward network between sensory and central brain and between the central brain and motor effectors in order to have a substrate for learning, and that this insertion may render the entire biological modeling exercise scientifically pointless, or at a minimum require a set of computational controls.

"In building virtual animal models, a motor policy is commonly learned by DRL so that the integrated, closed-loop virtual body successfully mimics the detailed kinematics of real animal behavior."<br /> The authors could define "motor policy" in simple terms and give a brief example.

"Additional realism is added when the motor policy network is constrained by a connectome dataset. However, many biophysical parameters for individual neurons and synapses remain un-measured."<br /> "motor policy network" is confusing; this is referring to the entire network model here, presumably.

(3) Methods

"We used the adult hermaphrodite C. elegans nematode connectome dataset [15, 16, 5], including the identities of its 302 neurons and their synapses (Fig. 1A)."<br /> We believe the authors should specify the dataset type, which is a structural, unsigned connectome lacking grounding in physiological function.

"The policy network was trained in closed loop using PPO as implemented by MIMIC-MJX"<br /> The authors should define "PPO" and "MIMIC-MJX" in simple terms and explain why they were used.

(4) Discussion

"Its role in the movement policy could be fulfilled equally well by a randomly connected RNN, akin to reservoir computing [20], since all the learning happens in the black-box ANN motor decoder."<br /> See above - this computational exercise should actually be performed.

"Looking further ahead, swapping brain and body models of related species may one day yield real insights into how their brains and bodies diverged through evolution. However, far more model development and experimental validation is needed before we can learn anything from such a digital sphinx."<br /> These two sentences about future possible cross-species chimeras feel superfluous and unsubstantiated, and weaken the main argument of the paper about whole-brain emulation.

Reviewer #2 (Public review):

Summary:

The authors use DRL to train a C. elegans connectome-based ANN to control stepping in a D. melanogaster body model. The resulting system can walk. This shows that one needs further constraints to derive biologically meaningful results from this approach.

Strengths:

The authors perform a very simple experiment with a clear outcome. The interpretation (or lack of interpretation) is a striking cautionary tale.

Weaknesses:

There is little analysis of precisely how robust this result is to parameter variation and network wiring. The worm also undulates in an oscillatory fashion. Thus, it is possible that the network is tapping into biologically meaningful motifs to generate oscillations for walking. As well, it would be useful to examine which heuristics one can use to determine whether modeling efforts are sufficiently constrained (i.e., how much biological data will be necessary to start obtaining fruitful, interpretable outcomes from DRL task optimization). For example, their "solution" using the worm connectome is not sparse (i.e., it uses many neurons). Perhaps a signature of a biologically-meaningful, interpretable result is one that is sparse?

Reviewer #3 (Public review):

Summary:

The authors construct a computational chimera by attaching a C. elegans connectome to a Drosophila body biomechanical model and use deep reinforcement learning to link neural activity to motor output. The model is able to produce walking, but is considered a priori to be scientifically meaningless, and the work is treated as a cautionary tale in complex interpretation layers unconstrained by experiment or data.

Strengths:

In a period of increasing excitement about linking AI and neuroscience, I respect very much that the authors work through a nontrivial example of nonsense results, rather than just making a theoretical case. It offers a clear and memorable existence proof that matching outputs of complex trained networks does not mean the internal dynamics are themselves emulated.

Weaknesses:

While I understand that the work was a rapidly produced comment on science-by-press-release, the message seems too important to be treated in quite as pithy a manner as it is. In particular, because the computational experiment is so memorable, it is worth getting the message right to avoid a set of readers who take from it that they should dismiss this category of neuroAI wholesale (which the authors absolutely do not imply!).

One part of me reads this work and thinks that by intentionally wiring up the sensory feedback in a particularly nonsense way, the authors have just made a bad model, and sometimes bad models can still generate sensible outputs, especially when expressive models are optimized to fit those sensible outputs. But I think this work is trying to say something more specific than this, and I would like it to be a bit clearer about that. The authors do a fairly good job of sharing a view about what should have been done instead, but this message would benefit from having some more concrete suggestions to avoid a simplistic interpretation. A few thoughts:

(1) It's not entirely obvious to me that the model is "scientifically meaningless." As the authors know extremely well, Drosophila walking is thought to be driven by simple central pattern generators coupled to leg-specific implementations. The C. elegans neural circuit is clearly capable of producing rhythmic activity as well. A version of the model they ran could have identified biologically valid rhythmic activity in the C elegans circuit and mapped it via the DRL to the right locomotor behavior in the fly. While this would not be a good emulation of the fly, it's not a concept devoid of scientific meaning. Similarly, if the ANN is converting a rhythmic signal to coordinated walking, it's not obvious to me that there aren't useful principles to identify in how it achieves this - it's basically the equivalent of that post-CPG circuitry, no?

(2) Similarly, is this outcome going to be relatively specific to rhythmic behaviors? I suspect that it would be harder to push the C. elegans connectome to produce some behaviors than others - for example, adding in visual navigation and other motor patterns, or a ring attractor. Rhythmic circuits arise in many places, and both biology and dynamical systems tell us they can come from numerous configurations of elements and interactions.

(3) Aside from the nonsense formulation of the problem, I would have liked to know more about what the authors should have done to know their model was useless. Put another way, if the authors hadn't known that their model was bad from the beginning (e.g., if they had stuck a fly brain in the middle of it, gotten the sensory feedback right), would there have been some way to figure out if it was meaningful or meaningless based on the results of the trained model itself?

Reviewer #1 (Public review):

This study by Alonso-Calleja and colleagues aimed to determine whether TGR5 regulates hematopoiesis and the bone marrow microenvironment under steady-state conditions and following transplantation. The revised manuscript substantially improves upon the original submission by providing additional characterization of TGR5 expression in hematopoietic and stromal populations, incorporating analyses in female mice, and expanding the investigation of bone marrow adipose tissue under aging and high-fat diet conditions. These additions more convincingly establish TGR5 as a regulator of bone marrow adipose tissue and stromal composition.

Major strengths of the study include the comprehensive characterization of the bone marrow adipose tissue phenotype across multiple experimental settings and the demonstration that TGR5 deficiency consistently alters the stromal compartment. The strongest and most convincing aspect of the work is the identification of TGR5 as a regulator of bone marrow adipose tissue and the bone marrow microenvironment. These findings provide useful insights into how metabolic signaling pathways influence the hematopoietic niche.

However, the evidence supporting a direct role for TGR5 in hematopoietic recovery following transplantation remains limited. Although reciprocal transplantation experiments and peripheral blood recovery analyses strengthen the manuscript, the conclusions regarding hematopoietic regeneration continue to rely largely on correlative observations. The study does not directly demonstrate that expansion of adipocyte progenitors is responsible for the enhanced recovery phenotype, nor does it establish improved regeneration of hematopoietic stem or progenitor cells within the bone marrow. Overall, the revised work addresses many of the concerns raised in the original review and provides useful new insights into the regulation of the bone marrow microenvironment by TGR5. Nevertheless, the conclusions regarding hematopoietic recovery should remain appropriately tempered, as the mechanistic basis linking the stromal phenotype to enhanced regeneration has not been directly demonstrated.

Reviewer #2 (Public review):

Summary:

The authors showed the expression of TGR5 in hematopoietic compartments and that loss of TGR5 doesn't impair steady-state hematopoiesis. Notably, TGR5 knockout significantly decreases BMAT, increase the APC population and accelerate the recovery upon bone marrow transplantation.

Strengths:

The role of TGR5 is interesting.

Weaknesses:

Additional mechanistic studies would further strengthen the work and provide deeper insight into how TGR5 regulates the bone marrow microenvironment.

Reviewer #1 (Public review):

Summary:

This manuscript combined rat fMRI, optogenetics and electrophysiology to examine the large-scale functional network of the olfactory system as well as its alteration in an aged rat model.

Strengths:

Overall methodology is very solid and the results provided an interesting perspective on large-scale functional network perturbation of the olfactory system.

Weaknesses:

The biological relevance and validation of the current results can be improved.

Comment on revised version.

Authors made satisfactory revision and I have no further comments.

Reviewer #2 (Public review):

Summary:

Ma and colleagues presented a study on the characterization of brain-wide spatio-temporal impact of olfactory cortical outputs. They take advantage of multi-modal techniques on rats: fMRI, optogenetics and electrophysiology. In addition, they used cutting-edge analytical techniques and modeling to support and interpret their data. The main findings of the study are:

(1) Neurons in Olfactory Bulb (OB) predominantly activate primary olfactory network regions, while stimulation of OB afferents in Anterior Olfactory Nucleus (AON) and Piriform Cortex (Pir) primarily orthodromically activates hippocampal/striatal and limbic networks, respectively.<br /> (2) Non-specified adaptation or habituation mechanisms may play a significant role in modulating olfactory outputs over subsequent fMRI sessions.

(3) Artificially induced aging in rats induces profound modification in the functional interaction between olfactory cortices and multiple brain regions.

The results on AON are of particular interest because of the lack of functional information on this region, despite its recognized importance in shaping OB output and behavior (odor localization tasks).

Strengths:

The manuscript is very accurate. The figures are well-crafted, clear and provide much information with the most appropriate plots and graphics. The study's amount and data quality are remarkable, and the experimental size adequately addresses the scientific questions. I particularly appreciated the details in the description of the methods regarding the missing data and the size of the different animal groups. The supplementary data complete the leading figures and provide information at a single animal level.

Weaknesses:

(1) One of the main reasons the Piriform Cx is understudied in rodents is because of the proximity to air, which creates artifacts in fMRI images. This issue becomes more critical at ultra-high magnetic fields, but I would expect it also at 7T. One main achievement of this study is, indeed, the acquisition of fMRI data from Piriform, and this point should be highlighted by showing raw functional data from a rat. The best would be if an fMRI data sample for a rat, no matter which stimulation, is shared on a public repository, like Zenodo or similar. I am curious to check the quality of the BOLD data from such an 'enormous' field of view, particularly in the OB, with a single-shot sequence. Also, the visual inspection of raw data is essential to appreciate how many 0.5 x 0.5 x 1 mm voxels fit into AON, and others analyzed small brain structures, like the amygdala, etc. Was the amygdala entirely visible in BOLD, or did the air in the ear channel make an artifact partially shadowing it?

(2) Surprisingly, the only information missing in the methods is the post-surgery period and the time between two consecutive fMRI sessions. How much time was accorded to rats to recover from the surgeries, and what time interval between two scans? This information is crucial for interpreting the decrease in most BOLD responses in subsequent recordings. The supposed adaptation should fit into the known time frames for odor adaptation. Usually, fast adaptation does not last for days (and it should be measured within a single experiment: is it the case?), while for long-lasting adaptation the stimulus (odor or opto) should be maintained constantly ON. This does not seem to be the case in this study. The hypothesis, alternative to adaptation, of a less efficient light activation, for example, due to gliosis around the fiber tips, should be discarded with more evidence than the preservation of OB > Pir responses or acknowledged in the manuscript.

(3) The D-galactose experiments were conducted only after administering the aging molecule, with no baseline/reference data on the same animals. Then, comparisons were made with healthy rats, but the two groups not only can be discriminated with respect to D-galactose administration but also with age (10 VS 18 weeks). A control group for 18-weeks-old rats with no D-galactose treatment would better compare the D-galactose effect and avoid any potential bias from group comparisons of rats at different ages. Do you confirm that D-galactose was injected into each rat 56 times/days in a raw, or am I mistaken?

The updated version of the manuscript partially addresses the flaws of the original submission. Here are my general concerns:

(1) Overall, the revised version comes with a few modifications/additions and no new data. Apart from a new correlation analysis, the improvements are mainly discursive, often non-convincing, justifications of the authors' choices. This may reflect a lack of interest in a publication that, in the meantime, lost its original peer-review value. However, it should be acknowledged that the Authors made an effort to partially address the concerns raised by the reviewers.

(2) My main concern was the quality of fMRI recordings. In the revised version, the Authors provided a new figure with an example single-mouse fMRI data. However, the depicted regions of interest (ROIs) mostly cover the brain spots that I expected to be the most impacted by the BOLD artifacts caused by the proximity of the air and the big field-of-view. In addition, these ROIs do not appear to match the mouse anatomy shown above the functional data. As an example, the EPI images in the OB are almost entirely covered by the colored mask. The feeling is that the fMRI data was indeed poor, as I worried, and the lack of any public repository of raw data reinforces that feeling. To make this point clear: I do not think the findings are not true, but poor fMRI data quality might have hidden more insightful results and does not foster the use of fMRI to monitor the olfactory pathway, which lowers the impact of this article.

Reviewer #2 (Public review):

Summary:

Overall, the authors aimed to provide evidence that clarifies two debates within metacognition research concerning subjective confidence reports:

(1) Does the post-decision confidence report arise from the same process that drives the initial decision, or does a separate, independent process support confidence computation?

(2) How do we stop accumulating evidence for the post-decision confidence report? Is it based on a self-imposed time limit, or on accumulated evidence crossing a boundary?

For the investigation, the authors constructed four models (2 × 2 factorial) to compare each combination of processes to account for random-dot motion tasks data with speed/accuracy manipulations. The models are generally embedded in the drift diffusion model framework, retaining basic parameters such as drift rate, boundary separation, starting point, and non-decision time, while adding linearly collapsing boundaries to model the initial choice. For the single vs. distinct process dimension, the difference lies in whether post-decision evidence accumulation is referenced to the endpoint of the initial decision process or restarts from a new, freely estimated starting point. For the time- vs. boundary-based stopping rule dimension, the key difference is that post-decision evidence accumulation stops either at a deadline sampled from a normal distribution or when the accumulated evidence hits a collapsing boundary.

Based on model comparison, the boundary-based stopping rule clearly outperformed the time-based stopping rule. However, models with the boundary-based stopping rule performed similarly regardless of whether a single or distinct process was used. Here, the authors drew additional insights from EEG recordings during the task, focusing on the centro-parietal positivity (CPP), which has been proposed as a neural correlate of the evidence accumulation process. By simulating evidence accumulation trajectories (with additional assumptions) and comparing the patterns of those trajectories with observed ERP waveforms, the authors argued that the single-process model provided a better match to the CPP findings and was therefore preferred. This was specifically demonstrated by the model's superior ability to match the pre-response CPP amplitude differences conditioned on the post-decision confidence-related variables.

Strengths:

(1) The authors translated existing theories into computational models of decision-making and systematically compared different cognitive processes by assessing model fits to the data. This provides strong evidence supporting the idea that post-decision confidence reports could be better explained by boundary crossing rather than a self-imposed deadline to respond.

(2) Beyond model evidence, an important result is that CPP amplitude predicted confidence before the initial choice was reported, which is a unique prediction of the single-process model. The use of EEG as an independent validation measure provided additional evidence in favour of this model.

(3) Combining points 1 and 2, this study successfully addressed the two key debates with solid evidence to favour one theory over another.

(4) Another strength of this study is the data quality. The high number of trials provided a strong foundation for model inference as well as ERP analysis. The experiment also contained a speed-accuracy manipulation to evaluate model performance across diverse situations.

Weaknesses:

I have two main concerns around the modelling work and neural analyses, which in my opinion could have limited the interpretation of the findings. My responses here will be lengthier, but this reflects the nature of the modelling work rather than implying stronger criticisms than those suggested by the strengths discussed above.

(1) There are a few assumptions in the models that lack psychologically meaningful interpretations, and this study placed more effort into model comparison while lacking discussion of the cognitive processes inferred from parameter estimates.

To start, I think some of the parameterisations were not properly justified. For the boundary models, it is not very clear why the upper and lower boundaries were different and collapsed at different rates for confidence decisions, given that a single boundary parameter and collapse rate were used for the initial decision. This allows more flexible shifts in the model's predictions of confidence ratings without strong justification. Specifically, it is unclear why the boundary-single model has such an implementation while the boundary-distinct model was only equipped with one boundary parameter (a2, compared to a2up and a2down).

Similarly, the inclusion of metacognitive noise creates another layer of flexibility in the predictions of confidence ratings. In most existing evidence accumulation models with a diffusion process, noise comes from two sources: within-trial noisy evidence accumulation and across-trial variability (e.g., drift rate variability). Beyond these, such models almost always assume that the decision is made deterministically once the evidence reaches a specific boundary. The inclusion of metacognitive noise here sounds more like a noisy decision-to-action mapping.

I also have similar doubts about allowing the non-decision time parameter for confidence accumulation in the distinct model to be negative. The authors argued that confidence accumulation may begin during initial evidence accumulation. However, this is a flawed implementation, as the non-decision time was simply added to the evidence accumulation time rather than being incorporated within it. Allowing negative non-decision times may achieve similar predictions, but it is ad hoc.

The inclusion of a collapsing boundary mechanism in the post-decision confidence accumulator helped the model reach more diverse levels of accumulated evidence and ultimately improved predictions of confidence ratings. However, no strong argument is presented for this implementation beyond the observation that the model performs worse without it. The collapsing boundary mechanism has traditionally been interpreted as reflecting a sense of urgency. For the boundary models, I noticed that the collapse rate of the upper boundary differed significantly between speed and accuracy conditions, which is consistent with the urgency interpretation. Overall, I would like to see more discussion of the specific model mechanisms included by the authors, interpreted in light of parameter estimates.

(2) While the ERP findings provided external evidence and validation of the modelling results, I find the simulation practices not particularly useful and potentially misleading for naive readers. Specifically, the authors attempted to draw a parallel between patterns of simulated evidence accumulation traces and observed CPP waveforms. While the CPP has received support as a correlate of the evidence accumulation process, the DDM is by no means a neural model capable of generating predictions of neural observations. To my understanding, the superior fit of the boundary-single model was primarily due to the fact that pre-response CPP amplitude predicts post-decision confidence ratings. Therefore, as the boundary-distinct model did not connect the two phases of evidence accumulation, it would fail to account for this observation. I think this point could be clearly demonstrated without the need to introduce additional assumptions into the model simulations in order to directly compare averaged trajectories with averaged ERP waveforms. While the authors did not explicitly claim otherwise, this approach creates an illusion that the model can mechanistically account for ERP data. I would like the authors to provide explicit clarification on this point.

Appraisal:

Overall, the authors have provided solid evidence in support of their research aims. The findings contribute to longstanding debates with insights from model mechanisms and neural findings that should not be overlooked by future studies on this topic. This study also offers a good starting point for future model development and refinement in broader contexts of confidence reporting, such as paradigms involving simultaneous initial decisions and confidence judgements. The high quality of the behavioural and EEG data will make a valuable contribution to future research.

Reviewer #1 (Public review):

Summary:

A central question in decision-making is whether confidence arises from the same evidence-accumulation process that led to the choice or from a separate second process. The manuscript addresses this question using a random-dot motion (RDM) task, with an initial choice followed by a confidence report, and a time-pressure manipulation on the confidence report. The authors fit a family of four models differing on two dimensions: the source of confidence (a continuation of the choice accumulator vs. a distinct accumulation process) and the stopping rule (time-based vs. boundary-based). The models are fitted to behavior, and then their simulated dynamics are compared with the CPP signal associated with evidence accumulation (not included in the fit). The main methodological contribution is the use of a neural signal to decide between the two boundary-based models, which are nearly indistinguishable behaviorally. The authors conclude that boundary-based stopping rules outperform time-based rules, and that the Boundary-Single model reproduces the certainty-related CPP dynamics better than the Boundary-Distinct model, supporting a single accumulation process for both choice and confidence.

Strengths:

The main strength is methodological: using a neural signal (CPP) as an out-of-sample arbiter between the two boundary-based models (which are nearly equivalent behaviorally). This addresses the model-identifiability problem: when behavior does not distinguish between competing models, a neural signal not included in the fit can provide external evidence.

The work is also thorough and empirically rigorous. The finding that CPP amplitude predicts subsequent certainty ratings several hundred milliseconds before the initial choice response is statistically supported and interesting in its own right, independent of its interpretation. The small-N/many-trials design (2,160 trials per participant) is well suited to capturing change-of-mind trials and is accompanied by thorough model and parameter recovery, with the generating model recovered in most simulations. The study also includes preregistration of the design and planned behavioral and neural analyses, and it replicates a previously reported behavioral pattern.

Weaknesses:

The central conclusion may well be correct, but in my view the current evidence does not fully support it. The behavioral comparison between the competing models did not resolve the issue and in fact showed a slight preference for the distinct-process model, so the weight of the decision falls mainly on the neural comparison.

(1) The neural evidence supports access to pre-choice variation, but does not necessarily establish a single continuous process. The critical difference between the single and distinct models is the presence of trial-to-trial variation in the evidence at choice commitment, which is inherited by the confidence process: the single model preserves it (the confidence process begins from the trial-specific endpoint of the choice DV), while the distinct model does not inherit it (z2 fixed). Thus, the neural test examines whether confidence has access to the state of evidence accumulation before the choice response but does not establish that the same accumulation process must continue seamlessly to determine confidence. The authors also test a model in which a distinct post-choice process is initialized using information from the endpoint of the choice process. However, its failure rules out one specific implementation of information transfer, rather than the broader class of two-stage models in which a distinct confidence process receives a readout of the decision state and may additionally integrate other metacognitive cues.

(2) A broader class of two-stage metacognitive models that combine a decision-state readout with additional cues is not tested. The distinct-process model implemented here captures only a limited subset of possible metacognitive architectures. Its starting point is independent of the choice-DV endpoint, and it remains driven by the available sensory evidence, potentially within a different reference frame. It does not capture second-order accounts in which confidence combines a readout of the decision state with additional cues not explicitly represented in the choice accumulator, such as response time, motor conflict, subjective stimulus clarity or attention. Moreover, the paradigm used in the manuscript provides few independently manipulated information sources that would allow such a process to be identified separately from the choice accumulator.

(3) The neural distinction between models is not quantitatively evaluated. The claim that the Boundary-Single model better reproduces the CPP rests primarily on a visual/qualitative comparison, without a numerical measure of the discrepancy between each model and the neural data. Although the plotted β coefficients (Figure 4D) provide estimates of the neural effects, no scalar summary of model-to-CPP fit is reported. Because the neural comparison carries much of the inferential weight, a quantitative comparison would strengthen the conclusion substantially.

(4) The fitted parameters raise a question about the post-choice process. Confidence responses were very fast (average median confidence RT = 243 ms), with no minimum RT threshold. The Boundary-Single model estimated a post-choice drift rate more than twice the pre-choice rate (1.69 vs. 0.75). In the model, confidence accumulation begins at commitment, before the initial response is executed. The measured confidence RT therefore does not capture the full accumulation window, which also includes the motor delay. Still, the sharp rise in drift rate at commitment requires an explanation. It may reflect stronger weighting of the still-available evidence, as the authors suggest. But it is also consistent with a fast readout of a decision state that was largely set before the initial response. The authors could compare the current model against a readout model, or against an intermediate variant that permits only a brief, bounded period of post-choice accumulation.

(5) The participant-level distribution of model preferences would clarify the comparison. Models were fit separately per participant and condition, but the comparison is summarized as mean BIC (a small average preference for Boundary-Distinct). A mean cannot distinguish two different situations: a consistent, weak preference for one model across all participants, versus a mixture in which some participants clearly favor one architecture and others the opposite. Reporting the distribution of per-participant ΔBIC and the number of participants favoring each model would clarify the result.

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.

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; and 3) 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.

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.

(1) 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.

(2) 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.

Comments on revised version.

The authors addressed all my concerns. I appreciate the authors' thoughtful and detailed response.

Reviewer #1 (Public review):

Summary:

The uniqueness of this paper is the study of the formation of temporal binding-dependent memories in the cntnap2 mouse, a long-standing mouse model of autism that has been used to test therapeutic modalities.

Strengths:

I liked the combination of optical recordings and interventions and the backup of primary observations with control experiments.

Weaknesses:

(1) Fiber photometry recordings are too coarse to give salient clues to the underlying mechanism.

(2) Are perturbed pyramidal cells causally responsible for the altered trace? What can be concluded about the possible role of inhibitory interneurons as potential drivers? The observations focus on abnormal regional activity as observed with fiber photometry and manipulated by optogenetics. The authors should state clearly the limits of their conclusions.

(3) I found the "trace" nomenclature confusing. "....in which mice are required to memorize the association between a tone (Conditioned Stimulus) and a mild electric foot-shock (Unconditioned Stimulus), separated by a time interval called Trace (Sellami et al., 2017)." It seems that the conceptual model invokes the creation of an [eligibility] trace, characterized by its progressive disappearance over time. It may be a convention in the field or a matter of language, but it seems perverse to use "trace" to label the time interval rather than the entity that is decaying. If this is an accepted convention going back to Howard Eichenbaum, the authors should cite the paper that first introduced the convention.

(4) I would advocate for the addition of some discussion points for the authors to consider.

a) Is the retention of activity in CA1 related to phenomena at the cellular or subcellular level in CA1 pyramidal cells? I'm thinking of dendritic, delayed, and stochastic CaMKII activation (DDSC) as defined by Yasuda's group or short-term and associative plasticity of calcium dynamics (STAPCD) as delineated by Caya-Bissonette and Beique.

b) Was the optogenetic intervention ever administered in a delayed fashion, capitalizing on the temporal advantages of optogenetics to probe dynamics?

c) Is the newfound reliance on corticostriatal pathways something more than compensation at the behavioral level? Could it be driven in part by the ASD-related genetic changes?

Reviewer #2 (Public review):

The authors investigate the contribution of dorsal CA1 hippocampal dysfunction to cognitive impairments in the Cntnap2 knockout mouse model of autism spectrum disorder. Building on previous evidence implicating the hippocampus in episodic and relational memory processes, they combine trace fear conditioning, fiber photometry, optogenetic manipulation, a relational/declarative memory radial maze task, and cFos mapping to test whether altered CA1 function contributes to deficits in temporal binding and memory flexibility.

The study has several important strengths. First, the work addresses a relatively understudied aspect of autism-related cognition, namely hippocampal-dependent memory processes, whereas much of the literature has focused on social behavior, cortical circuits, or striatal dysfunction. Second, the authors employ multiple complementary approaches that converge on a coherent mechanistic hypothesis. The behavioral data demonstrate a reduced ability of Cntnap2 knockout mice to retain associations across long temporal gaps. Fiber photometry recordings reveal reduced dorsal CA1 activity during conditions that challenge temporal binding, and optogenetic activation of dorsal CA1 neurons during the trace interval is sufficient to rescue memory performance. Together, these findings provide strong support for a causal contribution of dorsal CA1 activity to temporal binding deficits in this model.

The second major strength of the manuscript is the extension of these findings to a more complex hippocampus-dependent memory task. The radial maze experiments indicate that Cntnap2 knockout mice show impaired memory flexibility and a greater reliance on egocentric learning strategies. The accompanying cFos analyses suggest altered recruitment of hippocampal and striatal networks during learning, providing a systems-level framework that may explain the observed behavioral phenotype.

Overall, the main conclusions regarding impaired temporal binding and reduced dorsal CA1 engagement are well supported by the data. The optogenetic rescue experiments are particularly compelling because they move beyond correlation and directly test causality. The manuscript therefore makes a meaningful contribution to our understanding of how hippocampal dysfunction may contribute to cognitive abnormalities associated with autism.

Weaknesses:

Some conclusions are necessarily more inferential than others. In particular, the interpretation that the observed behavioral phenotype reflects a broader shift from hippocampal-dependent declarative memory toward striatum-dependent procedural learning is supported primarily by cFos activity patterns and behavioral strategy measures. While the data are consistent with this interpretation, they do not directly demonstrate a causal reorganization of memory systems. Similarly, although the findings identify a mechanism in the Cntnap2 model, caution is warranted when extrapolating these conclusions to autism spectrum disorder more broadly; but I believe this caution is addressed in the discussion.

Despite these limitations, the study presents a coherent and well-executed body of work that provides novel mechanistic insight into hippocampal contributions to cognitive dysfunction in a widely used autism model. The findings should be of considerable interest to researchers studying hippocampal function, memory systems, and neurodevelopmental disorders.

Reviewer #3 (Public review):

Summary:

The manuscript evaluated behavioral phenotypes in the Cntnap2 knockout mouse using two behavioral paradigms: trace fear conditioning and a radial maze task. The trace fear conditioning training is normal, but memory generalization is impaired. The inflexibility is suggested to be related to low activity in dCA1 neurons, which can be rescued by ChR2. The radial maze task data suggested a similar conclusion. Brain-wide cFos mapping indicated impairments in the Cntnap2 knockout mouse. The brain-wide cFos mapping does not show direct correlations with Cntnap2, limiting the interpretation of these data in the context of this paper.

Strengths:

The behavior data are solid.

Weaknesses:

The underlying mechanism is not fully investigated.

Major points：

(1) The authors should thoroughly check their manuscript as there are many typos in the current version that affect the readability.

(2) In trace fear conditioning, the tone test impairment can be rescued by ChR2. Have the authors tried rescue experiments with Cntnap2? Rescue experiments in the radial maze task are also essential, either with ChR2 or Cntnap2.

(3) The quality of the cFos example image in Figure 3 is too low. The authors should also provide example images for the other brain regions in the supplementary data, if possible.

(4) The causal link between the brain-wide cFos mapping and the Cntnap2 knockout is weak. How to explain the increase of cFos cell densities in some brain regions, but the decrease in others?

Reviewer #1 (Public review):

This manuscript describes a novel downstream mechanism of mTORC1 deficiency-mediated lifespan extension in C. elegans. The authors demonstrated that the biosynthesis and the nuclear hormone receptor daf-12 binding of a bile acid-like hormone, dafachronic acid (DA), are essential for TORC1 mutant raga-1 to extend lifespan. Through RNA-seq and RNAi lifespan screen, they also discovered that a dehydrogenase, dhs-26, which is expressed in the canal-associated neurons, is regulated by DA/daf-12 and downstream of the mTORC1-DA signaling for lifespan extension. The authors also explored the conservation of mTOR/DA/daf-12/dhs-26 signaling in the mouse model. This work demonstrates significant findings that will advance the aging field and will be of interest to many researchers in this field. The conclusions are mostly well supported by data with proper controls.

Some suggestions to strengthen the manuscript include:

(1) Other mTOR activity perturbation or mutants should be used to support some of the core lifespan experiments. It will strengthen the conclusions made from raga-1 mutant only, although there is evidence from TOR RNAi in Figure 1g to support the daf-12 data in Figure 1d.

(2) The authors showed in Figure 1h and 1i that DA supplementation rescued the shortened lifespan of raga-1;daf-9 but not raga-1;daf-12; and also rescued the shortened lifespan of raga-1; dnh-26 in Fig. 5e. Does DA supplementation itself extend lifespan? If its level is increased by mTORC1 inhibition and it is downstream of mTORC1 inhibition, it should theoretically extend lifespan. But from the reported publications, it seems that the DA supplementation lifespan modulation is highly dependent on genetic backgrounds. It will strengthen the conclusions if the authors provide the wild-type condition DA supplementation lifespan data and also related discussions about it.

Reviewer #2 (Public review):

Summary

This manuscript by Schilling et al. presents an important advancement in our understanding of how mTOR signaling regulates organismal aging. While the longevity-promoting effects of reduced mTOR activity have been extensively documented across species, the mechanisms by which mTOR communicates systemic metabolic information to regulate lifespan remain unclear. In this study, the authors provide strong evidence that longevity induced by reduced TORC1 signaling requires the bile acid-like steroid hormone dafachronic acid (DA) and its cognate nuclear receptor DAF-12. Furthermore, through a combination of transcriptomics and functional genomics, they identify the conserved short-chain dehydrogenase DHS-26/DHRS1 as a previously unrecognized downstream effector of this pathway. The work integrates genetics, lifespan analyses, sterol measurements, transcriptomics, proteomics, endogenous genome engineering, and comparative mammalian datasets. The resulting model, in which mTOR influences lifespan through regulation of endocrine steroid signaling, represents a conceptual advance that links nutrient sensing, metabolism, and organismal aging. Although several mechanistic questions remain unresolved, the study is comprehensive, technically rigorous, and likely to be of broad interest to investigators studying aging, metabolism, endocrine signaling, and cellular stress responses.

Strengths:

One of the major strengths of this manuscript is its conceptual novelty. Rather than reinforcing the well-established role of mTOR as a longevity regulator, the study proposes a specific endocrine mechanism that links reduced mTOR activity to increased lifespan through steroid hormone signaling. This advances the field beyond descriptive observations of mTOR-dependent longevity and introduces a model in which bile acid-like hormones function as systemic mediators of nutrient-sensing pathways. The idea that endocrine steroid signaling may serve as a downstream effector of mTOR provides a new perspective on how longevity signals are coordinated at the organismal level.

The genetic evidence supporting this model is particularly strong. In Figure 1, the authors use a series of epistasis experiments to demonstrate that mutations in daf-36, daf-9, and daf-12 suppress lifespan extension in raga-1 mutants. The DA supplementation experiments further strengthen the pathway ordering by rescuing longevity in hormone-deficient backgrounds while failing to restore lifespan in receptor-deficient animals. Importantly, the direct quantification of endogenous DA levels elevates the study by providing biochemical support for the proposed model.

The transcriptomic analyses presented in Figure 2 provide a valuable systems-level perspective on the interaction between mTOR and steroid signaling pathways. The observation that DAF-12 profoundly reshapes the RAGA-1 transcriptional program highlights the importance of steroid signaling in mediating the physiological consequences of reduced mTOR activity. The enrichment of metabolic, lysosomal, and peroxisomal pathways is consistent with established longevity-associated programs and generates a valuable resource for future mechanistic studies.

Figure 3 effectively integrates discovery-driven and hypothesis-driven biology. The authors use transcriptomic information to prioritize candidate genes and then perform a functional genomic screen to identify factors required for RAGA-1-mediated lifespan extension. This approach converges on DHS-26, which subsequently emerges as a central mechanistic component of the study. The progression from transcriptomics to functional validation is well executed.

In Figure 4, the generation of CRISPR-engineered dhs-26 deletion mutants and endogenous tagged reporter strains provides strong validation for DHS-26 function. The demonstration that dhs-26 deletion selectively abolishes RAGA-1-dependent longevity without substantially affecting wild-type lifespan strongly supports its role as a context-dependent mediator of mTOR signaling. Furthermore, the conservation analyses linking DHS-26 to mammalian DHRS1 provide biological context and enhance the broader significance of the findings.

In Figure 5, multiple independent experimental approaches converge on the conclusion that DHS-26 participates in DA-dependent lifespan regulation. The rescue of lifespan by DA supplementation, reductions in DA levels in raga-1;dhs-26 mutants, reporter-based analyses of DAF-12 activity, and proteomic profiling collectively support a mechanistic model. The proposed positive feedback relationship between DA/DAF-12 signaling and DHS-26 is intriguing and offers a plausible explanation for how endocrine signaling may amplify longevity-promoting responses. Finally, the incorporation of mammalian datasets showing regulation of DHRS1 by rapamycin and FXR signaling provides a promising avenue for future studies investigating conservation of this pathway.

Weaknesses:

Despite the many strengths of the study, important mechanistic questions remain unresolved. The most significant limitation is that the precise molecular connection between reduced mTOR activity and increased DA production remains unclear. While the genetic and biochemical data convincingly place DA/DAF-12 signaling downstream of mTOR, the study does not establish whether mTOR regulates DA biosynthesis, degradation, intracellular trafficking, sterol uptake, or hormone availability. The observed increase in endogenous DA levels is statistically significant but relatively modest, and the mechanistic basis for this increase remains speculative. Additional experiments examining sterol flux, enzyme activity, or intracellular sterol trafficking would substantially strengthen the proposed model.

The transcriptomic analyses in Figure 2 are informative but correlative. Because the RNA-sequencing was performed at a single adult time point, it remains difficult to distinguish primary transcriptional responses from secondary adaptive changes. Similarly, while pathway enrichment analyses identify plausible processes, they do not establish direct regulatory relationships. Additional temporal analyses or direct assessment of DAF-12 occupancy at candidate loci would strengthen mechanistic interpretations and help distinguish direct from indirect targets.

A major unresolved question concerns the biochemical function of DHS-26 itself. While the genetic evidence clearly establishes DHS-26 as an important regulator of RAGA-1-mediated longevity, its endogenous substrate and enzymatic activity remain unknown. The manuscript presents evidence linking DHS-26 to sterol metabolism, but direct biochemical characterization is lacking. Thus, the mechanistic model remains somewhat incomplete. Defining the substrates and products of DHS-26 activity would greatly strengthen the study and provide important insight into how this enzyme influences DA availability.

Another area requiring additional clarification is the proposed neuroendocrine role of DHS-26. The expression of DHS-26 in canal-associated neurons is interesting and raises the possibility that these cells participate in systemic longevity regulation. However, the current data do not establish whether DHS-26 functions autonomously within these neurons or whether expression in other cell types contributes to the observed phenotypes. Tissue-specific rescue or depletion experiments would strengthen the neuroendocrine model and help establish physiological sites of action.

Finally, the mammalian data presented in Figure 5 are supportive and suggestive of evolutionary conservation, but they remain correlative. While regulation of DHRS1 expression by rapamycin and FXR signaling is interesting, these observations do not yet demonstrate functional conservation of the longevity mechanism itself. Additional studies directly testing DHRS1 function in mammalian systems will be required before stronger conclusions regarding conservation can be drawn.

In summary, this manuscript provides a significant contribution to the aging field and introduces a model linking mTOR signaling, endocrine steroid hormones, and longevity. The study is comprehensive, technically sophisticated, and supported by multiple complementary approaches. Although some mechanistic questions remain open regarding the precise regulation of DA production, the biochemical function of DHS-26, and the extent of conservation, these limitations represent opportunities for future investigation. Overall, the work substantially advances our understanding of how nutrient-sensing pathways regulate aging and is likely to stimulate considerable interest within the fields of aging biology, metabolism, and endocrine signaling.

Reviewer #3 (Public review):

Summary:

This interesting manuscript provides evidence that the well-established consequences of (reduced) mTOR activity on longevity are, at least in part, mediated by regulation of dafachronic acid (DA) availability and its signalling via its nuclear receptor DAF-12 in C.elegans, with some supporting evidence derived from mouse studies that similar processes may be functional in mammalian systems, i.e., be evolutionarily conserved. Earlier studies by the group have established that DA/DAF-12 signaling promotes adult longevity in several contexts. DA is a bile acid look-alike, and DAF-12 is a homolog of mammalian bile acid-activated nuclear receptors FXR and VDR: recent experimental studies and human cohort studies have indicated a role of (specific) bile acids in mammalian longevity.

The hypothesis that mTOR and DA/DAF-12 signaling interact to modulate longevity in C.elegans is novel and of great potential interest. The hypothesis has rigorously been tested in a series of well-performed experiments employing mutant strains, functional genomic screens, and DA exposures, etc.. It is convincingly demonstrated that DA/DAF-12 does not directly impact mTOR (assayed on AMPK phosphorylation) and acts downstream of the pathway. The short-chain hydrogenase DHS-26 (mammalian homologue DHRS1) was identified as a downstream target and modulator of this mTOR-DA-DAF12 axis by modulating the lifespan of the mTOR regulator raga-1. As the components of this axis are expressed in different cell types of the worms, this finding indicates a neuroendocrine mode of action. Mode of action of DHS-26 appears to be based on modulation of cholesterol and lathosterol, i.e., substrate availability for DA production.

Strengths:

Overall, the manuscript is well-written and builds up the story in a clear fashion. The conclusions are based on solid data and of relevance for ageing research, also because the mechanism identified appears to be evolutionary conserved.

Weaknesses:

No overt weaknesses were identified by this reviewer.

Reviewer #1 (Public review):

Summary:

The authors used FBDD screening to identify numerous compounds interacting with the ORF9b dimer. They expanded the original fragment hit, soaked the derivatives into the crystals and confirmed their binding poses, and showed that the derivatives bind the target with higher affinity. The authors further targeted the ORF9b binding site on TOM70, and used a fluorescence polarization-based (FP) assay to screen a compound library and obtained several hits. Structure-activity relationship (SAR) optimization yielded hit analogs that have higher binding affinity to TOM70.

Strengths:

(1) The study adopted novel drug design strategies, including stabilizing ORF9b homodimer to prevent it from binding TOM70, and blocking ORF9b from binding TOM70 by screening compounds that compete with ORF9b for binding TOM70.

(2) The work established a feasible high-throughput screening assay. This FP-based assay screened ~50,000 compounds, from which two hit compounds were further optimized to yield analogs with higher binding affinity.

Weaknesses:

(1) The study lacks functional assays to evaluate whether the ORF9b-stabilized compounds or TOM70 binding compounds could affect IFN inhibition caused by ORF9b or virus infection.

(2) There is a lack of experimental evidence to reveal the binding mode of lipidated-compounds with ORF9b homodimer.

(3) There is a lack of experimental evidence to reveal the binding mode of HTS hits or analogs for TOM70.

(4) Overall, none of the compounds shown in the paper have promising potency warranting further development; their binding affinity is limited to the micromolar range.

Reviewer #2 (Public review):

Summary:

The authors investigate chemical strategies to disrupt the interaction between the SARS-CoV-2 accessory protein Orf9b and the host mitochondrial receptor Tom70, an interaction implicated in suppression of type-I interferon responses. They employ two discovery approaches: a crystallographic fragment screen against the Orf9b homodimer and a high-throughput fluorescence polarization screen for compounds that compete with Orf9b binding to Tom70. The study identifies fragment-binding hotspots on Orf9b, develops lipidated analogs that stabilize the Orf9b homodimer, and discovers Tom70-binding compounds with low micromolar activity that inhibit Orf9b binding in vitro.

Strengths:

An impressive amount of work using a variety of complementary approaches and methods to validate binding (FP, SPR, and computational modelling and SAR). The combination of crystallographic fragment screening on Orfb9 and HTS on Tom70 provides two independent routes for perturbing the Orf9b-Tom70 interaction. The structural work seems to be of very high-quality. The fragment campaign is extensive, yielding a substantial number of fragment-bound structures and identifying biologically meaningful binding hotspots on Orf9b.

Finally, the screen results in reporting useful chemical starting points. Although potency remains modest, the study provides tractable scaffolds and a clear framework for future optimization.

Weaknesses:

General comment:

(1) Although there is already an incredible amount of data presented, one limitation of this study is the lack of cellular validation - do these drugs enter cells, restore interferon signalling, reduce viral loads, or alter Orfb9 localization?

(2) The logic of locking Orfb9 as a dimer is that the monomer binds Tom70 - thus, a more stable dimer means less monomer. In Figure 2, the Orfb9 homerdimer stabilization by compounds should reduce binding affinity to Tom70. A direct binding experiment measuring reduced Tom70 binding with compound treatment would better strengthen this claim.

Reviewer #3 (Public review):

Summary:

This paper attempts to and succeeds in demonstrating that Orf9b is able to bind small molecules using X-ray fragment screening, SPR and FP assays. Exploration of sites from the fragment screening is performed along with fragment linking with inter-dimer lipid moieties.

Strengths:

The experimental work looks strong and well performed. The interpretation of the data is appropriate and was often validated through orthogonal methods and follow-up compounds. The use of Tom70 to find binders that might disrupt interactions between Orf9b and Tom70 is elegant.

Weaknesses:

The use of Chai-1 to predict co-folded structures with binding molecules was not properly described - no mention of this in the methods. It was not commented on whether the compounds which were found were attempted to be co-crystallised. If they were but negative data was collected (didn't crystallise, didn't diffract or no additional density was found), then this needs to be stated.

Reviewer #1 (Public review):

Summary:

Using sequences of short videos to elicit emotional changes in participants, Malamud and Huys demonstrate how a brief, controlled emotion regulation intervention (distancing) can effectively alter subsequent emotion ratings. A novel computational approach based on state-space models captures the trajectories of emotion ratings and leverages tools from control theory to quantify the intervention's impact on emotion dynamics.

Strengths:

The experiment is well designed and tailored to the computational modeling approach advanced in the paper. It also relies on a selection of previously validated stimuli. Within the constraints of a controlled experiment, the intervention successfully implements a relatively common tool used in psychotherapeutic treatment, supporting its clinical relevance.

The computational modeling is grounded in the well-established framework of dynamical systems and control theory. This foundation offers a conceptually clear formalization, along with powerful quantification tools that go beyond previous, more data-driven approaches.

Overall, this timely study presents a coherent approach that bridges concepts from clinical psychology and computational theory, providing a stepping stone toward more quantified, evidence-based psychological interventions targeting emotion control.

Weaknesses:

A limitation of this study is that the data were acquired online, resulting in some heterogeneity in the measured effects and reduced statistical power when testing for complex interactions. While the current data are sufficiently solid to demonstrate the validity of the general concept and computational approach, future work should aim to replicate these results in a more controlled laboratory setting.

Additionally, the repeated reminders of the distancing instruction during the second phase of the experiment raise questions about the generalizability of the findings to longer-term remediation strategies, as typically implemented in clinical settings.

Reviewer #3 (Public review):

Summary:

The manuscript takes a dynamical systems perspective on emotion regulation, meaning that rather than a simplistic model conceptualising regulation as applying to a single emotion (e.g. regulation of sadness), emotion regulation could cause a shift in the dynamics of a whole system of emotions (which are linked mathematically to one another). This builds on the idea that there are 'attractor states' of emotions between which people transition, governed by both the system's intrinsic characteristics (e.g. temporal autocorrelation of a particular emotion/person) and external driving forces (having a stressful week). Conceptually this is a very useful advance because it is very unlikely that emotions are elicited (or reduced) singly, without affecting other emotions. This paper is a timely implementation of these ideas in the context of a psychotherapeutic intervention, distancing, which participants were trained (randomised) to perform while watching emotion-inducing videos.

The authors' main conclusion is that distancing both stabilises specific emotional patterns and reduces the impact of external video clips. I would consider these results strong and believable, and to have the potential to impact models of emotion regulation as well as the field's broader views on the mechanisms of psychological therapies.

Strengths:

This paper has very many strengths: I would especially note the authors' very-well-matched active control condition and the robustness of their model comparison approach. One feature of the authors' approach in is that they explicitly add noise - not what you typically see in an emotion time-series analysis - which allows for participants to make errors in their own subjective ratings (a reasonable thing to assume); this noise can then be smoothed during filtering. In their model comparison approach, they explicitly test whether a true dynamical system explains emotion change/emotion regulation effect on emotions - demonstrating that both intrinsic dynamics and external inputs were needed to explain subjective emotion. Powerfully, they also used this approach to test the differential effects of the treatment groups (see below).

The main result seems quite robust statistically. Verifying the effects of the distancing intervention on emotion, the authors found an interaction between time (pre- to post-intervention) and intervention group (distancing vs. relaxation) suggesting that distancing (but not relaxation) reduced ratings of almost all emotions. Participants allocated to the distancing intervention also showed decreased variability of emotion ratings compared to those in the relaxation intervention (though note this interaction was not significant).

Using a model comparison approach, the authors then demonstrated that whilst the control group was best-explained by a model that did not change its dynamics of emotions, the active intervention (distancing) group was best-explained by a model that captured both changing emotion dynamics and a changing input weights (influence of the videos) - results confirmed in follow-up analyses. This is convincing evidence that emotion regulation strategies may specifically affect the dynamics of emotions - both their relationships to one another and their susceptibility to changes evoked by external influences.

The authors also perform analyses that suggest their result is not attributable to a demand effect (finding that participants were quicker during the control intervention, which one would expect if they had already decided how to respond in advance of the emotion question). I personally also think a demand effect is unlikely given the robustness of their control intervention (which participants would be just as likely to interpret as a mental health-enhancing training as distancing) and am convinced by the notion that demand effects would be unlikely to elicit their more specific effects on the dynamic quality of emotions.

Weaknesses:

The authors use an active control - a relaxation intervention - which is extremely closely matched with their active intervention (and a major strength). However, there was an additional difference between the groups: "in the group allocated to the distancing intervention, the phrasing of the question about their feelings in the second video block reminded participants about the intervention, stating: "You observed your emotions and let them pass like the leaves floating by on the stream." Therefore, some of the effects of distancing may have also been driven by different emotion regulation strategies, i.e. reappraisal, since this reminder might have evoked retrospective changes in ratings.

An unanswered question is exactly how distancing is producing these effects. As the authors point out, there is a possibility that eye-movement avoidance of the more emotionally-salient aspects of scenes could be changing participants' exposure to the emotions somewhat, which could vary by emotion, as the authors now discuss in their limitations.

Comments on revised version.

The authors have addressed my concerns.

Reviewer #1 (Public review):

Summary:

The study investigates how learning with combined visual and olfactory cues strengthens memory in fruit flies. It demonstrates that pairing colours with odours improves later memory performance, even when only one of the two cues is presented during testing. The authors show that multisensory learning recruits visually responsive Kenyon cells in the mushroom body into memory representations that would otherwise primarily encode odours. Their experiments indicate that the serotonergic DPM neuron links sensory representations that are normally separated, while the APL neuron regulates local GABAergic inhibition of separated learning subcircuits. Together, these findings provide a mechanistic explanation for how a single sensory cue can retrieve a broader memory of a multisensory experience.

Strengths:

A major strength of the paper is its integration of behavioural experiments, targeted neuronal manipulations, and detailed anatomical analysis to address a clear mechanistic question. The findings are supported by multiple complementary experiments showing that multisensory learning enhances memory and recruits visual pathways into olfactory memory representations. Overall, the work provides a coherent mechanistic framework for how multisensory experiences strengthen subsequent memory.

Weaknesses:

A limitation of the paper is that it represents an unusual case, as substantial parts of the broader study were previously published in Nature and subsequently retracted because the physiological findings could not be reproduced. Those physiological experiments would have helped resolve several mechanistic questions raised by the behavioural results and directly test how multisensory information is integrated within the fruit-fly learning circuit. Presenting only the reproducible behavioural and anatomical findings is therefore appropriate and preserves the reliable contribution of the work. Nevertheless, the absence of reproducible physiological evidence makes the mechanistic model less complete and more inferential than it would be in a fully comprehensive study. The conclusions should consequently be framed as a well-supported circuit model rather than a direct demonstration of the underlying physiological processes.

Reviewer #2 (Public review):

Okray et al. identify a novel form of multisensory memory in Drosophila, where pairing reward with a color+odor together gives a stronger memory than color alone or odor alone. Remarkably, this multisensory enhancement occurs even if only one modality is used during testing (i.e. training color+odor, then testing odor alone gives a stronger memory than training odor alone, then testing odor alone), showing that the two modalities are persistently linked following training. The manuscript presents compelling behavioural genetic evidence that the normally visual-selective gamma-d Kenyon cells acquire a functional role in the retrieval of odor memories following odor+color training, and that this occurs via transfer from gamma-main KCs via the serotonergic interneuron DPM.

The key pieces of evidence supporting this conclusion are that olfactory retrieval of multisensory memories requires:

(1) synaptic output from gamma-d KCs during retrieval (but not training);

(2) synaptic output from gamma-main KCs during training and retrieval (whereas it's only required during retrieval, not training, for pure-olfactory memory);

(3) synaptic output from DPM during training and retrieval, and expression of the serotonin receptor 5HT2A in gamma-d KCs.

In the absence of physiological data, the exact nature of the gamma-d KCs' participation in olfactory retrieval following odor+color training remains unclear. For example, do the gamma-d KCs encode the odor identity (i.e., is there an odor-specific pattern of gamma-d KCs activated for a particular odor+color combination), or does their activity provide a general activity boost to other neurons (e.g. gamma-m) that encode odor identity? This will be interesting to address in future studies.

That being said, the behavioural data are clear and back up the authors' conclusion that signaling between KC subtypes via DPM underlies multisensory integration for multimodal memories in the fly mushroom body.

Reviewer #1 (Public review):

Summary:

The authors sought to understand the impact of the decreased expression of the G-protein-coupled receptor GPR34 in Alzheimer´s disease (AD). They analyzed the transcriptional impact of GPR34 deficiency in mice and found that it induced a DAM-like phenotype in control mice and enhanced the DAM signature in the AD model 5xFAD, although it did not result in amyloid plaque clearance or gross changes in microglia or astrocytes. Next, the authors developed an in vitro model of GPR34 deficiency using a CRISPR/Cas9 strategy in human iPSCs to introduce functional mutations that resulted in GPR34 protein deficiency in induced microglial cells. In this model, the authors identified myelin as a ligand of GPR34 and showed that GPR34 deficiency resulted in reduced myelin debris engulfment and transcriptional changes related to lysosomal pathways.

Strengths:

The combined strategy of using in vivo and in vitro models of GPR34 depletion is robust, and the transcriptional analyses are thoroughly performed.

Weaknesses:

The paper´s two main findings related to the lack of GPR34 (enhancement of DAM signature in vivo and reduced myelin engulfment in vitro) are disconnected. At the very least, the authors should discuss what the relevance of myelin clearance in AD is, but the paper would strongly benefit from a more thorough assessment of the impact of GPR34 deficiency in vivo, particularly because no effects on amyloid clearance were observed. The authors could assess whether GPR34-deficient 5xFAD mice have reduced cognitive performance, which, based on their in vitro findings, could be related to the myelin pathology in AD (previously described: see PMID 36284351). The analysis showing reduced myelin content in GPR34-deficient microglia in vitro is superficial and does not allow for identifying whether GPR34 is related to reduced engulfment or increased degradation, which could be related to the changes in the lysosomal gene CD68 identified in vivo. In addition, it would be interesting to compare the transcriptional profile induced by myelin phagocytosis with that of 5xFAD or AD patients, to gain insight into the impact of the signature. Finally, the transgenic approach to delete GPR34 in vivo could have been complemented with experiments with the GPR34 antagonists (YL-365 or S-E49) or agonist (Compound 4B), possibly helping in identifying the source of discrepancy with previous papers showing that GPR34 promotes amyloid clearance.

Reviewer #2 (Public review):

Summary:

Using the 5xFAD model in combination with GPR34 mice, the authors explore the function of microglia in the context of neurodegeneration. Using a broad spectrum of methodology, they show that DAM signatures are increased in KO 5xFAD mice. Using several KO clones of GPR34 KO iMGLs and another set of broad methodologies, the authors show that GPR34 is important for microglia homeostasis,<br /> phagocytosis, specifically of myelin. GPR34 KO iMGLs also show a distinct transcriptional response to myelin. Together, they propose that GPR34 limits microglial activation in neurodegeneration.

Strengths:

All methods are state-of-the-art, and the combination of mouse and human microglia responses is a particular strength.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #1 (Public review):

"Learning is a fundamental source of individuality," by Manna and colleagues, interrogates different sources of variation in individual behavior. The authors place individual flies in a Y-shaped arena, which is a common design in the field, and illuminate the arms of the Y with blue versus green light. They track the color preference of individual animals and also perform operant conditioning, meaning that they teach the fly to avoid a particular color/arm by generating a foot shock when the fly enters that arm. There are a number of things that are impressive about this setup: The authors are able to collect data on thousands of individual flies of many different strain backgrounds, and they demonstrate a strong change in color preference after conditioning. This is nice, because in past papers visual learning ability has been modest and difficult to study. To put a number on it, in this paper animals on average don't show a color preference at the start of the assay, spending around 30% of their time in the one arm illuminated green, and the remaining time in the two arms illuminated blue. After conditioning, the average animal spends only 23% of its time in the green arm.

The authors run 64 animals through the assay for each of 88 wild type strains (maybe? see Major Point 1 below) and see considerable strain-specific (genetic) variation in the change in time spent in the shocked color after conditioning. Some strains show no learning, while others spend <10% of their time in the shocked color after conditioning. They also, I believe, see that some strains have more variability across individuals, which would suggest that some strains have stronger canalization at the development or circuit function level than others-i.e. some genotypes produce more consistent copies of the individual, others less consistent copies. (Or, some genotypes produce robust circuits, and others produce noisy circuits.)

Finally, the authors argue statistically that learning itself increases variability in individual performance. This makes a lot of sense to me intuitively. Learning changes the physical/chemical properties of circuits in the brain, and because it evolves over time and interacts with environmental variables, it seems like it should send different animals down different channels. Or, at a conceptual level, if I learn to play the piano and my sister doesn't (because of some genetic difference between us or something stochastic), this learning experience will cause all sorts of other differences in our behavior as time passes. I also think the authors do have enough data to be able to make this finding. However, the presentation of the argument in this portion of the paper is hard for me to understand, and I am not an expert in statistics, so the strength of the result is difficult for me to evaluate.

Major points:

(1) It's difficult to track through the paper the number of animals tested for different assays. At the beginning, it says N=5632, which works out to 64 flies for each of the 88 DGRP strains. 64 happens to be the number of parallel Y arenas they have. Later in the methods, there's description of more variation within the set of 64 for each strain-two different parent sets per strain, different sexes, conditioned and un-conditioned. And, while the results text focuses on the color learning, the methods discuss additional assays (place learning, multi-day learning).

Given the numbers, does each run of the 64 mazes include all the tested flies of one strain, or are flies of many strains included in each batch? Do different flies do different assays (color, place, multi-day) or do they all do all the assays? Perhaps there is a table including this information already in the supplement, but I recommend making it much clearer in the main results text and methods. While the dataset is large, if it is split over many conditions and/or if batch and genotype confound each other, this will affect the robustness of the results and how strong the conclusions can be.

(2) The data presentation in Figure 1 is elegant and easy to follow, but getting into Figure 2 and subsequently, I get lost in the statistics and have trouble understanding what is being measured. My understanding of the big picture is that while genetics and individual randomness contribute a lot to behavior, the evidence for learning as an amplifier of individuality is that variance in behavior among animals of the same strain increases over time in the conditioned group (i.e. the group that is doing the most learning, or a specific kind of learning), but not in the control group. This idea is illustrated in the flattening distributions in the cartoons in Figure 1A. The authors should include graphs of the real data that use the same format as in that cartoon. Instead, the graphs present "residuals," and I don't know what those are. I suspect it's "variation left over after accounting for effects of strain and individual stochasticity." I see the residuals being tracked per strain over time in Figure 2H, but I don't see the change over time in other graphs. I'm looking for something simple like, "variation within the strain at the beginning of learning and at later time points in learning." (But I'm not sure exactly what instantaneous measurement would be the focus in longitudinal analyses of learning behavior.)

(3) Figure 3 is a cool stab at tracking down the precise mechanism by which stochastic environment interacts with learning to send individuals along different behavioral routes. But again, like in Figure 2, I don't have the sophisticated understanding of statistics to understand exactly what the graphs are telling me, or how they relate to the underlying measurements. I'm relying on the results text alone to reach a conceptual understanding and just taking the graphs on trust.

So, overall, the authors have a very nice body of work here and with the potential to add a new facet to our understanding of the origins of diversity in animal behavior. In addition to the interpretations they focus on here, this dataset also represents an advance in studying visual associative learning in general, and quite an amazing ability to make longitudinal measurements of many behavioral decisions within the same animals. Improving the data presentation to make it easier to follow for a larger swathe of researchers, especially in figures 2 and 3, will increase its potential impact.

Comment on revised version:

The authors have addressed my main points, including adding description of their statistical analyses and providing more detail about the different assays run and which animals were included in the same assay batches.

Reviewer #2 (Public review):

Summary:

The authors set out to test the extent to which differences in learning capacity and experience contribute to behavioural variation in a genetically identical population under identical environmental conditions.

Strengths:

The authors developed and used a scaled-up version of a simple two-choice behavioural paradigm allowing them to test thousands of individuals across multiple genotypes. They then deployed clever and powerful statistical analysis methods and provided compelling evidence for a role of variability in learning in the expression of behavioural variation.

Weaknesses:

There are no major weaknesses, although some level of longitudinal analysis to strengthen the evidence for a strict definition of individuality would be a welcome extension of a future study. In addition, it would have been very interesting, although understandably beyond the current scope, to delineate a potential source of learning variability in the brain.

Reviewer #1 (Public review):

The authors demonstrate an innovative approach to investigate the effect of cone dropout on visual acuity using their newly developed Oz platform. By systematically reducing the coverage of real-world input to the cone photoreceptor mosaic ("cone dropout condition"), the authors are able to assess how having less cones leads to reduced vision, in comparison to existing approaches ("pixel dropout condition").

The observation of visual acuity maintenance with cone dropout has been a longstanding mystery since the 2013/2018 papers by Ratnam and Foote. The authors should be commended for their approach to address this important question. However, there are some simplifications and assumptions being applied to make this jump (i.e. that a 50% reduction in cone stimulation in a healthy eye is comparable to a 50% reduction in cone density in a patient). It seems unlikely that in a patient eye, with cone dropout, that there will be gaps in the mosaic. Not considering any other non-photoreceptor related reasons for visual acuity loss which can occur in patients, the cone aperture acceptance angle may be different due to changes in cone size or packing; the sensitivity of individual cones may also be reduced due to deficits in the visual cycle recovery which could be affected in disease. Some of these limitations could be addressed and acknowledged more explicitly.

The capture of a rich dataset including both cone imaging and eye motion is valuable. Since the C stimulus test relies on foveal fixation, and there is a high degree of subject-to-subject variation in peak cone density, the authors may wish to report on peak cone density measurements of the subjects being included in this study. In addition, evaluating whether the eye motion is affected by simulated cone dropout condition can help to rule out whether these observed effects can be attributed to eye motion.

Overall, this is an impressive study incorporating state-of-the-art technology to probe the fundamental limits of human vision.

Comments on revised version.

The authors have nicely addressed my concerns. The additional clarifications and revised text have strengthened the paper. Thank you also for pointing out the inaccuracy of referring to the system as the olo system; this has been corrected.

Reviewer #1 (Public review):

Summary:

This is important and significant work because it helps describe the complexity of interactions between system components where 2 herbivores interact with vegetation. Whereas other studies have shown that the larger ungulate (yaks, Bos grunniens, in this case) can facilitate the abundance and population growth of the smaller (the semi-fossorial lagomorph, Ochotona curzoniae, plateau pika hereafter), this study flips the tables, and shows that, at least under some conditions, moderate densities of the plateau facilitate the nutritional condition of yaks.

Strengths:

Notably, the strong inference the authors can claim for their results is supported by the careful experimental design. A weaker paper would have simply noted correlations between pika burrow density and yak feeding efficiency without experimental removal. This paper, to its credit, not only used experimental removals but also documented the various intermediary results that support the ultimate conclusions. The statistical approaches used appear to be appropriate. (Readers are encouraged to read the full Materials and Methods, which are available in the Supplementary Materials section).

Weaknesses:

Although the study was well designed and executed, and its conclusions appear strongly supported, readers interested in the management implications on the Qinghai-Tibetan Plateau should be mindful of its limitations. First, the study site, at approximately 3,200 m elevation, was relatively low by Qinghai-Tibetan Plateau standards. Stellera chamaejasme becomes less common at elevations > 4,000 m, where a majority of livestock grazing occurs. Thus, it would be instructive to learn, through follow-up studies, whether similar facilitation occurs where unpalatable (and mildly poisonous) species in such genera as Astragalus, Oxytropis, and Thermopsis replace S. chamaejasme as the problematic plant for pastoralists. Second, the authors make no mention of wild ungulates, so it is unclear what, if any, role they may have played in this system. At least one study in Qinghai Province, albeit at a slightly higher elevation, showed that not only pikas, but also Tibetan gazelles (Procapra picticaudata), which were commonly observed on grazed pastures, grazed more frequently on some dicots avoided by domestic sheep than did the livestock themselves (Harris et al. 2015). It would also be instructive to learn if similar facilitation as observed here applied to the other principal livestock species in the area, domestic sheep (which are often herded together with smaller numbers of domestic goats). Finally, as suggested by this study, the interactions between all components of the system are complex and interactive. If pika facilitation of yak nutrition at the densities documented results in herders increasing yak density, might the increased herbivory from the domestic animals provide the conditions for the pika population to increase beyond the densities observed here, and thus toward the levels where facilitation yields to competition?

Reviewer #2 (Public review):

This study uses a combination of field sampling and manipulative experiments to test for facilitative impacts of pikas on yaks via suppression of a poisonous forb. The authors found that, when Stellera forbs were present, yak weight increases over the growing season were greater in the presence of pikas compared to in their absence. This occurred because, although pikas do not consume Stellera, they clip it and use it in nest/burrow construction, thereby decreasing its relative abundance in the plant community. Thus, overall, the study contributes to our understanding of how herbivores of different size classes indirectly affect each other via use of shared resources.

It is well known that large herbivores on grasslands impact smaller animals, but the reciprocal interaction is rarely tested. Thus, this study asks a valuable question, and the experiment is well-designed to test it. The authors also do a good of demonstrating the potential conservation impacts of their research.

Reviewer #1 (Public review):

I thank the authors for the revised manuscript and for the detailed responses.

I think the main points raised in the review have now been addressed. In particular, the new experiment with TbPLK inhibition and mass spectrometry is an important addition, as it provides direct evidence that phosphorylation of KIN-G at Thr301 and Ser569 depends on TbPLK activity in cells.

I also appreciate that the authors have toned down the interpretation of the Golgi phenotype. The revised text now makes clear that the fluorescence data show altered Golgi/ERES organization or duplication, but do not prove a structural defect in Golgi biogenesis.

The added discussion of the T301A result is also helpful. The finding that only a small fraction of KIN-G is phosphorylated at Thr301 in asynchronous cells makes the lack of a strong T301A phenotype more understandable.

Overall, I am happy with the revision of the beautiful manuscript.

Reviewer #2 (Public review):

Summary:

The authors identify KIN-G as an in vitro substrate for phosphorylation by TbPLK and show that several of the in vitro P-ated sites, including T310, overlap with P-ation sites seen in live cells. The authors further show that PLK-mediated P-ation inhibits KIN-G binding to microtubules in vitro, as does a KIN-G-T301D mutant, and that expression of a KIN-G-T301D Phospho-mimic in T. brucei phenocopies KIN-G RNAi knockdowns, producing defects in cell division, morphogenesis of the centrin arm, FAZ and other cellular structures, as well as misplaced cytokinesis furrow.

Understanding cytoskeletal rearrangements that drive cell division in T. brucei is an important and unresolved problem, so the work addresses important questions that are of great interest. PLK and KIN-G have previously been shown to be important for cell division and morphogenesis of cytoskeletal structures that drive cell division in T. brucei. The current work advances our understanding by suggesting a potential mechanism by which PLK and KIN-G might participate, namely through PLK-dependent P-ation to control KIN-G MT binding activity.

Strengths:

The authors use a rigorous combination of biochemistry, phosphoproteomics, cell biology, and mutant analysis to support their conclusion that PLK-mediated P-ation of KIN-G negatively regulates KIN-G microtubule binding and this may explain the observation that a KIN-G T301 phosphomimic mutant blocks cell division and perturbs biogenesis of cytoskeletal structures that drive cell division and morphogenesis. Combining rigorous and informative in vitro studies with mutant analysis in live cells is a great strength. The work is solid and important, though a few pieces are needed to fully connect the in vitro findings with the in vivo observations, as detailed below.

Weaknesses:

Overall, I find this work to be solid, and to provide an important addition to our understanding of mechanisms controlling cell division in T. brucei. The biochemistry, in particular, is rigorous and convincingly demonstrates PLK can P-ate KIN-G, altering its MT-binding ability. Analysis of phospho-mutants of KIN-G in live T. brucei support the conclusion that P-ation of KIN-G at T301 negatively affects KIN-G function in vivo. I think, however, that the results fall short of supporting the title, because, although the data convincingly show that PLK can phosphorylate KIN-G at T301 in vitro, and that T301 is P-ated in vivo, they do formally demonstrate (nor even test) whether PLK is the kinase responsible for this phosphorylation in vivo (experiments to address this seem quite feasible). I also do not see where the authors try to reconcile the absence of phenotype for KIN-G-T301A with the implied importance of KIN-G phosphorylation by PLK in cell division, which calls into question the need for P-ation of KIN-G-T301 in cell division. Suggestions for addressing these concerns are provided below.

My two main questions are:

(1) What is the biological relevance of KIN-G P-ation at T301?<br /> a. The authors report no defect for the KIN-G-T301A mutant, so what then is the need for T301 P-ation, if the cell gets along fine without it? One step toward addressing this would be to ask what fraction of KIN-G shows P-ation at T301. Although published studies indicate P-ation at T301, it isn't known what percentage of KIN-G in the cell is P-ated. One might anticipate, for example, that T301-P is a small minority of the population in asynchronous cultures and that T301 P-ation increases at specific cell cycle stages.<br /> b. Published work links PLK to cell division, FAZ elongation, etc... The current work suggests that one role of PLK is to P-ate KIN-G at T301. In contrast, however, the current work also indicates that P-ation of KIN-G at T301 is unnecessary for normal cell division, FAZ elongation, etc....<br /> c. Some experiments or at least commentary on points a and b above would strengthen the paper.<br /> - The authors have now addressed this question by assessing what % of KING is phosphorylated at T301 and adding commentary on this point in the revised paper.<br /> - I would suggest that the model (new figure 8) include a dephosphorylation step, as that is proposed by the authors in the text. Also include in the legend some commentary on the role of phosphorylation, which is the center point of this paper, but not currently mentioned.

(2) Is PLK the kinase that P-ates Kin-G T301 in vivo?<br /> a. The authors show PLK P-ates T301 (and other residues) in vitro, and that T-301 is P-ated in vivo. To bring the analysis full circle, it would be informative to examine KIN-G P-ation in a PLK mutant or upon inhibition of PLK with published inhibitors. This seems to be a very doable experiment with the tools available.<br /> - The authors have addressed this question by demonstrating that T301 phosphorylation is reduced upon treatment with a PLK inhibitor, thus supporting that PLK phosphorylated T301 in vivo. It is noted that one might consider an alternate kinase is also able to phosphorylate T301 in absence of PLK activity, as that could explain the relatively low (~27%) reduction in phosphorylation by PLK inhibitor treatment.

Reviewer #3 (Public review):

Summary:

Here the authors investigate the role of the Trypanosoma brucei polo-like kinase TbPLK in the function of flagellum-associated cellular structures in trypanosomes. They set out to test the hypothesis that a key substrate of TbPLK is the kinesin protein KIN-G, and that TbPLK phosphorylation of KIN-G regulates its functions in cells.

Strengths:

Using in vitro biochemistry with purified proteins, the authors convincingly demonstrate that TbPLK phosphorylates KIN-G at 29 sites. Moreover, they convincingly show that phosphorylation at one site, T301, impairs the binding of purified KIN-G to purified microtubules. They further confirm that inhibition of TbPLK in cells reduces KIN-G phosphorylation at T301 (and S569). Using immunofluorescence-based imaging approaches, they also show that TbPLK colocalizes with KIN-G at centrin arms during early S-phase of the cell cycle. Centin arms are structures that are located near the basal body and flagellum and are important for new flagellum biogenesis, Golgi positioning, and cell division. To evaluate the function of KIN-G phosphorylation in cells, they depleted KIN-G by RNAi, simultaneously expressed phospho-mimetic (T301D) and phospho-ablative mutant proteins, and used immunofluorescene to examine the impact on flagellum-associated cellular structures. They show that expression of the phospho-mimetic mutant KIN-G-T301D causes the following defects: reduced cell proliferation, disruption of centrin arm and Golgi biogenesis, impairment of FAZ elongation and flagellum positioning, and misplacement of the cell division plane. The data convincingly support the conclusion that KIN-G phosphorylation on T301 plays an important role in regulating the cellular functions of this kinesin motor protein.

Weaknesses:

The authors have addressed prior weaknesses in the manuscript through additional experimentation and rewording of the conclusions.

Reviewer #1 (Public review):

Summary:

This study demonstrates that nutrient resorption efficiency (NuRE) in Phragmites australis is genetically canalized rather than plastic to salt stress. Using 110 genotypes in a common garden, the authors show that intraspecific variation in NuRE is explained by phylogeographic lineage, ecotype, and latitude, not by effective salinity. Element-specific regulatory strategies further reveal how N, P, and K resorption are differentially controlled. At the population level, this is an important study that fundamentally advances our understanding of plant functional trait evolution and its implications for ecosystem nutrient dynamics under global change.

Strengths:

This study is the first to demonstrate genetic determination of a key nutrient conservation trait under effective salt stress in a widespread macrophyte, directly testing the 'plastic acclimation versus inherent conservatism' paradigm in a non-nutrient stress context. The experimental design is rigorous: each genotype was paired across control and salt treatments, and multilevel stress effectiveness (metabolomics, biomass, Na accumulation) was confirmed before evaluating NuRE. The large sample size of a macrophyte and dual classification (phylogeography + ecotype) allow robust disentangling of genetic versus plastic sources of variation.

The analysis comprehensively tests three resorption control hypotheses using appropriate SMA regression, revealing element-specific and condition-dependent patterns. The latitudinal gradient and variation partitioning provide strong evidence that genetic origin and geographic context outweigh short-term plasticity, with important implications for predicting ecosystem nutrient cycling under global change. This study provides a clear empirical demonstration that a key nutrient conservation trait can remain homeostatic under non-nutrient stress, and that intraspecific variation is primarily a product of population differentiation rather than short-term plasticity.

Weaknesses:

First, the salinity treatment spanned only one growing season. The conclusion of genetic canalization therefore specifically refers to the absence of plasticity to an acute salt shock. Whether long-term, multigenerational chronic salinity could act as a selective agent or induce transgenerational plasticity remains an open and interesting question for further research. Likewise, the physiological mechanisms underlying the observed lack of plastic increase in NuRE (for example, phloem loading or senescence gene expression) are not directly resolved, leaving some inference about trade-offs versus true unresponsiveness. These points do not weaken the study's main conclusion. Instead, they suggest productive future directions, such as longer-term field manipulations and targeted molecular investigations.

Second, the test of nutrient limitation control relies on resorbed N:P and N:K ratios as proxies, an established but indirect approach. Direct nutrient addition experiments would provide stronger causal evidence. Also, the metabolomic analysis is used primarily to validate stress effectiveness; deeper integration of specific metabolites with NuRE variation across genotypes could have offered mechanistic insights but was not pursued. Additionally, the potential collinearity between ecotype and phylogeographic lineage among Chinese populations is not quantitatively addressed. None of these considerations undermines the main finding, which is supported by a robust experimental design and widely accepted analytical approaches.

Reviewer #2 (Public review):

Summary:

The study finds that nutrient resorption efficiency in Phragmites australis shows no plastic response to salinity stress but is canalized by phylogeographic lineage, ecotype, and latitude. In a common garden with 110 genotypes, salinity induced stress, yet no plastic change occurred for N, P, or K resorption. The authors conclude that intraspecific variation is historical and geographic; thus, predictions of wetland nutrient cycling need to account for phylogeographic composition.

Strengths:

The core finding that NuRE shows no plastic response to salinity, but is instead evolutionarily canalized by lineage and latitude, challenges a key assumption of broad trait plasticity. This conclusion is firmly supported by a robust common garden design with 110 genotypes, rigorous multi-level stress validation, and element-specific resorption analyses. The work provides compelling evidence that intraspecific variation in this critical nutrient cycling trait is shaped by phylogeographic history rather than short-term acclimation. The implications for predicting wetland responses to salinization are significant, as ecosystem-level nutrient dynamics may be constrained by the genetic composition of plant populations.

Weaknesses:

The experiment covers only one growing season, with salinity applied in June and measurements in December. While the stress is clearly effective, longer-term or multi-year stress might reveal acclimation or epigenetic effects that are not captured. Given the author team's expertise in parental and transgenerational effects in clonal plants, this limitation is particularly relevant and warrants more thorough discussion in the manuscript.

The salinity treatment uses a single moderate level of 10 ppt, which does not allow assessment of whether more extreme stress might trigger a plastic response. A dose-response design across a gradient would have provided stronger inference about the threshold at which NuRE canalization might be overcome. Additionally, the ecotype analysis in Figure 4 applies only to Chinese populations, as classification was not available for non-Chinese populations, which should be stated more explicitly in the Results.

The variation partitioning shows latitude as a significant predictor, but the R² values are relatively low, indicating that much variance remains unexplained. The manuscript should avoid overinterpreting latitude's explanatory power and more openly acknowledge the role of unmeasured factors. The interpretation of slopes greater than 1 for the resorbed N:P versus green N:P relationship, labeled as "inverted limitation", also needs further explanation regarding its functional significance.

Reviewer #1 (Public review):

Summary:

The manuscript introduces cuBNM, a GPU‑accelerated Python package for whole‑brain modeling. The authors demonstrate that running simulations on GPUs provides substantial benefits in computational speed, cost-efficiency, and scalability compared to traditionally used CPUs, making large‑scale and individualized brain network modeling computationally feasible. The usage of cuBNM has been demonstrated by running optimization of group-level and individualized low- and high-dimensional models. By investigating the test-retest reliability and heritability of simulated and empirical measures in the Human Connectome Project dataset, the authors showed that simulated features were fairly reliable and significantly heritable.

Strengths:

This study is timely and presents an important contribution to the field of whole-brain computational modeling. A major strength is that the authors go beyond introducing a GPU-accelerated framework by demonstrating its utility through comprehensive benchmarking and biologically relevant applications, including individualized model fitting, comparisons of homogeneous and heterogeneous models, and analyses of test-retest reliability and heritability.

The computational performance is evaluated comprehensively, assessing speed, computational cost, energy consumption, and scalability across different simulation settings. The Human Connectome Project dataset is used to demonstrate that the software enables individualized whole-brain modeling in large datasets.

Finally, the software is modular, open-source, and well-documented, and can facilitate the broader adoption of GPU-accelerated whole-brain modeling within the neuroscience community.

Weaknesses:

The test-retest reliability and heritability are estimated using high-quality Human Connectome Project data. The manuscript would benefit from discussion and/or demonstrations regarding how the software performs under more challenging conditions, such as clinical datasets, shorter data acquisitions, higher-motion datasets, or multi-site datasets.

Apart from demonstrating the benefits of GPUs over CPUs, the manuscript would benefit from a more direct comparison between cuBNM and other whole-brain modeling software, such as The Virtual Brain.

The manuscript demonstrates that heterogeneous models improve the fit to empirical functional connectivity. However, the biological interpretation of this improvement could be expanded. The heterogeneous models are also more complex than homogeneous models, and some improvement in model fit may be explained by the increased model flexibility.

In whole-brain brain network modeling, different parameter combinations can result in similar empirical functional connectivity measures. The manuscript would benefit from a discussion of how this influences the interpretation of individualized parameter estimates.

Reviewer #2 (Public review):

Summary:

The authors aim to address a major problem in brain network modeling: the high computational cost of simulating and fitting brain activity models, particularly for large samples, individualized models, and broad parameter searches. They introduce cuBNM, an open software package that uses graphics processing units to accelerate model simulation, fitting, and calculation of simulated brain activity features.

The manuscript is primarily a methods and software contribution, rather than a paper providing novel neurobiological insights. The authors demonstrate the tool using human imaging data, showing examples of group-level and individualized model fitting, comparisons between homogeneous and heterogeneous model parameterizations, and analyses of repeated-measurement stability and genetic influences of simulated features. They also provide speed and scaling tests to support the claim that the software can make large-scale and individualized brain network modeling more practical for the field.

Strengths:

A major strength of this work is that it addresses a clear computational bottleneck in brain network modeling. The authors provide an open software package that combines a user-friendly Python interface with an accelerated back-end, making large numbers of simulations and model fits more practical for other researchers.

The demonstrations are broad and relevant to real use cases. The authors show group-level and individualized model fitting, different optimization strategies, and comparisons between homogeneous and heterogeneous models, rather than limiting the paper to a narrow technical benchmark. The benchmarking and openness of the work further increase its value. The comparisons across hardware and network sizes give readers a practical sense of the tool's speed and scalability, while the availability of code, documentation, tutorials, and containers should make the method easier for the community to test and adopt.

Weaknesses:

(1) The benchmarking provides solid evidence for substantial speed improvements within the authors' implementation, but the generality of the performance claims is more limited. The largest reported speed-ups are measured relative to a single central processing unit thread, and the study does not fully benchmark cuBNM against other optimized brain modeling frameworks. This makes the results useful as evidence of strong acceleration in the tested setting, but less definitive as a general comparison across available implementations.

(2) The comparison between homogeneous and heterogeneous models is informative, but it is not fully controlled for model complexity. The best-fitting node-based heterogeneous model has more free parameters than the homogeneous and map-based alternatives, so its improved fit may partly reflect greater flexibility rather than a more biologically valid parameterization. As a result, the model comparison supports the conclusion that this parameterization fits better under the current setup, but not necessarily that it is generally superior or more biologically realistic.

(3) The reliability and heritability analyses are valuable demonstrations of what scalable individualized modeling can enable, but they do not establish the simulated features as validated biological mechanisms. Because these simulated features are derived from individualized structural and functional imaging data, their stability across repeated measurements and genetic influences may partly reflect information already present in the empirical inputs or fitting targets. These results therefore support a more cautious conclusion: the simulated features retain stable and genetically structured variation, but their biological interpretation remains model dependent.

(4) The empirical demonstrations are narrower than some of the broader claims made in the manuscript. Most analyses rely on one human imaging dataset, one cortical parcelation, one main brain model, and a specific fitting objective, while broader claims refer to diverse populations, dense networks, high-dimensional models, and biological applications. The current results show that cuBNM is a useful and scalable tool in the tested setting, but the extent to which the findings generalize across datasets, model classes, network resolutions, or clinical contexts remains to be established.

Reviewer #1 (Public review):

Summary:

This study revisits an important and controversial question in brain repair: whether NeuroD1 can convert brain immune cells into nerve cells in vivo. Using a virus-free genetic system, in vivo imaging, injury experiments, and single-cell profiling, the authors provide convincing evidence that NeuroD1-expressing cells do not become nerve cells under the tested conditions. Instead, these cells largely retain their original immune-cell identity, and some appear to undergo cellular stress or loss.

Strengths:

The main strength of the work is that it tests this question with a cleaner genetic strategy, avoiding some of the concerns associated with viral delivery and unintended cell labeling. Although the overall conclusion is consistent with the authors' previous work, the current study adds useful independent evidence, particularly through the virus-free fate-mapping system and live imaging in the brain.

Weaknesses:

There are some limitations. In the injury experiment, the labeled cells may include both resident brain immune cells and blood-derived immune cells recruited after injury, so the authors should be cautious when referring to all labeled cells as microglia. The level of NeuroD1 expression achieved by the genetic system is also not fully defined, which matters because the effects of such a cell-fate regulator may depend on expression level. Finally, the tested time window may not fully address very delayed or incomplete neuronal differentiation.

Overall, this is a useful and careful study that supports the conclusion that NeuroD1 does not drive brain immune cells to become nerve cells in the tested settings. It should be valuable for researchers studying brain repair, cell fate conversion, and genetic fate mapping, and it provides a clear caution against overinterpreting reprogramming results based only on viral labeling.

Reviewer #2 (Public review):

Summary:

In vivo glia-to-neuron conversion emerges as a potential regeneration-based therapeutic strategy for neural injuries and diseases. However, controversies exist in this exciting field, largely arising from the non-stringent methods employed for analyzing in vivo neuronal conversions. The study by Li et al. directly addressed this controversy regarding Neurod1-mediated microglia-to-neuron conversion. They took advantage of two transgenic mouse lines to specifically express Neurod1 in the microglia of adult mouse brains. Results from immunohistochemistry, in vivo live-cell imaging, and scRNA-seq convincingly demonstrate that microglia cannot be converted in vivo to neurons by ectopic Neurod1 expression under both normal and injury conditions. Instead, it induces microglia death, consistent with their earlier findings. These solid results, though negative, are critical additions to the field and further support that stringent lineage tracing methods are essential for studying in vivo cell reprogramming. Overall, the studies are rigorously designed and executed. Only minor issues need to be dealt with.

Joint Public Review:

The revised manuscript is much clearer, and the additional analyses address several of the original concerns. RAIN analyses (Rhythmicity Analysis Incorporating Nonparametric methods) now detects circadian rhythmicity in 7/11 recordings under light-dark conditions and 8/12 recordings in constant darkness, compared with 2/12 following treatment with the Orco antagonist. This supports circadian modulation of spontaneous firing and a role for Orco in its normal expression. The expanded qPCR analysis of Orco also supports the conclusion that Orco transcript abundance is not circadian, and the cAMP experiment shows that cAMP can modulate Orco-dependent activity.

The remaining issue concerns the mechanistic interpretation. The lack of rhythmic Orco transcript abundance does not distinguish an autonomous post-translational feedback-loop (PTFL) clock from a model in which the canonical transcriptional-translational (TTFL) clock acts upstream through cAMP, calcium, kinases, phosphatases, channel trafficking, or related pathways to regulate Orco.

Similarly, the new Figure 10 provides a useful representation of the authors' hypothesis, but the proposed delayed feedback and coupling mechanisms are not experimentally demonstrated.

We do not think any further experiments are necessary for the present study. Instead, we recommend that the manuscript should clearly distinguish between what the data show and what remains proposed. The data support circadian modulation of ORN firing a role for Orco in its normal expression, non-circadian Orco transcript abundance, and cAMP-sensitive modulation of Orco-dependent activity. The proposal that an Orco-centred membrane feedback loop generates the rhythm is intriguing and may remain a hypothesis generated through this study that needs formal testing in the future. This should be explicitly stated. While this has been done in the discussion section, elsewhere, including in the abstract and elsewhere, the original claim remains.

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate the physiological role of the Type VI secretion system (T6SS) in a naturally evolved gut microbiome derived from wild mice (the WildR microbiome). Focusing on Bacteroides acidifaciens, the authors use newly developed genetic tools and strain replacement strategies to test how T6SS-mediated antagonism influences colonization, persistence, and fitness within a complex gut community. They further show that the T6SS resides on an integrative and conjugative element (ICE), is distributed among select community members, and can be horizontally transferred, with context-dependent effects on colonization and persistence. The authors conclude that the T6SS stabilizes strain presence in the gut microbiome while imposing ecological and physiological constraints that shape its value across contexts.

This study is likely to have significant impact on the microbiome field by moving experimental tests of T6SS function out of simplified systems and into a naturally co-evolved gut community. The WildR system, together with the strain replacement strategy, ICE-seq approach, and genetic toolkit, represents a powerful and reusable platform for future mechanistic studies of microbial antagonism and mobile genetic elements in vivo.

The datasets-including isolate genomes, metagenomes, and ICE distribution maps-will be valuable community resources, particularly for researchers interested in strain-resolved dynamics, horizontal gene transfer, and ecological context dependence. Even where mechanistic resolution is incomplete, the work provides a strong experimental foundation upon which such questions can be directly addressed.

Overall, this study occupies a space between system building and mechanistic dissection. The authors demonstrate that the T6SS influences persistence and community structure in vivo, but the physiological basis of these effects remains unresolved. Interpreting the results as evidence of fitness costs or selective advantage therefore requires caution, as multiple ecological and host-mediated processes could produce similar abundance trajectories.

Placing the findings within the broader literature on microbial antagonism, particularly work emphasizing measurable costs, benefits, and tradeoffs, would help readers better contextualize what is directly demonstrated here versus what remains an open question. Viewed in this light, the principal contribution of the study is to show that such questions can now be addressed experimentally in a realistic gut ecosystem.

Strengths:

A major strength of this study is that it directly interrogates the physiological role of the T6SS in a naturally evolved gut microbiome, rather than relying on simplified pairwise or in vitro systems. By working within the WildR community, the authors advance beyond descriptive surveys of T6SS prevalence and address function in an ecologically relevant context.

The authors provide clear genetic evidence that Bacteroides acidifaciens uses a T6SS to antagonize co-resident Bacteroidales, and that loss of T6SS function specifically compromises long-term persistence without affecting initial colonization. This temporal separation is well designed and supports the conclusion that the T6SS contributes to maintenance rather than establishment within the community.

Another strength is the identification of the T6SS on an integrative and conjugative element (ICE) and the demonstration that this element is distributed among, and exchanged between, community members. The use of ICE-seq to track distribution and transfer provides strong support for horizontal mobility and adds mechanistic depth to the study.

Finally, the transfer of the T6SS-ICE into Phocaeicola vulgatus and the observation of context-dependent colonization benefits followed by decline is a compelling result that moves the study beyond simple "T6SS is beneficial" narratives and highlights ecological contingency.

Weaknesses:

Despite these strengths, there is a mismatch between the precision of the claims and the precision of the measurements, particularly regarding fitness costs, physiological burden, and mechanistic role of the T6SS.

First, while the authors conclude that the T6SS "stabilizes strain presence" and that its value is constrained by fitness costs, these costs are not directly measured. Persistence, abundance trajectories, and eventual loss are informative outcomes, but they do not uniquely identify fitness tradeoffs. Decline could arise from multiple non-exclusive mechanisms, including community restructuring, host-mediated effects, incompatibilities of the ICE in new hosts, or ecological retaliation, none of which are disentangled here.

Second, the manuscript frames the T6SS as having a defined physiological role, yet the data do not resolve which physiological processes are under selection. The experiments demonstrate that T6SS activity affects persistence, but they do not distinguish whether this occurs via direct killing, resource release, niche modification, or higher-order community effects. As a result, "physiological role" remains underspecified and risks being conflated with ecological outcome.

Third, although the authors emphasize context dependence, the study offers limited quantitative insight into what aspects of context matter. Differences between native and recipient hosts, or between early and late colonization phases, are described but not mechanistically interrogated, making it difficult to generalize beyond the specific cases examined.

Fourth is the lack of engagement with recent experimental literature demonstrating functional roles of the T6SS beyond simple interference competition. While the authors focus on persistence and competitive outcomes, they do not adequately situate their findings within recent work demonstrating that T6SS-mediated antagonism can serve additional physiological functions, including resource acquisition and DNA uptake, thereby linking killing to measurable benefits and tradeoffs. The absence of this literature makes it difficult to place the authors' conclusions about physiological role and fitness cost within the current conceptual framework of the field. Without this context, the physiological interpretation of the results remains incomplete, and alternative functional explanations for the observed dynamics are underexplored.

A further limitation concerns the taxonomic scope of the functional analysis. The authors state the role of the T6SS in the murine environment is functionally investigated using genetically tractable Bacteroides species, citing lack of genetic tools for Mucispirillum schaedleri. While this is a reasonable practical choice, it means that a substantial fraction of T6SS-encoding species in the WildR community are not experimentally interrogated. Consequently, conclusions about the role of the T6SS in the murine gut necessarily reflect the subset of taxa that are genetically accessible and may not fully capture community-level or niche-specific functions of T6SS activity. Given that M. schaedleri is represented as a metagenome-assembled genome, its isolation and genetic manipulation would be technically challenging. Nonetheless, explicitly acknowledging this limitation and slightly tempering claims of generality would strengthen the manuscript.

Finally, several interpretations would benefit from more cautious language. In particular, claims invoking fitness costs, selective advantage, or physiological burden should be explicitly framed as inferences from persistence dynamics, rather than as direct measurements, unless supported by additional quantitative fitness or growth assays.

Comments on revised version.

The authors have addressed my main concerns by more clearly distinguishing ecological outcomes from directly measured physiological mechanisms. They have moderated claims about fitness costs and benefits, replaced "physiological" with "ecological" where appropriate, expanded the discussion of potential downstream benefits of T6SS-mediated killing, and acknowledged the limited taxonomic scope of the functional analyses. The persistence trajectories support context-dependent relative fitness effects, although they do not identify the specific physiological basis of those effects. The revised manuscript now generally maintains this distinction. These revisions substantially improve the precision and balance of the manuscript.

Reviewer #2 (Public review):

Summary:

In this study, the authors set out to determine how a contact-dependent bacterial antagonistic system contributes to the ability of specific bacterial strains to persist within a complex, native gut community derived from wild animals. Rather than focusing on simplified or artificial models, the authors aimed to examine this system in a biologically realistic setting that captures the ecological complexity of the gut environment. To achieve this, they combined controlled laboratory experiments with animal colonization studies and sequencing-based tracking approaches that allow individual strains and mobile genetic elements to be followed over time.

Strengths:

A major strength of the work is the integration of multiple complementary approaches to address the same biological question. The use of defined but complex communities, together with in vivo experiments, provides a strong ecological context for interpreting the results. The data consistently show that the antagonistic system is not required for initial establishment but plays a critical role in long-term strain persistence, an insight that moves beyond traditional invasion-based views of microbial competition. The observation that transferable genetic elements can confer only temporary advantages, and may impose longer-term costs depending on community context, adds important nuance to current understanding of microbial fitness.

Weaknesses:

Overall, the study is not a lack of evidence, but a deliberate trade-off between ecological realism and mechanistic resolution, which leaves some causal pathways open to interpretation.

Comments on revised version.

The authors have addressed all previous concerns thoroughly and satisfactorily.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigate the contribution of the type VI secretion system of Bacteroidales to gut microbiome assembly and the targeting of closely related species. They demonstrate that B. acidifaciens relies on T6SS-mediated antagonism to prevent displacement by co-resident Bacteroidales and other members of the microbiome, allowing it to persist in the gut. They also developed new tools for analyzing the distribution of mobile genetic elements. This study advances our understanding of how molecular systems contribute to shaping complex microbial communities.

Strengths:

The use of a gnotobiotic model colonized with a wild-mouse microbiome is a significant strength of this study. This approach allows tracking of microbiome changes over time and evaluating the targeting by Bacteroidales carrying T6SS in a more natural setting. The development of ICE-seq for mapping the distribution of the T6SS in the microbiome is remarkable, enabling the study of how this bacterial weapon is transferred between microbiome members without requiring long-read metagenomics methods.

Weaknesses:

Some conclusions are based on a limited number of mice per condition. This could be due to the complexity of using a gnotobiotic mouse model, but this should be considered when interpreting the data.

Overall, the authors successfully achieved their objectives, and their experimental design and results support their findings. As mentioned in the discussion, it would be important to investigate the role of the T6SS in resilience to microbiome disturbances, such as antibiotics, diet, or pathogen invasion. This work represents a step forward in understanding how contact-dependent competition influences the gut microbiome in relevant ecological contexts.

Reviewer #1 (Public review):

[Editor's Note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. When experimentally feasible, the authors have adequately addressed the concerns of the reviewers in the revised manuscript to support the conclusions of the study.

Summary:

The study by Akita B. Jaykumar et al. explored an interesting and relevant hypothesis whether serine/threonine With-No-lysine (K) kinases (WNK)-1, -2, -3, and -4 engage in insulin-dependent glucose transporter-4 (GLUT4) signaling in the murine central nervous system. The authors especially focused on the hippocampus as this brain region exhibits high expression of insulin and GLUT4. Additionally, disrupted glucose metabolism in the hippocampus has been associated with anxiety disorders, while impaired WNK signaling has been linked to hypertension, learning disabilities, psychiatric disorders or Alzheimer's disease. The study took advantage of selective pan-WNK inhibitor WNK 643 as the main tool to manipulate WNK 1-4 activity both in vivo by daily, per-oral drug administration to wild-type mice, and in vitro by treating either adult murine brain synaptosomes, hippocampal slices, primary cortical cultures, and human cell lines (HEK293, SH-SY5Y). Using a battery of standard behavior paradigms such as open field test, elevated plus maze test, and fear conditioning, the authors convincingly demonstrate that the inhibition of WNK1-4 results in behavior changes, especially in enhanced learning and memory of WNK643-treated mice. To shed light on the underlying molecular mechanism, the authors implemented multiple biochemical approaches including immunoprecipitation, glucose-uptake assay, surface biotylination assay, immunoblotting, and immunofluorescence. The data suggest that simultaneous insulin stimulation and WNK1-4 inhibition results in increased glucose uptake and the activity of insulin's downstream effectors, phosphorylated Akt and phosphorylated AS160. Moreover, the authors demonstrate that insulin treatment enhances the physical interaction of the WNK effector OSR1/SPAK with Akt substrate AS160. As a result, combined treatment with insulin and the WNK643 inhibitor synergistically increases the targeting of GLUT4 to the plasma membrane. Collectively, these data strongly support the initial hypothesis that neuronal insulin- and WNK-dependent pathways do interact and engage in cognitive functions.

In response to our initial comments, the authors mildly revised the manuscript, which did not improve the weaknesses to a sufficient level. Our follow-up comments are labeled under "Revisions 1".

Strengths:

The insulin-dependent signaling in the central nervous system is relatively understudied. This explorative study delves into several interesting and clinically relevant possibilities, examining how insulin-dependent signaling and its crosstalk with WNK kinases might affect brain circuits involved in memory formation and/or anxiety. Therefore, these findings might inspire follow-up studies performed in disease models for disorders that exhibit impaired glucose metabolism, deficient memory, or anxiety, such as Diabetes mellitus, Alzheimer's disease, or most of psychiatric disorders.

The graphical presentation of the figures is of high quality, which helps the reader to obtain a good overview and to easily understand the experimental design, results, and conclusions.

The behavioral studies are well conducted and provide valuable insights into the role of WNK kinases in glucose metabolism and their effect on learning and memory. Additionally, the authors evaluate the levels of basal and induced anxiety in Figures 1 and 2, enhancing our understanding of how WNK signaling might engage in cognitive function and anxiety-like behavior, particularly in the context of altered glucose metabolism.

The data presented in Figures 3 and 4 are notably valuable and robust. The authors effectively utilize a variety of in vivo and in vitro models, combining different treatments in a clear manner. The experimental design is well-controlled, efficiently communicated, and well-executed, providing the reader with clear objectives and conclusions. Overall, these data represent particularly solid and reproducible evidence on the enhanced glucose uptake, GLUT4 targeting, and downstream effectors' activation upon insulin and WNK/OSR1 signaling crosstalk.

Weaknesses:

(1) The study used a WNK643 inhibitor as the only tool to manipulate WNK1-4 activity. This inhibitor seems selective; however, it has been reported that it exhibits different efficiency in inhibiting the individual WNK kinases among each other (e.g. PMID: 31017050, PMID: 36712947). Additionally, the authors do not analyze nor report the expression profiles or activity levels of WNK1, WNK2, WNK3, and WNK4 within the relevant brain regions (i.e. hippocampus, cortex, amygdala). Combined, these weaknesses raise concerns about the direct involvement of WNK kinases within the selected brain regions and behavior circuits. It would be beneficial if the authors provided gene profiling for WNK1, 2, 3, and -4 (e.g. using Allen brain atlas). To confirm the observations, the authors should either add results from using other WNK inhibitors or, preferentially, analyze knock-down or knock-out animals/tissue targeting the single kinases.

Revisions 1: The authors added Fig. S1A during the revisions to show expression of Wnt1-4. While the expression data from humans is interesting, the experimental part of the study is performed in mice. It would be more informative for the authors to add expression profiles from mice or overview the expression pattern with suitable references in the introduction to address this point. The authors did not add data from knock down or knockout tissue targeting the single kinases.

(2) The authors do not report any data on whether the global inhibition of WNKs affects insulin levels as such. Since the authors demonstrate the synergistic effect of simultaneous insulin treatment and WNK1-4 inhibition, such data are missing.

Revisions 1: The authors added Fig. S5A to address this point. It is appreciated that authors performed the needed experiment. Unfortunately, no significant change was found, therefore, the authors still cannot conclude that they demonstrate a synergistic effect of simultaneous insulin treatment and WNT1-4 inhibition. It is a missed opportunity that the authors did not measure insulin in the CSF or tissue lysate to support the data.

(3) The study discovered that the Sortilin receptor binds to OSR1, leading the authors to speculate that Sortilin may be involved in the insulin-dependent GLUT4 surface trafficking. The authors conclude in the result section that "WNK/OSR1/SPAK influences insulin-sensitive GLUT4 trafficking by balancing GLUT4 sequestration in the TGN via regulation of Sortilin with GLUT4 release from these vesicles upon insulin stimulation via regulation of AS160." However, the authors do not provide any evidence supporting Sortilin's involvement in such regulation, thus, this conclusion should be removed from the section. Accordingly, the first paragraph of the discussion should be also rephrased or removed.

Revisions 1: The authors added Fig. 5M-N to address this point. The new experiment is appreciated. However, the authors still do not show that sortilin is involved in insulin or WNK-dependent GLUT4 trafficking in their set up since the authors do not demonstrate any changes in GLUT4 sorting or binding. The conclusions should therefore be rephrased or included purely in the discussion. Moreover, the discussion was not adjusted either, leading to over interpretation based on the available data.

(4) The background relevant to Figure 5, as well as the results and conclusions presented in Figure 5 are quite challenging to follow due to the lack of a clear introduction to the signaling pathways. Consequently, understanding the conclusions drawn from the data is also difficult. It would be beneficial if the authors addressed this issue with either reformulations or additional sections in the introduction. Furthermore, the pulldown experiments in this figure lack some of the necessary controls.

Revisions 1: The Authors insufficiently addressed this point during the revisions and did not rewrite the introduction as suggested.

(5) The authors lack proper independent loading controls (e.g. GAPDH levels) in their immunoblots throughout the paper, and thus their quantifications lack this important normalization step. The authors also did not add knock-out or knock-down controls in their co-IPs. This is disappointing since these improvements were central and suggested during the revision process.

(6) The schemes that represent only hypotheses (Fig. 1K, 4A) are unnecessary and confusing and thus should be omitted or placed at the end of each figure if the conclusions align.

(7) Low-quality images, such as Fig. 5H should be replaced with high-resolution photos, moved to the supplementary, or omitted.

Reviewer #2 (Public review):

This study by Jaykumar and colleagues seeks to expand the field's appreciation of insulin responses in the brain, specifically by implicating WNK kinase function in various neuronal responses, ranging from behavioral / memory changes to GLUT4 trafficking to the cell surface with subsequent glucose uptake. This revised study is now comprehensive and presents a logical and reasonably documented cascade of molecular interactions responsible in part for GLUT4 trafficking under the regulation of WKK and insulin. Additional data allow the authors to dissect a plausible WNK/OSR1/SPAK-sortilin pathway for the modulation of GLUT4 trafficking, in part by capitalizing on an overlay of various techniques and systems. The data - much of it in vivo or ex vivo - showing a potential role for WNK function in brain glucose utilization remains a compelling part of the story, with the dissection of the signaling cascade and a potential role for sortilin in mediating WNK function via effects on GLUT4 cellular localization now more convincing.

Initially, the group shows that oral WNK463 treatment - an inhibitor of WNKs broadly - in mice augments a number of memory readouts. These findings fit within the context of the overall story the authors present: that WNK function is critical to brain glucose utilization, which impacts learning. Multiple approaches are used to show that WNK463 treatment, i.e. inhibition of WNKs, increases glucose uptake, including labeled 2-deoxyglucose uptake in vivo in the brain and in isolated synaptosome, and uptake in ex vivo hippocampal slices. These findings are solid and consistent. With the exception of some relatively minor comments regarding the data presentation made to the authors and now fully addressed, the findings showing that WNK463 treatment increases GLUT4-mediated glucose uptake and surface localization of GLUT4 are reasonable, with the hippocampal slice data being particularly relevant.

While the details of the WNK signaling cascade is dense, in the revised application one clearly appreciates the molecular interrogation and interactions the group is dissecting, supported by the use of multiple models. With the additional findings, these systems and the data now reinforce each other, presenting a strongly documented overall story.

A limitation of the study with the initial submission was the authors' reliance upon a single pharmacological tool (WNK463) to inhibit WNK kinases. WNK463 apparently has substantial specificity for WNKs and WNK463 treatment lessened OSR1 phosphorylation (a WNK substrate). Nevertheless, the cohesiveness of the findings in terms of the broader pathway engagement (GLUT4 trafficking, glucose uptake) is consistent with the author's proposed mechanisms and conclusions. The authors have additionally addressed this concern in the revised manuscript with more information supporting the specificity of WNK463 as well as the multiple approaches to confirm the effect of WNK463 on the WNK signaling pathway of interest.

The final few paragraphs of the discussion that weave the author's findings into the field more broadly, including Sortilin function and neurological disorders, are appreciated. Additional clarity in the Methods section is also helpful.