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

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

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

Which type of funder became more influential over time?

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

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

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

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

What tension exists within government arts funding?

How do wealthy individual patrons differ from institutional funders?

What is the central problem created by new funding sources?

What kinds of exhibitions do corporations tend to support?

What kinds of exhibitions do corporations tend to support?

What is the main motivation behind corporate arts funding?

What major change occurred in corporate arts sponsorship?

What were the main purposes of the NEA and NEH?

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

Why did federal arts funding become important?

How did early American museums differ from European museums?

What historical shift changed museum patronage in the United States?

Reviewer #2 (Public review):

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

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

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

Comments on revised version:

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

Reviewer #2 (Public review):

Summary:

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

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

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

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

Strengths:

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

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

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

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

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

Weaknesses:

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

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

Comments on revision:

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

Reviewer #2 (Public review):

This manuscript by Carmona, Zagotta, and Gordon is generally well-written. It presents a crude and incomplete structural analysis of the voltage-gated proton channel based on measured FRET distances. The primary experimental approach is Förster Resonance Energy Transfer (FRET), using a fluorescent probe attached to a noncanonical amino acid. This strategy is advantageous because the noncanonical amino acid likely occupies less space than conventional labels, allowing more effective incorporation into the channel structure.

Fourteen individual positions within the channel were mutated for site-specific labeling, twelve of which yielded functional protein expression. These twelve labeling sites span discrete regions of the channel, including P1, P2, S0, S1, S2, S3, S4, and the dimer-connecting coiled-coil domain. FRET measurements are achieved using acridon-2-ylalanine (Acd) as the acceptor, with four tryptophan or four tyrosine residues per monomer serving as donors. In addition to estimating distances from FRET efficiency, the authors analyze full FRET spectra and investigate fluorescence lifetimes on the nanosecond timescale.

Despite these strengths, the manuscript does not provide a clear explanation of how channel structure changes during gating. While a discrepancy between AlphaFold structural predictions and the experimental measurements is noted, it remains unclear whether this mismatch arises from limitations of the model or from the experimental approach. No further structural analysis is presented to resolve this issue or to clarify the conformational states of the protein.

The manuscript successfully demonstrates that Acd can be incorporated at specific positions without abolishing channel function, and it is noteworthy that the reconstituted proteins function as voltage-activated proton channels in liposomes. The authors also report reversible zinc inhibition of the channel, suggesting that zinc induces structural changes in certain channel regions that can be reversed by EDTA chelation. However, this observation is not explored in sufficient depth to yield meaningful mechanistic insight.

Overall, while the study introduces an interesting labeling strategy and provides valuable methodological observations, the analysis appears incomplete. Additional structural interpretation and mechanistic insight are needed.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on revised version:

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

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

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

Reviewer #2 (Public review):

Summary:

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

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

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

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

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

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

Strengths:

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

Reviewer #3 (Public review):

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

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

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

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

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

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

Additional comments and suggestions:

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

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

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

(1) Stationarity of neural response

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

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

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

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

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

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

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

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

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

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

Minor:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

Comments on revised version.

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Comments on revised version.

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on revised version:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Comments on revised version.

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #2 (Public Review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on revised version.

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

Reviewer #2 (Public review):

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

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

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

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

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

Comments on revised version.

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

Reviewer #2 (Public review):

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

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

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

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

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

Comments on revised version.

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public review):

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

Some comments and suggestions:

(1) General-

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

(2) Specific-

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

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

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

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

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

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

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

Reviewer #2 (Public review):

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

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

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

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

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

Reviewer #2 (Public review):

Summary:

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

Major Comments:

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

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

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

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

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

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

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

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

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

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

(4) It is stated that:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

Summary:

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

Strengths:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

Comments on the latest version:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

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

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

Comments on revised version.

Most of the comments have been addressed.

Reviewer #2 (Public review):

Summary:

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

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

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

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

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

Strengths:

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

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

Weaknesses:

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

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

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

Reviewer #2 (Public review):

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

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

Reviewer #2 (Public review):

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

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

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Conclusions:

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

Summary

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

Strengths

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

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

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

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

Weaknesses - whether the claims are supported by the data

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Major Comments:

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

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

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

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

Significance:

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

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

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Recommendations:

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

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

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

Comments on revised version.

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Comments on revisions:

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

Reviewer #2 (Public review):

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Comments on revised version.

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

Reviewer #2 (Public review):

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #2 (Public review):

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

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

Comments on revised manuscript:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

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

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary

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

Strengths

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

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

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

Limitations

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

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

Overall assessment

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

Reviewer #2 (Public review):

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

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

Reviewer #2 (Public review):

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

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

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

I have just two remaining concerns:

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

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

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

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

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

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

Reviewer #2 (Public review):

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

Major Comments:

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

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

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

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

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

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

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

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

Comments on revised version:

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

Reviewer #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 #2 (Public review):

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

Comments on revised version.

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

Reviewer #2 (Public review):

Summary

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

Strengths

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

Weaknesses/Limitations:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Reviewer #2 (Public review):

Short overview:

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

Comments:

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

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

Additional questions and context:

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

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Reviewer #2 (Public review):

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

Strengths:

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

Weaknesses:

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

Main specific issues:

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

Reviewer #2 (Public review):

Summary

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

Strengths

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

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

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

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

Weaknesses

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

There are no weaknesses.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #2 (Public review):

Summary

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

Strengths

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

Weaknesses

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

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #2 (Public review):

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

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

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

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

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

Weaknesses:

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

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

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

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

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

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

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

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

Reviewer #2 (Public review):

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Comment on revised version:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

Reviewer #2 (Public review):

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

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

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

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

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

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

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

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

Conclusion:

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

Reviewer #2 (Public review):

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

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

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

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

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

Reviewer #2 (Public review):

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

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

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

Comments on revised version.

The authors have satisfactorily addressed my concerns in the revised manuscript

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Comments on revised version.

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

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

Weaknesses:

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Major Points:

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

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

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

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

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

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

(3) The use of NG80 as a criterion for defining "high-quality libraries" requires further validation.

The authors use NG80 as a metric of library diversity and further apply NG80 > 1,000 as one criterion for defining high-quality libraries in Figure 6. However, because NG80 is based on gene counts, it may be affected by sequencing depth. In addition, gDNA contamination can artificially increase NG80, whereas genuinely abundant non-coding RNAs in WRR libraries can lower NG80. Therefore, a higher NG80 does not necessarily indicate better overall library quality, and the metric may reflect both technical quality and genuine RNA composition.

Recommendation: The authors are encouraged to re-evaluate NG80 after downsampling samples to a common number of mapped fragments and to examine the relationship between NG80 and sequencing depth. The rationale for the NG80 > 1,000 threshold should also be further justified, and the impact of alternative NG80 thresholds on high-quality library classification and the main conclusions should be assessed. Unless there is evidence that this threshold reliably predicts library reproducibility or biomarker-related information content, library diversity and overall library quality should be clearly distinguished, and NG80 should not be presented as a universal criterion for high-quality libraries.

(4) Conclusions regarding DNase treatment should be interpreted more cautiously.

The Toden study did not explicitly report DNase treatment. The manuscript infers that DNase digestion was performed based on the fact that this study originated from the same laboratory as other studies and used a similar workflow; this inference should not be treated as an established experimental fact. In addition, the authors state that double DNase treatment is the most effective approach among non-EB workflows, but this conclusion is mainly based on cross-study comparisons, in which DNase strategy varies together with laboratory, sample handling, and cohort-specific factors. The current evidence is therefore insufficient to establish that double DNase treatment itself is superior.

Recommendation: The authors are encouraged to label the DNase status of the Toden study as "not reported" or "inferred", unless confirmation can be obtained from the original authors. The conclusion that double DNase treatment is the most effective approach should also be tempered, with explicit acknowledgment that it requires direct parallel validation using the same samples under different DNase treatment strategies.

Reviewer #2 (Public review):

Summary:

The authors conducted a functional high-throughput drug screening using hiPSC-CMs derived from patients with LMNA-DCM. Cyproheptadine emerged as a therapeutic candidate.

Strengths:

The screen appears well designed.

Weaknesses:

The single candidate that emerged from the screen, cyproheptadine, raises issues with potency. In addition, validation studies that are both expected and necessary for a drug proposed as a novel therapeutic for human cardiomyopathy have not yet been performed. Rigor could be improved once the basic mechanistic and validation studies discussed below have been performed.

Reviewer #2 (Public review):

Summary:

In this study, the impact of prenatal alcohol (PAE) on amyloid precursor protein (APP) C-terminal fragments and notch intracellular domain (NICD) levels in adulthood is measured in 3xTg-AD mice.

Prenatal alcohol alters gamma secretase activity with development and aging. This could have implications for Alzheimer's disease risk in populations without inherited Alzheimer's risk genetics.

Strengths:

Strengths include the model, the use of orthogonal approaches, and the rigorous, high-quality data.

Weaknesses:

Some figures lack prenatal alcohol treatment in the 3xTg-AD mice.

Some overstatements should be tempered. For instance, one cannot conclude that the changes in CTFs are driving the changes in learning and memory (as suggested in the last line of the abstract) without a direct intervention testing this. For instance, though PAE caused a more robust learning deficit at 6 mo in WT, the impact on CTFs was less than it was at 3 mo. PAE did not significantly change CTFs or learning/memory in 3xTg-AD mice at 4 months, suggesting the genotype effect takes over at this point. The text should be adjusted to reflect this.

Conclusion:

In summary, this is a rigorous assessment of the long-term impacts of PAE on CTFs and learning/memory in adult WT and 3xTg-AD mice.

Reviewer #2 (Public review):

This manuscript reveals functional connectivity of two different classed of cortical neurons that respond in opposite ways to mismatches between sensory and top-down inputs. These data are very valuable because different theories of information processing in the cortex make different predictions on the patterns of connectivity of these neurons. Therefore, these data strongly constrain possible theories of cortical processing.

Comments on revised version.

I thank the Authors for answering my questions and updating the manuscript.

Congratulations on this important work!

Reviewer #2 (Public review):

Summary:

Toxoplasma gondii is an obligate intracellular parasite and the causative agent of toxoplasmosis. Parasite invasion of host cells, intracellular replication, and subsequent egress, which results in destruction of the infected cell, are central to pathogenicity. This manuscript focuses on understanding how maternal resources, specifically cellular organelles, are shared between daughter parasites during cell division. Many organelles are present as a single copy, making their division and inheritance essential for successful replication. In T. gondii, our understanding of how organelles are divided during cell division remains limited, and this study helps address this important knowledge gap.

Strengths:

The major strength of this study is the use of a Halo-based pulse-chase assay to characterize patterns of organelle inheritance and to monitor protein synthesis, turnover, and movement. This approach will be of considerable interest to the field. Using this method, the authors identify three major modes of organelle inheritance:

(1) Organelles present in multiple copies (such as micronemes and rhoptries) are partitioned between daughter parasites, with additional contributions from newly formed vesicles. Newly synthesized and pre-existing material remain as distinct populations within the cell.

(2) Single-copy organelles, such as the Golgi and apicoplast, are expanded through the incorporation of newly synthesized material before division.

(3) Cytoskeletal structures are synthesized de novo during each round of cell division.

These findings provide a more refined understanding of organelle inheritance and demonstrate that secretory organelles are not generated entirely de novo during each round of division, as was previously thought.

The paper places particular emphasis on the fate of maternal micronemes and rhoptries during division. The data show that (1) during division in wild-type cells, maternal micronemes and rhoptries are detectable in the residual body (RB); however, the majority of these organelles are localized within the parasite body, either at the apical or basal ends of the daughter parasites (Fig. 6). (2) In the absence of the myosin motor MyoF, micronemes and rhoptries accumulate in the residual body and are not properly trafficked to the daughter cells. Upon restoration of MyoF protein levels, these organelles redistribute to the daughter cells, although in an uneven manner.

Weaknesses:

The second half of the paper focuses on a more detailed characterization of microneme and rhoptry recycling. The authors strongly argue that the RB is a central hub for recycling micronemes and rhoptries; however, this conclusion is not fully supported by the data. For example, the authors state:

Line 227:<br /> "Notably, after endodyogeny was completed, M-MIC2 was redistributed from the RB to the apical tip of the daughter cells (Figure 6A, 11:30, 16:00 h), confirming that the RB serves as a temporary reservoir during microneme recycling (Periz et al., 2019)."

Line 231:<br /> "In approximately 90% of parasites undergoing replication, M-RON2 was integrated into daughter rhoptries prior to mother cell collapse and formation of the RB (Figure 6B, 4:15-4:30 h and 10:30-10:45 h). Like M-MIC2, M-RON2 was occasionally detected in the RB, though less prominently, suggesting more rapid, tightly regulated, or more efficient recycling due to their lower number."

Line 326:<br /> "However, we show that the RB temporarily stores maternal secretory organelles, such as micronemes and rhoptries, which are later redistributed to daughter cells in a MyoF-dependent manner (Figure 9B)."

Thus, the model that all microneme and rhoptry trafficking is RB-dependent is based primarily on the MyoF depletion phenotype (which results in RB accumulation) together with the observation that a relatively small amount of maternal microneme and rhoptry material is detectable in the RB of wild-type parasites. Although the authors' interpretation-that recycling is RB-dependent-is one possible explanation, alternative models are not discussed.<br /> For example, an alternative possibility is that the majority of micronemes and rhoptries are trafficked directly from the apical end of the mother parasite to the daughter cells without passing through the RB. In this scenario, only a subset of the organelles would enter the residual body, perhaps reflecting imperfect trafficking efficiency rather than an obligatory recycling step. Loss of MyoF would impair this trafficking pathway, resulting in the accumulation of secretory organelles within the RB. In other words, RB accumulation could be a consequence of MyoF depletion rather than evidence that all trafficking in wild-type parasites normally proceeds through the RB.

This alternative interpretation seems particularly relevant for the rhoptries, given that the authors themselves state that "M-RON2 was integrated into daughter rhoptries prior to mother cell collapse and formation of the RB."

Other comments:

Figure S10C<br /> To determine whether microneme degradation occurs in the RB, the authors quantified the fluorescence intensity of individual micronemes in control parasites and following auxin washout, showing that after redistribution the fluorescence intensity of individual vesicles is unchanged. However, this is not the appropriate analysis to address the question being asked. To conclude that micronemes are not degraded, the authors would need to quantify the total fluorescence intensity within the entire vacuole. For example, if half of the micronemes were degraded, the remaining micronemes would be expected to retain the same fluorescence intensity as those in the control parasites. Thus, unchanged fluorescence intensity of individual vesicles does not exclude the possibility that degradation has occurred.

Reviewer #3 (Public review):

Summary:

This excellent manuscript by Pinto, Sharp, and colleagues examines bovine tissue tropism for influenza viruses. They find that bovine flu, as well as other strains, have strong replication in mammary tissue. They also map the genetic changes to influenza that improve replication in bovine cells. Overall, the study is well designed and executed and the results are very timely.

Strengths:

(1) The experiments are well-controlled.

(2) The figures are well-constructed and easy to follow.

(3) The Methods and legends are detailed, with sufficient information.

Comment on revised version.

The authors have strengthened the manuscript by addressing comments from the three reviewers and I have no additional concerns/suggestions.

Reviewer #2 (Public review):

Summary:

This paper studies the interplay of evolutionary and in-lifetime learning. The authors develop a neural network model in which initial weight configurations evolve under selective pressure, while fitness is determined by the network's performance after a learning period. They show that such a network displays very distinct learning dynamics from those trained by either gradient descent or genetic algorithms alone: in particular, they do not learn the task, but they show evidence of learning-to-learn and unusual representational structure.

Strengths:

(1) The writing, figures, and presentation of ideas were clear.

(2) The question of how evolution on initial weights combines with learning from within-lifetime experience to structure a learning trajectory seems interesting.

(3) The analysis of existing experiments was well-done, highlighting that though these networks did not really learn, they show latent learning structure that makes the network perform better from less data.

(4) The interplay between Baldwin & learning dynamics seemed novel and interesting, presenting many attractive puzzles.

Weaknesses:

First, the authors point to an important distinction between performing evolutionary selection on the weights pre- or post- lifetime training, the latter of which is Lamarckian. They argue, correctly, that their model is interesting because it selects on the weight initialisation, unlike, for example, Shuvaev et al. However, my understanding is that a long line of papers beginning perhaps with Hinton & Nowlan also do non-Lamarckian evolution: Hinton & Nowlan have unspecified weights (denoted '?' in the paper) that can be inherited and then learnt. Is this not exactly inheritance of initial conditions (in this case, whether learnable or not)? This novelty is a primary motivation of the paper, whereas to me it seems it was already apparent in Hinton & Nowlan, and developed further in what seems to be a long line of uncited literature (see next paragraph). As such, this paper's conclusions seem poorly positioned within the existing state of knowledge/literature.

Second, the algorithm is framed as novel, but I think it is a rediscovery. This framework is very close to MAML, in which an initial weight configuration is optimised by gradient descent to be good after a few steps of fine-tuning (Finn et al., 2017). The authors' approach differs in using a genetic algorithm to perform the training of the initial weights, avoiding some of the computational complexities of MAML, especially after long fine-tuning. In this, the authors have, I think, rediscovered ES-MAML, MAML where the inner optimisation loop is gradient descent, while the outer is genetic (Song et al., 2020). Other similar work is "Meta-Learning by the Baldwin Effect" (Fernando et al., 2018).

Further, within Fernando et al. there is a rich literature review, almost none of which are cited by the authors. I point especially to Keesing & Stork, 1990, which appears to show a strong dependence of Baldwin-like improvements on the amount of data, something this paper also shows but explores less thoroughly.

To summarise my critique thus far: I think the literature already answers the main concern of the motivation (i.e. non-Lamarckian neural network evolution and learning), I think it has already discovered this particular algorithm, and I think past work has more thoroughly analysed behaviours similar to those presented in this paper. Without positioning correctly within this literature, the more general contribution of the paper is hard to establish. The true novelty of the authors' analysis seems to be the emphasis on Saxe et al.-like learning dynamics and its interplay with the Baldwin effect, but I am not certain of this without knowing the literature better.

Regarding experiments, there was an interesting effect where the EC networks didn't learn but did show latent learning (Figure 2, Figure 3), which sped up later learning (Figure 4). There were a few details I was surprised by on which I would appreciate clarity:

(1) The main result has basically no headline learning under EC. This will clearly be very dependent on parameters (e.g. if you add or remove enough training steps, the algorithm becomes SGD/GA, which both show learning). It seems like a natural analysis would examine this (e.g. a plot of final performance of EC after 4000 generations with different per-generation learning budgets).

(2) It is then shown that after 4000 generations EC can learn very quickly to perform the semantic task perfectly, at least within 200 generations (Figure 4C, and perhaps much sooner, Figure 4D, Figure 4F last panel; it was hard to say. This and the previous point seem somewhat inconsistent; was it just that Figure 3 used only 100 fine-tuning steps while the perfect-task-performing networks in Figure 4 required somewhere between 100 and 200? This seems to point to extreme parameter dependence. More broadly, how should I square this inconsistency/near-inconsistency?

(3) Figure 3k, and especially Figure 4f bottom right panel, seem to show networks that correctly separate all stimuli but cannot classify them. Should I understand this as networks learning to just push apart all pairs of datapoints without structure?

(4) If I understood the genetic algorithm correctly, only three individuals from each population seeded the next generation. This seems another important parameter to tune, since I think it is far lower than standard evolutionary work, but I am not sure.

Finally, the paper most interested me as a neural network learning puzzle: how can the network perform so badly, yet lead to such different post-fine-tuning results? The paper pointed to these as 'distinct' learning phenomena without explaining what was causing those differences. The only way I could square these results in my head was as above: that the 4000 generations pushed the initial representation to represent all datapoints differntly, effectively changing the learning problem gradient descent faces from one with a lot of structure (the semantic task) that leads to stepwise learning, to one in which it was basically linear regression on a set of well separated stimuli without the structure necessary for stepwise learning. Since the paper focuses so much on learning dynamics, and studies a task where such things can be precisely probed, it would have been nice to pin down exactly what was happening slightly more.

Reviewer #2 (Public review):

Summary:

The authors use simulations and empirical data fitting in order to demonstrate that informing a decision model using noisy single-trial estimates of an underlying fixed non-decision time can guide the model to more reliable parameter estimates, especially when the model has collapsing bounds.

Strengths:

The paper is well written and motivated, with clear depth of knowledge in the areas of neurophysiology of decision-making, sequential sampling models, and in particular, the phenomenon of collapsing decision bounds.

Two large-scale simulations are run to test parameter recovery, and two empirical datasets are fit and assessed; the fitting procedures themselves are state-of-the-art, and the study makes use of a very new and well-designed ERP decomposition algorithm that provides single-trial estimates of the duration of diffusion; the results provide inferences about the operation of decision bound collapse - all of this is impressive.

Weaknesses:

This is an interesting and promising idea, but a very important issue is not clear: it is an intuitive principle that information from an external empirical source can enhance the reliability of parameter estimates for a given model, but how can the overall BIC improve, unless it is in fact a different model?

Comment on revised version.

Thanks to the authors for their responses and inclusion of additional analyses and simulations. Thanks, in particular for clarifying a crucial detail, that the ndt-informed model actually assumes, like the uninformed model, that there is no variability in the non-decision time, and the idea is that the variable single-trial measurements of non-decision time are noisy estimates of an underlying, constant ndt. The revised paper itself has not made this clear - for example, throughout the Intro, there is no statement that the behavioural model assumes a trial-invariant ndt, and line 231 still calls tau the 'mean' non decision time, implying there is a distribution rather than an invariant single value in the behavioural model.

One implication of the above is that if the lognormal sigma is purely measurement noise that does not relate to actual variation in the underlying decision process generating behaviour, then the HMP latencies should not relate to behaviour, e.g. shorter latencies predicting shorter RT. I assume that even if the authors did find such a relationship, the principle still stands that a model with fixed ndt is more accurately fit when there are single-trial ndt estimates whose mean provides a constraint on that ndt value, than without such measurements. Still, given ndt variability is a core feature of many decision models, the authors could comment on whether the strategy would work in theory for a model with ndt variability (in the behavioural part), where the single trial estimates would then presumably reflect a mix of measurement noise and genuine ndt variability.

Another more important implication is that since it is in fact the same model being compared with and without the HMP data guiding the fixed ndt estimate, the reason the fit quality improves with HMP-information is not because it is a better model per se (it is the same model) but because without the HMP guidance, the search algorithm somehow gets lost and fails to find the 'optimal' parameter vector. That is, the parameter vector (just the parameters that relate to the behavioural model itself, not the HMP lognormally-distributed noise associated with VEP measurements) identified as optimal in the HMP-informed version of the model exists in the parameter space of the model without HMP information, but it is just not found? I raised this implication before, and it is still not clear whether it applies. I'm sorry to press on it, but it is critical for readers to understand why it is that neural information can improve overall model fit. Again, the enhancement of parameter recovery (like in Nunez 2025) makes sense, but the enhancement of the "model's fit to behavioural data" does not, without pointing to a deficiency in the search algorithm / fitting procedure.

The authors state in their replies that the onset of bound collapse is set at accumulation onset and imply that setting it instead at stimulus onset could "mathematically resolve the issue" but they don't do it because it is implausible. It is in fact not only plausible but clearly evidenced in empirical data - collapsing bounds are implemented neurally through urgency signals, and these can begin to dynamically build toward threshold well before, let alone at, stimulus onset. There is nothing bizarre about this - we can prepare movements without sensory input, and indeed even if choosing actions based on a sensory discrimination, motor preparation can launch well before the sensory evidence (e.g. Stanford, Salinas et al 2010) and this in effect collapses the bound on cumulative evidence for triggering action before any evidence actually arrives. So, Urgency/bound-collapse does not need to be triggered by a stimulus; it can start in anticipation of the stimulus. It seems critical, therefore, for the authors to clarify this point - does re-defining the onset of the collapse at stimulus onset remove the trade-off and render unnecessary the neurally-informed ndt estimation?