Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

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

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

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

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

I would like the authors to clarify two points.

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

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

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

Specific examples:

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary and Strengths:

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

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

Weaknesses:

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

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

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


Comments on revised version.

Previously raised concerns are addressed.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

Framing and motivation

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

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

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

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

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

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

Statistics

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

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

Belief parameter and terminology

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

Drift-diffusion modelling

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

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

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

Methods - EEG and analysis details

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths and Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Inhibition by disulfiram is convincing.

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

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

(1) A mouse model to better measure autophagy.

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

(3) Assessment of autophagy in many different tissues.

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

Weaknesses:

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

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

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

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

Reviewer #1 (Public review):

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

Major comments:

(1) Shortcomings in Immunofluorescence experiments:

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

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

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

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

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

(2) Shortcomings in biochemistry experiments:

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

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

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

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

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

https://typewriterdatabase.com/show.16666.typewriter

A script typewriter missing the number 1 and on which the lower case L doesn't suffice. Perhaps the Capital i would work?

Reviewer #1 (Public review):

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

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

Reviewer #1 (Public review):

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

Summary:

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

Strengths:

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

Comments on revised version.

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

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

Reviewer #1 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

Case Annotation Template

Case#: Patient 2, female, age 62

DiseaseAssertion: STGD

FamilyInfo: diagnosis of autosomal recessive STGD based on the pedigree and clinical phenotype of fleck deposits with or without genetic testing

CasePresentingHPOs: HP:0000608, HP:0000007, HP:0030610, HP:0030500

CaseHPOFreeText: Macular degeneration. autosomal recessive, Photoreceptor outer segment loss on macular OCT, Yellow/white lesions of the macula

CaseNotHPOs: n/a

CaseNotHPOFreeText: n/a

Genotyping Method: n/a

PreviouslyPublished: n/a

Variant: ENST00000370225.4:c.1222C>T

ClinVar: NM_000350.3(ABCA4):c.1222C>T (p.Arg408Ter)

CAID: CA179692

SupplementalData: composite mask analysis shown in figure 3 for patient 2, "Both patient 2 and 14 show foveal preservation of IS/OS and RPE," "Patients 2, 19, and 11 show large areas of matched degeneration and isolated IS/OS loss, "

Case#: Patient 10, female, age 19

DiseaseAssertion: STGD

FamilyInfo: diagnosis of autosomal recessive STGD based on the pedigree and clinical phenotype of fleck deposits with or without genetic testing

CasePresentingHPOs: HP:0000608, HP:0000007, HP:0030610, HP:0030500

CaseHPOFreeText: Macular degeneration. autosomal recessive, Photoreceptor outer segment loss on macular OCT, Yellow/white lesions of the macula

CaseNotHPOs: n/a

CaseNotHPOFreeText: n/a

Genotyping Method: n/a

PreviouslyPublished: n/a

Variant: Allele 1: NM_000350.3:c.2588G>C Allele 2: NM_000350.3:c.1222C>T

ClinVar: Allele 1: NM_000350.3(ABCA4):c.2588G>C (p.Gly863Ala) Allele 2: NM_000350.3(ABCA4):c.1222C>T (p.Arg408Ter)

CAID: Allele 1: CA119128 Allele 2: CA179692

SupplementalData: composite mask analysis shown in figure 3 for patient 10

Reviewer #1 (Public review):

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

Major concerns

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

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

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

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

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

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

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

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

Comment on revised version.

After revision the authors have addressed all of my concerns.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Comments on revised version.

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #1 (Public review):

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

Major comments

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

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

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

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

Reviewer #1 (Public review):

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

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

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

Reviewer #1 (Public review):

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

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on revised version.

Weaknesses:

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

This point has been confirmed by the authors.

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

Has not been addressed in their response.

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

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

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

The reviewer is happy with the response.

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

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

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

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

Impact:

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

The reviewer did not find the reply satisfactory.

Reviewer #1 (Public review):

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

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

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

Major points:

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Major Concerns

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

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

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

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

Reviewer #1 (Public review):

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

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

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

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

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

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

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

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

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

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

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

Minor remaining issues.

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

Reviewer #1 (Public review):

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

Summary:

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

Strengths:

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

Comments on the latest version:

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

Reviewer #1 (Public review):

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on revised version:

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

Reviewer #1 (Public review):

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

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

Strengths:

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

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

(3) The displayed EM images document interesting phenotypes.

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

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

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

Reviewer #1 (Public review):

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

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

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

Strengths:

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

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

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

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

Weaknesses

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

Reviewer #1 (Public review):

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

Joint Public Review

Summary:

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

Strengths:

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

Weaknesses:

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

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

(2) A second limitation concerns the developmental role assigned to Br-C and E93 during the larval-to-prepupal transition. The authors conclude that sustained Br-C expression is a defining molecular feature of the prepupal stage and discuss its potential role in prepupal specification. However, the functional analyses of both Br-C and E93 were initiated only after larvae had already entered the prepupal stage. Consequently, while the RNAi experiments convincingly demonstrate essential roles for Br-C during the prepupal-to-pupal transition and for E93 during adult differentiation, they do not directly address whether either factor is required to trigger the formation of the prepupal stage itself. Therefore, the molecular mechanisms governing the initiation of this distinctive developmental transition remain unresolved. In particular, the proposed lack of repression of E93 by Br-C is only weakly supported, yet may be an essential feature of the prepupal stage of Hermetia illucens.

(3) Although knockdowns of Kr-h1 and chinmo knockdowns look superficially similar, it would be good to confirm this with higher-magnification views of the cuticles for all three treatments (control, Kr-h1 RNAi, and chinmo RNAi). In other species, Kr-h1 knockdown leads to premature adult cuticle development, whereas chinmo knockdown typically leads to premature appearance of pupal characteristics. Similarly, in Fig. 4A and 4D, higher-magnification images of the cuticle would be helpful.

(4) (Relating to Line 336 and Figure 7): "This low but persistent prepupal Kr-h1 expression, together with modest chinmo expression from PPD0 to PPD8, may be correlated to a JH-dependent antimetamorphic effect that maintains the prepupal stage." However, we are not aware of a function of JH in extending the prepupal stage. In addition, in most insects, the prepupal stage expresses high Kr-h1 expression; this peak likely prevents the animal from turning into an adult instead of the pupa. We presume the same holds true for H. illucens (although the lower expression of Kr-h1 during that stage is curious). As a result, we suggest that Fig. 7D be revised as it may be difficult to distinguish between pupal formation and prepupal maintenance given the experimental set-up. Fig. 7E may also need to be modified since the development of the pupa may require Kr-h1. It is worth noting that at the prepupal stage, JH and Br-C are co-expressed in many insects. If the authors think that Kr-h1 expression needs to be low at this time, this would imply a novel interaction between Kr-h1 and Br-C, and should be discussed.

Reviewer #1 (Public review):

Summary

The authors build a "digital sphinx" by stitching together two neural network models: (i) a recurrent network with fixed parameters derived from the C. elegans connectome and imputed physiological (e.g. neural input/output) functions, and (ii) a feedforward encoder-decoder model with learnable parameters intended to represent a central brain - to - motor interface, then harnessing the combined model to a Drosophila biomechanical model situated in a physics simulator, and finally using deep reinforcement learning (DRL) training to optimize the parameters of the encoder-decoder model to reproduce a set of spatiotemporal patterns of jointed limb activations that together produce the overall organismal behavior of walking, within the physics simulator.

The primary intent of this paper is to dispel the recent grandiose claims made in the mainstream press by a private company, Eon Systems, to have achieved a major advance in biologically based brain simulation of the production of a set of ethologically relevant motor behaviors by the fly. Representatives of the company referred to this modeling and training process euphemistically and deceptively as "brain uploading". The authors proceed with a reduction-to-triviality exercise by constructing their own high-parameter dynamical brain-plus-body model situated in a physical simulation that produces, after training by reinforcement learning, satisfying ethological behavioral imitation in the same vein as the private company claim, but based on a clearly absurd and biologically unrealistic set of model assumptions.

Secondarily, the paper provides two overall admonitions that they assert their computational demonstration illustrates: that training high parameter network models to imitate behavior, even if they possess some biological detail, will deliver little or no biological insight, and that models of behavioral generation must be built from detailed biological data and, crucially, developed in a hypothesis generation/falsification loop with experimental validation, in order to be scientifically useful.

Appraisal

The authors are well justified in challenging the non-rigorous claims of "uploading" or even the delivery of a neurobehavioral simulation with potential scientific utility, in unison with the vocal criticisms of many other researchers in the fields of AI and neuroscience, and it is an important message to deliver to the world. However, the authors' own modeling counter-exercise, while clever and vivid in imagery, suffers from its own lack of rigor, both in disclosure of implementation and in scientific case-making. Some sacrifice of clarity and thoroughness in the interest of brevity is inevitable under the brief format of this manuscript; however, we suggest that crucial additions and modifications should be made to avoid falling into a similar trap of non-rigorous sensationalism.

Because the private company claims were not accompanied by a scientific paper, preprint, code repository, or much methodological disclosure of any kind, the authors have the particular challenge of building a refutation case against an undefined target. As a consequence, the authors chose their own task, model structure, and training paradigm.

The authors argue that brain models need to be built from biological data to be useful for yielding biological insight. We agree with the overall principle; however, in practice, this procedure is fraught with epistemological difficulty. Biological modeling suffers from a unique challenge within the larger endeavor of scientific/physical modeling, which is that it is generally unclear as to precisely what biological quantities should be measured and at what level of detail they should be measured. Additionally, biological data will by necessity be incomplete and noisy, and thus decisions of coarse-graining must be made at the outset of large-scale data collection projects, and some, possibly a substantial, level of data imputation will have to be performed in order to build testable models in our lifetimes. Despite the astonishing success of scaling (in both parameter count and corpus size) in engineered neural networks for certain human-like tasks, it is not at all clear that simply adding more detail to biological models will produce deeper scientific insight, or whether cataloging parameters from snapshot data will yield functional simulations. The failed Blue Brain mega-project should provide a lesson, as well as Marder's longstanding work on parameter variation in neural systems. The coupled, pernicious questions of choosing measurement detail and modeling detail represent a deep, unsolved challenge area for the field, and this context should be raised in the text.

The message about overinterpreting models trained with deep reinforcement learning, while valid and important, should be broadened to be a message about overinterpreting trained high-parameter models in general, in their ability to fit data or reproduce simple behavior. Other parameter optimization/learning procedures for building underdetermined and/or high-parameter models risk the same misinterpretation. The prescription of building models in conjunction with experimental prediction and validation is an important point.

The authors leave out an additional important and underappreciated challenge of brain-model-building, which is that imitating a time segment of behavior is a computational task of unspecified, and possibly low complexity. Successful recapitulation of behavioral time series may simply not be considered cognitively interesting, even if the model is built entirely on biological data. While quantifying task complexity is another open area of computational and neuroscientific research, the authors should, at a minimum, describe their particular task data in explicit mathematical terms and preferentially provide some complexity analysis. In the absence of task complexity analysis, at a minimum, computational controls should be applied to demonstrate the necessity of whatever structure or data is being asserted in the model. This epistemological practice is glaringly absent in much, if not most, of the neurobehavioral modeling literature. This paper would be a good opportunity to set an example of rigor.

Finally, the authors' description of prior work in the field of whole-organism neurobiological simulation feels incomplete and skewed toward work in Drosophila versus other model organisms. An internet search reveals many published efforts to build neurobehavioral models at varying levels of detail in C. elegans, of which only two are referenced.

We do feel this work constitutes an illustrative scientific exercise and important counterpoint to the sensationalism building around efforts in neurobiological simulation. It should inspire further work in defining a rigorous and scientifically productive epistemological framework for these kinds of brain modeling efforts.

Further Comments

(1) The authors oversell the completeness and quality of connectome datasets and what they lack.

Language such as "complete wiring diagrams," "nearly comprehensive connectomes" neglects the well-appreciated gaps in biological data that most practitioners believe necessary for useful, detailed models to be built. There is a brief mention that biological parameters "remain unknown" and that interfaces are "incompletely characterized", but beyond that, the authors do not explain which parameters are missing, why these parameters might matter, and what still needs to be addressed in order to make any plausible whole-brain emulation claims. This may also inadvertently bolster the sensationalist claims that the manuscript is trying to deflate by giving the impression that neurobiological and physiological data collection is a near-complete exercise.

(2) Prior work in C. elegans neurobehavioral modeling should be more acknowledged, if nothing else, for why it has been largely unsatisfying.

C. elegans is rarely discussed, while Drosophila is primarily focused on. The status of C. elegans connectomics, physiological mapping, biomechanics, and neurobehavioral modeling is worth more treatment.

(3) Critiques of Eon Systems announcements also, by and large, apply to more detailed and disclosed efforts in neurobehavioral modeling using RL for parameter imputation, and this should be recognized.

By way of reference to a tweet in the first paragraph, the authors are responding to a recent claim made by a startup that they have fully "uploaded" a fly brain, a significant advance vis-à-vis prior work in neurobehavioral modeling in Drosophila, such as references [3 and 9], which are mentioned as background in the paper but left out of the methodological critique. But one of the central warnings of the paper is around the challenge of interpretability when using reinforcement learning to optimize model parameters. The authors also should acknowledge that the use of RL has been justified by building neurobehavioral model builders as a proxy for the learning and tuning processes thought to occur during animal development.

(4) Substantiate the reservoir computing explanatory claim with appropriate computational controls.

The reservoir computing idea is the only piece of hypothesizing a necessary function for the central brain component model in the paper. This claim could be substantiated with some basic computational controls rather than just hypothesized. We suggest the following possibilities as additions to the model: (a) replace the connectome with an RRNN, (b) shuffle the connectome, or (c) use other simple dynamical systems in place of the worm brain model.

Specific Manuscript Comments

(1) Abstract

"New connectome datasets and musculoskeletal models now enable integrated, closed-loop simulations of the neural and biomechanical systems of the fruit fly Drosophila, an ideal model organism to investigate embodied intelligence."<br /> This sentence could mislead non-specialists into thinking all current simulations are novel because the connectome datasets are new. In fact, FlyWire (2024), NeuroMechFly (2022), and other connectomes have already been available for some years now. We believe that this sentence is a chance to make the opposite point that these resources have existed for a while, and that many simulations have been built before.

"However, many biological parameters of the nervous system and the body, as well as how they interface, remain unknown."<br /> Some examples of specific parameter/physiological data types that are missing and thought to be critical, such as neuronal input/output functions, are warranted. See below for a comment on the confusing construct of "interface" as a distinct entity from the neural network.

(2) Introduction

"Among animals that walk, the integration of brain wiring and body models is perhaps closest to fruition in Drosophila, due to the recent completion of multiple complete wiring diagrams (known as connectomes) of the fly nervous system." ...and... "The fly is the only animal with legs for which nearly comprehensive connectomes of its brain and nerve cord exist."<br /> The walking qualifier allows the authors to skirt around the substantial and decades-long work on connectomes in C. elegans, which crawls and does not walk. Yet sinusoidal crawling is a multidimensional, adaptive behavior, so it seems this exclusion was for narrative convenience rather than contextual accuracy.

"Despite this progress, closed-loop integration of biomechanical and neural models remains far from straightforward."<br /> Work (and shortcomings) in C. elegans neurobehavioral modeling should also be stated here alongside the fly.

"Where interfaces between brains and body models are missing or only partially characterized, one approach is to train an artificial neural network (ANN) to approximate these interfaces with deep reinforcement learning (DRL)."<br /> The choice of "interface" as a distinct, well-defined neurobiological entity is somewhat confusing and may mislead non-practitioner readers. If neuronal and muscular (and their interactions) physiology are incorporated into a neurobehavioral model, then in principle there is nothing left to call an "interface". It would be clearer to explain that prior neurobehavioral models have often inserted a trainable multilayer feedforward network between sensory and central brain and between the central brain and motor effectors in order to have a substrate for learning, and that this insertion may render the entire biological modeling exercise scientifically pointless, or at a minimum require a set of computational controls.

"In building virtual animal models, a motor policy is commonly learned by DRL so that the integrated, closed-loop virtual body successfully mimics the detailed kinematics of real animal behavior."<br /> The authors could define "motor policy" in simple terms and give a brief example.

"Additional realism is added when the motor policy network is constrained by a connectome dataset. However, many biophysical parameters for individual neurons and synapses remain un-measured."<br /> "motor policy network" is confusing; this is referring to the entire network model here, presumably.

(3) Methods

"We used the adult hermaphrodite C. elegans nematode connectome dataset [15, 16, 5], including the identities of its 302 neurons and their synapses (Fig. 1A)."<br /> We believe the authors should specify the dataset type, which is a structural, unsigned connectome lacking grounding in physiological function.

"The policy network was trained in closed loop using PPO as implemented by MIMIC-MJX"<br /> The authors should define "PPO" and "MIMIC-MJX" in simple terms and explain why they were used.

(4) Discussion

"Its role in the movement policy could be fulfilled equally well by a randomly connected RNN, akin to reservoir computing [20], since all the learning happens in the black-box ANN motor decoder."<br /> See above - this computational exercise should actually be performed.

"Looking further ahead, swapping brain and body models of related species may one day yield real insights into how their brains and bodies diverged through evolution. However, far more model development and experimental validation is needed before we can learn anything from such a digital sphinx."<br /> These two sentences about future possible cross-species chimeras feel superfluous and unsubstantiated, and weaken the main argument of the paper about whole-brain emulation.

Reviewer #1 (Public review):

This study by Alonso-Calleja and colleagues aimed to determine whether TGR5 regulates hematopoiesis and the bone marrow microenvironment under steady-state conditions and following transplantation. The revised manuscript substantially improves upon the original submission by providing additional characterization of TGR5 expression in hematopoietic and stromal populations, incorporating analyses in female mice, and expanding the investigation of bone marrow adipose tissue under aging and high-fat diet conditions. These additions more convincingly establish TGR5 as a regulator of bone marrow adipose tissue and stromal composition.

Major strengths of the study include the comprehensive characterization of the bone marrow adipose tissue phenotype across multiple experimental settings and the demonstration that TGR5 deficiency consistently alters the stromal compartment. The strongest and most convincing aspect of the work is the identification of TGR5 as a regulator of bone marrow adipose tissue and the bone marrow microenvironment. These findings provide useful insights into how metabolic signaling pathways influence the hematopoietic niche.

However, the evidence supporting a direct role for TGR5 in hematopoietic recovery following transplantation remains limited. Although reciprocal transplantation experiments and peripheral blood recovery analyses strengthen the manuscript, the conclusions regarding hematopoietic regeneration continue to rely largely on correlative observations. The study does not directly demonstrate that expansion of adipocyte progenitors is responsible for the enhanced recovery phenotype, nor does it establish improved regeneration of hematopoietic stem or progenitor cells within the bone marrow. Overall, the revised work addresses many of the concerns raised in the original review and provides useful new insights into the regulation of the bone marrow microenvironment by TGR5. Nevertheless, the conclusions regarding hematopoietic recovery should remain appropriately tempered, as the mechanistic basis linking the stromal phenotype to enhanced regeneration has not been directly demonstrated.

Reviewer #1 (Public review):

Summary:

This manuscript combined rat fMRI, optogenetics and electrophysiology to examine the large-scale functional network of the olfactory system as well as its alteration in an aged rat model.

Strengths:

Overall methodology is very solid and the results provided an interesting perspective on large-scale functional network perturbation of the olfactory system.

Weaknesses:

The biological relevance and validation of the current results can be improved.

Comment on revised version.

Authors made satisfactory revision and I have no further comments.

Reviewer #1 (Public review):

Summary:

A central question in decision-making is whether confidence arises from the same evidence-accumulation process that led to the choice or from a separate second process. The manuscript addresses this question using a random-dot motion (RDM) task, with an initial choice followed by a confidence report, and a time-pressure manipulation on the confidence report. The authors fit a family of four models differing on two dimensions: the source of confidence (a continuation of the choice accumulator vs. a distinct accumulation process) and the stopping rule (time-based vs. boundary-based). The models are fitted to behavior, and then their simulated dynamics are compared with the CPP signal associated with evidence accumulation (not included in the fit). The main methodological contribution is the use of a neural signal to decide between the two boundary-based models, which are nearly indistinguishable behaviorally. The authors conclude that boundary-based stopping rules outperform time-based rules, and that the Boundary-Single model reproduces the certainty-related CPP dynamics better than the Boundary-Distinct model, supporting a single accumulation process for both choice and confidence.

Strengths:

The main strength is methodological: using a neural signal (CPP) as an out-of-sample arbiter between the two boundary-based models (which are nearly equivalent behaviorally). This addresses the model-identifiability problem: when behavior does not distinguish between competing models, a neural signal not included in the fit can provide external evidence.

The work is also thorough and empirically rigorous. The finding that CPP amplitude predicts subsequent certainty ratings several hundred milliseconds before the initial choice response is statistically supported and interesting in its own right, independent of its interpretation. The small-N/many-trials design (2,160 trials per participant) is well suited to capturing change-of-mind trials and is accompanied by thorough model and parameter recovery, with the generating model recovered in most simulations. The study also includes preregistration of the design and planned behavioral and neural analyses, and it replicates a previously reported behavioral pattern.

Weaknesses:

The central conclusion may well be correct, but in my view the current evidence does not fully support it. The behavioral comparison between the competing models did not resolve the issue and in fact showed a slight preference for the distinct-process model, so the weight of the decision falls mainly on the neural comparison.

(1) The neural evidence supports access to pre-choice variation, but does not necessarily establish a single continuous process. The critical difference between the single and distinct models is the presence of trial-to-trial variation in the evidence at choice commitment, which is inherited by the confidence process: the single model preserves it (the confidence process begins from the trial-specific endpoint of the choice DV), while the distinct model does not inherit it (z2 fixed). Thus, the neural test examines whether confidence has access to the state of evidence accumulation before the choice response but does not establish that the same accumulation process must continue seamlessly to determine confidence. The authors also test a model in which a distinct post-choice process is initialized using information from the endpoint of the choice process. However, its failure rules out one specific implementation of information transfer, rather than the broader class of two-stage models in which a distinct confidence process receives a readout of the decision state and may additionally integrate other metacognitive cues.

(2) A broader class of two-stage metacognitive models that combine a decision-state readout with additional cues is not tested. The distinct-process model implemented here captures only a limited subset of possible metacognitive architectures. Its starting point is independent of the choice-DV endpoint, and it remains driven by the available sensory evidence, potentially within a different reference frame. It does not capture second-order accounts in which confidence combines a readout of the decision state with additional cues not explicitly represented in the choice accumulator, such as response time, motor conflict, subjective stimulus clarity or attention. Moreover, the paradigm used in the manuscript provides few independently manipulated information sources that would allow such a process to be identified separately from the choice accumulator.

(3) The neural distinction between models is not quantitatively evaluated. The claim that the Boundary-Single model better reproduces the CPP rests primarily on a visual/qualitative comparison, without a numerical measure of the discrepancy between each model and the neural data. Although the plotted β coefficients (Figure 4D) provide estimates of the neural effects, no scalar summary of model-to-CPP fit is reported. Because the neural comparison carries much of the inferential weight, a quantitative comparison would strengthen the conclusion substantially.

(4) The fitted parameters raise a question about the post-choice process. Confidence responses were very fast (average median confidence RT = 243 ms), with no minimum RT threshold. The Boundary-Single model estimated a post-choice drift rate more than twice the pre-choice rate (1.69 vs. 0.75). In the model, confidence accumulation begins at commitment, before the initial response is executed. The measured confidence RT therefore does not capture the full accumulation window, which also includes the motor delay. Still, the sharp rise in drift rate at commitment requires an explanation. It may reflect stronger weighting of the still-available evidence, as the authors suggest. But it is also consistent with a fast readout of a decision state that was largely set before the initial response. The authors could compare the current model against a readout model, or against an intermediate variant that permits only a brief, bounded period of post-choice accumulation.

(5) The participant-level distribution of model preferences would clarify the comparison. Models were fit separately per participant and condition, but the comparison is summarized as mean BIC (a small average preference for Boundary-Distinct). A mean cannot distinguish two different situations: a consistent, weak preference for one model across all participants, versus a mixture in which some participants clearly favor one architecture and others the opposite. Reporting the distribution of per-participant ΔBIC and the number of participants favoring each model would clarify the result.

Reviewer #1 (Public review):

This manuscript describes a multi-modal study of associative learning and memory in humans, that combines scalp EEG, pupillometry and behavioral analysis to explore the construct of mnemonic prediction errors (MPEs), in terms of their relationship to attention and cognitive control. Across two pooled studies, participants performed associative memory tasks in which they learned the relationship between a cue word (action verb) and subsequent picture (animate or inanimate) with a strong vs. weak (4 or 1 repetitions) encoding manipulation. At test, participants were encouraged to generate a prediction following the cue word to determine whether the subsequently presented picture was a match or mismatch. The timecourse of pupillary responses during match decisions were decomposed using temporal principal components analysis, which identified 6 distinct and overlapping processes. Some of the components (PC3/PC4) exhibited sensitivity to both the strength and mismatch conditions, as well as behavior (both RT and accuracy) and retrieval success on the subsequent trial. Furthermore, relationships were also observed between pupillary responses (specifically for PC4) and both frontal theta and posterior alpha power measures obtained from scalp EEG in Experiment 2, as well as for frontal theta and subsequent learning from mismatch stimuli (assessed using subsequent memory findings from a surprise recognition test). The authors suggest the findings indicate that MPEs elicit changes in attention, arousal and cognitive control which impact subsequent learning.

Strengths:

This manuscript has many strengths, including a clever study design, thoughtful integration of multiple neurocognitive measures, and a set of rigorous and technically sophisticated analyses, which reveal a large set of relationships among the measures and behavior. The findings demonstrating brain/physiology-behavior relationships are particularly important, in that they point to potential functional consequences of MPEs.

Weaknesses:

The technical proficiency and complexity of the study and analysis also presents a clear limitation and challenge for interpretation. It is likely that readers, even those that are quite knowledgeable about the methods, constructs, and questions being addressed will often struggle (as this reviewer did) to keep the large set of findings in mind and gain understanding of how they all fit together.

Indeed, it seems like there many threads running together in the paper which make it challenging to find the through-line of the key findings. The authors do address some of the key questions motivating the paper in the Introduction, but the results are somewhat ambiguous with regard to the primary question of the study as to whether the detection of MPEs leads to interaction among cognitive control, attention, and arousal. To their credit, the authors tackle this question through both cross-correlation and formal mediation analyses, and summarize these in diagrammatic figures (Figure 3, Figure 6). Yet it is not resolved whether the results represent a clear answer pointing to independence, or rather a lack of statistical power, or ill-resolved formulation of the mediational relationship. In particular, the cross-correlation suggests that posterior alpha suppression in response to MPEs does precede frontal theta, yet this indirect relationship does not explain the variation in trial-by-trial RTs on mismatches. This suggests a potential model misspecification.

In addition to the primary interaction issue mentioned above (between cognitive control, attention & arousal), the Introduction lays out a number of claims: 1) that pupil size will be more sensitive to strong than weak MPEs; 2) that MPE-linked increases in attention (indexed with posterior alpha suppression) and arousal (indexed with pupil size) will be linked to learning; and 3) MPE learning will vary as a function of prediction strength. Given the focus on learning, it is somewhat surprising that learning is not included in the mediation models. As the authors indicate in the Discussion, the use of trial-by-trial RT variation to drive the mediation model might be problematic, given that the RTs are sensitive to a range of factors beyond mnemonic prediction strength and also are under competing pressures (longer for mismatches than matches, due to surprise-linked slowing, but also faster following stronger rather than weaker mnemonic predictions). Thus, an alternative possibility might be to use trial-by-trial recognition of mismatches as the outcome variable in mediation models rather than trial-by-trial RT as the independent variable.

A large component of the results (Sections 2 and 3) is devoted to analyses of cue-linked pupil and EEG processes that putatively reflect mnemonic predictions (i.e., occurring before picture probes are presented and match/mismatch detection, i.e., MPEs occur). Yet these Results and the subsequent pupillary PCA components (PC1 and PC5) that are elicited are not well-integrated with the primary themes of the paper or the causal hypotheses. One finding that does seem to figure prominently (in that it is mentioned in Abstract, Introduction & Discussion) relates to the amount of attention allocated to the mnemonic prediction generation. Yet this finding is not well emphasized in the Results themselves. Possibly it refers to the negative relationship between posterior alpha during memory retrieval and the magnitude of pupillary PC3 component, described in Section 3. But it was quite challenging to identify amongst the wealth of results described in this Section as well as the others. More generally, the large amount of findings described across all four lengthy Results sections makes it challenging for readers to discern what are the key ones that the authors would like to highlight.

It is recommended that the authors do another pass through the paper to better highlight the most critical findings that they want to emphasize or which are most interpretable from a mechanistic and causal flow perspective and then de-emphasize or move other findings to the Supplemental Materials. Although the authors are to be commended for such a rigorous and comprehensive set of analyses, there are so many of them and findings, that the key points get buried and the reader needs to struggle potentially unnecessarily to identify the key take-away points.

Reviewer #1 (Public review):

Summary:

The uniqueness of this paper is the study of the formation of temporal binding-dependent memories in the cntnap2 mouse, a long-standing mouse model of autism that has been used to test therapeutic modalities.

Strengths:

I liked the combination of optical recordings and interventions and the backup of primary observations with control experiments.

Weaknesses:

(1) Fiber photometry recordings are too coarse to give salient clues to the underlying mechanism.

(2) Are perturbed pyramidal cells causally responsible for the altered trace? What can be concluded about the possible role of inhibitory interneurons as potential drivers? The observations focus on abnormal regional activity as observed with fiber photometry and manipulated by optogenetics. The authors should state clearly the limits of their conclusions.

(3) I found the "trace" nomenclature confusing. "....in which mice are required to memorize the association between a tone (Conditioned Stimulus) and a mild electric foot-shock (Unconditioned Stimulus), separated by a time interval called Trace (Sellami et al., 2017)." It seems that the conceptual model invokes the creation of an [eligibility] trace, characterized by its progressive disappearance over time. It may be a convention in the field or a matter of language, but it seems perverse to use "trace" to label the time interval rather than the entity that is decaying. If this is an accepted convention going back to Howard Eichenbaum, the authors should cite the paper that first introduced the convention.

(4) I would advocate for the addition of some discussion points for the authors to consider.

a) Is the retention of activity in CA1 related to phenomena at the cellular or subcellular level in CA1 pyramidal cells? I'm thinking of dendritic, delayed, and stochastic CaMKII activation (DDSC) as defined by Yasuda's group or short-term and associative plasticity of calcium dynamics (STAPCD) as delineated by Caya-Bissonette and Beique.

b) Was the optogenetic intervention ever administered in a delayed fashion, capitalizing on the temporal advantages of optogenetics to probe dynamics?

c) Is the newfound reliance on corticostriatal pathways something more than compensation at the behavioral level? Could it be driven in part by the ASD-related genetic changes?

Reviewer #1 (Public review):

This manuscript describes a novel downstream mechanism of mTORC1 deficiency-mediated lifespan extension in C. elegans. The authors demonstrated that the biosynthesis and the nuclear hormone receptor daf-12 binding of a bile acid-like hormone, dafachronic acid (DA), are essential for TORC1 mutant raga-1 to extend lifespan. Through RNA-seq and RNAi lifespan screen, they also discovered that a dehydrogenase, dhs-26, which is expressed in the canal-associated neurons, is regulated by DA/daf-12 and downstream of the mTORC1-DA signaling for lifespan extension. The authors also explored the conservation of mTOR/DA/daf-12/dhs-26 signaling in the mouse model. This work demonstrates significant findings that will advance the aging field and will be of interest to many researchers in this field. The conclusions are mostly well supported by data with proper controls.

Some suggestions to strengthen the manuscript include:

(1) Other mTOR activity perturbation or mutants should be used to support some of the core lifespan experiments. It will strengthen the conclusions made from raga-1 mutant only, although there is evidence from TOR RNAi in Figure 1g to support the daf-12 data in Figure 1d.

(2) The authors showed in Figure 1h and 1i that DA supplementation rescued the shortened lifespan of raga-1;daf-9 but not raga-1;daf-12; and also rescued the shortened lifespan of raga-1; dnh-26 in Fig. 5e. Does DA supplementation itself extend lifespan? If its level is increased by mTORC1 inhibition and it is downstream of mTORC1 inhibition, it should theoretically extend lifespan. But from the reported publications, it seems that the DA supplementation lifespan modulation is highly dependent on genetic backgrounds. It will strengthen the conclusions if the authors provide the wild-type condition DA supplementation lifespan data and also related discussions about it.

Reviewer #1 (Public review):

Summary:

The authors used FBDD screening to identify numerous compounds interacting with the ORF9b dimer. They expanded the original fragment hit, soaked the derivatives into the crystals and confirmed their binding poses, and showed that the derivatives bind the target with higher affinity. The authors further targeted the ORF9b binding site on TOM70, and used a fluorescence polarization-based (FP) assay to screen a compound library and obtained several hits. Structure-activity relationship (SAR) optimization yielded hit analogs that have higher binding affinity to TOM70.

Strengths:

(1) The study adopted novel drug design strategies, including stabilizing ORF9b homodimer to prevent it from binding TOM70, and blocking ORF9b from binding TOM70 by screening compounds that compete with ORF9b for binding TOM70.

(2) The work established a feasible high-throughput screening assay. This FP-based assay screened ~50,000 compounds, from which two hit compounds were further optimized to yield analogs with higher binding affinity.

Weaknesses:

(1) The study lacks functional assays to evaluate whether the ORF9b-stabilized compounds or TOM70 binding compounds could affect IFN inhibition caused by ORF9b or virus infection.

(2) There is a lack of experimental evidence to reveal the binding mode of lipidated-compounds with ORF9b homodimer.

(3) There is a lack of experimental evidence to reveal the binding mode of HTS hits or analogs for TOM70.

(4) Overall, none of the compounds shown in the paper have promising potency warranting further development; their binding affinity is limited to the micromolar range.

Reviewer #1 (Public review):

Summary:

Using sequences of short videos to elicit emotional changes in participants, Malamud and Huys demonstrate how a brief, controlled emotion regulation intervention (distancing) can effectively alter subsequent emotion ratings. A novel computational approach based on state-space models captures the trajectories of emotion ratings and leverages tools from control theory to quantify the intervention's impact on emotion dynamics.

Strengths:

The experiment is well designed and tailored to the computational modeling approach advanced in the paper. It also relies on a selection of previously validated stimuli. Within the constraints of a controlled experiment, the intervention successfully implements a relatively common tool used in psychotherapeutic treatment, supporting its clinical relevance.

The computational modeling is grounded in the well-established framework of dynamical systems and control theory. This foundation offers a conceptually clear formalization, along with powerful quantification tools that go beyond previous, more data-driven approaches.

Overall, this timely study presents a coherent approach that bridges concepts from clinical psychology and computational theory, providing a stepping stone toward more quantified, evidence-based psychological interventions targeting emotion control.

Weaknesses:

A limitation of this study is that the data were acquired online, resulting in some heterogeneity in the measured effects and reduced statistical power when testing for complex interactions. While the current data are sufficiently solid to demonstrate the validity of the general concept and computational approach, future work should aim to replicate these results in a more controlled laboratory setting.

Additionally, the repeated reminders of the distancing instruction during the second phase of the experiment raise questions about the generalizability of the findings to longer-term remediation strategies, as typically implemented in clinical settings.

Reviewer #1 (Public review):

Summary:

The study investigates how learning with combined visual and olfactory cues strengthens memory in fruit flies. It demonstrates that pairing colours with odours improves later memory performance, even when only one of the two cues is presented during testing. The authors show that multisensory learning recruits visually responsive Kenyon cells in the mushroom body into memory representations that would otherwise primarily encode odours. Their experiments indicate that the serotonergic DPM neuron links sensory representations that are normally separated, while the APL neuron regulates local GABAergic inhibition of separated learning subcircuits. Together, these findings provide a mechanistic explanation for how a single sensory cue can retrieve a broader memory of a multisensory experience.

Strengths:

A major strength of the paper is its integration of behavioural experiments, targeted neuronal manipulations, and detailed anatomical analysis to address a clear mechanistic question. The findings are supported by multiple complementary experiments showing that multisensory learning enhances memory and recruits visual pathways into olfactory memory representations. Overall, the work provides a coherent mechanistic framework for how multisensory experiences strengthen subsequent memory.

Weaknesses:

A limitation of the paper is that it represents an unusual case, as substantial parts of the broader study were previously published in Nature and subsequently retracted because the physiological findings could not be reproduced. Those physiological experiments would have helped resolve several mechanistic questions raised by the behavioural results and directly test how multisensory information is integrated within the fruit-fly learning circuit. Presenting only the reproducible behavioural and anatomical findings is therefore appropriate and preserves the reliable contribution of the work. Nevertheless, the absence of reproducible physiological evidence makes the mechanistic model less complete and more inferential than it would be in a fully comprehensive study. The conclusions should consequently be framed as a well-supported circuit model rather than a direct demonstration of the underlying physiological processes.

Reviewer #1 (Public review):

Summary:

The authors sought to understand the impact of the decreased expression of the G-protein-coupled receptor GPR34 in Alzheimer´s disease (AD). They analyzed the transcriptional impact of GPR34 deficiency in mice and found that it induced a DAM-like phenotype in control mice and enhanced the DAM signature in the AD model 5xFAD, although it did not result in amyloid plaque clearance or gross changes in microglia or astrocytes. Next, the authors developed an in vitro model of GPR34 deficiency using a CRISPR/Cas9 strategy in human iPSCs to introduce functional mutations that resulted in GPR34 protein deficiency in induced microglial cells. In this model, the authors identified myelin as a ligand of GPR34 and showed that GPR34 deficiency resulted in reduced myelin debris engulfment and transcriptional changes related to lysosomal pathways.

Strengths:

The combined strategy of using in vivo and in vitro models of GPR34 depletion is robust, and the transcriptional analyses are thoroughly performed.

Weaknesses:

The paper´s two main findings related to the lack of GPR34 (enhancement of DAM signature in vivo and reduced myelin engulfment in vitro) are disconnected. At the very least, the authors should discuss what the relevance of myelin clearance in AD is, but the paper would strongly benefit from a more thorough assessment of the impact of GPR34 deficiency in vivo, particularly because no effects on amyloid clearance were observed. The authors could assess whether GPR34-deficient 5xFAD mice have reduced cognitive performance, which, based on their in vitro findings, could be related to the myelin pathology in AD (previously described: see PMID 36284351). The analysis showing reduced myelin content in GPR34-deficient microglia in vitro is superficial and does not allow for identifying whether GPR34 is related to reduced engulfment or increased degradation, which could be related to the changes in the lysosomal gene CD68 identified in vivo. In addition, it would be interesting to compare the transcriptional profile induced by myelin phagocytosis with that of 5xFAD or AD patients, to gain insight into the impact of the signature. Finally, the transgenic approach to delete GPR34 in vivo could have been complemented with experiments with the GPR34 antagonists (YL-365 or S-E49) or agonist (Compound 4B), possibly helping in identifying the source of discrepancy with previous papers showing that GPR34 promotes amyloid clearance.

Reviewer #1 (Public review):

"Learning is a fundamental source of individuality," by Manna and colleagues, interrogates different sources of variation in individual behavior. The authors place individual flies in a Y-shaped arena, which is a common design in the field, and illuminate the arms of the Y with blue versus green light. They track the color preference of individual animals and also perform operant conditioning, meaning that they teach the fly to avoid a particular color/arm by generating a foot shock when the fly enters that arm. There are a number of things that are impressive about this setup: The authors are able to collect data on thousands of individual flies of many different strain backgrounds, and they demonstrate a strong change in color preference after conditioning. This is nice, because in past papers visual learning ability has been modest and difficult to study. To put a number on it, in this paper animals on average don't show a color preference at the start of the assay, spending around 30% of their time in the one arm illuminated green, and the remaining time in the two arms illuminated blue. After conditioning, the average animal spends only 23% of its time in the green arm.

The authors run 64 animals through the assay for each of 88 wild type strains (maybe? see Major Point 1 below) and see considerable strain-specific (genetic) variation in the change in time spent in the shocked color after conditioning. Some strains show no learning, while others spend <10% of their time in the shocked color after conditioning. They also, I believe, see that some strains have more variability across individuals, which would suggest that some strains have stronger canalization at the development or circuit function level than others-i.e. some genotypes produce more consistent copies of the individual, others less consistent copies. (Or, some genotypes produce robust circuits, and others produce noisy circuits.)

Finally, the authors argue statistically that learning itself increases variability in individual performance. This makes a lot of sense to me intuitively. Learning changes the physical/chemical properties of circuits in the brain, and because it evolves over time and interacts with environmental variables, it seems like it should send different animals down different channels. Or, at a conceptual level, if I learn to play the piano and my sister doesn't (because of some genetic difference between us or something stochastic), this learning experience will cause all sorts of other differences in our behavior as time passes. I also think the authors do have enough data to be able to make this finding. However, the presentation of the argument in this portion of the paper is hard for me to understand, and I am not an expert in statistics, so the strength of the result is difficult for me to evaluate.

Major points:

(1) It's difficult to track through the paper the number of animals tested for different assays. At the beginning, it says N=5632, which works out to 64 flies for each of the 88 DGRP strains. 64 happens to be the number of parallel Y arenas they have. Later in the methods, there's description of more variation within the set of 64 for each strain-two different parent sets per strain, different sexes, conditioned and un-conditioned. And, while the results text focuses on the color learning, the methods discuss additional assays (place learning, multi-day learning).

Given the numbers, does each run of the 64 mazes include all the tested flies of one strain, or are flies of many strains included in each batch? Do different flies do different assays (color, place, multi-day) or do they all do all the assays? Perhaps there is a table including this information already in the supplement, but I recommend making it much clearer in the main results text and methods. While the dataset is large, if it is split over many conditions and/or if batch and genotype confound each other, this will affect the robustness of the results and how strong the conclusions can be.

(2) The data presentation in Figure 1 is elegant and easy to follow, but getting into Figure 2 and subsequently, I get lost in the statistics and have trouble understanding what is being measured. My understanding of the big picture is that while genetics and individual randomness contribute a lot to behavior, the evidence for learning as an amplifier of individuality is that variance in behavior among animals of the same strain increases over time in the conditioned group (i.e. the group that is doing the most learning, or a specific kind of learning), but not in the control group. This idea is illustrated in the flattening distributions in the cartoons in Figure 1A. The authors should include graphs of the real data that use the same format as in that cartoon. Instead, the graphs present "residuals," and I don't know what those are. I suspect it's "variation left over after accounting for effects of strain and individual stochasticity." I see the residuals being tracked per strain over time in Figure 2H, but I don't see the change over time in other graphs. I'm looking for something simple like, "variation within the strain at the beginning of learning and at later time points in learning." (But I'm not sure exactly what instantaneous measurement would be the focus in longitudinal analyses of learning behavior.)

(3) Figure 3 is a cool stab at tracking down the precise mechanism by which stochastic environment interacts with learning to send individuals along different behavioral routes. But again, like in Figure 2, I don't have the sophisticated understanding of statistics to understand exactly what the graphs are telling me, or how they relate to the underlying measurements. I'm relying on the results text alone to reach a conceptual understanding and just taking the graphs on trust.

So, overall, the authors have a very nice body of work here and with the potential to add a new facet to our understanding of the origins of diversity in animal behavior. In addition to the interpretations they focus on here, this dataset also represents an advance in studying visual associative learning in general, and quite an amazing ability to make longitudinal measurements of many behavioral decisions within the same animals. Improving the data presentation to make it easier to follow for a larger swathe of researchers, especially in figures 2 and 3, will increase its potential impact.

Comment on revised version:

The authors have addressed my main points, including adding description of their statistical analyses and providing more detail about the different assays run and which animals were included in the same assay batches.

Reviewer #1 (Public review):

The authors demonstrate an innovative approach to investigate the effect of cone dropout on visual acuity using their newly developed Oz platform. By systematically reducing the coverage of real-world input to the cone photoreceptor mosaic ("cone dropout condition"), the authors are able to assess how having less cones leads to reduced vision, in comparison to existing approaches ("pixel dropout condition").

The observation of visual acuity maintenance with cone dropout has been a longstanding mystery since the 2013/2018 papers by Ratnam and Foote. The authors should be commended for their approach to address this important question. However, there are some simplifications and assumptions being applied to make this jump (i.e. that a 50% reduction in cone stimulation in a healthy eye is comparable to a 50% reduction in cone density in a patient). It seems unlikely that in a patient eye, with cone dropout, that there will be gaps in the mosaic. Not considering any other non-photoreceptor related reasons for visual acuity loss which can occur in patients, the cone aperture acceptance angle may be different due to changes in cone size or packing; the sensitivity of individual cones may also be reduced due to deficits in the visual cycle recovery which could be affected in disease. Some of these limitations could be addressed and acknowledged more explicitly.

The capture of a rich dataset including both cone imaging and eye motion is valuable. Since the C stimulus test relies on foveal fixation, and there is a high degree of subject-to-subject variation in peak cone density, the authors may wish to report on peak cone density measurements of the subjects being included in this study. In addition, evaluating whether the eye motion is affected by simulated cone dropout condition can help to rule out whether these observed effects can be attributed to eye motion.

Overall, this is an impressive study incorporating state-of-the-art technology to probe the fundamental limits of human vision.

Comments on revised version.

The authors have nicely addressed my concerns. The additional clarifications and revised text have strengthened the paper. Thank you also for pointing out the inaccuracy of referring to the system as the olo system; this has been corrected.

Reviewer #1 (Public review):

Summary:

This is important and significant work because it helps describe the complexity of interactions between system components where 2 herbivores interact with vegetation. Whereas other studies have shown that the larger ungulate (yaks, Bos grunniens, in this case) can facilitate the abundance and population growth of the smaller (the semi-fossorial lagomorph, Ochotona curzoniae, plateau pika hereafter), this study flips the tables, and shows that, at least under some conditions, moderate densities of the plateau facilitate the nutritional condition of yaks.

Strengths:

Notably, the strong inference the authors can claim for their results is supported by the careful experimental design. A weaker paper would have simply noted correlations between pika burrow density and yak feeding efficiency without experimental removal. This paper, to its credit, not only used experimental removals but also documented the various intermediary results that support the ultimate conclusions. The statistical approaches used appear to be appropriate. (Readers are encouraged to read the full Materials and Methods, which are available in the Supplementary Materials section).

Weaknesses:

Although the study was well designed and executed, and its conclusions appear strongly supported, readers interested in the management implications on the Qinghai-Tibetan Plateau should be mindful of its limitations. First, the study site, at approximately 3,200 m elevation, was relatively low by Qinghai-Tibetan Plateau standards. Stellera chamaejasme becomes less common at elevations > 4,000 m, where a majority of livestock grazing occurs. Thus, it would be instructive to learn, through follow-up studies, whether similar facilitation occurs where unpalatable (and mildly poisonous) species in such genera as Astragalus, Oxytropis, and Thermopsis replace S. chamaejasme as the problematic plant for pastoralists. Second, the authors make no mention of wild ungulates, so it is unclear what, if any, role they may have played in this system. At least one study in Qinghai Province, albeit at a slightly higher elevation, showed that not only pikas, but also Tibetan gazelles (Procapra picticaudata), which were commonly observed on grazed pastures, grazed more frequently on some dicots avoided by domestic sheep than did the livestock themselves (Harris et al. 2015). It would also be instructive to learn if similar facilitation as observed here applied to the other principal livestock species in the area, domestic sheep (which are often herded together with smaller numbers of domestic goats). Finally, as suggested by this study, the interactions between all components of the system are complex and interactive. If pika facilitation of yak nutrition at the densities documented results in herders increasing yak density, might the increased herbivory from the domestic animals provide the conditions for the pika population to increase beyond the densities observed here, and thus toward the levels where facilitation yields to competition?

Reviewer #1 (Public review):

I thank the authors for the revised manuscript and for the detailed responses.

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

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

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

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

Reviewer #1 (Public review):

Summary:

This study demonstrates that nutrient resorption efficiency (NuRE) in Phragmites australis is genetically canalized rather than plastic to salt stress. Using 110 genotypes in a common garden, the authors show that intraspecific variation in NuRE is explained by phylogeographic lineage, ecotype, and latitude, not by effective salinity. Element-specific regulatory strategies further reveal how N, P, and K resorption are differentially controlled. At the population level, this is an important study that fundamentally advances our understanding of plant functional trait evolution and its implications for ecosystem nutrient dynamics under global change.

Strengths:

This study is the first to demonstrate genetic determination of a key nutrient conservation trait under effective salt stress in a widespread macrophyte, directly testing the 'plastic acclimation versus inherent conservatism' paradigm in a non-nutrient stress context. The experimental design is rigorous: each genotype was paired across control and salt treatments, and multilevel stress effectiveness (metabolomics, biomass, Na accumulation) was confirmed before evaluating NuRE. The large sample size of a macrophyte and dual classification (phylogeography + ecotype) allow robust disentangling of genetic versus plastic sources of variation.

The analysis comprehensively tests three resorption control hypotheses using appropriate SMA regression, revealing element-specific and condition-dependent patterns. The latitudinal gradient and variation partitioning provide strong evidence that genetic origin and geographic context outweigh short-term plasticity, with important implications for predicting ecosystem nutrient cycling under global change. This study provides a clear empirical demonstration that a key nutrient conservation trait can remain homeostatic under non-nutrient stress, and that intraspecific variation is primarily a product of population differentiation rather than short-term plasticity.

Weaknesses:

First, the salinity treatment spanned only one growing season. The conclusion of genetic canalization therefore specifically refers to the absence of plasticity to an acute salt shock. Whether long-term, multigenerational chronic salinity could act as a selective agent or induce transgenerational plasticity remains an open and interesting question for further research. Likewise, the physiological mechanisms underlying the observed lack of plastic increase in NuRE (for example, phloem loading or senescence gene expression) are not directly resolved, leaving some inference about trade-offs versus true unresponsiveness. These points do not weaken the study's main conclusion. Instead, they suggest productive future directions, such as longer-term field manipulations and targeted molecular investigations.

Second, the test of nutrient limitation control relies on resorbed N:P and N:K ratios as proxies, an established but indirect approach. Direct nutrient addition experiments would provide stronger causal evidence. Also, the metabolomic analysis is used primarily to validate stress effectiveness; deeper integration of specific metabolites with NuRE variation across genotypes could have offered mechanistic insights but was not pursued. Additionally, the potential collinearity between ecotype and phylogeographic lineage among Chinese populations is not quantitatively addressed. None of these considerations undermines the main finding, which is supported by a robust experimental design and widely accepted analytical approaches.

Reviewer #1 (Public review):

Summary:

The manuscript introduces cuBNM, a GPU‑accelerated Python package for whole‑brain modeling. The authors demonstrate that running simulations on GPUs provides substantial benefits in computational speed, cost-efficiency, and scalability compared to traditionally used CPUs, making large‑scale and individualized brain network modeling computationally feasible. The usage of cuBNM has been demonstrated by running optimization of group-level and individualized low- and high-dimensional models. By investigating the test-retest reliability and heritability of simulated and empirical measures in the Human Connectome Project dataset, the authors showed that simulated features were fairly reliable and significantly heritable.

Strengths:

This study is timely and presents an important contribution to the field of whole-brain computational modeling. A major strength is that the authors go beyond introducing a GPU-accelerated framework by demonstrating its utility through comprehensive benchmarking and biologically relevant applications, including individualized model fitting, comparisons of homogeneous and heterogeneous models, and analyses of test-retest reliability and heritability.

The computational performance is evaluated comprehensively, assessing speed, computational cost, energy consumption, and scalability across different simulation settings. The Human Connectome Project dataset is used to demonstrate that the software enables individualized whole-brain modeling in large datasets.

Finally, the software is modular, open-source, and well-documented, and can facilitate the broader adoption of GPU-accelerated whole-brain modeling within the neuroscience community.

Weaknesses:

The test-retest reliability and heritability are estimated using high-quality Human Connectome Project data. The manuscript would benefit from discussion and/or demonstrations regarding how the software performs under more challenging conditions, such as clinical datasets, shorter data acquisitions, higher-motion datasets, or multi-site datasets.

Apart from demonstrating the benefits of GPUs over CPUs, the manuscript would benefit from a more direct comparison between cuBNM and other whole-brain modeling software, such as The Virtual Brain.

The manuscript demonstrates that heterogeneous models improve the fit to empirical functional connectivity. However, the biological interpretation of this improvement could be expanded. The heterogeneous models are also more complex than homogeneous models, and some improvement in model fit may be explained by the increased model flexibility.

In whole-brain brain network modeling, different parameter combinations can result in similar empirical functional connectivity measures. The manuscript would benefit from a discussion of how this influences the interpretation of individualized parameter estimates.

Reviewer #1 (Public review):

Summary:

This study revisits an important and controversial question in brain repair: whether NeuroD1 can convert brain immune cells into nerve cells in vivo. Using a virus-free genetic system, in vivo imaging, injury experiments, and single-cell profiling, the authors provide convincing evidence that NeuroD1-expressing cells do not become nerve cells under the tested conditions. Instead, these cells largely retain their original immune-cell identity, and some appear to undergo cellular stress or loss.

Strengths:

The main strength of the work is that it tests this question with a cleaner genetic strategy, avoiding some of the concerns associated with viral delivery and unintended cell labeling. Although the overall conclusion is consistent with the authors' previous work, the current study adds useful independent evidence, particularly through the virus-free fate-mapping system and live imaging in the brain.

Weaknesses:

There are some limitations. In the injury experiment, the labeled cells may include both resident brain immune cells and blood-derived immune cells recruited after injury, so the authors should be cautious when referring to all labeled cells as microglia. The level of NeuroD1 expression achieved by the genetic system is also not fully defined, which matters because the effects of such a cell-fate regulator may depend on expression level. Finally, the tested time window may not fully address very delayed or incomplete neuronal differentiation.

Overall, this is a useful and careful study that supports the conclusion that NeuroD1 does not drive brain immune cells to become nerve cells in the tested settings. It should be valuable for researchers studying brain repair, cell fate conversion, and genetic fate mapping, and it provides a clear caution against overinterpreting reprogramming results based only on viral labeling.

Joint Public Review:

The revised manuscript is much clearer, and the additional analyses address several of the original concerns. RAIN analyses (Rhythmicity Analysis Incorporating Nonparametric methods) now detects circadian rhythmicity in 7/11 recordings under light-dark conditions and 8/12 recordings in constant darkness, compared with 2/12 following treatment with the Orco antagonist. This supports circadian modulation of spontaneous firing and a role for Orco in its normal expression. The expanded qPCR analysis of Orco also supports the conclusion that Orco transcript abundance is not circadian, and the cAMP experiment shows that cAMP can modulate Orco-dependent activity.

The remaining issue concerns the mechanistic interpretation. The lack of rhythmic Orco transcript abundance does not distinguish an autonomous post-translational feedback-loop (PTFL) clock from a model in which the canonical transcriptional-translational (TTFL) clock acts upstream through cAMP, calcium, kinases, phosphatases, channel trafficking, or related pathways to regulate Orco.

Similarly, the new Figure 10 provides a useful representation of the authors' hypothesis, but the proposed delayed feedback and coupling mechanisms are not experimentally demonstrated.

We do not think any further experiments are necessary for the present study. Instead, we recommend that the manuscript should clearly distinguish between what the data show and what remains proposed. The data support circadian modulation of ORN firing a role for Orco in its normal expression, non-circadian Orco transcript abundance, and cAMP-sensitive modulation of Orco-dependent activity. The proposal that an Orco-centred membrane feedback loop generates the rhythm is intriguing and may remain a hypothesis generated through this study that needs formal testing in the future. This should be explicitly stated. While this has been done in the discussion section, elsewhere, including in the abstract and elsewhere, the original claim remains.

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate the physiological role of the Type VI secretion system (T6SS) in a naturally evolved gut microbiome derived from wild mice (the WildR microbiome). Focusing on Bacteroides acidifaciens, the authors use newly developed genetic tools and strain replacement strategies to test how T6SS-mediated antagonism influences colonization, persistence, and fitness within a complex gut community. They further show that the T6SS resides on an integrative and conjugative element (ICE), is distributed among select community members, and can be horizontally transferred, with context-dependent effects on colonization and persistence. The authors conclude that the T6SS stabilizes strain presence in the gut microbiome while imposing ecological and physiological constraints that shape its value across contexts.

This study is likely to have significant impact on the microbiome field by moving experimental tests of T6SS function out of simplified systems and into a naturally co-evolved gut community. The WildR system, together with the strain replacement strategy, ICE-seq approach, and genetic toolkit, represents a powerful and reusable platform for future mechanistic studies of microbial antagonism and mobile genetic elements in vivo.

The datasets-including isolate genomes, metagenomes, and ICE distribution maps-will be valuable community resources, particularly for researchers interested in strain-resolved dynamics, horizontal gene transfer, and ecological context dependence. Even where mechanistic resolution is incomplete, the work provides a strong experimental foundation upon which such questions can be directly addressed.

Overall, this study occupies a space between system building and mechanistic dissection. The authors demonstrate that the T6SS influences persistence and community structure in vivo, but the physiological basis of these effects remains unresolved. Interpreting the results as evidence of fitness costs or selective advantage therefore requires caution, as multiple ecological and host-mediated processes could produce similar abundance trajectories.

Placing the findings within the broader literature on microbial antagonism, particularly work emphasizing measurable costs, benefits, and tradeoffs, would help readers better contextualize what is directly demonstrated here versus what remains an open question. Viewed in this light, the principal contribution of the study is to show that such questions can now be addressed experimentally in a realistic gut ecosystem.

Strengths:

A major strength of this study is that it directly interrogates the physiological role of the T6SS in a naturally evolved gut microbiome, rather than relying on simplified pairwise or in vitro systems. By working within the WildR community, the authors advance beyond descriptive surveys of T6SS prevalence and address function in an ecologically relevant context.

The authors provide clear genetic evidence that Bacteroides acidifaciens uses a T6SS to antagonize co-resident Bacteroidales, and that loss of T6SS function specifically compromises long-term persistence without affecting initial colonization. This temporal separation is well designed and supports the conclusion that the T6SS contributes to maintenance rather than establishment within the community.

Another strength is the identification of the T6SS on an integrative and conjugative element (ICE) and the demonstration that this element is distributed among, and exchanged between, community members. The use of ICE-seq to track distribution and transfer provides strong support for horizontal mobility and adds mechanistic depth to the study.

Finally, the transfer of the T6SS-ICE into Phocaeicola vulgatus and the observation of context-dependent colonization benefits followed by decline is a compelling result that moves the study beyond simple "T6SS is beneficial" narratives and highlights ecological contingency.

Weaknesses:

Despite these strengths, there is a mismatch between the precision of the claims and the precision of the measurements, particularly regarding fitness costs, physiological burden, and mechanistic role of the T6SS.

First, while the authors conclude that the T6SS "stabilizes strain presence" and that its value is constrained by fitness costs, these costs are not directly measured. Persistence, abundance trajectories, and eventual loss are informative outcomes, but they do not uniquely identify fitness tradeoffs. Decline could arise from multiple non-exclusive mechanisms, including community restructuring, host-mediated effects, incompatibilities of the ICE in new hosts, or ecological retaliation, none of which are disentangled here.

Second, the manuscript frames the T6SS as having a defined physiological role, yet the data do not resolve which physiological processes are under selection. The experiments demonstrate that T6SS activity affects persistence, but they do not distinguish whether this occurs via direct killing, resource release, niche modification, or higher-order community effects. As a result, "physiological role" remains underspecified and risks being conflated with ecological outcome.

Third, although the authors emphasize context dependence, the study offers limited quantitative insight into what aspects of context matter. Differences between native and recipient hosts, or between early and late colonization phases, are described but not mechanistically interrogated, making it difficult to generalize beyond the specific cases examined.

Fourth is the lack of engagement with recent experimental literature demonstrating functional roles of the T6SS beyond simple interference competition. While the authors focus on persistence and competitive outcomes, they do not adequately situate their findings within recent work demonstrating that T6SS-mediated antagonism can serve additional physiological functions, including resource acquisition and DNA uptake, thereby linking killing to measurable benefits and tradeoffs. The absence of this literature makes it difficult to place the authors' conclusions about physiological role and fitness cost within the current conceptual framework of the field. Without this context, the physiological interpretation of the results remains incomplete, and alternative functional explanations for the observed dynamics are underexplored.

A further limitation concerns the taxonomic scope of the functional analysis. The authors state the role of the T6SS in the murine environment is functionally investigated using genetically tractable Bacteroides species, citing lack of genetic tools for Mucispirillum schaedleri. While this is a reasonable practical choice, it means that a substantial fraction of T6SS-encoding species in the WildR community are not experimentally interrogated. Consequently, conclusions about the role of the T6SS in the murine gut necessarily reflect the subset of taxa that are genetically accessible and may not fully capture community-level or niche-specific functions of T6SS activity. Given that M. schaedleri is represented as a metagenome-assembled genome, its isolation and genetic manipulation would be technically challenging. Nonetheless, explicitly acknowledging this limitation and slightly tempering claims of generality would strengthen the manuscript.

Finally, several interpretations would benefit from more cautious language. In particular, claims invoking fitness costs, selective advantage, or physiological burden should be explicitly framed as inferences from persistence dynamics, rather than as direct measurements, unless supported by additional quantitative fitness or growth assays.

Comments on revised version.

The authors have addressed my main concerns by more clearly distinguishing ecological outcomes from directly measured physiological mechanisms. They have moderated claims about fitness costs and benefits, replaced "physiological" with "ecological" where appropriate, expanded the discussion of potential downstream benefits of T6SS-mediated killing, and acknowledged the limited taxonomic scope of the functional analyses. The persistence trajectories support context-dependent relative fitness effects, although they do not identify the specific physiological basis of those effects. The revised manuscript now generally maintains this distinction. These revisions substantially improve the precision and balance of the manuscript.

Reviewer #1 (Public review):

[Editor's Note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. When experimentally feasible, the authors have adequately addressed the concerns of the reviewers in the revised manuscript to support the conclusions of the study.

Summary:

The study by Akita B. Jaykumar et al. explored an interesting and relevant hypothesis whether serine/threonine With-No-lysine (K) kinases (WNK)-1, -2, -3, and -4 engage in insulin-dependent glucose transporter-4 (GLUT4) signaling in the murine central nervous system. The authors especially focused on the hippocampus as this brain region exhibits high expression of insulin and GLUT4. Additionally, disrupted glucose metabolism in the hippocampus has been associated with anxiety disorders, while impaired WNK signaling has been linked to hypertension, learning disabilities, psychiatric disorders or Alzheimer's disease. The study took advantage of selective pan-WNK inhibitor WNK 643 as the main tool to manipulate WNK 1-4 activity both in vivo by daily, per-oral drug administration to wild-type mice, and in vitro by treating either adult murine brain synaptosomes, hippocampal slices, primary cortical cultures, and human cell lines (HEK293, SH-SY5Y). Using a battery of standard behavior paradigms such as open field test, elevated plus maze test, and fear conditioning, the authors convincingly demonstrate that the inhibition of WNK1-4 results in behavior changes, especially in enhanced learning and memory of WNK643-treated mice. To shed light on the underlying molecular mechanism, the authors implemented multiple biochemical approaches including immunoprecipitation, glucose-uptake assay, surface biotylination assay, immunoblotting, and immunofluorescence. The data suggest that simultaneous insulin stimulation and WNK1-4 inhibition results in increased glucose uptake and the activity of insulin's downstream effectors, phosphorylated Akt and phosphorylated AS160. Moreover, the authors demonstrate that insulin treatment enhances the physical interaction of the WNK effector OSR1/SPAK with Akt substrate AS160. As a result, combined treatment with insulin and the WNK643 inhibitor synergistically increases the targeting of GLUT4 to the plasma membrane. Collectively, these data strongly support the initial hypothesis that neuronal insulin- and WNK-dependent pathways do interact and engage in cognitive functions.

In response to our initial comments, the authors mildly revised the manuscript, which did not improve the weaknesses to a sufficient level. Our follow-up comments are labeled under "Revisions 1".

Strengths:

The insulin-dependent signaling in the central nervous system is relatively understudied. This explorative study delves into several interesting and clinically relevant possibilities, examining how insulin-dependent signaling and its crosstalk with WNK kinases might affect brain circuits involved in memory formation and/or anxiety. Therefore, these findings might inspire follow-up studies performed in disease models for disorders that exhibit impaired glucose metabolism, deficient memory, or anxiety, such as Diabetes mellitus, Alzheimer's disease, or most of psychiatric disorders.

The graphical presentation of the figures is of high quality, which helps the reader to obtain a good overview and to easily understand the experimental design, results, and conclusions.

The behavioral studies are well conducted and provide valuable insights into the role of WNK kinases in glucose metabolism and their effect on learning and memory. Additionally, the authors evaluate the levels of basal and induced anxiety in Figures 1 and 2, enhancing our understanding of how WNK signaling might engage in cognitive function and anxiety-like behavior, particularly in the context of altered glucose metabolism.

The data presented in Figures 3 and 4 are notably valuable and robust. The authors effectively utilize a variety of in vivo and in vitro models, combining different treatments in a clear manner. The experimental design is well-controlled, efficiently communicated, and well-executed, providing the reader with clear objectives and conclusions. Overall, these data represent particularly solid and reproducible evidence on the enhanced glucose uptake, GLUT4 targeting, and downstream effectors' activation upon insulin and WNK/OSR1 signaling crosstalk.

Weaknesses:

(1) The study used a WNK643 inhibitor as the only tool to manipulate WNK1-4 activity. This inhibitor seems selective; however, it has been reported that it exhibits different efficiency in inhibiting the individual WNK kinases among each other (e.g. PMID: 31017050, PMID: 36712947). Additionally, the authors do not analyze nor report the expression profiles or activity levels of WNK1, WNK2, WNK3, and WNK4 within the relevant brain regions (i.e. hippocampus, cortex, amygdala). Combined, these weaknesses raise concerns about the direct involvement of WNK kinases within the selected brain regions and behavior circuits. It would be beneficial if the authors provided gene profiling for WNK1, 2, 3, and -4 (e.g. using Allen brain atlas). To confirm the observations, the authors should either add results from using other WNK inhibitors or, preferentially, analyze knock-down or knock-out animals/tissue targeting the single kinases.

Revisions 1: The authors added Fig. S1A during the revisions to show expression of Wnt1-4. While the expression data from humans is interesting, the experimental part of the study is performed in mice. It would be more informative for the authors to add expression profiles from mice or overview the expression pattern with suitable references in the introduction to address this point. The authors did not add data from knock down or knockout tissue targeting the single kinases.

(2) The authors do not report any data on whether the global inhibition of WNKs affects insulin levels as such. Since the authors demonstrate the synergistic effect of simultaneous insulin treatment and WNK1-4 inhibition, such data are missing.

Revisions 1: The authors added Fig. S5A to address this point. It is appreciated that authors performed the needed experiment. Unfortunately, no significant change was found, therefore, the authors still cannot conclude that they demonstrate a synergistic effect of simultaneous insulin treatment and WNT1-4 inhibition. It is a missed opportunity that the authors did not measure insulin in the CSF or tissue lysate to support the data.

(3) The study discovered that the Sortilin receptor binds to OSR1, leading the authors to speculate that Sortilin may be involved in the insulin-dependent GLUT4 surface trafficking. The authors conclude in the result section that "WNK/OSR1/SPAK influences insulin-sensitive GLUT4 trafficking by balancing GLUT4 sequestration in the TGN via regulation of Sortilin with GLUT4 release from these vesicles upon insulin stimulation via regulation of AS160." However, the authors do not provide any evidence supporting Sortilin's involvement in such regulation, thus, this conclusion should be removed from the section. Accordingly, the first paragraph of the discussion should be also rephrased or removed.

Revisions 1: The authors added Fig. 5M-N to address this point. The new experiment is appreciated. However, the authors still do not show that sortilin is involved in insulin or WNK-dependent GLUT4 trafficking in their set up since the authors do not demonstrate any changes in GLUT4 sorting or binding. The conclusions should therefore be rephrased or included purely in the discussion. Moreover, the discussion was not adjusted either, leading to over interpretation based on the available data.

(4) The background relevant to Figure 5, as well as the results and conclusions presented in Figure 5 are quite challenging to follow due to the lack of a clear introduction to the signaling pathways. Consequently, understanding the conclusions drawn from the data is also difficult. It would be beneficial if the authors addressed this issue with either reformulations or additional sections in the introduction. Furthermore, the pulldown experiments in this figure lack some of the necessary controls.

Revisions 1: The Authors insufficiently addressed this point during the revisions and did not rewrite the introduction as suggested.

(5) The authors lack proper independent loading controls (e.g. GAPDH levels) in their immunoblots throughout the paper, and thus their quantifications lack this important normalization step. The authors also did not add knock-out or knock-down controls in their co-IPs. This is disappointing since these improvements were central and suggested during the revision process.

(6) The schemes that represent only hypotheses (Fig. 1K, 4A) are unnecessary and confusing and thus should be omitted or placed at the end of each figure if the conclusions align.

(7) Low-quality images, such as Fig. 5H should be replaced with high-resolution photos, moved to the supplementary, or omitted.

Joint Public Reviews:

This manuscript presents an algorithm for identifying network topologies that exhibit a desired qualitative behaviour, with a particular focus on oscillations. The approach is first demonstrated on 3-node networks-where results can be validated through exhaustive search-and then extended to 5-node networks, where the search space becomes intractable. Network topologies are represented as directed graphs, and their dynamical behaviour is classified using stochastic simulations based on the Gillespie algorithm. To efficiently explore the large design space, the authors employ reinforcement learning via Monte Carlo Tree Search (MCTS), framing circuit design as a sequential decision-making process.

This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

Strengths:

The evidence presented is strong and compelling. The authors validate their results for 3-node networks through exhaustive search, and the findings for 5-node networks are consistent with previously reported motifs, lending credibility to the approach. The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

Weaknesses:

Although the proposed approach substantially expands the scale of tractable searches, the systems explored remain relatively small, being limited to five-node networks. The authors now discuss possible avenues for improving scalability, but extending the framework to substantially larger networks remains an important future challenge.

Another important limitation concerns the assumption of identical reaction rates for all circuit connections. As the authors' own analysis shows, relaxing this assumption leads to significant qualitative and quantitative changes in oscillatory dynamics. Consequently, it remains unclear how the properties of the identified fault-tolerant oscillators translate to more biologically realistic regulatory circuits, where kinetic parameters vary across interactions.

The conclusions should also be interpreted within the chosen modelling framework and parameter space. In particular, the sampled parameter ranges and restriction to relatively low Hill coefficients define the subset of regulatory architectures explored. Whether broader parameter regimes, including higher Hill coefficients, would reveal additional oscillatory architectures remains unclear.

Reviewer #1 (Public review):

Summary:

The ciliary photoreceptor cells and its downstream neurons of larval annelid must be orchestrated in a specific pattern to promote downward swimming in response to long duration of UV exposure. The authors first conducted neuroanatomical examination of the circuit to identify NOS-expression neurons (INNOS) that are immediately downstream to the ciliary photoreceptor cells. The INNOS is activated by UV and produce NO. The NOS is required for UV avoidance by Platynereis larvae and neural dynamics of the photoreceptor cells and their downstream circuit. Following up the RNA-seq data with in-situ hybridization experiments, the authors found that two unconventional guanylate cyclases, NIT-GC1 and NIT-GC2, are expressed and localized in different subcellular domain of the photoreceptor cells. Experiments using the culture cells ang genetically encoded sensors demonstrated that NIT-GC1 can generate cGMP in response to nitric oxide. Finally, authors build mathematical model that fit the live imaging data and used it to predict how the magnitude of the photoreceptor activation varied by intensity and duration of UV light.

Strengths:

The authors conducted comprehensive interrogations of the UV avoidance pathway at the molecular and circuit levels and constructed mathematical model. The main conclusions are supported with layers of evidence from different assays.

Weaknesses:

The authors addressed these weaknesses in the previous version of the manuscript. Statistics are missing in both figure legends and methods. The perturbations of genes and molecules were not cell-type-specific and therefore the observed behavioral defect could be attributed to the malfunction of the circuit elsewhere not examined in this study. I suggest adding more explanation about the functions of other NOS-expressing cells and conducting a control experiment to test behavioral response to a non-visual stimulus.

Reviewer #1 (Public review):

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

The authors point out that the fitness estimates obtained from different experimental assays (monoculture, pairwise competition or bulk competition) are not generally equivalent, not even with regard to the fitness ranking of different genotypes. Using a computational model based on experimentally measured growth phenotypes for knockout strains in yeast, as well as data from Lenski's Long Term Evolution Experiment (LTEE), they derive a set of best practice rules aimed at extracting the optimal amount of information from such experiments.

The study is very complete on a technical level, and the conceptual weaknesses raised in the first round of reviews have been fully addressed in the revision.

Reviewer #1 (Public review):

Summary:

This is a careful, well-powered treatment of age effects in resting-state MEG. Rather than extracting (say) complex connectivity measures, the authors look at the 'simplest possible thing' : changes in the overall power spectrum across age.

Strengths:

They find significant age-related changes at different frequency bands: broadly: attenuation at low-frequency (alpha) and increased beta. These patterns are identified in a large dataset (CamCAN) and then verified in other public data.

Weakness:

Some secondary interpretations (what is "unique" to age vs global anatomy) maybe go beyond what the statistics strictly warrant in the current form, but these can be tightened with (I think pretty quick) additions already foreshadowed by the authors' own analyses.

Aims:

The authors set out to replace piecemeal, band-by-band ageing claims with t-maps, and Cohen's f2 over sensors×frequency ("GLM-Spectrum").

On CamCAN, six spatio-spectral peaks survive relatively strict statistical controls. The larger effects are in low-frequency and upper-alpha/beta ranges (f2 approx. 0.2-0.3), while lower-alpha and gamma reach significance but with small practical impact (f2 < 0.075). A nice finding is that the same qualitative profile appears in three additional independent datasets.

Two analyses are especially interesting. First, the authors show a difference between absolute and relative spectral magnitude (basically within-subject normalization). Relative scaling sharpens spectral specificity of the spatial maps while absolute magnitude is dominated by a broad spatial mode that correlates positively across frequencies, likely reflecting head-position/field-spread factors. The replication of the main age profile is robust to preprocessing decisions (e.g. SSS movement compensation choices) - the bigger determinant of the effect is whether they apply sensor normalization (relative vs absolute).

Second, lots of brain-related things might be related to age and the authors spend some time trying to back out confounds / covariates. This section is handled transparently (in general I found the writing style very clear throughout) - they examine single covariates (sex, BP, GGMV, etc.) and compare simple vs partial age effects. For example, aging is correlated with reductions in global grey-matter volume (GGMV) but it would be nice to find a measure that is independent of this : Controlling for GGMV (via a linear model) reduces age-related effect sizes heterogeneously across space/frequency but does not eliminate them, a nuance the authors treat carefully.

This is a nice paper and I have only a few concrete suggestions:

(1) High-gamma<br /> There can be a lot of EMG / eye movement contamination (I know these were RS eyes closed data but still...) above 30-40 Hz and these effects are the weakest anyway. Could you add an analysis (e.g. ICA/label-based muscle component removal) and show the gamma band's sensitivity to that step. Or just note this point more clearly?

(2) GGMV confound control<br /> Controlling for GGMV reduces, but does not eliminate, age effects. I have a few questions about this: a) Could we see the residuals as a function of age? I wonder if there are non-linear effects or something else that the regression is not accounting for. Also, b) GGMV and age are highly colinear - is this an issue? Can regression really split them apart robustly? I think by some cunning orthogonalisation you can compute the effect of age independent of GGVM. I don't think this is the same as the effect 'adjusted' for GGMV (which is what is shown here if I'm reading it correctly). Finally, of course, GGMV might actually be the thing you want to look at (because it might more accurately reflect clinical issues) - so strong correlations are not really a problem: I think really the focus might even be on using MEG to predict GGMV and controlling for age.

Minor presentation edits:

It would be handy to see a single table listing each tested "analysis family" (e.g., sensors×frequency, source parcels×frequency), the multiple control used, and the permutation count. I kept wanting to see this as I was reading to compare back and fore.

I loved the power-planning content (section 3.2, the table with peak f2, CIs, contour plot). I think you could somehow make this even more explicit because people will use it a lot - both for this age/MEG domain and more generally as a template for other types of power planning in the field. Perhaps a "How to use this paper to plan N" guide in a paragraph? Power analysis is surely both "important and difficult" - but also not impossible. A flowchart?

Comments on the latest version:

The authors address all my initial points in their revisions and I have no further comments.

Reviewer #1 (Public review):

Summary:

This manuscript investigates how rhythmically presented stimuli support working memory by using task-trained recurrent neural networks (RNNs) endowed with short-term synaptic plasticity. RNNs trained with rhythmic sequences have a marginal performance increase (0.4%) over models trained with jittered input and show increased phase-locking and oscillatory organisation during the sample period. While the question addressed in this paper is highly relevant, the core conclusion that regular temporal structures provide a functional scaffold for sequence working memory lacks evidence. The extensive post-hoc filtering pipeline obscures whether there is phase coding or not, and whether or not the found oscillatory phenomena are truly emergent or a mathematical artefact of the analytical selection criteria.

Strengths:

(1) The manuscript addresses a highly relevant question.

(2) The introduction is nicely written and presents relevant background work.

(3) The authors' results are robust in the sense that they analysed and trained an ensemble of models instead of single networks.

Weaknesses:

(1) Misalignment between analysis epoch and core claims. The manuscript argues that temporal regularity supports sequence working memory. However, the majority of analyses focus on the sample/encoding period rather than the delay period during which memory maintenance occurs.

(2) Ambiguity in the neural code (rate vs. phase). The decoding accuracies suggest that the memory can be well decoded from the instantaneous activity, implying a rate (not a phase) code. This raises two questions:<br /> a) Can memory-related information be decoded directly from the oscillatory phase, particularly during the delay period?<br /> b) What would be the mechanism with which the increase in phase organisation improves a representation that seems otherwise decoded/represented from activity levels?

(3) Absence of any RNN activity plots. The manuscript would benefit from showing, e.g., single neuron response plots, raster plots, phase histograms of units, etc. Are there actually spontaneous oscillatory dynamics as the paper writes (line 243)? Can the authors show baseline activity (which is also supposed to be oscillatory, line 219)?

(4) Potential concerns in the analysis pipeline: The data undergo an intensive, selective pipeline that might be susceptible to introducing circularity and selection bias. I highlighted some points here:<br /> a) Many analyses are performed on (summed) data projected on demixed PCs (extracted from time-warped data). Crucially, dPCAs are not unsupervised; they already explicitly maximise the variance of interest.<br /> b) For the phase extraction during sample encoding: after dPCA percentile clipping, z-scoring, and z-score clipping are applied (lines 762-764), low-amplitude trials are excluded (lines 779-781), and there is further selection based on a valid-point criterion and r2 thresholding (lines 804-805). Do all of these selection criteria risk introducing bias?<br /> c) Some statistical assumptions are not explicitly evaluated. E.g., the sign-flip permutation test (lines 735-742) relies on sign-exchangeability.<br /> d) For selectivity analysis of oscillatory organisation (Figure 4C, lines 893-896): Units are first selected by ANOVA, and then on the selected units further stats (Power and PLV) are computed. Unless the further stats are completely independent of the ANOVA, this may introduce selection bias.<br /> e) The finding of stronger power around f0 given rhythmic inputs of that exact frequency seems somewhat circular (Figure 3A)?<br /> f) The dPCA description seems a little odd, e.g., line 674, for the ordinal component you would normally actually average (i.e., marginalise out) everything except the ordinal axis.

(5) Conflation of RNN learning dynamics with working memory mechanisms. The authors show that rhythmic input makes learning marginally easier, but in principle, both RNNs reach full performance (so working memory can be done as well with either case). To avoid the findings depending on learning dynamics, it could be of interest to test the RNNs trained with jittered input on fixed input (or retrain RNNs with both jittered and non-jittered input). It is also unclear if the small increase in performance (0.4%) can be expected to hold across different initialisations and/or learning rate /regularisation strengths.

(6) STSP. It is unclear if the findings rely on STSP being present or not (or what the role of STSP is in the model at all currently). Note that in Liebe et al. 2025, RNNs were trained on an almost identical task without STSP, and phase-coding was demonstrated in the models.

(7) Writing redundancy. The methods subsections "Population signal construction for oscillatory analysis" and "Oscillatory phase organization during sample encoding" seem to define exactly the same quantity with different characters (activity projected in dPCA space), which leads to confusion (in one section, z is the PC component, in another, it's the complex signal). There are also slightly different definitions of the wavelets in either section, for which the reasoning is unclear.

Reviewer #1 (Public review):

Summary:

The authors explore how temporal information and decision-related dynamics are represented across FOF and ADS in rats. The authors used Neuropixels to record neurons simultaneously from FOF and ADS during a free-response auditory change-detection task. They then applied single-trial temporal decoding to estimate both the time elapsed since stimulus onset and the time remaining until movement initiation. When neurons in both FOF and ADS were sorted based on decoder weights, they showed ramping and transient bump-like dynamics aligned to stimulus onset. However, around the decision report, FOF showed a clearer ramping signal and stronger movement-aligned population reorganization than ADS. These results suggest that FOF and ADS share similar temporal dynamics during evidence evaluation, but that FOF undergoes a stronger reorganization near decision commitment.

Strengths:

(1) The authors recorded large-scale neural populations simultaneously from FOF and ADS, allowing direct and fair comparison between them in the same sessions.

(2) The free-response auditory change-detection task, which requires rats to evaluate sensory evidence over time and initiate a decision report, is suited to address the question. The behavioral results support that rats used sensory evidence to guide their choices.

(3) The authors used multiple approaches, including single-trial temporal decoding, decoder-weight PCA, PC loading trajectory, and population-geometry analyses, to explore the FOF and ADS dynamics. These methods provide converging evidence supporting that FOF and ADS share similar temporal dynamics during evidence evaluation but diverge around movement/decision commitment.

(4) The population-geometry analysis is quite strong and interesting because it compares epoch-specific neural subspaces and quantifies dimensionality and subspace alignment, showing stable subspaces during evidence evaluation and stronger subspace reorganization in FOF near movement initiation.

Weaknesses:

(1) The manuscript failed to include histological confirmation of probe placement.

(2) The direct FOF-ADS decoding comparison in fig 3f and 4f includes only 16 of 61 sessions because of imbalanced unit counts. While controlling for unit number is important, excluding most sessions may waste data. Restricting analyses to only 16 sessions questions the generalizability of the result.

(3) Fitted regression curves, and ideally confidence intervals, were missing from Figures 3c and 4c. Also, the confusion matrices in Figures 3a/b and 4a/b show a strong preference for predictions in the first and last time bins. The authors did not explain whether this reflects meaningful event-aligned neural activity or an endpoint artifact from decoding time as bounded discrete classes.

(4) The interpretation of the neuron groups defined by PCA on the decoder-weight matrix was confusing. The authors perform PCA on an N units by T time-bin matrix of LDA decoder weights, then group neurons according to their PC1 and PC2 scores. This is an interesting approach, but the current wording could make readers think that neurons at the extremes of PC1 or PC2 are necessarily the most important neurons for temporal decoding. In fact, these groups appear to represent neurons whose decoder-weight profiles project strongly onto the dominant weight-space patterns. They are not necessarily the neurons that contribute most strongly to decoding accuracy, nor are they necessarily the most common firing-rate dynamics in the raw neural population.

(5) Discussion is missing some needed context. First, given the causal role of ADS in evidence-accumulation-based choices (Yartsev et al., 2018), and its position as a key node that may integrate input from FOF (Brody & Hanks, 2016), the weaker decision-aligned transition in ADS compared with FOF should have been further discussed. If ADS contributes causally to the decision process, why does it show a much weaker population-state transition near decision commitment in the present data? Second, in DePasquale et al. (2024), more extensive choice vacillation was found in ADS, while greater choice certainty was found in FOF. Does this follow the same principle as the current manuscript, where FOF shows stronger reorganization near decision commitment compared to ADS?

(6) Current analyses do not fully exploit the simultaneous nature of the recordings. Apart from the comparison of decoding accuracy, most analyses could have been performed and compared based on the data collected independently from two regions.

(7) Figures 3-10 are hard to read and unpolished. Fonts are too small, and legends/labels are redundant.

Reviewer #1 (Public review):

Summary:

Here, the authors examine how CRH neurons in the PVN track social behaviours. They use fiber photometry to record the bulk activity of PVN CRH neurons during the resident-intruder test. They find that PVN CRH activity increases when the intruder enters, and also when mice make movements to approach the intruder. They further show that the magnitude of this response differs depending on the familiarity of the mouse. Specifically, if the intruding mouse is unfamiliar, there is a greater PVN CRH response relative to a familiar mouse. The authors argue that this is specific to social familiarity, as they do not see the same differentiation in the PVN CRH response when mice approach a familiar or unfamiliar object. Finally, the authors conduct optogenetic experiments and show that inhibition of PVN CRH neurons reduces social investigative behaviour. The authors then conclude that PVN CRH neurons are a part of a decision-making circuit to influence behaviour in ambiguous settings, specifically that they are a "key component of the neural circuitry underlying rapid social appraisal, linking endocrine regulation to real-time behavioural decision making".

The data are interesting and novel. They help us understand the dynamics and range of situations in which PVN CRH neurons are activated. There is some overinterpretation of the data and restriction of what this signal means (i.e., specifically driven by unfamiliar social situations), which doesn't seem to be supported by the data. Indeed, PVN CRH neurons are robustly activated by scenarios outside unfamiliar social ones.

Strengths:

The experiments are run and presented very beautifully in a sophisticated way. The data are novel and interesting. They help us understand the time course of PVN CRH responding in social and object settings, and how this differs with the familiarity of a social stimulus.

The optogenetic manipulation is also very nice. The authors optically inhibit during just the first 20 seconds of the resident-intruder test. They find that this inhibition results in a long-term reduction in social behaviours. To me, this supports an idea that the PVN CRH signal triggers a cascade of behaviours, but is not necessarily driving these behaviours per se.

Weaknesses:

It would be great to see more sophisticated analysis of the fiber photometry data, which may reveal interesting effects that are currently being occluded by static AUC analysis. One pipeline that is freely available that could be used is found in Jean-Richard-dit-Bressel, Clifford, and McNally (2020) Frontiers in Molecular Neuroscience. Referred to as waveform analysis, this would allow the authors to examine the significance of their data across time. There are multiple points at which this would be interesting. For example, in Figure 3F, it is possible that differences between the familiar and unfamiliar objects emerge. Also, there seems to be one outlier in this figure in the familiar object group. What happens if it is removed (Figure 3H)?

Similarly, what do these signals look like when aligned with making contact with the social or object stimuli? It is possible that the objects do not elicit a difference depending on familiarity when approaching because: (1) they are not moving, and (2) it is unclear whether they are familiar or not until contact is made, consistent with the object recognition literature. What would inhibition of the PVN CRH signal do to investigative behaviours directed towards objects?

Finally, given the robust nature of the response to the approach to the objects and familiar mouse, why is this signal being argued to predominantly act in unfamiliar social settings? The lack of difference between the familiar and unfamiliar objects doesn't negate the importance of this signal. To me, this is the most interesting finding: PVN CRH neurons that are usually activated in stressful situations can also be robustly activated by familiar objects. Relatedly, while the authors argue that inhibition of PVN CRH neurons only reduces social behaviours in the unfamiliar case, there is likely a floor effect in the behaviours that they are looking at, which occludes observation of a reduction via optical inhibition.

Reviewer #1 (Public review):

[Editors' note: the authors have revised the work in response to the original reviews.]

Summary:

This paper describes experiments with alpha-synuclein (aS) with acetylated lysines (acK) at various positions. Their findings on how to use non-canonical amino acid (ncAA) mutagenesis to generate aS with acetylated lysines are valuable. The paper then continues with a range of experiments to characterise the acetylated alpha-synuclein constructs at different positions, with the aim of providing insights into which sites are relevant to disease or their function inside cells. The paper concludes these experiments with the suggestion that inhibiting the Zn2+-dependent histone deacetylase HDAC8 to potentially increase acetylation at lysine 80 may have therapeutic benefit. However, the relevance of most of these experiments is unclear, mainly as the filaments that form from these constructs are different from those observed in human disease (but see below for more details). Moreover, using the recombinantly produced acetylated versions of alpha-synuclein to normalise mass-spectrometry data, the authors themselves report that acetylation of alpha-synuclein does not differ between individuals with Parkinson's disease or healthy controls.

Strengths:

The authors report difficulties with chemical synthesis and then decide to make these constructs using non-canonical amino acid (ncAA) mutagenesis, which seems to work reasonably well (yields vary somewhat). In the Conclusion section, the authors report that they used these recombinant proteins to obtain quantitative insights into the levels of acetylation of lysines in individuals with PD versus healthy controls, for which they find no significant differences. This part of the work is valuable.

Weaknesses:

The authors then use circular dichroism to show that aSyn with acK at position 43 has less alpha-helical content. From this result, they deduce that "only this site could potentially perturb aS function in neurotransmitter trafficking", but no experiments on neurotransmitter trafficking were performed.

Reviewer #1 (Public review):

In the manuscript by Fabian-Fine et al., the authors employ neuroanatomy to investigate aquaporin-4 expression in cells they consider tanycytes and their supposed involvement in tau tangles and amyloid-beta plaques in the hippocampus. This study includes samples from three mice and two Alzheimer's disease (AD) patients.

My key concern and question is whether the cells presented in the manuscript are tanycytes. Tanycytes are specialized ependymoglial cells located in the circumventricular organs and are known to express specific markers. Importantly, they are not myelinated cells, which is a crucial distinction that the authors do not address.

Additionally, the methodologies described in the manuscript lack clarity and controls. For instance, the use of Cdh5-GCaMP882 mice is not adequately justified. It is unclear what these mice contribute to the study's objectives, particularly concerning the aim of investigating waste removal processes in the brain. Moreover, the rationale behind the purported "fluorophore uptake experiments" is unclear and appears to involve the uptake of fluorophore-labeled goat anti-rabbit secondary antibody, which seems implausible to me.

The hypotheses and claims presented in this manuscript are not sufficiently substantiated and are conceptually unclear. The notion that amyloid beta and tau proteins play structural roles in a hypothesized "tanycytes"-derived canal network is not sufficiently supported by the evidence. Furthermore, the study lacks rigorous data to convincingly establish the proposed interactions between these proteins and the processes of waste internalization.

In conclusion, due to conceptual and methodological issues, I consider the current evidence as inadequate to support the primary claims.

Reviewer #1 (Public review):

Summary:

This manuscript describes the development and validation of a low-cost device to identify viruses from saliva samples of animals non-invasively. This device was tested under laboratory conditions to assess whether viruses could be recovered in different environmental conditions and after different durations of time. The devices were then used to sample mice and cats in shelters to assess utility.

Strengths:

Sampling animals is cost-effective and highly labour-intensive, and this device has the potential to substantially improve surveillance. The device is relatively low-cost, and the authors demonstrate that the virus can be obtained from these filter papers after different durations of time and in different environmental conditions.

Weaknesses:

The authors do not discuss if different volumes were obtained from different animals (for example, due to different behaviours or attractiveness of the odour baits). Additionally, it appears the virus results were cross-validated using the serological status of the animals. While I am not an expert on FIV, there seems that there could be potential for different levels of viral shedding, and it would be more prudent to cross-validate against blood or another gold standard sample. Finally, the statistical analysis could be improved as there appear to be relatively few replicates and limited analysis conducted.

Reviewer #1 (Public review):

Summary:

This carefully executed study uncovers the functional relevance of curl signals that impinge on the retina every time an observer's gaze direction and movement direction are not aligned. This finding is important, highlighting the functional role of an abundant incidental signal (curl in retinal motion) that has thus far believed to be a nuisance that needs to be filtered out of the retinal motion stream. As such, the study forms an important contribution to the emerging recognition that incidental sensory signals are not a challenge to the sensorimotor system, but contain functionally relevant and effectively used visual signals. The study's evidence is compelling: A combination of psychophysical experiments and critical manipulations, control theory and neural modeling makes an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Strengths:

The study has its strengths in the combination of psychophysical experiments and critical manipulations, control theory and neural modeling, which together make an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction.

This study uncovers the functional relevance of curl signals that occur on the retina when an observer is moving and gaze is not straight ahead. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Another clear strength is that the study uses tightly controlled experimental manipulation to provide strong test cases for the hypothesis that curl is used for visual navigation. These conditions are important to constrain the proposed model (and future models) of heading control.

The modeling is very clearly described and the modeling and analysis code is published and freely available. The authors go beyond a back-of-the-envelope control model and show how it might be implemented at the neural-circuit level. The model is biologically plausible.

Weaknesses:

I see no major weaknesses of the study. I expect it to inspire future research that extends these findings to a wider range of visual environments (including walking in natural scenes), motion speeds and kinds of movements.

Comments on revised version.

I have no additional comments for the authors.

Reviewer #1 (Public review):

Summary:

Poh and colleagues investigate dopamine signaling in the nucleus accumbens (ventromedial striatum) in rats engaged in several forms of go/no-go tasks, that differed in reward controllability (self-initiated reward seeking or cue-evoked/quasi-pavlovian), and in the specific timing of the action-reward contingencies. They analysis dopamine recordings made with fast scan cyclic voltammetry and find that dopamine signals vary most consistently to cues that signal a required action (go cues) vs cue signaling action withholding (no go cues). Through various analysis they report that dopamine signals align most clearly with action initiation and with the approach to the reward-delivery location. Collectively these data support aspects of a variety of frameworks related to accumbens dopamine signaling in movement, action vigor, approach, etc.

Strengths:

These studies use several task variants that consolidate a few different components of dopamine signal functions and allow for a broad comparison of many psychological and behavioral aspects. The behavioral analysis is detailed. These results touch on many previous findings, larger showing consistent results with past studies.

Weaknesses:

The paper is dense and could benefit from some revision to increase clarity of the figures, the methods and analysis. The inclusion of many tasks is a strength but also somewhat overshadows specific points in the data, which could be improved with some revision to focus. There is a lack of strong connection between some of the findings, which if revised would help to emphasize the impact of the work.

Reviewer #1 (Public review):

This study by Gangadharan and colleagues provides significant progress towards a quantitative biochemical mechanism for Stu2 polymerase activity. A key conceptual advance is the novel application of an enzyme-like model, initially developed for the actin polymerase Ena/VASP, to Stu2.

Strength:

New refined affinity measurements for a Stu2 TOG domain using Bio-layer interferometry show more than an order of magnitude higher affinity of TOG domains to tubulin compared to previously published reports.

The findings reinforce the "concentrating reactants" or, more specifically, for TOG-domain proteins, the "tubulin-shuttling antenna" model, compared to the "polarized unfurling" model, a more speculative structural hypothesis.

The manuscript builds upon a series of previous manuscripts that showcase the profound intellectual engagement with microtubule polymerization mechanisms by TOG-domain proteins from the Rice lab, a thought leader in microtubule polymerization for over a decade.

Minor weakness:

The affinity discrepancy is not fully resolved by side-by-side measurements, which seem to be not feasible as not all buffer conditions are compatible with all assays.

Reviewer #1 (Public review):

Summary:

The factors that create and maintain diversity in host-associated microbiomes remain poorly understood. A better understanding of these factors will help in the efforts to leverage the adaptive potential of the microbiome to help solve pressing problems in health and agriculture.

Experimental evolution provides a promising path forward as we can track the causes and consequences in the emergence of novel variants, but experimental evolution remains underutilized in host-microbiome interactions. Here, Gracia-Alvira utilizes a long-term experimental evolution study in Drosophila simulans under hot and cold temperature regimes to identify strain-level variation in an important fly bacterium, Lactiplantibacillus plantarum. They identify three strains of L. plantarum, which are most prevalent in their respective three temperature regimes, suggesting that these are locally adapted bacteria. Then, using a combination of genomics, in vitro, and in vivo, Gracia-Alvira et al attempt to understand the factors that led to the differentiation of the hot and cold L. plantarum and their impacts on the fly host.

Strengths:

This is an excellent use of experimental evolution to track the emergence of novelty in the microbiome. The genomic analyses are all solid and appropriate for the data sets. It is especially striking that the comparisons with the other, independent experimental evolution studies in different labs (and across continents between Portugal and South Africa) show a consistent response to temperature. Many have disregarded the microbiome as it is something that is too sensitive to seemingly innocuous variables (particularly in the fly microbiome), such that we cannot find generalities. However, this finding highlights the potential for experimental evolution to uncover these dynamics. The question of how strains emerge and are maintained is timely and is one of the key open questions in host-microbiome evolution currently.

Comments on revised version:

I thank the authors for their thoughtful responses to my concerns, and I appreciate the additional experiments to help resolve the questions about subspecies competition. The manuscript remains strongest in the genomic assessment of changes in the L. plantarum genomes, and it is striking and noteworthy that the isolates across multiple countries but same temperature conditions group together phylogenetically.

I appreciate the additional clarity also incorporated in this revision, but there are still a few key concerns that are unresolved about the microbial ecology described here. I will also note that I apologize if I missed something in the text as no line numbers were provided to point me to where the changes were incorporated in the revised manuscript.

(1) Competition has many different meanings and many different measurements (see Hart 2018 https://doi.org/10.1111/1365-2745.12954) -and incorporating the effects of competition in shaping an ecological community is, has been, and will continue to drive much research in community ecology. Measuring strain level competition is one of the major questions in host-associated microbiomes, and it is difficult-though there have been significant advances in doing so (see isogenic barcodes, e.g., Daniel 2024 doi: https://doi.org/10.1038/s41564-024-01634-9b, Ordon 2024 https://doi.org/10.1038/s41564-024-01619-8, as well as my previous suggestion to track the outcomes of competition). The inability to directly track and measure competition of the isolates remains a limitation of this manuscript. The authors' explanation of measuring competition is unusual, simplistic, and at times inconsistent.

They need to be crystal clear about their definitions, logic for making these inferences, and weaknesses in their approach. I think what the authors mean is that competition between the unevolved and C or H in their respective regimes leads to the decrease of the U clade over experimental evolution. But it is not clear how the authors are thinking about competition between C and H clades in the different temperatures.

The authors state that competition is inferred because changes in relative abundance across the time series-and this is unusual because there are alternative explanations that require no ecological interactions among sub-strains, as I described in my comments on the prior version. This is then combined with in vitro work that shows that the H and C clades can both grow in their mismatched temperature regimes-and thus I think it is to be inferred that because they can grow alone in vitro (and C isolates show lower growth than H isolates in hot temperature), then changes in the relative abundance over fly generations can be attributed to competitive interactions among C and H clades. But then the logic is inconsistent because then the authors just say that in vitro growth curves don't support the differences in relative abundance observed in the flies (lines 224-225). Then the authors argue is it about a combination of diet/sugar metabolism and temperature (line 373), which doesn't make any sense because temperature previously didn't matter (lines 224-225).

All of this is to say is that the authors need to make clear their logic to the readers-and explain these inconsistencies appropriately. To me, it suggests that there are clear methodological weaknesses that inhibit the ability to track competitive microbial dynamics. Because you can't really assess the microbial dynamics in vivo, it remains further unresolved why clade C isolates have such strong negative fitness effects on the fly but reach such high relative abundances in the C evolving flies. I find that this series of logical inconsistencies (and see my point #2) distracts from the important finding that the C and H clades evolved to utilize sugars differently from the U clade, which is an interesting finding!

(2) There are also inconsistencies in the patterns observed between the text and the figures. Some of this arises because the authors are not clear what comparisons they are making. For example, line 450 says that clade C outcompeted the other clades, which I presume means only in the cold temperature. Line 456 says that C and H isolates grow faster in the sugar-rich lab diet, but that is not really true because U and C have similar growth rates in Fig. 5, and U and H have similar growth rates in Fig. S4. The text about microbial load is a bit misleading (lines 271-273), as it is confusing that clade C is significantly higher load in both hot and cold temperatures (Fig. S6), which is counterintuitive given Fig. 4, 5, S4. But it is also overly speculative to say that these results suggest that fitness effects depend on microbial load of clade C without connecting the load to the fly fitness measures (and also given the inconsistency with the time series data from evolving lines). Please take care to more carefully phrase these statements to ensure the inference is supported by the experiment design (e.g., clarifying comparison) and statistics (e.g., ensuring agreement with what the figure shows).

(3) I understand the concern about focusing the reader on the L. plantarum strains. However, it should be clear to the readers that you did not examine the other parts of the microbiome, and that L. plantarum is often very rare in lab and wild fly populations. The data presented on Table S4 (cited line 552, I think citation at line 176 is incorrect) is confusing. If these were colonies picked and then identified, this should be explicit. If it is based off on colonies, then please clarify if this was sampled randomly or occurred when trying to enrich/focus on L. plantarum isolates. If the data was computational (e.g., Kraken to classify), then only taxa richness is not necessarily relevant, but please also include to the relative abundance of each taxa.

To me, this is relevant information to contextualize these results, particularly because you test this in both D. mel and D. simulans (apologies for the confusion over Mazzucco & Schlotterer 2021), and we have insight into how combinations of Lactobacillus and other taxa impact fitness (Gould PNAS 2018). If the results from D. melanogaster are not applicable to D. simulans, then the authors need to explain this. I understand if incorporating analysis of the broader microbiome is beyond the scope of this manuscript, but at least acknowledging the general rarity in Lactobacillus frequency in Drosophila microbiome and variation in fitness effects will more accurately contextualization these results.

One small point is that line 452 the citations are OK, but there are fly-specific examples to support this statement, like Gould PNAS 2018, Henry Proceedings B 2025.

Reviewer #1 (Public review):

Summary:

In this manuscript, Uphoff et al. propose a structural and mechanistic model in which the multidomain ECM protein SVEP1 enables Angiopoietin (ANG) binding to the orphan receptor TIE1, thereby promoting downstream receptor phosphorylation and signaling. Using AlphaFold-based modeling, the authors predict that the CCP20 domain of SVEP1 binds to TIE1, creating a composite surface that facilitates Angiopoietin association and TIE1 activation. The resulting ternary model (SVEP1-TIE1-ANG) offers a structural rationale for how SVEP1 converts TIE1 into a functional, ligand-responsive receptor. Additional models and biological assays suggest roles for other domains of SVEP1, such as CCP5-EGF-L7, although these interactions are predicted with low confidence. The authors interpret these findings as the first structural framework for how SVEP1 enables ANG-TIE1 signaling.

Strengths:

(1) The central hypothesis - that SVEP1 enables ANG binding to the orphan receptor TIE1 - is biologically compelling and addresses an important question in vascular biology.

(2) The AlphaFold-predicted ternary complex (SVEP1-TIE1-ANG) is plausible, high-confidence, and structurally consistent with prior functional data (e.g., poly-Ala scanning from Sato-Nishiuchi et al.).

(3) The authors' model offers a potential explanation for the previously observed role of SVEP1 in enhancing ANG signaling through TIE1 and may represent the first structural insight into TIE1's transition from orphan to ligand-activated receptor.

(4) The potential clinical implication - that a combinatorial ligand (ANG+SVEP1) can activate TIE1- could have translational relevance for vascular leak and inflammatory disease.

Comments on revised version:

The authors have adequately addressed my concerns.

Reviewer #1 (Public review):

Summary:

This manuscript describes a study examining the relationship between microsaccades and covert attention. This question has been widely investigated, with numerous studies showing that during sustained fixation, when subjects covertly attend to a peripheral stimulus, microsaccades tend to be biased toward the attended location. Here, the authors ask whether this microsaccade bias reflects a shift of covert attention or the maintenance of covert attention. They conclude that the bias is primarily driven by attention shifts, a finding that also helps reconcile the seemingly conflicting results of prior research, where the bias was questioned in paradigms that largely involved attention maintenance rather than shifting.

Strengths:

A large sample size was used.

Weaknesses:

The main weakness is that the authors' response does not adequately resolve concerns about the robustness of the microsaccade analyses. The newly reported event counts reveal that the number of microsaccades per participant is very low, especially in Experiment 2, and highly variable across subjects. Because the key analyses rely on proportions of microsaccades toward versus away from the attended location, estimates based on so few events are likely unstable and may not provide reliable subject-level measures.

A second major concern is that several additional analyses introduced in the revision appear to suffer from the same limitation. The permutation analyses and angle-partition analyses may give the impression of statistical rigor, but if the underlying averages are based on very few microsaccadic events, the resulting probabilities are difficult to interpret. Further subdividing already sparse data into narrower angular bins likely makes the estimates even less reliable.

A third concern is that the authors have not fully addressed issues related to microsaccade detection and fixation control. The presence of very small-amplitude events with relatively high velocities raises the possibility that some detected microsaccades may be artifacts. The authors also did not implement the requested exclusion of microsaccades smaller than 5 arcmin or the suggested reanalysis using stricter fixation criteria. These omissions leave open the possibility that the reported effects are influenced by detection errors.

A fourth weakness is that some of the requested analyses or clarifications were addressed only superficially. The comparison with Brandolani et al. remains minimal, despite being highly relevant to interpreting whether the observed microsaccade-direction effect is transient or sustained. Similarly, the gaze-density plots do not show the raw gaze-position distributions that were requested and may therefore be misleading, because difference maps cannot determine whether subjects were actually fixating centrally.

Overall, the revision raises additional concerns rather than resolving the original ones. The main conclusions remain insufficiently supported unless the authors can demonstrate that the effects are robust at the individual-subject level, based on adequate numbers of microsaccadic events, reliable detection criteria, and appropriate controls for fixation behavior.

Reviewer #1 (Public review):

In this article, the authors investigate how glutamate transporter function regulates excitability and synaptic coding in T-stellate cells in the mouse ventral cochlear nucleus. They test this in acute brain slices using whole-cell electrophysiology and artificially raise the relative local concentration of glutamate via pharmacological inhibition of transporter proteins. The main finding is that when sub-saturating doses of DL-TBOA are applied, cells become much more sensitive to synaptic input, diminishing the normally high fidelity of EPSP-spike coupling in these neurons. Notably, high-frequency stimulation in the presence of DL-TBOA reveals a large and slowly decaying AMPA receptor component that underlies persistent/rebound firing in earlier recordings. These effects are not seen in other ventral cochlear neurons, suggesting that rapid glutamate clearance in T-stellate cells, particularly, is important for auditory intensity coding. Overall, these experiments are well-performed, and the findings are robust, though there are some aspects that could be expanded to make the work more impactful. These include a better understanding of the relative contribution of neuronal vs glial transporters and an ability to separate the relative contributions of tonic glutamate concentrations in the cleft vs changes in membrane potential in action potential output. Additionally, there were some minor issues of clarity in both the figure presentation and the main text language that should be addressed.

Major Points:

(1) Given the dramatic effect of saturating DL-TBOA on tonic leak/RMP and that the sub-maximal concentration used in most of the experiments still varied between 25-50 uM, Figure 1 would be strengthened substantially by a dose-response curve. Ideally, 5 or 6 concentrations, plotting the effect on tonic current or RMP increase.

(2) Examining the contribution of glial (EAAT1/2) vs. neuronal (EAAT3) transporters (Fig 8) is intriguing but comes across as incomplete here, especially given the small number of recordings. Using a different non-selective EAAT inhibitor (TFB-TBOA) to chase the EAAT1/2 blocker combo seems like an odd choice, given that you have already characterized the effects of DL-TBOA well. One could also try a lower concentration (~50-100 nM) of TFB-TBOA since it is somewhat selective itself for glial EAAT1/2. Given the data presented, neuronal transporters (presumably EAAT3) appear to dominate the rapid clearance of glutamate at this synapse, but this point isn't emphasized or explored sufficiently.

(3) Separating the effects of depolarization vs. glutamate clearance was never explored. What effect does depolarizing the cell ~10 mV in control conditions (i.e., without TBOA) have on AP number/fidelity during synaptic stimulation experiments? The authors state that submaximal DL-TBOA generally causes no more than a 5 mV change in RMP, but tonic depolarization could also influence spike fidelity. This experiment could demonstrate that the increase in excitability during/after stimulation is not due to increased engagement of voltage-gated channels.

Joint Public Review:

Summary:

This manuscript couples a 32-parameter model with simulation-based inference (SBI) to identify parameter changes that can compensate for three canonical hyperexcitability perturbations (interneuron loss, recurrent-excitatory sprouting, and intrinsic depolarisation). The study demonstrates a careful implementation of SBI and offers a practical ranking of "compensatory levers" that could, in principle, guide therapeutic strategies for epilepsy and related network disorders.

Strengths:

(1) By analysing three mechanistically distinct hyper-excitable regimes within the same modelling and inference framework, the work reveals how different perturbations require different compensatory interventions.

(2) The authors adopt posterior estimation to systematically rank the efficiency of different mechanisms in balancing hyperexcitability.

(3) Code and data are available.

Comments on revised version:

I appreciate the authors' extensive efforts in revising the manuscript and responding to the previous review. The revised version is substantially improved in clarity, organization, and presentation. In particular, the addition of schematic figures, the reorganization of the Methods section, the improved explanation of the model, and the inclusion of replication analyses all strengthen the manuscript.

The manuscript remains entirely computational, and therefore its conclusions should be interpreted as predictions generated by a specific model rather than validated biological mechanisms. I believe the work has the potential to make a useful methodological contribution. However, several concerns remain regarding validation, interpretation of inferred posteriors, organization of the manuscript, and presentation.

Major comments:

(1) The manuscript states that simulation-based calibration showed the amortized posterior estimator was unreliable (85-88), but these results are not shown. The manuscript explicitly states that simulation-based calibration demonstrated substantial failures of the amortized posterior estimator, yet the corresponding analyses are not presented. Since these results motivate the transition to sequential NPE and are central to assessing inference reliability, they should be reported quantitatively, either in the main text or supplementary material.

(2) The authors present two independently trained estimators and show strong agreement between them. This is a useful robustness analysis. However, the rebuttal occasionally presents this as addressing concerns regarding cross-validation and generalization. The new analysis does not constitute cross-validation in the usual sense and does not directly assess generalization to held-out targets or posterior accuracy.<br /> I recommend that the authors explicitly describe Figure 4 as a reproducibility analysis and avoid presenting it as a substitute for validation.

(3) Posterior correlations are useful for generating hypotheses about compensatory mechanisms, but they should not be interpreted as direct evidence of compensation. The compensatory interpretation should instead be supported by the perturbation analyses (e.g., Figure 6), which provide mechanistic validation.

The manuscript consistently treats posterior correlations and conditional posterior shifts as direct evidence of compensatory mechanisms. These are consistent with compensatory mechanisms, but they do not by themselves establish that the corresponding biological parameters causally compensate for the perturbation. I recommend clarifying this distinction and emphasizing that the conditional posterior analyses generate hypotheses regarding compensation, which are then partially supported by the perturbation experiments shown later in the manuscript.

The language throughout the manuscript should therefore be softened.

(4) The manuscript repeatedly suggests that the inferred conditional distributions may be useful for identifying precise interventions or guiding personalized treatments (examples include lines 24-29, lines 217-223, lines 242-246, lines 277-282, lines 283-286). These claims go beyond what is directly demonstrated.

The study does not evaluate treatment outcomes, patient-specific inference, intervention efficacy, or clinical decision-making. Rather, it demonstrates differences in inferred parameter distributions within a computational model. While these results are valuable and may generate clinically relevant hypotheses, they do not yet establish predictive utility for treatment selection or precision medicine. I therefore recommend substantially softening these translational claims and emphasizing that the current findings generate hypotheses that could be tested experimentally in future work.

(5) The revised manuscript still mixes presentation of findings with interpretation.

For example, lines 217-226 largely continue to describe findings from Figure 6 and would fit better in the Results section. The Discussion would be strengthened by focusing more exclusively on biological implications, limitations, and future directions.

A similar issue appears later in the discussion comparing posterior correlations and conditional distributions. Much of this section effectively reinterprets Figures 2 and 3 rather than discussing broader implications.

(6) The discussion around lines 271-282 overstates what can be concluded from the inferred posteriors.<br /> The statement that correlations "discover broadly applicable mechanisms" whereas conditionals "identify specific mechanisms" is stronger than the presented evidence supports. Likewise, the conclusion that conditional distributions are more useful for precision treatments is speculative and not directly demonstrated.

I recommend reformulating these statements as interpretations or hypotheses rather than conclusions.

(7) Around line 84, the manuscript introduces q(theta|x) without clearly defining θ, x, or q. Readers unfamiliar with SBI may struggle to follow the notation. All quantities should be defined when first introduced.

(8) The manuscript equates larger KS distances between conditional posteriors with greater compensatory potential. While KS distance provides a useful measure of posterior redistribution, it is not obvious that it should be interpreted as a measure of biological efficacy.

(9) The manuscript would benefit from a discussion of parameter identifiability. The inference problem maps 32 model parameters to 7 summary statistics, implying substantial non-identifiability. While complete identifiability analysis is likely beyond the scope of the current work, this limitation should be discussed explicitly.

All in all, the revised manuscript is significantly improved and addresses several concerns raised in the previous review. However, important issues remain as discussed above.

Reviewer #1 (Public review):

This work evaluates the impact of reproductive history on growth, body weight and body composition in mammals. In mice, somatic growth is stimulated by the first pregnancy while the second pregnancy increases body weight mainly by increasing adiposity. To probe the role of pituitary growth hormone (GH), the key regulator of somatic growth in these processes, was addressed by comparing the impact of reproduction on growth in normal ("wild type") and genetically GH-deficient females and by detailed characterization of the profile of fluctuations in circulating GH levels in both types of animals. Additional studies addressed the possible role of other endocrine pathways (ghrelin and estrogen) in the pregnancy-related growth. Surprisingly, reproduction-related growth was independent of GH, ghrelin and estrogen. To determine whether these results may apply ("translate") to human physiology, data on various parameters of somatic growth were collected from women with hereditary GH deficiency. The findings indicate that GH-independent stimulation of growth by reproductive events also occurs in women.

Use of multiple animal models, rigorous characterization of GH levels in normal and GH-deficient females, and inclusion of data derived from a unique and well-characterised population of people with hereditary isolated GH deficiency and no GH replacement therapy are important strengths of these elegant and innovative studies. The results address a broader and clinically significant issue of permanent changes in body size, composition and function that result from pregnancy and lactation. This work also provides important background for further studies aimed at the identification of the mechanism involved and the role of specific reproductive events in the regulation of growth.

Reviewer #1 (Public review):

Summary:

Zhang et al. investigated EEG neurofeedback as a method to modulate brain activity prior to painful stimulation and its effect on pain perception. Neurofeedback was designed to train participants to upregulate alpha power contralateral to the site of painful stimulation. Real or sham neurofeedback was administered to two independent groups. Each group performed two tasks: one in which participants were asked to modulate their brain signals (training task) and another in which they were asked to passively watch the feedback (non-training task). The authors reported an increase in alpha power during real neurofeedback training compared with sham training and non-training conditions. The authors also reported a decrease in pain perception during the training task, both in the real and sham neurofeedback groups. Additionally, in an offline analysis, the authors investigated brain dynamics with microstate analysis during the neurofeedback training. Also, they implemented a mediation analysis to infer which brain responses to neurofeedback training mediated changes in pain perception.

Strengths:

(1) The research question is licit and sound. EEG neurofeedback is a promising non-invasive technique with the potential to alleviate at least the sensory component of pain. The rationale for applying neurofeedback at the alpha band in the somatosensory cortex is well justified by the alpha-gating theory in pain modulation.

(2) The sample size is adequate to capture neurofeedback effects. The effort to conduct a double-blind study with a complex design paradigm and an adequate sample size is valuable and appreciated.

Weaknesses:

(1) Reported behavioral effects on pain reduction might be due to the placebo effect rather than neurofeedback, as pain ratings were reduced both in the real and sham neurofeedback groups during training. It is important that authors report this effect appropriately and disclose which information was given to the participants when they enrolled in the study, i.e., whether the paradigm was designed to reduce pain perception.

(2) The utility of training effects, especially in the sham group, is unclear. I understand that including the non-training condition allows the distinction between neurofeedback effects and arousal effects. However, interpreting training effects should not be the point of this study. What does it tell us that participants who received sham stimulation increased or decreased alpha power in the training session vs the non-training session?

(3) There might be hidden time effects (habituation/sensitization) on pain responses and/or on brain responses to neurofeedback. A within-session analysis comparing the first half of the training with the second half should be conducted to discard them.

(4) Connectivity analysis reflects spurious effects. In EEG, deriving phase-based functional connectivity at the sensor level is problematic due to volume conduction effects. EEG functional connectivity should be performed after source reconstruction, and measures discarding instantaneous phase lags should be preferred, which is not the case with magnitude-squared coherence. See (Bastos and Schoffelen, 2015).

Although neurofeedback is a promising technique for modulating pain perception, the current study adds limited novelty to the field, as its design could not disentangle whether behavioral effects (reductions in pain intensity and unpleasantness) were specific to neurofeedback training or due to non-specific effects (e.g., placebo). Nevertheless, the authors corroborated that brain states before painful stimuli could be modulated with neurofeedback (enhancement of alpha power).

Reviewer #1 (Public review):

Summary:

This manuscript investigates whether the human brain contains a shared category-general representation of gender across faces, bodies, and gender-associated objects. The authors acquired fMRI data while participants viewed male and female stimuli from three categories in a one-back task. They then used searchlight MVPA, cross-category decoding, regression-based RSA, CNN vs. brain representational comparisons, and PPI analyses. Their main finding is that gender information could be decoded from distributed occipitotemporal regions within each category, whereas a cluster in the rMTG showed convergence across cross-category decoding and RSA. The authors concluded that this rMTG representation resembles intermediate layers of fine-tuned CNNs and that face and body gender processing share similar functional connectivity patterns.

Strengths:

The question is potentially important, particularly for social cognition, object recognition, and the use of neural network models to interpret high-level visual representations. Previous behavioral studies have shown cross-category adaptation between bodies and faces, and even between gender-associated objects and faces, so the attempt to test for a neural counterpart using fMRI is well motivated. The use of multiple complementary analyses including within-category decoding, cross-category decoding, regression RSA, CNN comparisons, and effective connectivity analyses is also a strength. The convergence of cross-category MVPA and RSA in a right MTG cluster is potentially interesting and deserves attention.

Weaknesses:

The largest problem is conceptual. The term gender is used as if it refers to the same construct across faces, bodies, and objects. This is not self-evident. In faces and bodies, the stimuli seem to contain visual cues from which observers infer binary gender categories. In objects, however, the relevant information is almost gender stereotype, cultural association, or learned semantic association. These are not equivalent constructs. The manuscript therefore needs to distinguish much more carefully between perceived gender, biological sex cues, gender-associated visual features, and gender stereotypes. Without this distinction, the title and main conclusion are too broad. The object condition is particularly problematic. Javadi & Wee (2012) showed that gender-associated objects can bias subsequent judgments of ambiguous face gender, and they discussed two possible mechanisms, including shared neural substrates or top-down modulation induced by the gender concept. However, their behavioral adaptation study does not directly demonstrate that objects, faces, and bodies are encoded in the same neural representational format. The present manuscript treats these object stimuli as if they provide evidence about the same kind of gender representation as faces and bodies, but that step requires additional empirical support. Independent ratings of object gender association, cultural familiarity, visual similarity, and semantic category are essential here.

A second major concern is stimulus control. The face images were taken from Chinese male and female actors, the body images were headless bodies in underwear, and the object images were selected because of prior gender associations. This design introduces many possible confounds: hairstyle, makeup, skin texture, body shape, clothing, color, luminance, object category, object function, curvature, spatial frequency, and cultural familiarity. Cross-category decoding can be significant even when a classifier relies on shared visual statistics rather than an abstract gender code. For example, female-associated stimuli may differ from male-associated stimuli in color, shape, brightness, texture, or semantic category in ways that are consistent across faces, bodies, and objects. The present analyses do not adequately rule out these alternatives. Foster et al. (2019) are especially relevant in this respect. They reported that body sex could be decoded from both body- and face-responsive regions. However, the sex of well-controlled faces, for example faces excluding hairstyle cues, could not be decoded from face- or body-responsive regions. This finding should make the authors more cautious. The fact that the present study used more ecological face stimuli may increase sensitivity to gender-related cues, but it also increases the possibilities that decoding is driven by uncontrolled external features rather than by an abstract gender representation. Accordingly, because no additional visual, semantic, or stereotype-based model RDMs were included in the RSA analysis, this result alone cannot establish an abstract, category-independent gender representation. Any systematic difference between male- and female-associated images will load onto the gender RDM. At least, the authors should include additional model RDMs for low-level visual features. In addition, the current RSA analysis has another limitation. The neural RDMs are based on only six condition-level patterns, producing a 6 × 6 matrix. The theoretical model includes only binary gender and category RDMs. This is too coarse to support the claim of category-independent gender representation. Ideally, all the RSA analysis should be performed at the item level rather than at the condition level.

The cross-category decoding result in rMTG is promising but not yet conclusive. The authors identify a right MTG cluster by overlapping thresholded maps from three cross-category decoding analyses. This is useful descriptively, but it does not by itself establish a common representational code. The overlap of thresholded maps depends on the chosen threshold. If the authors want to make a formal conjunction claim, they should use a valid conjunction-null approach such as a minimum-statistic conjunction evaluated under the appropriate conjunction null, rather than simply displaying the intersection of thresholded maps. Even if this approach cannot be adopted in this study, the issue should be included as a limitation.

In the PPI analysis, the reported similarity between face and body connectivity matrices is a little bit small (r = 0.08). The claim of a shared functional network should therefore be softened unless the authors test whether this correlation is significantly larger than the face-object and body-object correlations, correct for multiple comparisons, account for the non-independence of matrix elements, and report participant-level distributions and confidence intervals.

Reviewer #1 (Public review):

In this paper, Pal and colleagues propose a mechanistic unification of two influential accounts of inter-areal communication: communication through coherence and communication subspaces. A major strength of the paper is that it does not treat coherence and communication subspaces as independent phenomena, as typically done, but instead derives both from the same circuit with divisive normalization. In this framework, noise-driven fluctuations around the normalized fixed point determine covariance and cross-power structure (which, in retrospect, makes so much sense to be related). Then, they show how these determine linear prediction performance and the effective dimensionality of the communication subspace. They also show (however not very visually, see recommendation below for a figure) how divisive normalization is crucial to shape inter-areal coherence and the dimensionality of communication.

I found this conceptual contribution potentially very influential, but somewhat obscured by the technical complexity of the model. The central intuition (I think) is that recurrent normalization can organize cross-area fluctuations, both frequency-specific correlations and cross-covariances. Took me a while to grasp this insight, mostly because I was stuck with the model details. Note that I have some experience with network dynamics, but not with this particular model.

Reviewer #1 (Public review):

Summary:

Pang et al. investigated the expression pattern of the transcription factor foxQ2II in an adult beetle brain. They find nine distinct clusters, with many neurons expressing Glut/ChaT and dopamine. Some of the dopamine neurons resemble cell types described in Drosophila. Several neurons seem to project to prominent higher brain regions such as the MB and CX, and might even connect to both.

Strengths:

The authors use state-of-the-art labeling techniques for the analysis of individual cell types, such as beetle brainbow, to investigate the until now unknown expression of the transcription factor in the adult beetle brain.

Rigorous cell reconstruction and image analysis revealed a better understanding of the anatomy of the labeled cells.

Weaknesses:

The brainbow labeling seems to include all cells labeled by the enhancer trap line, as well as the ones not expressing foxQ2II. Thus, it is unclear how useful this data is to compare individual cells to other insects.

The functional relevance of this transcription factor in the adult brain cell is still unknown. It is therefore unclear if the described neurons have any specific function and if they require this transcription factor for normal function.

Overall, the neural reconstructions are missing single-neuron details; it is difficult to compare the shown cell types to specific cell types in Drosophila based on the presented data, and this finding remains speculative.

Reviewer #1 (Public review):

Summary:

Esfahany et al. describe a new platform (Toothy) to identify and analyze dentate spikes and sharp wave ripples from silicon probe electrophysiology data. The goal is to facilitate and standardize the extraction of DS1 and DS2 events, which have highly variable properties across recordings from different labs. The manuscript describes the basic workflow of the Toothy pipeline, including loading data, assigning channels along a linear probe, customizing parameters, selecting ideal channels for analysis, and classifying DS1 and DS2 events.

Strengths:

The manuscript is clear and easy to follow and does a good job of describing the platform. Overall, this will be a useful analysis pipeline that can help to standardize DS analysis across labs and datasets.

Weaknesses:

The current version has several bugs that prevent analysis, and the documentation of analysis parameters needs to be improved.

(1) In limited testing, the pipeline had several bugs, and I was not able to complete the full analysis of a dataset. Loading data from .mat or .npy files gave errors (it seemed that the metadata was not loaded correctly from the pop-up window). I was able to load a .nwb file, which worked well. The probe configuration tool was a bit difficult to understand, and there was not much documentation to help, although it worked when simply entering the x-y coordinates of the channels. It also crashed several times while trying to make a probe configuration due to it trying to save when a small typo was briefly entered. The initial analysis worked well, and the auto-selected channels matched our recording notes and seemed appropriate. DSs and ripples were extracted. An error came when trying to classify DSs, and the program repeatedly crashed across a variety of parameters. Overall, parts of the pipeline worked well, but others had significant bugs that need to be addressed.

(2) The authors should provide test data that can be run through the pipeline. Ideally, this could use a variety of data types, probes, and conditions so that it is clear how they differ.

(3) There are a lot of parameters that can be adjusted, but very little information about how they are chosen and what goes into parameter selection for a dataset. Additional documentation with more information on adjustable parameters, channel selection, and best practices would help improve the utility of the tool. Ideally, this could also integrate citations (either in the manuscript or documentation) to support some of the choices made during parameter selection.

(4) There is no validation presented against other analysis methods or datasets. While there is no ground truth of when DSs occur, this may limit the ability of this tool to become the standard for DS analysis. A section comparing the analysis used in the pipeline to other published analyses would be helpful.

(5) In the manuscript, it would be helpful to further describe the rationale for initially detecting DSs and SPW-Rs on all channels, when they are network events that occur across channels.

(6) A section on what hardware and software are necessary to run the pipeline should be added.

Reviewer #2 (Public review):

Summary:

In natural visual behavior, such as when one is looking for a face in the crowd, the eyes are moved from site to site, seeking possible matching targets. This involves attention both to the current view at center of vision (the foveal location) as well as to upcoming views via attention to targets in the periphery. While it has been established that attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This study thus moves the field towards understanding the neural encoding of active vision.

This study examines the neuronal basis of feature selective attention during active, freely behaving visual search. Traditional electrophysiological studies on visual attention in monkeys commonly used an eye fixation with covert attention paradigm, but have not sufficiently addressed the roles of both foveal and peripheral attention in play during natural looking behavior. Here, the authors present a novel paradigm in which, during eye movement mediated search neuronal receptive fields are recorded in multiple cortical areas (sensory V4, temporal and prefrontal areas). In this manner, as the eye foveates, items in the array fall into foveal or non-foveal recorded sites. Thus, the experimental paradigm is elegant, offering the opportunity to make multiple types of comparisons: target/distractor, towards/away from fovea, areal. Specifically, following a category cue (face, house, hand, flower), freely initiated saccades are made to locate a categorically matching 'target' in an array of distractors. Feature attention is assessed by comparing eye saccades made to targets vs to distractors. Spatial attention is assessed by comparing saccades made 'towards' vs 'away' from targets. Statistics are rigorous and nicely designed. Detailed association of simultaneously obtained eye movement sequences and neural parameters are well done. These are valuable data which will contribute to our understanding of attentional modulation in visual search.

The significance of these findings is fundamental. Decades of attention research in vision have been based on the paradigm of visual fixation and covert peripheral attention. However, increasingly the field has moved towards understanding how the visual system works during active vision. Here, the authors use an active visual search paradigm and record from key mid-tier (V4) and higher order (IT, PFC) areas. They find enhancement of attention both in the foveal and peripheral locations, and, furthermore, marked by a high degree of feature and categorical specificity. That is, while attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This provides valuable data for the concept of a foveal-peripheral spatiotemporal attentional window in natural vision. The controls (comparisons of neuronal response during looks to targets vs distractors and looks towards and away from the target) and statistical rigor make these findings compelling. There will likely be additional future impacts of this study. For example, the eye movement patterns collected in this study may also provide a valuable dataset for future study of understanding search strategies. Goal-directed vs non-goal-directed task comparisons could be designed to test possible circuit models. Although much remains unknown regarding how and where frontal and temporal signals are integrated during active search, these data contribute important guideposts for future models of active visual search.

Reviewer #1 (Public review):

Summary:

Based on previous work showing that viral evolution follows reproducible patterns in diverse animals, the authors sought to examine whether the antibody response operates under similar constraints. By analyzing over 17,000 B cells isolated from 6 monkeys at 3 different time points, the authors convincingly show that the immune response does follow specific patterns of responses to different classes of epitopes based on the infecting virus. Moreover, each of these clusters has characteristic (cross-) binding and neutralization properties. Importantly, these classes are independent of the underlying immunogenetics, which (as expected) vary significantly between monkeys. This last point is particularly relevant for vaccine design, as it means that immunogens may not need to be as narrowly focused on specific germline genes as previously thought.

Strengths:

The large number of B cells cultured for this study is a particular strength, as is the fact that they were isolated in an antigen-unbiased fashion. The experiments are well-designed and comprehensive.

Weaknesses:

The genetic element is a relatively minor component overall and more qualitative than quantitative. It would be nice to investigate other properties of the repertoire like CDRH3 length and possible public clones, as well.

Reviewer #1 (Public review):

Summary:

Foik et al. report that hypochlorous acid, a reactive chlorine species generated during host defense, activates the transcription of the froABCD in P. aeruginosa. This gene cluster had previously been associated with a potential role during flow of fluids and appears to be regulated by the sigma factor FroR and its anti-sigma factor FroI. In the present study, the authors show that froABCD is expressed both in neutrophils and macrophages, which they claim is likely a result of HOCl but not H2O2 production. Fro expression is also induced in a murine model of corneal infection, which is characterized by immune cells invasion. Expression of the fro system can be quenched by several antioxidants, such as methionine, cysteine, and others. FroR-deficient cells that lack froABCD expression during HOCl stress, appear more sensitive to the oxidant.

Strengths:

The authors provide a number of data supporting their claim that transcription of the froABCD system is induced by reactive chlorine species. This was shown by RNAseq, qRT-PCR, and through microscopy using a transcriptional reporter fusion. Likewise, elevated expression of froABCD was shown in vitro and in vivo, excluding potential in vitro artifacts. The manuscript, while mostly descriptive, is easy to follow and the data were presented clearly and convincingly. The authors have also been responsive to concerns from the previous review.

Weaknesses:

(1) Line 10: "HOCl preferentially oxidizes....". Please consider modifying the language to: "the second-order rate constant of HOCl is significantly higher with Met/Cys compared to other aa."

(2) I am not sure I completely understand Fig 1B. Is the promoter right upstream of yfp or is yfp located downstream of froA? If the latter is the case, wouldn't this be a translational fusion?

(3) My previous comment regarding why fro expression is higher during phagocytosis in macrophages compared to neutrophils has been somewhat (albeit not convincingly), addressed by the authors in the response to the reviewer, but this discussion should be part of the manuscript as the macrophage data were shown.

(4) Line 122: The statement "The degree of fro inhibition by 4-ABAH...." is incorrect unless the authors can provide experimental evidence. Fro expression is not upregulated because MPO is inhibited by 4-ABAH, which results in less hOCL production.

(5) Can Supp Fig. 1 be quantified in a similar way it was done for HOCl to allow for a better comparison if HOCl or flow is the more potent inducer?

(6) Overall, the fro expression (YFP/mCherry) seems highly variable for treatment with HOCl (Fig. 2C: ~65; 2D: ~20; why is fro expression 3x lower?

(7) The authors should provide evidence that N-chlorotaurine can activate fro expression also. They said they weren't able to obtain chlorinated taurine, but this is quite simple to produce: PMCID: PMC1219228

(8) Fig. 4 supplement 1: Please provide concentrations for the oxidants used in these experiments.

(9) Lines 251/252: change to: upregulation of instead of in

(10) Chaperones and other heat-shock genes are more upregulated in ∆froR, indicating elevated HOCl-mediated oxidative damage, which supports their findings.

(11) Complementation of ∆froR is missing

(12) Line 198: The growth experiment at 4 uM shows differences between WT and mutant, but at 2 uM cells showed already low fro expression due to cell death (which has not been proven by CFU counts). This discrepancy should at least be discussed.

(13) The critical in vitro experiment is missing: does purified FroI get oxidized by HOCl and dissociated from FroR?

(14) Lines: 350-355: The claim that the fro system is the first-line defense is unproven.

Reviewer #1 (Public review):

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

Summary:

The authors aim to use state-of-the art behaviour, imaging and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

The investigation follows a logical strategy to collect huge dataset of sleep, appetitive memory and live imaging. The authors identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation dependant manner. The author also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the new data provide TRIC-LUC provided better temporal resolution of neural activity correlates for PAMalpha1-DPM inhibition. Importantly, the author demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP at the memory consolidation period after training.

Weaknesses:

Although the revised version carries arguments to satisfy the reviewers' concern, the writing is now less cohesive. Crucially an explanation however remains required for the following experimental contradiction: the central observation of the study indicates that PAM alpha1 activation cause DPM inhibition which disrupt sleep and memory consolidation. Therefore, one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found the opposite is true from now enhanced TRIC-LUC dataset. The authors indicate this data reinforce the inhibitory nature of PAM-alph1-DPM, but it does not explain why such a reduced DPM activity is observed after training.

Reviewer #2 (Public review):

Summary:

This manuscript presents the "NoSeMaze", a novel automated platform for studying social behavior and cognitive performance in group-housed male mice. The authors report that mice form robust, transitive dominance hierarchies in this environment and that individual social rank remains largely stable across multiple group compositions. They further demonstrate that social dominance and aggressive behaviors, like chasing, are partially dissociable and that dominance traits are independent of non-social cognitive performance. The study includes a genetic manipulation of oxytocin receptor expression in the anterior olfactory nucleus, which showed only transient effects on social rank.

Strengths:

(1) Innovative Methodology:<br /> The NoSeMaze platform is a technically elegant and conceptually well-integrated system that enables fully automated, long-term monitoring of both social and cognitive behaviors in large groups of group-housed mice. It combines tube-test-like dominance contests, voluntary chase-escape interactions, and an embedded operant olfactory discrimination task within a single, ethologically relevant environment. This modular design allows for high-throughput, minimally invasive behavioral assessment without the need for repeated handling or artificial isolation.

(2) Experimental Scale and Rigor:<br /> The study includes 79 male mice and over 4,000 mouse-days of observation across multiple group reshufflings. The use of RFID-based identification, automated data logging, and longitudinal design enables robust quantification of individual trait stability and group-level social structure.

(3) Multidimensional Behavioral Profiling:<br /> The integration of social (tube dominance, proactive chasing), physical (body weight), and cognitive (olfactory learning task) measures offers a rich, multi-dimensional profile of each individual mouse. The authors' finding that social dominance traits and non-social cognitive performance are largely uncorrelated reinforces emerging models of orthogonal behavioral trait axes or "animal personalities".

(4) Clarity and Data Analysis:<br /> The analytical framework is well-suited to the study's complexity, with appropriate use of dominance metrics, mixed-effects models, and permutation tests. The analyses are clearly explained, statistically rigorous, and supported by transparent supplementary materials.

Weaknesses:

(1) Scope Limitations (Sex):<br /> The study is limited to male mice, which represents a common but problematic bias.

(2) Ambiguity of Dominance as a Construct:<br /> While the study robustly quantifies social rank and hierarchy structure, the broader functional meaning of "dominance" remains unclear.

Reviewer #2 (Public review):

Summary:

This convincing study builds on previously published findings in both mice and humans to advance quantitative insights into the coupling between noradrenergic activity fluctuations during mouse NREM sleep and heart rate fluctuations. The work reaffirms the presence of coordinated infraslow fluctuations in sigma power and heart rate during NREM sleep and that this coordination is enabled by noradrenaline-releasing neurons in the locus coeruleus. Also supporting previously published work in mice and humans, the authors describe a link between the strength of these infraslow fluctuations and memory consolidation in mice and humans.

Strengths:

A major finding of this study is the mechanistic insight it provides into the regulation of the previously understudied very-low-frequency (0-0.15 Hz) component of heart rate variability, and the demonstration, through elegant optogenetic bidirectional interference, that infraslow noradrenergic fluctuations are an underlying driving force. This finding will promote recognition of heart rate variability in sleeping mice as a read-out of neuronal activity patterns that control autonomic balance.

Another strength of the study is its translational part, whereby the sigma power-heart rate coupling in mouse is used to identify a previously unrecognized correlation between such coupling and memory consolidation in humans. This widens the applicability of heart rate variability measures, highlighting their use as biomarkers for noradrenergic fluctuations and associated sleep-dependent memory consolidation.

Weaknesses:

The study impresses by the thorough parallel analysis of both mouse and human correlational data between electrophysiological and fluorescent activity measures of the sleeping brain. Further work will be needed to disentangle the mechanisms by which heart rate is regulated, notably the contribution of parasympathetic and sympathetic nervous systems, to establish the very low frequency heart rate variability in mice as a novel biomarker for noradrenergic dynamics in the sleeping brain.

Reviewer #1 (Public review):

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

Summary:

In this manuscript, Scheib et al. identify distinct calcium dynamics in the somata and tuft dendrites of layer 5 pyramidal cells in mice performing a licking task. Animals are trained to lick water ports on the left or right following an acoustic cue, and can adjust their targeting when the ports are displaced. For tongue premotor cortical neurons projecting to the ventromedial thalamus, calcium transients in tuft dendrites are tightly locked to the direction-instructive cue, while somatic calcium signals are more broadly dispersed and more frequently synchronized with tongue motion and port contact. Finally, when the targets are shifted, tufts exhibit a sparse but large corrective signal on an improperly-targeted first lick, and the changes in population activity in the tufts and somata differ after adaptation to the new port locations.

Strengths:

In my opinion, this is a very strong manuscript which reports several novel and significant observations, contains high-quality data and (for the most part) reasonable analyses, and is clear and well-written. Most prior studies of cortical sensorimotor processing have measured the output of neurons using extracellular recording - an approach which obscures potentially important signaling differences between neuronal compartments. This study leverages cutting-edge imaging techniques in mice to document large, time-dependent differences between calcium signals at cortical somata and tuft dendrites. This phenomenon could have major implications at the cellular level for synaptic plasticity, and at the systems and behavioral levels for motor adaptation.

Weaknesses:

At a conceptual level, the authors may wish to elaborate a bit on what sensorimotor computation they think the circuit is implementing, and how their results help explain this implementation. Several possibilities are raised: tuft activation could "prime" the pyramidal cells in advance of movement initiation (line 319ff), or could track errors to engage plasticity (line 351ff) and solve the credit assignment problem (line 362ff). It might be helpful to make one of these proposals more concrete with a computational model, but this is not strictly necessary. [The authors explain that they will address this with modeling work in subsequent research.]

Reviewer #1 (Public review):

Summary:

The paper investigates how AVP modulates pancreatic alpha and beta cell activity using acute mouse pancreatic tissue slices, calcium imaging, hormone secretion assays, RNAscope, and newly synthesized receptor-selective ligands. The Authors report that AVP regulates islet cell activity in a glucose- and state-dependent manner, with maximal effects occurring within physiological AVP concentrations and a bell-shaped concentration-response profile. They conclude that V1b receptors are the principal mediators of these effects and propose that IP3 receptor-dependent signaling underlies the observed nonlinear responses.

Strengths:

The use of fresh pancreatic tissue slices preserves islet architecture and cell-cell interactions, providing a physiologically relevant experimental model compared with isolated islets or immortalized cell lines.

The combination of live calcium imaging, hormone secretion measurements, RNAscope, and pharmacological characterization of newly synthesized receptor-selective ligands represents a technically comprehensive experimental approach that addresses AVP signaling from multiple complementary perspectives.

Weaknesses:

(1) The central mechanistic model of the manuscript is not supported by the experimental data. Although the Authors repeatedly attribute the observed bell-shaped responses to IP3 Receptor activation and inactivation, no direct mechanistic evidence is provided to implicate IP3 receptors. Experiments assessing IP3 receptor function using genetic manipulation and direct measurements of IP3 signaling are necessary before such mechanistic conclusions can be drawn.

(2) The Authors should directly demonstrate V1b receptor expression in β cells using complementary approaches, since the RNAscope data indicate broader expression but do not convincingly establish receptor localization within specific endocrine populations.

(3) In my opinion, the central conclusion that V1b receptors are the predominant mediators of the observed effects is insufficiently supported because definitive loss-of-function experiments are lacking. Genetic deletion or selective silencing of V1b receptors should be provided to validate the proposed mechanism.

(4) The heterogeneous responses observed among islets substantially weaken the proposed mechanistic model. Data should be provided to identify the determinants responsible for activation, absence of response, or inhibition in individual islets.

(5) Please explain why the marked changes in alpha-cell calcium activity were not accompanied by corresponding alterations in glucagon secretion. This apparent discrepancy requires additional experimental evidence.

(6) The Authors need to provide stronger evidence linking the observed calcium dynamics with insulin secretion, since calcium measurements alone cannot establish the proposed functional consequences.

(7) Proper assays should be provided to assess whether the newly synthesized ligands exhibit comparable selectivity and efficacy at murine receptors rather than relying primarily on pharmacological characterization performed using human receptor-expressing cell lines.

(8) The proposed absence of V1a receptor involvement is based primarily on pharmacological inhibition. Independent experimental approaches should be provided to exclude a contribution of this receptor subtype.

(9) They must provide additional quantitative analyses demonstrating that the reported bell-shaped concentration-response relationship is robust across individual experiments rather than reflecting substantial biological variability.

(10) The Authors should include experiments evaluating endogenous AVP signaling under more physiological conditions instead of relying predominantly on exogenous agonist administration.

(11) I believe the role of forskolin deserves further clarification because many conclusions were obtained under cAMP-permissive conditions that may substantially influence AVP responses. Additional experiments without pharmacological cAMP stimulation should be presented.

(12) Please clarify how beta cells and alpha cells were identified exclusively from functional activity patterns during calcium imaging and provide independent validation of cell identity within the analyzed recordings.

(13) In my opinion, the manuscript relies heavily on changes in intracellular calcium activity as a surrogate for endocrine function, whereas the secretion data do not consistently support the proposed functional conclusions. Additional evidence is needed to establish a direct relationship between the observed calcium dynamics and hormone release.

(14) The Authors should better reconcile their findings with previous reports showing minimal or absent AVP receptor expression in β cells and explain how the current data resolve these discrepancies rather than adding another possible interpretation.

Reviewer #1 (Public review):

Summary:

This paper develops a formalism for quantifying epidemic dynamics in terms of relative fitnesses of circulating variants, uses the formalism to elucidate fundamental tradeoffs of epidemics driven by variants with increased transmissibility versus immune escape capability, shows the formalism implies a natural quantity measuring the impact of selection on epidemic growth, and demonstrates that the formalism enables a decomposition of epidemic dynamics into circulation among different immunity groups. The relative fitness formalism enables these analyses to be performed with genetic sequence data only, a major benefit of the model given the relatively high availability of sequence data compared to other data streams such as case counts and titers.

Strengths:

Linking epidemic dynamics to pathogen evolution is a fundamental problem in studies of antigenically variable pathogens, with models of epidemic dynamics and immune-driven evolution going back decades in applications to respiratory pathogens such as influenza. The COVID-19 pandemic heightened the urgency for developing methods for quantifying epidemic growth in contexts where novel variants emerge, leading to differential susceptibility among individuals with diverse exposure histories with implications for vaccination strategies. Real-world data streams such as case counts and immunological measurements have a variety of shortcomings that pose major challenges for quantitative models aiming to inform policy. In recent years, genetic sequencing data has become widely available for pathogens including SARS-CoV-2 and influenza, allowing tracking of pathogen evolution at unprecedented detail in real time, yet biases in the collection of sequence data across different populations make connections between absolute epidemic size and variant frequencies from sequence data not immediately transparent.

This paper's contributions are exciting because they demonstrate new ways to link pathogen evolution and epidemic dynamics using very accessible data. From a theoretical perspective, the model is appealing because of its simple derivation in terms of compartmental models of epidemics, which are standard in the literature, and its clear extension to populations with heterogeneous immune histories. The latter extension leads directly to new methods for inferring immune groups with differential susceptibility to antigenically distinct variants in populations with heterogeneous immune histories without access to immunological data such as titers, an important advance given the wide applicability of quantification of antigenic relationships among variants in real populations.

Weaknesses:

While the demonstrated methods for forecasting short-term epidemic growth and for quantifying population immunity using sequence data are exciting as proofs of principle, the validation and statistical support provided in the analyses have drawbacks that are not fully addressed in the manuscript, weakening the evidence for the usefulness of the methods in their current form.

The analyses forecasting epidemic growth using Gaussian process models are justified using Pearson correlation coefficients whose values are extremely low for the test data period. The explanation given for this is that the case data used to validate the predictions has worse ascertainment over time, but it is not shown directly that the model may be working well despite the low correlations. Whereas, by eye, the predicted epidemic growth curves appear to capture features of the observed epidemic growth curves, the computed metrics don't support the claim of success of the predictions. Additionally, nearly all the model fits lack estimates of uncertainty, so it is not possible to discern the significance of departures between the model and data, or subtle differences in relative fitness calculations across geographies.

The analysis of latent pseudo-immune components also suffers drawbacks that render it more of an interesting proof of principle than a convincing tool for prediction at this point. In particular, in figures S18 and S19, metrics meant to quantify the statistical significance of the results show no difference from null models computed by permuting variants and their escape vectors, yet no interpretation is given for the lack of significance. Moreover, the model fits relating titer distance to pseudo escape distance seem unsuccessful for JN.1 infection and XBB infection histories, which is not adequately accounted for in the text, which cites just "weaker correlations" in these cohorts.

In several instances, the evidence for the new data analyses is weakened by a lack of clarity in the presentation of the technical details of the methods. For example, in the discussion of the Gaussian process models, it was not clear what features of the problem inform the choice of kernel (Matern 5/2), which hyperparameters were used, and how novel this use of Gaussian processes is. In the section describing methods for predicting epidemic growth rate from selective pressure, the discussion of the gradient boosting regressor model provided no intuition as to why this method performed better than the others tested or whether this was particularly important to the conclusions, and the lack of discussion of uncertainty or variability in the model predictions makes it difficult to assess the significance of the time series estimates alone. In the discussion of the latent immune factor model, the mismatch between the notation used in Equation 5 compared to that in Equation 18 made the derivations more difficult to follow. Subsequently, the explanation of the fitting of the pseudo-immune model left out details, such as an explicit definition of distance in pseudo-escape space, to what extent the group-level mean aggregated titer measurement captured features of the titer data (despite ignoring interindividual variability), and a thorough discussion of the successes and shortcomings of the fits in different scenarios. More explicit presentation of the mathematical choices going into the methods, sources and quantification of uncertainty, and cases where the model performs well or poorly could significantly bolster the case for the usefulness of sequence data in quantitatively predicting epidemic growth and antigenic relationships among variants in practice, in more general settings than those carried out here.