Reviewer #2 (Public review):

Summary:

Shimogawa et al. studied the effect of lysine acetylation at different sites in the alpha-synuclein (aS) sequence on the protein-membrane affinity, seeding capacity in the test tube and in cells, and on the structure of fibrils, using a range of biophysical methods. They use non-canonical amino acid (ncAA) mutagenesis to prepare aS lysine acetylated variant at different sites.

Strengths:

The major strength of this paper is the approach used for the production of site-specific lysine acetylated variants of aS using ncAA mutagenesis, as well as the combination of a range of biophysical methods together with cellular assays and structure biology to decipher the effect of lysine acetylation on aS-membrane binding, seeding propensity, and fibril structure. This approach allowed the author to find that lysine acetylation at positions 12, 43, and 80 led to lower seeding capacity of aS in the test tube and in cells, but only acetylation at lysine 80 did not affect aS-membrane interaction. These results suggest that lysine acetylation at position 80 may be protective against aggregation without perturbing the proposed functional role of aS in synaptic plasticity.

Weaknesses:

SDS is not a good membrane model to investigate the effect of lysine acetylation on aS membrane-binding because it is a harsh detergent and solubilizes membranes. Negatively charged vesicles or vesicles made of a mixture of lipids mimicking the lipid composition of synaptic vesicles are more accepted in the field to study aS-membrane interactions. The authors used such vesicles for the FCS experiments, and they could be used for the initial screening of the 12 lysine acetylated variants of aS.

Reviewer #2 (Public review):

Summary:

In this study, the authors propose the existence of an AQP4-positive tanycyte-associated canal system in the hippocampus and suggest that this system participates in waste clearance and contributes to Alzheimer's disease pathology. Using histological, ultrastructural, immunohistochemical, and RNA-based approaches, the manuscript attempts to reinterpret amyloid-β plaques and tau-associated structures as components of a tanycyte-derived waste-internalization system. The work is conceptually ambitious and raises observations that may stimulate discussion regarding glial organization and waste clearance in the diseased brain.

Strengths:

A strength of the manuscript is the combination of imaging modalities and anatomical observations across mouse and human tissue. Some of the reported morphological features are intriguing and may warrant additional investigation. The study also attempts to integrate structural observations with broader hypotheses regarding neurodegeneration and Alzheimer's disease.

Weaknesses:

The central interpretation depends almost entirely on identifying the observed hippocampal structures as tanycytes, and the evidence supporting this conclusion remains insufficient. Tanycytes are classically associated with ventricular regions in circumventricular organs, particularly in the third ventricle and median eminence region, yet the manuscript does not provide sufficiently specific anatomical or molecular evidence to convincingly distinguish the described structures from astrocytic, ependymal, radial glial-like, oligodendroglial, myelin-associated, vascular-associated, or degenerative elements. The marker profile used throughout the study, particularly the reliance on AQP4 labeling and Luxol-positive structures, is not sufficiently selective to establish tanycyte identity, especially in pathological tissue where reactive glial changes may occur.

This becomes particularly important because the manuscript repeatedly interprets Luxol-positive and myelin-associated structures as tanycytic processes or "myelin-derived tanycyte protrusions," despite tanycytes not being known to produce myelin. Alternative explanations are not sufficiently explored. Some of the canal-like structures shown in Figure 4 also resemble vascular profiles, and additional vessel markers would be necessary to exclude this possibility.

Several of the proposed structures and mechanisms are also difficult to reconcile with established cell biology and neuroanatomy. The introduction of new terminology such as "tanysomes," "waste receptacles," and "toroids" further extends the interpretation beyond what is currently demonstrated experimentally.

The discussion and integration of the existing literature on tanycytes are also insufficient. Tanycytes themselves are not clearly introduced; the manuscript does not adequately discuss what is currently established regarding tanycyte anatomy, ventricular localization, morphology, and function. Foundational literature defining tanycyte biology, including work from the Prévot group or others, is largely absent despite its central importance to the field. Because the manuscript proposes a substantial departure from established neurobiological concepts, it is particularly important that previous literature be discussed comprehensively and critically. The current version does not sufficiently contextualize the proposed model within the existing literature on tanycyte, AQP4, glymphatic, and Alzheimer's disease, making it difficult to evaluate what is genuinely novel versus what is merely being reinterpreted. It is also not entirely clear what is genuinely new here compared with the authors' previous work, particularly reference 11, which appears to present a highly similar conceptual framework.

More broadly, several of the manuscript's mechanistic conclusions extend well beyond the available evidence. The proposal that amyloid-β plaques and tau pathology represent hypertrophic tanycyte-derived waste structures is provocative and potentially interesting, but currently remains largely correlative and speculative. At several points, it becomes difficult to distinguish direct observations from broader mechanistic interpretation. The manuscript itself acknowledges that the proposed glial-canal hypothesis contradicts the current understanding of nervous system organization and states that ultrastructural serial-section analysis would be required to unambiguously determine the origin of the myelinated profiles described. This point is critical because the study's central conclusions depend on the assumption that these structures are tanycyte-derived. At present, this interpretation remains insufficiently demonstrated, which substantially limits the strength of the broader pathological and mechanistic conclusions proposed throughout the manuscript.

Although access to human material is understandably limited, the study appears to include only one male and one female AD patient, making it difficult to assess the reproducibility or frequent these structures are across individuals and pathological conditions. The manuscript would benefit from clearer characterization of prevalence, reproducibility, and variability across samples.

Overall, the manuscript presents an unconventional and thought-provoking model that may stimulate discussion. However, the evidence currently provided does not convincingly establish tanycyte identity for the described hippocampal structures, and several of the broader disease-related interpretations would require substantially stronger anatomical and molecular evidence before the proposed model can be convincingly supported.

Reviewer #2 (Public review):

Summary:

The study introduces an innovative device designed to collect non-invasive saliva samples from animals using disposable cassettes with odor attractants and filter paper. The authors aimed to validate this tool for pathogen monitoring, specifically by detecting pathogen RNA in animal models. While the concept is compelling and the problem statement well-framed, the validation of the device for pathogen detection was not achieved. For example, the rabies virus was not detected in the chosen model, and results were limited primarily to FeLV. The work highlights the potential of saliva-based sampling for microbiota analysis, but the rationale for virus selection and the experimental design require further clarification. Overall, the study presents a novel approach with promise, though its current scope is better suited to microbiota monitoring rather than pathogen surveillance.

Strengths:

The innovative design of the device, which enables non-invasive saliva collection through disposable cassettes with odor attractants, represents a creative and practical advance in sampling methodology. The authors undertook an extensive experimental effort, generating a substantial amount of data that highlights the feasibility of saliva-based monitoring. The rationale for exploring saliva as a medium is valid, and the work successfully shows that the device can be applied to microbiota profiling, where the strongest results were obtained. This methodological innovation could be valuable for expanding non-invasive approaches to animal health monitoring.

The authors acknowledge that metabarcoding sequencing has limitations; however, the study could be refocused on the microbiota in general rather than on pathogen detection. They could give greater prominence to the taxonomic composition of microorganisms in saliva using high-throughput sequencing. That is where they obtained the most results.

Weaknesses:

Despite the enormous experimental effort undertaken, the results fall short of the expected success of the proposed test. The rationale and criteria for virus selection are not clearly explained, leaving the experimental design insufficiently justified.

The central aim of validating the device for pathogen detection was not achieved, particularly in the case of the rabies virus. The mouse infection model used for the rabies virus does not seem to adequately replicate the natural course of the disease. This could explain, at least in part, the negative results obtained.

Of the three viruses evaluated, satisfactory results were obtained only for FeLV, and the sample size remains limited. According to the literature reviewed, this virus is not common in wild cats, so the applicability of the results would appear to be limited primarily to domestic cats.

The collected samples were stored at −80 {degree sign}C for later analysis, which likely contributed to the high Ct values observed with the device. The need to store samples at low temperatures may be a limitation to applying this technique in wildlife sampling scenarios where access to dry ice or liquid nitrogen tanks may be difficult.

Stating that the device can be used for pathogen monitoring in wild animals is not desirable, since the viruses for which results were obtained are not relevant in wild animals. On the other hand, claiming that this is a tool for monitoring diseases in endangered species is also misleading. Endangered species are typically scarce and therefore would not be the reservoirs that these surveillance efforts should target. In fact, groups such as wild rodents would be a better target for monitoring zoonotic pathogens.

Reviewer #2 (Public review):

This study examines how curl in the retinal flow field can be used as a control variable for estimating and controlling the heading of a moving observer. The basic idea (which is not entirely new, see Matthis et al. 2022) is that translation along a path with eccentric gaze (meaning that the subject is not heading toward the point they are looking at) produces a pattern of optic flow on the retina with a rotational component around the point of fixation (which can be captured by the mathematical "curl" operator). The sign and magnitude of retinal curl varies with heading relative to the point of fixation, such that curl can be used as a control variable to steer rightward or leftward to move toward the fixated target. The authors perform behavioral experiments and show that there are biases in perceived heading that seem to be largely governed by retinal curl. They also show that a simple controller model can use curl to steer toward a target, and they provide a neural network model that provides a biologically-plausible implementation of the controller (although there are some questions about that).

There is a core of interesting work here that I think can be important to the field. However, there is a lack of clarity on several important fronts, including design of the behavioral experiments, presentation of the behavioral data, conceptual framing of what curl can and cannot do, etc. Equally importantly, the manuscript is not written in a manner that will make it accessible to most vision scientists. I consider myself to be pretty knowledgeable about optic flow, and I had to read most of the manuscript 3 or 4 times to be able to understand the bulk of it. And my experience is that most vision scientists do not understand optic flow well, so I fear that most of the readers that the authors should want to reach would struggle to understand the work. As written, this is mainly going to make an impact on a handful of optic flow gurus. Thus, this manuscript is going to need a major overhaul to clarify important issues and make this more accessible.

Major issues:

(1) The manuscript contains inconsistent, if not misleading, messaging about what information retinal curl does, and does not, provide regarding heading estimation. In the Abstract, the authors state: "We propose an alternative: the visual system utilizes retinal curl directly to estimate heading, rendering the explicit recovery of the FOE unnecessary." Based on my understanding of the rest of the manuscript, I find this statement to be a misrepresentation for two main reasons:<br /> a. To "directly estimate heading" relative to what? When not qualified, most people interpret "heading" to mean an observer's heading relative to the world (or some allocentric reference frame). But retinal curl only gives information about an observer's heading relative to the point on which their eyes are fixated. Moreover, that point of fixation will change every few hundred milliseconds in natural viewing, so the retinal curl will change with each new fixation even as heading relative to the world remains unchanged. So, I think most readers would grossly misinterpret the claim that retinal curl can be used "directly to estimate heading". Indeed, in the authors' controller model, the initial heading needs to be given and then the controller can work. But from where does the visual system get the initial heading, since it does not come from curl? These issues are left hanging. Thus, while curl can provide a very useful input for steering toward a fixated target, other signals are needed to estimate heading relative to the world. This has to be made much clearer early on, and a conceptual schematic diagram might help. Also, the authors generally do not specify the reference frame of the variables they are talking about, leaving lots of room for misinterpretations. It should be clear each time they are talking about a variable, such as heading, whether it is relative to the fixation target, body, world, etc.<br /> b. It seems to me that retinal curl will depend on other variables, in addition to heading relative to the fixation target. For example, it seems to me that the magnitude of retinal curl will depend on self-motion speed, the depth structure of the scene, the angle of elevation of the fixated target, and perhaps others. This is not discussed at all, and many readers would get the misguided impression that there is a 1:1 mapping from curl to heading (relative to fixation). If I am right that this is not correct, it means that retinal curl can tell the observer whether to steer right or left to move toward the fixated target, but it cannot tell them how much to steer. Indeed, in the authors' controller model, there is a free parameter that calibrates curl to angle. It makes sense that this works to fit trajectory data that are given from a fixed environment, but it is unclear how the brain would use retinal curl to control steering when these other variables are uncertain or changing unpredictably. Moreover, how does the system change the mapping from curl to steering command as the location of fixation changes relative to the current heading? These are issues that need to be brought up in framing the problem and discussed at some length. If the authors can show mathematically that retinal curl is only dependent on heading (relative to fixation) and not any of these other variables, it would be very valuable to show the equations for this relationship.

(2) The description of the behavioral experiment and presentation of behavioral data leaves a lot to be desired.<br /> a. First, it is stated (line 158) that "Participants continuously reported their perceived direction of self-motion while maintaining fixation on the yellow dot." Again, reference frame is completely unspecified. Participants were reporting their perceived heading relative to what? The fixation target? The world? What exactly were the instructions given to the subjects to perform the task? Based on the description of how perceived paths are computed (line 166-), it seems to be presumed that subjects are reporting their heading relative to the world because those angles are then converted into x and z coordinates in what I presume is a world-centered reference frame. But how do we know that subjects are accurately reporting their heading relative to the world? What if they are biased in their reports by the location of the fixation target relative to the scene, or by some other reference signal? Is it possible for the authors to rule out the possibility that perceptual biases seen in the unaltered curl condition result from observers not fully adopting the assumed reference frame of the task? If this cannot be firmly excluded, it seems to create problems for the rest of the study.<br /> b. I also feel that there is a mismatch between what the behavioral task requires and what the controller model does. Subjects are apparently asked to report their heading relative to the world, but the controller model only controls their heading relative to the point that they are fixating. I understand how this is resolved in the model, but I think this type of distinction is buried and will not be apparent to most readers. Again, the reference frames of what is being measured and controlled need to be specified explicitly in all parts of the paper, and the authors needs to explain how the system would combine curl-based control with some other measures of (at least initial) heading for world-centered heading to be computed. All of the assumptions need to be clearly specified.<br /> c. Second, I found it frustrating that the authors never present raw perceptual data from the observers. Rather, in Figure 2, we see reconstructed trajectories that are perfectly smooth with no indications of noise whatsoever. Since these paths are computed from the perceptual reports, there must be some noise inherent in them. The figures should represent this uncertainty somehow, and it should be explained how these perfectly smooth trajectories are obtained.

(3) "...the magnitude of retinal curl in the fovea can specify the body trajectory relative to gaze (Matthis et al., 2022)." The main idea put forward by the authors here seems to overlap heavily with this statement that they attribute to Matthis et al. 2022. While I think this paper still adds importantly to the topic, the authors do not discuss how their findings are different from those of Matthis et al. 2022, why they are an important extension, etc. Readers should not have to go read this other paper to have any idea how the present findings are placed in importance relative to the literature.

(4) The analysis and treatment of eye movements is extremely weak. The authors discarded trials for which gaze deviated from the fixation point by more than 3 degrees (which is a LOT given that the eye speeds are generally in the neighborhood of 0.5 deg/sec), and they provide basic stats on the distribution of positions. But this largely misses the point: it is not small position errors that are likely to matter, but rather velocity errors. Even a small amount of retinal slip of the target while it is being pursued will cause image motion that is going to alter the optic flow field around the fixation target. So, for example, the retinal curl field may no longer be centered on the fixation target. How do we know that some of the perceptual biases are not influenced by image motion resulting from imperfect tracking of the fixation target? This needs to be analyzed and discussed.

(5) I found the sections of text comparing the separate and joined fits (starting line 287) to be a bit too rosy. The authors show the separate fits in the main text, and it is not very surprising that these fits are good given that the model has 30 parameters, and these data are pretty low dimensional. The authors only show the joined fits in the supplement, and they say that they are almost as good as the separate fits (indeed they are better in a model comparison sense, but this is 30 parameters vs. 2 parameters). However, when I look at the fits of the joined model in the supplement, I don't find them to be very impressive. In particular, the model grossly misses the data for the straight paths for several subjects (e.g., id5, id6, id8, id10). And fitting the straight paths would presumably be easiest. This implies that the joined model is really missing something and that fitting the curved paths interacts strongly with fitting the data for different fixation target locations on the straight path. I think that the authors should discuss the results a bit more soberly and tone down their conclusions here.

(6) The section of the paper on neural simulations (starting line 387) has a few weaknesses. First, why are only straight paths simulated here? This does not seem to provide a very rigorous test of the model. Second, it is awkward that the simulation results are presented in units of pixels, rather than degrees. Third, the authors seem to downplay the fact that the neural estimates of heading seem to oscillate rather wildly (over a range of hundreds of pixels, whatever that means, see especially Fig. S16). It was far from clear to me how an estimate of heading with these large oscillations is useful. It would seem to require that heading estimates are integrated over substantial lengths of time to be reliable. It was therefore unclear how the model produces such smooth paths from these oscillating estimates.

Comments on revised version.

Overall, the authors have done a responsible job of responding to the comments of my previous review, and the manuscript is substantially improved. There are a few points on which I still do not completely agree with the authors, and I think these are important to document for the record:

(1) Introduction: "Pure visual decomposition should function regardless of 3D depth or whether the rotation stems from an active eccentric fixation." Perhaps in a world of noiseless perfect computation, this might be true. But I generally disagree. When there is more depth structure in an environment, then translation of the observer is generally going to create greater motion parallax. That is a fact that I don't think can be disputed. And greater motion parallax should help to decompose optic flow into components related to translation and rotation (the latter of which is not depth dependent), especially when there is noise in estimating location motion vectors.

(2) Related to point #9 of my previous review: I had asked why the authors believed that retinal curl was computed in area MSTd. Their response is that previous studies (i.e., Graziano et al. 1994) show selectivity to spiral motion stimuli in MSTd. That is true, but those studies typically placed the spiral stimulus centered on the MSTd receptive field, hence they were not presenting something like retinal curl as defined here. So, I think it is still an open question as to where in the brain retinal curl is encoded, and from which areas it would be possible to decode retinal curl from population responses.

(3) Related to point #10 of my previous review: I had asked about biological plausibility of the gaze-centered inhibition signal in the model. The authors' response is that parietal neurons show gain fields in which response depends (usually monotonically) on eye position. This is true, but it is not a trivial jump from gain fields in individual neural responses to a gaze-centered inhibition signal, and I think the authors should have been more forthcoming about the lack of an established neural signal that directly signals what they want in their model.

(4) The authors point out that the perceptual biases they measure take a few seconds to emerge and they attribute this to temporal integration. But in their curl manipulations, they temporally average over a 2.4 second window in computing the curl signals that they use to cancel or over-cancel curl. So, it is not clear whether some of the delay in the behavioral effects might result from their computations.

(5) Related to point #13 of my previous review: I had asked about empirical evidence for the assumption of a relationship between the heading preferences of MSTd neurons and their receptive field locations. In response, the authors state that such a relationship is built into the Layton and Browning (2014) model. While that is a precedent, citing another model as a response to a question about empirical evidence is not a convincing response. If there is no empirical evidence to support such a relationship, it would have been better for the authors to acknowledge this.<br /> Given the way that the eLife review model works, it is not necessary for the authors to address these comments, but I think they should be included in the public review record.

Reviewer #2 (Public review):

Here, the authors record dopamine release using fast-scan cyclic voltammetry in the nucleus accumbens/ ventromedial striatum (VMS) while rats perform variants of a go/no-go task. Two versions are self-paced, in that the rat can initiate a trial by nosepoking at the odor port at any time once the ITI had elapsed, whereas the other two require the rat to wait for a cue-light before responding. Two "long" variants also require either more lever-presses on go trials, or a longer nosepoke time for no-go trials, and also incorporate "free" trials in which the rat is rewarded for just heading straight to the food tray. The authors find that dopamine levels increase more during the response requirement for go than no-go trials, indicating a role for invigorating to-be-rewarded actions. Dopamine levels also steadily increased as rats approached the site of reward delivery, and the authors demonstrate quite elegantly that this was not due to orientation to the food tray, or time-to-reward, or action initiation, but instead reflects spatial proximity to the rewarded location. Contrary to previous reports, the authors did not discern any differences in dopamine dynamics depending on whether the trials were cue- or self-paced, and dopamine release did not scale with effort requirements.<br /> The manuscript is well-written and the authors use figures to great effect to explain what could otherwise be a hard-to-parse set of data. The authors make good use of the richness of their behavioral data to justify or negate potential conclusions.

Reviewer #2 (Public review):

Summary:

The manuscript from the Rice lab by Gangadharan et al., submitted to eLife, investigates the polymerization mechanism of the yeast microtubule polymerase Stu2. The lab has published a number of articles demonstrating the structural basis by which the two TOG domains of Stu2 each bind free tubulin heterodimers and has developed a tethered polymerization model by which the TOG domains drive polymerization by shuttling those tubulin subunits onto the microtubule plus end. A second model was proposed by Nithianantham et al. (eLife, 2018) based on a closed - to - open transitional state in which Stu2 unfurls and loads two longitudinal associated tubulin heterodimers onto the microtubule plus end. While the second model is not directly tested, the current work aims to further characterize/model the tethered polymerization model using a kinetic framework developed by developed by Breitsprecher et al. for Ena/VASP actin polymerization activity, using a model that is enzymatic (EMBO J., 2011). The general architecture and function of Ena/VASP on actin polymerization versus Stu2 on microtubule polymerization is a reasonable relation and hits upon, as the authors note, potential convergent mechanistic evolution across distinct cytoskeletal networks. The model effectively treats tubulin as the substrate, and the polymerized microtubule plus end as the product. If Stu2 is "enzymatic" in this framework, the model predicts it would behave with Michaelis-Menten kinetics, that there would a Vmax, and polymerase activity would either be "affinity limited" by TOG:tubulin affinity (KD) and/or "kinetically limited" by TOG:tubulin association (Kon) and transfer of tubulin to the microtubule plus end (Kt). The authors find that the Brietsprecher model works well for Stu2 activity, and that Stu2 best aligns with a "kinetically limited" model. The work is interesting and adds to the growing elucidation of the Stu2 microtubule polymerase model. While yeast microtubule polymerases are somewhat distinct in their architecture, there is significant overlap that findings from the manuscript can be utilized to inform the mechanisms of larger, more complex microtubule polymerases such as human ch-TOG.

Strengths:

The manuscript invokes the enzymatic model of Breitsprecher et al. used for Ena/VASP and conducts an elegant series of (mostly established) experiments to determine whether Stu2 microtubule polymerase activity aligns with the model - which they conclude does align, supported by the data/results obtained.

Weaknesses:

The authors used biolayer interferometry to measure TOG:tubulin affinity. The affinities obtained were significantly higher affinities than the lab obtained in an earlier publication using analytical ultracentrifugation. While differences in buffer and salt conditions may underlie these differences, additional runs using comparable buffer systems, or use of a third independent assay to measure affinities would have added rigor.

The discussion could be expanded to better compare and contrast the results with both existing polymerase models introduced in the introduction, as well as expanded to look at reversible enzymatic activity (microtubule depolymerization at low to zero tubulin concentrations) and microtubule plus versus minus end activity.

Comments on revised version.

The revised submission has addressed these comments adequately.

Reviewer #2 (Public review):

Summary:

In this manuscript, Gracia-Alvira et al. investigated how environmental temperature affects competition among members of the microbiome, with a focus on intraspecific diversity, using the Drosophila model.

Notably, the authors identified three clades of Lactiplantibacillus plantarum from a natural population of Drosophila simulans collected in Florida. They tracked the dynamics of these three bacterial clades under two temperature conditions over the course of more than ten years. Using comparative genomics and phylogeny, they showed that these three bacterial clades likely adapted to their host independently in a temperature-specific manner. Further, by combining in vitro culture and in vivo mono-association assays, they demonstrated the functional divergence of these three bacterial clades phenotypically, including their growth dynamics and effects on host fitness. Lastly, they performed pathway analysis and speculated on key genomic variance supporting such functional divergence.

Strengths:

The laboratory evolutionary experiment in response to cold or hot environmental temperature is impressive, given its more than ten years of experimental time period. This collection of achieved microbiome samples paired with the fly host data can be a valuable resource for the field.

Comments on revised version:

The revised version has addressed my major points raised in the original review.

Reviewer #2 (Public review):

Uphoff and colleagues present the results of a study focused on characterizing the binding of SVEP1 to TIE1 along with Angiopoietin-2. Starting with computational prediction of SVEP1 binding to TIE1, the authors identify the region of SVEP1 that serves as a high-affinity ligand for TIE1. Advanced studies identify a weak secondary binding site within SVEP1 that appears to be sufficient but not necessary for its interaction with TIE1 based on in vivo rescue experiments. The most novel contribution of the manuscript seems to be the identification of angiopoietin-1 and -2 as co-factors that seem to enhance the binding of SVEP1 with TIE1 and impact downstream AKT signaling. They propose a complex in which SVEP1 binds to TIE1 and ANG2.

Although the first set of results is essentially confirmatory, the identification of ANG-2 as a "co-factor" enhancing the binding of SVEP1 to TIE1 and associated downstream signaling (i.e., Figures 3 and 4) is novel and is of interest. However, the manuscript and its conclusions would greatly benefit from some clarifying details and additional experiments to ensure rigor and support specific claims.

Comments on revised version:

I have no further comments. The authors have addressed my concerns.

Reviewer #2 (Public review):

Summary:

This study aims to test the hypothesis that microsaccades are linked to the shifting of spatial attention, rather than the maintenance of attention at the cued location. In two experiments, participants were required to judge an orientation change at either a validly cued location (80% of the time) or an invalidly cued location (20% of the time). This change was presented at varying intervals (ranging from 500 to 3,200 ms) after cue onset. Accuracy and reaction times both showed attentional benefits at the valid versus invalid location across the different cue-target intervals. In contrast, microsaccade biases were time-dependent. The authors report a directional bias primarily observed around 400 ms after the cue, with later intervals (particularly in Experiment 2) exhibiting no biases in microsaccade direction towards the cued location. Noteworthy, it would have been interesting to observe whether directional biases in microsaccades are also evident when compared to a neutral condition. The authors argue that this finding supports their initial hypothesis that microsaccade biases reflect shifts in attention, but that maintaining attention at the cued location after an attention shift is not correlated with microsaccade direction.

Strengths:

The results are straightforward given the chosen experimental design. The manuscript is clearly written, and the presentation of the study and its visualisations are of a high standard.

Weaknesses:

The link between attention and microsaccades has been the subject of extensive research over the past two decades. The authors present a potential solution to the conflicting past findings, arguing that attention should be considered a dynamic process that can be broken down into an attention shift and a sustained attention phase. To differentiate between the two components, the authors varied the interval between the onset of the attention cue and the test stimulus. It would have been nice to use a theory-driven criterion (or an independent measure), in addition to their data-driven approach, to distinguish between these components of a dynamic attention concept. Moreover, it is important to note that the current experiments take a purely correlational approach.

Reviewer #2 (Public review):

Summary:

This manuscript addresses an important and mechanistically interesting question: whether plasma membrane glutamate transporters contribute only to slow clearance of ambient glutamate or whether they can rapidly shape synaptic signaling during high-frequency auditory activity. This manuscript provides important evidence that EAAT-mediated glutamate uptake is not merely a slow background clearance mechanism but is essential for maintaining reliable synaptic transmission and linear stimulus-intensity coding in ventral cochlear nucleus T-stellate cells during sustained auditory nerve activity.

Strengths:

The finding that EAATs may be required for rapid, local control of glutamate during high-frequency auditory nerve activity is interesting and could have broad relevance to auditory processing. The electrophysiological evidence is generally strong, particularly the use of patch-clamp recordings, stimulus trains, partial versus complete EAAT blockade, and comparison with bushy cell/endbulb synapses. The comparison between T-stellate cells and bushy cells/endbulb synapses strengthens the manuscript. The authors demonstrate that EAAT blockade disrupts coding in T-stellate cells but has little effect on bushy cell spike transmission, supporting a cell-type- and synapse-specific role of glutamate uptake.

Weaknesses:

However, some mechanistic conclusions, especially the specific contribution of neuronal versus glial EAATs and the absence of glutamate crosstalk between auditory nerve inputs, rely mainly on pharmacological and indirect electrophysiological inference and would be strengthened by additional anatomical, genetic, or direct glutamate-sensing evidence.

(1) Clarification of DL-TBOA concentration.

The authors used bath application of 200 µM TBOA and 25-50 µM in the other experiments, stating that "sub-maximal concentrations (25-50 µM)". The authors should provide a clearer rationale for why different concentrations were used across experiments rather than a fixed concentration.

The reversibility of DL-TBOA effects should be demonstrated by washout experiments. In addition, potential off-target effects of DL-TBOA on postsynaptic receptors, intrinsic membrane excitability, or presynaptic release (e.g., PPR measurement) should be carefully considered. It would also be useful to test the effects of the submaximal DL-TBOA concentrations (25-50 µM) on membrane potential and inward currents, shown in Figure 1, to determine whether these concentrations depolarize the membrane potential in current-clamp mode or induce inward currents under voltage-clamp conditions.

(2) Potential contribution of altered intrinsic excitability.

In Figures 3B and 3C, DL-TBOA appears to induce additional action potentials even immediately after the first stimulation, whereas Figures 6 and 7 suggest that the first EPSC is not substantially altered. This raises the possibility that the enhanced firing may partly result from a modest depolarization caused by background glutamate accumulation or from other changes in intrinsic membrane properties after drug treatment. To address this, the authors should provide a quantitative analysis of physiological parameters under submaximal DL-TBOA conditions, including spontaneous action potential frequency, resting membrane potential, input resistance, and spike threshold.

(3) Spillover/ crosstalk between AN-fiber-synpases.

The authors should provide more explanation of how altering the number of active auditory nerve fibers demonstrates the absence of glutamate spillover/crosstalk between bouton synapses. Strong stimulation likely recruits more AN fibers, but it may also change release probability, axonal synchrony, or stimulation spread. The authors should more clearly justify the interpretation that strong stimulation recruits additional independent AN fibers rather than altering release probability or activating fibers with different intrinsic properties.

(4) Interpretation of glial versus neuronal EAAT contributions.

The authors claim that both neuronal and glial transporters contribute to rapid uptake using pharmacological approaches. The pharmacological data demonstrate that glial EAATs play a major role in glutamate clearance at T-stellate cell synapses. The strong increase in EPSC decay time and synaptic charge after UCPH-101/DHK application supports the conclusion that glial transporters contribute substantially to limiting glutamate accumulation during sustained auditory nerve activity. However, the conclusion that neuronal EAATs contribute directly should be stated with some caution. The evidence for neuronal EAAT involvement is indirect and depends on the pharmacological specificity and completeness of glial EAAT blockade. The conclusion would be strengthened by additional evidence, such as EAAT subtype expression/localization in T-stellate cells or auditory nerve terminals, transporter current recordings, immunohistochemistry, or genetic manipulation of neuronal EAATs. In addition, fitting the decay phase with a double-exponential model may help determine whether glial and neuronal EAATs contribute over distinct temporal windows.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

This manuscript describes the fascinating phenomenon of growth hormone (GH)-independent growth occurring in the mother during pregnancy. This growth was most pronounced in dwarf mice that are lacking the receptor for growth hormone-releasing hormone (GHRH) and therefore showing isolated GH deficiency. However, the pregnancy-induced growth could also be observed in wild-type mice, suggesting that it is a normal part of the maternal adaptation to pregnancy. The study falls short of identifying the mechanism(s) driving this pregnancy-induced growth response, but it certainly reveals a novel insight into maternal physiology. The authors have completed a range of experiments in mice to prove that, as well as being GH independent, the pregnancy-induced growth also did not require GH signaling in the liver (i.e. not another pregnancy-specific ligand operating through the GHR to promote IGF). They also provided complementary data from a population of humans with untreated isolated GH deficiency that are broadly consistent with the hypothesis. While it is important to consider the significant species differences between rodents and humans, both in terms of growth physiology and also in terms of evolution of placental somato-mammotrophic hormones, this unique population are a valuable resource and adds credence to the study. Overall, I find this a compelling research story, but disappointingly unfinished. There are some areas where additional information could improve the ability to interpret the data, and some additional concepts that could be considered in the discussion. There are also areas where additional experiments might provide important insights. However, I think that such suggestions can be considered as appropriate for future research, rather than delaying consideration of the current manuscript.

Main comments:

(1) Data in Figure 1 are remarkable - not so much the growth in pregnancy in the wildtype mice, because while elevated GH is well known in pregnancy, but growth in the dwarf mice is indicative of GH-independent growth. From these data, it seems that there is good evidence that growth in pregnancy is an adaptive function. However, it is possible that growth is achieved in dwarf mice and that in wildtype mice may have been mediated through different mechanisms. The dwarf mice showed an increase in liver and plasma IGF1, suggestive of an additional ligand driving IGF in pregnancy. One could hypothesize that such an effect could be mediated by an additional pregnancy-specific ligand activating the GH receptor. In humans, placental growth hormone could be such a ligand, but as far as we know, there is no placental GH in mice. In contrast, the wildtype animals showed suppression of liver and circulating IGF1, and low levels of pSTAT5 in the liver during pregnancy. These data (in Figure 5) are very surprising. Given the high circulating GH in pregnancy, as well as high placental lactogen (which would be expected to activate STAT5 in the liver through the Prlr), the low levels of pSTAT5 are unexpected and would seem to indicate some sort of acquired insensitivity to GH. Is this entirely driven by down-regulation of STAT5b protein, or could there be activation of other, negative regulators of STAT signalling, such as SOCS? What is causing such a profound suppression of STAT5? Regardless of the mechanism, this suggests that pregnancy-induced growth in wildtype mice is independent of circulating IGF1 (potentially a different mechanism or in addition to that seen in IGHD mice).

The data shown in Figure 6 are a major strength of the study, showing that the pregnancy-induced changes are not specific to one particular transgenic model, but still occur in a variety of models affecting GH through different approaches. Given the pregnancy-specific nature of the changes, however, it seems an oversight not to have evaluated the role of placental lactogens. Prlr is highly expressed in the liver, but the function of this hormone in the liver is not well established. Could the extremely high levels of PL be mediating this growth response? Given the low expression of STAT5 in the liver and the fact that plasma IGF1 is not markedly elevated, it seems more likely that this growth response may be mediated by locally produced IGF1 in target tissues.

I think these possibilities could be addressed by an expanded discussion of species variation in placental hormones, to highlight that humans have expansion of the GH locus, but rodents have expansion of the prolactin axis (see Soares, M. J. The prolactin and growth hormone families: pregnancy-specific hormones/cytokines at the maternal-fetal interface. Reprod Biol Endocrinol 2, 51, 2004). Importantly, placental GH and chorionic somatomammotropins (CSM) in humans are all variants of the GH gene, but CSM have preferential activity at Prlr. This seems to be a fundamental species difference in pregnancy biology, but has been interpreted as an example of convergent evolution, with conservation of prolactin and GH-like functions at the maternal-fetal interface, mediated by different mechanisms, likely contributing to the metabolic adaptations of the mother (see Newbern D, Freemark M. Placental hormones and the control of maternal metabolism and fetal growth. Curr Opin Endocrinol Diabetes Obes. 2011; 18: 409-416). While the preceding function has focused on explaining the evolution of placental lactogens (either prolactin or GH variants), the present data suggest that there are also mechanisms to maintain growth in pregnancy, independent of GH (even in the absence of a placental GH).

(2) The human data are very interesting, and my initial impression was that it seemed unlikely to be the same phenomenon. Was there any real evidence for "growth" in pregnancy? Pubertal maturation of long bone growth might be expected to prevent further growth in adulthood. However, these issues were appropriately discussed, and it seems well justified to evaluate this unique population of women with IGHD who underwent pregnancy. It would be very interesting to know if these women experienced elevated IGF1 during pregnancy, indicative of placental GH contributing to growth. Mechanistically, this might be more like the dwarf mouse situation of IGHD, that the situation in wildtype mice (associated with liver insensitivity to GH and low IGF1).

(3) It would be useful to include investigations that isolate the effects of pregnancy and the placental hormones. Such studies could include evaluating growth in pseudopregnant mice with IGHD (pregnancy-like changes in hormones but lacking the placental contribution) and in IGHD animals that experience pregnancy but not lactation (pups removed at birth). I accept that this might be too large an additional study to add for the present manuscript.

(4) It is an important and translationally relevant observation that pregnancy increased the risk of long-term weight gain, and that after the first pregnancy, the pregnancy-induced growth response was more directed to promoting fat deposition. Does this provide any mechanistic insight? Could a metabolic adaptation result in growth?

Reviewer #2 (Public review):

Summary:

This study uses neurofeedback to modulate alpha-band activity and examines how this influences pain-related processing. The question is timely and methodologically elegant, because it addresses whether noninvasive modulation of ongoing oscillatory activity can causally shape pain perception and/or expectation-related processes.

Strengths:

The use of neurofeedback as a tool to modulate alpha activity is a major strength, because it provides a noninvasive and conceptually clean approach to probing the functional role of oscillatory brain activity. The design is also attractive because it links neurophysiological regulation to a psychologically meaningful outcome, namely pain processing. Further, the induced changes were also related to different EEG microstates and ERP components during the processing of the pain stimulus, and therefore the authors demonstrate a clear relation between preparatory prestimulus states and stimulus processing.

The manuscript appears to address an important and clinically relevant question, and the idea of testing whether alpha regulation can alter pain-related responses is of high interest for systems neuroscience and pain research.

Weaknesses:

Methodologically, it is unclear what alpha values were used in the analyses. It is stated that alpha was extracted within 2s windows of the 16s long feedback period. However, the values change across this period. Which value is used for the correlation with the pain ratings and all other analyses? Using the average across the 16s could reflect large values in the first half and low values in the final half, but for the relationship between alpha and pain, the last segments should be more relevant. If the initially elevated alpha activity subsides several seconds before the onset of the pain stimulus, it is difficult to see how it could influence subsequent pain processing.

Related, after the 16s feedback period, a fixation period is used with a 3-5s length. If alpha band activity is relevant for the consecutive pain processing, the amount of alpha in this period should be relevant. The authors should demonstrate that the induced alpha change during the feedback period remains stable during the fixation period and that the activity in this period is related to pain processing.

Further, it should be noted that the alpha band modulations related to alpha band training were accompanied by significant effects in other frequencies. Therefore, a clear relationship between alpha and behavioral pain ratings is not the only interpretation. Correlations with other frequencies or combinations of frequency band modulations should be incorporated to allow a more precise interpretation. Furthermore, in the sham feedback group, an increase in alpha band activity was observed (p=0.06), and the small difference in the pain intensity rating may be related to a clear outlier in the Sham group (Figure 4a).

In both groups, a main effect of training, regardless of sham or real feedback, was reported with a small difference between groups. But the main modulator seems to be related to the instruction to modulate the neural activity, and this large effect should be discussed in more detail regarding, for example, possible attentional processes.

A further central concern is that the visual feedback signal (the ball movement) may generate expectations that are not specific to alpha activity and that these expectation processes modulate the pain processing (ball down may indicate more pain). It is well known that intensity cues can generate expectations about upcoming perceptions, and the used feedback signal with an increasing or decreasing visual curve clearly signals what intensity should be expected. Therefore, it is important to show that the amount of positive (ball up) and negative visual displays is matched between the sham and real feedback group. Further, the authors should report whether the final ball position can predict the latter pain rating in both groups or differentially. Following this interpretation, alpha band activity is not directly related to pain processing but only serves as a signal that is transformed to a visual stimulus that then generates expectations.

Finally, the manuscript would benefit from a more explicit analysis of whether individual alpha changes are related to pain ratings within each subject. If higher alpha is truly linked to reduced pain perception, this should be visible at the participant level during learning of the neurofeedback procedure. Relatedly, there is no learning period incorporated, and usually participants are not able to regulate their alpha activity from the first trial on. The authors should include an analysis of the development of alpha band activity over learning and a relation of these individual alpha values and the corresponding pain ratings.

I cannot find a link to the preregistration in the current manuscript.

In summary, a "causal" relation of alpha activity with pain perception -that is mentioned several times in the manuscript- is not fully supported by the present results

Reviewer #2 (Public review):

Summary:

The study tests whether male/female-related information is represented in a form that generalizes across faces, bodies, and gender-associated objects. Using within- and cross-category MVPA, regression RSA, comparisons with fine-tuned CNNs, and connectivity analyses, the authors identify a right middle temporal gyrus region whose patterns generalize across the three stimulus classes. They conclude that this region provides a category-general, mid-level representation of gender and acts as a neural hub.

Strengths:

The question is novel and important, while the logic of the study is straightforward. Examining faces, bodies, and objects within the same participants provides a useful extension beyond the predominantly face-based literature. Cross-category decoding is also a stronger test of shared information than simple anatomical overlap between within-category maps. The combination of MVPA, RSA, computational modelling, and connectivity analysis is ambitious, and the replication of the CNN layer profile with both AlexNet and VGG16 is a useful characterization of relevant information.

Weaknesses:

(1) The construct labelled "gender" is not equivalent across stimulus classes. For faces and bodies, the male/female label is intended to track a property of the depicted person, albeit one inferred imperfectly from appearance; for objects, masculinity or femininity is not an intrinsic property of the object but a culturally contingent association that may vary across observers and contexts. Treating both as levels of a single binary factor risks conflating person-category information with gender-stereotypic object associations and interpreting their common neural discriminability as evidence for one abstract concept of gender. The term "object gender" could also be confused with grammatical gender in some languages (e.g., French or German).

(2) The CNN analysis does not isolate the shared male/female component. The authors correlate the complete six-condition neural RDM with the complete CNN RDM. However, rMTG also carries substantial information about whether an image is a face, body, or object. Consequently, the peak correspondence with Conv4 may reflect category structure rather than the representation that supports cross-category male/female decoding. The current analysis does not establish that shared gender-related information specifically depends on mid-level features.

(3) The connectivity interpretation is overstated. PPI measures task-dependent covariance; it does not establish information transmission, directionality, or an upstream-to-downstream processing sequence. The reported face-body connectivity similarity is also small (r=.08). Also, describing rMTG as a "hub" is not justified without network-centrality measures, lesion evidence, or causal perturbation.

The authors partly achieve their aims. The results provide credible evidence that patterns in rMTG contain information that generalizes across binary male/female-labelled faces and bodies and masculine/feminine-associated objects. They do not yet establish a genuinely abstract representation of gender, a specifically gender-related correspondence with intermediate CNN layers, or a neural hub that transmits information through a directed network. With more precise framing and targeted reanalysis, the study could make a useful contribution to research on social vision and cross-category representation.

Reviewer #2 (Public review):

Summary:

The authors extend the ORGaNICs framework (a recurrent circuit that dynamically implements divisive normalization) to connected cortical areas with explicit top-down feedback. Because the network has a known analytical fixed point that coincides with (or closely approximates) the normalization equation, the authors can linearize about that fixed point and derive closed-form expressions for the power spectral density, inter-areal coherence, and communication subspaces. Using a two-area instantiation (V1 & V2) with a single fixed parameter set and no data fitting, they show the model reproduces: (i) contrast-response functions with steeper slope V2; (ii) gamma-band power and coherence peaks that shift to higher frequency with contrast; and (iii) a low-dimensional inter-areal communication subspace that is lower-dimensional than the within-area subspace. They derive parallel predictions of what happens by changing model parameters: feedback gain enhances inter-areal and suppresses within-area communication, and normalization is necessary for both the oscillatory dynamics and the reduced subspace dimensionality. A three-area extension (V1&V4, V1&V5/MT) is used to argue that differential top-down feedback can dynamically route functional connectivity.

Strengths:

(1) Analytical tractability: Deriving power spectra, coherence, and communication-subspace structure in closed form from a known fixed point is genuinely valuable.

(2) Conceptual unification: Framing coherence and communication subspaces as arising from the same normalization-driven dynamics is an elegant and useful contribution.

(3) Breadth from few assumptions: A large range of phenomena (contrast gain, gamma dynamics) emerges from normalization-based model assumptions.

(4) Biological grounding: The mapping of model variables onto identified cell types connects the abstract computation to known cortical microcircuitry.

(5) The prediction that input-gain versus feedback-gain modulation produce distinct spectral signatures gives experimentalists a clear way to test the framework.

Weaknesses:

(1) Comparisons are qualitative, not quantitative: The theory/experiment panels are visual side-by-side comparisons. There is no quantitative goodness-of-fit for any predictions.

(2) The simulations use τ ≈ 1 ms for all cell types, which the authors acknowledge is unrealistically short; realistic values would shift the gamma peaks to lower frequencies.

(3) Divisive normalization is a special case and is recovered exactly only for the identity recurrent matrix (self-normalization). Some statements that the circuit implements divisive normalization exactly need softening.

(4) The element-wise (multiplicative) interaction in the modulator dynamics is not tied to a specific cellular mechanism.

Reviewer #2 (Public review):

Summary:

The authors provide the first thorough profiling of neurons in Tribolium characterized by the expression of the transcription factor foxQ2, which will be useful for developmental neurobiology. They use state-of-the-art methods convincingly to not only identify the neurons, but also to further characterize them anatomically and neurochemically.

Strengths:

Thorough and meticulous application of state-of-the-art anatomical methods in a non-standard laboratory organism.

Weaknesses:

No weaknesses were identified by this reviewer.

Comments:

I don't really have any major suggestions at all. Loved the work.

There is only one tiny nitpicking aspect:

P21: "Biogenic amines are involved in learning and memory and setting arousal threshholds (Davis, 2023), which are functions performed by the mushroom bodies and related to the function of the central complex in goal directed navigation, respectively."

MBs mainly process olfactory memory. At least in Drosophila, most other kinds of memories are being supported elsewhere.

https://pubmed.ncbi.nlm.nih.gov/10454381/

such as, e.g., visual pattern learning in the CX

https://pubmed.ncbi.nlm.nih.gov/16452971/

or motor learning in motor neurons

https://pubmed.ncbi.nlm.nih.gov/38779314/

or ventral ganglion, antennal lobes, and median bundle for place learning:

https://pubmed.ncbi.nlm.nih.gov/10706599/

If the authors focus on MBs, this sentence ought to reflect the fact that the function of the MBs is much narrower than the current sentence appears to suggest.

Reviewer #2 (Public review):

Summary:

This work provides an open-source, Python-based, graphical user interface for curating the detection and classification of dentate spikes (DSs) from hippocampal local field potential (LFP) recordings. The tool may also be used to detect, but not classify, sharp wave-ripples (SPW-Rs). The tool utilizes previously published Python packages for loading LFP files and creating experiment-specific probe objects. Detection and classification parameters are clearly defined and logged in a parameter file before starting processing. Once LFP data has been mapped to the probe object, event detection occurs across all channels. DSs are detected as qualifying peaks in the filtered DS band LFP, while SPW-Rs are detected as qualifying peaks in the filtered ripple band amplitude envelope. An initial curation step allows visualization of the LFP, instantaneous current source density (CSD), and depth-by-frequency band power plots for determining the approximate channel locations of key anatomical regions (i.e., CA1, the hippocampal fissure, and the hilus of the dentate gyrus). The optimal channel for detection is further refined in the next step by comparing event waveforms and quality metrics across channels. Artifacts and noisy waveforms can also be manually excluded during this step. Finally, DSs detected from the optimal channel are classified by computing the CSD profile around events and then clustering the first two principal components of all CSDs. The authors claim that this customizable tool will standardize DS detection and classification.

Strengths:

Toothy's detection and classification algorithms are appropriate and well-validated in the literature. The ability to change many parameters, the CSD calculation method, and clustering algorithm is helpful for precise replication of methodology that has varied previously. Default parameters optimized for mouse recordings provide a standardized starting point for rodent researchers.

The authors' commitments to transparency and user-friendliness are to be commended (e.g., clear instructions, defined and logged parameters, multiple visualization options, etc.) and are likely to be appreciated by new users. Researchers with little-to-no coding experience should find this tool especially powerful for jumpstarting their own DS analyses.

While not the focus of the paper, the capability to detect SPW-Rs provides an additional use case for Toothy and streamlines simultaneous analysis of SPW-Rs and DSs.

Weaknesses:

I encountered unexpected errors while trying to load LFP data into Toothy for testing, indicating that the "data ingestion" stage of Toothy requires minor code revision.

Toothy's utility for recordings that do not produce an LFP depth profile is unclear. According to the authors, Toothy allows probe designs with irregular spatial sampling (e.g., tetrodes) to be used. However, recording from a linear probe with electrodes spanning from approximately the hippocampal fissure to the hilus of the dentate gyrus is required for Toothy's full functionality. For example, Toothy uses a DS type classification algorithm that relies on sufficiently sampled CSD depth profiles that tetrode recordings cannot provide. As such, usage is currently restricted to detection only for certain recording setups.

The documentation on Toothy's output could be improved. Specifically, the work does not state which files different data are saved to or list the properties saved per detected event. Furthermore, the work does not discuss the potential importance of DS properties that are saved besides those related to the timing of the DS and its type.

Reviewer #3 (Public review):

In this manuscript, the authors investigate the role of attention in foveal processing during a naturalistic task. They record neural activity from extrastriate visual areas V4 and inferotemporal cortex, as well as from the lateral prefrontal cortex, in macaques performing a free-gaze visual search task. In this task, animals searched for a face or house target among multiple complex stimuli, with no constraints on eye movements. Unlike classic studies of visual attention, which often rely on controlled fixation, this work examines neural activity in both foveal and peripheral receptive fields during naturalistic eye movements.

The main question addressed by the authors is how feature-based attention is distributed and coordinated across foveal and peripheral visual fields during active search, and how this attentional processing influences saccade behavior. The authors show that foveal units in visual areas exhibit feature-based attentional enhancement, with stronger responses when a fixated stimulus is a target compared to when the same stimulus serves as a distractor. Peripheral units in visual and prefrontal areas show both feature-based and spatial attentional modulation, consistent with prior work. Finally, the authors show that attentional modulation depends primarily on stimulus category rather than response magnitude, with neurons showing similar enhancement for all images within the target category regardless of how strongly individual images drive the cell.

There are several notable strengths of this paper including:

(1) Disentangling feature-based and spatial attention during naturalistic vision remains a central challenge. This paper tackles both simultaneously, parsing neural populations by object selectivity (face-selective, house-selective, non-selective) and RF position (foveal vs. peripheral).

(2) The unconstrained search task (Fig. 1A) moves beyond the dominant fixed-gaze, cued-attention designs (Zhou & Desimone, 2011) to study attention as it operates during natural behavior, with sequential fixations and voluntary saccades.

(3) The scale of the multi-area recordings is a major strength and is well aligned with current trends in primate and human neuroscience toward large-scale, multi-area recordings. Simultaneous recordings from visual and prefrontal areas, comprising over 4,900 foveal units and more than 1,500 peripheral units, enable meaningful cross-area latency comparisons and area-specific analyses of attentional modulation. This study builds on the authors' previous analyses of this dataset by expanding the scope to show that feature-based attention generalizes across neuronal classes and operates on categorical identity rather than response magnitude.

(4) The combination of simultaneous multi-area recordings and a rich behavioral paradigm provide a dataset that is well suited for population decoding, cross-area interaction analyses, and trial-by-trial prediction of saccade choices, which could substantially deepen mechanistic understanding beyond the largely univariate comparisons presented here.

While the data broadly support the paper's main conclusions, several issues limit the strength of the mechanistic interpretation and should be taken into consideration:

(1) Receptive field size is not explicitly quantified and may confound foveal-peripheral comparisons. Units are classified as foveal or peripheral based on responsiveness to the cue versus the search array (Methods, p. 17), but the manuscript lacks essential information about receptive field sizes, eccentricities, and the number of search stimuli falling within each receptive field and related proper controls. This is critical because receptive fields in visual area V4 at foveal eccentricities are relatively small (Gattass et al., 1988; Desimone & Schein, 1987), whereas receptive fields in inferotemporal cortex can span several degrees to tens of degrees and often include the fovea (Op de Beeck & Vogels, 2000; DiCarlo & Maunsell, 2003; Zoccolan et al., 2007). Given the 2{degree sign} × 2{degree sign} stimulus size, multiple search items could potentially fall simultaneously within peripheral receptive fields. This introduces a potential confound, as attentional modulation is known to be strongest when multiple stimuli appear within a single receptive field (Reynolds et al., 1999). Although the authors acknowledge this issue for visual area V4 (p. 17), it is neither quantified nor controlled for. Without explicit receptive field mapping relative to the search array, comparisons between foveal and peripheral units, as well as between visual areas, are difficult to interpret cleanly.

(2) Attentional modulation is difficult to dissociate from saccade planning and decision-related signals. The free-gaze paradigm enhances ecological validity but introduces a temporal confound: mean distractor fixation durations are approximately 156 ms (p. 9), while attentional effects emerge between 137 and 170 ms after fixation onset (Fig. 2). As a result, the reported attentional modulation coincides with preparation of the subsequent saccade. Neural activity measured in the primary analysis window (150-225 ms; p. 19) therefore likely reflects a mixture of visual, attentional, motor planning, target recognition, and behavioral relevance signals, all of which are known to modulate responses in visual areas at similar latencies (e.g., Chelazzi et al., 1998). Moreover, target fixations (~257 ms) and distractor fixations (~156 ms) occur on fundamentally different behavioral timescales, which may inflate apparent foveal attentional effects. While the authors suggest that these timing differences support the idea that foveal feature-based attention facilitates prolonged fixation on target stimuli, this interpretation is not fully supported by the current analyses. That said, the saccade-aligned analyses of peripheral units (Fig. S3) partially mitigate this concern by demonstrating that feature-based modulation persists through saccade execution.

(3) The "attention-out" condition for spatial attention lacks directional control. In the spatial attention analyses (Fig. 4D-F), the "attention-out" condition appears to include all fixations followed by saccades directed away from the receptive field, regardless of saccade direction. This differs from classic spatial attention designs, which typically use controlled anti-saccades or saccades to fixed locations opposite the receptive field (e.g., Moore & Armstrong, 2003; Gregoriou et al., 2009). Saccades directed toward locations adjacent to, but outside, the receptive field may still partially engage spatial attention mechanisms near the receptive field via broad attentional fields or motor preparation gradients (Bisley & Goldberg, 2010). In addition, the "attention-out" condition likely contains a heterogeneous mixture of trials in which the stimulus in the receptive field is either a target or a distractor, since feature-based attention effects are derived from this same pool of trials. As a result, spatial and feature attention effects are not fully orthogonal, and variance related to feature attention may already be embedded in the spatial attention baseline.

[Editors' note: the authors have provided responses to each of these points.]

Reviewer #2 (Public review):

Summary:

Song et al. comprehensively analyzed the SHIV-infected macaque B cell repertoires and commonalities among their antibody responses, despite their diverse genetic background. They suggest these studies would inform HIV-1 vaccine design.

Strengths:

This study is well-designed and used proper analysis methods, and the figures are clear and effectively presented.

Weaknesses:

However, it tends to overstate its novelty and significance, emphasizing points that are relatively obvious (e.g., different classes of antibodies can recognize a common epitope) and appears to have been overwritten and unnecessarily fancy ("conceptually analogous to ecomorph evolution", "epitopic convergence"). Moreover, some limitations of the rhesus macaque model and the differences between bnAbs and nAbs should be discussed. That said, the underlying data are solid and important in their detail, and the manuscript will be a useful resource for HIV-1 vaccine and pathogen studies.

Reviewer #2 (Public review):

Summary:

Foik et al. studied the regulation of the fro operon in response to HOCl, an oxidant derived from immune cells, especially neutrophils. They use a transcriptional fusion of YFP to the froA promotor in an mCherry expressing P. aeruginosa strain to determine fro-induction under the microscope. They use this system to study fro expression in medium, in the presence of neutrophils and macrophages, neutrophil-conditioned medium, and several chemical stimuli, including NaCl, HOCl, hydrogen peroxide, nitric acid, hydrochloric acid, and sodium hydroxide. They also use a corneal infection model to demonstrate that froA is upregulated in P. aeruginosa 20 h post infection and perform transcriptional analyses in WT and a froR mutant in response to HOCl.

Strengths:

Their data clearly shows that HOCl is a strong inducer of the fro Operon. Addition of HOCl-quenching chemicals together with HOCl abrogates the response. They also show that a froR mutant is more susceptible to HOCl than WT. Their transcriptomic data reveals genes under control of the FroR/FroI sigma factor/anti sigma factor system.

Weaknesses:

Although the presented evidence is mostly solid, some of their findings need to be evaluated more carefully; explaining the rationale behind some of the experiments might enhance the article; and some of the models proposed by the authors seem far-fetched, as outlined below:

Unexpected outcomes and open questions for future research:

(1) As outlined above, HOCl seems to be the main inducer of the fro operon. Interestingly, during interaction with immune cells, macrophages and neutrophils seem to induce a reporter gene under fro control in a similar manner, although macrophages are generally thought to produce less HOCl, when compared to neutrophils. May be this view needs to be revised, or another reactive species, produced by macrophages, can activate the fro operon as well.

(2) HOCl is typically unstable in the presence of biomolecules. Nevertheless, medium conditioned by activated neutrophils is a strong inducer of the fro operon. The medium used by the authors for this experiment contains taurine, and, as the authors acknowledge, this taurine will likely react with HOCl to form the more stable taurine N-chloramine. Similarly, the MinA bacterial medium used to treat P. aeruginosa with HOCl directly also contains ammonium ions at mM concentrations, which could potentially react with HOCl to form monochloramine. It could be speculated that taurine N-chloramine and other chloramines are as effective as HOCl in activating the fro-operon.

(3) The fro operon was originally described to be activated by shear stress ("flow-regulated operon"). How shear stress and HOCl-stress are related, or if fro activation by both stimuli is a coincidence, remains unclear. The authors propose a model, in which flow transports oxidizing molecules, which ultimately activate the fro operon. However, the initial work by Sanfilippo et al. (2019, Nat Microbiol) used plain LB medium in a fluidic chamber to induce the shear stress, which should be free of oxidants, and certainly of HOCl.

Comments on revised version:

The authors have addressed my concerns appropriately.

Reviewer #2 (Public review):

Summary:

Sleep plays a critical role in memory consolidation, but the neural mechanisms underlying this relationship remain incompletely understood. The authors examined a specific subset of PAM dopaminergic neurons, PAM-α1, and DPM neurons in Drosophila. These neurons have previously been implicated in memory, and DPM neurons have also been linked to sleep. The study explores whether this circuit provides a mechanistic link between sleep and memory consolidation.

Strengths:

The authors report several novel findings. Brief activation or inhibition of PAM-α1 neurons, or brief inhibition of DPM neurons during the first few hours after training, impairs 24-hour LTM. Notably, these brief manipulations disrupt sleep for many hours afterward, particularly during the night. The authors further show that perturbation of PAM-α1 and DPM neurons impairs sleep and appetitive memory consolidation under starvation conditions, and that pharmacological sleep induction during the night rescues the LTM defects. Together, these findings suggest that PAM-α1 and DPM neurons are involved in sleep regulation and LTM consolidation under starvation. These are important observations that advance our understanding of the circuits regulating sleep and memory consolidation.

Weaknesses:

Some claims require additional evidence or clarification.

(1) Previous studies linking impaired memory to reduced sleep have primarily examined conditions involving severe sleep deprivation. In contrast, this manuscript argues that relatively modest decreases in total sleep, accompanied by sleep fragmentation, are sufficient to impair memory consolidation. It remains unclear whether sleep fragmentation of this magnitude is itself critical for LTM consolidation. An independent method for inducing comparably mild sleep loss and fragmentation would be needed to directly test this interpretation.

(2) It is unclear why both activation and inactivation of PAM-α1 neurons produce similar effects on sleep and memory. In addition, MB299B-labeled neurons exert stronger effects on memory than MB043B-labeled neurons, whereas MB043B-labeled neurons have stronger effects on sleep. If sleep disruption is the primary driver of impaired memory consolidation, a stronger correspondence between the sleep and memory phenotypes might be expected. The authors speculate that MB043B may affect sleep through non-PAM neurons, but without identifying the relevant neurons, this remains speculative.

(3) The complex schematic model (Fig. 12), with parallel circuits and unidentified neuronal groups, underscores the difficulty of interpreting the current data. In the "less activity" arm of the model, distinct circuits are proposed to regulate sleep and LTM, respectively, and DPM neurons are not included. This makes it difficult to reconcile the model with the central claim that the PAM-α1-to-DPM microcircuit links sleep and LTM consolidation.

(4) The TRIC-LUC reporter system is not ideal for resolving dynamic changes in neuronal activity. Activity-dependent Ca²⁺ signaling must first reconstitute the TRIC transcriptional system, which then drives luciferase transcription, translation, and accumulation. The original characterization of TRIC indicates that TRIC signals accumulate and decay over several hours. Thus, the kinetics of the TRIC-LUC reporter should be interpreted cautiously, particularly when inferring transient or precisely timed changes in neuronal activity.

(5) Including data from training under fed conditions would provide a more complete understanding of state-dependent neural activity and would help distinguish starvation-specific effects from more general circuit mechanisms.

Reviewer #2 (Public review):

Summary:

The authors set out to compare functional encoding in the tuft dendrites and somata of a specific cortical cell type during motor planning and learning.

Strengths:

The investigation of a specific projection type (L5 ET) is a strength that aids reproducibility and interpretation. The elegant approach to increasing the depth of field of dendritic imaging is another strength. The data analyses are largely clear in their methods, scope, and interpretation. The writing is extremely clear and appropriately referenced, with an excellent Introduction, in particular.

Weaknesses:

This work is largely observational, describing signals that might reflect computational transformations and/or instruct plasticity, but those possibilities have not yet been deeply investigated. The manuscript does a good job of laying out these as future directions.

Reviewer #2 (Public review):

Summary:

In this paper Drs. Kercmar, Murko and Bombek make a series of observations related to the role of AVP in pancreatic islets. They use the pancreatic slice preparation that their group is well known for. The observations on the slide physiology are technically impressive. However, I am not convinced by the conclusions of this manuscript for a number of reasons. At the core of my concern is perhaps that this manuscript appears to be motivated to resolve 'controversies' surrounding the actions of AVP on insulin and glucagon secretion. This manuscript adds more observations, but these do not move the field forward in improving or solidifying our mechanistic understanding of AVP actions on islets. A major claim in this manuscript is the beta cell expression of the V1b Receptor for AVP, but the evidence presented in this paper fall short of supporting this claim. Observations on the activation of calcium in alpha cells via V1b receptor align with prior observations to this effect and can explain the effects of beta cell calcium and insulin secretion better than an explanation where beta cells express functional V1BR, for which direct evidence is lacking.

I have focused my main concerns below. I hope the authors will consider these suggestions carefully - please be assured that they were made with the intent to support the authors and increase the impact of this work.

Strengths:

The main strength of this paper is the technical sophistication of the approach and the analysis and representation of the calcium traces from alpha and beta cells.

Weaknesses:

(1) There are excellent data that indicate that the actions of AVP are mediated via V1bR on alpha cells and that V1bR is 1) not expressed by beta cells and 2) does not activate beta cell calcium at all at 10 nM - which is the same concentration used in this paper (Figure 4G) for peak alpha cell Ca2+ activation (see https://doi.org/10.1016/j.cmet.2017.03.017; cited as ref 30 in the current manuscript). Any published stimulatory actions of AVP on insulin secretion can be explained by the potentiating effects of glucagon, released in response to AVP stimulation of alpha cells.

(2) The RNAscope data offered in the revision as a second line of evidence for the expression of the V1bR in beta cells do not convince. I applaud the authors for trying as these are hard experiments to do well, as evidenced from the Gcg RNAscope signal that is not at all concentrated in the islet periphery, and in fact both color puncta occur outside of the islet at similar density. Absent a convincing concentration of Gcg signal (which is a very abundant transcript in alpha cells), it is hard to depend on these results. They certainly do not substitute experiments to determine cell autonomous activation of isolated beta cells by AVP. Claim of beta cell expression of V1br, require a more direct demonstration by staining (if appropriate antibodies exist), by beta cell-specific deletion of V1br, or by documenting the direct calcium activation in isolated beta cells in the absence of alpha cells. This should include a demonstration of Gaq-dependence in isolated beta cells.

(3) We know from bulk RNAseq data on purified alpha, beta, and delta cells from both the Huising and Gribble groups that there is no expression of V2a. I will point you to the data from the Huising lab website published almost a decade ago (http://dx.doi.org/10.1016/j.molmet.2016.04.007) - which is publicly available and can be used to generate figures (https://huisinglab.com/data-ghrelin-ucsc/index.html). They indicate the absence of expression of not only AVP2 receptors anywhere in the islet - but the lack of expression of V1bra, V1brb, and Oxtr in beta cells. These AVP/OXT receptor expression data are largely and helpfully confirmed by the efforts in this paper that involved the generation of the V1aR agonist and V2R antagonist.

(4) Importantly, the lack of V1br from beta cells does not invalidate observations that AVP affects calcium in beta cells, but it does indicate that these effects are mediated 1) indirectly, downstream of alpha cell V1br or 2) via an unknown off-target mechanism (less likely). The different peak efficacies in Figure 4G would also suggest they are not mediated by the same receptor. The recent work by Huixia Ren and colleagues (PMID: 41916313) that demonstrates that glucagon accelerates the frequency of beta cell calcium is in line with such a scenario.

(5) The use of forskolin across almost all traces complicates the interpretation of the results. The design does not account for the elevation of cAMP in alpha cells and subsequent release of glucagon - particularly upon co-stimulation with AVP which permits glucagon release by activating a calcium response in alpha cells. This glucagon then could activate beta cells. If resolving the mechanism of action is the goal, often less is more. The activation of Gaq-mediated calcium is not cAMP dependent (although the downstream hormone secretion clearly often is). As was shown, AVP does not activate calcium in beta cells in the absence of cAMP. The experiments should have been completed in the absence of cAMP/forskolin, which would likely have had different outcomes on the beta cell responses and to the hormone secretion.

(6) It is motivated by a desire to 'study the AVP dependence of both alpha and beta cells at the same time'. As best as I can determine, the design choice to conduct most studies under sustained forskolin stimulation is related to the permissive actions of AVP on hormone secretion in response to cAMP-generating stimuli. The permissive actions by AVP that are cited are on hormone secretion - which in many cell types requires activation of both calcium and cAMP signaling. Whether the activation of V1br and subsequent calcium responsive is permitted by cAMP is unclear. I believe the argument the authors are making here is that the activation of beta cell calcium by AVP is permitted by forskolin. i.e. the cAMP stimulated by it in beta cells.

(7) Figure 9 suggests a pharmacological activation of beta cell V1bR in the low pM range. How do the authors reconcile this compare with the apparent absence of an effect of AVP stimulation at low pM to low nM doses in beta cells (Figure 5A). I note that there are changes over time with sustained beta cell stimulation with 8 mM glucose, but these changes are relatively subtle, gradual and quite likely represent the progression of calcium behaviors that would have occurred under sustained glucose irrespective of these very low AVP concentrations. I will note that the Kd of the V1bR for AVP is around 1 nM, with tracer displacement starting around 100 pM according to the data in figure 6B, which is hard to reconcile with changes in beta cell calcium by AVP doses that start 10-100-fold lower than this dose at 1 and 10 pM (Figure 9).

Reviewer #2 (Public review):

Summary:

The authors first introduce a framework to understand how different phenotypic drivers of viral evolution, i.e., changes in transmissibility versus immune escape, complicate epidemic forecasting using only genetic data. To overcome these complications, they advance an evolutionary "selective pressure" metric to predict population-wide epidemic growth from genetic data alone. Separately, they introduce a latent space model to infer a "pseudo" population immune structure from geographic variation in viral lineage dynamics, and find that the inferred pseudo-structure predicts human serological data.

Strengths:

This paper begins with a useful pedagogical exposition on the connection between fitness-driven frequency dynamics and underlying mechanisms of viral-immune co-evolution. A major contribution of this paper - a method to infer variant-specific escape properties from geographically non-uniform variant frequency dynamics alone - is an interesting and potentially timely one, given the advance of sequencing-based surveillance.

Weaknesses:

The logical flow of the pedagogy part of the text works against the reader, which is problematic since it motivates the rest of the text. Moreover, some important modelling choices and procedures, particularly with respect to the selective pressure metric, are only cursorily described in the methods section. The lack of explanation and detail, especially relative to more simple choices that are seemingly motivated by the authors' own theory, makes it difficult to understand and therefore assess their validity and/or necessity.

Reviewer #2 (Public review):

Summary:

dbGIST appears to be the first dedicated multi-omics resource worldwide that is specifically focused on GIST.

Strengths:

The main value of the paper is not simply that the authors collected datasets, but that they built a usable resource around them, with cohort-aware analyses, curated clinical labels, interactive visualizations, downloadable results, selected API access, and an optional LLM-assisted interface. The work is solid, and the database is likely to be useful for GIST researchers interested in target discovery, cross-dataset validation, drug-response hypotheses, and translational follow-up.

The MCM7 analysis is a reasonable use case. It shows how a user can start from one candidate gene and then move across transcriptomic, proteomic, clinical, single-cell, immune-related, drug-response, and experimental evidence. I do not see this as the main discovery of the paper, but rather as a practical demonstration of what the database can do. That is appropriate for a resource manuscript.

Weaknesses:

(1) The authors should make the organization of the platform a little easier to follow. The manuscript refers to five primary omics layers, six omics-focused pages, and eight analytical modules. This structure is understandable after reading the relevant sections, but it may not be immediately obvious to readers. A brief clarification of how the omics layers, web pages, and analytical modules relate to each other would help.

(2) Since dbGIST is a live web resource, the authors should provide a clear versioning statement. The manuscript should indicate which version of the database corresponds to the analyses and figures reported in the paper, and how future updates will be distinguished from the version evaluated here. This is a small point, but it matters for reproducibility.

(3) The API function is a strength of the resource, but it is still described rather generally. The authors should give more concrete documentation of what can be accessed through the API, what inputs are required, and what type of output is returned. This could be placed in the supplementary materials. It would make the database more useful for computational users.

(4) The manuscript should clarify the status of downloadable data. It is clear that figures, source-data tables, and selected derived outputs are available, but it is less clear whether the full processed matrices used internally by the platform are downloadable or only maintained for deployment. This distinction should be stated plainly.

(5) The statistical reporting in the MCM7 clinical-association analyses needs a little more care. Several p-values are shown across different cohorts and clinical variables. The authors should state whether these are nominal p-values or adjusted p-values. If they are nominal, that is acceptable for a resource demonstration, but the exploratory nature of the analyses should be made clear.

(6) The ROC analyses for imatinib response should include sample sizes, and confidence intervals for AUC values would be useful if available. Some of the AUC values are high, and without group sizes, it is difficult to judge how stable those estimates are. The authors should avoid implying that these ROC results are validated predictive models.

(7) The interpretation of MCM7 should be slightly more cautious. MCM7 is a well-known DNA replication and cell-cycle gene, and the single-cell analyses seem to support its association with proliferative cell states. This is biologically consistent, but it also means that MCM7 expression should not be presented as tumour-cell-specific without qualification. The manuscript should frame it mainly as a proliferation-associated signal in the current analysis.

(8) The drug-response section would benefit from a clearer explanation of the response metric. The authors report correlations between MCM7 expression and predicted response to C6-ceramide, but readers need to know whether the predicted value represents IC50, AUC, sensitivity score, or another metric. The direction of interpretation should also be made explicit, since a negative correlation can mean different things depending on the scoring system.

(9) The single-cell annotation would be more convincing if the authors provided a compact marker-gene summary for the major cell types in each single-cell cohort. The current description of annotation by source labels, marker inspection, and manual curation is reasonable, but users of the database would benefit from seeing the marker evidence behind the labels.

(10) The experimental validation section should include a few routine details that are currently not easy to find. The siRNA sequences or target regions, number of biological replicates, statistical tests for the CCK-8 and wound-healing assays, and details of wound-closure quantification should be reported. These additions would make the in vitro part more reproducible.

(11) The wound-healing result should be interpreted with caution. Since MCM7 knockdown reduces proliferation, reduced wound closure could reflect changes in proliferation, migration, or both. Unless proliferation was controlled during the wound-healing assay, the authors should avoid describing this result as purely migratory.

(12) The LLM-related claims should remain conservative. The assistant is a useful feature for navigation, plain-language explanation, and user support, especially for clinicians or wet-lab researchers. However, the strongest statements about the LLM transforming interpretation or automating analysis should be toned down. The important point is that the LLM layer helps users interact with the resource, while the numerical analyses come from predefined dbGIST modules.

Reviewer #2 (Public review):

This is a study of the dynamics of plant volatile emissions, using a curve-fitting approach to describe salient properties of the dynamics of plant volatile chemicals. The study is interesting and unique in taking this approach. Some of the dynamics uncovered (e.g. lagged emission of many sesquiterpenes) are already well known using less sophisticated approaches, while other properties (diurnal cycles in emission dynamics) are newly uncovered. The approach in general is new for the topic of plant volatile emissions, but curve-fitting is widely used to describe the dynamics or function-valued responses of plants and other organisms. The study thus reads as rather methods-focused, giving tidbits of interesting properties of the dynamics of plant VOCs rather than being structured strongly around clear biological hypotheses. The method seems like a logical and robust way to analyze the dynamics of plant VOCs. I believe the impact of the work will largely depend on whether there are substantial and meaningful outcomes (for herbivores, downstream processes of induction, etc) due to the differences in VOC dynamics described via these methods that would be hard to observe in other ways. If so, there will be a need to adopt robust methods such as this to describe the salient features of those dynamics. At present, I do not believe there is evidence one way or another as to whether the subtle differences in VOC dynamics have large consequences.

The paper sells itself as describing a new technique for describing response curves generally across biological systems, but it only uses this technique to look at the dynamics of induced plant volatiles. I believe to show general utility of this approach, a wider range of examples of plastic responses to stimuli across organismal groups would be needed. I am, however, convinced that this approach is both novel and useful within the scope in which the examples are shown (i.e. in describing the dynamics of induced plant responses). Some of the text purporting novelty in uncovering shared and divergent responses across the tree of life seems pretty overstated.

Much of the introductory and discussion text is quite broad, and I wonder if the technique is really meant to be applicable to the specific case that is described (repeated measures of an induced volatile response). Likewise, there has been considerable work in such realms as behavioral science, function-valued traits (e.g. Stinchcombe et al 2012), performance curves (Kingsolver various papers), etc to describe dynamic or variable responses phenomenologically, and there are approaches including GAMs, parametric curve fitting, and other techniques that probably report the same salient features as the approach here. Indeed, there are already statistical techniques to assess the macroevolution of response curves (e.g. Goolsby 2015) and wide discussions as to how to compare function-based responses among organisms (The Functional Phylogenies Group 2012). So in the broad scheme of biology, I am not sure I'm convinced of the novelty of the approach. However, I believe it is novel within the context in which it is used here. The salient part of the methods is that it uses predefined attributes of dynamics (onset, duration, etc) based on a gamma distribution that the researchers (with good reason) believe to be biologically meaningful. This is in contrast to multivariate approaches (e.g. Izem et al 2005) that attempt to find salient dynamic features in a less constrained way.

I would have liked to see a clear description of model fits (e.g., how much of the variation in the real data is described by the fitted model). This seems important because there are quite a number of constraints placed on model fitting - so presumably when a model blind to those constraints picks unrealistic parameters, that would suggest that the constrained model probably does not fit the data all that well.

I am curious about the normalization process in the 'normalized emission' that is analyzed throughout the study. Normalization to leaf size makes sense, though I was less clear about L459: "Additionally, values were normalized to the maximum response observed in each experiment, yielding a range of positive values < 1." Why was this needed? Is the 'maximum response observed in each experiment' across all plants/compounds/treatments or within a single plant? In general, is there a way of reporting VOC emission rates in absolute values (e.g. umol / Liter air)? Normalization would presumably not impact most curve properties very much, but it could have effects on 'integral', and the need for within-experiment normalization would suggest a lack of transferability or comparability among datasets from different experiments (at least as regards 'integral'), which is suggested as a major advantage of this approach in the discussion.

Reviewer #2 (Public review):

Summary:

In this manuscript Zhu, Emanuelli and colleagues describe a novel pharmacological activator of the Integrated Stress Response kinase GCN2. The work is conclusive and biochemically solid. This work significantly adds to the pharmacological arsenal targeting the ISR and in particular GCN2.

Strengths:

Strong biochemistry, novel molecular activator of GCN2 (GCN1 independent).

Weaknesses:

Rationale for the screen not exploited in the results (e.g. pathogenic GCN2 mutants), lots of cell-based read-outs not endogenous.

Comments on revised version.

The authors did a great job at addressing my initial critique on their manuscript and consequently I have no further comment.

Reviewer #2 (Public review):

Summary:

The authors showed the localization pattern of nucleolus components, including Pre-rRNA, a precursor of rRNAs, changes during meiotic prophase I, particularly with the localization of these nucleolar components to the X-Y body, which shows inactivation of RNA polymerase II transcription, during pachynema. The localization of Pre-rRNA depends on ATR kinase and gammaH2AX. The chemical inhibition of rRNA transcription disrupts the binding of pre-rRNA to the X-Y body and suppresses the inhibition of the RNA polymerase II-mediated transcription on the sex chromosomes.

Strengths:

The cytological analysis, combined with the chemical inhibition, provided solid evidence to support the idea that, together with the remodeling of the nucleolus structure, pre-rRNA is an essential component of sex chromosome inactivation in male mouse meiosis. The role of pre-rRNA in sex chromosome inactivation in male meiosis helps our understanding of how the X-Y body, which would be a biological condensate, would be formed; e.g. for example, this Pre-rRNA may promote phase separation.

Weaknesses:

However, there is limited information on how Pre-rRNA is recruited to only sex chromosomes and how the RNA promotes the inactivation of sex chromosomes. Of course, these will be a target of future study. One major weakness of this paper is a poor description of the results, with fair presentation and interpretation of the data.

Reviewer #3 (Public review):

Summary:

In their manuscript, the authors investigate how glutaminolysis (GLS) and mitochondrial pyruvate import (MPC2) jointly shape B cell fate and the humoral immune response. Using inducible knockout systems and metabolic inhibitors, they uncover a "synthetic auxotrophy": When GLS activity/glutaminolysis is lost together with either GLUT1-mediated glucose uptake or MPC2, B cells fail to upregulate mitochondrial respiration, IL 21/STAT3 and IFN/STAT1 signaling is impaired, and the plasma cell output and antigen-specific antibody titers drop significantly. This work thus demonstrates the promotion of plasma cell differentiation and cytokine signaling through parallel activation of two metabolic pathways. The dataset is technically comprehensive and conceptually novel, but some aspects leave the in vivo and translational significance uncertain.

Strengths:

(1) Conceptual novelty: the study goes beyond single-enzyme deletions to reveal conditional metabolic vulnerabilities and fate-deciding mechanisms in B cells.

(2) Mechanistic depth: the study uncovers a novel "metabolic bottleneck" that impairs mitochondrial respiration and elevates ROS and directly ties these changes to cytokine-receptor signaling. This is both mechanistically compelling and potentially clinically relevant.

(3) Breadth of models and methods: inducible genetics, pharmacology, metabolomics, seahorse assay, ELISpot/ELISA, RNA-seq, two immunization models.

(4) Potential clinical angle: the synergy of CB839 with UK5099 and/or hydroxychloroquine hints at a druggable pathway targeting autoantibody-driven diseases.

Comments on revised version.

Authors extensively modified the text with great care and provided new data e.g. Fig. 5. Collectively, this is convincing and hence, I have no further comments.

Reviewer #2 (Public review):

Summary:

The study demonstrates that CRISPR-Sirius provides a powerful approach to investigating chromosome dynamics in living cells during environmental stress. By focusing on serum starvation, the authors show that this process induces global nuclear changes, including a reduction in nuclear area and increased morphological dynamism, while at the same time driving specific reorganization of chromosome 1. Chromosome 1 relocates toward the nuclear periphery and displays distinctive patterns of motion, maintaining overall motility but punctuated by occasional long-distance displacements, particularly near the nuclear envelope. Importantly, the analysis reveals that homologous copies of chromosome 1 do not behave uniformly: peripheral loci become more mobile and responsive to starvation, whereas central homologs remain comparatively stable, often associated with nucleolar subcompartments. By integrating live imaging with machine learning and explainable AI analysis, the study highlights the complexity of nuclear organization and provides valuable insights into how chromosome-specific and locus-specific responses to stress are orchestrated within the three-dimensional nuclear landscape.

Strengths:

The study uses live-cell imaging to investigate the dynamics of loci during starvation. Live-cell tracking and data interpretation are carried out using machine learning and AI models, which is a major strength.

Weaknesses:

The manuscript is at times difficult to follow, partly because the methodological descriptions are highly specialized, especially for non-expert biologists. In addition, the observations are not tested for a mechanistic basis. Experiments that could provide deeper insights are missing, for example, why chromosome 1 moves, why the peripheral homologue dislocates, or why a "long jump" is observed at the periphery even though the speed of the loci does not change. It is also unclear whether a displacement of 0.5 μm is functionally meaningful.

Comments on revised version:

The authors have added some technical information and provided a better discussion of the data, but beyond that, they have not strengthened the conclusions. The observations are not supported by any perturbation assays.

Reviewer #2 (Public review):

Summary:

The Drosophila executioner caspase Dcp-1 has established roles in cell death, autophagy, and imaginal disc growth. This study reports previously unrecognized factors that work together with Dcp-1. Specifically, the authors performed a turboID-based proximal ligation experiment to identify factors associated Dcp-1 and Drice. Dcp-1-specific interactors were further examined for their genetic interaction. The authors report autophagy-related genes, including Debcl and Buffy, to be required for Dcp-1 activation. In addition, the authors present evidence of an interaction between Bruce and Dcp-1. Bruce expression blocks the Dcp-1 overexpression phenotype. Inhibition of effector caspases or overexpression of Bruce commonly reduced wing growth, suggesting a relationship between the two proteins.

Strengths:

The study identifies new Dcp-1-interacting proteins and provides a functional link between Dcp-1 and Sirt1, Fkbp59, Debcl, Buffy, Atg2, and Atg8a. During the revision, the authors have also added convincing new data supporting the interaction between Dcp-1 and Bruce. They further make a strong case regarding the quality of the turboID-proteomics data. Overall, this is a strong manuscript supporting an interesting discovery.

Reviewer #2 (Public review):

Summary:

This study functionally characterizes a single KCNE-like gene, kcne0, from a jawless vertebrate. The authors conducted multiple experiments, including TEVC, VCF, RT-PCR, and RNA-seq to show that KCNQ1 and kcne0 exhibited a broadly overlapping organ distribution in lamprey species, and KCNE0 produced a constitutively active current when co-expressed with lamprey KCNQ1, similar to the effects of human KCNE3 on KCNQ1. This modulation was species-specific, as co-expression of KCNE0 with other species' KCNQ1 was less effective. Moreover, the authors found that truncating the N-terminal had a more significant reduction of the modulatory effects than truncating the C-terminal of KCNE0. Interestingly, the introduction of the tetra-leucine motif from human KCNE4 into KCNE0 conferred KCNE0 with comparable effects of human KCNE4 on KCNQ1.

Strengths:

The authors clearly introduced an early-diverging member of the KCNE family, and convincingly demonstrated the function of this gene, KCNE0. The results are supported by experiments of multiple approaches and are clearly written. The work is significant and will interest readers from the extended research area.

Weaknesses:

No major concerns were identified with the manuscript in general.

Reviewer #2 (Public review):

This manuscript aims to characterize how semantic information is prioritized relative to perceptual details in visual working memory. The central claim is that semantic judgements benefit from faster pre‑decisional access (shorter non‑decision time), and that advantages in evidence accumulation emerge under higher cognitive demands (e.g., when items are outside the focus of attention or must be maintained under interference). Based on this, the paper argues that unattended working‑memory contents are reformatted into more abstract, long‑term‑memory‑like semantic representations that remain more readily accessible than fine‑grained perceptual features.

Strengths:

(1) The question is timely and relevant to current research about the format of visual working memory.

(2) Behaviorally, the semantic advantage is carefully documented in many conditions across datasets.

(3) The use of hierarchical drift-diffusion modelling is helpful to decompose the semantic advantage into cognitive processes such as non‑decision time and drift‑rate components.

Weaknesses:

(1) The strong claims about visual working‑memory representation and "long‑term‑memory‑like" formats rest on an indirect inference from decision‑model parameters to representational content, and this link is not convincingly established. Non‑decision time, as implemented here, bundles many things, such as probe processing, cue processing, retrieval/access, and motor preparation, so reduced non‑decision time for semantic probes could reflect easier question reading, simpler response mapping, or more efficient decision preparation rather than a genuine advantage in accessing semantic memory representations. Although the manuscript acknowledges that non‑decision time includes multiple processes, it nonetheless treats this parameter as primary evidence for a retrieval‑stage semantic advantage, which overstates what the data can uniquely support.

(2) The modelling approach is relatively constrained and does not fully address the underdetermination inherent in mapping latent drift-diffusion parameters onto specific psychological mechanisms. The preferred model that allows multiple parameters (non‑decision time, drift rate, threshold) to vary provides only modest improvements in predictive accuracy over simpler models, and several key drift‑rate effects are present only in particular load or lag conditions. As a result, the theoretical interpretation that semantic prioritization primarily reflects faster access and secondarily more efficient accumulation under high demand appears rather post hoc, and alternative accounts focused on generic task efficiency or strategy differences remain plausible.

(3) The operationalization of "semantic" is narrow and largely categorical, focusing on animacy (animal/object) and a perceptual format dimension (photo/drawing), rather than richer semantic or associative relations among items. This makes it difficult to generalize the conclusions to broader claims about semantic structure and its integration into working‑memory representations. Important recent work on how semantic and associative relationships facilitate the formation, maintenance, and retrieval of visual working memory is not adequately integrated into the theoretical framing. Consequently, the discussion tends to generalize from a specific probe structure to a broader semantic prioritization theory without engaging fully with the existing literature on semantic facilitation and neural decoding of working‑memory content.

(4) The paper contrasts its behavioral/model‑based results with prior neural decoding findings, but the comparison is not fair. Neural decoding provides complementary evidence about the content and format of working memory representations, whereas drift-diffusion parameters reflect downstream decision dynamics given a probe. Because the current work does not include any direct representational or neural measure, its conclusions about representational "reformatting" and long‑term‑memory‑like access remain speculative and, in places, feel like a stretch.

(5) Overall, while the data show a semantic advantage in decision‑stage measures and the modelling provides an informative decomposition of this advantage, the manuscript does not fully achieve its stated aim of characterizing the representational format of visual working memory or demonstrating a mechanistic shift toward long‑term‑memory‑like semantic representations. The work primarily informs decision‑process analyses of the conditions under which semantic judgements are faster and more robust, rather than the nature of visual working‑memory representations themselves.

Reviewer #2 (Public review):

Summary:

The authors used Cryo-EM to obtain a complex between MEK1 and ERK2. They used the same method as previously used by the same authors to form a stable complex between MKK6 and p38, an extra-strong KIM replacing the wild-type KIM in MEK1. Three conformers were resolved, with the highest resolution of 3.0 Å. The multiple conformers indicate more flexibility in MEK2 than in ERK2. These data suggest that nucleotide exchange is possible while maintaining MEK1-ERK2 interactions. SAXS and HDX data reinforce the idea of flexibility. They point to interactions between the two N-terminal domains between histidines at the N-terminus of helix C and between the G helices that are maintained in each of the 3 conformers, and sequence and structure suggest these histidines may be a source of specificity in MEK1-ERK2 versus MKK6-p38 interactions. A 2.2 Å structure of a complex between ERK1 (88% identical to ERK2) and the docking peptide used was also presented. Molecular dynamics simulations suggest that the MEK1-ERK2 complex can assume a fully active configuration of MEK1.

Strengths:

This is the first structure of a MEK1-ERK2 complex. The structural data are valuable additions to our understanding of MAP2K-MAPK interactions. The discussion points offered in the results section are palatable. These include the origins of specificity and the idea of flexibility in the MAP2K in support of a processive mechanism for the dual phosphorylation activity of MAP2Ks.

Weaknesses:

(1) This reviewer considers that the abstract is overstated. Specifically, this paper does not reveal the molecular details of phosphoryl transfer, nor does it demonstrate that substrate binding releases the catalytic machinery.

(2) The discussion is in some places not supported by evidence and in others has superfluous text. Examples follow:

- "Once the αG-helix is docked, and the C-lobe histidine triad is in place, the N-lobe interactions must then be fulfilled." The data in this paper does not suggest an order of events.<br /> - "If the substrate MAPK is incorrect, the N-lobe interaction will not be stabilised, preventing alignment of the MAPK A-loop with the MAP2K active site." This statement could be described as obvious.

(3) Much of the discussion is embedded in the results, such that it is difficult to separate new facts offered by the paper from speculation.

Reviewer #2 (Public review):

Summary:

Here, Sutlief et al. use a novel patch-foraging task to investigate the role of dorsomedial striatal (DMS) neurons in determining when animals disengage from a resource. They show that mice, contrary to canonical optimal-foraging predictions, adopt a strategy in which reward receipt resets timing behavior, with decisions further shaped by both cumulative time spent in a patch and the overall quality of the environment. The authors further demonstrate that a subset of DMS neurons exhibits step-like activity patterns during task performance. Importantly, the accumulation of these state transitions across the neuronal population predicts the timing of patch-leaving decisions on a trial-by-trial basis, providing a potential neural mechanism underlying decisions about when to abandon a currently exploited resource.

Strengths:

This study addresses an important question using a well-designed, interesting behavioral task. The finding that mice employ a reward-triggered exit-timing policy is particularly interesting, as it is pertinent to the many patch foraging-style tasks that have been developed for use in mice, where rewards are delivered as discrete events. The identification of step-like activity in DMS neurons is mostly compelling, and the authors' trial-by-trial analysis linking this activity to behavior provides some support for its relevance to patch-leaving decisions.

Weaknesses:

A key interpretational issue is whether the DMS signal reflects timing specifically, rather than movement initiation or other task-related factors. The authors argue that once a sufficient number of neurons transition, the animal exits the time-investment port. However, it remains unclear whether this population threshold reflects a timing computation that determines when to leave in the more abstract sense, or a signal more directly related to movement onset (that may also be initiated after some proportion of the population has changed its activity). An important control would be to examine neural activity while animals are engaged at the context port. In this epoch, animals presumably do not need to time their departure in the same way, but they still eventually initiate movement. If the DMS signal reflects timing rather than movement, one would not expect the same accumulation-to-threshold pattern of step-like transitions at the context port.

It would also be helpful for the authors to clarify the behavioral definition of the leaving decision. Can mice return to the time-investment port after exiting it if they do not subsequently enter the context port? How exactly is "exit" defined: as withdrawal from the time-investment port, entry into the context port, or some other behavioral event? Is there variability in the latency between time-investment port exit and context-port entry, and if so, is this latency related to DMS step-like activity? These details are important for interpreting whether the neural activity is aligned with a timing decision, movement initiation, or the execution of a transition between task states.

The classification approach for identifying step-like activity seems generally reasonable, and the low false-positive rate against homogeneous Poisson controls is reassuring. However, one potential issue is that the identification of trial-by-trial state transitions is not independent of the session-level characterization of each neuron. The algorithm first fits a sigmoid to the pooled session data and then uses the resulting high- and low-firing-rate states to constrain interval-level fits. This may bias the analysis toward finding step-like transitions in neurons whose activity is only approximately step-like at the session level, effectively reducing the space of alternative solutions available to the interval-level fits. As implemented, the approach therefore functions more as a detector of consistency with a session-defined step model than as an unbiased test of whether individual intervals are better described by discrete state transitions versus alternative dynamics such as ramps or gradual drifts. This concern could be addressed by comparing the constrained sigmoid model against alternatives, such as constant-rate or ramping models, on held-out intervals, or by deriving state parameters from an independent subset of trials and testing classification on the remaining trials.

The inclusion threshold for the accumulation analysis is difficult to evaluate. Sessions were included if they contained at least seven simultaneously recorded step-like units, but this number is hard to interpret without knowing the total number of recorded units per session and the fraction classified as step-like. Seven units may be sufficient for fitting a population accumulation trajectory, but because the cutoff is based on an absolute number rather than a proportion of the recorded population, it is unclear whether included sessions reflect robust population-level step-like dynamics or a relatively small selected subset of DMS activity. Reporting the number and fraction of step-like units per session, as well as the sensitivity of the accumulation results across different inclusion thresholds, would help clarify this point.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

This article looks at differences in how the brain entrains to, or tracks, the rhythmic presentation of syllables and words in speech in infants at increased likelihood versus low likelihood for autism. The authors first sought to characterize how brain responses are modulated by learning the statistical probability of a given syllable following the one before it over the first two years of life. They then sought to identify at which stages of word learning infants at increased likelihood for autism showed difficulties, and whether those difficulties worsened over time. Finally, they sought to indicate whether infants' statistical learning and word learning abilities could predict later verbal skills. The authors found similar developmental trajectories of neural entrainment to syllables in infants at high and low likelihood for autism, but infants at high likelihood for autism had overall weaker syllable-level entrainment. Infants at high versus low likelihood for autism showed different developmental trajectories for word entrainment. Lower syllable entrainment in high-likelihood infants corresponded with poorer verbal outcomes, but word entrainment was not associated with verbal outcomes. Event-related potential responses to words and part words were positively associated with verbal outcomes, however, but only in low-likelihood infants.

Strengths:

Overall, the article provides rigorous statistical analysis of longitudinal EEG data to provide strong support for the claims that neural entrainment to syllable and word features of speech may be a useful marker for language development difficulties, particularly in infants at increased likelihood for neurodevelopmental disorders. The EEG data collection and preprocessing procedures are well within standards within the field. Readers should take care to note that authors indexed neural entrainment to speech using phase-locking values instead of spectral power.

Comments on revised version.

While the statistical analyses are rigorous, there are a few potential confounds to the results. The authors now do a nice job addressing these limitations to the work. For example, sleep status may modulate some of the biomarkers relevant for language learning. Exposure to additional languages may influence performance on the verbal assessment, though the authors do clarify that participants came from majority French-speaking households. As a result, readers should be encouraged to interpret that neural entrainment to speech features is likely a useful mechanism to explain differences in language development, while taking this interpretation with some caution.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors studied transgenic reporter mice to profile Nestin expression in the postnatal mouse cerebellum. They discovered a small population of Nestin+; Atoh1+ granule neuron precursors (GNPs) in the external granule cell layer (EGL). Using immunostaining, qPCR, and RNA-sequencing, the authors showed that these Nestin+ cells are not identical to Sox2+ cells (e.g., the majority of Nestin+ cells are Sox2-). Using various mouse genetic strategies, including an elegant intersectional strategy that specifically targets Nestin+; Sox2+ cells, the authors showed that Nestin+ cells are capable of initiating Sonic hedgehog (SHH) medulloblastoma when they express the SmoM2 allele that drives constitutively active SHH signaling. Lastly, the authors profiled the transcriptomes of these cells and showed that they display enriched stem cell genes and are closer to the transcriptomes of GNP-like cells in medulloblastoma compared to Nestin- GNPs in the developing cerebellum.

Strengths:

(1) The comprehensive mouse genetics experiments, in combination with immunostaining, lineage tracing, and RNA-seq studies, provided compelling evidence that rare Nestin+ cells are present in the EGL, predominantly at the posterior lateral cerebellum in early postnatal mice.

(2) The intersectional genetics experiment unequivocally show that Nestin+; Atoh1+ cells can be oncogenically transformed by SmoM2, leading to SHH medulloblastoma.

(3) The more stem cell-like transcriptomic features of the Nestin+ GNPs compared to Nestin- GNPs provide support for the heterogeneity of this transient progenitor cell population, with implications for development, congenital diseases, and tumors from the cerebellum.

Weaknesses:

Main comments:

My main concern relates to whether these Nestin+; Atoh1+ cells are restrictively localized in the EGL. Both the title "A Rare Nestin-Expressing Granule Cell Precursor Subpopulation Underlies SHH Medulloblastoma Formation" and what the authors described throughout the manuscript propose that Nestin+; Atoh1+ cells in the EGL are the cell-of-origin of SHH medulloblastoma. To definitively conclude this, the authors need to comprehensively analyze all regions of the developing cerebellum.

Most importantly, are Nestin+; Atoh1+ cells present in the rhombic lip? Are there any rhombic lip cells genetically labeled in their intersectional mouse mutants (e.g., the Atoh1Frt-Cre/+; Nes-FlpoER; R26LSL-SsmoM2/+ mice)?

If Nestin+; Atoh1+ cells are present at non-EGL regions in the developing cerebellum, the authors would have to reconsider many of their conclusions and also the title of this paper.

Additional comments:

(1) To investigate Nestin expression, the authors used Nes-CFP transgenic mice expressing CFP from promoter/enhancer sequences from the rat Nes gene (Encinas et al., 2006). Given that Nestin expression is of central importance for this study, it is important to validate that these reporter mice faithfully report Nestin protein expression (e.g., by co-labeling CFP with Nestin antibody and systemically comparing signals throughout the cerebellum, ideally in several developmental stages).

(2) The authors mostly presented immunostaining data of the cerebellum from P1 mice. It is important to systematically profile the appearance and disappearance of these Nestin+, Atoh1+ cells in mouse cerebellum across developmental stages (e.g., embryonic, early, and late postnatal stages).

(3) How different is the proliferative ability of the Nestin+ versus Nestin- GNPs at various developmental stages? Also, the difference between EdU+; Barhl1+; Nestin+ and EdU+; Barhl1+; Nestin- cells is quite small despite statistical difference (Figure 1N). Do the authors think this very small EdU incorporation difference can translate into a biological difference (in developmental and/or disease context)?

(4) Lines 145-147: "Compared to double-negative cells, Atoh1 and Nes were significantly higher in the double-positive fraction, supporting the identity of the cells as a previously unrecognized rare population of GCPs at P1 that expresses both the GCP marker Atoh1 and ventricular zone marker Nes." Nestin is not a ventricular zone marker. This should be rephrased.

(5) In Figure 3, the authors showed mouse survival data and concluded that Nes-driven and Atho1-driven SHH medulloblastoma models show similar tumor penetrance. This is not an entirely accurate description of their data. The Nes-SmoM2 mice displayed significantly longer survival compared to the Atoh1-SmoM2 mice (Figure 3B). This conclusion needs to be revised.

(6) In Figure 5, the authors showed that genes enriched in cluster 10 included Sox2, Nes, Wls, and Wnt1, while Neurod1 and Rbfox3 were preferentially expressed in the other GCP clusters. They conclude that cluster 10 represents a less differentiated, more stem-like GCP state, potentially positioned upstream in the lineage hierarchy. While these few markers are useful, it is more informative to formally support this conclusion by comparing the stem cell transcriptomic signature (using a larger gene list) between cluster 10 and other GCPs.

(7) In Figure 5, the authors performed gene ontology analysis and showed that cluster 10 is enriched for biological processes linked to WNT signaling and proposed that this molecular profile supports their identity as a transient, developmentally plastic population within the GCP lineage related to the rhombic lip. The authors are recommended to use an orthogonal approach (i.e., immunostaining to compare nuclear localization of beta-Catenin) to validate their transcriptome-based finding.

Reviewer #2 (Public review):

Summary:

In this work, the authors investigated the regulation of the transcription factor PPARγ by the post-translational modification lysine methylation The data demonstrate that the lysine methyltransferase SETD6 targets PPARγ for methylation using biochemical and cell-based assays. Methylation of PPARγ occurs in its DNA binding domain, and the authors demonstrate that loss of methylation limits PPARγ chromatin binding, particularly to lipid storage and metabolism genes promoters. As a physiological output, the authors demonstrate that deletion of SETD6 and loss of PPARγ methylation also disrupt lipid droplet accumulation in hepatocytes. In addition, the authors uncover a positive feedback loop in which SETD6 methylation of PPARγ also regulates its binding to the SETD6 promoter and expression of the gene.

Strengths:

One of the key strengths of this manuscript is the novelty of the findings in terms of identifying a new mode of regulation of PPARγ that modulates its chromatin association in cells and thereby regulating lipid metabolism genes. The authors nicely combine biochemical studies of SETD6 activity with cell-based assays investigating PPARγ and SETD6 function in regulating lipid storage. Data supporting this conclusion is largely convincing and frequently, multiple assays are used to provide sufficient support to the conclusions. This work therefore expands regulatory modes of PPARγ and identifies a new target for SETD6, an enzyme that targets a number of other transcription factors. Furthermore, the regulatory loop that controls SETD6 expression via PPARγ methylation is likely important for understanding SETD6 function in different cell types that have high levels of lipid accumulation or regulation. The gene expression and lipid accumulation assays are useful for testing the physiological outcome of loss of SETD6 activity or PPARγ methylation directly. In the revised manuscript, the authors have added useful structural modeling to better define potential roles of methylation of PPARγ in regulating its function, particularly relative to DNA binding, and to better define the physical interaction between PPARγ and SETD6.

Weaknesses:

The revised manuscript substantially improved on the presentation of the data and broadened the discussion to provide more context to both the role of SETD6 and to elaborate on potential mechanisms by which methylation impacts PPARγ function and under what physiological conditions this interaction and regulation is important. This improves and strengthens the manuscript and its impact overall.

Comments on revised version.

The authors addressed all of my major concerns following this round of review and I do not have additional recommendations. The presentation of the manuscript including text and figures is improved compared to the previous version. I have updated my public review to reflect these changes.

Reviewer #2 (Public review):

Summary:

DNA double-strand breaks (DSB) in repeated DNA pose a challenge for repair by homologous recombination (HR) due to the potential of generating chromosomal aberrations, especially involving repeats on different chromosomes. This conceptual caveat led to a long-held notion that HR is not active in repeated DNA, which was disproven in groundbreaking work by Chiolo showing in Drosophila that DSBs in pericentromeric repeats are mobilized to the nuclear periphery for repair by HR. A similar mechanism operates in mouse cells, as shown by the Gautier laboratory, but the mobilization goes to the nucleolar periphery, called nucleolar caps. In this manuscript, the authors reexamine the role of MDC1 in the mobilization of DSBs in rDNA in human cells. Previous work has shown that MDC1 is replaced by Treacle, the gene associated with Treacher Collins syndrome 1, in its role as the main adaptor of the DNA damage response, and these results are confirmed here. The novelty of this contribution lies in the discovery that MDC1 is required downstream in the recruitment of BRCA1 and RAD51 to nucleolar DSBs that were mobilized to the nucleolar cap. Using multiple MCD knockout models and DSBs induced by the nuclease PpoI, which cleaves at nuclear sites as well as in the 28S rDNA, convincingly documents this role of MDC1 and shows that it acts upstream of the RNF8-RNF168 ubiquitylation axis. Using a proxy assay of co-localization of EdU incorporation at DSBs (gammaH2AX), evidence is provided that MDC1 is required for HR in rDNA. MDC1 was not required for RAD51 recruitment to IR-induced foci, but it is unclear whether this is related to the different DSB chemistry (enzymatic versus IR) or to the localization of the DSB (rDNA versus unique sequence genome).

Strengths:

(1) The manuscript is well-written, and the experimental evidence is nicely presented.

(2) Multiple MDC1 knockout models are used to validate the results.

(3) Convincing back-complementation data clarify the relationship between MDC1 and RNF8.

Reviewer #2 (Public review):

Summary:

This manuscript describes the results of an interesting study examining the rate of degradation of extracellular DNA in soil ecosystems using a clever experimental approach. 16S ribosomal RNA genes were amplified from soil samples, and then purified PCR amplicons, containing a 5' linker sequence on the forward primer, were introduced to soils and monitored over time using real-time quantitative PCR and NGS amplicon sequencing. The study was able to measure rates of overall extracellular DNA degradation, but also sequence-specific degradation rates. I like the idea and execution of the study, and the results are interesting. The manuscript needs some help to improve the overall readability. Please see general and editorial comments below.

Strengths:

Innovative experimental design that is well deployed across a large number of soil types, revealing interesting variability in extracellular DNA degradation.

Weaknesses:

(1) The manuscript needs another review to improve the readability of the document.

(2) The authors have used 16S genes to look at sequence-specific degradation. But 16S rRNA genes are actually pretty well conserved, and there isn't as much genetic variation across this gene among organisms as there is for other genes. It might be more relevant to look at metagenomic DNA degradation from high AT, high GC organisms, etc. This would be more generalizable than 16S genes.

(3) Consideration of differential cell lysis during soil DNA extraction needs to be considered as well.

(4) It is not clear why the authors didn't put GAPDH linkers on the reverse primer as well. This would have given an easier amplicon to amplify (no degeneracies at all).

Reviewer #2 (Public review):

Summary:

In this EEG study, Huang et al. investigated the relative contribution of two accounts to the process of conflict control, namely the stimulus-control association (SC), which refers to the phenomenon that the ratio of congruent vs. incongruent trials affects the overall control demands, and the stimulus-response association (SR), stating that the frequency of stimulus-response pairings can also impact the level of control. The authors extended the Stroop task with novel manipulation of item congruencies across blocks in order to test whether both types of information are encoded and related to behaviour. Using decoding and RSA they showed that the SC and SR representations were concurrently present in voltage signals and they also positively co-varied. In addition, the variability in both of their strengths was predictive of reaction time. In general, the experiment has a solid design and the analyses are appropriate for the research questions.

Strengths:

(1) The authors used an interesting task design that extended the classic Stroop paradigm and is effective in teasing apart the relative contribution of the two different accounts regarding item-specific proportion congruency effect.

(2) Linking the strength of RSA scores with behavioural measure is critical to demonstrating the functional significance of the task representations in question.

Weaknesses:

I still have some doubts on the effectiveness of the experimental manipulation on Phase 2: although the ISPC effect is still present, it is much weaker in comparison, suggesting the participants did not learn the contingency statistics in Phase 2 as well as they did in the other phases, due to either the lingering effect of the previous phase or an inherent bias towards one color pairs. Perhaps by separately plotting the earlier and later blocks of Phase 2 any difference can be revealed if it exists. This behavioral difference could result in unequal levels of SC/SR representation across phases, which may raise problems when data were combined for analyses that assume the neural effects are equivalent.

Reviewer #2 (Public review):

Summary:

This manuscript presents a large-scale combinatorial transcription factor overexpression screen in mouse embryonic fibroblasts coupled with single-cell RNA-seq to systematically map the relationship between TF combinations, gene regulatory networks, and transcriptional reprogramming. Using approximately 100 transcription factors, the authors identify TF combinations that shift cells toward diverse transcriptional states, organize TF combinations into "perturbation clusters" with shared transcriptional outputs, infer modular gene regulatory networks, model pairwise TF interactions, and use pseudotime analyses to nominate candidate reprogramming TF combinations. The resulting dataset represents a potentially valuable resource for studying combinatorial TF activity and transcriptional reprogramming.

Strengths:

The primary strength of the study is its experimental scale and the breadth of the generated dataset. The Reprogram-Seq platform enables systematic interrogation of thousands of TF combinations that would be difficult to test individually, and the authors develop several computational frameworks to organize these data and generate biological hypotheses. The epicardial reprogramming analyses, including independent qPCR and protein localization experiments, provide proof-of-principle that the platform can recover biologically relevant TF combinations.

Weaknesses:

Many of the manuscript's central conclusions rely on a complex computational pipeline that is not sufficiently justified or independently validated. Identification of transcriptionally reprogrammed cells depends on co-embedding with reference atlases, yet the robustness of this analysis and the interpretation of cells occupying primary-cell clusters are not explored in depth. Similarly, the conclusions regarding modular gene regulatory networks depend critically on the perturbation clusters defined by MDE embedding and HDBSCAN clustering. These perturbation clusters form the basis for nearly all downstream analyses, including differential expression, gene module identification, gene specificity, and TF modularity, yet little evidence is provided that the clusters are robust to alternative embedding strategies, clustering parameters, or resampling approaches.

The manuscript also provides relatively limited orthogonal validation of its computational predictions. Although the epicardial analyses are validated experimentally, comparable validation is not performed for most other predicted cell fates, TF interaction classes, or perturbation modules. Consequently, many conclusions regarding the generality of modular TF activity, TF cooperativity, and the predicted reprogramming cocktails remain supported primarily by computational inference.

In addition, several aspects of the analytical workflow-including the quality filtering of TF combinations, interpretation of unclustered perturbations, selection of genes for downstream visualization, and robustness of pseudotime analyses across lineages-would benefit from greater methodological transparency.

Overall, this work provides a valuable dataset and introduces analytical approaches that will likely be useful to the community. However, in its current form, I believe the strongest biological conclusions are insufficiently validated. Additional analyses demonstrating the robustness of the computational framework, together with broader orthogonal validation of representative predictions, would substantially strengthen confidence in the proposed principles governing combinatorial transcription factor activity and transcriptional reprogramming.

Reviewer #2 (Public review):

This manuscript introduces DrosoMating, an integrated hardware-software pipeline designed to automate quantification of Drosophila courtship and mating behavior. The authors aim to provide a low-cost, scalable alternative to existing behavioral tracking systems, focusing on extracting key temporal metrics including courtship index, copulation latency, and mating duration from high-throughput video recordings.

A major strength of the work is the clear emphasis on experimental scalability and practical usability. The system is designed for multi-chamber recording and performs robustly under low-quality imaging conditions where conventional pose-tracking pipelines often fail. The revised manuscript substantially improves its scientific positioning through the inclusion of systematic benchmarking against widely used tools (Ctrax and FlyTracker), demonstrating that both fail to complete end-to-end analysis under these recording conditions: Ctrax due to segmentation instability and trajectory fragmentation, FlyTracker due to runtime errors during feature computation. A comparative table (Table 1) summarizes key features across tools, and the authors appropriately qualify that these limitations are specific to the low-quality video conditions tested and should not be interpreted as general shortcomings of those tools. The addition of individual-level behavioral ethograms (Figure S3) further strengthens the evidence by allowing direct assessment of the system's temporal resolution at the single-fly level.

The authors also appropriately address statistical concerns raised in review. The re-analysis using ANOVA frameworks - one-way ANOVA with Tukey's HSD for multi-strain comparisons, two-way ANOVA with Sidak's correction for genotype × training interactions - improves the rigor of the behavioral comparisons and supports the revised conclusions regarding strain and learning effects. The addition of locomotor control analyses (Figure S4) further clarifies interpretation of mutant phenotypes by partially disentangling motor from courtship-specific effects, with the revised text appropriately acknowledging contributions from both general hypoactivity and sensory impairments.

A key limitation remains the conceptual scope of the system. DrosoMating is optimized for state-based temporal segmentation rather than fine-grained behavioral annotation or posture-level decomposition. While the authors now clearly acknowledge this and position the tool appropriately, it inherently restricts its use cases compared to modern pose-estimation and classifier-based frameworks. Additionally, the benchmarking comparison is necessarily asymmetric: DrosoMating is tested on low-quality videos where it excels by design, while Ctrax and FlyTracker are evaluated under conditions outside their intended operating range. A comparison under more favorable conditions for the tracking-based tools, or an evaluation of whether modest improvements in video quality would bring conventional pipelines within functional range, would further contextualize the practical boundary between approaches. The comparison also does not include modern deep-learning-based tools (e.g., DeepLabCut, SLEAP), which may be more robust to low-contrast conditions than classical segmentation-based pipelines. These points do not diminish the demonstrated utility of DrosoMating for its intended niche but would help users make more informed decisions about tool selection.

Overall, the revised manuscript presents a well-validated and clearly positioned contribution. It defines the niche in which DrosoMating provides substantial practical value while appropriately delimiting its limitations relative to more general behavioral analysis frameworks.

Reviewer #2 (Public review):

Summary:

Protein tyrosine kinases are submitted to diverse regulatory mechanisms controling their activity in normal situation. The authors previously identified SLAP (Src-like adaptor protein), a negative regulator of receptor tyrosine kinase (RTK) signaling, as a key suppressor of the cytoplasmic tyrosine kinase SRC in the normal colon and demonstrated that SLAP is downregulated in a majority of colorectal cancers (CRCs).

In this study, the authors further explored slap functions in mouse models using constitutive and inducible epithelial-specific Slap deletion (villin-CreERT2 model). They found that loss of slap augments colonic epithelial cell proliferation and that induction of tumorigenesis by the AOM/DSS protocol mimicking CRC leads to more aggressive tumors in the absence of slap. This effect is apparently cell-autonomous as growth of normal and tumoral colonic organoids is SLAP-dependent in in vitro settings. Finally, the authors define that, in colon, SLAP represses EphB2, an RTK lying upstream of SRC, and show that inhibitors of EphB2 can partially limit tumorigenic development in vitro.

Strengths:

The manuscript is clearly and concisely written, making it easy to follow. Data obtained in the mouse models are very convincing.

Weaknesses:

Direct evidence that EphB2 is activated/phosphorylated in the absence of SLAP is lacking as conclusions are only based on results obtained with inhibitors. Some other issues have to be addressed before acceptance, in particular the relevance of the findings in CRC patients.

Comments on revised version.

The authors have satisfactorily addressed my concerns.

Reviewer #2 (Public review):

In K. Kostanjevec et al., the authors study a possible mechanism for the formation of spiral patterns in the cornea. First the authors analyze an inferred velocity field, which is deduced from images of fixed corneas, and then determine the position-dependent spiral angle of this velocity fields. Next, the authors analysed two possible markers of cell polarity: the direction of the centrosome-nuclei and the axis of mitosis. Then the authors introduce a stochastic agent-based model of self-propelled particles with over-damped dynamics and with aligning interactions to the orientation of the nearest neighbors and to the particle's velocity. The authors claim to be able to reproduce the equal-time autocorrelation function and the velocity Fourier spectrum. Then the authors introduce the geometry of the cornea by constraining the dynamics on a spherical cap and show that their model can reproduce a typical trajectory in experiments. Finally, the authors produce a phase diagram of the states at a fixed time point as a function of the spherical cap radius and the strength of the coupling aligning constant. Finally, the authors propose an interpretation of the cell fluxes based on the equation of mass conservation.

Reviewer #3 (Public review):

Here the Wang et al resubmit their manuscript describing the events in the establishment of polarity in MDCK cells cultured in vitro. As with the original version, the description is throughout and is important to the field to report as it establishes a hierarchy of events in polarization, placing Par3 upstream of centrosome positioning and apical membrane component trafficking. Unfortunately, in the revised version, the authors addressed almost none of my points. They did a cursory job of responding in the rebuttal letter but made little attempt to actually address what was being asked or to incorporate any of my suggestions into the manuscript. The particularly egregious examples are cited below:

Comments on revisions:

(1) My original main experimental concern was not addressed: I had originally asked what role microtubules play in the process of polarization (either centrosomal or non-centrosomal). An obvious model is that Gp135, Rab11, etc. are delivered to the AMIS on centrosomal microtubules. Centrosomes might also be pulled to the AMIS via cortically derived microtubules as is the case in the C. elegans intestine where the centrosome moves apically on apical microtubules via dynein directed transport to the cortically anchored minus ends. The authors do not explore the role of microtubules in the revision, citing that it was not possible to observe the microtubules directly or to perform nocodazole experiments during polarization. Instead, the authors use a relatively new genetic tool to disrupt centrosomal microtubules. They appear to succeed in displacing centrosomal g-tubulin using this tool, but without being able to observe microtubules, a remaining caveat of this experiment is that it is still unclear whether the authors have removed centrosomal microtubules. Compounding this issue is that this tool has never been used in MDCK cells. The authors conclude "we found that cells lacking centrosomal microtubules were still able to polarize and position the centrioles apically.", but they have not shown this, instead the data suggest this conclusion and the authors should acknowledge the caveat that they have no idea whether centrosomal microtubules are abolished. Similarly, the authors also state: "Additionally, although PCNT knockout cells show reduced microtubule nucleation ability, they still recruit a small amount of γ-tubulin". Where are the data that show that microtubule nucleation is reduced in these PCNT knock out cells?

(2) Many of my comments were addressed in the rebuttal, but not in the text.

The non-centrosomal GP135 in Figure 2 is not acknowledged or explained.

That the polarity index does not actually measure polarity, but nuclear-centrosome distance is not acknowledged or explained in the paper.

I still don't believe that the quantification in Figure 3D matches the images I am being shown in Figure 3A. In the centrinone treatment condition, there is certainly an enrichment of GP135 at the AMIS that is not detected in the quantification. The method described in the rebuttal might miss this enrichment if it is offset from line drawn between the centroid of the two nuclei.

Cell height changes in the centrosome depleted cysts are still referenced in the text ("the cell heights of the centrosome-depleted cysts are less uniform"), but no specific data or image is called out. Currently, Figure 3G is referenced, but that is a graph of GP135 intensity.

In my original review, I called on the authors to comment on the striking similarity of the mechanisms they documented in MDCK cells to what has been shown in in vivo systems. The authors did not do this, instead restating in the rebuttal some features of what they found. But the mechanisms shown here are remarkably similar to the polarization of primordia that generate tubular organs in vivo. Perhaps most striking is the similarity to the C> elegans intestine where Par3 localizes to the cortex at the site of an apical MTOC that pulls the centrosome to the apical surface via dynein (Feldman and Priess, 2012). Instead of discussing this similarity, the authors state: "Par3 is likely to regulate centrosome positioning through some intermediate molecules or mechanisms, but its specific mechanism is still unclear and requires further investigation." Given the acetylated tubulin signal emanating from the Par3 positive patch in Figure 5E and F, I suspect similar mechanisms to the C. elegans intestine are at play here. Such a parallel should be noted in the Discussion.

I had originally commented that "I find the results in Figure 6G puzzling. Why is ECM signaling required for Gp135 recruitment to the centrosome. Could the authors discuss what this means?" The authors responded that "The data in Figure 6G do not indicate that ECM signaling is required for the recruitment of Gp135 to the centrosome". In Figure 6G, the localization of GP135 to the centrosome appears significantly delayed compared to its localization to the centrosome in images where cells were cultured in Matrigel. Indeed, the authors argue that the centrosomal localization precedes and contributes to its localization to the AMIS. In the absence of ECM, GP135 localizes to the membrane before it localizes to the centrosome and its localization to the centrosome appears significantly reduced. Thus, my original and current interpretation is that ECM signaling is somehow required for the centrosomal targeting of GP135. One could make a competition argument, i.e. that the cortex in the absence of ECM is somehow a more desirable place to localize than the centrosome, but this experiment also argues that the centrosome does not need to be a source of this material in order for it to end up on the cortex.

(3) There needs to be precision in the language used in many places:

I don't understand this line in the abstract: "When cultured in Matrigel, de novo polarization of a single epithelial cell is often coupled with mitosis." If a cell has divided, it is no longer a single cell.

The authors state in the Introduction "Because of its strong ability to nucleate microtubules, the centrosome functions as the primary microtubule organizing center", but then state ""In polarized epithelial cells, the centrosome is localized at the apical region during interphase, which contributes to the construction of an asymmetric microtubule network conducive to polarized vesicle trafficking". In the latter statement, I assume the authors are describing the well-characterized apical microtubule network in epithelial cells that is non-centrosomal. Thus, the latter sentence is at odds with the former.

The authors continually refer to Par3 as a tight junction protein. "Par3, which controls tight junction assembly to partition the apical surface from the basolateral surface". To my knowledge, PARD3 is an apical protein with similar localization to C. elegans PAR-3 and Drosophila Bazooka. PARD3B is a junctional protein. I assume that the antibody that the authors are using is to PARD3 and not PARD3B? Can the authors please clarify this in the text.

Reviewer #2 (Public review):

Summary:

The manuscript by Garcia-Alcala et al. reports an interesting paradox: the cost of gene expression slows the population-average growth rate, whereas at the single-cell level, expression levels from these genes positively correlate with the growth rate. The effect is observed in the expression of flagellar genes and a gene under a synthetic promoter in E. coli. The findings are explained by the inheritance of growth factors, including ribosomes, during asymmetric division.

Strengths:

(1) The manuscript adds strength to an emerging body of literature showing that the population-level bacterial growth laws do not match correlations based on single-cell data. The evidence presented here is more striking than in previous works (such as Pavlou et al., Nat. Commun. 2025), as the trends in population-level data and single-cell data are reversed.

(2) A relatively simple model correctly explains the trends in the data.

Weaknesses:

(1) The differing behavior of the MG1655 and MC4100 strains remains a lingering question concerning the generality of the conclusions. It appears unlikely that ribosomes or other growth factors partition significantly differently in the MC4100 strain than in the MG1655 strain. Furthermore, based on Fig. S15, it is still unclear to what extent MC4100 exhibits growth-rate fluctuations, as stated in the text, rather than primarily size fluctuations, as shown in the figure. It is also unclear why such very slow fluctuations would lead to qualitatively different behavior, given that the proposed mechanism appears to be rather fundamental. It would be helpful for the authors to discuss these two points.

(2) It is unclear what fraction of the total proteome mVenus represents in different measurements. Adding this information would strengthen the conclusions.

Reviewer #3 (Public review):

Summary:

This is a very interesting paper extending the use of SHG to the study of relaxed muscle and its use to assess the order- disorder (and on /off) states of myosin heads in the thick filament. The work convincingly shows that SHG, and the parameter gamma, provide a reliable measure of the state of the myosin heads in a range of different relaxed muscle fibres, both intact and skinned and in myofibrils. In mini pig cardiac fibres the use of dATP and mavacamten increased or decreased the number of heads in the disordered state respectively. On the assumption that these treatments push myosins fully into the disordered or ordered state then this allows the fraction of ordered heads to be assessed under a wide variety of conditions. The extension of this part of the study to mouse heart and rabbit psoas samples extends the validation of the approach.

The results with the myosin mutant R403Q support the idea that this mutation reduces the fraction of myosin heads in the ordered state and that mavacamten can recover the WT situation.

The results from SHG were compared with parallel studies using X-rays to validate the conclusions. Independent fibre ATPase data further support the conclusions.

The work is solid and provides a novel approach assessing the activity state of muscle thick filaments. The authors point out some of the potential uses of this approach in the future including time resolved SHG measurements. Indeed, jumps in mavacamten or dATP concentration with time resolved SHG could measure the rates of entry and exit from the ordered , off state of the filament. A measurement urgently needed in the field.

Strengths:

(1) The SHG signal is convincingly shown to assess the fraction of ordered/disordered myosin heads in the thick filament of a variety of muscle fibres.

(2) The results are similar for rabbit psoas, mouse and minipig cardiac fibres, Skinning the fibres and production of myofibrils does not change the SHG signal.

(3) Use of myosin R403Q mutant in mini pig confirms a loss of ordered myosin heads and the ordered heads can be recovered by mavacamten.

(4) Parallel X-ray scattering and ATPase data support the conclusions.

(5) Assuming that dATP and mavacamten generate 100% disordered vs ordered myosin heads respectively then the % ordered heads can be calculated for a variety of conditions.

(6) The potential of extending the technique with time resolved studies and sub sarcomere variations of SHG are very exciting prospects.

Weaknesses:

Issues like the effect of fibre disarray on the SHG signal are not well defined.

Reviewer #2 (Public review):

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

Reviewer #2 (Public review):

Summary:

Neurons have varied responses to external stimuli that cannot be explained by naive Poisson models. Previous work has quantified and partitioned higher-than-Poisson variability in the brain into different components. The authors improve on these methods to infer how both the stimulus drive and internal gain dynamics impact neuronal variability continuously in time. The clean and well-reasoned model is rigorously developed and then applied to neural data across the visual hierarchy. This lends new insights into how variability is partitioned, agreeing with and extending previous work on how that variability changes from early visual areas (LGN, V1) through to higher, motion-sensitive areas (area MT). Another key contribution is that this partitioning can be fully addressed as a continuous-time process, which allows for dissection of how the timescale of fluctuations in these two components changes across the brain's processing arc.

Strengths:

(1) The model is cleanly derived and thoroughly documented, including useable code shared in a GitHub repo. This makes the method immediately portable to other neural systems.

(2) The figures and writing are clear and understandable and all pieces of the derivations are included in the main text and supplementary information.

(3) Comparisons to other models, particularly the one from Goris et al., 2014 shows how this Continuous Modulated Poisson (CMP) model outperforms previous work.

(4) New insights about how variability partitioning changes across the visual stream from LGN to MT are revealed, including how the gain fluctuates on longer timescales in higher visual areas. Another key result about the anticorrelation between the variance in stimulus drive and gain fluctuations comports with theories about how neurons maintain efficient, reliable encoding.

(5) In addition to the results reported here, this work will serve as an excellent tutorial for students and postdocs first delving into the sources of variability in the brain.

Weaknesses:

(1) The work builds off previous studies of the partitioning of variability in the brain, but provides important new extensions as noted above. Sub-poisson variability cannot be addressed in the current framework, but ideas for extensions are included in the Discussion.

Comments on revised version.

The revisions have thoroughly addressed my previous comments and concerns and the paper's clarity and scope have improved.

Reviewer #2 (Public review):

Summary:

Phon-Amnuaisuk and colleagues address an important question in cognitive psychology and computational psychiatry: how does the established relationship between depression and metacognitive under-confidence unfold over time? The study is grounded in a hierarchical view of metacognition, in which local confidence in individual decisions contributes to global estimates of performance. To test this, the authors used an intensive longitudinal design in which participants repeatedly reported depressive symptoms and completed a gamified perceptual decision-making task over eight weeks. This allowed them to examine whether depressive symptoms and confidence fluctuate together within individuals, whether one predicts the other over time, and whether trait depression alters the way local confidence is carried forward and integrated into global confidence. The main findings are that higher trait depression is associated with lower local and global confidence, that within-person mood fluctuations show limited temporal precedence over confidence at the two-day timescale, and that trait depression is linked to weaker temporal persistence of local confidence and reduced carry-over into later global confidence.

Strengths:

A major strength of the study is its repeated-measures design across a large sample. Participants completed up to 28 metacognitive task sessions over eight weeks, alongside repeated ratings of depressive symptoms. This allows the authors to separate stable between-person differences from within-person changes over time, which is a clear advantage over standard cross-sectional studies. The analytical strategy is also appropriate: the authors use multilevel vector autoregressive models to examine temporal, contemporaneous, and between-person associations; Bayesian ordinal models to test interactions; and computational modelling to examine how confidence is formed.

The strongest results concern stable individual differences. Participants with higher average depressive symptoms reported lower local and global confidence. While this pattern is consistent with prior work showing reduced confidence in depression, the computational model further suggests that higher depression is associated with a more conservative confidence criterion: these participants required more evidence before reporting high confidence. This adds nuance by suggesting that under-confidence in depression may not reflect poorer metacognitive sensitivity, but rather a bias in how confidence is reported.

The study also proposes a novel temporal account. Trait depression was associated with weaker autocorrelation of local confidence across sessions, which in turn reduced the extent to which local confidence carried over into later global confidence. This indirect pathway was supported by a moderated mediation analysis and was consistent across most individual depressive symptoms. The finding is theoretically interesting and supported by strong statistical evidence.

Weaknesses:

The main limitation concerns the interpretation of the temporal and mechanistic claims. The strongest effects are observed at the trait level, whereas the within-person temporal associations between mood and confidence are weak or absent. The study therefore provides stronger evidence that people with higher average depressive symptoms are generally less confident than evidence that momentary changes in depressive mood drive later changes in confidence, or vice versa.

The sample also constrains the conclusions. Depression scores were strongly concentrated near the lower end of the scale, and the final sample was highly selected, with many of the initial 976 participants excluded because they did not complete enough task sessions. This means that the study may be better suited to detecting stable individual differences than dynamic mood-confidence processes.

The temporal spacing of the metacognitive assessments is a further constraint. Because the metacognition task was administered every two days, the cross-lagged analyses could only test mood-confidence dynamics across this interval. If depressive mood and confidence influence each other over shorter timescales, such effects may have been missed. The absence of cross-lagged effects should therefore be interpreted with caution: it shows that temporal precedence was not detected at the two-day lag in this sample, but it does not rule out shorter-term directional effects or effects in more symptomatic clinical populations.

Related to this, the manuscript moves between several related but distinct terms - "mood," "depressive mood," "depression," "depressive symptoms," and "trait depression" - without always clarifying whether these are intended as interchangeable or as conceptually distinct constructs. This matters for a study whose central claims concern temporal precedence and trait-versus-state distinctions, and it is compounded by a sample with generally low depressive symptom levels, where the boundary between transient low mood and a trait-like depressive disposition is harder to draw.

Reviewer #2 (Public review):

The authors tackle the question of whether conceptual absence is neurally encoded in the same way as perceptual absence. On one hand, the neural bases of perceptual absence have been largely investigated, as exemplified by the study of neural correlates of perception of aware vs. unaware stimuli, and on the other hand, the overlapping neural encoding of symbolic ('0') and non-symbolic (number of dots) formats of numerical absence has been previously established (Barnett & Fleming, 2024). However, the direct comparison of neural representations between numerical and perceptual absences remained to be investigated.

This article fills this gap by designing a Magneto-EncephaloGraphy (MEG) study using multi-voxel pattern analysis (MVPA) and temporal generalization to probe the similarity of neural patterns across the representation of perceptual absence (lack of stimuli), symbolic ('0'), and non-symbolic (number of dots) formats of numerical absence. They confirmed previously obtained evidence for shared neural representation across both formats of numerical absence. They report evidence for an absence of shared representation between both formats of numerical absence on one hand and perceptual absence on the other hand, while controlling for the confounding effect of low-level visual features. Their results overall support the conclusion that conceptual and perceptual absence are neurally encoded in a distinct way and speak in favour of a boundary between the representation of the concepts and the percept of absence.

Major strengths:

(1) Behavioral and neuroimaging results convincingly demonstrate that neural encoding of symbolic and non-symbolic absences is shared and situated on a graded, abstract neural number line, replicating previous results, notably from the authors themselves (Barnett & Fleming, 2024).

(2) They show that neural encoding of perceptual and numerical absence do not generalise across each other, while controlling for spurious confounds due to visual stimuli that are commonly shared in the cases of non-symbolic numerical absence and perceptual absences.

(3) They adequately use Bayesian analysis to distinguish absence of evidence vs. evidence of absence to support their claim.

(4) The interpretation of numerical absence as a representation of the concept of "nothingness" is adequate, although it might be further discussed by distinguishing the concept of "zero" on a number line from the concept of nothingness and that of an empty set (Nieder, 2016).

(5) The discussion about development and metacognition paves an interesting road for further investigation on the acquisition of the concept of zero, especially in light of debates on the progressive development of metacognitive abilities in children (Goupil & Kouider, 2019).

(6) Data and code are published with open-source access, allowing the community to further investigate the points as major weaknesses evoked below, if desired.

Major weaknesses:

(1) Interestingly, restricting the neural decoding method to the alpha band shows distinct representations across formats of numerical absence. This begs for providing more details on how neural representations of perceptual, symbolic, and non-symbolic absences differ at the source and frequency level and to report the decoding weights to better assess what drives the performance of the neural decoding algorithm in each case and whether they overlap with each other.

(2) Task-demands between perceptual (present vs absent) and numerical (lower vs higher) are different, raising concerns about whether these aspects of experimental design could drive, at least partially, the shared representational patterns across numerical representation of absence and their distinction from perceptual representation of absence.

(3) The same argument can also be raised for the way that the neural decoders of perceptual and numerical absences are trained and tested. Both formats of numerical absence are built using the same procedure (zero vs. rest) and differ from the way the decoder is built for perceptual absence (hits vs misses), which might possibly drive the difference observed here.

(4) It is thus unknown whether the claim supporting the evidence of absence holds as long as other counterfactual hypotheses that might drive these results are not excluded, such as the nature of the decoded features, the effect of task demands, or the way the neural decoder is trained, as mentioned above.

Overall, I was pleased by the quality of the methods and the clarity with which the question of the boundary between cognition and perception is addressed for the case of numerous and perceptual absence. While the methods used are well established in the field, the choice and rigor of their analysis and the controls provided stand as a convincing methodology to test their hypotheses, although they do not fully exclude alternative interpretations nor explore the wider extent of possibilities that may provide exhaustive evidence for showing that perceptual and numerical absences are distinctly encoded in the brain.

This work will be of great appeal to neuroscientists interested in comparing the representation of percepts and concepts across different formats, to psychologists interested in the origin of number representation, and to philosophers interested in debates on the boundary between cognition and perception.

Nieder, A. (2016). Representing something out of nothing: The dawning of zero. Trends in Cognitive Sciences, 20(11), 830-842.

Goupil, L., & Kouider, S. (2019). Developing a reflective mind: From core metacognition to explicit self-reflection. Current Directions in Psychological Science, 28(4), 403-408.

Barnett, B., & Fleming, S. M. (2024). Symbolic and non-symbolic representations of numerical zero in the human brain. Current Biology, 34(16), 3804-3811.

Reviewer #2 (Public review):

Summary:

The current work investigates the evolution of sideward locomotion in Brachyura in light of a single evolutionary origin. To this end, the authors first analysed the mode of locomotion in 50 crab species and observed mutually exclusive presence of sideways vs. forward movement. The phylogenetic analysis confirmed that there is indeed a single evolutionary origin for sideways movement, which was sometimes followed by several reversions to forward locomotion. This way, authors demonstrate how locomotor movement modes shape evolutionary diversification in animals by showing that species richness is much higher in side-ways-moving crabs than in the nearest groups. This is an interesting work that integrates behavioural analysis and phylogenetic relations, capitalising largely on crabs.

Original questions/suggestions:

Firstly, I think the paper spends too much time on a straightforward analysis of the mode of locomotion. I was also wondering whether the phylogenetic analysis could be simply achieved by maximising an objective function in which the modes of movement are inversely coded for two putative groups, with all values calculated at all possible nodes.

Unfortunately, I find that the authors did not sufficiently discuss differences in the ecological niches of species with forward vs. sideways locomotion modes (including challenges of locomotion and substrate).

Likewise, what are the anatomic correlates of forward vs. sideways locomotion? For instance, how are the advantages assumed for sideways movement associated with a flattened body? Is it possible that the mode of motion is secondary to flattened/narrow body structure, which basically limits the distance between legs and thus makes the forward movement difficult - under this logic, the mode of movement would be a secondary phenomenon to body shape traits. How can one differentiate between this alternative and the one that puts the mode of movement in the centre of the story? On a related note, how do different modes of movement relate to the ability to fit into tight spaces - how does it relate to differences in leg joints?

Is it possible that the sideways movement maximises the scanned visual field per unit time/displacement, which may be beneficial for mostly forward-moving predators?

Briefly, although I find the study interesting, the presented complexity may not be necessary given the endpoints; it can be achieved much more simply. Furthermore, the degree to which the conceptual analysis of different modes of locomotion was exercised was limited. The general approach may serve as a good model for the evolutionary analysis of other traits. The demonstration of traceability of the relations in question is a major contribution of the work.

Comment on revised version:

I am not fully convinced by the authors' handling of the complexity of the paper, but this seems like a moot point.

Reviewer #2 (Public review):

Summary:

This paper investigates whether large language models (LLMs) of increasing size more accurately align with brain activity during naturalistic language comprehension. The authors extracted word embeddings from LLMs for each word in a 30-minute story and regressed them against electrocorticography (ECoG) activity time-locked to each word as participants listened to the story. The findings reveal that larger LLMs more effectively predict ECoG activity, reflecting the scaling laws observed in other natural language processing tasks.

Strengths:

(1) The study compared model activity with ECoG recordings, which offer much better temporal resolution than other neuroimaging methods, allowing for the examination of model encoding performance across various lags relative to word onset.

(2) The range of LLMs tested is comprehensive, spanning from 82 million to 70 billion parameters. This serves as a valuable reference for researchers selecting LLMs for brain encoding and decoding studies.

(3) The regression methods used are well-established in prior research, and the results demonstrate a convincing scaling law for the brain encoding ability of LLMs. The consistency of these results after PCA dimensionality reduction further supports the claim.

Comments on revised version.

I thank the authors very much for their efforts in addressing my comments. One remaining concern is the extent of the paper's conceptual advance. Several recent studies have made broadly similar claims regarding the increasing alignment between large language models and human language processing, although using fMRI data (Antonello et al., 2023; Gao et al., 2025). I would therefore encourage the authors to more clearly articulate what additional insights are gained from using ECoG. Clarifying this point would help better establish the novelty and contribution of the present study.

Antonello, R. J., Vaidya, A. R., & Huth, A. G. (2023). Scaling laws for language encoding models in fMRI. Advances in Neural Information Processing Systems, 36, 21895-21907.

Gao, C., Ma, Z., Chen, J., Li, P., Huang, S., & Li, J. (2025). Increasing alignment of large language models with language processing in the human brain. Nature Computational Science, 5(11), 1080-1090.

Reviewer #2 (Public review):

Summary:

The authors use a postnatal mouse model of E. coli bacterial meningitis and a mouse brain endothelioma cell line combined with cell type specific gene deletion to study the function of endothelial TLR4, a cell surface receptor that recognizes gram positive bacterial wall components, in the local leptomeningeal (LPM) response with a focus on endothelial barrier breakdown mediated by TLR4. Single cell transcriptional profiling and imaging studies using wholemount preps of the LPM support that LPM endothelial, CD206+ local macrophage and LPM fibroblast and arachnoid barrier cell inflammatory response and is abrogated in endothelial specific KO of TLR4, pointing to a role for endothelial TLR4 in local LPM response. Culture studies using Bend3.1 cells (a mouse brain endothelioma cell line) support a direct role for TLR4 in the bacteria-mediated inflammatory response and in internalization of Cldn5 via the endosomal-lysosomal pathway, resulting in loss of barrier integrity

Strengths:

The local LPM cell response in meningitis and the role of specific LPM cells in inflammation and CNS barrier breakdown has not been extensively studied, despite ample evidence for primary immune response in the meninges in human patients and in animal models. The authors employ a robust, multi-model approach using both in vivo and in vitro models with cell-type specific knockout to study the function of TLR4 in brain endothelial cell response. The authors nicely combine functional barrier assays with IF for junctional localization in their experimental design and they delve into potential mechanisms of Cldn5 internalization using markers of endosomal-lysomal pathway localization. The authors also describe a new type of barrier assay using a streptavidin-coated plates upon which barrier forming cell cultures can be plated, this could be a very useful alternative or complement to other size-selective barrier assays and presumably could work for other barrier forming cell types, like epithelial cells.

Comments on previous version.

In their revision, the authors addressed prior noted weaknesses with new data and analysis. They now show that TLR4-VE-cad cKO mice have a largely similar disease progression as control mice, including increased bacterial burden in the LPM and brain. This underscores that that the reduced vascular leakage and blunted inflammatory response is due to loss of TLR4 response to bacteria on VE-cad recombined cells and not because the mice are protected from meningitis. The authors also performed additional experiments to show that Cldn5 internalization via the endosomal-lysosomal pathway is independent of NFKB signaling. The authors also added in important discussion points about how their results fit into the broader literature on TLR4 in BBB endothelial cell junctional protein localization and prior work on meningitis in global TLR4.

Reviewer #2 (Public review):

In the study by Reynolds et al., the authors propose a new behavioral approach for ADHD evaluation using a genetically modified zebrafish model. The study is interesting and has potentially important implications for the field. However, there are several methodological, analytical, and validation-related issues that should be addressed before the study can be considered scientifically rigorous.

Comments:

(1) Introduction section

What is the epidemiological evidence supporting the prevalence of ADGRL3 dysfunction in the human population? The authors should consider adding this information, as well as clarifying where ADGRL3 mutations rank among other genetic variants associated with ADHD.

I suggest reconsidering the sentence "quantifiable behavioural repertoires that complement rodent approaches." Zebrafish studies do not simply complement rodent studies; they can serve as independent pharmacological and toxicological tools that may be used in parallel with rodent models.

The statement "forced light/dark (FLD) locomotion test" is too broad. Are the authors referring to the Visual Motor Response Test? If so, this is a robust assay that can evaluate not only anxiety-like responses but also locomotor state, arousal, decision-making, and potential cognitive impairment in larvae. A clearer description of the assay is necessary, especially to justify the statement that "while useful for detecting overall activity differences, it cannot determine whether increased movement reflects hyperactivity, altered arousal, disrupted behavioural control, or anxiety-like responses." In contrast, subtle behavioral changes across light and dark phases can be highly informative when velocity, time moving, and anxiety-like responses are analyzed together.

(2) Methods section

The zebrafish husbandry section lacks several essential details. The authors should include fundamental information, such as the embryo medium used, how embryos were obtained, the age of the breeding adults, and how larval age was determined in hours post-fertilization. These details are necessary for proper interpretation and reproducibility of the data.

Why was the mutant DNA not sequenced? Although agarose gel electrophoresis can provide useful preliminary evidence of mutation, it cannot precisely determine the number or nature of base-pair changes. This information is essential because different mutations can have distinct impacts on gene function.

The sentence "All statistical analyses and tests were completed on Prism10 (GraphPad)" is insufficient. It should be specified which statistical tests were used, including assumptions tested, post hoc comparisons, correction methods, and how experimental replicates or batch effects were handled.

Overall, the methods section lacks sufficient information to support the scientific rigor of the study. It is unclear whether every individual evaluated behaviorally was injected and then only a subset was genetically confirmed, or whether stable breeding matrices were generated and all experimental individuals were derived from these parents. The manuscript mentions "2-6 parent batches per experiment, with batches collected and run on separate days," but the genetic origin and validation of these batches remain unclear.

If embryos were injected for each batch, how did the authors ensure that the mutation was homogeneous enough across individuals to consider them equivalent? How did the authors confirm that the mutation was homozygous or present across all relevant cells? Zebrafish embryos remain at the single-cell stage for only a short period before mitosis begins. Without detailed information regarding breeding timing, embryo collection, injection timing, and sequencing validation, it is difficult to determine whether the injected embryos developed homogeneous mutations or mosaic patterns.

Additionally, to claim a knockout model, protein-level validation, such as Western blotting or another protein expression assay, should be provided. At present, there appears to be some confusion between a knockout and a knockdown model.

To validate a new behavioral protocol, the authors should compare their assay with an established gold-standard behavioral paradigm using the same experimental batch. They should clarify why this comparison was not performed.

Reviewer #4 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

One aspect of the current results that puzzles me is the strength of the current stimulation effects. Meta-analyses suggest that tDCS shows only small-to-moderate effect sizes (with Cohen's d around 0.5). While the authors report no effect sizes for their statistical models, the small p values, in combination with the unusually small sample size of 18 participants per group, suggests that the effect size must be rather large. Can the authors provide an estimate of the effect size of their stimulation effects? If they are considerably larger than to be expected, could the authors give an explanation for why their stimulation setup is showing much stronger effects than comparable high-definition tDCS studies on cognition or decision making?

Regarding the strengths of the stimulation effects, I moreover found remarkable that the post-test procrastination rate was 100% in all (!) participants in the DLPFC group (figure 3F). I admit that it is hard to trust results that have no individual variation at all. This means that all participants are perfect responders to tDCS, which is again at variance what one typically expects for tDCS (where one usually has many non-responders). Do the authors have an explanation for this?

In any case, I am surprised by the rather small sample size. Due to the small effect sizes for tDCS, it is common to have a minimum of 30 subjects per group in between-subject designs. According to G*Power, a between-subject design with 17 subjects per group could detect only relatively large effect sizes of Cohen's d = 0.99 (alpha = 5%, power = 80%, independent-samples t-test). As explained above, this is far above the effect size that can be expected for tDCS. In addition, small samples bear the risk that results strongly depend on outliers in the data, which might explain the strong effect size observed in the current study. The small sample size should be discussed as a major limitation of the current study and that the results need to be replicated by studies with larger sample sizes. Moreover, to rule out that the results are driven by outlier in the data, the authors should show individual data points in all plots showing empirical data.

Related to this, in the figure showing individual data points (3B/F), I count only around 10 data points per tDCS group for the 18 participants per group. I ask the authors to modify the plot that the data points from all participants can be seen (for example, by adding some noise on the x-axis for participants with the same value on the y axis).

Another surprising aspect of the data is that repeated sessions of tDCS change procrastination behavior up to six months after stimulation. Do the authors think that their tDCS setup leads to such long-lasting neuroplastic changes, and if yes, can they cite prior work where similar dosages of tDCS also showed such long-lasting effects? Or could the results be explained by learning effects, for example because participants in the DLPFC group learned during the repeated tDCS sessions that it feels internally rewarding to finish one's tasks instead of procrastinating them, and they still benefit from this kind of "learned industriousness" 6 months later? In any case, in my view it is important to be more specific about how seven sessions of tDCS can affect behavior half a year later.

Lastly, the link to the data repository works, but I could not inspect the data because I was asked to request access to the data, which I did not do in order to remain anonymous.

Reviewer #2 (Public review):

Summary:

In this study, the authors developed a human forebrain organoid model incorporating both iPSC-derived microglia and CD8⁺ T cells, enabling them to recreate and investigate multicellular aspects of AD pathology in a human-relevant system.<br /> Their findings show that microglia help clear amyloid-β deposits, but they also promote inflammatory responses. Activated microglia recruit CD8⁺ T cells by releasing the chemokines CCL4, CCL5, and CXCL10, which signal through the receptors CCR1/CCR5 and CXCR3. Pharmacological inhibition of CCR5 or CXCR3 prevents T-cell recruitment and alters autophagy pathways in a microglia-dependent manner.

Strengths:

The study presents a versatile human organoid platform for investigating neuron-immune interactions in Alzheimer's disease. It highlights the critical role of microglia-driven recruitment of CD8⁺ T cells in sustaining neuroinflammation and identifies CCR5 and CXCR3 signaling pathways as promising therapeutic targets for neuroinflammatory conditions.

This study is interesting and presents novel findings supported by state-of-the-art approaches, including single-cell RNA sequencing, a three-dimensional cerebral organoid model, and co-culture systems involving two distinct immune cell populations.

Weaknesses:

Several aspects of the study require clarification and further improvement. For example:

(1) Figure 1H is missing statistical analyses.

(2) The scRNA-seq analysis shows a reduction in the proportion of cells occupying transcriptional states associated with later pseudotime values, which the authors interpret as evidence that Aβ treatment inhibits neuronal maturation. However, the data presented do not appear sufficient to support this conclusion. An alternative explanation is that Aβ preferentially affects the survival of more mature neuronal populations, leading to their depletion, consequently, an apparent enrichment of cells at earlier pseudotime states. Therefore, the observed pseudotime shift does not necessarily demonstrate impaired maturation per se. The authors should revise the interpretation of these results in the first paragraph and either provide additional evidence supporting a maturation defect or discuss alternative explanations such as selective loss of mature neurons.

(3) A similar concern applies to the scRNA-seq data presented in Figure 3. The authors interpret the shift toward later pseudotime states in the presence of microglia as evidence of enhanced neuronal maturation. However, the data do not exclude alternative explanations. For instance, microglia may preferentially promote the survival of more mature neuronal populations or protect them from cell death, thereby increasing their relative abundance in the dataset. Consequently, the observed pseudotime distribution cannot be taken as direct evidence of enhanced maturation. The authors should revise their interpretation accordingly and discuss the possibility that the observed effect reflects differential survival rather than accelerated neuronal maturation.

(4) In Figures 4A-E, the authors should report the levels of the secreted proteins in pg/mL instead of relative values, as this would better reflect the actual amounts produced. In Figure 4H, the inhibitor-treated control T-cell samples should be included. Furthermore, it should be explicitly stated that the inhibitor-treated data points currently shown refer to T cells cultured in the presence of myeloid Aβ.

Reviewer #2 (Public review):

Summary:

In the manuscript titled "Multiple Molecular Pathways to Longevity: Opposing Gene Expression Programs Define Distinct Aging Strategies", the authors investigated diverse genetic pathways that contribute to lifespan extension in Caenorhabditis elegans and aimed to identify shared and distinct molecular mechanisms among various longevity mutants. Through comprehensive RNA sequencing of different longevity mutants representing seven distinct pathways, the authors showed that these mutants cluster into three primary groups based on their gene expression profiles. This transcriptomic analysis revealed that while some longevity genes are commonly regulated across multiple pathways, others exhibit opposing expression patterns, suggesting that distinct molecular strategies can lead to increased lifespan. Specifically, they identified a set of 196 genes that are consistently upregulated in most longevity mutants, many of which are involved in innate immunity and stress defense. By performing RNAi-based screening, the authors further validated the functional roles of several candidates, including C08F11.7, ugt-62, and K05C4.9, supporting their contributions to longevity and stress resistance. The authors conclude that longevity is mediated through multiple molecular pathways and provide a public online tool to study these complex transcriptomic landscapes.

Significance:

This study provides a systematic, side-by-side transcriptomic comparison of nine genetically distinct long-lived C. elegans mutants, revealing that lifespan extension arises from both shared and opposing gene expression programs. By identifying three distinct longevity groups and demonstrating that key pathways can be modulated in opposite directions to achieve long life, the work challenges the notion of a single universal transcriptional signature of aging. Importantly, functional validation shows that select commonly regulated genes can directly modulate lifespan and stress resistance, highlighting actionable molecular targets for promoting healthy aging.

Comments on revised version:

The authors addressed my concerns successfully.

Reviewer #2 (Public review):

Summary:

The authors present a creative approach using visual anagrams matched on low-level image statistics to isolate animacy from low-level visual features and report consistent effects of animacy on visual working memory and attention.

Strengths:

(1) An important methodological advance in controlling low-level confounds that have historically complicated the study of animacy.

(2) The converging effects across multiple experiments, together with the pre-registered design, strengthen the reliability of the reported findings.

Reviewer #2 (Public review):

Summary:

Sleep plays a critical role in memory consolidation, but the neural mechanisms underlying this relationship remain incompletely understood. The authors examined a specific subset of PAM dopaminergic neurons, PAM-α1, and DPM neurons in Drosophila. These neurons have previously been implicated in memory, and DPM neurons have also been linked to sleep. The study explores whether this circuit provides a mechanistic link between sleep and memory consolidation.

Strengths:

The authors report several novel findings. Brief activation or inhibition of PAM-α1 neurons, or brief inhibition of DPM neurons during the first few hours after training, impairs 24-hour LTM. Notably, these brief manipulations disrupt sleep for many hours afterward, particularly during the night. The authors further show that perturbation of PAM-α1 and DPM neurons impairs sleep and appetitive memory consolidation under starvation conditions, and that pharmacological sleep induction during the night rescues the LTM defects. Together, these findings suggest that PAM-α1 and DPM neurons are involved in sleep regulation and LTM consolidation under starvation. These are important observations that advance our understanding of the circuits regulating sleep and memory consolidation.

Weaknesses:

Some claims require additional evidence or clarification.

(1) Previous studies linking impaired memory to reduced sleep have primarily examined conditions involving severe sleep deprivation. In contrast, this manuscript argues that relatively modest decreases in total sleep, accompanied by sleep fragmentation, are sufficient to impair memory consolidation. It remains unclear whether sleep fragmentation of this magnitude is itself critical for LTM consolidation. An independent method for inducing comparably mild sleep loss and fragmentation would be needed to directly test this interpretation.

(2) It is unclear why both activation and inactivation of PAM-α1 neurons produce similar effects on sleep and memory. In addition, MB299B-labeled neurons exert stronger effects on memory than MB043B-labeled neurons, whereas MB043B-labeled neurons have stronger effects on sleep. If sleep disruption is the primary driver of impaired memory consolidation, a stronger correspondence between the sleep and memory phenotypes might be expected. The authors speculate that MB043B may affect sleep through non-PAM neurons, but without identifying the relevant neurons, this remains speculative.

(3) The complex schematic model (Fig. 12), with parallel circuits and unidentified neuronal groups, underscores the difficulty of interpreting the current data. In the "less activity" arm of the model, distinct circuits are proposed to regulate sleep and LTM, respectively, and DPM neurons are not included. This makes it difficult to reconcile the model with the central claim that the PAM-α1-to-DPM microcircuit links sleep and LTM consolidation.

(4) The TRIC-LUC reporter system is not ideal for resolving dynamic changes in neuronal activity. Activity-dependent Ca²⁺ signaling must first reconstitute the TRIC transcriptional system, which then drives luciferase transcription, translation, and accumulation. The original characterization of TRIC indicates that TRIC signals accumulate and decay over several hours. Thus, the kinetics of the TRIC-LUC reporter should be interpreted cautiously, particularly when inferring transient or precisely timed changes in neuronal activity.

(5) Including data from training under fed conditions would provide a more complete understanding of state-dependent neural activity and would help distinguish starvation-specific effects from more general circuit mechanisms.

Reviewer #2 (Public review):

Summary:

In this paper the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the item values and not their accumulation whereas the pre-SMA, IPS and dlPFC correlated more strongly with their accumulation. Further analysis revealed that the pre-SMA was the only one of the three integrator regions to also exhibit gaze modulation.

The study uses a highly innovative design and addresses an important and timely topic. The manuscript is well-written and engaging, while the data analysis appears highly rigorous.

Weaknesses:

With 23 subjects the study has relatively low statistical power for fMRI although the within-subjects design and relatively high trial count reduces these concerns.

Reviewer #2 (Public review):

Summary:

The ADP-ribosyltransferase tankyrase controls many biological processes, many of which are relevant to human disease. This includes Wnt/beta-catenin signalling, which is dysregulated in many cancers, most notably colorectal cancer. Tankyrase is a positive regulator of Wnt/beta-catenin signalling in that it counters the activity of the beta-catenin destruction complex (DC). Catalytic inhibition of tankyrase not only blocks PAR-dependent ubiquitylation and degradation of AXIN1/2, the central scaffolding protein in the DC, but also tankyrase itself. As a result, blocking tankyrase gives rise to tankyrase accumulation, which may accentuate its non-catalytic functions, which have been proposed to drive Wnt/beta-catenin signalling. Most tankyrase catalytic inhibitors have shown limited efficacy and substantial toxicity in vivo. By developing tankyrase-directed PROTACs, the authors aim to block both catalytic and non-catalytic functions of tankyrase, aspiring to achieve a more complete inhibition of Wnt/beta-catenin signalling. The successfully developed PROTAC, based on the existing catalytic inhibitor IWR1, IWR1-POMA, induces the degradation of both TNKS and TNKS2, blocks beta-catenin-dependent transcription without stabilising the DC in puncta/degradasomes, and inhibits cancer cell growth in vitro. Mechanistically, this points to a scaffolding role of tankyrase in the DC, at least under conditions of tankyrase catalytic inhibition, in line with previous proposals.

Strengths:

The study clearly illustrates the incentive for developing a tankyrase degrader, namely, to abolish both catalytic and non-catalytic functions of tankyrase. By and large, the study achieves these ambitions, and the findings support the main conclusions, although the statement that a more complete inhibition of the pathway is achieved requires corroboration. The proteomics studies are powerful. IWR1-POMA constitutes a very useful tool to re-evaluate targeting of tankyrase in oncogenic Wnt/beta-catenin signalling. The paired compounds will benefit investigations of tankyrase scaffolding functions across many different biological systems controlled by tankyrase. The findings are exciting.

Comments on previous version:

I thank the authors for responding to the queries raised in the original review, most of which have now been addressed. This further strengthens this well-conducted study and well-presented manuscript. I congratulate the authors for this interesting and insightful work.

Reviewer #2 (Public review):

Summary:

In this manuscript, Khamari and colleagues investigate how HGF-MET signaling and the intracellular trafficking of the MET receptor tyrosine kinase influence invadopodia formation and invasion in triple-negative breast cancer (TNBC) cells. They show that HGF stimulation enhances both the number of invadopodia and their proteolytic activity. Mechanistically, the authors demonstrate that HGF-induced, RAB4- and RCP-RAB14-KIF16B-dependent recycling routes deliver MET to the cell surface specifically at sites where invadopodia form. Moreover, they report that MET physically interacts with MT1-MMP - a key transmembrane metalloproteinase required for invadopodia function- and that these two proteins co-traffic to invadopodia upon HGF stimulation.

Although the HGF-MET axis has previously been implicated in invadopodia regulation (e.g., by Rajadurai et al., Journal of Cell Science 2012), studies directly linking ligand-induced MET trafficking with the spatial regulation of MT1-MMP localization and activity have been lacking.

Overall, the manuscript addresses a relevant and timely topic and provides several novel insights.

Comments on revised version:

I appreciate the authors' efforts to revise the manuscript and address the reviewers' comments. While the revised version includes additional experiments and several improvements in data presentation, the major methodological and conceptual concerns raised in the initial review remain largely unresolved. In my opinion, these issues critically undermine the central mechanistic conclusions of the study.

(1) Inappropriate experimental design for studying MET trafficking

A major concern remains the use of prolonged HGF stimulation times (2-6 hours) to study MET endocytosis and recycling. This is not an appropriate experimental design for investigating receptor tyrosine kinase trafficking dynamics. Ligand-induced internalization of MET occurs within minutes, with maximal endosomal accumulation typically observed within 5-15 minutes, whereas recycling occurs over approximately 15-60 minutes.

Importantly, the authors have not included short stimulation time points or any kinetic analysis that would allow a proper assessment of MET internalization or recycling. The additional surface biotinylation experiment does not address this issue, as it still does not provide temporal information regarding receptor trafficking.

Therefore, the current data do not support the conclusions regarding MET endocytosis or recycling, and this major methodological concern has not been adequately addressed in the revised manuscript.

(2) Insufficient validation of antibody specificity in immunofluorescence

The validation of antibody specificity for MET, phospho-MET, and MT1-MMP in immunofluorescence experiments remains insufficient. While the authors demonstrate knockdown efficiency by immunoblotting and show some reduction in fluorescence signal, they do not provide rigorous evidence that the immunofluorescence signal is specifically abolished upon gene silencing under identical imaging conditions. Such validation is essential, particularly because the manuscript relies heavily on imaging-based localization and colocalization analyses. Without these controls, it cannot be excluded that the observed signal represents non-specific staining.

Importantly, the authors attempt to justify antibody specificity primarily by citing previous publications that used the same antibodies. However, this is not an adequate substitute for experimental validation within the current study. Previous reports do not guarantee specificity under the present experimental conditions, particularly in immunofluorescence, where staining patterns can be strongly influenced by fixation procedures, antibody concentrations, imaging settings, and cell type. Moreover, those studies may themselves lack sufficiently rigorous validation of antibody specificity. Therefore, antibody specificity should be demonstrated directly in the experimental system used in this manuscript, especially given that the principal conclusions rely extensively on the subcellular localization of MET, phospho-MET, and MT1-MMP.

(3) Questionable MET localization in TIRF microscopy

The presence of punctate MET signal in TIRF microscopy under unstimulated conditions raises additional concerns. Under basal conditions, MET is generally expected to exhibit a predominantly diffuse distribution at the plasma membrane, whereas prominent punctate structures are typically associated with ligand-induced clustering, endocytosis, or trafficking events.

The observation of numerous MET-positive puncta in unstimulated cells, together with the insufficient validation of antibody specificity, raises the possibility that at least part of the observed signal represents non-specific staining or imaging artefacts rather than bona fide MET localization. This concern is further compounded by the lack of rigorous immunofluorescence antibody validation discussed above and significantly undermines the interpretation of all TIRF-based trafficking analyses presented in the manuscript.

(4) The evidence supporting a MET-specific role in invadopodia remains unconvincing

The authors argue that the role of MET in invadopodia formation is validated using three independent approaches: shRNA-mediated knockdown, SMARTpool siRNA-mediated knockdown, and pharmacological inhibition with PHA665752. However, I do not agree that these constitute three independent orthogonal validations of MET function.

First, the shRNA-mediated knockdown presented in this study achieves only modest depletion of MET protein. The authors themselves acknowledge this limitation and therefore selected cells with visibly reduced MET staining for imaging. Consequently, the shRNA experiments cannot be considered a robust or independent validation of MET function.

Second, although pooled SMARTpool siRNAs are widely used to improve knockdown efficiency, they cannot exclude off-target effects, as each individual guide RNA contributes its own potential off-target profile. Therefore, pooled siRNAs cannot by themselves establish that an observed phenotype is specifically attributable to depletion of the intended target and do not replace validation using independent individual siRNAs or rescue experiments.

Third, the pharmacological data should also be interpreted with caution. Throughout the manuscript, PHA665752 is presented as a MET inhibitor supporting the specificity of the observed phenotype. However, there is essentially no such thing as a truly selective receptor tyrosine kinase inhibitor. PHA665752 inhibits multiple kinases in addition to MET, particularly at concentrations commonly used in cell-based assays. Consequently, the inhibitor cannot be considered an independent validation of MET-specific function.

Importantly, the newly added siRNA experiments do not resolve my original concern regarding the role of MET in invadopodia formation. Although siRNA-mediated MET depletion is substantially more efficient than the shRNA-mediated knockdown presented in the original manuscript, this marked difference in MET depletion is not accompanied by a correspondingly stronger inhibition of invadopodia formation or ECM degradation. If MET were indeed the principal driver of the observed phenotype, one would expect the magnitude of the biological effect to correlate with the efficiency of MET depletion. This inconsistency raises the possibility that the observed phenotype is not solely attributable to MET depletion and calls into question the specificity of the proposed mechanism.

Taken together, the three perturbation approaches used by the authors cannot be regarded as independent orthogonal validation of MET function. One approach provides only modest target depletion, another relies on pooled RNAi reagents that cannot exclude off-target effects, and the third employs a multi-kinase inhibitor rather than a MET-specific compound. Collectively, these limitations substantially weaken the conclusion that the reduction in invadopodia formation is specifically attributable to loss of MET. A convincing demonstration of MET-specific function would require rescue experiments or another truly orthogonal validation strategy.

(5) Weak evidence for MET-MT1-MMP interaction

The evidence supporting a physical interaction between MET and MT1-MMP remains unconvincing. The newly added co-immunoprecipitation experiment does not reveal a convincing MET-MT1-MMP interaction, and I am unable to appreciate a specific co-immunoprecipitated MT1-MMP signal in the presented blot. As presented, these data do not convincingly demonstrate a specific or functionally relevant interaction. Given that this interaction constitutes a central component of the proposed mechanistic model, this remains a major weakness of the study.

(6) Overinterpretation of the data

Taken together, the study proposes a mechanistic model linking MET trafficking to MT1-MMP localization and invadopodia function. However, the experimental evidence largely supports correlative observations rather than demonstrating a direct mechanistic relationship.

Specifically, MET endocytosis and recycling are not properly demonstrated because of the inappropriate temporal resolution of the trafficking experiments; the localization data remain uncertain owing to insufficient validation of the immunofluorescence reagents; and the proposed interaction between MET and MT1-MMP is not convincingly demonstrated. Consequently, the manuscript establishes correlation rather than causality, and the central mechanistic conclusions appear to be substantially overstated relative to the presented data.

Conclusion:

While the manuscript addresses an interesting and biologically relevant question, the current experimental evidence does not adequately support the proposed mechanistic model. The combination of inappropriate experimental design for trafficking studies, insufficient validation of key imaging reagents, questionable interpretation of the localization data, lack of convincing evidence for the proposed MET-MT1-MMP interaction, and the absence of a clear relationship between the degree of MET depletion and the biological phenotype substantially limits the reliability of the conclusions.

In my opinion, these issues cannot be addressed by further revision of the current manuscript, as they require substantial additional experimentation, including appropriately designed trafficking assays with short kinetic time points, rigorous validation of antibody specificity for immunofluorescence, and stronger mechanistic evidence linking MET trafficking to MT1-MMP-dependent invadopodia function.

Reviewer #2 (Public review):

Summary:

This study investigates how inhibitory synaptic plasticity can stabilize recurrent neural circuits while also shaping their functional connectivity. The authors analyze inhibitory spike-timing-dependent plasticity rules and show that different temporal kernels promote distinct E/I motifs, including reciprocal E/I connectivity and lateral inhibition. Using reduced circuit analyses and larger spiking network simulations, they demonstrate that inhibitory plasticity can generate structured effective connectivity, including Mexican-hat-like interactions in ring networks, while maintaining stable activity. The work therefore extends the view of inhibitory plasticity from a primarily homeostatic mechanism to one that may contribute to computationally useful circuit organization.

Strengths:

A major strength of the study is that it identifies a concrete mechanism by which the temporal shape of iSTDP rules determines the structure of learned inhibitory connectivity. The comparison between rules favoring reciprocal E/I motifs and those favoring "lateral" inhibition is shown across both reduced circuit models and larger spiking networks. The ring-network simulations further connect these learned motifs to circuit-level outcomes, including Mexican-hat-like effective connectivity, surround-suppression, and modular spontaneous activity.

Weaknesses:

The main limitations concern the extent to which the learned motifs are fully self-organized and how broadly the results generalize. In particular, the ring-network results rely on a pre-specified ring-like excitatory architecture and on two inhibitory populations with distinct plasticity rules, making it important to clarify which aspects of the Mexican-hat effective connectivity emerge from iSTDP itself. The conclusions would also be strengthened by intermediate plasticity rules. Finally, the ring-network simulations provide an interpretable proof of principle, but the authors should clarify whether the PV/SST effects depend on this specific architecture or would also arise in a more generic recurrent or cortex-like connectivity motif.

The authors largely achieve their aim of showing that inhibitory synaptic plasticity can provide structured stabilization of recurrent circuits. The results support this claim within the model framework by demonstrating that different temporal forms of iSTDP lead to distinct learned E/I motifs and can shape effective connectivity and cortical-like response patterns. However, the broader biological interpretation remains more suggestive because some results depend on specific assumptions for the network architecture and plasticity rules.

The work is likely to be valuable for researchers studying inhibitory plasticity, E/I balance, cortical circuit development, and biologically plausible learning because it provides a clear theoretical link between local inhibitory learning rules and circuit-level organization. The combination of analytically tractable motifs, spiking network simulations, and publicly available code makes the framework useful for future research.

The significance of the work lies not in showing that inhibitory plasticity can have functions beyond homeostatic stabilization, which has been established by previous theoretical and experimental studies, but in formalizing how the temporal form of iSTDP rules can bias the emergence of distinct E/I motifs. At present, the work identifies rules that are sufficient to generate these motifs in model networks, while the mapping of these rules onto specific interneuron types remains for future experimental testing.

Reviewer #2 (Public review):

Summary:

This paper describes a study that uses a combination of observational and experimental techniques to investigate the hypothesis that the zona incerta is a neural loci where sensory information is integrated to interpret the motivational value of reward-associated cues. They show that manipulation of GABAergic neurons in this region bidirectionally modulates responding during a progressive ratio test, that activating these neurons recovers motivational deficits incurred by chronic stress, and that they fire in response to reward-associated visual or auditory cues. They also showed that activity in these neurons is not necessary for incentive salience of reward-associated cues, because inactivating them did not prevent Pavlovian-instrumental transfer. However, activating them did enhance responding during the presentation of reward-associated cues in females but not in males.

Strengths:

The study has a very systematic and elegant approach to assess how this region responds first to intrinsic motivation and then to motivation-enhancing effects of reward-associated cues.

Weaknesses:

Males and females are used throughout, but sample sizes are generally too small to make a meaningful interpretation of sex differences (which is not the focus of the study, but is worth bearing in mind). In the last experiment, the lack of discrimination between CS+ and CS- conditions across training for males confounds any interpretation of sex-differences in the outcomes.

The ZI is known to be a region where there is notable convergence of neural inputs from a diverse and heterogenous range of sensory and other cortical inputs. To my knowledge, this is the first study that has directly tested whether it may serve to encode motivational/incentive properties of reward-associated cues. The outcomes are not definitive - it appears that they are sufficient but not necessary. However, this study represents an important first step - the ZI also has notable heterogeneity in the genetic identity of neurons, and properly dissecting the function of ZI microcircuits will likely require characterising function based on more than one molecular marker. This is addressed by the authors in the discussion.

In summary, this study will have a significant impact on our understanding of how motivation is calculated based on complex environmental signals.

Reviewer #2 (Public review):

Summary:

A relatively new measure of flexible brain state engagement (SEV - State Engagement Variability) is used here. It simply measures time-to-time variation in brain activity in terms of how it matches pre-specified motifs of activity. This metric seems to be predictive of behavioural data measuring cognitive control abilities. This was found to be the case in two independent datasets with different (though related) behavioural measures.

Strengths:

Use of multiple datasets is a clear strength. The use of both replication and out-of-sample model prediction is another.

Weaknesses:

(1) It is not clear to me how specific the SEV metric is for telling us about brain state engagement flexibility. Resting state fluctuations have been described as quasi-periodic changes that can be mapped onto "states", but the fluctuations could easily be a reflection of vascular flow, which may indirectly correlate with cognition.

(2) If SEV is calculated using other state descriptors (e.g. a random parcellation of the brain into 4 networks) - would the result still hold? Or are the motifs important (this would rule out, to some extent, the vascular argument from (1) above)?

(3) Figure 1 confused me a little. Why not show all the combinations (patient v full sample), inhibition vs shift, and main vs validation? Instead, a subset of 4 was selected?

(4) The inhibition/patient/main correlation seems to be driven by 4 patients with particularly high inhibition measures?

(5) Why is SEV negative in some cases (e.g., Figure 1) if it's a std measure? Has it been demeaned or orthogonalised wrt another variable?

(6) The external analysis is great, but why should the model predict a relationship between SEV and inhibition if the claim is that it is only true for patients? Why would it only be true for patients in the first place?

(7) I can't get my head around the results shown in Figure 3. How can one have both positive and negative correlations being significant or meaningful in the same pairs of networks? I think this set of results could benefit from more explanation.

(8) I struggled with Figure 4 analysis. What is the SEV network? How do we know that it is specific enough to the SEV concept? Looking at co-fluctuations with the cognitive network, are we not simply looking at the old anti-correlation between the default mode and the rest of the brain (I note that the correlations in the y-axes of Figure 4 are negative)?

Reviewer #2 (Public review):

Summary:

Renard, Foustoukos and colleagues present a study of rapid sensorimotor learning in the mouse barrel cortex. Head-fixed water-restricted mice already trained on an auditory detection task are introduced to a novel C2 whisker stimulus, and the authors show that reward-paired mice acquire the whisker-lick association within a single behavioral session, with the two groups (rewarded vs non-rewarded) diverging behaviorally within ~22 whisker trials and ~14 minutes. Both pharmacological inactivation of wS1 across Days 0/+1/+2 and optogenetic inactivation on Day 0 impair whisker-guided performance, while fpS1 manipulations do not, establishing that wS1 activity is required for whisker-guided behavior during the initial learning period. Longitudinal two-photon imaging of GCaMP6f-expressing L2/3 neurons across five days (-2 to +2 relative to whisker introduction) reveals a bidirectional, reward-dependent reorganization of population responses to passive whisker stimuli: rewarded mice show enhancement, non-rewarded mice show suppression. The authors use a logistic-regression decoder trained to discriminate pre- vs post-learning passive trials and then project Day 0 active whisker trials onto this learning axis; the projection rises monotonically across Day 0 in R+ mice and is significantly correlated with behavioral performance, with no such trajectory in R- mice. Finally, the authors detect reactivation events during catch trials by template-matching to the average passive whisker response, and show that on Day 0, the neurons most positively modulated by learning (LMI-positive) participate in these reactivations more than LMI-negative neurons in R+ but not R- mice. The authors interpret this as evidence that online, reward-gated reactivations may act as an upstream selection mechanism for which neurons undergo learning-related plasticity, operating on the minutes-timescale of within-session learning. There is much to like in this paper, with some moderate-to-major concerns that could largely be addressed with re-analysis or re-framing.

Strengths:

The single-session learning paradigm is a key aspect of this paper, given the rapid learning observed. Coupled with the R+ and R- design, there's a lot to like with the behavioral approach. The bidirectional response change across these R+ and R- groups (enhancement vs suppression) is also a nice finding.

The causal manipulations demonstrate that the imaged region is used during the task. By doing both pharmacological and optogenetic inactivation, each with a control in the spatially adjacent region (fpS1), the authors make a strong case that wS1 activity is necessary for whisker-guided behavior during the initial learning period (though see below about the limitations of the current approach).

The longitudinal two-photon imaging of the same L2/3 neurons across five days underlies essentially every neural analysis in the paper and enables the single-cell LMI and population-trajectory analyses.

The pathway-specific analysis in Figure 3 - figure supplement 2 is very interesting, but not much time is spent on it (lines 151-155). The dissociation between wS2-projecting neurons (which show learning-related enhancement in R+ and suppression in R-) and wM1-projecting neurons (which do not) is (in my opinion) a nice instance of projection specificity - it also aligns with the known routing of task-relevant whisker information through the wS1→wS2 pathway. I would encourage the authors to motivate this experiment in the main text rather than leaving it all to the discussion (lines 256-262).

The methods are generally well documented and easy to follow.

Weaknesses:

(1) Conflation of de novo association learning with generalization from auditory pre-training.

All mice have already learned a task structure with the auditory task - "detect the salient sensory cue → lick → reward". Under these conditions, the rapid emergence of licking to the whisker stimulus could reflect either de novo formation of a whisker-specific association or generalization of an instrumental policy to a novel salient cue. The manuscript frames the result as the former ("acquisition of a novel sensorimotor association"), but the experiment cannot distinguish between the two alternatives. This distinction between de novo learning and generalization may have a meaningful impact on the interpretation, though it doesn't impact the specific results. It would be helpful for the authors to discuss the two possibilities and generally consider the contribution of generalization from auditory pre-training to Day 0 performance.

Relatedly, the R- group is introduced (lines 69-74) and later used (lines 244-247) as a passive-exposure control that rules out representational drift. While R- group is an important control for repeated whisker stimulation and task context, it does not appear to be a pure passive-exposure control: Figure 1B shows that on Day 0 the mice lick more to the R- stimulus than with no stimulus and then extinguish that licking by Day 1. Thus, one possibility is that R- mice actively learn to suppress licking to an unrewarded stimulus (whisker) in a context where other stimuli (auditory) remain rewarded. This would be a different cognitive operation (response suppression) from a purely passive exposure condition. The manuscript therefore lacks a true passive-exposure baseline, and several claims that rely on R- as such a baseline (including that bidirectional changes are reward-driven rather than reflecting passive drift, lines 244-247) need to be reframed.

(2) The inactivation experiments establish that wS1 is necessary on Day 0, but they cannot separate detection, acquisition, and expression.

Both the muscimol manipulation (whole session, Days 0/+1/+2) and the optogenetic manipulation (0.1 s before stimulus onset through the 1 s reporting window) silence wS1 during the moments when the whisker stimulus must be detected for a successful trial. Under these conditions, impaired performance could reflect that the animal cannot detect the stimulus, cannot express the learned response on that trial, or cannot acquire the association. These are causally distinct processes, and the manuscript currently treats them as equivalent.

Specifically, on Day +1 of the opto experiment (light off), do mice learn at the same rate as a naive Day 0 cohort (e.g., the R+ imaging mice on Day 0), or is performance already higher than the naive group? If higher than the naïve group, this would suggest that there is learning occurring and would suggest that something that may have been acquired during Day 0 inactivation, even if it could not be expressed.

(3) The interpretation of the LMI-participation correlation is complicated by the peaked LMI distribution and neuron-level pooling.

Two related issues arise from the results shown in Figure 4I. First, the LMI distribution in Figure 3F (and visible in 4I) is sharply peaked near zero. The reported r = 0.24 in R+ mice is therefore difficult to interpret biologically because the distribution is dominated by near-zero-LMI neurons and the slope may be disproportionately influenced by neurons in the tails. The key claim is better tested by comparing significantly LMI-positive, LMI-negative, and non-modulated neurons. The authors do address this in Figure 4J - showing that participation rate rises across days for significantly LMI-positive R+ neurons (p = 5×10⁻⁴) but not for LMI-negative neurons (p = 0.05) - but this analysis is not the lead result. To my understanding, Figure 4J is more interpretable and should be the key piece of data supporting their claim.

Second, the p-value of p = 1×10^-41 in Figure 4I comes from treating thousands of neurons pooled across 19 mice as independent observations. Neurons within an animal are correlated through shared behavioral state, shared imaging session, and circuit-level interactions, so it would be helpful to consider a different statistical unit of comparison (FOV, animal, etc). For example, a linear mixed-effects model with mouse as a random effect could work.

(4) The reactivation-LMI relationship is partially circular, and the framing in the abstract could be more constrained.

The "reactivation template" is the trial-averaged passive whisker-evoked population vector from each session, and reactivations are detected as moments in catch-trial activity that correlate with this template above a shuffled threshold. This approach is reasonable, but it means that the reactivation-LMI relationship is not fully independent of template construction, and the framing in the abstract blurs that line. LMI-positive neurons are defined as neurons whose passive whisker-evoked responses increase from pre- to post-learning. Therefore, neurons with strong whisker responses, or neurons that become stronger components of the whisker-evoked template across learning, may be more likely to contribute to template-matching events by construction. Thus, the LMI-participation relationship could partly reflect template weighting or sensory-response amplitude, rather than showing that reactivation events selectively recruit neurons for future learning-related plasticity. It would be helpful and more reassuring if the authors could control for each neuron's whisker-template weight, baseline whisker responsiveness, and overall calcium event rate when relating LMI to reactivation participation.

A complementary unsupervised approach could also help: rather than starting from the whisker template, one can derive co-activity assemblies directly from spontaneous activity (e.g., via PCA or ICA on the catch-trial population activity), and then ask, separately, whether any of these assemblies overlap with the whisker ensemble. The interesting test is then whether whisker-like assemblies become more frequently expressed across Day 0 in R+ but not R- mice, and whether LMI-positive neurons are preferentially loaded onto these whisker-like assemblies. This logic inverts the current pipeline and can be complementary to the current analysis. By identifying structure in nominally spontaneous activity first and then comparing to the whisker response, this could help avoid the circularity in which the template both defines the events and contains the cells being tested. The Figure 4 - figure supplement 1B partial-correlation analysis is a step in this direction but addresses only spontaneous firing rate, not template coupling. Without such a complementary approach, the authors may want to clarify that the reactivation detection is anchored to a template defined in part by the same cells whose participation is being tested.

(5) The reactivation-as-selection-mechanism interpretation is not supported by the current data.

The Discussion (lines 278-281) acknowledges that the authors have not shown necessity, but the end of the intro and part of the discussion (Lines 275-277) frame reactivations as a "reward-gated selection mechanism" for plasticity. An equally plausible alternative is that neurons whose synaptic inputs or intrinsic excitability have been potentiated by reward-driven learning will simply co-fire more often during quiet periods - meaning reactivations would be a consequence of plasticity that has already occurred rather than a mechanism that selects which neurons to potentiate. The current data cannot distinguish these.

A separate concern is the use of the term "spontaneous." The authors' usage is defensible in one sense - catch trials are stimulus-free, so the activity is not externally driven. However, "spontaneous" in the reactivation literature typically connotes offline, internally generated activity during quiet wakefulness or sleep, which carries different implications for plasticity than activity during active task engagement. Catch trials in this paradigm occur within the behavioral session, with the animal still engaged in the task, potentially anticipating reward or licking. The authors should either acknowledge this distinction in the text or qualify the term - "within-session" or "inter-trial" reactivations would be more accurate and would avoid borrowing the conceptual weight of the offline-replay literature.

The authors should also clarify whether catch-trial activity around licks (false alarms, anticipatory licks) is excluded from the reactivation analysis, and whether reactivation rates depend on recent reward, recent whisker trial outcome, or behavioral state. Specificity controls - template-matching with shuffled templates and with auditory templates - would help establish that detected events reflect whisker-specific patterns rather than generic high-coactivity moments.

(6) Motor, lick, and behavioral-state confounds in the neural analyses are not fully addressed.

I have two specific concerns. First, for the Day 0 active-trial projection, mean whisker reaction times in Figure 1 - figure supplement 1G are around 350-500 ms, but the distributions extend into the 0-300 ms analysis window. The correlation between the projection trajectory and the behavioral learning curve (Figure 4E, lines 196-198) is the key piece of evidence that the neural shift tracks learning. However, on hit trials the lick may fall within or close to the analysis window, so a motor confound could in principle contribute to the rising projection. The authors could repeat the projection using an earlier/shorter window, exclude trials with early licks, or regress out lick timing. It would be helpful to better understand whether this effect is, in part, driven by licking activity.

Second, the central evidence for representational reorganization (Figure 3) rests on a post-session passive epoch in which 50 whisker stimulations are delivered after "task disengagement" (lines 131, 387-389). The concern is that the brain state during this epoch is unlikely to be matched across groups or across days. R+ mice receive additional water rewards on whisker trials, whereas R- mice receive rewards only on auditory trials. This could lead to systematic differences in satiety, arousal, and disengagement state during the passive block. Because cortical sensory responses are strongly modulated by arousal, some of the apparent learning-related enhancement (R+) or suppression (R-) of passive whisker responses across days could reflect systematic state differences during the passive epoch rather than plasticity. The disengagement criterion ("stopped licking in all trial types") is also qualitative - no consecutive-miss or time-window threshold is specified - so the epoch may begin at slightly different behavioral states across mice. To resolve this, the authors could (i) specify the disengagement criterion quantitatively and (ii) compare pupil diameter and whisker self-motion (if available) across R+ vs R- and across days during the passive epoch.

Reviewer #2 (Public review):

Le and Wei present a novel estimation method for information-limiting correlations. Information-limited correlations are shared noise fluctuations that affect neural encoding, but they can be hard to estimate (even to detect their presence) because they can be very small and buried under other common sources of variability that do not affect encoding. The newly proposed method bypasses two central limitations of previous approaches: extrapolation or assuming the noise structure to be Gaussian. The authors proposed a split-trial analysis where the population is split into two, and the correlations between the decoding errors arising from each population are computed. These correlations provide an unbiased measure of information-limiting correlations. The method is very simple and sound, and it is shown to deliver stable estimates with sensible magnitudes across several brain data sets. Further, even if the decoders are suboptimal, the method can detect the presence of information-limiting correlations, as only shared fluctuations of the two population decoders can possibly be observed if there are correlations that limit information.

Comments:

(1) The name "split-trial analysis" does not seem to reflect well the nature of the method introduced. I would propose something like "split-ensemble analysis" or "split-population decoding-correlation analysis".

(2) Previous work has proposed a related - but different - bootstrap method, which can be mentioned in the current paper (Nogueira et al, J of Neuroscience, 2020).

(3) The authors proposed a deconvolution method to study the shape of the distribution of information-limiting noise. An alternative would be to split neural populations into 3 or more subpopulations and compute 3rd- and 4th-order correlations between the decoding errors. This would lead to estimates of higher-order moments that can be compared to Gaussian ones and test for non-Gaussian distributions. Further, this N-split-ensemble method could be used to compare the deconvolution method results to test their consistency.

Reviewer #2 (Public review):

Summary:

The work by Nagvekar et. al., reports the development of a new model in the African Killifish to study the engulfment of extracellular proteins. Specifically, they expressed oScarlet with a signaling peptide under the control of a neuronal promoter/gene to induce secretion into the extracellular space. Using this model, they found that the secreted protein was predominantly taken up by brain macrophages. Leveraging this finding, they were able to conduct RNAseq on brain macrophages from young and aged fish, where they reported differences in translation and vacuolar acidification at the transcriptional level among others. Finally, they show that the engulfment capacity of brain macrophages from old killifish is reduced when compared to their young counterparts.

Major comments:

(1) Red fluorescent proteins are notorious for being prone to aggregation. Are oScarlet proteins being internalized by macrophages aggregates or soluble proteins? This distinction is important as the clearance of extracellular molecules could be mediated by most cells, yet aggregates could be removed specifically by macrophages. Can experiments be conducted to distinguish between these two possibilities? We realize this may be challenging. If not feasible, the discussion should be tempered to reflect this possibility.

(2) Brain dissociation tends to generate a lot of debris, especially from sheared neurons. Therefore, the high level of oScarlet inside macrophages could be an artifact of dissociation rather than a reflection of in vivo clearance. Authors should use internalization inhibitors during dissociation (CytoD, Dynasore, and pitstop) to exclude this possibility. Alternatively, if they have a transgenic killifish that expresses another fluorescent reporter in neurons (and preferably at a similar level to that of oScarlet), authors should dissociate brains together and quantify how many oScarlet+ cells are now also positive for that other fluorescent reporter. This could give an idea of how much engulfment is occurring due to the dissociation processes. It is not ideal, as macrophage eating could be happening during dissociation but before cells are in single cell suspension. However, given that RNAseq is needed to identify macrophages, this reviewer would be satisfied by this alternative approach if the aforementioned pitfall is also presented in the discussion.

(3) Related to the above, it appears based on the scRNAseq that dissociation heavily enriched for brain macrophages. Therefore, the claim that clearance is mostly macrophage mediated could be due to an enrichment of this population during dissociation rather than this cell type being responsible for most of the extracellular waste disposal. Authors should quantify the % of total oScarlet that is specifically in macrophages in the brain sections they already have that are stained against oScarlet and CSF1R/ApoEB transcript.

(4) The flow cytometry strategy used does not distinguish between oScarlet protein that has been internalized versus that which is sticking to the surface of macrophages. Authors should stain non-premeabilized and permeabilized cell suspensions with a flow antibody against mCherry/RFP to get a sense of how much oScarlet is inside versus outside of the macrophage. For most antibodies this can be done on the same sample sequentially if the antibodies have a different fluorophore.

(5) It is concerning that dextran and oScarlet are almost perfectly colocalized in the image presented (Figure 3a). It raises the possibility, among others, that dextran is sticking to potential oScarlet aggregates and then being internalized by macrophages. Therefore, it could be an artifact of the transgenic line. Authors should repeat the experiment in wildtype fish and use HCR against CSF1R/ApoEB to address this issue.

Significance:

We believe that this is an important finding as such a model in African Killifish lays the groundwork to study the pathways that mediate the clearance of extracellular molecules by brain macrophages, the impact that this process has on brain homeostasis, and how it changes in aging. In particular, this reviewer is excited about the future potential of this model to uncover the molecular processes behind macropinocytosis, a process that occurs frequently in brain macrophages yet the mechanisms regulating it remain elusive, and how it contributes to overall brain health.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

(1) To show the importance of the Tat system in bacterial cells, it would be good to describe in the introduction how many proteins are translocated via the Tat system.

(2) Figure 2B and D show that a sufficient amount of MPIase is important in SufI translocation. However, the reason why MPIase level was upregulated in the BL21 strain but not in the KS46 strain remains unexplained. The authors should address this point.

(3) In Figures 4A and B, the authors explain that MPIase first works as a receptor of TorA-GFP without recognizing the RR motif. This conclusion is based on the results of the fractionation assays, where "sup" indicates the cytoplasmic and periplasmic fractions, and "ppt" indicates the membrane fraction. In Figure 4B, under the TatABC+++, (RR), +MPIase condition, the substrate is secreted most efficiently via the Tat pathway and should therefore be recovered in the periplasm fraction (sup). However, the authors point out that efficiently processed substrate was recovered in the ppt fraction rather than the sup fraction. The authors should explain why this occurred.

Reviewer #2 (Public review):

Summary:

The work provides mechanistic insights into the establishment of the anterior-posterior axis of the mouse embryo by quantifying multiple cellular and tissular parameters from high-quality live imaging data. It shows that the direction of the axis is predetermined by the embryo geometry, that the cells whose migration defines the direction of the axis (the anterior visceral endoderm) have a ratchet-like movement probably depending on transient relaxation events of the epithelial cells lying in their way, and that the adjacent cell layer (the epiblast) moves in the opposite direction.

Strengths:

The dataset is large, with multiple embryos from relevant reporter lines integrally imaged at high resolution for long periods of time, and the analysis tools are novel, original and powerful.

Weaknesses:

Since all data are obtained from wild-type unchallenged embryos, the direct causality between events may not be fully guaranteed.

Reviewer #2 (Public review):

Overall, I think these are exciting results that make a very nice contribution to the literature. I thought the picture-tagging of sequence locations in the fMRI study was clever, and the across-sequence RSA results were especially compelling. But there are several aspects of the presentation of the results that reduced my confidence and enthusiasm.

(1) This is an unusual paper in that there is one human intracranial study and two fMRI studies. The paradigm for the intracranial study is very different than the fMRI paradigm. The key differences are that the fMRI paradigm is hierarchical, while the intracranial is flat, with no sequence learning component, and the fMRI is auditory, while the intracranial is auditory. The justification for the switch from intracranial to fMRI was that intracranial does not allow anterior-posterior axis analysis, but there are so many differences between the studies that this feels like an awkward transition and justification. Also, anterior-posterior analysis in the MTL may not be feasible in EC with intracranial data, but it can be feasible in the hippocampus, and indeed this could be very worthwhile and relevant to pursue (see point 2).

While the two independent fMRI datasets is a strength, the replications would have been much more compelling had the analysis for the second dataset been preregistered.

(2) The intracranial results are pitched as a novel "abstract coordinate representation" but there is a substantial prior literature on MTL "ordinal position codes", which I believe is the same thing in this paradigm. Most of this literature is in the hippocampus, which is, of course, very relevant given the hippocampal findings here, but there is also evidence for this kind of information in EC, e.g., https://elifesciences.org/articles/45333.

(3) Given the intracranial results in the hippocampus as well as the prior relevant literature on position coding, it was not clear why the hippocampus was not an ROI in the fMRI studies.

(4) It wasn't until reading the Methods section carefully that I understood that the results do not hold for the right EC, only the left. This deserves more acknowledgment.

(5) The use of one-sided t-tests with an alpha of .05 reduced my confidence in the robustness of the results.

Reviewer #2 (Public review):

Summary:

The authors first surveyed metazoan genomes to identify homologs of Drosophila errantiviruses and classified them into two groups, "insect" and "ancient" elements, supporting the hypothesis of an early evolutionary origin for these retrotransposons. They subsequently identified two distinct types of envelope proteins, one resembling the glycoprotein F of paramyxoviruses and the other akin to the glycoprotein B of herpesviruses. Despite differences in their primary amino acid sequences, these proteins display notable structural similarity in their predicted domain architectures. The congruence between the phylogenies of the envelope and pol genes further supports the ancient origin of the envelope genes, challenging earlier hypotheses that proposed recent recombination events with baculoviruses. Additional analysis of the Pol "bridge region" corroborated the divergence among these elements, consistent with a pattern of limited cross-species recombination. Finally, by comparing these elements with non-envelope-containing Gypsy retrotransposons, the authors concluded that errantiviruses originated from multiple elements independently.

Strengths:

The conclusions of this study are based on a comprehensive collection of errantiviruses identified across a wide range of metazoan genomes. These findings are further supported by multiple lines of evidence, including phylogenetic congruence and the diverse evolutionary origins of envelope genes. AlphaFold2-assisted protein domain structure analyses also provided key insights into the characterization of these elements. Together, these results present a compelling case that errantiviruses arose independently through multiple evolutionary events, extending well beyond previous hypotheses.

Original Weaknesses:

It would be beneficial to emphasize in the Abstract the potential impact of this work by more clearly articulating the current knowledge gap in the field. While the second paragraph of the Introduction briefly touches on this point, highlighting the broader significance in the Abstract would better capture readers' interest. Additionally, some methodological choices would benefit from clearer justification and explanation. For instance, in Figure 6, the selection of the bridge region/RNase H domain is not explicitly explained, leaving the rationale for its choice unclear.

Reviewer #2 (Public review):

Summary:

The authors aimed to develop a large-scale drug screen to identify B-lp modulators in a vertebrate whole-animal system. Using the zebrafish LipoGlo system that the authors had previously published and validated, the authors screened 2762 drug candidates to generate 49 hits and ultimately validated 19 drugs as genuine ApoB-lowering drugs. Using LipoGlo-Electrophoresis, the authors are able to obtain insights into the ApoB-lipoprotein size/subclass distribution. The authors further validate and study the mechanism of a strong hit, Enoxolone, known as also known as 18β-Glycyrrhetinic acid, which has previously been reported to modulate lipid metabolism. The authors also show that Enoxolone effects are mediated through HNF4⍺, which has been previously shown in the mouse system, but this is the first time it has been shown in the zebrafish.

Strengths:

The study was methodical and robust, using a published and well-validated zebrafish LipoGlo model. The authors validated the hits from the screen independently and considered the possibility that some drugs may have been detected as false positive results due to effects on the enzymatic activity of NanoLuciferase; only one hit, verteporfin, was shown to be a false positive. Using LipoGlo-Electrophoresis, the authors are able to obtain extra insights into the ApoB-lipoprotein size/subclass distribution. They showed that while enoxolone treatment reduces total B-lps, there are no overt changes in B-lp size distribution compared to vehicle-treated animals, other than a slight increase in the zero mobility (ZM) fraction, which contains very large particles and/or tissue aggregates. In contrast, the positive control, lomitapide, does show a change in B-lp size distribution compared to vehicle-treated animals - an increase in frequency of LDLs (low-density lipoprotein), but a decrease in VLDLs (very low-density lipoprotein). This study also assesses the LipoGlo-Electrophoresis profile of HNF4⍺ inhibitors. Work in the zebrafish larvae means that the effect on overall development and an entire vertebrate organism can also be assessed. Finally, the authors applied a thorough statistical measure to define a hit, using the Strictly Standardized Mean Difference (SSMD) method.

Reviewer #2 (Public review):

Summary:

The authors have improved clarity overall and have spoken to most of the issues raised by the reviewers. There are still two outstanding problems however, where issues raised during the review were inappropriately dismissed in the manuscript. These should be explicitly addressed as limitations to the results presented (no eye tracking), and early pilot experiments that informed the experiments as presented (pink noise) rather than brushed off as 'unnecessary' and 'would be uninformative'.

Eye tracking:<br /> It is generally accepted that experiments testing stimuli presented at specific locations in peripheral vision require eye tracking to ensure that the stimulus is presented as expected, in particular, in the correct location. As I stated in the previous round of review, while a stimulus presentation time of 200ms does help eliminate some saccades, it does not eliminate the possibility that subjects were not fixating well during stimulus onset. I am also unclear what the authors mean by 'trained observer' in this context, though the authors state that an author subject in a different portion of the paper is an 'expert observer'. Does this mean the 'trained observers' are non-expert recruited subjects? Given the conditions tested differ from previous work (Freeman & Simoncelli, 2011) *these differences are a main contribution of the paper!* which DID include eye tracking in a subset of subjects, it is entirely possible to get similar results to this work in the context of non eye-tracking controlled stimulus presentation. The reasons now in the manuscript are not reasons that make eye tracking 'considered unnecessary'.

I appreciate that the authors now state the lack of eye tracking explicitly, but believe the paper needs to at least state that this is a limitation of the results reported, and eyetracking being 'considered unnecessary' is unreasonable, nor a norm in this subfield.

N=1:<br /> The authors now state clearly the limitations of a single subject in the manuscript, and state the expertise level of this subject.

Large number of trials:<br /> The authors now address this, and include an enumeration of the large number of trials.

Simple Models / Physiology comparison:<br /> I support the choice to reduce claims regarding tight connections to physiology, and appreciate the explanation of the luminance model.

Previous Work:<br /> I appreciate the author's changes to the introduction, both in discussing previous work and citation fixes.

Blurred White, Pink Noise:<br /> While the authors now address pink noise, the explanation for such stimuli being expected to be uninformative is confusing to me. The manuscript now first states that pink noise is a natural choice, then claims it would be uninformative, while also stating in the rebuttal (not the manuscript) that they tried it and it indeed reduced the artifacts they note. The logic of the experiments indeed relies on finding the smallest critical scaling value, which is measured by subjects determining if a synthesis is similar or different to a target or second synth. A synthesis free from artifacts would surely affect the subjects' responses and the smallest critical scaling measured.

The statement that the authors experimented with pink noise early on and found this able to address the artifacts should be stated in the manuscript itself, not just in the rebuttal, and the blanket statement that this experiment would be 'uninformative' is incorrect. Surely this early pilot the authors mention in the rebuttal was informative to designing the experiments that appear in the final paper and would be an informative experiment to include.

Comments on revised version.

The authors have addressed my outstanding concerns, adding discussion about the limitations of not having eye tracking in the study, details about the subject pool, limitations of a subset of the study which contains a single subject, and experiments with pink noise seeds, and this relationship to largest vs smallest critical scaling. In addition, they have added clarity around internal noise vs metamerism in the context of this study as raised by the other reviewer.

Reviewer #2 (Public review):

In this manuscript, the authors investigate how the oncogenic fusion protein NUP98-KDM5A alters gene expression in leukemia, using a combination of cellular experiments with model and patient cell lines, as well as in vitro studies. Upon transfection of U2OS cells with mEGFP-tagged NUP98-KDM5A, the authors show that the fusion proteins form sub-micrometer puncta, whereas KDM5A alone does not. These foci are also observed at expected native expression levels (using OpenCell data). The tag has an effect here, as switching to an mCherry tag raises the apparent saturation concentration for phase separation. Finally, the authors show via super-resolution imaging that the foci correlate with H3K4me3 distribution.

In vitro, the fusion protein forms amorphous, gel-like condensates at double-digit nanomolar concentrations. Truncation analysis identifies PHD3 of KDM5A as required for maximal phase separation, consistent with the ability of the protein to bind H3K4me3 peptides. Addition of polynucleosomes increases the amount of fusion protein partitioning into the condensate in an H3K4me3-binding-dependent manner. Condensates are gel-like with slow internal dynamics in vitro; in cells, however, the dynamics depend on the position of the EGFP tag (no corresponding experiments with mCherry are shown). Reconstitution with H3K4me3- and H3K4me0-modified arrays shows colocalization with both wild-type NUP98-KDM5A and the binding mutant. Here, H3K4me3 arrays recruit ~20% more protein and yield gel-like structures in a manner dependent on the PTM and on the PHD finger.

In cells, the fusion protein colocalizes with H3K4me3-marked loci, including the HOX clusters, as confirmed by FISH. Finally, re-analysis of published expression datasets from patient cells shows that genes are predominantly upregulated and that the upregulated genes are H3K4me3-marked.

This is a well-executed mechanistic study. The data convincingly establish that NUP98-KDM5A forms sub-micrometer foci at realistic expression levels, that these foci correlate with H3K4me3-marked sites, that the PHD3-H3K4me3 interaction mediates chromatin binding while the NUP98 moiety drives phase separation in vitro, that foci in cells overlap genes heavily decorated with H3K4me3, and that H3K4me3-marked genes are those found to be upregulated in patient datasets. These are important mechanistic findings and of interest to the community.

Still, the functional/causal link is a bit more tentative, as the data is mostly correlative, since it is not directly established that there is feedback between H3K4 methylation, NUP98-KDM5A recruitment, phase separation and target gene overexpression. An experiment that could further bolster this claim would be a direct test of whether NUP98-KDM5A expression drives overexpression of bound genes, e.g. expression of the fusion protein vs PHD- and NUP98-mutant variants, followed by qPCR of target genes, such as the HOX cluster, and possibly H3K4me3 ChIP at the same loci. As all the constructs and cell lines exist, this could be feasible and would substantially strengthen the manuscript.

Reviewer #2 (Public review):

Summary:

In this manuscript, Gini et al. investigate the mechanisms by which CDK4 and CDK6 upregulation drives resistance to EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutant lung adenocarcinoma (LUAD). The study utilizes preclinical models, including cell line-derived xenografts (CDXs), patient-derived xenografts (PDXs), and primary organoids, alongside large-scale clinical genomic datasets. The authors demonstrate that CDK4 or CDK6 overexpression allows cancer cells to bypass EGFR TKI-induced G1/S arrest, leading to continuous cell cycle progression. This sustained proliferation during EGFR inhibition induces DNA replication stress, activates DNA damage response pathways (such as ATM and TPX2), and ultimately causes genomic instability. The authors also show that this leads in turn to the upregulation of tumor-promoting genes (e.g., AGR2, ASNS, STEAP1) and an epithelial-mesenchymal transition (EMT) phenotype. Moreover, the authors show that combinatorial treatment utilizing TKIs such as osimertinib alongside CDK4/6 inhibitors effectively suppresses proliferation, mitigates DNA damage, and restores TKI sensitivity in preclinical models.

Overall, this is a highly translational study that provides a strong mechanistic rationale for biomarker-driven clinical trials combining EGFR and CDK4/6 inhibitors. However, there are a few experimental and analytical areas that require clarification or additional data to fully support the authors' conclusions.

Major Comments:

(1) Reliance on overexpression models over loss-of-function

The mechanistic studies mainly rely on overexpression of CDK4 and CDK6 to simulate the amplified state. Although the authors argued that the level of overexpression mimics that observed in resistant tumors, a complementary study in which CDK4/CDK6 were suppressed in a model where CDK4/CDK6 is amplified (such as HCC827 or TH116), and replication stress and osimertinib sensitivity tested would greatly strengthen their observations. Indeed, there is mention of CDK4 constructs to perform knockdown studies in the methods, but those studies are not included in this submission.

(2) Mechanistic link between genomic instability and specific gene amplifications

The authors highlight that CDK4/6 activation leads to recurrent copy number gains and transcriptional upregulation of specific pro-tumor genes including AGR2, ASNS, and STEAP1. While the paper establishes that CDK4/6 overexpression causes general genomic instability (increased FGA), it does not mechanistically explain why these specific genes are consistently amplified. The authors should investigate or discuss whether these specific loci are inherently fragile under replication stress, if they are direct downstream targets of the E2F transcriptional program, or if this is a result of random genomic instability followed by strong positive selection under osimertinib pressure.

(3) Discrepancies in tumor mutational burden (TMB) reporting

There is a slight contradiction regarding the TMB data that needs to be clarified for readers. The manuscript states that in the clinical datasets, "EGFR-mutant LUAD harboring cell cycle gene alterations exhibited significantly elevated FGA and TMB relative to cell cycle-negative tumors" (Line 265-266). However, in the next section, the authors say, "Notably, no corresponding increase in TMB was observed with CDK4 or CDK6 CNA, similar to our findings in preclinical models" (Line 271-273). The authors should clarify or discuss why broad cell cycle alterations correlate with high TMB, while CDK4/6-specific alterations drive structural instability (FGA) without increasing TMB.

Reviewer #2 (Public review):

Summary:

Jiang et al. sought to elucidate the molecular basis of polymyxin antibiotic interaction with the renal transporter hPepT2, a transporter previously implicated in polymyxin-induced nephrotoxicity. They combined molecular dynamics simulations with transporter mutagenesis, functional uptake assays, kinetic analyses, protein expression studies, antibacterial susceptibility testing, and mouse nephrotoxicity experiments to develop a structure-interaction relationship (SIR) model and apply this model to the rational design of polymyxin analogues.

Overall, the study represents a substantial multidisciplinary effort that integrates computational and experimental approaches. The identification of transporter residues involved in polymyxin recognition and the subsequent design of analogues with reduced hPepT2-mediated uptake provide a valuable framework for developing safer polymyxin antibiotics. In particular, the identification of FADDI-795 as an analogue that retains antibacterial activity while exhibiting reduced nephrotoxicity represents an encouraging proof of concept.

Strengths:

The computational predictions are strengthened by extensive experimental validation, including site-directed mutagenesis, transport kinetics, fluorescence uptake assays, membrane expression analyses, and in vivo toxicity studies. The consistency between multiple independent experimental approaches increases confidence in many of the authors' conclusions.

Weaknesses:

Several conclusions would benefit from a more cautious interpretation. A major limitation is that several transporter mutations substantially altered total or membrane protein expression, making it difficult to distinguish effects on substrate binding from indirect effects caused by impaired transporter stability or trafficking. The authors acknowledge this limitation in the Discussion, but some mechanistic conclusions remain stronger than the available evidence supports.

Similarly, while the proposed binding model is biologically plausible and supported by mutagenesis, it remains an inferred model derived from molecular simulations rather than a direct structural determination. Statements describing the model as "validated" should therefore be moderated to indicate that the experimental data provide support rather than definitive structural confirmation.

The translational implications are promising but remain preliminary. Although FADDI-795 demonstrated reduced nephrotoxicity in the mouse model while maintaining antibacterial activity, no pharmacokinetic studies were presented to demonstrate reduced renal accumulation or altered tissue distribution, and additional efficacy studies in infection models would further strengthen the therapeutic claims.

Reviewer #2 (Public review):

The study addresses the conundrum of how the mRNAs of SARS-CoV-2 are efficiently translated since the 5' leader, which has common elements for all the viral genes, is highly structured. The authors test the hypothesis that m6A modification at position 74 is key to this translation. First, the authors show convincingly that this site is modified. Then, with extensive transfected reporter experiments using luciferase assays as well as sucrose gradient sedimentation, this modification is shown to be key for efficient translation. While the mechanism of this effect is not entirely clear (see comments/suggestions below), the authors show that it is independent of YTH "reader" proteins and likely involves altered interactions between SL3 (which contains A74) and downstream elements in the UTR. These results are important because they both offer insight into the function of m6A in gene expression and suggest how they may be important for the translation of viral mRNA in particular.

While the data on their own make the overall case that the m6A modification in the 5'UTR of the viral genes is important to their expression, there are several things worth considering that could refine the model and make it more convincing.

It is not clear whether putative uORF translation, particularly translation of the uORF that begins with a CUG codon at position 59 in the 5'UTR (as shown in Finkel et al., Nature 2020), would be impacted by this modification (as it includes the putative m6A site at position 74). It is also worth considering whether SL3 melting by translation of this uORF would alter the proposed mechanism.

It is a bit unclear why the A74T mutant was put in the longer construct while the C75G mutant was put in a shorter construct. While not essential, the mechanistic arguments would be stronger if the same construct had been used to compare the mutations.

While the authors show that "global depletion of m6A modification does not grossly alter translation efficiency" in a general sense (page 11), it would be of interest to know whether any host mRNAs with 5'UTR m6A (i.e., ACTA2 and COX8A, mentioned in this study) are affected by the mechanism here (i.e. run them in the luciferase assay).

The authors show that the YTH "reader" proteins have a very small inhibitory effect (1.6-fold) on the translation of the viral mRNA with 5'UTR m6A. However, it remains unclear how important this is or whether it is generally true for host mRNAs with this modification.

It is reassuring to see controls for changes in RNA levels in the supplemental material. The RNAs were generally stable under the experimental parameters explored, which would rule out RNA-decay-based mechanisms of m6A regulation. However, it should be noted that mRNA level experiments appear to have been done at 24 h while luciferase measurements were done at 48 h (as noted on p. 23, gene expression vs luciferase activity). It is not clear whether any RNA decay phenotypes would be apparent at 24 h.

The authors use the term "ribosome profiling" (for example, on page 10), but it would appear the experiment performed is actually "polysome profiling" or "sucrose gradient sedimentation" since it did not involve ribosome footprinting.

Reviewer #2 (Public Review):<br /> <br /> Summary:

Rademacher et al. present a paper showing that chronic chemogenetic excitation of dopaminergic neurons in the mouse midbrain results in differential degeneration of axons and somas across distinct regions (SNc vs VTA). These findings are important. This mouse model also has the advantage of showing a axon-first degeneration over an experimentally-useful time course (2-4 weeks). 2. The findings that direct excitation of dopaminergic neurons causes differential degeneration sheds light on the mechanisms of dopaminergic neuron selective vulnerability. The evidence that activation of dopaminergic neurons causes degeneration and alters mRNA expression is convincing, as the authors use both vehicle and CNO control groups, but the evidence that chronic dopaminergic activation alters circadian rhythm and motor behavior is incomplete as the authors did not run a CNO-control condition in these experiments.

Strengths:<br /> This is an exciting and important paper.<br /> The paper compares mouse transcriptomics with human patient data.<br /> It shows that selective degeneration can occur across the midbrain dopaminergic neurons even in the absence of a genetic, prion, or toxin neurodegeneration mechanism.

Weaknesses:

Major concerns:

(1) The lack of a CNO-positive, DREADD-negative control group in the behavioral experiments is the main limitation in interpreting the behavioral data. Without knowing whether CNO on its own has an impact on circadian rhythm or motor activity, the certainty that dopaminergic hyperactivity is causing these effects is lacking.

(2) One of the most exciting things about this paper is that the SNc degenerates more strongly than the VTA when both regions are, in theory, excited to the same extent. However, it is not perfectly clear that both regions respond to CNO to the same extent. The electrophysiological data showing CNO responsiveness is only conducted in the SNc. If the VTA response is significantly reduced vs the SNc response, then the selectivity of the SNc degeneration could just be because the SNc was more hyperactive than the VTA. Electrophysiology experiments comparing the VTA and SNc response to CNO could support the idea that the SNc has substantial intrinsic vulnerability factors compared to the VTA.

(3) The mice have access to a running wheel for the circadian rhythm experiments. Running has been shown to alter the dopaminergic system (Bastioli et al., 2022) and so the authors should clarify whether the histology, electrophysiology, fiber photometry, and transcriptomics data are conducted on mice that have been running or sedentary.

Reviewer #3 (Public review):

Summary:

Core conclusions are well-supported by data: co-folding outperforms docking in known ligand pose/affinity prediction (validated by RMSD and IC₅₀ correlation), struggles with false positive discrimination in virtual screens (lower AUC values), and is complementary to docking (non-correlated errors, distinct strengths in drug discovery stages).

Strengths:

Unprecedented prospective design with 557 novel Mac1-ligand complexes ensures rigorous, independent evaluation of co-folding methods, provides an unbiased and rigorous benchmark dataset, which contains structures and compounds absent from the co-folding models training sets. Comprehensive comparison of 3 co-folding tools (AlphaFold3, Chai-1, Boltz-2) with DOCK3.7 across diverse targets and metrics enables nuanced performance assessment. The revised results clarify an intriguing finding: co-folding can predict correct ligand poses even when protein formations are mispredicted. The study clearly demonstrates complementary roles of co-folding (superior pose/affinity prediction for known ligands) and docking (better hit prioritization), and addresses deep learning memorization concerns via ligand similarity analysis.

Weaknesses:

The study identifies a major limitation of co-folding-failure to capture rare protein conformational changes, which deserve future investigation. The authors include uncalibrated Boltz-2 affinity data (addressing a prior comment) but note that large-scale free energy perturbation (FEP) comparisons are beyond their capabilities.

Appraisal of Aims Achieved:

The authors successfully achieved their primary aims and the results provide strong, well-supported evidence for their core conclusions. Key conclusions are grounded in the study's unbiased, training-set independent data, ensures the conclusions are not confounded by model memorization and are broadly applicable to the field's use of these co-folding models.

Field Impact:

This study provides a critical reality check for the field: co-folding models are powerful tools for pose prediction but are not yet standalone solutions for virtual screening, a key distinction that will prevent over-reliance on these models and guide more rational tool selection.

Reviewer #2 (Public review):

Summary:

Savage et al. investigates the synchronization of retinal Ca2+ waves with developmental cell death, microglia activation, and vascular outgrowth. These developmental processes occur through a mechanism where apoptotic cells release ATP through Panx-1 channels to stimulate both Ca2+ retinal waves and microglia activation. Using scRNAseq, the authors classify autofluorescence cell clusters (ACCs) at the leading edge of vasculature outgrowth as Hmox-1+ microglia. From here they show microglia engulfment of apoptotic RGCs and the potential release of ATP may contribute to Ca2+ wave generation. The authors demonstrate these mechanisms through the use of two pharmacological to agents to either block the ATP release from Panx-1 or by blocking receptor binding to ATP. Furthermore, while previous studies have described the site of initiation of retinal Ca2+ waves as random, this study shows the initiation of Ca2+ waves are biased to the leading edge of vascular growth in the developing retina. To do this, the authors use a combination of wide-field Ca2+ imaging and multi-electrode arrays to pinpoint the sites of Ca2+ wave initiation in the developing retina.

Strengths:

Savage et al. uses a several techniques to interrogate these mechanisms, including single cell RNAseq, wide-field Ca2+ imaging, and multi-electrode arrays. With these experiments, this manuscript proposes several novel ideas, such as ATP as the Ca2+ wave initiating cue, and the localization the Ca2+ wave initiation to the leading edge of vascular growth.

Weaknesses:

The main limitation of this study is the reliance on only two pharmacological agents to test their central hypotheses. In future studies, these conclusions could be strengthened if they used genetic knockout models to perturb programmed cell death and/or ATP release (i.e. BAX-KO, Panx-1 KO).

Reviewer #2 (Public review):

The determination of macromolecular structures directly within their native cellular environment is becoming increasingly routine, making standardized data collection strategies essential. In this manuscript, Tuijtel et al. provide a timely and valuable contribution by benchmarking key acquisition parameters and establishing practical guidelines for in situ cryo-electron tomography (cryo-ET). Critically, the authors present a systematic framework for optimizing data collection to achieve the highest attainable resolution.

Using Dictyostelium cells as a model system, the authors generate multiple datasets at a constant total dose while varying the tilt increment. They demonstrate that tilt-series acquired with finer increments (1-3 degrees) yield superior alignment accuracy and improved template-matching performance, resulting in higher-quality reconstructions than those collected with coarser increments (5 degrees or above). Furthermore, the authors show that for subtomogram averaging, a 3-degree tilt increment outperforms all other conditions tested, particularly after per-particle refinement as implemented in M.

Comments on revised version.

The authors have addressed all my concerns, and I have no further issues.

Reviewer #2 (Public review):

In the manuscript entitled "Adaptive plasticity of aspartate metabolism in succinate dehydrogenase-deficient cancer cells," Sokolov et al. delineate the metabolic adaptations that succinate dehydrogenase (SDH)-deficient cancer cells undergo over time to overcome the initial aspartate limitation. To do so, the authors generated five clonal osteosarcoma SDH subunit B (SDHB) knockout cell lines using the CRISPR/Cas9 system and compared the proliferation rates of early- and late-passage cells, revealing that the latter rewired central carbon metabolism to increase aspartate levels and therefore replicate faster than their early-passage counterparts. Using a series of pharmacological and/or genetic interventions, the authors show that this rewiring can occur via two different routes: either through reduced Complex I (CI) activity, whereby glutamine is channelled towards aspartate synthesis via reductive carboxylation, or through a metabolic rewiring in which aspartate is produced from glucose via the PC-GOT2 pathway while CI activity is preserved. The CI-suppression-independent route depends on PC expression, as evidenced by an analysis of DepMap cell-line data, in which higher PC expression is associated with decreased SDH dependency. Moreover, they find that other consequences of aspartate deprivation observed in SDH-deficient cells, including impaired pyrimidine synthesis, replication stress, and DNA damage, are ameliorated in late-passage cells.

Overall, this study is interesting because it disentangles the different metabolic rewiring routes that SDH-deficient cells can undergo to reverse aspartate limitation and sheds light on previously reported, seemingly contradictory results in the field. However, the study's major premise requires further validation, and important controls are missing, diminishing the overall strength of the conclusions.

Major points:

(1) The main conclusion that two separate routes allow SDH-deficient cells to overcome aspartate limitation, defined by their CI-activity status, is not convincingly proven. Indeed, to show this dichotomous behaviour, the authors performed Western blots for two CI subunits and determined the basal oxygen consumption rate. However, these assays are insufficient to demonstrate that LP clones 2 and 3 maintain functional CI, in contrast to LP clone 1. Moreover, it is not ruled out that these clones show dysfunction in ETC complexes other than CI. To assess these points, the activities of all individual ETC complexes should be carefully measured, for instance, by Seahorse assay after permeabilization. Furthermore, given the complex nature of CI, a reduction in two subunits does not necessarily reflect a reduction in its assembly. Therefore, CI assembly should be assessed directly by BN-PAGE analysis of isolated mitochondria.

(2) It is difficult to reconcile why the authors used an NDUFA8 KO in clone 2 EP to mimic the physiological long-term CI-suppression-dependent adaptation. Indeed, this approach seems to represent an extreme scenario of Complex I loss that may induce non-physiological adaptations that override the effects of SDH KO. To assess the distinct metabolic fluxes between the two proposed routes, it would be advisable to use a more physiological model and instead compare the tracing data from LP clone 2 with those from LP clone 1, which exhibits a "natural" CI-suppressed state. Does clone 1 LP show similar metabolic changes to A8KO, including increased reductive carboxylation?

(3) It is unclear whether the loss of Complex I at late passage is an intrinsic progression of osteosarcoma cells rather than a feature specific to SDH-deficient cells. A proper comparison between SDHB-deficient cells and WT cells, both at early and late passage, should be carried out. This is essential to fully understand the adaptive trajectories of SDH-deficient cells. This comparison is essential to identify the baseline metabolic hardware of the osteosarcoma cells. Indeed, the authors state that "While wild-type 143B cells synthesize most aspartate from glutamine via oxidative TCA cycling and GOT2 activity, ..." (Page 7, third paragraph), but these data are not included in the manuscript and would represent an important control for assessing the observed metabolic changes in comparison with the wild-type context.

(4) The data showing that the PC-GOT2 pathway is mainly driven by enhanced PC activity are not fully convincing, as PC activity seems to be equally important for maintaining aspartate levels in the NDUFA8 KO compared with clone 2 LP. Moreover, the extracted expression data from DepMap suggest that increased PC expression might not be transcriptionally regulated, as only a slight association between PC mRNA levels and SDH dependency was observed. Are PC mRNA levels increased in clone 2 LP? If not, PC might be regulated post-transcriptionally. To test this, the nascent translation of PC could be assessed.

Reviewer #2 (Public review):

Summary:

Barrett et al. examine how neural activity in the mouse motor cortex varies when a movement is performed with the ipsilateral or contralateral forelimb. First, they train animals to grasp and manipulate a pellet of food with either the left forepaw, the right forepaw, or both. Next, they measure activity in the primary and secondary forelimb motor areas (fl-M1 and fl-M2) and in the classical tongue-jaw area (tj-M1 / LOM). While responses in the forelimb areas are diverse, with some neurons preferring ipsilateral or bilateral movements, a plurality of cells prefer the contralateral limb. In LOM, by contrast, little limb selectivity is observed. At the neural population level, structure is preserved across conditions in LOM, but not in the forelimb areas. Finally, paw position can be decoded from activity in all three areas, and the LOM decoder generalized across limbs.

Strengths:

While previous studies in macaques have compared motor cortical activity during movement (and perturbation) of the contralateral and ipsilateral arms, no analogous work has been undertaken in rodents. This paper closes this knowledge gap by showing, for the first time, moderate-to-strong lateralization in the forelimb motor cortical areas of mice transporting grasped food pellets to the mouth, and a relative absence of lateralization in the classical tongue-jaw area. On the whole, I think this is a solid paper that reports novel observations of interest to the motor systems community.

Weaknesses:

The central question posed is whether cortical activity depends on the effector(s) used (ipsi forelimb, contra forelimb, or both). The corresponding hypotheses (Figure 1) are somewhat coarse-grained and are not mutually exclusive. One might expect to see condition-independent, limb-selective, and uni-/bimanual-selective signals in motor cortex (though their magnitudes could differ substantially), and to find these signals intermingled at the level of single neurons. The authors may wish to consider setting up a more focused question. For example, can bimanual responses be explained as a sum of the unimanual responses from the left and right limbs?

In the area usually identified as tongue-jaw motor cortex (here referred to as LOM), unit and population activity look quite similar for ipsilateral, contralateral, and bilateral forelimb reaches. The most parsimonious explanation is that the activity is related mostly to mouth and tongue movements, rather than limb movements. Systematic mapping studies with microstimulation in the rat (Neafsy et al., Brain Res. Rev. 1986) and optogenetic stimulation in the mouse (Mayrhofer et al., Neuron 2019) tend to support the idea that tjM1/LOM is specialized for control of the tongue and mouth. Thus, I'm not entirely convinced that it "encodes ingestion-related forelimb parameters necessary for oromanual coordination." The authors could say more about this issue: what specific limb-related parameters do they think are encoded, why would these parameters be effector-independent, what evidence for this encoding is presented here, and how can limb- and mouth-related components be distinguished? The problem could potentially be addressed experimentally, as well, by delivering food pellets directly to the mouth while preventing manipulation with the paws, but this experiment isn't strictly necessary.

Because the corticospinal tract is strongly lateralized, cortical activity presumably has a smaller effect on ipsilateral than contralateral motor output. Somatosensory feedback should also be relatively lateralized for the forelimb areas. The authors could say a bit more about this issue and how it relates to their data and conclusions in the Discussion.

An important limitation of the behavioral task is that it involves only a single stereotyped movement for each limb, instead of multiple directions, speeds, or loads. This issue and its consequences for the analyses (especially those in Figures 7-10) and conclusions could be discussed.

Reviewer #2 (Public review):

Major concern 1: The manuscript does not clearly establish a bona fide GS filament state.

The authors repeatedly refer to GS "filaments," but the data presented appear to support primarily a di-decameric assembly rather than a well-defined filamentous polymer.

A two-decamer reconstruction can define a putative inter-decamer interface, but it cannot by itself demonstrate propagation of a repeating filament geometry. To establish a bona fide filament, the authors should provide evidence for a reproducible one-dimensional assembly, such as at least three consecutive repeating units or equivalent quantitative evidence that the same inter-decamer transform propagates along an assembly axis.

In the current manuscript, many of the supporting 2D classifications appear to contain at most two adjacent GS decamers. This is particularly evident in the time-resolved cryo-EM datasets shown in Supplementary Figures 9-10, where I do not see convincing 2D classes corresponding to filaments. The same concern applies to other datasets, including Supplementary Figures 2, 6, and 11, where the apparent assemblies are primarily two-decamer particles.

Moreover, many of the selected "filament" classes show only one well-resolved GS decamer, while the neighboring decamer density is blurred. This suggests substantial variability in the relative position and/or orientation of adjacent decamers. Such heterogeneity is difficult to reconcile with a stable repeating filament geometry.

Therefore, the authors should explicitly define what they mean by "filament." If their evidence supports only a di-decameric or short oligomeric assembly, the terminology should be changed accordingly throughout the manuscript.

Symmetry concern

Given the low quality and heterogeneity of the 2D classifications for the putative "filament" classes, the use of D5 symmetry requires stronger justification. The current reconstructions primarily show the result after applying D5 symmetry to a two-decamer assembly. The authors should show reconstructions of the same particle sets processed under C1, C5, and D5 symmetry, and explain why D5 symmetry is justified.

This is particularly important because the claimed interface density and ligand interpretation are sensitive to symmetry averaging. Without showing how the reconstruction behaves under less restrictive symmetry assumptions, it is difficult to determine whether the final D5 map reflects a true biological assembly or a symmetry-imposed interpretation.

Filament abundance and physiological relevance

Even under the authors' broad classification criteria, the filament-like population appears to be a minor species. In some datasets, especially Supplementary Figure 10, the apparent filament fraction is very low, approximately 2-10%. This raises a major concern: if GS filaments are rare even under high-concentration cryo-EM conditions, are they expected to form to a meaningful extent under physiological conditions?

The authors propose a concentration-dependent assembly mechanism. If so, the relevance of GS filamentation in the lower-concentration cellular environment becomes even less clear. The authors should quantify filament abundance as a function of GS concentration and glutamine concentration, ideally under conditions closer to physiological ranges.

K52/C53 interface mutations

The authors use K52 and C53 as filament-interface residues, but the mechanistic contribution of these residues to filament assembly remains insufficiently explained. Why should K52A or C53A disrupt filament formation? Is the effect due to loss of a specific side-chain contact, altered local electrostatics, reduced crosslinker accessibility/reactivity, local structural destabilization, or nonspecific disruption of the interface?

The manuscript states that the interface is "concentration dependent and driven primarily by electrostatic interactions," but the data presented before that statement do not clearly establish this. The authors should explicitly identify the interacting electrostatic partners and provide structural or biochemical evidence supporting this interpretation.

Functional linkage between filamentation and kinetics is weak.

The authors should establish the oligomeric state of GS under the actual assay conditions. In particular, what is the filament fraction during the Figure 2F / Supplementary Figure 15 kinetic assays? Is the change in KM ammonia quantitatively correlated with filament abundance?

This is currently unclear. The direct comparison between decamer and 2-decamer fractions does not robustly show a functional difference, and the later glutamine-addition assays are interpreted as filament-mediated without directly demonstrating the filament fraction under the same assay conditions.

In Figure 2F, WT, K52A, and C53A already show different ammonia-dependent kinetic parameters in the absence of added glutamine. K52A and C53A appear to have lower basal kcat/KM ammonia and higher KM ammonia than WT even without glutamine. The authors should explain why these interface mutants already alter basal ammonia kinetics. Without such an explanation, K52A and C53A cannot be treated as clean controls that selectively disrupt glutamine-stabilized filamentation.

Major concern 2: The interface density is not convincingly assigned to glutamine.

The second foundational issue is the assignment of the interface density to glutamine. At present, the evidence is not sufficient to support the conclusion that glutamine is the ligand at this interface.

The local density at the interface appears weak and likely has lower local resolution than the reported global resolution. The current density could represent a low-occupancy or symmetry-averaged amino-acid-like density rather than a confidently assigned glutamine molecule.

Ligand pose and hydrogen bonding.

The proposed glutamine pose also requires more rigorous validation. The authors state that glutamine forms hydrogen bonds with interface residues, including K52, C53, and E55. These hydrogen bonds should be shown explicitly in a figure, with distances listed.

The proposed interaction involving C53 appears unusual and should be justified chemically and geometrically.

Glutamate has not been excluded.

The largest problem is that the authors do not adequately consider glutamate as an alternative ligand. They compare the density with phosphate and ATP/ADP, but this is not sufficient. Glutamate is present at high concentration during turnover, and it is chemically and structurally very similar to glutamine. Given the limited local density and possible orientational averaging, distinguishing glutamine from glutamate from the current cryo-EM density alone is not justified.

The authors should report or estimate the concentrations of glutamate and glutamine at the vitrification time point used for the high-resolution turnover-filament reconstruction. If glutamate is present at a much higher concentration than glutamine, the authors must explain why the interface density should be assigned to glutamine rather than glutamate.

The authors should fit both glutamine and glutamate into the interface density using the same validation criteria and compare the results. Stronger support would come from direct structural experiments, such as cryo-EM structures of GS incubated separately with glutamate and glutamine under controlled conditions.

Unless stronger evidence is provided, the claim that "glutamine binds to the filament interface" cannot be made.

Specific comments

Interface assembly statement:<br /> "These data suggest that the formation of the interface is concentration dependent and driven primarily by electrostatic interactions."

What specific data support "concentration dependent" at this point in the manuscript? Which residues or chemical groups are proposed to form the electrostatic interactions? The authors should provide a more explicit explanation.

Line 149-150:<br /> "In both scenarios, any signal is likely to be averaged out and experiments with symmetry expansion and focused classification did not yield any convincing density."

Please show these analyses. Negative results are important here because they bear directly on the reliability of the interface interpretation.

"Glutamine stabilizes larger GS filaments":<br /> What does "larger" mean? Longer filaments, more decamers per filament, or larger diameter? The authors should define this quantitatively, preferably by reporting filament-length distributions or the number of decamers per assembly.

Filament classification:<br /> The criteria used to classify particles or 2D classes as "filament" are not sufficiently clear. The authors should provide the full 2D classification results for each time-resolved dataset, including selected and discarded classes, particle numbers, and objective selection criteria. Some selected and discarded classes appear visually similar, especially in Supplementary Figures 9-10.

R298A decamer:<br /> The R298A mutant is presented as a turnover-decamer structure, not a filament structure. The authors should clarify whether R298A forms filament-like particles under comparable turnover conditions. If R298A does not form filaments, this should be reported and explained. If filament-like particles were present but excluded during processing, the authors should provide their abundance and justify why only the decameric form was analyzed. This point matters because R298A is used to connect E305-loop disorder with the proposed filament-associated mechanism, although R298A is a loop-stabilization mutant rather than a filament-interface mutant.

Line 231-233:<br /> "a reaction time that should yield a high concentration of product due to the higher enzyme concentration than previous experiments"

What is the estimated product concentration at vitrification? What concentration range qualifies as "high"? The authors should provide a quantitative estimate.

Glutamine hydrogen bonds:<br /> The proposed hydrogen bonds linking glutamine to K52, C53, and E55 should be shown explicitly with atom identities and distances.

Glutamate comparison:

What is the glutamate concentration in the same sample? Given that glutamate is chemically similar to glutamine and likely present at high concentration, why is the interface density not glutamate? The authors should compare glutamine and glutamate fitting using the same validation criteria.

Line 248-253:<br /> The speculation that apo filaments may arise from high GS concentration or residual glutamine should be moved to the Discussion. In the Results, this reads as an ad hoc explanation rather than a result directly supported by data.

Actual assay-state oligomeric distribution:<br /> What is the filament fraction under the actual kinetic assay conditions? Is the KM ammonia change quantitatively correlated with filament abundance?

Figure 2F:<br /> Why do WT, K52A, and C53A differ in basal ammonia-dependent activity even without added glutamine? The authors should explain whether these mutations alter intrinsic ammonia kinetics independent of filamentation.

Supplementary Figure 15 / Figure 2F:<br /> Please clarify the relationship between Figure 2F and Supplementary Figure 15. The kinetic constants in Figure 2F appear to depend on global fitting of progress curves shown in Supplementary Figure 15. The authors should provide replicate-level raw progress curves, between-replicate variability, fitting residuals, and individual fitted parameters.

Supplementary Figure 19:<br /> Supplementary Figure 19 should be presented consistently with Supplementary Figure 18, including the corresponding 2D classification results.

In summary, although the revised manuscript improves the presentation of cryo-EM map processing, the two foundational claims remain unresolved. The current data establish, at most, a di-decameric or filament-like GS assembly, but not a rigorously defined filamentous polymer. In addition, the interface density is not convincingly assigned to glutamine, particularly because glutamate has not been excluded as the most relevant alternative ligand. Since the proposed negative-feedback mechanism depends directly on these two points, the current evidence does not support the strength of the title, abstract, or mechanistic conclusions.

Reviewer #2 (Public review):

Summary:

The authors developed a deep learning model based on a DenseNet CNN architecture to predict two cognitive functions: working memory and episodic memory, from functional connectivity matrices. These matrices were recorded under three conditions: during rest, a working memory task, and a movie, and were treated as images for the CNN algorithm. They tested their model's performance across different conditions and a separate dataset with a different age distribution (using the same MRI scanner, scanning configurations, and cognitive tests). They also calculated the "brain cognition gap" based on the model trained on resting functional connectivity to predict working memory. Extending from the commonly used index "brain age," the brain cognition gap was defined as the difference between the working memory score predicted by their model (predicted working memory) and the working memory score based on the working memory test itself (observed working memory). This brain cognition gap was found to be associated with physical activity, education, and cardiovascular risk. The authors also conducted additional mediation tests to examine whether regional functional variability mediated the relationship between PET-derived measures of dopamine and the brain cognition gap.

Strengths:

The major strength of this manuscript is the extensive effort the authors have put into creating a new 'biomarker' that links deep learning with fMRI, PET, physical activity, education, and cardiovascular risk across two studies. This effort is impressive.

Concerns from the previous round of review:

(1) The primary issue is still the lack of baseline models against which to benchmark the predictive performance of the proposed DenseNet model. This concern was raised independently by two reviewers. Without such benchmarks, it is difficult to interpret the reported results in the context of prior work on MRI-based cognition prediction.

Notably, the authors state: "While we compared our model with the connectome predictive modeling (CPM) approach and observed better performance with our deep learning framework, we did not conduct a comprehensive benchmark across all available machine learning methods, nor was this the aim of the present study."

However, I could NOT find any discussion or results related to the CPM model in the manuscript. It is therefore unclear whether the DenseNet model was actually statistically compared with CPM, and, if so, how the comparison was conducted.

Note that the statement, "While Vieira et al. show that the majority (76%) of prior studies used linear modeling approaches, including CPM and penalized regressions, these models are often vulnerable to overfitting, especially when applied to high-dimensional fMRI data," is not entirely accurate. Linear models typically have far fewer parameters than deep-learning models and are therefore often less prone to overfitting. In fact, it is well established that deep-learning models are particularly susceptible to overfitting and usually require substantially larger sample sizes to achieve stable and reliable performance. Although deep-learning models may outperform shallower models once sufficient data are available and training is well controlled, this does not justify the authors' claim as stated. I therefore disagree with the argument put forward by the authors.

The authors further justify the absence of benchmarking by stating: "In this context, deep learning was employed as a flexible framework capable of modelling high-dimensional functional connectivity patterns across cognitive states, rather than as a claim of inherent methodological superiority. Thus, our goal was not to propose a universally superior prediction model, but rather to test how brain state influences predictive utility for WM and EM using a deep learning approach." However, most shallow models can likewise be applied across different brain states and cognitive targets. This rationale does not establish deep learning as a uniquely appropriate or necessary choice. If deep learning is indeed a better approach in this context, the authors should demonstrate this empirically through appropriate benchmarking against established baseline models.

(2) Additional analysis shows that "BCG is not significantly associated with cognition itself". This is the most perplexing result. This is like saying Brain Age Gap is not related to chronological Age. It is counterintuitive since the Brain Age Gap is calculated by chronological age minus actual age, and most research has shown a strong relationship between the Brain Age Gap and age.

If the brain cognition gap is not related to cognition, is it possible that the results found are mainly due to the predictive model not fitting well with another dataset? Regardless, the lack of association between BCG and cognition deserves a discussion.

(3) I still do not fully understand the rationale of the mediation analysis. The analysis and findings are still not related to aims 1 and 2, since DA and entropy are not part of the prediction models. But I appreciate the explanation that this part is related to the authors' previous work, and that the authors attempted to link to them somehow.

[Editors' note: the authors have responded to these points.]

Reviewer #2 (Public review):

Earhart et al. investigated the role of the complement system in trained innate immunity (TII) in alveolar macrophages (AM). They used a WT and C3 knockout murine model primed with locally administered heat-killed P. aeruginosa (HKPA). Additionally, they employed ex vivo AM training models using C3 knockout mice, where reconstitution of C3 and blockade of C3R were performed. The study concluded that the C3-C3R axis is essential for inducing TII in macrophages in the ex vivo model. The manuscript is well-written and easy to follow.

Comments on revised version.

My concerns have been addressed, and the provided data is convincing supporting the manuscript's claims.

Reviewer #2 (Public review):

Summary:

This work by Waltner, et. al. provides a comprehensive single cell multiomics analysis of plasticity in gene regulatory networks present in Ewing sarcoma using single cell RNA-sequencing (scRNA-seq) and single cell assay for transposase accessible chromatin with sequencing (scATAC-seq). They find that Ewing sarcoma cell lines models have distinct patterns of chromatin accessibility compared to non-Ewing sarcoma models, and that there is significant variability across Ewing sarcoma cell lines, and sometimes within a single cell line. These differences across models are linked to 3 distinct gene regulatory modules, 2 of which are present across the range of model systems studied here. The first modules present across models is activated when the fusion is expressed and includes genes enriched for the known EWSR1::FLI1 response element, GGAA microsatellites along with other neural crest transcription factors. The other module primarily consists of genes repressed by EWSR1::FLI1, which are activated in EWSR1::FLI1-low states. Interestingly, EWSR1::FLI1-low cells have already been tied to more migratory and metastatic phenotypes and the data here suggest these cells are more responsive to external signals from TGF-β and this may be mediated through FOSL2-mediated gene regulation. This is a technically rigorous study, with a variety of different analytical techniques used to address similar questions and this approach elevates confidence in the answers provided. This is further strengthened by the diverse set of model systems used, including patient-derived cell lines, cell line xenograft models, patient-derived xenografts, mining available single cell data from patient samples, and validation of the gene modules identified in a larger set of patient microarray samples. In whole, this study provides a valuable resource for understanding heterogeneity, plasticity, and gene expression networks in Ewing sarcoma. This may be a useful resource for future studies of metastatic disease and provide a framework for similar questions in other fusion-driven sarcomas.

Comments on revised version.

The authors have addressed comments from my prior review. Thank you!

Reviewer #2 (Public review):

Summary:

This paper presented a hypothesis testing procedure for the independence of two time-series that was potentially suitable for nonlinear dependence and for small-sample cases. This should bring potential benefits for biology data.

Strengths:

The test offers good flexibility for different kinds of dependence (through adjusting \rho) and seems to have good finite sample performance compared to the literature. The justification regarding the validity of the test procedure is clear.

Reviewer #2 (Public review):

Summary:

Otgonbaatar et al. sought to investigate β-catenin/Arm protein lifetime and stabilization dynamics in vivo during embryonic development. To address this question, the authors developed an endogenous tandem fluorescent protein timer (tFP) system that enables the visualization of newly synthesized versus long-lived Arm protein in vivo. Using this approach, the authors sought to determine where stabilized Arm accumulates during development and how it contributes to dorsal closure.

Strengths:

A major strength of the study is the development and application of the endogenous Arm timer system, which provides a powerful approach for monitoring protein stabilization dynamics in living tissues. Using this system, the authors unexpectedly found that the strongest Arm stabilization occurs not in Wnt signaling regions, but at the leading edge cells during dorsal closure. The study combines quantitative live imaging, optogenetic perturbation, genetic analysis, and structure-function approaches to demonstrate that stabilized junctional Arm interacts with α-catenin and contributes to tissue mechanics required at the leading edge for dorsal closure. Particularly compelling is the combination of multiple perturbations, including optogenetic disruption of Arm or α-catenin, Axin overexpression, and Arm mutants, which produce consistent dorsal closure defects.

Some conclusions are generally supported by the presented data. The work provides strong evidence that Arm plays an important role in dorsal closure. The identification of a requirement for the Dishevelled DEP domain and JNK signaling supports a non-canonical regulatory mechanism controlling dorsal closure.

Weaknesses:

(1) Conclusions are made regarding force transmission;(however, no experimental evidence is provided to support these conclusions.

(2) The conclusion was made that Wingless does not affect dorsal closure. However, this was based solely on Wingless overexpression in the amnioserosa, and the level of Wingless expression was not quantified. One possibility is that this level was not sufficient to see an effect. Alternatively, Wingless may have a role in migrating epithelium rather than the amnioserosa. Indeed, it is known that wingless mutants display a defect in dorsal closure.

(3) The effect of JNK knockdown on Arm localization maybe is indirect, and due to a secondary consequence on disruption of epithelial morphology rather than a direct effect of JNK on Arm.

(4) Some conclusions rely on overexpression-based perturbations (e.g., Axin or Arm mutants), which may not fully recapitulate endogenous physiological regulation.

(5) The Arm timer was not able to detect Wingless-dependent Arm stabilization in stripes. This finding demonstrates that the timer is not sensitive enough to thoroughly analyze Arm dynamics.

Overall, this work provides important conceptual advances in understanding junctional β-catenin/Arm function during dorsal closure. The endogenous fluorescent timer approach will likely be broadly useful to the community for studying protein stability dynamics in vivo, and the findings expand current views of β-catenin by highlighting its mechanical and junctional functions during tissue morphogenesis.

Reviewer #3 (Public review):

Despite the abundance of RNA velocity tools, there are still major limitations, and there is strong skepticism about the results these methods lead to. In this paper, the authors try to address some limitations of current RNA velocity approaches by proposing a unified framework to jointly infer transcriptional and splicing dynamics. The method is then benchmarked on 6 real datasets against the most popular RNA velocity tools.

Comments on revised version:

The Authors addressed my 2 follow-up comments suitably.

Thanks for the time you took addressing them. I have no further comments.

Reviewer #2 (Public review):

Summary:

The authors use a postnatal mouse model of E. coli bacterial meningitis and a mouse brain endothelioma cell line combined with cell type specific gene deletion to study the function of endothelial TLR4, a cell surface receptor that recognizes gram positive bacterial wall components, in the local leptomeningeal (LPM) response with a focus on endothelial barrier breakdown mediated by TLR4. Single cell transcriptional profiling and imaging studies using wholemount preps of the LPM support that LPM endothelial, CD206+ local macrophage and LPM fibroblast and arachnoid barrier cell inflammatory response and is abrogated in endothelial specific KO of TLR4, pointing to a role for endothelial TLR4 in local LPM response. Culture studies using Bend3.1 cells (a mouse brain endothelioma cell line) support a direct role for TLR4 in the bacteria-mediated inflammatory response and in internalization of Cldn5 via the endosomal-lysosomal pathway, resulting in loss of barrier integrity

Strengths:

The local LPM cell response in meningitis and the role of specific LPM cells in inflammation and CNS barrier breakdown has not been extensively studied, despite ample evidence for primary immune response in the meninges in human patients and in animal models. The authors employ a robust, multi-model approach using both in vivo and in vitro models with cell-type specific knockout to study the function of TLR4 in brain endothelial cell response. The authors nicely combine functional barrier assays with IF for junctional localization in their experimental design and they delve into potential mechanisms of Cldn5 internalization using markers of endosomal-lysomal pathway localization. The authors also describe a new type of barrier assay using a streptavidin-coated plates upon which barrier forming cell cultures can be plated, this could be a very useful alternative or complement to other size-selective barrier assays and presumably could work for other barrier forming cell types, like epithelial cells.

Comments on revised version.

In their revision, the authors addressed prior noted weaknesses with new data and analysis. They now show that TLR4-VE-cad cKO mice have a largely similar disease progression as control mice, including increased bacterial burden in the LPM and brain. This underscores that that the reduced vascular leakage and blunted inflammatory response is due to loss of TLR4 response to bacteria on VE-cad recombined cells and not because the mice are protected from meningitis. The authors also performed additional experiments to show that Cldn5 internalization via the endosomal-lysosomal pathway is independent of NFKB signaling. The authors also added in important discussion points about how their results fit into the broader literature on TLR4 in BBB endothelial cell junctional protein localization and prior work on meningitis in global TLR4.

Reviewer #2 (Public review):

Summary:

In this manuscript, dos Santos et al seek to identify cell-specific programs that drive neuronal wiring patterns. They focus on two chemosensory and mechanosensory neurons in the Drosophila nervous system, as they both display stereotyped connectivity in the ventral nerve cord. Single-neuron RNA sequencing identified cell surface molecules that distinguish the sensory neurons and may instruct their respective wiring patterns. They functionally test several of these candidates and observe miswiring phenotypes upon knockdown experiments. Additionally, they attempt to miswire the chemosensory neurons. Overall, this manuscript addresses an important question about how neurons identify appropriate synaptic partners through precise cell surface molecular codes. However, there are significant deficiencies in the experimental logic and rigor, and the manuscript can be very difficult to digest.

Strengths:

The use of two sensory neurons with stereotyped connectivity is a significant strength, as this enables the authors to identify genes that are required for wiring. Additionally, analyzing the transcriptomes of single neurons repeatedly could potentially be a robust approach to identifying cell-specific cell-surface molecules that drive wiring.

Weaknesses:

(1) The authors perform RNAseq for single identifiable neurons, as opposed to neuronal subclasses, which has been reported before. It would be beneficial to elaborate on the significance of using single neurons for answering the scientific question. This is briefly mentioned toward the end of one of the results subsections: "Repeated RNA sequencing of an identifiable neuron seeks to address the fundamental nature of variability in connectomics, axonal branching, and cellular identity." But this should be in the Introduction.

(2) The authors chose the P14 pupal stage for one of the analyses. It is not clear why this specific stage is chosen. Does pSc and aPa connectivity occur at this stage?

(3) This reviewer is confused as to why looking at differentially expressed CSMs between pupal and adult stages of two different neurons is useful. This does not seem like an appropriate comparison. This data might be better in the supplemental material, especially given the lack of precise age synchronization across pupal samples (as reported).

(4) It is very difficult to follow the logic because the manuscript seems to jump around between different results and lacks a compelling through line.

(5) "Single cell sequencing of the same neuron reveals transcriptome precision": What are the controls here? An aPa neuron is shown in Figure 3 as an example of a different neuronal subtype, but were other factors (e.g., lack of Repo expression) checked to ensure that samples were not contaminated?

(6) "However, whether any of these exon 6 or 9 splicing specificities are biologically significant can only be determined using exon 6 and 9 isoform-specific RNAi." The authors could alternatively use CRISPR techniques to target specific isoforms that they hypothesize might be important for neural wiring, enabling them to assess isoform-specific wiring defects.

(7) In the section "The set of cell surface receptors required to wire up the pSc mechanosensory neuron": Several previous subsections of the Results use RNAseq to identify molecules expressed in pSc neurons across different stages. It's unclear why the authors did not start with the identified list of candidate cell surface receptors identified in their RNAseq experiments.

a. Were any of the genes screened the same as those identified by the authors as differentially expressed in pSc mechanosensory neurons, either across developmental stage (pupa vs. adult) or across neuronal subtype (pSc vs. Gr59d)? If so, it would be helpful to state this here. (They do mention later on that five CSMs identified were more highly expressed in pSc than aPa. However, changes in expression across developmental stages within the pSc neuron would still be helpful to comment on, especially since the authors identified greater transcriptomic differences across developmental stages than they did between different neuronal subtypes.)

b. The 39 genes not expressed in pSc neurons served as their negative control, but the average axonal targeting grade was 2.3 (between moderate and severe). This calls into question the use of this method as an appropriate measure of whether a gene expressed by pSc neurons is truly required for proper axon targeting; there seems to be a strong probability of significant off-target effects. Performing a global knockdown and cell-specific rescue could potentially complement these experiments and serve as a stronger indicator of candidate receptors' roles in pSc-specific axon targeting.

(8) It seems as though the purpose of the experiments described in the last results subsection ("Re-wiring the Gr59 chemosensory neuron") is to redirect the Gr59d neuron toward the pSc neuron's axonal targeting phenotype. However, the authors do not state whether they were able to do so effectively (i.e., whether or not there were significant differences between the rewired Gr59d neuron and the pSc neuron). This leaves the story unfinished.

(9) At the end of the discussion, the authors state that "...if a Gr59d chemosensory neuron is functionally rewired to a pSc mechanosensory circuit, activation of the Gr59d neuron using a bitter tastant molecule should elicit a grooming (mechanosensory) response...". The authors should attempt this experiment, especially given that they have developed the PXGS technique.

Reviewer #2 (Public review):

The manuscript reports protection of midlobular hepatocytes from APAP toxicity by activation of Atf4-CHOP (Ddit3)-mediated cell cycle arrest and stress response. The authors acknowledge that their finding is unexpected because CHOP typically induces cell death. Therefore, they functionally validate several aspects of the proposed Atf4-CHOP mechanism. Along these lines, the mitigation of APAP toxicity by AAV expression of Atf4 or Btg2, the latter identified as CHOP effector, is impressive. Whether Atf4 indeed acts through CHOP and whether midlobular hepatocytes are protected because of cell cycle arrest is less clear. These and other criticisms are described in the following.

Major points:

(1) Starting with the basics, one wonders why midlobular hepatocytes manage to mount a defensive response to APAP, but PC hepatocytes don't. Is this because midlobular hepatocytes express the relevant Cyps (2e1 but also 1a2 and 3a11) at lower levels, which mitigates toxicity and buys them time? This would be supported by F2A but not by F3B, at least not for the most important Cyp2e1. A moderate difference is shown for Cyp1a2 expression in F3D but is that enough to explain the different fates? Or are additional post-transcriptional effects on these Cyps at work? The difference in baseline Cyp2e1 expression between F2A and F3B remains unexplained after revision.

(2) The evidence presented in support of cell cycle arrest of midlobular hepatocytes is not fully convincing: there is no overt difference in S and G2/M gene scores in F2F; the marker genes used for S phase and G1 to S progression in F2G are unusual. Along these lines, one wonders if spatial transcriptomics confirmed the Ki67 immunostaining results in F1 also for specific zones, not only overall as shown in F2E? In contrast to the revised discussion, the abstract does not reflect that limited evidence for a cell cycle arrest in pericentral hepatocytes was found.

(3) The authors conclude in line 364 that halting of proliferation by Btg2 favors survival, which raises the question of whether Btg2 knockout causes death in midlobular hepatocytes in F6K. Data addressing this question, that is, localization and extent of tissue necrosis and ALT levels after APAP, are missing. The efficiency of knockout of Btg2 is also not given. Additional Btg2 knockout data support its proposed role in the revised manuscript.

(4) Related to the previous question, the BTG2 immunostaining in F6F is not convincing when compared to F6D. One also wonders if it is necessary to apply APAP to find induction of BTG2 by AAV-Ddit3? The BTG2 immunostaining remains weak, not only in in F6F but now also in F6D of the revised manuscript, which together with lack of high-resolution immunostaining of AAV-Ddit3-induced BTG2 in the absence of APAP results in limited support for the conclusion that APAP promotes nuclear localization of BTG2.

(5) Related to the previous question, the proposed Atf4-Ddit3 axis is challenged by the lack of midlobular induction of Atf4 in the APAP scRNA-seq data published by another group presented in S4F and G. Further analysis of AAV-Atf4 samples generated for F5 could address if it is really Atf4 that acts on Ddit3 in APAP toxicity. The extended list of transcription factors (from 30 to 50) includes Atf4 but direct evidence for an interaction with Ddit3 is missing from the revised manuscript.

(6) Related to the previous question, the ATF4 immunostaining in F5A doesn't look convincing, with many brown pigments appearing to be outside of the nucleus. The ATF4 immunostaining after APAP challenge remains weak.

(7) It is not ruled out that AAV expression of Atf4 or Btg2 reduces hepatocyte sensitivity to APAP by affecting expression of the Cyps needed for activation. In other words, does AAV-Atf4 or AAV-Btg2 change the expression of any of the Cyps relevant to APAP in the 3 weeks before APAP application (F5B)? S5A of the revised manuscript rules out loss of Cyp2e1 expression as a confounding factor.

(8) It is laudable that the authors tried to extend their findings to human by using snRNA-seq data from a published study (line 391) but it is unclear why they didn't analyze all 10 patients in that study but instead focused on 2 and stated that this small sample number prevented drawing definitive conclusions and could therefore only be mentioned in the discussion. The revised manuscript continues to focus on rare spatial transcriptomics analyses of patients with APAP toxicity although more snRNA-seq analyses of such patients are available which should also allow for analysis of hepatocyte zonation.

Comments on revised version.

After revision, the proposed role of Btg2 is substantiated but it remains unclear why midlobular hepatocytes don't proliferate after APAP challenge and whether the observed protective effects are indeed mediated by Atf4 acting directly through CHOP.

Reviewer #2 (Public review):

Summary:

In this manuscript, Hossaim and colleagues investigate the role of the ITK kinase in modulating inflammation in a pristane-induced lung hemorrhage model. Using a germline ITK KO mouse, they report that loss of ITK skews the T cell compartment toward a memory-like state, expanding Tregs, and conferring protection against alveolar hemorrhage, inflammatory monocyte recruitment, proteinuria, and systemic cytokine elevation. They further show that transfer of ITK-deficient Tregs into wild-type hosts with established disease attenuates injury and shifts the cytokine balance toward resolution, and that ITK-deficient Tregs carry a transcriptional signature enriched for OXPHOS, mTORC1, MYC, and cell-cycle programs. While these observations are interesting for the development of potential immunotherapies, there are several issues with the methodological approach that support the authors' claims, tempering my enthusiasm for this manuscript.

Strengths:

(1) The clinical motivation and potential targeted therapies are relevant.

(2) The murine phenotype seems robust.

Weaknesses:

(1) All loss-of-function experiments are from a global ITK knockout. This is a major limitation and weakness of this study. The protection observed in the intact knockout, therefore, cannot be attributed to Tregs specifically. The Treg-intrinsic claim rests almost entirely on a single adoptive-transfer experiment. In order to show that this effect is Treg-specific, the authors would need to generate a Treg-specific ITK-deficient mouse

(2) In their sufficiency experiment (adoptive Treg cell transfer), donor and/or host cells are not congenically marked, so persistence, lung trafficking, and in vivo expansion of transferred Tregs are not demonstrated.

(3) The authors claim that ITK-deficient Tregs possess enhanced metabolic fitness. This conclusion is based on transcriptional profiling of isolated splenic Tregs from unchallenged mice, yet it concerns lung protection during active disease. A disease-state and ideally lung-relevant transcriptome would more directly support the mechanistic narrative. Additional functional validation would be needed (Seahorse assay, mitochondrial mass/potential, etc). Some of these GSEA programs enriched in ITK-deficient Tregs could reflect a more general proliferative signature.

Reviewer #2 (Public review):

Summary:

This is an interesting paper from Alonso-Caraballo and colleagues that examines the influence of opioid use, acute and prolonged abstinence, and sex on cue-induced relapse and paraventricular thalamus (PVT) to nucleus accumbens shell (NAcSh) medium spiny neurons circuit physiology. The study presents a valuable finding that following prolonged, but not acute abstinence from oxycodone self-administration, female rodents exhibit higher relapse rates to drug paired cues. Additionally, the study presents the useful finding that prolonged abstinence increased PVT-NAcSh MSN synaptic strength in both sexes, an effect that is likely due to presynaptic adaptations. While the evidence to support these two findings is solid, further experiments are required to determine the functional role of the PVT-NAcSh MSN circuit in relapse following prolonged oxycodone abstinence, and the mechanism underlying the heightened relapse vulnerability in females in this model of opioid use disorder.

Strengths:

The paper is interesting, well written and presented, and the experiments are well designed and conducted. The revised analysis of spike count data that models the hierarchical structure of the data is appropriate to overcome low animal numbers and the potential for oversampling. The authors are transparent in reporting the results related to this analysis in figure 5 and acknowledge the study is underpowered to confirm the trend of increased intrinsic excitability in male MSNs following prolonged oxycodone analysis.

Impact:

The topic is of interest to the field of substance use disorders and gives solid evidence for the need to consider targeted therapeutics aimed at relapse prevention in opioid use disorder.

Reviewer #2 (Public review):

Tittelmeier et al. investigated the role of sphingolipid (SL) metabolism in the maintenance of endolysosomal vesicle integrity. They find that both impaired SL biosynthesis and degradation in C. elegans decreases the fluidity of endolysosomal membranes and promotes their rupture, while it has little effect on plasma membrane fluidity. Endolysosomal membrane fluidity is also negatively affected in human cells upon knockdown (KD) of a gene (SPHK2) involved in the SL degradation pathway. Aggregated forms of tau in both models (C. elegans and human cells) can also cause rigidification of the endolysosomal membrane, with SL homeostasis disruption having an additive effect, exacerbating endolysosomal rupture. Notably, KD of SPHK2 also increased the formation of tau foci, suggesting that compromised endolysosomal integrity may promote tau aggregation. These data provide a clearer understanding of how genetic manipulation of SL metabolism affects endolysosomal membranes and their rigidification in the context of tau aggregation. Supplementation of polyunsaturated fatty acids (PUFAs), which has a beneficial effect on Alzheimer's patients, improved membrane fluidity and reduced tau propagation in human cells and tau-associated neurotoxicity in C. elegans, suggesting a possible mechanism of action.

Comments on revised version:

The authors have:<br /> Corrected editorial errors (Points 1 and 2).

Clarified the experimental rationale, added new data to rule out alternative explanation, and improved the presentation of the C. elegans model (Point 3).

Provided experimental evidence and appropriate discussion regarding the specificity and broader physiological context of SL gene knockdown effects (Point 4).

Overall, the authors' responses are thorough, supported by new data where appropriate, and demonstrate a clear understanding of the concerns raised. All points raised have been satisfactorily resolved.

Reviewer #3 (Public review):

Summary:

This multi-omics study by Zhou et al elucidates the context-dependent roles of the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway (JSP) across different cellular compartments in the breast cancer tumor microenvironment. While bulk JSP activity is associated with a favorable prognosis, single-cell analysis reveals a paradoxical landscape: high JSP in T cells drives anti-tumor cytotoxicity and reduces exhaustion, whereas high activity in tumor epithelial cells promotes malignancy and immunosuppression via the MIF-CD74 signaling axis. The JSP score (immune-related) serves as a robust predictive biomarker for response to anti-PD-1 immunotherapy, particularly in triple-negative breast cancer (TNBC). Furthermore, the study identifies the STAT4/SLC47A1 axis as a critical mechanism through which tumor cells resist ferroptosis, facilitating disease progression. These findings suggest that broad JAK-STAT inhibition may be counterproductive in cancer therapeutics; instead, therapeutic success depends on precise modulation and carefully timed interventions to preserve its T-cell-associated functions. This study may inspire future studies to explore specific factors that selectively modulate JAK-STAT activity in immune cells to achieve favorable therapeutic outcomes.

Strengths:

Significant therapeutics implications

Weaknesses:

Limited molecular mechanisms

Comments on revised version:

The authors have addressed my comments

Reviewer #3 (Public review):

In this study, Liu and colleagues utilize TRAP-seq to profile the repertoire of actively translated mRNAs in different intestinal cell types (anterior INT1 vs. posterior INT2-9 cells) in C. elegans. A key goal of this study was to identify transcripts differentially expressed/translated between these intestinal cell subtypes in the context of animals being well fed or subjected to acute (30 minutes) or chronic (3 hours) starvation, followed by refeeding.

The authors identify a number of differentially expressed genes across all of the conditions tested. They then provide an initial survey of the landscape of translatome changes through Weighted Gene Network Correlation Analysis (WGNA), and some high-level functional surveys via Gene Ontology (GO) term analysis and protein domain analysis. The authors validate the enriched expression patterns of some of their identified candidate genes using fluorescent promoter fusion reporters, confirming INT1-specific expression. The authors further implicate the role of several other candidate genes in pathogen avoidance and in response to nutritional cues by knocking them down specifically in INT1 cells by RNAi. Finally, the authors identify pyruvate as a major nutrient signal coming from the bacterial diet that suppresses the release of a key insulin peptide (INS-7) and identify some of the genes expressed in INT1 that are required for this response.

Strengths:

(1) Good use of and justification for TRAP-seq, because scRNA-seq would be difficult under the varied conditions used (starvation, refeeding)

(2) The manuscript is generally clear to read, and the data are generally well-presented with good supporting data that includes replicates, sample sizes, error measurements, and associated statistics.

(3) The dataset will be an interesting resource to mine for future studies focusing on mechanisms of how particular intestinal cell types respond to different environmental signals.

Weaknesses:

(1) A limitation of TRAP-seq, although powerful, is that only relative comparisons can be made between genotypes/conditions to identify differentially-expressed genes, rather than assessing whether a given gene is expressed at a certain level in a cell type under a certain condition. This limitation is due to the non-specific association of sticky RNA species to the beads during the immunoprecipitation step. This is a minor point however, and the authors do a nice job of focusing their analysis on differentially expressed transcripts in the current study.

(2) Another limitation of the current study is that the experiments testing the role of candidate genes identified by their profiling experiments do not dive a bit deeper into providing a mechanistic understanding of the phenotypes being studied. At present, the results are thus viewed more as a genomics-based screen with some limited follow-up on interesting hits. However, this reviewer appreciates that when placed in context of the work presented, a presentation of the profiling data along with some validation is an excellent starting point for future mechanistic studies elaborating on these interesting candidates.

Appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

The main goal of the study was to survey the dynamic responses at the level of actively translated mRNAs of the INT1 vs INT2-9 cells in response to metabolic challenge.

Overall, the authors use established methods to perform their genome-wide analysis, and the set of differentially regulated genes are enriched for expected molecular functions and form coherent networks in anticipated pathways.

The validation experiments (promoter::GFP fusion reporters, INT1-specific knockdowns of highly regulated genes) further corroborate the quality of the TRAP-seq datasets generated.

I have a few points for the authors that would further strengthen this work:

(1) The authors rightfully focus on the top differentially-regulated candidates, but it's unclear at present how far down their fold change list would lead to expression pattern validations. It would be useful to test a few more promoter::GFP fusion reporters at different enrichment/fold-change/statistical cutoffs.

(2) Although the INT1-specific RNAi provides a convenient strategy for rapidly perturbing and testing genes of interest for phenotypes, independently validating the knockdowns with genetic mutants, or alternatively (if genes are essential), degron alleles.

Likely impact of the work on the field, and the utility of the methods and data to the community.

The TRAP-seq data and list of differentially-expressed candidate genes will form an interesting set of high-priority candidates to study for their role in the reception and transduction of nutritional cues in response to food status and pathogens. This data will thus benefit the C. elegans community of researchers studying the mechanisms governing these phenomena.

Comments on revised version:

I think the authors have done a good job of addressing the suggestions from the previous round of review in this new version.

Reviewer #2 (Public review):

Summary:

The authors set out to enable large-scale measurement of fat in skull bone marrow using routine structural brain MRI scans. They present a neural-network pipeline trained largely on realistic simulated examples and show that the resulting skull marrow measure is highly repeatable in test-retest data and consistent in monozygotic twins. Applying it to ~33,000 UK Biobank participants, they report expected population patterns (including sex- and menopause-related differences) and identify genetic and health-related associations, creating an important resource that can be built upon by researchers interested in BMA, imaging, bone, metabolism, neuroscience, ageing, haematology, and other fields.

Major strengths:

A notable methodological strength is the training strategy: by using a large, simulated dataset that captures plausible variation in skull-layer thickness and MRI intensity, the authors reduce reliance on scarce expert-labelled images. The modelling choice (using 1D intensity profiles through the skull rather than analysing the full 3D volume) appears well matched to the anatomy and offers an efficient approach for thin, layered structures. Multiple validation steps (including test-retest reliability and twin concordance) support the robustness of the measurement pipeline.

On the biological and genetic side, the study demonstrates that the skull BMA estimate relates to known correlates of marrow fat (e.g., age/sex/menopause patterns/bone density) and integrates population imaging with large-scale genetic analysis to highlight loci and candidate genes with plausible relevance to skeletal and marrow biology. The inclusion of cross-ancestry analyses and integration with cell-type-resolved gene-expression resources further improves interpretability and usability for the community.

Major limitations:

The main limitation is conceptual rather than technical: the phenotype is derived from T1-weighted MRI intensity, which does not directly separate fat and water signals and can vary with scanner and sequence settings. The manuscript provides convincing evidence that the measure is reproducible and biologically meaningful, but it should still be interpreted as a semi-quantitative proxy for marrow fat rather than a direct fat-fraction measurement. Accordingly, the genetic and phenotypic associations are likely informative, but the most direct claims about "adiposity" would be stronger if anchored to established quantitative fat-measurement imaging or spectroscopy in the skull.

The genetic "replication" analysis in a smaller, ancestrally heterogeneous non-European-ancestry sample is useful as a test of transferability, but it is not equivalent to replication in an independent cohort of similar ancestry and is expected to show reduced SNP-level reproducibility because of differences in sample size and genetic background. This should be clearly framed so readers understand what level of generalisation is supported by the current evidence.

Likely impact and utility:

Overall, the work provides a practical method for extracting new biological information from widely available brain MRI scans and should be particularly useful to researchers working with large imaging biobanks and those studying connections between bone, blood, metabolism, and brain ageing. The combination of a scalable measurement approach and openly reported genetic results is likely to accelerate follow-up studies, including cross-cohort comparisons and mechanistic work on candidate pathways.