Reviewer #1 (Public review):

Summary:

This paper investigates the physical basis of epithelial invagination in the morphogenesis of the ascidian siphon tube. The authors observe changes in actin and myosin distribution during siphon tube morphogenesis using fixed specimens and immunohistochemistry. They discover that there is a biphasic change in the actomyosin localization that correlates with changes in cell shapes. Initially, there is the well-known relocation of actomyosin from the lateral sides to the apical surface of cells that will invaginate, accompanied by a concomitant lengthening of the central cells within the invagination, but not a lot of invagination. Coincident with a second, more rapid, phase of invagination, the authors see a relocalization of actomyosin back to the lateral sides of the cells. This 2nd "bidirectional" relocation of actin appears to be important because optogenetic inhibition of myosin in the lateral domain after the initial invaginations phase resulted in a block of further invagination. Although not noted in the paper, that the second phase of siphon invagination is dependent on actomyosin is interesting and important because it has been shown that during Drosophila mesoderm invagination that a second "folding" phase of invagination is independent of actomyosin contraction (Guo et al. eLife 2022), so there appear to be important differences between the Drosophila mesoderm system and the ascidian siphon tube systems.

Using the experimental data, the authors create a vertex model of the invagination, and simulations reveal a coupled mechanism of apicobasal tension imbalance and lateral contraction that creates the invagination. The resultant model appears to recapitulate many aspects of the observed cell behaviors, although there are some caveats to consider (described below).

Strengths:

The studies and presented results are well done and provide important insights into the physical forces of epithelial invagination, which is important because invaginations are how a large fraction of organs in multicellular organisms are formed.

Weaknesses:

(1) This reviewer has concerns about two aspects of the computational model. First, the model in Fig. 5D shows a simulation of a flat epithelial sheet creating an invagination. However, the actual invagination is occurring in a small embryo that has significant curvature, such that nine or so cells occupy a 90-degree arc of the 360-degree circle that defines the embryo's cross-section (e.g., see Fig. 1A). This curvature could have important effects on cell behavior.

(2) The second concern about the model is that Figure 5 D shows the vertex model developing significant "puckering" (bulging) surrounding the invagination. Such "puckering" is not seen in the in vivo invagination (Fig. 1A, 2A). This issue is not discussed in the text, so it is unclear how big an issue this is for the developed model, but the model does not recapitulate all aspects of the siphon invagination system.

(3) In Fig. 2A Top View and the schematic in Fig. 2C, the developing invagination is surrounded by a ring of aligned cell edges characteristic of a "purse string" type actomyosin cable that would create pressure on the invaginating cells that has been documented in multiple systems. Notably, the schematic in Fig 2C shows myosin II localizing to aligned "purse string" edges, suggesting the purse string is actively compressing the more central cells. If the purse string consistently appears during siphon invagination, a complete understanding of siphon invagination will require understanding the contributions of the purse string to the invagination process.

(4) The introduction and discussion put the work in context of work on physical forces in invagination, but there is not much discussion of how the modeling fits into the literature.

Comment on revised version.

This is an extensively revised version of a previously submitted manuscript that, as detailed in their 20-page response to the first reviews, satisfactorily addresses the reviewers' comments. In particular, the revised manuscript makes it much clearer how this work fits into and advances the field. The added experiments strengthen the rigor of the manuscript as well. Overall, this paper is ready to go.

Reviewer #4 (Public review):

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

Summary:

The authors demonstrate a computational rational design approach for developing RNA aptamers with improved binding to the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. They demonstrate the ability of their approach to improve binding affinity using a previously identified RNA aptamer, RBD-PB6-Ta, which binds to the RBD. They also computationally estimate the binding energies of various RNA aptamers with the RBD and compare against RBD binding energies for a few neutralizing antibodies from the literature. Finally, experimental binding affinities are estimated by electrophoretic mobility shift assays (EMSA) for various RNA aptamers and a single commercially available neutralizing antibody to support the conclusions from computational studies on binding. The authors conclude that their computational framework, CAAMO, can provide reliable structure predictions and effectively support rational design of improved affinity for RNA aptamers towards target proteins. Additionally, they claim that their approach achieved design of high affinity RNA aptamer variants that bind to the RBD as well or better than a commercially available neutralizing antibody.

Strengths:

The thorough computational approaches employed in the study provide solid evidence of the value of their approach for computational design of high affinity RNA aptamers. The theoretical analysis using Free Energy Perturbation (FEP) to estimate relative binding energies supports the claimed improvement of affinity for RNA aptamers and provides valuable insight into the binding model for the tested RNA aptamers in comparison to previously studied neutralizing antibodies. The multimodal structure prediction in the early stages of the presented CAAMO framework, combined with the demonstrated outcome of improved affinity using the structural predictions as a starting point for rational design, provide moderate confidence in the structure predictions.

Reviewer #1 (Public review):

Summary:

Authors have investigated the role of FMRP in the formation and function of RNA granules in mouse brain/cultured hippocampal neurons. Most of their results indicate that FMRP does not have a role in the formation or function of RNA granules with specific mRNAs but may have some role in distal RNA granules in neurons and their response to synaptic stimulation. This is an important work (though the results are mostly negative) in understanding the composition and function of neuronal RNA granules. the last part of the work in cultured neurons is disjointed from the rest of the manuscript and the results are neither convincing nor provide any mechanistic insight.

Strengths:

(1) The study is quite thorough, the methods and analysis used are robust and the conclusion and interpretation are diligent.

(2) The comparative study of Rat and Mouse RNA granules is very helpful for future studies

(3) The conclusion that the absence of FMRP does not affect the RNA granule composition and many of its properties in the system the authors have chosen to study is well supported by the results

(4) The difference in the response to DHPG stimulation concerning RNA granules described here is very interesting and could provide a basis for further studies though it has some serious technical issues (see below)

Weaknesses:

(1) The system used for the study (P5 mouse brain or DIV 8-10 cultured neuron) is surprising as the majority of defects in the absence of FMRP are reported in later stages (P30+ brain and DIV 14+ neurons). It is important to test if the conclusions drawn here hold good at different developmental stages.

(2) The term 'distal granules' is very vague. Since there is no structural or biochemical characterization of these granules it is difficult to understand how they are different from the proximal granules and why FMRP has an effect only on these granules.

(3) Since the manuscript does not find any effect of FMRP on neuronal RNA granules, it does not provide any new molecular insight with respect to the function of FMRP

Comments on revised version.

The authors have answered several questions raised by the reviewers. But for me, the critical issue of using only the brain from P5 animals and relatively early DIV neurons is still not convincingly addressed. FMRP may still play a role in determining the stalled ribosomes on its target mRNAs at a later stage of development, when there is more scope for activity-mediated protein synthesis.

I agree with the authors that this work helps the molecular understanding of FMRP functions by disproving one of the long-standing hypotheses.

Reviewer #1 (Public review):

Summary:

The article by Zdraljevic et al. reports the discovery of a third toxin-antidote (TA) element in C. elegans, composed of the genes mll-1 (toxin) and smll-1 (antidote). Unlike previously characterized TA systems in C. elegans, this element induces larval arrest rather than embryonic lethality. The study identifies three distinct haplotypes at the TA locus, including a hyper-divergent version in the standard laboratory strain N2, which retains a functional toxin but lacks a functional antidote. The authors propose that small RNA-mediated silencing mechanisms, dependent on MUT-16 and PRG-1, suppress the toxicity of the divergent toxin allele. This work provides insights into the evolutionary dynamics of TA elements and their regulation through RNA interference (RNAi).

Overall, there are many things to like about this paper and only a few small quibbles, which will not require more than a little rewriting or relatively minor analyses.

Strengths of the Paper:

(1) The discovery of a maternally deposited TA element with delayed toxicity due to delayed mRNA translation of the maternally deposited toxin mRNA is a significant addition to the literature on selfish genetic elements in metazoans.

(2) Identifying three haplotypes at the TA locus provides a snapshot of potential evolutionary trajectories for these elements, which are often inferred but rarely demonstrated in naturally occurring strains. The genomic analysis of 550 wild isolates contextualizes the findings within natural populations, revealing geographic clustering and evolutionary pressures acting on the TA locus.

(3) The study employs various techniques, including CRISPR/Cas9 knockouts, FISH, long-read RNA sequencing, and population genomics. The use of inducible systems to confirm toxicity and antidote functionality is particularly robust. This multifaceted approach strengthens the validity of the findings.

(4) The authors provide compelling evidence that small RNA pathways suppress toxin activity in strains lacking a functional antidote. This highlights an alternative mechanism for neutralizing selfish genetic elements.

Comments on revised version.

The authors have addressed all my (relatively minor) comments from the first round of reviews. However, the most substantial comments came from Reviewer 2, mostly focused on the conclusions that "Multiple lines of evidence suggest that the N2 tmrl-1 allele is recognized by piRNAs, leading to MUT-16-dependent 22G siRNA production and post-transcriptional silencing of the transcript." This is beyond my expertise to fully evaluate what is state-of-the-art in terms of acceptable evidence, so I will defer to Reviewer #2 for this.

Reviewer #3 (Public review):

Summary:

This study uses large-scale all-atom molecular dynamics simulations to examine the conformational plasticity of the HIV-1 envelope glycoprotein (Env) in a membrane context, with particular emphasis on how the transmembrane domain (TMD), cytoplasmic tail (CT), protomer cleavage, and membrane environment influence ectodomain orientation and antibody epitope exposure. By comparing Env constructs with and without the CT, explicitly modeling glycosylation, and embedding Env in an asymmetric lipid bilayer, the authors aim to provide an integrated view of how membrane-proximal regions and lipid interactions shape Env antigenicity, including epitopes targeted by MPER-directed antibodies.

Strengths:

The authors have made a heroic effort to address the concerns raised in the first two rounds of review, and the revised manuscript is substantively improved. The addition of dynamical cross-correlation maps, expanded citation of prior computational work, clarification of the membrane composition rationale, data deposition to Zenodo, and new contextualization has improved the flow and interpretation of the manuscript throughout. Several scientifically interesting aspects of the work merit highlighting with a brief discussion on how future studies can leverage this data to build upon its impact.

A key strength of this work remains the scope, scale, and realism of the simulation systems. The authors construct a very large, nearly complete-Env-scale model that includes a glycosylated Env trimer embedded in an asymmetric bilayer, enabling analysis of membrane-protein interactions that are difficult to capture experimentally. The inclusion of specific glycans at reported sites, and the focus on constructs with and without the CT or cleavage, are well motivated by existing biological and structural data.

The observation that R696 orientation and its interacting partners give rise to asymmetric protomer conformations and distinct TMD tilts is a notable finding. The statement that interactions between R696 and lipid headgroups or CT residues can be strong enough to introduce a kink into the TMD is well-supported by representative snapshots and consistent with prior isolated-TMD simulations. The use of two initialization depths ("high" and "low") to probe R696 leaflet preference is methodologically interesting and the authors' interpretation - that there is a slight bias toward cytoplasmic leaflet interactions, but that these contacts could be highly dynamic over the course of viral entry - is appropriately cautious. It would be valuable to explicitly frame this as a hypothesis with testable predictions that future experimental or enhanced-sampling work could address. Similarly, the equilibration-driven kinking of the TMD core, consistent with prior isolated-TMD studies, represents a useful validation that extends those earlier observations to the intact trimeric context.

The simulations reveal substantial tilting motions of the ectodomain relative to the membrane, with angles spanning roughly 0-30{degree sign} (and up to ~40{degree sign} in some analyses), while the ectodomain itself remains relatively rigid. This framing, that much of Env's conformational variability arises from rigid-body tilting rather than large internal rearrangements, is an important conceptual contribution. The authors also provide interesting observations regarding asymmetric bilayer deformations, including localized thinning and altered lipid headgroup interactions near the TMD and CT, which suggest a reciprocal coupling between Env and the surrounding membrane.

The analysis of antibody-relevant epitopes across the prefusion state, including the V1/V2 and V3 loops, the CD4 binding site, and the MPER, is another strength. The study makes effective use of existing experimental knowledge in this context, for example by focusing on specific glycans known to occlude antibody binding, to motivate and interpret the simulations.

Finally, the revised text provides clear context that situates the study's findings and discrepancies within the broader literature, strengthening the manuscript's clarity and interpretability.

Future work in the field:

As the authors appropriately acknowledge within in the text, these microsecond simulations capture only the closed ground state and with limited sampling due to the already computationally intensive nature of these simulations. Their simulation setup provides interesting foundational knowledge of this state and a framework for these additional important questions.

Additionally, the authors appropriately acknowledge that CT-TMD and CT-ectodomain correlations are difficult to interpret given limited structural confidence in these regions. Future experimental and computational work in the field can extend and build upon the author's framework, particularly as the authors have made their trajectories available for the public. Re-analysis of the authors' deposited MD trajectories-such as probing for exposure of cryptic epitopes and potential allosteric coupling-could serve as valuable extensions of this work, particularly as advancements in computational analysis has reached an inflection point.

Comments on revised version.

Bravo! The improved clarity was a delight to read and will increase the impact this study has on the field.

Reviewer #1 (Public review):

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

Summary:

Mitotic kinesins carry out crucial roles in intracellular motility and mitotic spindle organization. Although many mitotic kinesins have been extensively studied, a few conserved mitotic motors remain poorly explored, including chromosome-associated kinesins. Here, Furusaki et al reconstitute recombinant chromosome-associated kinesin or chromokinesin (Kid) and reveal processive plus-end motility along microtubules. The authors purify multiple versions of Kid, revealing dimeric organization and their processive microtubule plus-ended motility which depends on their conserved motor domains, neck linkers, and coiled-coil regions. The study reveals for the first time that KID can recruit and transport duplex DNA along microtubules using its conserved C-terminal DNA binding domain. The work provides crucial revised thinking about the mechanisms of Chromokinesins mitosis as physical processive motors that mobilize chromosomes towards the microtubule plus ends in early metaphase.

Strengths:

The authors reconstitute multiple chromosome-associated kinesin (KID) orthologs from Xenopus and humans with microtubules and determine their oligomerization. The study shows how coiled-coil and neck linker regions of KID are essential for its function as its deletion leads to non-processive motility. Chimeras placing the KID coiled-coil and neck linker on the KIF1A motor domain led to the production of a processive recombinant motor supporting the compatibility of their motility mechanisms. The KID c-terminal tail binds and transports only double-stranded DNA and its deletion or single-stranded DNA leads to defects in this activity.

Reviewer #1 (Public review):

This is a well-written and fully documented methods paper.

The authors have established a clear rationale for their new packages, especially for real-time use, and demonstrate significant speed improvements that will likely appeal to many users of tools like DLC, SLEAP, and LightningPose. The inclusion of a graphical user interface will help make the package more accessible to neuroscientists with limited computational expertise. While it may be challenging to get users to switch from their established workflows for video analysis, the speed gains offered by this package make it worth considering. The hardware aspects of the project are well-documented, and the GitHub repository for this part of the setup is also thorough. Overall, this paper provides a clear summary of the tools, their uses, setup, and benefits.

I have a few minor questions about the collective set of tools.

First, the GitHub repository for SqueakPoseStudio appears to be missing a testing routine and associated badge, and the package has not been formally released. This means users would need to download the repository to install it, correct? I suggest the authors consider publishing a formal release of the package, making it installable via pip, and including a basic testing routine to clearly display the package's status on the repository page. Adding a DOI from Zenodo would also be helpful. A testing routine is especially useful when updates are made, as many users avoid repositories with failing tests.

Second, the installation instructions simply state "Create a virtualenv and install:". This may not be sufficient for many researchers, as most neuroscientists are not experienced Python programmers and require clear guidance on the environment specific to this package. The installation instructions should be expanded to provide more detailed guidance and encourage more users. It would also be helpful to verify that the setups work across Windows, Mac, and Linux.

Third, the package defaults to UMAP for non-linear dimensionality reduction, which has some known issues. Can the package be modified to allow for alternative mapping methods, such as PaCMAP, PyDiffMap, or the more comprehensive topometry package?

Finally, what specific GPUs have been tested with the package, and are there any limitations based on the age of the video card or the available libraries for the deep learning component of the package?

Reviewer #1 (Public review):

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

Summary:

In this manuscript, the role of the insulin receptor and the insulin growth factor receptor was investigated in podocytes. Mice, where both receptors were deleted, developed glomerular dysfunction and developed proteinuria and glomerulrosclerosis over several months. Because of concerns about incomplete KO, the authors generated and studied podocyte cell lines where both receptors were deleted. Loss of both receptors was highly deleterious with greater than 50% cell death. To elucidate the mechanism of cell death, the authors performed global proteomics and found that spliceosome proteins were downregulated. They confirmed this directly by using long-read sequencing. These results suggest a novel role for insulin and IGF1R signaling in RNA splicing in podocytes.

This is primarily a descriptive study and no technical concerns are raised. The mechanism of how insulin and IGF1 signaling regulates splicing is not directly addressed but implicates potentially the phosphorylation downstream of these receptors. In the revised manuscript, it is shown that the mouse KO is incomplete potentially explaining the slow onset of renal insufficiency. Direct measurement of GFR and serial serum creatinines might also enhance our understanding of progression of disease, proteinuria is a strong sign of renal injury. An attempt to rescue the phenotype by overexpression of SF3B4 would also be useful but may be masked by defects in other spliceosome genes. As insulin and IGF are regulators of metabolism, some assessment of metabolic parameters would be an optional add-on.

Significance:

With the GLP1 agonists providing renal protection, there is great interest in understanding the role of insulin and other incretins in kidney cell biology. It is already known that Insulin and IGFR signaling play important roles in other cells of the kidney. So, there is great interest in understanding these pathways in podocytes. The major advance is that these two pathways appear to have a role in RNA metabolism.

Reviewer #1 (Public review):

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

Summary:

The authors describe the results of a single study designed to investigate the extent to which horizontal orientation energy plays a key role in supporting view-invariant face recognition. The authors collected behavioral data from adult observers who were asked to complete an old/new face matching task by learning broad-spectrum faces (not orientation filtered) during a familiarization phase and subsequently trying to label filtered faces as previously seen or novel at test. This data revealed a clear bias favoring the use of horizontal orientation energy across viewpoint changes in the target images. The authors then compared different ideal observer models (cross-correlations between target and probe stimuli) to examine how this profile might be reflected in the image-level appearance of their filtered images. This revealed that a model looking for the best matching face within a viewpoint differed substantially from human data, exhibiting a vertical orientation bias for extreme profiles. However, a model forced to match targets to probes at different viewing angles exhibited a consistent horizontal bias in much the same manner as human observers.

Strengths:

I think the question is an important one: The horizontal orientation bias is a great example of a low-level image property being linked to high-level recognition outcomes and understanding the nature of that connection is important. I found the old/new task to be a straightforward task that was implemented ably and that has the benefit of being simple for participants to carry out and simple to analyze. I particularly appreciated that the authors chose to describe human data via a lower-dimensional model (their Gaussian fits to individual data) for further analysis. This was a nice way to express the nature of the tuning function favoring horizontal orientation bias in a way that makes key parameters explicit. Broadly speaking, I also thought that the model comparison they include between the view-selective and view-tolerant models was a great next step. This analysis has the potential to reveal some good insights into how this bias emerges and ask fine-grained questions about the parameters in their model fits to the behavioral data.

Weaknesses:

I'll start with what I think is the biggest difficulty I had with the paper. Much as I liked the model comparison analysis, I also don't quite know what to make of the view-tolerant model. As I understand the authors' description, the key feature of this model is that it does not get to compare target and probe at the same yaw angle, but must instead pick a best match from candidates that are at different yaws. While it is interesting to see that this leads to a very different orientation profile, it also isn't obvious to me why such a comparison would be reflective of what the visual system is probably doing. I can see that the view-specific model is more or less assuming something like an exemplar representation of each face: You have the opportunity to compare a new image to a whole library of viewpoints and presumably it isn't hard to start with some kind of first pass that identifies the best matching view first before trying to identify/match the individual in question. What I don't get about the view-tolerant model is that it seems almost like an anti-exemplar model: You specifically lack the best viewpoint in the library but have to make do with the other options. I sort of understand the reasoning that this enforces tolerance of viewpoint variability, but I'm not clear on whether or not this is a version of face familiarity and recognition that the authors think has an analog in human visual processing.

I do think that this model is interesting in terms of the differential tuning it exhibits, but don't find it easy to align with any theoretical perspective on face recognition. Specifically, do the authors think there is a stage of face processing in which tolerance as they've operationalized it in the model is extant? What I'm looking for is a concrete description of the circumstances that the authors are saying lead to this kind of model potentially being a meaningful analog of face recognition. For example, is the idea that one may become familiar with a face in some very limited set of viewpoints and then be presented with that face in other views?

Alternatively, if the authors prefer to say that they simply thought this was a nice exercise in terms of identifying a different model and that it may not be a meaningful proxy for face recognition. I think that's fine, to be clear! I just still don't see anything in the text that convinces me of the ecological validity of this version of view-tolerance.

Reviewer #1 (Public review):

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

Summary:

The authors address whether theta/beta ratio /TBR) can be used as a clinical biomarker for ADHD.

Strengths:

The data were acquired independently from 2 separate datasets, and there are sufficient subjects for adequate statistical power. The authors applied up-to-date EEG data preprocessing, state-of-the-art feature extraction, and statistical analyses, using a multiverse approach. By testing and comparing all meaningful approaches, defined a priori in the previous meta-analysis, the author convincingly demonstrates that TBR cannot be used as a clinical biomarker, and previous positive results can be explained by interactions between different factors (alpha peak frequency, aperiodic component, age).

Weaknesses:

There are no apparent issues with data, separate datasets, large sample sizes, and state-of-the-art data analysis.

Reviewer #1 (Public review):

Summary:

GPR52 is an orphan receptor implicated in neuropsychiatric disorders; however, the absence of tools capable of monitoring GPR52 activity in real time has stalled both mechanistic research and ligand discovery. This study addresses this gap by reporting the development of GPR52-1.0, a genetically encoded fluorescent sensor designed to detect activation of GPR52. The sensor was systematically engineered using the established GRAB platform, yielding a construct with micromolar sensitivity and high selectivity in cell culture. The authors largely achieve their stated aims, however the biological relevance of their aims is unclear, as GPR52 is reported to be a constitutively active receptor (PMID: 32076264, PMID: 26384023). GPR52-1.0 is a validated, specific, and sensitive sensor that functions in vitro and ex vivo. The claim that electrically stimulated endogenous GPR52 ligand release occurs in the striatum is supported by the specificity of the GPR52 antagonist block using ex vivo brain slices, however, once again this aim is clouded by evidence that GPR52 is constitutively active. The sensor is presented as a tool for future deorphanization; however, this assumes that the physiological ligand is an agonist, which is unclear based on the evidence that GPR52 is constitutively active. If the authors can explain or adapt their experiments and manuscript in the context of GPR52 constitutive activity, this will be useful work to the community. The impact of this work is likely to be moderate to high within the specialized communities studying orphan GPCRs, neuronal signaling, and neuropsychiatric disease. The GRAB sensor strategy has already generated widely adopted tools for other receptors, and a validated GPR52 sensor would fill a genuine gap. The GRAB technology makes GPR52-1.0 directly applicable to in vivo studies. It is likely that GPR52-1.0 could be replicated for other orphan receptors to facilitate their deorphanization.

Strengths:

(1) Systematic and rigorous sensor optimization and characterization by screening ~800 variants with iterative linker and cpEGFP mutation step. The resulting EC50 values are characterized in HEK293T and cultured neurons.

(2) Testing GPR52-1.0 against a broad panel of neurotransmitters with no detectable off-target activation strengthens confidence in sensor specificity.

(3) The use of a selective antagonist to confirm specificity, both in cell lines and in brain slices, strengthens the conclusions significantly.

(4) Electrically stimulated GPR52-1.0 fluorescence changes in ex vivo striatal slices are blocked by a GPR52 antagonist. This is the most biologically significant result in the manuscript, as GPR52-related diseases can involve the striatum.

Weaknesses:

(1) The work, both experimentally and in its presentation, is not put into the context of what is known about GPR52 pharmacology and signaling. It is reported by multiple groups that GPR52 has high constitutive activity and does not require a ligand for high levels of signaling (PMID: 32076264, PMID: 26384023). The authors should clarify whether GPR52-1.0 senses constitutive activation and whether baseline fluorescence is stable over the timescale of their experiments. The cell and mouse work needs to be reframed and conducted in the context of the high basal activity of the receptor, or the authors need to explain the differences between their study and other studies.

(2) The electrical stimulation used in brain slice experiments is non-specific. This could be activating many cell types and neurotransmitter systems simultaneously. The pharmacological block by the GPR52 antagonist is reassuring, but the identity of the molecules driving the signal remains unknown. It could be that GPR52 is constitutively active, and that the electrical stimulation drives higher expression of GPR52 and thus constitutive signaling. This constitutive signaling can then be inhibited by the GPR52 antagonist. In this scenario, there would be no endogenous GPR52 agonist invoked by electrical stimulation.

(3) The ex vivo brain slice data rely on n=9 slices without reporting the number of animals that the slices come from. Given the importance of this result, more biological replicates and clear reporting of animal numbers would strengthen confidence.

(4) The manuscript does not benchmark GPR52-1.0 against existing approaches (e.g., HTRF, BRET, or calcium mobilization assays) to contextualize its advantages in a drug-discovery or screening workflow.

(5) The paper's title references deorphanization, but the authors have made no attempts toward this deorphanization. No candidate ligand molecules are identified or tested.

Reviewer #1 (Public review):

The manuscript titled," Sleep-Wake Transitions Are Impaired in the AppNL-G-F Mouse Model of Early Onset Alzheimer's Disease", is about a study of sleep/wake phenomena in a knockin mouse strain carrying, "three mutations in the human App gene associated with elevated risk for early onset AD". Traditional, in-depth, characterization of sleep/wake states, EEG parameters and response to sleep loss are employed to provide evidence, "supporting the use of this strain as a model to investigate interventions that mitigate AD burden during early disease stages". The sleep/wake findings of earlier studies (especially, Maezono, et al., 2020, as noted by the authors) were extended by several important, genotype-related observations, including age-related hyperactivity onset that is typically associated with increased arousal, a normal response to loss of sleep and to multiple sleep latency testing, and a stronger AD-like phenotype in females.

The authors conclude that the AppNL-G-F mice demonstrate many of the human AD prodromal symptoms and suggest that this strain may serve as a model for prodromal AD in humans, confirming the earlier results and conclusions of Maezono, et al. Finally, based on state bout frequency and duration analyses, it is suggested that the AppNL-G-F mice may develop disruptions in mechanism(s) involved in state transition.

The study appears to have been, technically, rigorously conducted with high quality, in depth traditional assessment of both state and EEG characteristics with the concordant addition of activity and temperature.

The major strengths of this study derive from observations that the AppNL-G-F mice: 1) are more hyperactive in association with decreased transitions between states; 2) maintain a normal response to sleep deprivation and have normal MSLT results; and 3) display a sex specific, "stronger" insomnia-like effect of the knockin in females.

The weaknesses stem from the study's impact being limited due to its being largely confirmatory of the Maezono et al. study with advances of import to a potentially, more focused field. Further, the authors conclude that AppNL-G-F mice have disrupted mechanism(s) responsible for state transition, however these were not directly examined. The rationale for this conclusion is stated by the authors as based on the observations that bouts of both W and NREM tend to be longer in duration and decreased in frequency in AppNL-G-F mice. Although altered mechanism(s) of state transition (it is not clear what mechanisms are referenced here) cannot be ruled out, other explanations require careful consideration. It is acknowledged in the discussion that increased arousal in association with hyperactivity would be expected to result in increased duration of W bouts during the active phase. This would also predictably result in greater sleep pressure that is typically associated with more consolidated NREM bouts, consistent with the observations of bout duration and frequency. The results from the MSLT tests and lack of increased EEG slow wave activity are problematic to interpret in the context of increased arousal (evidenced by the hyperactivity) since these phenomena, known to be enhanced in association with increased sleep pressure, may be masked by arousal (or by some other effect of the altered genotype). Perhaps, the effect on consolidation is less sensitive. Thus, understanding the underlying mechanism(s) involved is needed for conclusion(s) about sleep pressure.

Overall, this study's findings are valuable but with respect to the claims, incomplete.

Reviewer #1 (Public review):

Freas and Wystrach present a computational and experimental study of ant navigation. The main innovation of the computational model is the insertion of an oscillatory element between the steering signal and the motor control that results in a trajectory whose heading oscillates around a goal direction. Additionally, the model imposes periodic cessations of forward movement and inversely couples rotational speed to forward velocity. As a result the model periodically makes larger reorientations reminiscent of those seen in behaving ants.

The behavioral data consists of two experimental sets: experienced Melophorus bagoti foragers, recorded in 2010 and inexperienced M. bagoti foragers, recorded in 2023-2024 at the same site. The behavioral data is qualitatively compared to the model in Figures 3 through 6. In figures 3-5, all ant sets are grouped together while in Figure 6 they are separated. In Figure 6, the authors should do a careful job of making sure the reader is aware that comparisons are being made between behavioral data sets captured more than a decade apart and of justifying the validity of a quantitative comparison between these sets.

The manuscript also describes Myrmecia ants and makes comparisons between modeled Myrmecia ants and supplemental videos of these ants (Videos 3,4). These videos are not described in the methods. While the captions describe these as ants "homing in an unfamiliar environment," the videos show tethered ants walking on a ball. Without more information and absent any analysis, it is difficult for me to understand how these videos support granular points in the text about coupling between rotation and forward velocities.

Strengths:

The manuscript's main thesis, that an oscillatory element interspersed between the control signal and the motor unit can reproduce aspects of ant navigation, appears supportable.

Weaknesses:

Qualitative agreement between aspects of a model and aspects of a behavioral measurement do not prove the correctness of a model. In the section (802), "An ancestral design? Striking parallels with crawling Drosophila larvae," the authors argue that behavioral data in larvae support their model, despite the larva's lack of a (known) central complex. C. elegans navigation can also be segmented into longer runs and shorter exploratory behaviors (Chen 2025), comparable to the runs and scans described here. C elegans definitively does not have a central complex. In general, multiple internal mechanisms are capable of producing the same macroscopic behavioral outcome. This fact limits the ability of behavioral data to confirm the details of a particular model; it does not imply that observation of similar behaviors in multiple species shows that a particular model is correct or generalizable.

Here the ability of the behavioral data to confirm or constrain the model is further limited by the qualitative nature of the comparisons. Some of the comparisons are trivial (e.g. Figure 5E-F: any first order process will produce a Poisson distribution, and in the model a Poisson process was explicitly coded in with parameters chosen (1070) to match the behavioral data). Finally, the number of adjustable parameters (13) is comparable to the number of comparisons made; it is unclear that the model could not be adjusted to fit any set of behavioral measurements.

While the introduction is improved, there is still room to eliminate confusion as to what aspects of the model reflect hypothesized rather than measured neural circuits. For instance, if there is data showing LAL oscillations in insects, the authors should cite it and call it out clearly. Alternately they should say that the oscillator is hypothesized based on measured bistability. They should also clarify whether they are discussing neural oscillations or motor oscillations and whether these oscillations are measured, modeled, or hypothesized.

As one example: Lines 283-284 "This oscillator [referring to the model's intrinsic oscillator described in the previous paragraph], which is widespread in insects (Cheng, 2024; Kanzaki, 2005; Kanzaki and Mishima, 1996), resides in the lateral accessory lobes (LAL)" reads as though it is known that a neural oscillator occupies the LAL. Cheng 2024 is a brief review of behavioral oscillation. Kanzaki et al. 2005 describes numerical modeling and simulation with a physical robot. Kanzaki and Mishima, 1996 demonstrates bistability (flip-flopping) in moth descending neurons. None of these show neural oscillations and none of them describe the LAL. The authors should review the paper and be scrupulously careful that the claims made in the text are supported in the cited references. These difficulties were pointed out in a previous round of review; hopefully they can be fully corrected this time.

Kevin S. Chen, Jonathan W. Pillow*, Andrew M. Leifer*, "State-switching navigation strategies in C. elegans are beneficial for chemotaxis," arXiv:2508.00191 31 July 2025.

Reviewer #1 (Public review):

Overview:

This study examines cellular computations in the dendrites of neurons in the medial superior olive (MSO) required for computing sound location based on interaural time differences (ITD). This field had, for many decades, depended on the so-called Jeffress model, which stated that an array of binaural coincidence detector neurons fire only when a given sound lateralization is balanced by a given difference in presynaptic axonal conduction time. The apparent absence of such calibrated axonal delay lines has left the field with little mechanistic handle for the strong ITD computations in MSO. This study suggests that dendritic delay along the dendrites of the bipolar MSO neurons makes a significant contribution to a calibrated delay line.

Strengths:

The authors used a combination of in vitro patch-clamp recordings, morphological analysis of a large dataset, and computational modelling to gain experimental access to dendritic computations. A technical tour-de-force set of distal dendritic patch-clamp recordings allowed an evaluation of this otherwise inaccessible parameter, and detailed modeling based on large datasets revealed the functional consequences. The use of this broad methodological toolbox enabled a detailed study of dendritic integration in MSO neurons and revealed a prominent role for graded variation in dendrite structure in shaping the coincidence detection in MSO neurons. In addition, the modeled effects of synaptic inhibition were quite striking and shaped our understanding of ITD coding in the MSO.

Weaknesses:

The paper's organization does not set up the reader very well for the major point to be made about exactly how dendritic asymmetry could bias ITD curves. This point only arises later in the paper after discussion of uncorrelated physiological measures that merely hint that what is important is "larger morphological and electrotonic structure". The paper could also benefit from a more complete description of the methodology. As an example, bridge balance goes unmentioned, and series resistance is hardly mentioned, even though both could distort the measurements of simulated EPSP amplitudes made through tiny electrodes used for dendrite recording.

Reviewer #1 (Public review):

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

The central pair apparatus of motile cilia consists of two singlet microtubules, termed C1 and C2, each of which is associated with a set of projections, referred to as the C1 and C2 projections. Each projection comprises multiple distinct structural domains, designated a, b, c, and so on. Biochemical studies combined with genetic analyses in Chlamydomonas identified three proteins as the major components of the C2a projection, and subsequent cryo-EM studies confirmed these findings.

In this paper, the authors aim to study the homologues of these three proteins-CCDC108/CFAP65, CFAP70, and MYCBPAP/CFAP147-using knockout mouse models. Biochemical and cell biological analyses demonstrate that, as in Chlamydomonas, these proteins are components of the C2 projection and form a complex that depends on the presence of each other. In addition, the authors use affinity purification to identify two previously uncharacterized proteins and show that they are central pair apparatus proteins that associate with the aforementioned complex. Knockout mice lacking any of the three core proteins exhibit phenotypes consistent with primary ciliary dyskinesia (PCD).

Overall, the manuscript is clearly written, and the data are convincing and support the authors' conclusions. However, given the previous findings in Chlamydomonas, this work provides limited conceptual advances to the field. Nonetheless, it represents a useful and well-documented resource for understanding the conserved organization of the central pair apparatus in motile cilia. It will be of interest to cell and developmental biologists, biochemists, and clinicians studying and treating human ciliopathies.

Reviewer #1 (Public review):

In this work, Gaurav et al. present an extensive study of phase-separated condensates formed by the foci-forming region (FFR) of the MUT-16 protein. The authors first report in vitro experiments showing that these condensates exhibit upper critical solution temperature (UCST) behavior. They then provide a detailed analysis based on atomistic simulations of MUT-16 FFR condensates, identifying key interactions responsible for LLPS, including salt bridges, cation-π interactions, and the role of Na⁺ ions.

Overall, the manuscript is well written. However, there are several concerns that should be addressed.

Major Concerns:

(1) I have several questions regarding the system preparation that require clarification. The authors state that "65 copies of the coarse-grained MUT-16 FFR were embedded in a slab-shaped simulation," but it is not clear how this initial configuration was generated. Were the molecules randomly distributed in the simulation box, or were they initially arranged in a preformed condensate? Alternatively, were they randomly inserted and allowed to self-assemble into a condensate during NpT simulations?

In Figure 1, the atomistic snapshot appears to show a well-defined condensate at the center of the simulation box. It would be important to clarify how this configuration was obtained: Was it generated from coarse-grained simulations starting from random initial conditions? Or was a preassembled condensate used as input?

Related to this, how do the authors ensure that the simulations are equilibrated? While 20 μs appears to be a reasonably long simulation time for coarse-grained simulations, it would be useful to demonstrate equilibration explicitly. For example, the authors could plot the center-of-mass positions (in the long axis of the simulation box) of individual proteins over time to show that all molecules reach a steady state and remain within the condensate without systematic drift.

(2) The authors experimentally observe UCST behavior for these condensates. Do the coarse-grained or atomistic simulations reproduce this behavior?

While atomistic simulations may be too computationally demanding to systematically explore temperature dependence, coarse-grained simulations could be used to test whether condensates are stable at lower temperatures and dissolve at higher temperatures. Such an analysis would provide valuable support for the experimental observations.

(3) Regarding the analysis of ions, several points could be clarified and extended:

a) It would be helpful to report the total number of ions and quantify how many are located inside vs. outside the condensate. While qualitative trends can be inferred from density profiles, quantitative analysis would strengthen the conclusions.

b) It would also be interesting to analyze the number of contact ion pairs (e.g., Na⁺-Cl⁻ pairs), as described in J. Chem. Phys. 156, 044505 (2022). It is known that some ion models tend to overestimate ion pairing and underestimate solubility (e.g., J. Chem. Phys. 153, 010903 (2020)).

c) In this context, the use of scaled-charge models has been shown to improve the description of ionic solutions and biomolecular systems (e.g., J. Phys. Chem. Lett. 2019, 10, 23, 7531-7536). I would suggest that, at least for one trajectory, the authors perform a test simulation using scaled charges (e.g., scaling by ~0.8) to evaluate whether ion distributions and protein-ion interactions are significantly affected.

d) Finally, while the selected water model is known to be accurate, it would be useful to assess its performance for concentrated salt solutions. For example, the authors could estimate the density of a 6 m salt solution and compare it with experimental data or validated models (e.g., J. Chem. Phys. 151, 134504 (2019)). This would help clarify to what extent the conclusions depend on the chosen force field.

Minor Concerns

(1) In the Introduction, it would be helpful to elaborate further on the possible driving forces of LLPS in this region. Are there prior hypotheses or evidence pointing to specific interactions (e.g., cation-π, π-π, electrostatic interactions)? While this work addresses these questions, a brief discussion of previous experimental or theoretical insights would provide useful context.

(2) On page 18, the authors state:<br /> "MUT-16 FFR satisfies the length (172 residues), aromatic content (20.35%), and Arg enrichment (85.71%) criteria. Its charge content (10.47%) and charge balance (38.89% positive charge fraction) are slightly below the nominal thresholds."<br /> It would be very helpful to include a schematic representation of the protein sequence highlighting these features (aromatic residues, charge distribution, etc.) in the corresponding figure, to provide a more intuitive understanding.

(3) A question regarding ion hydration: What is the coordination environment of the ions that bridge proteins? Are they still hydrated by water molecules, or does the reduced water content inside the condensate significantly affect their solvation?<br /> Typically, Na⁺ and Cl⁻ ions have coordination numbers around 5-6 in aqueous solution. Do protein interactions and reduced solvent conditions within the condensate alter this coordination? A brief analysis or discussion would be valuable.

Reviewer #1 (Public review):

Summary:

The authors investigated the relationship between physical activity (PA) and both structural (MRI) and cognitive brain health in the LIFE-Adult Study, with total baseline recruitment of 2576. Hippocampal volume, an MRI-derived BrainAGE marker, and scores from the Trail Making Test were used as outcomes, with the majority of participants measured at baseline and subsets also measured in a follow up session. The key findings were a lack of direct association between PA and outcomes, but longitudinal evidence for a higher BrainAge at baseline leading to lower physical capacity at follow-up. This supports a reverse-causation hypothesis in contrast to prevailing understanding of the positive effects of physical activity on brain health.

Strengths:

The Life-Adult study is a rich and carefully acquired dataset, with multiple follow-up time points. The statistical analyses were conducted carefully with appropriate control for confounds and multiple testing. The study design enables the important assessment for reverse causality. The authors are scrupulous in their consideration of a number of factors that could potentially bias their results, performing an age-stratified analysis, and emphasising discrepancies in PA measurements (specifically and age-reporting bias) across the dataset and other limitations.

Weaknesses:

This is an observational study with inconsistent measures of physical activity. Previous studies have used physical activity interventions, and might be more strongly weighted when considering evidence for these effects (specific confounders involved in interventions notwithstanding) .

The model identifying potential reverse causality is relatively limited - it seems possible/likely that brainAge could reflect more general health status, which would expand the potential range of factors underlying this observation. The authors comment on these possibilities.

The important quantitative actigraphy subset is small (n=227) as are the longitudinal subsets. Along with the discrepancy of physical activity/capacity at baseline and follow-up, and other complexities of the dataset, it is difficult to make firm conclusions. The authors point out that the actigraphy subset was quite inactive, and discuss this as a limitation.

Joint Public Review:

Summary:

Lengyel et al. present a normative model of single-neuron activity in area MT, which is known for its role in processing visual motion. The authors focus on responses to a center and a surround that move at different velocities. Both the center and surround are rigid: picture a set of dots all moving at the same velocity. The center dots are arranged in a disc; the surround dots in an annulus, and in both cases, the velocity of each is time-varying.

The core proposal is that the brain does not process motion in a fixed coordinate system, but instead infers a latent reference frame, and that MT neurons encode motion either in retinal coordinates or relative to this inferred reference frame. The model is meant to overcome a challenge in the existing literature on area MT: on the one hand, experimental findings are heterogeneous, including both surround suppression and surround facilitation of neural responses; on the other, existing models are either designed ad hoc to capture specific phenomena or they are somewhat general (e.g., divisive normalization), but in either case they can't explain the full range of responses. This manuscript proposes that the full range of responses in MT is explained as Bayesian inference over the reference frame in which center motion speed and direction should be estimated. The model extends one introduced in a previous publication from the same lab (Shivkumar et al. 2025). That publication focused on human perception of motion; this one makes predictions about MT mean responses and across-trial variability.

Strengths:

Processing visual motion is important for normal visual function, including for the integration and segmentation of visual objects. This manuscript presents a normative theory, supported by recent human perceptual data, and extends it to make predictions about neural firing rate and variability in area MT. The theory is well motivated and supported by the simulation analysis and comparison to data. It provides new insight into how causal inference of relative motion reference frames can modulate neural activity in MT. The richness of the theory's prediction can guide future experiments. In particular, the theory explains both center-surround suppression and facilitation, unifying disparate empirical observations in MT for which no unified explanation had been proposed. The manuscript also demonstrates a new method to map ideal observer predictions (posterior distributions over speed and direction, which are dependent on the posterior inference over reference frames) onto predicted neural activity for center-surround stimuli, by only considering basic tuning curves measured in the center-alone condition. This is a useful methodological contribution. The manuscript offers a thorough review of CS modulation studies in MT.

Weaknesses:

We found this paper difficult to read for two reasons. First, math is generally explained in words. This made it extremely difficult (impossible for some reviewers) to understand the details of the model, which are important. We're not against words, but it's critical that they be accompanied by equations.

Second, the manuscript is not self-contained in the sense that many of the motivations, assumptions, and limitations of the approach are only evident if one carefully reads the groups' prior work, Shivkumar et al. (2025). Following up on previous work isn't necessarily a flaw, but the introduction of the paper is written from a very broad perspective that does not effectively summarize the prior work and lay out the specific questions that motivate the current study. For example, it is not clear from the introduction whether the authors believe this framework can explain all sorts of center-surround interactions (including in non-motion stimuli and in other areas like the retina), or if the focus is only on area MT.

Finally, the connection to neural data is confusing and mostly qualitative. The authors create a library of "hypothetical but plausible tuning curves" and show that their modeling framework is flexible enough to capture a variety of center-surround interactions. Although they do state that their model can't explain all possible tuning curves, it's still hard to tell whether they have particularly strong evidence for the Bayesian causal inference hypothesis.

We also have several technical, but potentially important, comments.

Line 427: 'Our framework not only reinterprets past findings but also generates new, testable predictions. The model makes directly testable predictions for surround modulation. Facilitation, for instance, is predicted for neurons encoding retinal-centric motion (v_center) under high sensory uncertainty. In contrast, suppression is the hallmark of neurons encoding relative motion (v^relative_center) with respect to a surround-influenced reference frame.' It seems that to test the predictions of the model, one would need to first determine if a neuron encodes retinal or relative motion, without relying on the patterns predicted by this model, and then test if the two types of neurons behave as predicted. It is unclear how one can obtain this labeling of neurons independently of the model predictions.

Line 492: 'This offers a principled account of how the same population of neurons can support both perceptual states (integration and segmentation)'. However, because the theory assumes each neuron encodes either center velocity or center velocity relative to a moving reference frame, but not both, it does not explain that the same neuron could shift from suppression to facilitation. It may be worth considering another possibility, using V1 surround modulation as an analogy. Different neuron types are required to implement the surround computation: in mouse V1, SST interneurons are surround-facilitated, and they are necessary to implement surround suppression of pyramidal neurons https://pmc.ncbi.nlm.nih.gov/articles/PMC3621107, but their (SST) outputs are not communicated to downstream targets. In that view, facilitation is therefore not a signature of some neurons encoding a type of latent variable; it is only there as an intermediate step in the computation of the other latents (those that require suppression).

Misspecification of either the prior or likelihood can be a problem for Bayesian inference. Discussion of this point -- and in particular evidence (say from analysis of natural scene statistics in the case of the prior) that both are well-specified -- would strengthen the manuscript.

Reviewer #1 (Public review):

In this work, Jiqi Shao and colleagues evaluate the microbial iron competition and siderophore-mediated interactions combining (a) a dynamic modeling framework based on the consumer-resource model, including multiple siderophore and siderophore-receptor types, and (b) a graph-theory framework based on directed graphs to quantify the ecological dependencies of the community (referred to as Benefit Transfer Graph). Through a plethora of simulation experiments, by changing the number of species in the community, the ratio of pure-cheaters, and the number of foreign siderophores a partial-producers can utilize (referred to in this study as 'Cheating Breadth'), the authors found:

(1) Using simulations of small communities of 5 or fewer members, they observe that closed benefit-transfer loops (commensalism/mutualism loops) serve as the structural scaffold for diversity, observing coexistence, dominance, or dynamic fluctuations in function of the fraction of receptors in species and the number of community members.

(2) Using simulations of large communities of 50 members, they observed a paradox on the capacity of partial producers to utilize different foreign siderophores (referred to in this study as 'The Paradox of Cheating'). They observed that broad 'Cheating Breadth' of partial-producer members increases the probability of community-wide extinction and can act as destabilizing forces. However, at the same time, 'Cheating Breath' of partial-producer members promotes species richness and community biodiversity.

(3) The application of graph-theory framework helps to unveil ecological complexities of small and large microbial communities, explaining the aforementioned Paradox of Cheating.

As major strengths of this work, the authors present a novel modeling framework considering the ecological complexity of siderophore-mediated interactions by differentiating types of community members (pure-producers, partial-producers, and pure-cheaters), siderophore/receptor pairs, and exploring a wide range of situations (such as the number of community members, the ratio of pure-cheaters, or the siderophore breadth of partial-producers). Moreover, the discussion and conclusions of this study are mechanistically well-founded with a graph-theory framework (Benefit Transfer Graph). All computer code and scripts to replicate the simulations, analysis, and figure generation are public in the Zenodo repository.

However, this study still has some work to do before it meets the expected standards, presenting some weaknesses to be addressed. Please regard the following paragraph as constructive feedback aimed at improving your work. The main weakness of the actual version is the Abstract, the missing Methods section, the structure of the Results section, and the results displaying (i.e., Figures), and how partial-producers are considered as cheaters (including how they referred to the capacity of partial-producers to use different siderophores as 'Cheating Breath'). The Abstract could be significantly improved with a better introduction of the system (cooperators and cheaters, and the concept of the 'Tragedy of Commons'), a better description of the modeling framework, and other details included in 'Recommendations for the authors'. The current version of the manuscript misses a proper 'Methods' section.

Moreover, the authors could include (1) a section with the simulated systems and parameter choices of simulation experiments, (2) the key model assumptions, and (3) a separate (and more detailed) section explaining the graph-theory framework applied in this study (Benefit Transfer Graph). Most of this information is included in Supporting Information, but including it in the main text will facilitate the comprehension of the work. The structure of the results displayed (i.e., Figures) is quite confusing, especially in the section 'Closed Benefit Loops Drive Transitions from Exclusion to Coexistence and Chaos'. Moreover, important results are included in Supportive Information when they should be in the main text. Also, the lack of a proper Method section makes it harder to follow the Results sections. I have included some recommendations/suggestions to improve the Results structure. This study reveals an interesting ecological dynamic in siderophore-mediated interactions. The authors suggest the existence (and further explanation) of the 'Paradox of Cheating'. However, this paradox (and their discussion) may come from a misunderstanding of concepts and/or terminologies used by the authors applied here (and maybe widely applied in cooperator-cheaters systems). The authors refer to the capacity of 'partial-producers' to utilize foreign siderophores (i.e., siderophores of other species) as cheating. Also, they refer to the number of foreign siderophores that a 'partial-producer' can utilize as 'Cheating Breadth'. A microbial cheater is one that has receptors for siderophore uptake but does not pay the cost of producing siderophore themselves. Because 'partial-producers' are generating at least one type of siderophore, these are not technically cheaters (although they may act as 'pure-cheaters', changing their gene expression and do not synthesize any siderophore for the community). All this may entail a misleading of the results and a potentially overstated title and conclusions of this work. Community members 'pure-producers', 'partial-producers' cheaters may be called in a different way, e.g., 'single-receptor producer', 'multiple-receptor producers' and 'nonproducers', respectively [Gu. et al. (2025), doi: 10.1126/sciadv.adq5038]. A better terminology for 'the number of foreign siderophores that a partial-producer can utilize' could be 'Siderophore Breadth', and instead of stating a 'Paradox of Cheating', it can be a 'Paradox of Multiple-receptor Producers'. The discussion of the authors aligns better with the presented results if the proposed terms 'single-receptor producer/multiple-receptor producer and cheater' are used, considering multiple-receptor producers as cooperative members rather than 'moderate cheating'. On the other hand, the Paradox of Multiple-receptor Producers (or Paradox of Cheating by the authors) could be a modeling artifact. Although some species possess multiple siderophore receptors in their genome (some studies suggest that Pseudomonas species and other environmental strains' genomes can have up to 20-30 siderophore receptors), that does not mean that they are all expressed simultaneously.

Regardless of the weaknesses and the major points to be improved, the findings presented in this work substantially advance our understanding of complex ecological interactions between cooperators and cheaters mediated by siderophore and siderophore-receptor syntheses, especially when multiple-receptor producers are present. Moreover, the modeling and graph-theory frameworks presented by the authors can be applied in other microbial systems, such as collaboration/competition/cheating for substrates or nutrients. Fundamental modeling exercises are indispensable to unveil ground ecological rules of complex microbial communities, accelerating the advances in ecology by developing theory-based hypotheses for future experimental and environmental studies.

Reviewer #1 (Public review):

Summary:

One of the most important fundamental questions in base excision repair (BER) is how chromatin structure affects the action of specific components of the BER pathway. Previous work from this and other groups has began to address this question. In this report, the authors study the activity of Pol beta on a gapped or nicked DNA substrate 23 bases from the entry/exit site of a 603 nucleosome core particle in the presence and absence of PARP1, PARP2, HPF1, or FEN1. They show that H1 and PARP block pol beta incorporation, which is relieved by NAD+.

Strengths:

They show, not unexpectedly, that HPF1 and PARP activity help to displace H1, allowing Pol beta incorporation. PARP1 and PARP2 suppress Pol beta activity, which is mitigated by autoparylation. PARP2 has a strong impact on strand displacement synthesis. This is an important contribution to the field.

Weaknesses:

This present work incrementally builds upon their previous work, and what has been known previously about the activity of PARP1/2, HPF1, and the modification of histones.

Reviewer #1 (Public review):

This manuscript investigates the conformational flexibility and membrane-interaction behavior of the N-terminal segment of the VP4 protein from non-enveloped viruses, such as Coxsackievirus B3, with particular emphasis on the role of myristoylation, an essential process implicated in viral entry and transmission. The authors employ a multiscale simulation framework, combining all-atom (AA) and coarse-grained (CG) molecular dynamics simulations, to characterize the behavior of VP4 peptides in both bulk aqueous and membrane environments.

AA simulations suggest that the VP4 N-terminus remains predominantly disordered in bulk water, whereas CG simulations highlight the importance of conformational flexibility during interactions with a POPC membrane. The CG approach is further used to demonstrate an enhanced aggregation tendency of myristoylated VP4 monomers compared to non-myristoylated forms and to estimate the free-energy barriers associated with VP4 translocation across the membrane in monomeric and aggregated states. The study proposes a connection between VP4 aggregation, membrane remodeling, and peptide insertion into the membrane. Finally, well-tempered metadynamics simulations are used to explore changes in VP4 helicity during pore formation.

Overall, the study addresses an important problem and applies appropriate computational approaches. However, several aspects of the methodology, interpretation of results, and consistency with existing literature require clarification before the conclusions can be fully supported. The authors should revise the manuscript with due attention to the comments below.

(1) Disordered State of VP4 in Bulk Water

Figures 1(f-g, i-j) indicate that both myristoylated and non-myristoylated VP4 peptides adopt largely disordered conformations in bulk water. This finding appears to contradict prior experimental and computational reports discussed in the Introduction, which suggest partial or transient helicity in this region. A more detailed explanation is required to reconcile these differences with the existing literature. Additionally, since α-RMSD (aRMSD) is a direct and quantitative measure of helicity, the authors may consider reporting helical content explicitly using this metric to strengthen the analysis.

(2) Lack of Backmapped Atomistic Data for Membrane-Bound States

Figure 2 presents membrane-bound conformations of VP4 obtained from CG simulations. While this provides useful qualitative insight, the absence of backmapped all-atom representations limits the ability to extract detailed information regarding residue-level interactions, peptide conformations, and specific binding modes at the membrane interface. Inclusion and analysis of backmapped atomistic data would significantly strengthen the mechanistic interpretation of VP4-membrane interactions.

(3) VP4 Binding to Membrane

Figure 2(H): The key takeaway from the exercise using multiple different rigidity for the peptide was that the different sections of the peptide have reduced membrane contacts, particularly the N-terminus. However, the contribution from each membrane component is not very apparent due to stacked transparent plots. Re-plotting using bars placed side to side or using a line representation will help to make this clearer.

(4) Aggregation Stability in Bulk Versus Membrane Environments

The manuscript states that the aggregation rate and stability of VP4 20-mers in bulk water are weaker than in the presence of a membrane, as shown in Figure S5. However, no clear or significant reduction in aggregation stability is apparent from the figure as currently presented. The authors should clarify which quantitative metrics support this claim and, if necessary, provide additional analysis to substantiate the reported difference.

(5) Decoding the Role of MYR on the VP4 n-mer Aggregation

The authors have suggested that the MYR tail plays a key role in the recruitment of VP4 peptides into the aggregate. This is based solely on visual evidence from the simulation. This can be tested directly by using a combination of MYR and non-MYR VP4 molecules, with MYR VP4 acting as membrane anchors. The change in aggregation rate or the number of clusters will give a more complete picture of this phenomenon. In the case of 20 non-MYR VP4 peptides, the aggregate forms within 2 µs, which is comparable to the complete aggregation in the case of MYR-VP4 6-mer. This further brings into question whether the faster aggregation for MYR cases is due to the proximity to the membrane or due to the lipid recruitment aspect of the MYR group.

(6) Interpretation of Umbrella Sampling Results and Membrane Remodeling

Figure 4 reports CG umbrella sampling results indicating a reduced translocation free-energy barrier for VP4 in aggregated (condensate) form, which is linked to membrane curvature and remodeling. Additional methodological details are required to support this interpretation:<br /> (a) What is the nature of the membrane used in the umbrella sampling simulations? Specifically, was the membrane initially flat or curved, and was the same membrane (with identical curvature and properties) used for the single, 6-mer, and 20-mer cases? Differences in membrane geometry would directly influence the translocation free-energy profiles.<br /> (b) Additional details regarding the peptide models used in umbrella sampling simulations should be provided, including peptide length, aggregation state definition, restraints applied (if any), and reference configurations, to improve clarity.

(7) VP4 n-mer Condensate Dynamics

The authors have performed an autocorrelation analysis of Rg of VP4 in the 6 and 20-mer condensates and found that the decay is slower in the 6-mer. This suggests a higher degree of rearrangement within the VP4 20-mer. This could be due to a faster relaxation time upon formation for the 6-mer compared to the 20-mer owing to its smaller size. It would be informative to look at whether these differences still hold when the 20-mer simulations are extended beyond 10 µs.

(8) Comparison Between Metadynamics and Backmapped Membrane-Bound Structures

Figure 5 presents Well-Tempered Metadynamics results for VP4 in a membrane environment. To strengthen the conclusions regarding peptide binding and conformational behavior, it would be valuable to directly compare the peptide conformations and interaction characteristics observed in the Metadynamics simulations with those obtained from the backmapped structures corresponding to Figure 2.

(9) Interpretation of the Z-Coordinate in Free-Energy Profiles

Figure 5(a) shows the free-energy landscape of the VP4 peptide as a function of reaction coordinates. However, the corresponding Z-position of the peptide relative to the membrane is not clearly defined. The authors should clarify whether the reported Z-values correspond to peptide conformations at the membrane surface, within the hydrophobic core, or fully translocated across the membrane, as this is essential for proper interpretation of the free-energy minima.

(10) Helicity in Bulk Water from Metadynamics Simulations

Figure 5(b) shows a free-energy minimum at relatively high helicity (~0.6) even at a peptide-membrane distance of approximately 3.6 nm, which appears to correspond to a bulk-water-like environment. This observation contradicts the predominantly disordered peptide behavior reported in bulk water simulations (Figure 1). The authors should provide a mechanistic explanation for this inconsistency between the bulk AA simulations and the Metadynamics results.

(11) Folding and Insertion Free Energy of VP4

The free energy calculation for folding of VP4 using metadynamics in the POPC membrane and the 2D free energy calculated using umbrella sampling do not show the same picture. As in the first case, the deeper insertion into the membrane promotes a higher helicity, which is not present in the 2D free energy landscape. Assuming the same scale bar for the free energy between the two plots, as that is not mentioned for the free energy obtained from the metadynamics simulations, we see a massive preference towards a helicity fraction of >0.6. This is absent, both in the aqueous and the membrane-embedded environment of the 2D free energy simulations. It will also be useful to mention the plane of the phosphate groups to demarcate the hydrophilic and hydrophobic sections of the membrane

Final Recommendation

The manuscript presents interesting and potentially impactful findings on the conformational dynamics and membrane interactions of VP4. However, substantial clarification and additional analysis addressing the points above are required to ensure consistency, rigor, and alignment with existing literature. I recommend major revisions.b

Reviewer #1 (Public review):

Summary:

The authors utilize genetic code expansion to tag TDP-43 and G3BP1, and evaluate this protein tagging system (ANAP) compared to antibodies and evaluate protein trafficking and stress granule formation in response to stress with sodium arsenite treatment. They find similar staining to antibodies in HeLa cells, mouse embryonic stem cells and primary mouse cortical neurons. By incorporating the intrinsically fluorescent noncanonical amino acid Anap at carefully selected sites, the authors enable live-cell and neuronal visualization of protein localization, stress-induced redistribution, and dynamic behavior without the structural and functional compromises often associated with large fluorescent protein tags. The work provides technical framework that will be useful for live imaging of tagged proteins.

Strengths:

A key strength is the demonstration of the specificity of the Anap fluorescence signal through appropriate controls and the agreement between Anap labeling and antibody-based detection across multiple cell types, including primary neurons. The ability to visualize stress-induced redistribution of both G3BP1 and TDP 43 in living cells highlights the practical value of this approach.

The functional validation of TDP 43-Anap is compelling. The rescue of both cell viability and RNA splicing defects in TDP 43 knockout models provides evidence that Anap incorporation preserves core protein functions. This is important, as functional disruption is a central concern for any alternative tagging strategy applied to aggregation-prone or RNA-binding proteins.

Weaknesses:

While some inherent limitations of genetic code expansion remain (e.g., variable amber suppression efficiency and the inability to directly assess endogenous protein behavior), these are acknowledged and discussed appropriately. Importantly, these limitations do not undermine the central contributions of the study.

Reviewer #1 (Public review):

Summary:

This study by Damphousse, Calvin, and Redish investigates how the hippocampus represents competing future outcomes during approach-avoidance conflict. Using an ethologically relevant robotic predator foraging paradigm, the authors aimed to dissociate hippocampal activity associated with reactive defensive responses (escape) from that linked to anticipatory withdrawal decisions. The central finding is that dorsal hippocampal representations differentiate these two modes of defensive behavior within a single naturalistic assay. Specifically, the authors show that attack-triggered retreats and mid-track aborts differ in movement dynamics and hippocampal spatial decoding despite sharing a common behavioral endpoint, that hippocampal representations during pauses predict subsequent behavioral outcomes, and that these representational biases emerge before overt behavioral divergence. The main importance of the study lies in moving beyond viewing the hippocampus as merely encoding spatial location or threat salience, instead suggesting that hippocampal ensemble activity dynamically tracks and differentially weights threat-related, reward-related, and safety-oriented future states to bias behavior before overt action occurs.

Strengths:

The study has several notable strengths. First, the behavioral decomposition into retreats, mid-track aborts, and mid-track continues is rigorous and provides a highly interpretable analytical framework. Second, replication across two independent cohorts - despite differences in arena configuration, robot design, and extinction procedures - meaningfully strengthens confidence in the robustness of the findings. Third, the unified reanalysis pipeline across cohorts reflects strong analytical discipline, and the Bayesian decoding framework is well-suited to addressing the central representational questions. Fourth, the ethological relevance of the robotic predator paradigm is a major advantage, allowing the authors to examine a richer repertoire of defensive and decision-related behaviors than is possible in conventional fear-conditioning assays. Overall, the experiments are well designed, the data are clearly presented, and the findings make a valuable contribution to understanding how the hippocampus supports decision-making under threat.

Weaknesses:

The study is technically strong, but a few modest revisions would further enhance it.

(1) First, the abstract mentions extinction and reinstatement effects, but neural analyses focus primarily on the attack phase. It would be helpful to clarify or adjust the abstract accordingly.

(2) Second, some interpretive language ("guide," "bias") leans toward causal phrasing. Given the correlational data, using "predict" or "correlate with" would be more precise.

(3) Third, given the relationship between running speed and hippocampal theta, considering speed-related contributions to decoding differences would be useful.

(4) Fourth, reporting turnaround positions for mid-track abort and continue trials (Figure 7) would provide helpful context.

(5) Fifth, a figure comparing stimulated vs. non-stimulated sessions in cohort 2 would support the claim that closed-loop stimulation had no measurable effect.

(6) Finally, reporting effect sizes for key decoding comparisons would add clarity.

Reviewer #1 (Public review):

Summary:

The manuscript by Sustar et al. takes a methodical approach to document the types of glutamate receptor subunits that reside in Drosophila muscles, examining developmental stages spanning from larvae to adults. Prior work thoroughly documented the subunits operating in Drosophila larval body wall muscles. Most subsequent research focused on the glutamate receptor heterotetramers found in the body wall, composed of GluRIIA/C/D/E or GluRIIB/C/D/E subunits, along with auxiliary subunits like isoforms of Neto.

For the current work, the authors report that the larval muscle glutamate receptor composition is not universal for all Drosophila muscles. They examine the following muscle systems: larval body wall, adult abdomen, adult leg coxa, and adult indirect flight. They also briefly examine adult muscle structures associated with the proboscis, neck, and haltere. The authors find that the receptor subunits in the adult abdomen (mostly) match those in the larval body wall. This makes sense given that the adult abdominal muscles are derived from the larval body wall. Yet not much else matches the larval body wall. For example, all (or most) of the GluRII-type subunits are missing from the adult indirect flight muscles. Leg muscles have GluRII-type subunits, but they do not have all of them expressed prominently, and they are missing GluRIIB. Additionally, leg muscles express a glutamate-gated chloride channel, which could be a source of inhibitory glutamatergic transmission. Interestingly, when it comes to non-abdominal adult muscles, one general theme seems to be an active promoter (GAL4 driver) for the kainate-type glutamate receptor called Clumsy. The authors propose that Clumsy could be key to understanding how functional GluR complexes are assembled in adult insects.

Strengths:

(1) Documenting the types of glutamate receptors that operate in diverse insect muscle systems is important because it uncovers fundamental information.

(2) Much of the prior research focus has been on how the body wall muscle tetramers assemble and operate. It is a strength to demonstrate the other receptor solutions used by adult NMJs.

(3) The work uses GAL4 drivers and immunohistochemistry (when possible) in combination to draw conclusions.

(4) The muscle anatomical analyses are of high quality. This allows the research group to reach refined conclusions.

(5) The confocal-level images of synaptic active zones and their apposed glutamate receptor clusters are of high quality.

Weaknesses:

(1) There is a strawman argument that is used repeatedly to highlight the significance of the work. The argument implies that the field broadly assumes (or "tacitly" assumes) that the larval body wall glutamate receptor composition extrapolates to all muscles of the fly, including the adult. This reviewer cannot find evidence that this assumption or argument has been explicitly promulgated by others. More likely, others have not examined these muscles directly, and thus, they have not speculated one way or the other.

(2) Related - to the extent that there has been any tacit assumption about GluRIIC/D/E-anchored receptors being ubiquitous among adult muscles, tacit doubt was raised by Rivilin et al., 2004 (cited by the authors but not as a source of doubt) and by RNAseq datasets like FlyAtlas from 2022 (replicated in Figures s11 and s12). To be clear, the current analysis is better than a bulk transcript analysis from adult tissues. But rather than "overturning" a field or being paradigm-shifting, the current data seem confirmatory of FlyAtlas - and confirmatory of Rivlin et al., 2004, which explicitly concluded that larval and adult NMJs were different .

(3) One can draw expression-level conclusions from these data. But genetic tests (e.g., would clumsy losses of function impair leg muscles?) could help the authors and the field draw stronger conclusions about the roles of some of these glutamate receptor gene products. The current dataset falls short of definitively establishing the function of alternate glutamate receptor modules.

(4) The confocal synaptic images are of high quality. They are good enough that one could analyze how well Brp directly apposes a specific glutamate receptor subunit for all the associated imaging data underlying Figures S1-S8. No such analysis is done, but understanding what components seem to directly oppose the site of release could lead to better conclusions.

Overall Assessment and Discussion:

The data in this study are of high quality, and the results support the main conclusion: adult muscle glutamate receptor clusters do not recapitulate the "canonical" larval body wall clusters. This is important, and the data stand on their own. That is the most important part. This reviewer does have suggestions on how to put the current work in proper context; the current draft appears to overstate the novelty of the findings. Additionally, some sentences need editing for accuracy. None of those concerns impeach the excellent foundational data.

Joint Public Review:

Summary:

Kalburge et al. investigate a task in which human subjects make a decision based on the accumulation of noisy evidence. Tasks like this have been studied for decades, but always with the same essential ingredient: noisy moment-by-moment evidence has to be integrated internally by the subjects, and so is not observed by the experimenter.

In this study, the authors depart from this scenario and make the evidence visible. Specifically, subjects see a pigeon moving stochastically on a screen, and they have to determine whether the net motion is to the right or to the left. This provides the experimenter direct access - on a trial-by-trial basis - to the bounds the subjects use to make their decision.

The authors apply this paradigm across a range of tasks, each one differing in how the signal-to-noise ratio (SNR; defined to be the ratio of the drift rate of the pigeons to the standard deviation of the noise) changes over time and across trials. The tasks range from the standard case of constant SNR to the non-standard case where the SNR changes abruptly in the middle of the task.

The authors determined, on a trial-by-trial basis, the bounds used by the subjects. Setting the bounds optimally when the SNR changes over time or across trials is a non-trivial problem; not surprisingly, then, the subjects were suboptimal. However, they weren't very suboptimal; instead, their behavior was "satisficing" (in the words of the authors), meaning their bounds were reasonably close to the optimal ones. Since the loss is relatively flat near the maximum, and finding the optimal bounds is hard, this is a sensible strategy.

Strengths:

The main strength of this work is the introduction of a new paradigm that supports a trial-by-trial measure of the decision bound. This allows direct measurement of the bound at decision time within individual trials. This, in turn, allows experimenters to determine whether the decision bound differs across decision time or fluctuates for the same decision time across trials. This is harder, although not impossible, to do with tasks in which decision bounds have to be estimated across multiple trials, especially when the SNR is changing.

The authors use this paradigm to show that the decision bounds are mostly constant when the SNR is constant within and across trials. This has been shown indirectly before by fitting models with different parametric boundary shapes, but not directly by measuring the boundary separately for different decision times (but see Kira, Yang, and Shadlen, 2015). They also demonstrate that variability in these bound estimates arises from measurement noise rather than trial-by-trial variability in bound heights, something that could not have been done with previous paradigms.

They furthermore replicate findings that subjects adjust their bounds, including weak collapse, to changing reward contingencies and SNRs, further validating their paradigm. And finally, the work demonstrates an apparent within-trial bound change if the SNR changes (predictably) mid-trial, as predicted by their previous work (Barendregt et al., 2022). This is -- to our knowledge -- the first confirmation of this prediction.

Weaknesses:

There are two non-technical weaknesses.

First, comparison to optimal behavior was mainly qualitative; a quantitative comparison would greatly strengthen the work.

Second (although not exactly a weakness), the work does not leverage the full potential of trial-by-trial estimates of the decision bound, which is a missed opportunity. To our understanding, the only finding that relied on trial-by-trial access to the bound was that the variability in the bound estimate was a major source of measurement noise. Their finding that the bound changes to reward contingencies and SNR, on the other hand, did not require such a trial-by-trial estimate. However, with this task (and not standard paradigms), the authors could determine how the bounds change during learning, which would give insight into the learning rules that participants use to adjust their bounds.

There are also a few technical issues.

(1) The authors argue that they don't observe a collapsing bound when the SNR varied across blocks (Figure 5). However, they only seem to perform this analysis on the difference in boundaries between trials with different SNRs (Figsures 5B, D). Observing a zero difference implies that the boundary shape is the same across SNRs, but does not rule out a collapse.

(2) The evidence for a within-trial boundary change for conditions with a within-trial SNR change could be stronger. The data shown in Figures 6C, D is very noisy, and there are no error bars. For individual participants, is the estimated change in bound larger than the variability in bound estimates before and after the SNR changepoint? Are there potentially other measures that could be used to make the point of a clear change in boundary within individual trials more convincing?

(3) The work assumes that bound height estimates are biased due to the bounded accumulation nature of the decision process, and it corrects for these biases with a simulation-based correction (Methods and Figure 7). To our understanding, this correction assumes that the decision time is the first time that this boundary is crossed. However, the authors do not demonstrate that this is the strategy that participants use; they need to explicitly rule out the possibility that there are significant pigeon excursions across the boundary before the decision time.

(4) The authors did not consider other stopping rules, such as a decision based on the last few trials. Showing that a stopping rule based purely on the bound fits the data better than other possible rules would strengthen the manuscript.

DOI: 10.1016/j.isci.2026.116206

Resource: RRID:ZFIN_ZDB-GENO-120113-1

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-120113-1

What is this?

Reviewer #1 (Public review):

This manuscript presents compelling evidence from a wild chickadee population linking heritable spatial cognition to extra-pair paternity success, supporting sexual selection via good genes in a food-caching species. The integration of RFID cognition tests with ddRAD paternity assignment is methodologically strong and timely for behavioral ecology, though causal mechanisms and confounds warrant clarification.

Overall, a major revision of the manuscript is recommended, addressing the points below.

(1) Confirmation of manipulation and treatment effects. The central claim hinges on spatial cognition driving EP siring, but direct evidence that cognition predicts observed copulations (vs. post-copulatory mechanisms) is absent. While territories do not cluster by performance (Figure S4), quantify male aggression/movement data during fertile periods to rule out intrusion-based EPP. The authors should provide metrics like nearest-neighbor distances for EP sires or playback responses linking cognition to dominance, as in prior chickadee work. Without this, causal female preference remains correlational.

(2) Female cognition-EPY link inconsistency. Poor female cognition predicts more EPY (first-20-trials: offspring-level χ²=6.21, P=0.013; nests: χ²=6.79, P=0.009), but not for full-task (P>0.5). The authors should discuss why (e.g., learning speed vs. memory stability) and add exploratory correlations (female errors vs. EPY proportion). They should soften claims in the Discussion section of "female-driven" without consistent support and should frame this as a hypothesis.

(3) Cognitive task sensitivity and validity. Mean errors aggregate learning curves effectively, but single feeder-assignment (non-preferred) confounds neophobia/motivation with spatial ability. The authors should report trial-by-trial improvements (Figure S7 subset) or criterion-to-learn metrics. Justify excluding high-error birds (<3 mean); sensitivity analysis needed to check bias toward high performers.

(4) Paternity assignment robustness. ddRAD-CERVUS with bimodal LODs (Figure S8) is solid, but unassigned EPY (social-genotyped but no sire) implies missing sires (~?% of EPY?). Include all alive males as candidates yearly? Test power simulations for LOD thresholds. 2019 exclusion justified, but multi-year SNP alignment could boost resolution.

(5) Mechanistic speculation vs. data. Discussion invokes hippocampus genes (GWAS priors) and good genes, but no offspring cognition/survival data. Label as hypotheses; suggest tracking EPY recruitment. No brood size costs for EP sires is key, but monitor long-term nest investment (e.g., feeding rates).

Reviewer #1 (Public review):

Summary:

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

The study was not designed to investigate the reasons that pikas clip Stellera chamaejasme. That said, based on other studies and general knowledge of the ecology of these pikas, it is likely that they clip (although do not eat) this plant because its relatively large size hinders predator detection. This species of pika does better where vegetation height is low than where it is higher.

Strengths:

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

Weaknesses:

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

Citation:

Harris RB, Wang, WY, Badinqiuying , Smith AT, Bedunah DJ (2015) Herbivory and Competition of Tibetan Steppe Vegetation in Winter Pasture: Effects of Livestock Exclosure and Plateau Pika Reduction. PLoS ONE 10(7): e0132897. doi:10.1371/journal.pone.0132897

Reviewer #1 (Public review):

The multi-species approach of testing the model in macaque and mouse is excellent, as it improves the chances that the observed findings are a general property of mammalian visual cortex. It would be useful to delineate however any notable differences between these species, which are to be expected given their lifestyle.

The overall performance of the model appears to be excellent in V1, with over 80% performance, but falls substantially in V4. It would be important to consider the implications of this finding; for example, in the context of studying temporal lobe structures that are central to recognizing objects. Would one expect that model performance decreases further here, and what measures could be taken to avoid this? Or is this type of model better restricted to V1 or even LGN?

While the manuscript delineates novel axes of inhibitory interactions, it remains unclear what exactly these axes are and how they arise. What are the steps that need to be taken to make progress along these lines?

Comments on revised version.

The authors have adequately addressed the points I raised in my review during the revision.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed all the comments raised in the previous round of review. The revised manuscript includes new labeling experiments revealing boundary compression at the cardiac poles consistent with the authors predicted dynamic model of heart tube formation.]

Summary:

The study by Raiola et al. conducted a quantitative analysis of tissue deformation during the formation of the primitive heart tube from the cardiac crescent in mouse embryos. Using the tools developed to analyze growth, anisotropy, strain, and cell fate from time-lapse imaging data of mouse embryos, the authors elucidated the compartmentalization of tissue deformation during heart tube formation and ventricular expansion. This paper describes how each region of the cardiac tissue changes to form the heart tube and ventricular chamber, contributing to our understanding of the earliest stages of cardiac development.

In order to understand tissue deformation in cardiac formation, it is commendable that the authors effectively utilized time-lapse imaging data, a data pipeline, and in silico fate mapping. The study clarifies the compartmentalization of tissue deformation by integrating growth, anisotropy, and strain patterns in each region of the heart.

Reviewer #1 (Public review):

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

Summary:

The manuscript by Rayan et al. aims to elucidate the role of RNA as a context-dependent modulator of liquid-liquid phase separation (LLPS), aggregation, and bioactivity of the amyloidogenic peptides PSMα3 and LL-37, motivated by their structural and functional similarities.

Strengths:

The authors combine extensive biophysical characterization with cell-based assays to investigate how RNA differentially regulates peptide aggregation states and associated cytotoxic and antimicrobial functions.

Reviewer #1 (Public review):

Summary:

Here the authors address the organization of reach-related activity in layer 2/3 across a broad swath of anterodorsal neocortex that included large subregions of M1, M2, and S1. In mice performing a novel variant water-reaching task, the authors measured activity using two-photon fluorescence imaging of a GECI expressed in excitatory projection neurons. The authors found a substantial diversity of response patterns using a number of metrics they developed for characterizing the PETHs of neurons across reach conditions (target locations). By mapping single-neuron properties across cortex, the authors found substantial spatial variation, only some of which aligned with traditional boundaries between cortical regions. Using Gaussian mixture models, the authors found evidence of distinct response types in each region, with several types prominent in multiple cortical regions. Aggregating across regions, four primary subpopulations were apparent, each distinct in their average response properties. Strikingly, each subpopulation was observed in multiple regions, but subpopulation members from different regions exhibited largely similar response properties.

Strengths:

The work addresses a fundamental question in the field that has not previously been addressed at cellular resolution across such a broad cortical extent. I see this as truly foundational work that will support future investigation of how the rodent brain drives and controls reaching.

The quantification is thoughtful and rigorous. It is great that the authors provide explanation for and intuition behind their response metrics, rather than burying everything in the Methods.

The Discussion and general contextualization of the Results is thorough, thoughtful, and strong. It is great that the authors avoid the common over-interpretation of classical observations regarding cortical organization that are endemic in the field.

All things considered, this is the best paper regarding spatial structure in the motor system I have ever read. The breadth of cellular resolution activity measurement, the rigor of the quantification, and the clear and open-minded interrogation of the data collectively have produced a very special piece of work.

Weaknesses:

There are two important issues left unaddressed that the authors plan to address in their future work. The first is the relation between observed neural activity patterns and movement kinematics, and in particular how much the activity variation across target locations may relate to the kinematic differences across these different conditions, as opposed to true higher-order movement features like reach direction. The second issue is how to interpret the results in relation to existing ideas about behavioral organization in motor/premotor cortex.

Comments on revised version:

The authors have done an excellent job addressing my previous concerns. I have no additional concerns with the manuscript.

Reviewer #1 (Public review):

Summary:

Simoens and colleagues use a continuous estimation task to disentangle learning rate adjustments on shorter and longer timescales. They show that participants rapidly decrease learning rates within a block of trials in a given "location", but that they also adjust learning rates for the very first trial based on information accrued gradually about the statistics of each location, which can be viewed as a form of metalearning. The authors show that the metalearned learning rates are represented in patterns of neural activity in the orbitofrontal cortex, and that prediction errors are represented in a constellation of brain regions including ventral striatum, where they are modulated by expectations about error magnitude to some degree. The work opens the door to future work focusing on how exactly these signals contribute to adaptive behavior.

Strengths:

The authors build on an interesting task design allowing them to distinguish moment-to-moment adjustments in learning rate from slower adjustments in learning rate corresponding to slowly gained knowledge about the statistics of specific "locations". Behavior and computational modeling clearly demonstrate that individuals adjust to environmental statistics in a sort of metalearning. fMRI data reveal representations of interest including those related to adjusted learning rates and their impact on the degree of prediction error encoding in the striatum.

Weaknesses:

It was nice to see that the authors could distinguish differences between the OFC signals that they observed and those in the visual regions based on changes through the session. However, the linkage between these brain activations and a functional role in generating behavior remains somewhat unclear, opening the door for alternative interpretations.

Comments on revised version.

I appreciate the authors responses and they have largely addressed my concerns. I understand the concerns about power with regard to the individual differences/behavioral analyses included in the rebuttal. However, my personal view, which is perhaps a matter of taste, is that the paper would benefit from a description of these results - along with a clear description of why the authors are hesitant to draw a strong interpretation from the negative result.

Reviewer #1 (Public review):

Summary:

Cai et al. investigated the role of ripples in the hippocampus and coupled between the hippocampus and the neocortex in visual short-term memory (VSTM) using a similar lures match-to-sample task. The main findings are that hippocampal, but not neocortical ripples, ramp up during the maintenance period, peaking shortly before the memory response is given. This ramping-up effect was stronger for correct compared to incorrect trials. Furthermore, the authors show that stimulus category could be better decoded during coupled hippocampo-neocortical ripples compared to uncoupled ripples. These results provide compelling novel evidence for a role of ripples in supporting human visual short-term memory.

Strengths:

(1) State-of-the-art intracranial EEG in 13 patients during a well-designed visual short-term memory task, with simultaneous hippocampal and neocortical recordings.

(2) Thorough analysis pipeline with validation to detect ripple events, and distinguish them from spurious ripple activity (i.e., as induced by IEDs).

(3) Use of multivariate classifiers to resolve the neural representation of the stimuli.

Weaknesses:

It is difficult to find clear weaknesses in this paper, as the analyses are thorough, the results are clear, and the writing is excellent. However, some more sanity checks on the validity of ripples could have been conducted (i.e., making sure that ripple events have multiple peaks in the unfiltered raw signal at the ripple frequency). Also, the time window for coupled ripples appears to be a bit long, which makes it questionable to what degree these ripples are coupled (i.e., the time window is ~5 times longer than the duration of a ripple event). Lastly, the ramping-up effect could have been more clearly depicted in the figures, but that's a fairly minor point.

Reviewer #1 (Public review):

Summary:

This study combines representational similarity analysis (RSA) with 7T layer-specific fMRI and EEG to examine how neural representations in specific cortical layers of EVC and LOC correspond to the temporal dynamics of visual processing. The authors interpret these correspondences as reflecting feedforward and feedback processes, based on their relative timing and their similarity to representations in different layers of a deep neural network (DNN).

Strengths:

The combination of RSA with laminar fMRI is a promising approach for dissociating the functional roles and dynamics of different cortical layers within the same functional region, and it holds considerable potential for elucidating computational mechanisms both within and between levels of the visual hierarchy. However, several issues should be addressed before the authors' conclusions can be fully supported.

Weaknesses:

(1) The authors report that the representation in the LOC superficial layer resembles EEG-derived neural representations at ~400 ms post-stimulus, and that this similarity is best explained by representations in the higher layers of the DNN. From these two observations, they conclude that activity in the LOC superficial layer is driven by feedback signals. However, neither line of evidence directly dissociates feedforward from feedback contributions.

Specifically, late-stage representations in LOC could instead reflect the outcome of local recurrent computation, given that the superficial layer also serves as an output layer of the local cortical circuit. Moreover, the correlation with the DNN peaks at higher layers rather than being dominated by them, and feature tuning in higher DNN layers does not necessarily map onto higher-order cortical regions such as PFC.

While a feedback contribution to the LOC superficial layer is consistent with theoretical predictions and known cortical anatomy, the current evidence is indirect. I would recommend that the authors either tone down this conclusion or, at a minimum, explicitly clarify the strength and limitations of the evidence in the Discussion.

(2) I could not find information regarding the fMRI slice orientation or whether temporal regions beyond LOC were covered. The reported FOV (192 × 192 mm) seems quite large if only EVC and LOC were targeted. Did the authors acquire data from other object-selective regions in the temporal cortex, and if so, did they analyze these?

It would strengthen the feedback interpretation considerably if the RDM of the LOC superficial layer could be shown to resemble RDMs from more anterior temporal regions, which would be consistent with feedback originating from higher-order object-processing areas.

(3) Related to the previous point, LOC is a relatively large region, and based on the figures, it appears that the LOC ROI may contain two subregions. It would be helpful for the authors to show the location and extent of the LOC ROI in example participants.

If the ROI does indeed span two subregions, do these subregions share the same laminar profile and temporal dynamics?

(4) The authors report no feedback-related information in EVC, which contrasts with a number of prior fMRI studies that have demonstrated object-related feedback signals in EVC. One plausible explanation for this discrepancy is task relevance: in the present study, participants performed only a fixation color-change task, whereas in previous work they were required to attend to object features or identity (e.g., Morgan et al., 2019, J Neurosci; Kok et al., 2016, Curr Biol; Mohsenzadeh et al., 2018, eLife; Hou et al., 2026, eLife). Task demands on object processing may substantially modulate the strength of feedback signals to EVC, and this possibility warrants discussion.

(5) A substantial body of work has used specialized paradigms to dissociate feedforward and feedback signals in EVC (e.g., Williams et al., 2008, Nat Neurosci; Fan et al., 2016, PNAS; Hou et al., 2026, eLife). These studies are directly relevant to the current work but are not cited.

(6) Multidimensional scaling (MDS) visualizations of the RDMs (as in, e.g., Mohsenzadeh et al., 2018) are not included in the manuscript. These visualizations are important for interpreting the representational format across different layers of LOC and EVC, and I would encourage the authors to include them.

Reviewer #1 (Public review):

Summary:

Hüppe and colleagues characterized the network of neurons in the central nervous system of Antarctic krill that contained pigment-dispersing hormone (PDH), an important output factor in the circadian clock of insects. These neurons in the brain are putative clock neurons since a subset also expressed the clock genes period and cryptochrome 2. As one of the ocean's major contributors to biomass, krill is an ecologically important marine species that experiences challenging daily and seasonal environmental fluctuations in its high-latitude habitat. A comprehensive study of krill's internal clock may help to understand the extent of its resilience to the rapidly changing climate.

The authors used antibody staining against PDH across the whole central nervous system and additional in situ hybridization for cry2 and per mRNA, with a focus on the supraesophageal ganglion. There, they identified the major neuropils in the eye stalks and central brain of Antarctic krill. The resulting staining pattern aligns with the identified circadian clock network in insects and PDH-expressing networks in other crustaceans, making these neurons highly likely candidates for krill clock neurons.

Strengths:

(1) This study provides the first clues about the circadian clock architecture in a non-model organism in chronobiology, Antarctic krill, with a clear 3D reconstruction of the putative clock network.

(2) The authors effectively place their results within the extensive body of literature on arthropod circadian clock networks to argue that the neurons they describe are likely the circadian clock in krill.

Weaknesses:

(1) The data presented here are not sufficient to support the claim that the described network is the circadian clock because functional evidence is missing.

(2) Additionally, the study falls short of identifying any elements of the positive limb of the canonical circadian clock transcriptional-translational feedback loop, e.g., clk or cyc, in the PDH-expressing neurons.

(3) No sample sizes are reported, making it difficult for readers to assess the generalizability of the presented data.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

My only major technical concern relates to the analyses in Figures 4F-H, 5G-I, and 6H-K (c.f. equations 2-5). Typically, one identifies population-level factors by projecting neural activity onto fixed dimensions of interest; this makes it possible to see how activity evolves over time along interpretable coordinates. Here, however, the coding directions are redefined at each time point, so the "choice" activity at time t is actually a different signal from the "choice" activity at t+1. This procedure is a bit like comparing the activity of one neuron at one time point with the activity of a different neuron at a later time point. It also makes the physiological interpretation more complicated: if the dimensions are fixed, one can see how a downstream neuron could "read out" the signal by computing a weighted sum of the activity of upstream neurons, but it is harder to see how this could happen if the weights are always rotating.

A few comments on the behavioral task and results. After the port shift, the error rate is quite high, and doesn't diminish much between the early and late epochs (approximately 42% and 38% error rate, respectively; Figure 1I). That is, mice do not seem to fully master the task. Clearly, animals do alter their aim, but even this does not seem to change much between early and late periods (Figure 1J). I recommend that the authors show the behavioral data at a finer level of granularity (e.g., by plotting the change in exit trajectory on all individual trials across sessions, with a loess fit) to allow an assessment of the adaptation rate and when adaptation saturates. It would also be more conventional to refer to the behavioral changes as "motor adaptation," instead of "skill learning." (The latter would be appropriate if the port offset were randomized across trials, and animals received two separate cues for direction and offset, but I suspect this task would be too difficult for mice to learn.)

This is perhaps a semantic point, but it might not be entirely accurate to refer to the activity evoked by the directional cue as "sensory." Typically, a "sensory" response should encode some feature of a stimulus - in this case, the frequency of a tone. Here, it seems likely that the cue-aligned activity reflects the instructed lick direction, rather than the auditory information per se. (Presumably, these premotor neurons do not have well-behaved auditory tuning curves.) By comparison, in macaques performing center-out reach tasks, activity in dorsal premotor cortex rapidly ramps up following a visual cue instructing the direction of an upcoming reach, but one usually wouldn't refer to this activity as "visual" or "sensory" (though this is sometimes done). I suggest the authors either use "Instruction" or similar (e.g., in Figure 4F), or clarify in the text whether they think the activity is a genuine auditory response or something else.

Reviewer #1 (Public review):

Summary:

The non-social task was a classic risky decision-making task with a binary choice between an option with a sure gain and a risky option with a probabilistic gain or loss. In the social task, the sure option was an individual gain (as in the non-social option) and the probabilities in the risky option, which were shown to participants, were framed as probabilities of other previous participants (i.e., "partners") to cooperate or not; a probabilistic gain (when the partner cooperated) also led to a gain of the partner, while a probabilistic loss meant that the partner would receive the amount lost by the participant. This loss was framed as "betrayal." The authors show differences in how probabilities and amounts (of gains/losses) affected choices, RTs, and ERPs (P3 and LPP).

Strengths:

Since participants faced decisions with the same individual payoffs in a non-social and a social condition, this setup made it possible to use identical standard analyses for choices, RTs, and ERPS as well as (almost) identical economic models for the two conditions.

Weaknesses:

(1) The task does not include many components that are usually considered central for cooperation or "betrayal" and this is not discussed appropriately. At the same time, the "emotional aspects" of the operationalized "betrayal" are not directly assessed.

a) The standard economic game for cooperation is the prisoner's dilemma, in which participants make independent choices at the same time without getting any explicit information on the cooperation probability of their partner before they make their decisions. Furthermore, most of the time the interactions are repeated. Actually, the trust game as one other frequently used economic game, also includes a back and forth of transfers between the partners. So, here, I am not so convinced by the operationalization of a low cooperation probability, which is shown before the decision, as "betrayal." The authors should motivate and explain their rationale more clearly in reference to such other tasks.

b) The setup of the task, especially the fake interaction with the fake partners, should be made clearer in the main text (before reporting the results). I would argue for including the task picture in the main text.

c) In general, I am in favour of taking participants' choice behaviour as the main outcome measure. But given the strong implications of "emotional costs" made by the authors, I would have expected some ratings of "betrayal" on a trial-by-trial basis. I would at least include this as a shortcoming.

d) Also, given the framing of the study, I would have expected some exploratory analyses regarding individual differences with respect to, e.g., social value orientation, etc. I would at least include this as an outlook.

(2) The standard statistical analyses could be improved.

a) It is good that the authors have rather long sections using standard regression analyses. But they are a bit lengthy, and the modelling should be more prominent.

b) In a couple of places, the authors say something like "this is significant, but that is not." Here, it has been made very clear that the interaction term needs to be looked at. As far as I can see, this has not always been done.

c) For this binary choice, the difference in expected value (EV) between the sure and the risky options is one crucial comparison. But the authors never take that into account. This difference does not depend on the amount, which the authors dub "principal." That is, the sure option simply has an EV of x, i.e., the amount. The risky option has the EV = p2x + (1-p)0.5x, with p being the probability of gain/cooperation. That is, the two options have the same EV at p=1/3, independent of x. This should be made clear.

d) Relatedly, RTs should depend on the differences in EV (and not so much on p or on x per se). This can be seen by the more or less quadratic relationship between p and RTs (Fig 1A), with a peak around a p of 1/3.

e) RTs are often log-transformed. It should be briefly mentioned why this was not done here.

(3) The modelling evidence is relatively weak. This is my main point.

a) (Cumulative) prospect theory should be introduced.

b) The models seem overly complicated with many free parameters. I would have expected some simpler versions and more comparisons between models that differ in just one parameter.

- e.g., it is really nice that the authors used a probability weighting function. BTW: Please describe this more clearly in the introduction and in the results. But for this limited range of probabilities, this might be too much.

- e.g., why directly assume two different exponents in the utility function for gains and losses, and in addition a loss aversion parameter lambda? Only lambda would be a better starting point here.

c) The differences in AIC (Figure 2A) seem rather minuscule, and the distribution of winning models is not very peaked. I am not convinced that Model 3 is the winning model.

d) Crucially, and related to the previous points, judging from Fig 2C, the "betrayal" parameter kappa seems to be zero for about half of the participants. The authors should look into this.

- Would a model just like model 3 but without kappa (i.e., kappa set to zero) perform better? Is this just model 2?

- How is kappa set in the non-social condition?

- This massive skew, to say the least, is never discussed.

- A correlation is definitely not warranted.

(4) The ERP results seem to me rather superficial. But I am not an EEG expert.

a) The authors do not seem to look at the outcome phase, which could be interesting for differences in reward/loss processing in the two task versions.

b) Again, differences in EV seem to be more important from a conceptual point than probabilities or amounts; see my comment 2d.

c) Also, the authors report ERPs for the two task types separately but do not seem to run proper comparisons between them, see my comment 2b.

(5) Preregistration: It should be made very clear early on that this study was not preregistered.

(6) Quality checks: The authors should check if some participants are outliers in terms of the number of missed trials, always choosing the same option, etc. It is notoriously difficult to find good post hoc reasons for excluding participants (one reason why replications and preregistrations are important). In any case, the data quality should be checked and described a bit more.

Reviewer #1 (Public review):

Summary:

In this manuscript, Demeshkina and Ferré-D'Amaré showed that extrachromosomal circular DNA (eccDNA) and chromatin-associated proteins are present in stress granules, based on proteomic and sequencing analyses. Using HCR-FISH combined with imaging, the authors showed the colocalization of eccDNA with stress granule proteins. Furthermore, they found that CRISPR machinery targeting the eccDNA component of stress granules disrupts stress granule assembly, and that this effect is largely independent of Cas9 endonuclease activity. Notably, expression of cytoplasmic chromatin factors restores stress granule formation in the presence of CRISPR machinery in yeasts. This also rescues the growth defect caused by hypoxic stress, which correlates with impaired stress granule formation. Together, this manuscript provides insight into the presence of eccDNA in cytoplasmic membraneless organelles, specifically stress granules, and suggests a functional role for eccDNA within these structures under stress conditions.

Strengths:

The authors used a panel of ribonucleases to demonstrate that stress granule cores isolated from yeast and HEK293 cells are resistant to plasmid-safe DNase, an enzyme that does not degrade circular double-stranded DNA. To further support the presence of extrachromosomal circular DNA (eccDNA) in stress granules, they performed Circle-Seq on stress granule cores. The gel electrophoresis and sequencing experiments complement each other well, providing consistent evidence for eccDNA within these granules. Overall, this study provides insight into potential cytoplasmic roles for eccDNA, an area that remains largely unexplored.

Weaknesses:

(1) Figure 1F suggests that stress granule cores are susceptible to DNase I but not to plasmid-safe DNase (psDNase). However, its smearing pattern in the psDNase condition appears similar to that in the DNase I treatment shown in Figure 1E, although psDNase produces more discrete bands. The authors should comment on these differences between Figures 1E and 1F, or consider revising Figure 1F to improve consistency with Figures 1E and 1D.

(2) The authors should clearly define "colocalization". Does it refer to complete spatial overlap between two signals (i.e., VCP and T30), or partial overlap (i.e., AHNAK DNA and G3BP)? Figure 3 and the associated text are descriptive. Quantitative analysis would strengthen the conclusions. For example, the authors could analyze the fraction of molecules localized to stress granules or provide Pearson's correlation coefficient or similar measurements.

(3) The authors used a CRISPR-based approach to target the Ty1 LTR retrotransposon, an abundant stress granule eccDNA, and they observed a loss of stress granule formation. However, this phenotype may be specific to Ty1 eccDNA rather than representative of all eccDNA species present in granules. In particular, the title "Cytoplasmic circular DNA is a key constituent of stress granules" implies a broader role. To support this claim, the authors should consider approaches that more globally deplete eccDNA rather than targeting a single eccDNA.

(4) The authors should provide additional experimental evidence to support the claim that eccDNA is packaged in a chromatin-like state. The rescue of stress granule formation by ectopic expression of modified chromatin-associated proteins (CHD1NES and GCN5NES) following CRISPR treatment does not necessarily demonstrate that eccDNA is packaged like chromatin under basal conditions.

https://www.reddit.com/r/typewriters/comments/1u06wh4/duplex_jewett_no_1/

Reviewer #1 (Public review):

In this manuscript, the authors study optimal chemotactic navigation of bacteria in disordered environments. Most previous work has studied bacterial chemotaxis in free liquid, but navigation in obstructed environments is gaining more attention. Here, the authors first used the classic swim plate assay to select E. coli for chemotaxis in soft agar at two agar concentrations. In the higher concentration, they observed that the population's migration speed increased and the mean run duration decreased over selection cycles. Importantly, the growth rate did not change, so the change in migration speed was due to improved chemotaxis. Then, using a strain in which they could control the mean run duration with an inducible promoter, they measured population migration speed as a function of mean run duration, observing a peak. In liquid, theory predicts a peak when the run duration is comparable to the time scale of rotational diffusion. Here, the peak is at a much shorter run duration, and the optimal run duration decreased with agar concentration. A key feature in previous studies of bacterial motion in obstructed environments has been the dynamics of cell trapping and escape via tumbling. By directly visualizing the flagella in single cells, the authors found that the majority of trap events in semisolid agar did not end with a tumble. This is important because it means that the peak in the migration speed has a different origin from the peak typically seen in the diffusion coefficient, which is due to a balance between longer runs and less time spent trapped. Instead, using a minimal theoretical model, the authors argue that the peak in the migration speed is due to a balance between longer runs, which improve chemotaxis, and having those runs terminate with a tumble rather than a trap event, because runs that end with trapping do not result in up-gradient bias. Qualitatively similar behavior is seen in simulations of a more complex model of chemotaxis.

Overall, we find the results to be significant and the evidence to be strong. We have some comments, which the authors need to address to improve/clarify their work:

(1) The authors' model predicts that, because cells spontaneously escape traps without tumbling, the diffusion coefficient should depend monotonically on mean run length even though the chemotaxis coefficient is non-monotonic. It would strengthen the paper if the authors could show this to be true in experiments. Part of the reason for this comment is that the flagella labeling experiments were done in agar that was rapidly cooled in a freezer and then thawed, whereas the migration experiments were performed in agar cooled at room temperature. Our (anecdotal) understanding is that the cooling rate dramatically affects the properties of the agar mesh. Verifying that diffusivity is monotonic in mean run length would therefore show that cells' spontaneous escape from traps is not an artifact of the cooling protocol.

(2) Two agar densities were used in their study (0.2%, 0.3%). As shown in Figure 1, while cells in the 0.3% agar showed significant improvements during the directed evolutionary experiments, the cells in 0.2% agar didn't. Correspondingly, the evolved average run time did not show significant changes in the 0.2% agar, but it decreased in the 0.3% agar. What is the reason for this difference? Does it mean the cells are already optimized for the 0.2% agar medium?

(3) Related to the previous comment, the comparison between Figure 1 and Figure 2 should be made clearer. In Figure 2, a peak performance at an intermediate run time is shown, with the optimal run time decreasing with the agar density. Qualitatively, this result, i.e., the existence of the peak performance, gives the evolution experiments shown in Figure 1 a nice explanation. However, quantitatively, the run times shown in Figures 1 and 2 are quite different. For example, for the 0.3% agar case, the change of run time decreases from ~0.6sec. in cycle-1 to ~0.4sec in cycle-40. However, in Figure 2, the optimal run time is ~0.9sec., which means that the migration speed would decrease if the run time is decreased from 0.6sec to 0.4sec. We understand this may only be considered as a qualitative result. However, it does raise the question of what the molecular mechanisms are that drive the directed evolution, which the authors should address.

(4) In Figure 3B, the distributions of speed in different media (liquid versus agar) for cells with bundled and split flagella are shown. While the distribution for the bundled flagella shows nicely the emergence of the trapped state (peak near zero speed), the distribution for the split flagella shows a significant shift of the distribution. Does this mean the agar medium also changes the tumble state significantly? In fact, we are puzzled by the observation that in bulk liquid, the run speed distribution for cells with split flagella seems to be quite similar to that of cells with bundled flagella, which might indicate problems in determining run speed.

(5) Finally, none of the points plotted have error bars. Error bars would allow the readers to evaluate i) whether the changes in mean run speed during selection are significantly resolved and ii) whether the peaks in the migration speeds are significantly resolved.

Reviewer #1 (Public review):

Summary:

Eroglu and Hobert demonstrate that injecting CRISPR guides and repair constructs to target three genes at a time, tagging each with a different fluorescent protein, and selecting which gene to tag with which fluorophore based on genes' expression levels, can improve efficiency of gene tagging.

Strengths:

This manuscript demonstrates that three genes can be targeted efficiently with three different fluorophores. It also presents some practical considerations, like using the fluorophore least complicated by agar/worm autofluorescence for genes with low expression levels, and cost calculations if the same methods were used on all genes.

Weaknesses:

Eroglu has demonstrated in a previous publication that single-stranded DNA injection can increase efficiency of CRISPR in C. elegans, while inserting two fluorescent proteins and a co-CRISPR marker into three loci, and Paix et al 2015 demonstrated simultaneous insertion of two fluorescent tags. The current work is valuable and incremental advance. In general, I applaud the authors' willingness to strategize about how whole proteome tagging might be accomplished. I predict that the advance here will be one of many small advances that will get the field to that goal. The title oversells the advance presented, in my view, since seems like one among many key advances, and the first sentence of the Discussion seems a more apt summary of the key advance here.

Some injections targeted genes on the same chromosome together, which will create unnecessary issues when doing crossing that will be useful for some future experiments. This made me wonder if injecting 3 together really is helpful vs targeting each gene separately, since only 5 worms need to be injected. It cuts time down by 2/3, but perhaps avoiding targeting the same chromosome with two tags would be useful.

The limited utility of current blue fluorescent proteins makes me wonder if it's worth using at this stage, before there are better blue fluorescent proteins, or better yet, far red, to avoid issues with live imaging under phototoxic UV or near-UV illumination.

Reviewer #1 (Public review):

The manuscript has been improved in response to the reviewing. Although overinterpretation has been partially reduced compared to the previous version, the main concerns on the manuscript remain. The experiments have been conducted according to rigorous standards and the limitations of the results have been discussed to provide a comprehensive interpretation. However, this still represents an incomplete study in which the conclusions are insufficiently supported by the data provided.

Reviewer #1 (Public review):

Summary:

The authors identify and investigate a specific population of PVNOT neurons (oxytocin neurons of the paraventricular hypothalamus) that seem to be involved in both behavioral and autonomic thermoregulation. These cells are activated by social thermoregulatory behaviors, but can influence thermoregulation in both social and social contexts, specifically during transitions and when mice are at low core body temperature (Tb).

Strengths:

The manuscript has many strengths.

This is a novel study, with a clear question that is addressed using an array of well-designed experiments employing integrative methods. Most of the Figures are well developed, and the analysis is generally rigorous and well detailed. The authors are clearly very experienced in this field, and indeed their scholarly introduction and discussion sections is in their credit.

The link between thermoregulation and the oxytocin system is well established, as is the link between social behavioral and the same broad system. However, the link between these three things is novel, if it can be well substantiated. I am not persuaded that was achieved here, but I do think this manuscript has many novel and useful offerings.

The authors use a cooling floor and only go town to 10 degrees Celsius. This is fine, but I would like to see the effects using ambient temperature also. This is not a crucial issue, as it is not necessary for the authors' interpretations, but it could improve measurement sensitivity.

Through an elegant behavioral experiment in Fig. 1, the authors identify c-Fos patterns in the PVN that are activated by active social huddling, and they show that at the RNA level these cells overlap with oxytocin, indicating that they are oxytocin producing cells. But this is not well discussed or indeed quantified.

The authors engage in deep analysis of fiber photometry experiments, first by observing PVNOT neuron overall activity during a variety of different behaviors in the context of three different temperatures. Activity was associated with nesting, quiescence, and both types of huddling (when social opportunities exist). Social situations did not strongly effect this, not did temperature conditions. These analyses indicate that the PVNOT neurons are involved in mediating specific behavioral outputs.

With more detailed analysis, the authors investigated how PVNOT neuronal activity relate to behavioral state transition. They found that the probability of peak PVNOT neural activity strongly predicts the offset of quiescence or quiescent huddling and therefore can be argued to signal an increase in physical activity, and as such increased metabolism. However, the opposite pattern was observed for huddling and nesting (onset being associated with PVNOT activity), again arguing for increased thermogenesis as a function.

What is particularly compelling is that these peaks of activity tend to occur during low Tb, again arguing for the function in increasing body warmth.

The authors then employ an impressive set-up where they image brown dispose tissue (BAT) in tandem with DeepLabCut (DLC) based animal tracking. Crucially, BAT activity and surface temperature correlated with the calcium peak of PVNOT neurons.

Lastly, optogenetic activation of PVNOT neurons increased Tb when it was in the lower range, but not when in the higher ranger. It also affected BAT and rump temperature, again at low Tb. However, there is no real affect on behavior, except a trend in activity.

The authors do some interesting tracing work at the end, though this is not functionally explored. That's not a criticism as it does seem like this would be a follow-up whole study.

Comments on revised version.

As discussed before, the authors employ a wide range of techniques (FOS IHC, FP for fine scale PVN OXT population dynamics, behavioural analysis, core and surface temperature tracking, physiological recordings to assess AAV specificity, optogenetic activation of PVN OXT neurons, and projection tracing) to address a clear question. The outcomes of these techniques seem to drive the same conclusion that PVN OXT neurons signal transitions from rest to arousal (behavioural and thermogenic) in a state-dependent manner:

- FOS data identifies PVN OXT population activity following behavioural onset

- Ca activity in these cells peaks at behavioural and thermogenic state transitions

- Rump temperature and BAT activity increase at state transition points

- Optogenetic stimulation of these cells recapitulates the thermogenic effects seen during physiological state transitions (in low body temperature animals) with a trending increase in physical activity

Despite the inconclusive IHC results when validating the specificity of their AAV, the virgin female/ lactation experiment is convincing that they are specifically targeting PVN OXT neurons. The rationale for this experiment is clearer in the revised manuscript.

Generally, in terms of the revised manuscript, the authors give strong responses to reviewer comments, either incorporating feedback, or giving clear explanations for the choices they made in the original manuscript. The revised manuscript is clearer about the question the authors aim to address, the reasons for their choice of experiments, and the limitations of the techniques used.

Criticisms:

I appreciate and agree with the authors' point that this manuscript is more fundamental than simply social basis oxytocin neuron function. This is point is well made by their data, and in the revised text. However, I still believe more behavioural analysis would be welcome to any reader.

They partly justify the lack of behavioural analysis in Figure 6 with the problem of "animal merging" on the SGBS images. However, in Figure 6C, they confirm that, in solo conditions, the SGBS readings are consistent with core body temperature readings. So why not stick to core body temperature, opto stimulate and analyse the social behaviour with DLC (with normal video recordings)?

The lactation validation still seems out of place in manuscript order. It is a very valuable validation, but it feels more like supplementary data for Figure 1. I feel the authors wanted it as a main figure because of how much work it must have been. In that case, it still makes more sense to include it in Figure 1.

Though their lactation experiment validates that they are targeting PVN OXT neurons, their optogenetic stimulation protocol may not be specifically inducing OXT release from these cells. PVN OXT neurons co-release glutamate but can also release glutamate independently of OXT following lower frequency tonic stimulation. OXT release from PVN neurons requires pulsatile stimulation at a higher frequency (Leithead et al., 2021; Piñol et al., 2014; Lincoln & Wakerley, 1975). In this paper, the authors use a low stimulation frequency (10Hz) and continuous pulse train (20s) to optogenetically manipulate the target PVN population which may bias the cells towards glutamate release over OXT. Therefore, though they find evidence that PVN OXT neurons are involved in driving the transition between states in their other experiments, their optogenetic stimulation may not necessarily involve OXT release/signalling. It may be valuable to separate this out to identify the signalling molecule underlying this behavioural/ thermogenic transition. This could be done by using an opto protocol that recapitulates physiological OXT release.<br /> The authors do however mention that isolating the specific contribution of OXT signalling compared to other co-transmitted molecules was not the aim of this study, so this is not an essential question for this manuscript.

A loss of function experiment to test for sufficiency would be a nice addition to further confirm their claims, but the authors mention that there were technical limitations to their attempts at inhibiting PVN OXT neurons. I appreciate the authors declaring that the DREADDs attempt suffered from unfortunate confounds. But for optogenetic attempts, I don't think they need a closed-loop system to get some useful results. They still can shine the light at "random" moments (that will correspond to random body temperatures) and then separate the data per body temperature.

Lastly, the mention of Raam et al. 2026 is insufficient. The authors just mention it regarding the potential differences with males, to be explored in future experiments. Even if not using males in the current study doesn't affect the stated conclusions, the fact that they chose females because "their thermo-behavioural states were readily discernible" is a considerable bias. Testing males in this very study might be out of scope, but more discussion is warranted.

References

Leithead, A. B., Tasker, J. G., & Harony-Nicolas, H. (2021). The interplay between glutamatergic circuits and oxytocin neurons in the hypothalamus and its relevance to neurodevelopmental disorders. Journal of neuroendocrinology, 33(12), e13061. https://doi.org/10.1111/jne.13061

Lincoln, D. W., & Wakerley, J. B. (1975). Factors governing the periodic activation of supraoptic and paraventricular neurosecretory cells during suckling in the rat. The Journal of physiology, 250(2), 443-461. https://doi.org/10.1113/jphysiol.1975.sp011064

Piñol, R. A., Jameson, H., Popratiloff, A., Lee, N. H., & Mendelowitz, D. (2014). Visualization of oxytocin release that mediates paired pulse facilitation in hypothalamic pathways to brainstem autonomic neurons. PloS one, 9(11), e112138. https://doi.org/10.1371/journal.pone.0112138

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the reviewers' comments adequately and revised the manuscript accordingly.]

Summary:

In the submitted manuscript, Steinbach et al describe the formation of a detergent-resistant "cloud" around the Legionella-containing vacuole (LCV) that functions as a protective barrier. The authors show that formation of the "cloud" barrier is contingent upon the phosphoribosyl-ubiquitination activity of the SidE/SdeABC effector family, and is temporally regulated, with the assembly and subsequent disassembly of the "cloud" coinciding with replication and vacuolar expansion. The authors postulate a model of "cloud" barrier formation that relies upon a wave of initial ubiquitination by the SidC effector family, after which the SidE/SdeABC family expands the ubiquitination and forms cross-links that render the ubiquitin cloud resistant to harsh detergents. Additionally, Steinbach et al. also demonstrate that Rab5 is recruited to the LCV and remains associated for a considerable period.

Strengths:

This manuscript is very well written, with clear justification provided for experiments that make it very easy to follow along with the experimental logic. The figures have clearly been designed with much thought and are easy to interpret. Steinbach et al have also done a commendable job of addressing the previous reviewers' comments, even though some may suggest that some of these comments could be viewed as slightly unreasonable. This work would be of interest to both the Legionella and ubiquitin fields. Legionella researchers would potentially be interested to explore the proposed barrier model as the function for the ubiquitin "cloud," whereas ubiquitin researchers may be interested in exploring the mechanisms underlying SidE's crosslinking ability.

Reviewer #2 (Public review):

Summary:

The authors conducted a time-course of whole-body transcriptional analysis of a pest aphid, Rhopalosiphum padi, and identified four major clusters of the genes that show diurnal rhythmicity in transcription. In addition, they have conducted the analysis of aphid feeding behaviour and showed that aphids salivate longer from the end of the day toward the beginning of the night while their phloem feeding time does not change throughout a day. The genes up-regulated at nighttime were enriched with the genes involved in metabolic activities, collaborating with the results showing higher number of honeydew excretion at night. The authors identified the list of candidate salivary genes that show diurnal rhythmicity in the transcription and silenced a salivary gene C002 and the candidate salivary gene E8696. Silencing of these genes reduced aphid fecundity and survival rate on the host plant but not on the artificial diet.

Strengths:

The time-course transcription study and its analysis will be of interest to researchers studying diurnal rhythms in insect biology. Also, the analysis of aphid feeding behaviour at different time of day is interesting. This study provides variable resources for those who study insect biology.

Weaknesses:

Without the knowledge of the functions of the salivary effectors, especially their targets, it is hard to conclude that the rhythmical expression is important for the aphid performance. In addition, it is not clear whether increase of gene expression is directly corelated with the increase of protein secretion into the saliva and the plant.

Reviewer #1 (Public review):

Huang et al. examined ACC response during a novel discrimination-avoid task. The authors concluded that ACC neurons primarily encode post-action variables over extended periods, reflecting the animal's preceding actions rather than the outcomes or values of those actions. The authors have made considerable revision to address the raised the concerns. However, it appears that some important issues remain unresolved.

To what extent ACC neurons encode post action content remain as a major concern. This may be at least partially attributed by the analysis methods. If I understand it correctly, the authors compared pre- vs post-event neural activity and looked for significant changed. By default, this is to look for post-event changes, rather than pre-event. As a result, it would lead to the conclusion 'Our study also reveals that ACC neurons play a limited role in encoding pre-action variables associated with decision-making or planning, as evidenced by their minimal responses to auditory cues and the modest activity changes prior to shuttle initiation'.

To determine whether ACC encode pre-action variables or planning, different time windows should be used in the analysis.

Reviewer #1 (Public review):

Summary:

This study investigates whether the distribution of receptors and transporters of neurotransmitters accounts for the topography of cortical activity of confidence and surprise in probability learning. The authors first examined the invariance of functional correlates of confidence and surprises with multiple fMRI studies and then investigated whether 20 PET-derived receptor and transporter density maps account for this cortical invariant activity of confidence and surprise in probabilistic learning. Beyond these specific findings, the main novelty of this study lies in its attempt to bridge neuromodulatory systems and cognitive processes using neuroimaging data. This integrative approach is particularly valuable, as it showcases a framework to combine neurochemical architecture and cognitive computations.

Strengths:

This study attempts to link neuromodulatory systems with cognitive processes involved in probabilistic learning. Although the role of neuromodulatory systems in learning has been highlighted in several influential previous studies, it has not yet been widely investigated or systematically related to functional neuroimaging data so far. The authors used an efficient approach to address this question by combining group-averaged neurotransmitter maps with functional results from multiple fMRI studies using probabilistic learning tasks with similar structures. This approach provides informative insights into the relationship between the distribution of neuromodulatory systems and cognitive processes from neuroimaging data.

Weaknesses:

One limitation of the study stems from the unavoidable constraints of relying on pre-existing datasets rather than data specifically collected to address the present research question. Because the four fMRI studies differed in their measurements and task structures, the authors defined confidence and surprise on the basis of ideal observer behavior. Thus, "confidence" and "surprise" are not related to individual decision or subjective value, and the PET data is also from group-level data. Thus, it certainly has a limitation in linking with individual learning performance and brain activity. Also, "surprise" in this study does not seem to capture the nature of "surprise" in the learning process, which is a violation of expectation, as it was calculated with improbability. Moreover, the correlation of Study 1-4 for surprise was not consistent and not strong enough to argue for spatial invariance. Thus, these results may not yet be fully conclusive.

Reviewer #1 (Public review):

Summary:

This study uses stacked encoding models to characterize differences in sensory (visual and auditory) processing between autistic and non-autistic children and adolescents. The authors found no significant enhancement of low-level feature encoding in either visual or auditory cortex, but reduced high-level visual representations and a relative shift toward low-level over high-level visual feature encoding in the posterior superior temporal sulcus (pSTS). The shift in pSTS correlated with social symptom severity (SRS scores). These findings support weak central coherence (WCC) theory over enhanced perceptual functioning (EPF) theory, suggesting an altered visual feature encoding in pSTS in autism.

Strengths:

This study uses sophisticated methodology and an open data set with a relatively large sample size. fMRI data are acquired during a naturalistic paradigm (i.e., movie watching), which promotes attention and engagement among participants, and provides greater ecological validity. The use of encoding models to explore population-level differences in neural representations of stimulus-computable features is novel. Overall, results provide somewhat modest yet still informative evidence for adjudicating between possible theories of altered sensory processing in autism.

Weaknesses:

Some important methodological details are missing and/or require justification. Some potential confounding factors or unconsidered differences between individuals and/or diagnostic groups should be explored and possibly addressed. Specific major and minor points are raised below.

Major comments:

(1) Unclear description of noise ceiling calculation (line 205-206, 632-634) and potential heterogeneity: it is not clear what data were "split" for the split-half correlation used to calculate noise ceilings. To our knowledge, each participant watched each movie once each, so there is no within-subject repetition available. Were these correlations across participants (i.e., ISC)? If so, does this across-subject metric provide a fair representation of the true noise ceiling, given that a) encoding models themselves are trained within subjects and b) autistic individuals are known to exhibit more idiosyncrasy in responses to naturalistic stimuli (e.g., Hasson et al., 2008)? Moreover, do noise ceilings differ between individual participants, diagnostic groups, and/or with age? If so, how might these differences affect the interpretation of results (e.g., R2 differences)?

(2) Possibly underperforming visual model: given that the visual model in general performed worse than the audio model, the visual vs audio perceptual preference analyses (line 281-290) might be affected by the underlying mismatch between model performance. Though the visual and auditory regions showed similar noise ceilings (Figure 2 S1B), the stacked model performed better in auditory regions than in visual or multimodal regions (Figure 2 S1A). Supporting the same idea, the visual model in general showed lower fitting R2 than the audio model (Figure 2 S2A, Figure 2 S3A vs B). Instead of using mean motion (line 608-614), applying PCA on the raw features might help reduce noise inherent in the raw motion energy features (Malik et al., 2026), therefore improving model performance.

(3) The clipping procedure for unique variance (lines 634-637) requires justification: the unique variance is defined by subtracting high-level R² from stacked R² with explicit clipping when high-level R² is negative or exceeds stacked R². However, in the original stacked regression framework (Lin et al., 2024), unique variance is defined by simple subtraction without such post-hoc adjustment, as the negative R2 is still meaningful, indicating the model performs worse than predicting using the mean value. This requires justification. How frequently does clipping occur, and in which brain regions? Is it an indicator of overfitting or poor model performance? How substantially do results change if clipping is removed? E.g., the hemisphere dominance comparison (line 271-280, Figure 6). Critically, does this procedure affect the key finding regarding SRS/sensory symptom severity correlations in pSTS?

(4) The interpretation of the correlation between SRS with neural patterns is misleading (line 237-242, line 364-366): based on Figure 3, SRS and SSS showed more significant and robust relationship with unique variance of high-level visual feature, meaning that the decrement of high-level feature encoding in STSvp and STSdp, rather than the relative low-level preference, is likely driving the relationship with autism severity and sensory symptom.

(5) Details are missing about how data from the two movie runs were combined. Were the time series concatenated without regard to which movie they originally came from, or was the distinction between movies taken into account for purposes of splitting data into train/test cross-validation folds? The results would be stronger if the authors could show that results replicate across the two movies when they are each analyzed independently, though we recognize that there is perhaps not enough data, especially in the shorter [~4min] movie, to do this. The authors discussed this in lines 412-417, but it would be helpful to provide a justification in the Methods section as well.

(6) Potential feature weight differences across individuals and/or diagnostic categories: since the encoding models were trained for each subject, is there significant variability in feature weights across individuals and/or diagnostic categories (e.g., did the model predictions heavily rely on face for the non-ASD group but not for the ASD group)? If so, how does this change the interpretation of the R2 comparisons? The authors showed the results of stacked feature weight differences between diagnostic categories and their relationship with autism severity and sensory symptoms, but it might be informative to show the raw feature weightings before diving into stacked-weight differences.

Reviewer #1 (Public review):

Summary:

Evidence for visual representation of animacy.

Strengths:

This is a very cool paper that casts light on a persistent problem in the psychology and philosophy of visual representation: is there high-level perception? Every vision scientist agrees that low-level features such as shape, color, texture, motion and spatial frequency are represented in visual perception, but there is a great deal of controversy about the representation of high-level properties such as causation, faces, agency and animacy. Animacy is especially problematic because there are large differences in line curvature between stimuli that represent animate and inanimate items.

This article uses a novel approach-visual "anagrams" that are exactly the same image, except one is rotated 90 degrees relative to the other. They found persistent differences in visual processing between animate and inanimate stimuli. (Of course, the stimuli aren't animate-they represent animate items.). For example, there were processing differences between changes between animate and inanimate items (rabbit to boot) that were not present in rabbit to dog. They also showed such differences in two kinds of visual search tasks.

Of course, there are feature differences that exploit orientation. A classic example is the difference between a square and a diamond that is produced from the square by rotating it 45 degrees.

They addressed an aspect of this challenge having to do with some features using silhouettes. There was no search advantage for silhouetted stimuli.

Weaknesses:

I thought this was an excellent submission. I have two suggestions for revision:

(1) I thought that experiment 7 should have been described in more detail, with the upshot explained better. What exactly do the authors take it to show?

(2) There should be a candid discussion of what the loose ends are and how they might be addressed. It would be good to have some examples like the square/diamond case with some indication of what would address such challenges.

Reviewer #1 (Public review):

Summary:

In this study, Deepak V. Raya and colleagues combined behavioral measures with EEG recordings to investigate how distractors presented during the working memory delay influence memory representations. Using oriented gratings as stimuli and a continuous estimation task, the authors systematically manipulated factors that may modulate distractor interference, including the behavioral relevance of the WM item (cued vs. uncued) and the spatial relationship between the distractor and the WM item. By analyzing the relative orientation between the WM item and the distractor, the authors showed that distractors presented at the same location as the WM item induced an attractive bias (i.e., reported orientations biased toward that of the distractor), whereas distractors presented at the opposite location produced a weaker effect, with any systematic bias tending to be repulsive. Through a combination of behavioral analyses and EEG-based decoding, the authors further examined and revealed factors that modulate the magnitude of distractor interference, including cueing status, the strength of memory maintenance, distractor timing, and neural indices of distractor encoding and gating. Lastly, the authors propose a computational account of these effects by implementing a two-layer ring attractor model that captures several key behavioral patterns observed in the data.

Strengths:

The influence of distractors on working memory has been extensively studied both behaviorally and with neuroimaging. The present study advances this literature by providing a more comprehensive account that jointly manipulates and quantifies many key factors, including cueing (behavioral relevance), the spatial relationship between WM items and distractors, and distractor timing. This integrative approach enables a more systematic characterization of how different sources of interference interact. A particular strength of the study is the use of EEG combined with multivariate decoding to track the dynamics of memory and distractor representations. Compared to prior fMRI work, this approach provides a time-resolved view of how encoding, maintenance, and distractor processing unfold over time. This is especially valuable for dissociating memory maintenance and stimulus encoding, or gating contribute to behavioral interference, which is more difficult to achieve with fMRI.

Behaviorally, while most previous studies have reported attractive biases by distractors, the current study identified a repulsive effect when distractors were in the opposite hemifield from the WM item. Overall, the study provides a rich investigation of distractor interference in working memory and will be of interest to researchers studying the neural and computational mechanisms that protect memory representations from distraction.

Weaknesses:

(1) In the paragraph starting around line 125, the authors reported a 2-way ANOVA (cue/uncued × same/opposite side) restricted to trials in which a distractor was present. However, the subsequent post-hoc analyses compared distractor-present trials (same or opposite side) with no-distractor trials, which were not included in the ANOVA. While both analyses were informative, presenting them together in this way was somewhat confusing, as the post-hoc tests extended beyond the factors and conditions analyzed by the ANOVA. I suggest presenting these analyses separately and clarifying their distinct purposes. Additionally, Figure 1C appeared to reflect only the pairwise comparisons; including a figure that directly visualizes the two-way ANOVA results would improve clarity.

(2) In lines 138-150, the authors fitted von Mises functions to the distributions of memory error and reported that the effect of distractor location (same vs. opposite) was stronger in the uncued condition than in the cued condition. However, this result appears difficult to reconcile with the earlier 2-way ANOVA, which showed no interaction between cueing and distractor location. It is unclear whether this discrepancy arose from differences in the dependent measures (CSD vs. κ), statistical procedures, or other factors. Clarifying how these two sets of results should be interpreted together would improve the clarity of the findings.

(3) For the analyses in Figures 1B and 1D, parametric functions were fitted to the distributions of memory error using aggregated data. Models of memory error distributions have been central to ongoing debates in the working memory literature (e.g., Schurgin, Wixted, & Brady, 2020; van den Berg, Awh, & Ma, 2014). Fitting functions/curves to aggregated data can be problematic, as it distorts the underlying distributions at the individual level. I suggest performing the fits on the individual data and analyzing the fitted parameters across participants using appropriate group-level statistical tests.

(4) At the end of the first Results section (lines 234-235), the authors concluded that cued memoranda were "better shielded from interference" than uncued memoranda. However, I did not see a clear statistical test directly supporting this. This statement appeared to rely mainly on Figure 1D, which showed a stronger location effect (same vs. opposite) when the memory item was uncued. However, this analysis does not directly test whether distractors impair uncued items more than cued items overall. Supporting this broader claim would require a direct comparison of distractor effects (e.g., distractor vs. no-distractor) between cued and uncued conditions, or an interaction test involving cueing and distractor presence (e.g., either by pooling different distractor locations, or focusing on the same-location condition if opposite-location distractors show no significant effect).

(5) While the attractive and repulsive biases are an interesting finding, it was demonstrated only at the behavioral level. It would be informative to examine whether the biases are reflected in the decoding results. For example, after deriving trial-wise orientation tuning functions, one could estimate decoded orientations (e.g., via vector averaging or the peak of the tuning curve) and assess bias at the neural level. Although EEG SNR may limit recovery of full function of the memory error (e.g., Figure 1F-G), grouping trials into fewer bins (even with just two bins) may still allow detection of the overall direction of the bias in the decoding results. This type of decoding bias has been reported in other contexts (GY Bae - NeuroImage, 2021).

(6) The analysis P2/P3a requires more explanations. Typically, these components are extracted from trial-averaged ERP. The methods section also mentioned "averaged across channels and trials to obtain the ERP waveform." However, to split the trials, these components have to be identified at a single-trial level. More details are needed in the Methods.

(7) Components such as P3a are often linked to attentional capture and orienting, which would predict increased, rather than decreased, distractor interference. The interpretation of this signal as reflecting gating appears to be inferred from the observed relationship between larger P3a amplitudes and weaker interference. The N2pc component is a well-established index of spatial attention allocation and may be particularly relevant (and useful) here, given the lateralized distractor. Have the authors tested whether distractor-evoked N2pc can be used to split trials and examine its relationship with the bias?

(8) Line 676 in the Discussion states "possibly by error-correcting top-down control mechanisms." It is unclear which results provide support for this interpretation, except that there are stronger feedback connections at the cued location in the attractor ring model.

Joint Public Review:

Cardiolipin, is a key lipid constituent of mitochondrial membranes. Perturbation of its abundance is thus poised to affect broad aspects of mitochondrial function. Given the important role of mitochondria, it is not surprising that cardiolipin deficiency would have pervasive effects on cell physiology.

The original version of this paper advanced the idea that cardiolipin deficiency, and the attendant mitochondrial dysfunction, plays a causative role in the progression of fatty liver (a common feature in the human population) to a more pathogenic inflammatory state known as steatohepatitis. Given the prevalence of this form of liver disease in the human population this claim for discovery was deemed sufficiently interesting to merit peer review at eLife.

Peer review reaffirmed the importance of the claim but also revealed important limitations in the experimental support provided. Specifically, the lack of experimental interventions that uncouple the correlation between progression in a mouse model and changes in cardiolipin abundance to test the causal relationship. The review process also recognised the utility of other aspects of the paper, namely the evidence implicating cardiolipin deficiency in altered properties of the mitochondrial membrane, its contribution to an electron leak and the potential for these features to contribute to pathology.

The revised version of the manuscript now focuses on the importance of cardiolipin sufficiency to mitochondrial integrity and contains various improvements to the data supporting this aspect. At the same time the revised paper retreats from the most interesting claim of a causal role for cardiolipin deficiency in disease progression. We are left with a more convincing but less significant paper.

Reviewer #3 (Public review):

The authors find that DNA methylation-based clocks are generally less accurate at predicting age in cohorts with large proportions of non-European (especially African) ancestry, compared to cohorts with high European ancestry proportions (which more closely reflects the genetic composition of individuals included in training sets). They provide evidence for this ancestry bias via ancestry-stratified analyses, and in analyses of continuous ancestry proportion effects on clock error. They then test two hypothesized underlying causes of ancestry bias: that ancestry-differentiated SNPs disrupt CpG sites preventing methylation, and that ancestry-differentiated SNPs influence DNA methylation levels. They find clear evidence especially for the second cause, in the form of meQTL that influence clock CpG sites and vary in frequency across ancestry groups. Finally, the authors provide key discussions of potential paths forward to alleviate bias and improve portability for future clock algorithms.

The topic is timely due to the increasing popularity of DNA methylation-based clocks and the acknowledgment that many algorithms (e.g., polygenic risk scores) lack portability when applied to cohorts that substantially differ in ancestry or other characteristics from the training set. This has been discussed to some degree for DNA methylation-based clocks, but could of course use more discussion and empirical attention, which the authors nicely provide using an impressive and diverse collection of data. The inclusion of data from multiple cohorts, the analysis of ancestry as a continuous variable, and the attempts to address the underlying causes of ancestry-based differences in accuracy provide comprehensive evidence that genetic background influences clock portability.

Reviewer #1 (Public review):

Ono et al., compared the activity of prime editor nickase PE2 and primer editor nuclease PEn in introducing SNPs and short exogenous DNA sequences into the zebrafish genome to model human disease variants. They find the nickase PE2 prime editor had a higher rate of precise integration for introducing single nucleotide substitutions, whereas the nuclease PEn prime editor showed improved precision of integration of short DNA sequences. In somatic tissue the percentage of SNP variant precision edits improved when using PE2 RNP injection instead of mRNA injection, but increased precision editing correlated with elevated indel formation. While PEn overall had higher rates of precision edits, the indel rate was also elevated. Similar rates were observed when introducing a 3 bp stop codon into the ror gene using a standard pegRNA with a 13-nucleotide homology arm, or a springRNA driving integration by NHEJ. Inclusion of an abasic sequence in the springRNA prevented imprecise edits caused by scaffold incorporation, but did not improve the overall percentage of precise edits in somatic tissue. Both PE2 and PEn showed higher frequency of 3 bp precision integration, compared to CRISPR HDR mediated knock-in using a single strand donor DNA template with short homology. Recovery of a germline ror-TGA integration allele using PEn with RNP was robust, resulting in 5 out of 10 founders transmitting a precise allele. The authors demonstrate PEn was effective at integration of a 30 bp nuclear localization signal into the 5' end of GFP in an existing muscle-specific reporter line. PEn-mediated integration of long sequences was further demonstrated by integration into the wls gene of a 46bp attP sequence for phiC31 integrase recombination. Additional analyses are needed to determine if the approach can be used to isolate stable germline alleles of variants that are potentially dominant negative or gain of function in nature.

The conclusions of the paper are well supported, demonstrating PE2 increases precision, while PEn increases efficiency, for integrating short DNA sequences. Introducing longer sequences up to 46 bp wit PEn highlights the potential broad utility of this approach for insertion of functional motifs for protein modification and gene expression.

(1) In Figure 3 the data indicates a significant increase in precise edits of the 3 bp TGA using PE2 RNP (11.5%) vs. PE2 mRNA (1.3%). At the adgrf3b locus both PE2 RNP, PE2 mRNA, PEn RNP and PEn mRNA were tested for introducing the 3 bp TGA and a longer 12 bp insertion. PEn RNP showed the highest rate of precision for integration of the longer 12 bp sequence. A comparison of somatic precision editing at additional loci, and analysis of germline transmission rates using PE2 vs. PEn, would support the conclusion that PEn is preferred for precise integration of longer templates, and recovery of germline integration alleles.

(2) Figure 4 shows the results of introducing a TGA stop codon that is predicted to result in nonsense mediated decay. Testing the ability to also isolate different substitution mutations in the germline would be useful information for identifying the most effective approach for generating human disease variant models.

Reviewer #1 (Public review):

Summary:

This paper carefully compares intramural vs. extramural National Institutes of Health funded research during 2009-2019, according to a variety of bibliometric indices. They find that extramural awards more cost-effectively fund outputs commonly used for academic review such as number of publications and citations per dollar, while intramural awards are more cost-effective at generating work that influences future clinical work, more closely in line with agency health goals.

Strengths:

Great care was taken in selecting and cleaning the data, and in making sure that intramural vs. extramural projects were compared appropriately. The data has statistical validation. The trends are clear and convincing.

Reviewer #1 (Public review):

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

Summary:

It is well known that neurons in the medial prefrontal cortex (mPFC) are involved in higher cognitive functions such as executive planning, motivational processing and internal state mediated decision-making. These internal states often correlate with the emotional states of the brain. While several studies point to the role of mPFC in regulating behavior based on such emotional states, the diversity of information processing in its sub-populations remains a less explored territory. In this study, the authors try to address this gap by identifying and characterizing some of these sub-populations in mice using a combination of projection-specific imaging, function-based tagging of neurons, multiple behavioral assays and ex-vivo patch clamp recordings.

Strengths:

The authors targeted mPFC projections to the nucleus accumbens (NAc) and basolateral amygdala (BLA). Using the open field task (OFT), the authors identified four relevant behavioral states as well as neurons active while the animal was in the center region ("center-ON neurons"). By characterizing single unit activity and using dimensionality reduction, the authors show differentiated coding of behavioral events at both the projection and functional levels. They further substantiate this effect by showing higher sensitivity of mPFC-BLA center-ON neurons during time spent in the open arms of the elevated plus maze (EPM). The authors then pivoted to the three-chamber social interaction (SI) assay to show the different subsets of neurons encode preference of social stimulus over non-social. This reveals an interesting diversity in the function of these sub-populations on multiple levels. Lastly, the authors used the tube test as a manipulation of the anxiety state of mice and compared behavioral differences before/after in the OFT and social interaction tasks. This experiment revealed that "losers" of the tube test spend less time in the center of the open field while "winners" show a stronger preference for the familiar mouse over the object. Using patch-clamp experiments, the authors also found that "winners" exhibit stronger synaptic transmission in the mPFC-NAc projection while "losers" exhibit stronger synaptic transmission in the mPFC-BLA projection. Given the popularity of the tube test assay in rank determination, this provides useful insights into possible effects on anxiety levels and synaptic plasticity. Overall, the many experiments performed by the authors reveal interesting differences in mPFC neurons relative to their involvement in high or low anxiety behaviors, social preference and social rank.

Weaknesses:

The authors have addressed all comments.

Reviewer #1 (Public review):

Summary:

The authors attempted to identify if a new deep learning model could be applied to both resting and task state fMRI data to predict cognition and dopaminergic signaling. They found that resting state and moving watching conditions best predict episodic memory, but only movie watching predicts both episodic and working memory. A negative 'brain gap' (where the model trained on brain connectivity predicts worse performance than what is actually observed) was associated with less physical activity, poorer cardiovascular function, and lower D1R availability.

Strengths:

The paper should be of broad interest to the journal's readership, with implications for cognitive neuroscience, psychiatry, and psychology fields. The paper is very well-written and clear. The authors use two independent datasets to validate their findings, including two of the largest databases of dopamine receptor availability to link brain functional connectivity/activity with neurochemical signaling.

Weaknesses:

The deep learning findings represent a relatively small extension/enhancement of knowledge in a very crowded field.

It's unclear from these results how much utility the brain gaps provide above and beyond observed performance. It would be helpful to take a median split the dataset on observed performance, and plot aside the current Fig 3 results to see how the cardiovascular and physical activity measures differ based on actual performance. Could the authors perform additional analyses describing how much additional variance is explained in these measures by including brain gaps?

Some of the imaging findings require deeper analysis. For figure 1f - Which default mode regions have high salience? DMN is a huge network with subregions having differing functions.

Along the same lines, were the striatal D1R findings regionally specific at all? It would be informative to test whether the three nuclei (Accumbens, Caudate, Putamen) and/or voxelwise models would show something above and beyond what is achieved from averaging D1R across the striatum. What about cortical D1R, which are highly abundant, strongly associated with cognitive (especially WM) performance, and have much unique variance beyond striatal D1R? https://www.science.org/doi/full/10.1126/sciadv.1501672. The PET findings are one of the unique strengths of this paper and are underexplored. It's also unclear if the measure of brain entropy should simply be averaged across all regions.

It is not clear from the text that the authors met the preconditions for mediation analysis (that is, demonstrating significant correlations between D1R and entropy, in addition to the correlation with brain gap. Could they please report this as well?

Was age controlled for in the mediation analysis? I would not consider this result valid unless that is the case.

The discussion is long, but the authors would do better to replace some less helpful sections (e.g., the paragraph on methodological tweaks to parcellations and model alignment) with a couple of other important points, including:

(1) Discuss the 'sweet-spot' of movie watching for behavior prediction in the context of studies showing that task states 'quench' neural variability: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007983. This may not be mutually exclusive of the discussion on dopamine and signal-to-noise ratio, but it would be helpful for the authors to discuss their potential overlap vs. unique contributions to the observed findings.

(2) The argument that dopamine signaling increases signal-to-noise ratio is based on some preclinical data as well as correlational data using fMRI with pharmacological challenges. It is less clear how PET-derived estimates of D1R and D2R availability equate to 'dopamine signaling' as it is thought of in this context. Presumably, based on these data, higher D1R or D2R availability would be related to greater levels of tonic dopaminergic signaling. However, in the case of the COBRA dataset with D2R estimates, those are based on raclopride -- which competes with endogenous dopamine for the D2 receptor. Therefore, someone with higher levels of endogenous dopamine signaling should theoretically have lower raclopride binding and lower D2R estimates. I'm not arguing that the authors logic is flawed or that D1R and D2R are not good measures of dopamine signaling, but I'd ask the authors to dig into the literature and describe more direct potential links for how greater receptor availability might be associated with greater dopamine signaling (and hence lower entropy). Adding this to the discussion would be very valuable for PET research.

Comments on revised version:

I thank the authors for their extensive efforts to revise the manuscript. I have no further concerns.

Reviewer #1 (Public review):

Summary:

This study used whole genome data to investigate Beefalo ancestry for the first time, filling the gap in the field of Beefalo ancestry. The authors used preserved semen samples to generate genomic data on 47 registered Beefalo and 3 bison hybrids, further questioning the ABA's stated goal of ⅜ bison ancestry. In addition, the authors also show that ancestry profiles of Beefalo and bison hybrid genomes are consistent with repeated backcrossing to either parental species, demonstrate the value of genomic information in examining gene flow between species in the genus Bison. Overall, these data thus demonstrate the utility of genomic information in validating specific breeding claims for a more complete understanding of gene flow and genetic variation among bovine species. This is an interesting study, but there are still some major weaknesses that exist.

Strengths:

Numerous genetic analysis methods such as PCA, ADMIXTURE, F4 ratios, and local ancestry inference techniques revealed that no single Beefalo set meets the ancestry requirements set by the American Beefalo Association (ABA) and some beefalo had detectable indicine cattle ancestry.

Comments on revised version:

The authors have made further revisions in the revised manuscript, and these revisions have undoubtedly helped improve the article. No further comments.

Reviewer #1 (Public review):

Summary:

The authors set out to better understand how Drosophila responses to CO2 can be aversive or attractive depending on context (especially presence of food odors, temperature, humidity). While some aspects of this circuit had been previously identified, the authors uncovered additional, critical aspects of the circuit to more fully explain these phenomena. One important discovery was the identification of the LN23 interneuron, which receives input from the V glomerulus. LN23 relays sensory input via an extraglomerular CO2 pathway, and manipulation of LN23 activity revealed a dominant role in CO2-induced avoidance behavior.

Through a careful series of experiments, the authors demonstrate important aspects of these parallel (and sometimes converging) circuits - differential sensitivity to CO2 concentration changes, synaptic plasticity, circuit connectivity, developmental origins, and the effect of chemo and optogenetic manipulations on behavior. Together, they piece together a complex and interconnected circuit diagram for CO2-dependent behaviors that can be modulated by external factors. This finding will be impactful not only for the fly olfactory/gustatory field but also for many others in the sensory neuroscience community who are very interested in understanding state-dependent modulation of sensory circuits.

Strengths:

The experiments were well described and controlled. The addition of the developmental trajectory of the LN23 neurons was interesting. The inclusion of multiple levels of analysis from synaptic contacts and activity-dependent labeling of synapses, circuit analysis guided by connectomes, and detailed behavior analysis for each part of the circuit were all strengths.

Weaknesses:

The circuit is very complex and interconnected. This is important for its function, but it makes reading through the manuscript a challenge. The diagrams are helpful, but still somewhat confusing, and some of the experimental findings do not completely support the model outlined in the final figure.

The main difficulty is visualizing the "default/predominant aversive" LN23 circuit - in the final diagram, there is no "stop" sign on that side, although it's depicted as an inhibition of a "go".<br /> Also, importantly, the findings shown in Figure 5 demonstrate pretty convincingly that LN23 inhibition reduces CO2 avoidance "almost entirely". Also supporting a central role for LN23 is the opposite effect of silencing LN23, with chronic CO2 inducing attraction. If this is the case, then where is the contribution of the other canonical aversive pathway? How does the silencing of LN23 override the PNvbi/uni pathways to aversion? Incorporating this into the figure more prominently would improve the understanding of this contribution to the circuit.

A minor weakness is that CO2 levels were not reduced below ambient air. For the first part of the paper addressing the activation of these circuits, there seemed to be a ceiling effect for the LN23 neurons at ambient CO2 levels. It would be interesting to see if there would be some change to the activity labeling experiments if CO2 were reduced or eliminated from the air.

Reviewer #1 (Public review):

Summary:

This manuscript offers a careful and technically impressive dissection of how subpopulations within the subthalamic nucleus (STN) support reward-biased perceptual decision-making. The authors recorded STN neurons in monkeys performing an asymmetric-reward visual motion discrimination task, then combined single-unit analyses, regression modeling, and drift-diffusion model (DDM) fitting to identify functionally distinct neuronal clusters. Each subpopulation shows unique relationships to computational decision variables - evidence accumulation rate, decision bound, and non-decision time - as well as to post-decision evaluative signals including choice accuracy and reward expectation. The revised manuscript substantially strengthens the original submission by improving both the objectivity of neuron selection and the robustness of the clustering solution.

Strengths:

The asymmetric-reward paradigm cleanly separates perceptual and motivational contributions to STN activity, allowing the authors to characterize how neurons blend these distinct sources of information. The dataset is extensive and well-controlled, and the behavioral and neural analyses are tightly integrated. Relating cluster-specific activity to DDM parameters provides an interpretable computational link between population signals and behavior. The clustering solution is now validated across two algorithms, two monkeys, and subsets of trials - establishing that the three-cluster structure is robust. The new Figure 9 offers a conceptually useful, if necessarily speculative, synthesis connecting the identified subpopulations to distinct basal-ganglia pathways (hyperdirect versus indirect). The new Figure 8 documenting the anatomical intermingling of subpopulations is also important, as it directly informs the interpretation of prior and future STN stimulation studies.

Weaknesses:

The inferred relationships between neural clusters and DDM parameters remain correlational - the authors now appropriately flag this throughout, and the causal inference gap is acknowledged in the Discussion with concrete proposals for future targeted perturbation strategies. While a generative multi-cluster model would further strengthen mechanistic interpretation, the conceptual framework in Figure 9 provides a reasonable intermediate step given the scope of the study and the absence of simultaneous population recordings, which preclude direct inter-cluster covariation analyses. These remaining limitations are inherent to the experimental design rather than analytical oversights.

Reviewer #1 (Public review):

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

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

My main issue concerns the identity and biological origin of the proposed bat odor cue, (−)-limonene. Limonene seems like an unusual compound to be emitted endogenously by a mammal, particularly by an insectivorous bat. It would be helpful if the authors could clarify whether mammals are known to synthesize this compound de novo, and, if not, what the likely source of this plant-associated terpene would be in S. kuhlii. Possible sources could include environmental exposure, diet, roosting material, handling, or temporary housing conditions.

I do not doubt that crickets avoid synthetic (−)-limonene. Indeed, this result is quite plausible given that limonene is widely used in insect repellent or repellent-associated fragrance products. However, this also makes contamination an important issue to address explicitly. How did the authors exclude the possibility that limonene entered the samples from human-associated sources, such as insect repellents, soaps, cleaning products, field equipment, cloth bags, cages, gloves, or other materials used while handling wild-caught bats? It would strengthen the manuscript to report limonene levels for individual bat odor collections, all relevant blanks, and any handling or housing controls.

More broadly, given the common occurrence of limonene in plants and human-associated products, I am not yet convinced that it would function as a reliable "keystone kairomone" as suggested around line 253. How would crickets distinguish bat-associated limonene from limonene emitted by a mint leaf, citrus peel, pine material, or other non-threatening environmental sources? The authors may wish to soften this interpretation or provide additional evidence that crickets respond to limonene in a bat-specific context, perhaps through concentration, temporal patterning, co-occurring volatiles, or enantiomeric composition.

Reviewer #1 (Public review):

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

Summary:

Noell et al have presented a careful study of the dissociation kinetics of Kinesin (1,2,3) classes of motors moving in-vitro on a microtubule. These motors move against the opposing force from a ~1 micron DNA strand (DNA tensiometer) that is tethered to the microtubule and also bound to the motor via specific linkages (Fig 1A). Authors compare the time for which motors remain attached to the microtubule when they are tethered to the DNA, versus when they are not. If the former is longer, the intepretation is that the force on the motor from the stretched DNA (presumed to be working solely along the length of the microtubule) causes the motor's detachment rate from the microtubule to be reduced. Thus, the specific motor exhibits "catch-bond" like behaviour.

Strengths:

The motivation is good - to understand how kinesin competes against dynein through the possible activation of a catch bond. Experiments are well done and there is an effort to model the results theoretically.

Weaknesses from original round of review:

The motivation of these studies is to understand how kinesin (1/2/3) motors would behave when they are pitted in a tug of war against dynein motors as they transport cargo in bidirectional manner on microtubules. Earlier work on dynein and kinesin motors using optical tweezers has suggested that dynein shows catch bond phenomenon, whereas such signatures were not seen for kinesin. Based on their data with DNA tensiometer, the authors would like to claim that (i) Kinesin1 and kinesin2 also show catch-bonding and (ii) The earlier results using optical traps suffer from vertical forces, which complicates the catch-bond interpretation.

Reviewer #1 (Public review):

This manuscript by Zhang et al addresses how Pi scarcity/depletion drives PMB resistance in Enterobacteriaceae, because it proposes a mechanistically distinct pathway from the better-known PhoBR-linked phospholipid-remodeling responses in other Gram-negatives. The authors also suggest an intervention strategy based on Mg repletion or Fe chelation. The results are substantial and include genetic analyses, mass spectrometry, reporter assays, phospho-signaling readouts, metal quantification, and comparative analyses across enterobacterial species.

The paper reads well with the emphasis on the Mg loss followed by Fe mobilization during Pi depletion that induces PmrAB TCS activation for lipid A modification through transcriptional activation of ugd and arn genes. However, PmrAB is a well-known TCS responsible for PMB resistance through lipid A modification in the extensive studies by the Groisman lab. PmrA is a well-known transcriptional regulator to activate the transcription of the ugd gene in Salmonella and Yersinia by Mg depletion and Fe mobilization. Therefore, the current paper should focus more on the upstream signaling to connect the dots between Pi depletion and Mg loss. This is important because Ugd gene expression is not affected by PmrAB in Pi depletion. It should also be considered that Mg loss is temporally associated with Fe mobilization, but the manuscript does not quantitatively show that Mg dissociation/redistribution is sufficient to trigger Fe mobilization in the absence of Pi depletion, considering that Mg is a macronutrient, whereas Fe is a micronutrient.

Second, the relationship between arn and ugd regulation needs a clearer mechanistic resolution to orchestrate the synthesis of the L-Ara4N during Pi depletion, because the manuscript shows that arn activation is PmrAB-dependent, whereas ugd is only partially PhoBR-dependent and not dependent on PmrAB. Yet the current model and narrative treat the system as a unified "ugd-arn" output. This should be carefully addressed, given that Pi depletion and Mg depletion might trigger different signaling modules.

Third, the manuscript argues that this is a "conserved" circuit in Enterobacteriaceae. The evidence for conservation is presently strongest in E. coli MG1655 and includes supportive observations in E. coli O157, one UTI strain by lipid A MS, several UTI isolates by killing assay, and S. Typhimurium for key phenotypes. No direct mechanistic validation is shown in other important genera belonging to Enterobacteriaceae, which include Klebsiella, Enterobacter, Citrobacter, Yersinia, Serratia, or other clinically important Enterobacteriaceae.

Fourth, the reversal and translational claims are a bit stronger than the current evidence supports. The title and Abstract state that identifying and targeting the circuit reverses Pi depletion-driven PMB, and the manuscript suggests a pharmacological intervention framework based on Mg supplementation or Fe chelation. The actual intervention evidence is limited to in vitro killing assays under acute Pi-depleted minimal-medium conditions in E. coli and S. Typhimurium, without in vivo testing, in that the experiments are performed under an acute 3-hour starvation in MOPS medium, not in host-mimicking or infection-relevant environments. The reversal needs to be shown not only at the level of survival curves, but also by the quantitative MIC/MBC measurements.

More importantly, the authors demonstrated that the signaling module upon Pi limitation in Enterobacteria differs from that in other Gram-negative bacteria such as Pseudomonads. However, they did not discuss why this difference would impact the life of Enterobacteria. The authors should consider the glycolytic pathways (i.e., EMP pathway for enterobacteria vs ED pathway for pseudomonads), in that the ED pathway requires less Pi, whereas the EMP pathway requires more Pi. It should be noted that Pi supply is highly limited in the natural environment for the free-living bacteria, rather than in the host environment for the commensals.

Reviewer #1 (Public review):

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

Summary:

Knowing that small pupil-size variations accompany brightness variations (even when these are illusory), the authors asked whether pupil constrictions would accompany the synesthetic perception of a brighter color (compared with a darker one), induced by the presentation of a black-white character. This grapheme-colour synesthesia is only experienced by few participants, sixteen of whom were enrolled in this study. The results reliably showed that a relative pupil constriction would "betray" the perception of a brighter color in these participants, while no such effect would be observed in control participants who were asked to report a color in association with each grapheme, even though they did not perceive any.

Strengths:

The main strength of the study lays in its combination of psychophysics (brightness ratings) and pupillometry, which allowed for showing clear-cut results.

Impact:

This work is likely to improve our understanding of synesthesia, providing a new tool to quantify the subjective sensations; an interesting potential extension would be using pupillometry for tracking changes over time of the synesthetic experiences, opening up the possibility to evaluate the importance of learning for this peculiar experience.

Reviewer #1 (Public review):

Summary:

This retrospective study provides a new data regarding the prevalence of pain in women with PCOS and its relationship with health outcomes. Using data from electronic health records (EHR), the authors found a significantly higher prevalence of pain among women with PCOS compared to those without the condition: 19.21% of women with PCOS versus 15.8% in non-PCOS women. The highest prevalence of pain was conducted among Black or African American (32.11%) and White (30.75%) populations. Besides, women with PCOS and pain have at least a 2-fold increased prevalence of obesity (34.68%) at baseline compared to women with PCOS in general (16.11%). Also, women with PCOS had the highest risk for infertility and T2D, but women with PCOS and pain had higher risks for ovarian cysts and liver disease. Regarding these results, authors suggested the critical need to address pain in the diagnosis and management of PCOS due to its significant impact on patient health outcomes.

Strengths:

The problem of pain assessment in PCOS patients is well described and authors provided a clear rationale selection of the retrospective design to investigate this problem.

A large number of analyzed patient's records (76,859,666 women) and its uniformity increases the power of the study. Using the Propensity Score Matching makes possible to reduce the heterogeneity of the compared cohorts and influence of comorbid conditions.

Analysis in different ethnic cohorts provides actual and necessary data regarding the prevalence of pain and its relationship with different health conditions that will be helpful for clinicians to make a diagnosis and manage the PCOS in women of different ethnicity.

Assessment of risk of different health conditions as including PCOS-associated pathology as other common groups of diseases in PCOS women with or without pain allows to differentiate the risk of comorbid conditions depending on the presence of one symptom (pelvic or abdominal pain, dysmenorrhea).

Weaknesses:

The significant weakness of the study is the absence of Latin American cohort. Probably the White cohort includes Latin Americans or others, but results of the study cannot be extrapolated to particular White ethnicities.

Comments on revised version:

At present, I have no questions or recommendations for the authors, as they have exhaustively addressed the previous comments and incorporated the necessary corrections.

Reviewer #1 (Public review):

Summary:

The authors aim to demonstrate that PGLYRP1 plays a dual role in host responses to B. pertussis infection. PGLYRP1 signaling is known to activate bactericidal responses due to recognition of peptidoglycan. Through NOD1 activation and TREM-1 engagement, it appears PGLYRP1 also has immunomodulator activities. The authors present mouse knockout studies and gene expression data to illustrate the role of PGGLYRL1 in relation to B. pertussis peptidogylcan. Mice lacking PGLYRP1 had slightly lower pathology scores. When TCT peptidoglycan was removed from the bacteria, surprisingly IL23A, IL6, IL1B and other pro-inflammatory genes encoding cytokines increased. The relationship to TCT and PGLYRP1 suggest the pathogen uses this strategy to decrease immune activation. The authors when on to show the relationship between PGLRP1 and TREM-1 as mediated with PGN using various versions of peptidoglycan. The study presents multiple angles of data to back up its findings and demonstrates an interesting strategy used by B. pertussis to down-regulate innate responses to its presence during infection.

Strengths:

Use of knockout mice of the key factor being considered paired with isogenic B. pertussis strains to reveal the mechanism of immune modulation to benefit the bacteria. The authors used in vivo gene expression paired with in vivo assays to establish each aspect of the mechanism.

Weaknesses:

The main focus was on innate responses, but some analysis of antigen specific antibody responses could improve the impact of the findings.

Comments on revised version.

I have no further input to add.

Reviewer #1 (Public review):

Summary:

In their manuscript, Zhou and colleagues present a detailed look at how the JSP functions differently in the various cells of a breast tumor. The authors have effectively shown that the JSP acts as a double-edged sword, as it helps T cells fight cancer but also allows tumor cells to grow and avoid ferroptosis. These findings are important because they identify a useful biomarker to predict how TNBC patients might respond to PD-1 inhibitors.

Strengths:

This work is important because it provides a clear explanation for the conflicting roles of the JSP in the tumor environment. The evidence is solid, as it combines data from thousands of patients with single-cell analysis and lab experiments to confirm the role of STAT4 in cancer progression and immunity.

Comments on revised version:

The authors made a significant effort to improve the manuscript. My comments were sufficiently addressed.

Reviewer #2 (Public review):

Summary:

This study aims to establish a rational framework for designing bacterial probiotics against respiratory infections. The central hypothesis is that in vitro antagonism, particularly through metabolic niche overlap with a pathogen, predicts in vivo efficacy.

Strengths:

(1) Systematic pipeline: The study integrates bacterial isolation, in vitro characterization, model development, and in vivo validation into a cohesive workflow.

(2) Quantitative model: The introduction of the Niche Index (NI) and Niche Index Fraction (NIF) provides a novel, quantitative tool for predicting probiotic efficacy based on ecological principles.

(3) Mechanistic insight: The work dissects different modes of action, clearly demonstrating that inhibition can be driven by specialized metabolite production (CP8) or carbon resource competition (e.g., CP7), with lactate utilization identified as a key factor.

Weaknesses:

(1) Limited model generalizability: The predictive power of the NI model is not universal. It fails to account for the in vivo inefficacy of CP8 (a metabolite-dependent inhibitor) and cannot explain the short-term protection conferred by some non-inhibitory CPs in vivo, suggesting unmodeled mechanisms like immune priming are at play.

(2) Preliminary nature of key findings: The emphasis on lactate consumption as a critical predictor, while interesting, is not sufficiently explored to establish its general importance beyond the specific strains and conditions tested.

Appraisal:

The authors successfully achieve their aim of establishing a rational probiotic-design pipeline. The data robustly support the conclusion that metabolic niche overlap predicts efficacy for many strains, while also clearly delineating the model's limitations, as acknowledged by the authors.

Impact:

This work provides a valuable methodological framework for hypothesis-driven probiotic discovery. The quantitative Niche Index offers immediate utility to the field and, with further refinement, has the potential to become a fundamental tool for developing respiratory therapeutics.

Comments on revised version.

I thank the authors for their meticulous revisions.

Reviewer #1 (Public review):

Summary:

This study presents a potentially important integrative model linking spontaneous retinal waves, apoptosis, microglial activity, and vascular development during postnatal retinal maturation. Its significance lies in proposing a mechanistic framework that could reshape understanding of how neural activity and tissue remodeling are coordinated in the developing central nervous system. The evidence is strengthened by the use of multiple complementary techniques, including Ca++ imaging, high-throughput electrophysiology, transcriptomics, histology, and pharmacology.

Strengths:

(1) Multimodal Validation: The authors correlate large-scale functional imaging (calcium imaging and MEA) with high-resolution structural and molecular data (scRNA-seq and IHC), providing strong topographical evidence for the "centrifugal expansion" pattern.

(2) The primary significance lies in identifying apoptotic Retinal Ganglion Cells (RGCs) as the physiological "pacemakers" for stage II retinal waves. By linking programmed cell death directly to neural activity and subsequent angiogenesis, the authors propose a self-regulating developmental loop.

Weaknesses:

(1) While the PANX1 pharmacological data provide compelling functional support, extending these conclusions to the broader CNS may be premature. Additional direct mechanistic validation would further strengthen the claim of causality.

(2) While the manuscript beautifully illustrates the co-occurrence of events during retinal development, strengthening the distinction between correlation and direct causation would enhance the impact of the findings.

Reviewer #1 (Public review):

Summary:

In this manuscript, Lei and co-workers aim to uncover the genetic underpinnings of thermal adaptation across three strains of the diamondback moth (Plutella xylostella) through experimental evolution over three years under three different thermal regimes. They identify systematic differences in trait responses (e.g., survival, fecundity), metabolic profiles, gene expression, and in the amino acid sequence of the PxSODC gene, among others. These results suggest that the diamondback moth has a strong potential for rapid physiological adaptation to different thermal regimes. Overall, this is a comprehensive and generally well-executed study that addresses an important question in the face of ongoing climate change.

Strengths:

The authors employ multiple approaches to identify signatures of thermal adaptation across the three strains, such as trait performance comparisons, metabolomics, transcriptomics, and amino acid sequence comparisons. All these different angles form a convincing picture of the underlying factors that underpin thermal adaptation in this experimental system. The manuscript is also generally well written and easy to understand.

Reviewer #1 (Public review):

Summary:

This research sheds light on the nuanced role of ABHD6 in regulating AMPARs, highlighting its interaction with TARP γ-2 as a critical factor in modulating receptor gating kinetics. It is crucial to understand that although ABHD6 alone does not alter AMPAR kinetics, its presence alongside TARP γ-2 accelerates AMPAR deactivation and desensitization, thereby affecting synaptic transmission dynamics.

Strengths:

Important findings in the research include:<br /> - ABHD6 does not affect the gating kinetics of GluA1 and GluA2(Q) homomeric receptors independently.<br /> - In the presence of TARP γ-2, ABHD6 accelerates deactivation and desensitization of these receptors, regardless of their splicing or editing isoforms.<br /> - The effect is consistent for both homomeric GluA1 and GluA2(Q) receptors and heteromeric GluA1i/GluA2(R)i-G receptors.<br /> - The recovery from desensitization of GluA1 with the flip splicing isoform is slowed by ABHD6 in the presence of TARP γ-2.

Reviewer #1 (Public review):

Summary:

Brunsdon et al. present a zebrafish model of mosaic PIK3CA activation to investigate mechanisms underlying PIK3CA-related overgrowth spectrum (PROS), with a particular focus on non-cell-autonomous mechanisms of tissue overgrowth. The study is timely and addresses an important gap in the understanding of how mosaic activation of PI3K signaling leads to tissue-specific developmental abnormalities.

Using a Tol2-based mosaic expression system combined with single-cell transcriptomics, the authors provide evidence suggesting that mutant PIK3CA-expressing cells influence surrounding wild-type tissues through indirect signaling mechanisms, contributing to vascular malformations and tissue overgrowth.

Overall, the work presents an interesting and potentially impactful model for studying mosaic PIK3CA-driven overgrowth and non-cell-autonomous signaling mechanisms. However, several aspects require clarification, additional controls, and improved presentation to strengthen the mechanistic conclusions and overall impact of the study.

Strengths:

This study addresses an important and timely question by investigating the mechanisms underlying mosaic PIK3CA activation in the context of PROS, a condition for which developmental mechanisms remain poorly understood. The use of a mosaic zebrafish model is particularly appropriate, as it closely reflects the mosaic nature of PIK3CA mutations observed in patients and allows the investigation of non-cell-autonomous effects.

Another major strength of the study is the integration of single-cell transcriptomics, which provides valuable insight into potential signaling pathways involved in indirect tissue overgrowth and offers a rich dataset for hypothesis generation. The authors also propose an interesting conceptual framework in which PI3K-activated cells influence surrounding tissues through paracrine signaling, which could have broader implications beyond PROS and contribute to understanding mosaic developmental disorders more generally.

Finally, the work has potential translational relevance, as identifying mechanisms driving mosaic PI3K activation and non-cell-autonomous signaling could inform future therapeutic strategies for PROS and related conditions.

Weaknesses:

Despite these strengths, several aspects of the study require clarification and additional experimentation.

Major comments:

(1) The Tol2-based system results in mosaic overexpression of mutant PIK3CA in the presence of endogenous wild-type PIK3CA, making it difficult to determine how co-expression of WT and mutant proteins influences the observed phenotypes. While mosaic expression is relevant to PROS, a complementary approach in which endogenous PIK3CA is knocked out prior to introducing mutant variants would allow clearer interpretation of mutant-specific effects.

(2) The authors do not clearly describe the validation of editing or integration efficiency. It would be important for the authors to clarify whether sequencing was performed to confirm integration, to quantify the proportion of mosaic expression, and to measure transgene expression levels. These controls would strengthen confidence in the model and interpretation of the results.

(3) The manuscript would benefit from rescue experiments to strengthen causal conclusions. It remains unclear whether the phenotypes induced by PIK3CA PROS variants can be rescued, either through expression of wild-type PIK3CA, pharmacological inhibition of PI3K signaling, or assessment of developmental reversibility. Such experiments would strengthen the link between PI3K activation and the observed phenotypes.

(4) The authors propose candidate signaling molecules mediating non-cell-autonomous effects downstream of PI3K hyperactivation; however, these conclusions remain speculative, as no functional validation is provided. Testing selected candidate mediators identified in the RNA-seq dataset would significantly strengthen the mechanistic conclusions.

Reviewer #1 (Public review):

Summary:

The authors performed seqFISH in 26 gastruloids and performed a variety of computational analyses on these novel spatial data sets. Whilst the data is valuable and the computational concepts useful (exposure index, L-metric, ... ), the article falls short on novelty and is written using a very clunky language, often with contradictory conclusions.

Major issues:

(1) The authors did well in explaining and detailing the provenance of data and the individual experiments performed. However, their 26 gastruloid data still constitute a very limited sampling from their total organoids: one experiment pooled 4 plates at an 80-94% success rate; 6 different aggregation experiments were done, making a total of 1843 gastruloids, sampled 26 (~1-2%). A simple IF stain of 2-3 markers in a bigger sample could have given a more accurate picture of specific domains of interest and their proximity. Regardless, more information should be given about the existing samples: variation across experimental batches, differences between 300-cell vs 100-cell gastruloids that were used.

(2) Language in the manuscript should be revised. Overall the manuscript is very long, descriptive and written "impressions and beliefs" are often not adequately justified and indeed can be contradictory, e.g. in Section 1: the title states "cell types' locations ...are consistent", a few sentences down we find "there was substantial variation" and "within range of what would be considered a 'morphologically normal' gastruloid". "quite consistent", "compelling patterning", "we don't believe"... these types of expressions are best avoided and replaced with data or used and bolstered with quantitative numbers such as percentages when a given cutoff is used. Another example: "location of each cell type relative to gastruloid morphology was quite consistent the posterior region ... mainly consisted in NMPs." Given T expression in the posterior, this result phrased as such appears quite inflated, in fact, looking at cell types in Figures S1, 2a/b/c, this reviewer would state they are all but consistent and indeed it takes sophisticated analyses to find a pattern (of sorts) beyond the coarse domains expected!

(3) Figure 6 is one of the most valuable parts of the work, as the authors use the battery of analyses developed to investigate the variable and not-so-robust endothelial clusters in gastruloids. However, this investigation is still very preliminary, and it should be further linked with known biology. It is still unclear what the unique organization of this cell type is (circularity isn't convincing) and whether any signalling cues of adjacent cells could explain it. Is there any evidence that more mature endodermal cell types are generated (like the suggested "liver") to give rise to endothelial cells? It would certainly be interesting to perform IF for this cell type together with mesodermal and endodermal markers to validate seqFISH predictions on a bigger sample.

(4) Figures 1c and 6b need statistical significance assessments.

(5) The article should include an analysis of Hox colinearity expression in these gastruloids as a validation of the system.

Reviewer #2 (Public review):

Summary:

Protein synthesis - translation - involves repeated recognition and incorporation of amino-acyl-tRNAs by the ribosome. This process is a trade-off between the rate and accuracy of selection (for review see (Johansson et al, 2008; Wohlgemuth et al, 2011)). The ribosome does not just maximise the rate or the accuracy, it balances the two. Therefore, it is possible to select mutants that translate faster than the wt (but are sloppy) or that are very accurate (more than the wt) but translate slower. Slow translation is detrimental as it limits the rate of protein synthesis (and, therefore, growth) and hyper-accurate mutants accumulate mis-translated proteins, which is detrimental for the cell.

Bi and colleagues employ genetics, MIC measurements, reporter assays and structural biology to characterise the role of GidB rRNA methylase in translational accuracy in Mycobacterium smegmatis.

Strengths:

The genetics and phenotypic assays are convincing and establish the biological role of the methylase. The authors use a powerful set of complementary assays that convincingly demonstrates that the loss of GidB results in mistranslation.

Weaknesses:

Cryo-EM analysis of vacant 70S ribosomes is not sufficient for understanding the mechanisms underlying the accuracy defects in the gidB KO. Ideally, one should assemble and solve structurally near-cognate and non-cognate complexes.

References:

Johansson M, Lovmar M, Ehrenberg M (2008) Rate and accuracy of bacterial protein synthesis revisited. Curr Opin Microbiol 11: 141-147

Wohlgemuth I, Pohl C, Mittelstaet J, Konevega AL, Rodnina MV (2011) Evolutionary optimization of speed and accuracy of decoding on the ribosome. Philos Trans R Soc Lond B Biol Sci 366: 2979-2986

Reviewer #1 (Public review):

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

In this manuscript, the authors investigate the relationship between genetic codes and their robustness to single-point mutations. They construct ten alternative genetic codes by reassigning nine codons to Leu, Ser, or Ala, and assess mutational robustness using three reporter proteins subjected to error-prone PCR. This represents an interesting experimental approach to addressing the hypothesis that the standard genetic code is optimized for mutational robustness.

Reviewer #1 (Public review):

Summary:

In their article, Guo and coworkers investigate the Ca²⁺ signaling responses induced by Enteropathogenic Escherichia coli (EPEC) in epithelial cells and how these responses regulate NF-κB activation. The authors show that EPEC induces rapid, spatially coordinated Ca²⁺ transients mediated by extracellular ATP released through the type III secretion system (T3SS). Using high-speed Ca²⁺ imaging and stochastic modeling, they propose that low ATP levels trigger "Coordinated Ca²⁺ Responses from IP₃R Clusters" (CCRICs) via fast Ca²⁺ diffusion and Ca²⁺-induced Ca²⁺ release. These responses may dampen TNF-α-induced NF-κB activation through Ca²⁺-dependent modulation of O-GlcNAcylation of p65. The interdisciplinary work suggests a new perspective on calcium-mediated immune response by combining quantitative imaging, bacterial genetics, and computational modeling.

Strengths:

The study provides a new concept for host responses to bacterial infections and introduces the concept of Coordinated Ca²⁺ Responses from IP₃R Clusters (CCRICs) as synchronized, whole-cell-scale Ca²⁺ transients with the fast kinetics typical of local events. This is elegantly done by an interdisciplinary approach using quantitative measurements and mechanistic modelling.

Comments on revised version.

The revised version of the manuscript has addressed all my raised points. I'd like to thank the authors for the work they have put into the revision to make this a very compelling publication.

Reviewer #1 (Public review):

The authors present a compelling case for the necessity of age-specific templates in functional hyperalignment. Given that the brain undergoes substantial developmental, structural, and functional changes across the lifespan, a 'one-size-fits-all' canonical template is often insufficient. This study effectively demonstrates that incorporating age-congruent features significantly enhances the performance and sensitivity of hyperalignment models. By validating these findings across two independent datasets (Cam-CAN and DLBS), the paper provides robust evidence that accounting for age-related functional organization is a critical prerequisite for accurate functional alignment in lifespan research

Comments on revised version:

The authors have been exceptionally thorough in addressing the concerns raised by the reviewers. In particular, the inclusion of the supplemental analysis on the middle-aged cohort is a valuable addition that strengthens the manuscript. Furthermore, the rationale for employing a congruent template is well-articulated; this approach clearly provides a more robust and accurate foundation for reconstructing individualized connectomes. I appreciate the authors' detailed responses and have no further comments.

Reviewer #1 (Public review):

Summary:

This manuscript presents a tunable Bessel-beam two-photon fluorescence microscopy (tBessel-TPFM) platform that enables high-speed volumetric imaging with stable axial focus. The work is technically strong and broadly significant, as it substantially improves the flexibility and practicality of Bessel-beam-based two-photon microscopy. The demonstrations are generally strong and bridge a wide range of neuroimaging applications, namely vascular dynamics, neurovascular coupling, optogenetic perturbation, and microglial responses. These convincingly show that the approach enables biological measurements that are difficult or impractical with existing methods.

The evidence supporting the technical and biological claims is generally strong. The optical design is carefully motivated, clearly described, and validated through a combination of simulations and experimental characterization. The biological applications are diverse and well chosen to highlight the strengths of the proposed method, and the data are of high quality, with appropriate controls and comparative measurements where relevant.

Strengths:

(1) The optical innovation addresses a well-recognized limitation of existing Bessel-TPFM implementations, namely axial focus drift during tuning, and does so using a relatively simple, light-efficient, and cost-effective design.

(2) The manuscript provides convincing experimental evidence for this being a versatile platform to map flow dynamics across diverse vessel sizes and orientations in both healthy and pathological states.

(3) Biological demonstrations are comprehensive and span multiple domains such as hemodynamics, neurovascular coupling, and neuroimmune responses.

(4) Quantitative analyses of blood flow across vessel sizes and orientations, including kilohertz line scanning, are particularly compelling and clearly beyond the reach of standard Gaussian TPFM.

(5) Particular advantages are that higher blood slow speeds become measurable up to 23mm/sec (20x more than conventional frame scanning), and that simultaneous (Bessel-)imaging and (Gaussian-)perturbation are possible because of the stable axial focus.

Weaknesses:

(1) At present, the paper does not properly position the new Bessel-beam method against previous work, and fails to compare it to alternative fast volumetric imaging methods without Bessel beams.

(2) The cost-effectiveness of the proposed method is not well described or supported by evidence; it would be useful to include more detail or remove this claim.

(3) Some biological conclusions, e.g., regarding novel features of microglial dynamics (i.e., the observed two-wave responses and coordinated extension-retraction), are based on relatively limited sample size and would benefit from clearer discussion of variability across animals and fields of view.

(4) The use of neural network-based denoising for microglial imaging is reasonable but introduces potential concerns about trustworthiness; additional clarification of validation or failure modes would strengthen confidence in these results.

To conclude, most of the authors' claims are well supported by the data. The central conclusion, namely that tBessel-TPFM provides tunable volumetric imaging enabling experiments not feasible with existing two-photon approaches, is justified. Some biological interpretations would benefit from a more cautious framing, but they do not undermine the main technical and methodological contributions of the study. This is a strong and technically rigorous manuscript that makes a substantial methodological advance with clear relevance to neuroscience and intravital imaging. Minor clarifications and a slightly more measured discussion of certain biological findings are recommended.

Reviewer #2 (Public review):

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

The authors pair analysis of replication timing and allele-specific expression in clonal populations of primary human cells. They combine these data with previously published data on clones from transformed human cell lines. They identify a number of genomic regions that display asynchronous replication timing in at least one clone and correlate these regions with allele-specific expression of genes within them. They also observe that several interesting gene sets, including genes that are associated with human diseases, map to asynchronously replicating regions. This is a good experimental approach that builds on already published data demonstrating the connection between allelic imbalance and replication timing.

Joint Public Review:

This manuscript puts forward the provocative idea that a posttranslational feedback loop regulates daily and ultradian rhythms in neuronal excitability. The authors used in vivo long-term tip recordings of the long trichoid sensilla of male hawkmoths to analyze spontaneous spiking activity indicative of the ORNs' endogenous membrane potential oscillations. This firing pattern was disrupted by pharmacological blockade of the Orco receptor. They then use these recordings together with computational modeling to predict that Orco receptor neuron (ORN) activity is required for circadian, not ultradian, firing patterns. Orco did not show a circadian expression pattern in a qPCR experiment, and its conductance was proposed to be regulated by cyclic nucleotide levels. This evidence led the authors to conclude that a post-translational feedback loop (PTFL) clockwork, associated with the ORN plasma membrane, allows for temporal control of pheromone detection via the generation of multi-scale endogenous membrane potential oscillations. The findings will interest researchers in neurophysiology, circadian rhythms, and sensory biology. However, the manuscript has limited experimental evidence to support its central hypothesis and is undermined by several assumptions that underlie their data analysis and model builds, as well as insufficient biological data including critical controls to validate and/or fully justify the model the authors are proposing.

Strengths:

The authors raise several intriguing model-based hypotheses regarding the mechanisms that underlie the generation of olfactory rhythms. The electrophysiological approach and the long-term recording paradigm are elegant and technically impressive. In the revised version, the authors have added additional qPCR data supporting the lack of rhythmic Orco transcript expression and included a new figure suggesting that cAMP can modulate Orco conductance.

Major weaknesses:

(1) The cAMP experiment was only conducted at one time-point, which is insufficient to support the central claim that "AMP and cGMP may have ZT-dependent effects on Orco conductivity".

(2) The revised manuscript continues to rely heavily on prior publications or defers key mechanistic questions (or important manipulations) to future studies. In its current form, the evidence presented remains insufficient to support the central claim that a PTFL constitutes the primary underlying circadian clock mechanism. The proposed model is intriguing, but the data provided do not yet directly demonstrate the novel mechanism.

Reviewer #1 (Public review):

M. tuberculosis exhibits metabolic flexibility, enabling it to adapt to various environmental stresses, including antibiotic treatment. In this manuscript, Serafini et al. investigate the metabolic remodeling of M. tuberculosis used to survive iron-limited conditions by employing LC-MS metabolomics and 13C isotope tracing experiments. The results demonstrate that metabolic activity in the oxidative branch of the TCA cycle slows down, while the reductive branch is reverted to facilitate the biosynthesis of malate, which is subsequently secreted.

Overall, this study is experimentally well-designed, particularly the use of 13C isotope tracing to monitor TCA cycle remodeling under iron-limited conditions. The findings are valuable as they offer potential new targets for antibiotics aimed at non-replicating M. tuberculosis occurring in the hosts.

Comments on revised version:

All concerns are well addressed.

I have one minor concern: Page 3 line 16 - Fig. 1G & H: The kinetics of ATP levels between H37Rv and Erdman seem different; Erdman induces greater ATP at days 2 and 3 after DFO treatment, which was not clear in H37Rv. Fig. 1I shows NAD/NADH ratio not NADH/NAD ratio. Please change it to NADH/NAD+ to be consistent with Supplement Fig. 1 result. Include the 17-day result of NADH/NAD+ in the discussion section to explain the different viability between the two strains.

Reviewer #1 (Public review):

This paper aims to improve the accuracy of predictions of the impact of ITN strategies by developing a method to estimate duration of ITN access and use over time on a subnational scale from cross-sectional survey data and the numbers ITNs received annually. The subnational estimates are then input into a mathematical model to predict clinical cases under different ITN distribution strategies.

Strengths:

The approach is novel and addresses a useful and timely topic. It makes use of available routine data, and has considered all of the relevant components of ITN distributions.

The authors have made revisions, particularly to the methods, appendices and title - leaving the paper easier to follow, and with a clear, consistent aim. The assumptions are clearly stated.

Weaknesses:

The weaknesses are shared with other models of a similar complexity - it is not easy for a casual reader to fully understand the model or the implications of the assumptions which were required to be made. That routine data is used is good for availability, but data quality may be an issue in some places.

Reviewer #1 (Public review):

Summary:

Launay et al., conducted a screen of PDE in 25 new Rhabditidae species through cytological approaches and found PDE is detected in 17 out of 25 species, representing 12 out of 17 genera within the family. This work is significant because it expands PDE from a few known nematodes to a much broader set of Rhabditidae species.

Strengths:

By demonstrating PDE across many genera with the exception of C. elegans and some other Caenorhabditis species, the study provides an important resource for investigating PDE's evolutionary origins, mechanisms of genome reorganization and DNA repair, and its functional consequences.

Most of the observed PDEs were supported by solid evidence through a survey-style cytological screen (PDE detected in 17/25 species and in 12/17 genera), which supports the main claim of widespread occurrence.

Weaknesses:

Although most PDE claims are supported by solid evidence, some of the existing data do not describe the depth of characterization, e.g., how many replicates were conducted for each species? How reproducible are the claimed PDEs between embryos in terms of timing and cell identities destined for PDE? Is it possible to validate a subset of PDE with independent evidence, especially for those with marginal PDE? This is important because some dying embryos may fail to maintain their chromosome integrity and release some of the broken DNAs, some others may suffer from noise such as intracellular parasites, for example, microsporidia, or even highly condensed mitochondrial DNAs.

Reviewer #1 (Public review):

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

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

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

Major points

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

This paper presents a toolkit for the transformation of Blastocystis. The authors have screened a number of selectable agents, promoters and reporter genes and present their findings. This resource will be of immense use to those in the Blastocystsis field, as well as those seeking to establish transformation tools in other species where such tools do not yet exist. Establishing new transformation tools is extremely challenging, and the authors have done an excellent job.

Strengths:

The authors have carried out a systematic screen of promoters, reporter genes and selectable agents. They have screened numerous for each, and all the data is presented. It is good to see when things did not work as well as when things did, so this data set is extremely useful indeed.

Weaknesses:

The findings are reported by reporter gene assay (microscopy). No evidence is given using genetics. The authors claim that the DNA is maintained episomally. However, could it be possible that there is integration? No PCRS/RT-PCRs are shown (although it can safely be assumed that the DNA/RNA is present where the transformation was successful), nor are any Western blots. These would have been useful to show that the P2A ribosomal skipping had occurred, and that proteins were expressed individually rather than as a polyprotein.

Joint Public Review:

In this manuscript, the authors proposed an approach to systematically characterise how heterogeneity in a protein signalling network affects its emergent dynamics, with particular emphasis on drug-response signalling dynamics in cancer treatments. They named this approach Meta Dynamic Network (MDN) modelling, as it aims to consider the potential dynamic responses globally, varying both initial conditions (i.e., expression levels) and biophysical parameters (i.e., protein interaction parameters). By characterising the "meta" response of the network, the authors propose that the method can provide insights not only into the possible dynamic behaviours of the system of interest but also into the likelihood and frequency of observing these dynamic behaviours in the natural system.

The authors study the Early Cell Cycle (ECC) network as a proof of concept, focusing on pathways involving PI3K, EGFR, and CDK4/6 with the aim of identifying mechanisms that may underlie resistance to CDK4/6 inhibition in cancer. The biochemical reaction model comprises 50 state variables and 94 kinetic parameters, implemented in SBML and simulated in Matlab. A central component of the study is the generation of large ensembles of model instances, including 100,000 randomly sampled parameter sets intended to represent intra-tumour heterogeneity. On the basis of these simulations, the authors conclude that heterogeneity in kinetic rate parameters plays a stronger role in driving adaptive resistance than variation in baseline protein expression levels, and that resistance emerges as a network-level property rather than from individual components alone. The revised manuscript provides additional clarification regarding aspects of the simulation and filtering procedures and frames the comparison with experimental data as qualitative. Nonetheless, the study is best interpreted as a theoretical and exploratory analysis of the model's behaviour under heterogeneous conditions. Consequently, questions remain regarding the biological grounding of the sampled parameter regimes and the extent to which the reported frequencies of resistance-associated behaviours can be directly interpreted in physiological terms.

While the authors propose a potentially useful computational framework to explore how heterogeneity shapes dynamic responses to drug perturbation, a number of important conceptual and methodological concerns remain to be addressed:

(1) The sampling of kinetic parameters constitutes the backbone of the manuscript, yet important concerns remain regarding its biological grounding and transparency. Although the revised version provides additional clarification on the exploration of "model instances", it is still not sufficiently clear how parameter values and initial conditions are generated, nor how the chosen ranges relate to biological measurements. The kinetic rates are sampled over broad intervals without explicit justification in terms of experimentally measured bounds or inferred distributions. As a consequence, it remains uncertain whether the ensemble of simulated behaviours reflects physiologically plausible cellular regimes or primarily the properties of the assumed parameter space. In this context, the large-scale sampling (100,000 parameter sets) resembles a Monte Carlo exploration of the model rather than a biologically calibrated representation of tumour heterogeneity.

Furthermore, the adequacy of the sampling strategy in such a high-dimensional space (94 free parameters) remains open to question. In the absence of biologically informed constraints, the combinatorial space of possible parameter configurations is vast, and it is unclear to what extent the sampled ensembles can be considered representative. This issue is particularly relevant because the manuscript interprets the frequency of resistance-associated behaviours as indicative of their likelihood.

The validation presented in Figure 7 does not fully resolve these concerns. The comparison with experimental data is qualitative, and the simulations are performed in arbitrary time units, which complicates direct interpretation alongside time-resolved experimental measurements. Moreover, certain qualitative discrepancies between simulated and experimental trends (e.g., persistent versus decreasing CDK4/6 activity) are not thoroughly discussed. As this figure represents the primary empirical reference point in the manuscript, the extent to which the model captures experimentally observed dynamics remains uncertain.

Finally, aspects of presentation continue to limit transparency. Parameter ranges are described at different points in the manuscript but are not consolidated clearly in the Methods, and the definition of initial conditions remains ambiguous - particularly whether these correspond to conserved quantities or to the dynamic variables used to initialise simulations. In addition, the exact number of model instances underlying specific analyses and figures is not always explicit. Greater clarity on these issues is essential for assessing reproducibility and for interpreting the quantitative claims of the study.

(2) A central conclusion of the manuscript is that heterogeneity in protein-protein interaction kinetics is a stronger driver of adaptive resistance than heterogeneity in protein expression levels. To assess the latter, the authors fix a nominal set of kinetic parameters and generate 100,000 random initial concentrations for the 50 model species. However, according to the simulation protocol described in the manuscript, each trajectory includes three phases: (i) simulation under starvation conditions to equilibrium, (ii) mitogenic stimulation to a second ("fed") equilibrium, and (iii) application of drug treatment. The equilibrium concentrations reached in phases (i) and (ii) are determined by the kinetic parameters of the model and are independent of the initial concentrations, provided the system converges to a stable steady state. In dynamical systems terms, stable equilibria are defined by the parameter set and attract all initial conditions within their basin of attraction. Since the kinetic parameters are fixed in this experiment, the pre-treatment equilibrium that serves as the starting point for drug application should likewise be fixed. Under these conditions, it is therefore not unexpected that sampling a large number of initial concentrations has limited influence on the treated dynamics.

This raises conceptual questions about the interpretation of the comparison between kinetic and expression heterogeneity. If the system converges to a unique stable steady state prior to treatment, then variability in initial concentrations does not propagate into variability in drug response, and the observed dominance of kinetic heterogeneity may partly reflect this structural property of the model rather than a biological principle. Clarification is needed regarding whether multiple steady states exist under the nominal parameter set, and if so, how basins of attraction are explored.

More broadly, it remains unclear why initial protein concentrations can be sampled independently of the kinetic parameters. In biological systems, steady-state expression levels are typically determined by the underlying kinetic rates. A more consistent approach might require constraining initial concentrations to correspond to equilibrium states of the chosen parameter set, thereby introducing relationships between at least some of the 50 initial conditions and the 94 kinetic parameters. Finally, the manuscript employs a non-standard terminology regarding "initial conditions," which may further obscure interpretation of these results and would benefit from clarification.

(3) The technical implementation of the modelling and simulation framework remains difficult to evaluate due to insufficient methodological detail. Although the authors state that kinetic parameters are randomly sampled, the manuscript does not specify the distributions from which parameters are drawn, nor whether potential correlations between parameters are considered or explicitly ignored. Without this information, it is not possible to assess how implicit modelling assumptions shape the ensemble of simulated behaviours. Given that the conclusions rely on frequency-based interpretations across sampled parameter sets, greater transparency regarding the sampling procedure is essential.

A further concern relates to the parameter filtering step. The authors report that the "vast majority" of sampled parameter sets produced systems that were "too stiff," and that these were excluded on the grounds that stiff dynamics are not biologically plausible. However, the manuscript does not clearly define how stiffness is assessed, nor why stiffness is interpreted as biologically unrealistic rather than as a numerical property of the formulation. In standard practice, stiff systems are typically handled using appropriate implicit solvers rather than being discarded. Similarly, parameter sets that produce negative state values are excluded, yet such behaviour may arise from numerical artefacts rather than from intrinsic model inconsistency. The rationale for excluding these parameter sets, rather than adapting the numerical scheme, is not sufficiently justified.

The reported rejection rate - approximately 90% of sampled parameter sets - is substantial and raises questions regarding the interplay between model structure, parameter ranges, and numerical methods. As currently described, the filtering step appears to select parameter sets based primarily on computational tractability rather than on experimentally motivated biological criteria. The manuscript would be strengthened by clarifying whether the retained parameter sets are representative of biologically meaningful regimes, and by distinguishing clearly between exclusions based on biological plausibility and those arising from numerical considerations.

Finally, important aspects of the simulation protocol require clarification. The model is simulated under "fasted" and "fed" conditions until equilibrium is reached, yet the criterion used to determine convergence is not specified. It would be important to describe how equilibrium is assessed (e.g., based on the norm of the time derivatives). Additionally, it remains unclear whether the mitogenic stimulus applied in the "fed" phase is assumed to be constant over time and, if so, how this assumption relates to biological experimental conditions. Greater detail on these implementation choices is necessary to ensure interpretability and reproducibility.

(4) The manuscript states that the modelling conclusions are strongly supported by existing literature; however, the validation presented does not fully substantiate this claim. As noted above, the comparison with CDK2 and CDK4/6 experimental data remains qualitative, and the use of arbitrary simulation time units complicates interpretation of temporal agreement. The extent to which the model quantitatively or mechanistically recapitulates experimentally observed dynamics therefore remains uncertain.

The claim that the model reproduces known resistance mechanisms is also difficult to assess in light of Figure S10, where a large fraction of network nodes (~80%) appear implicated in resistance under some conditions. If most components of the network can, in at least some parameter regimes, be associated with resistance phenotypes, the resulting lack of selectivity weakens the strength of model-based validation. It becomes challenging to distinguish specific mechanistic insights from generic consequences of network connectivity.<br /> In addition, the Supplementary Information notes that certain components of the mitogenic and cell-cycle pathways were abstracted or excluded in order to maintain computational tractability. While such abstraction is understandable in a large ODE framework, it raises interpretative questions. Proteins identified as potential resistance drivers within the model may, in some cases, represent aggregated or simplified pathway effects. Clarifying in the main text how such abstractions may influence the attribution of resistance mechanisms would strengthen the biological interpretation of the results.

Drug inhibition is central to the manuscript's conclusions. The revised version clarifies that inhibition is implemented as a fixed fractional modification of specific kinetic rate laws. This abstraction is appropriate for exploring network-level responses, but it represents a stylised perturbation rather than a pharmacologically calibrated model of drug action. For full interpretability and reproducibility, the mathematical form of the modified rate laws, as well as the timing of inhibition relative to network equilibration, should be specified unambiguously. The biological implications of the findings depend critically on understanding this modelling choice.

The one-at-a-time perturbation analysis presented in Figure 5 provides an interpretable ranking of first-order control points across the ensemble and offers mechanistic insight into primary sensitivities of the network. However, many targeted therapies act on multiple components, and resistance frequently arises through combinatorial mechanisms. The reported rankings should therefore be interpreted as identifying primary influences under isolated perturbations, rather than as a comprehensive account of multi-target drug behaviour.

Overall, the manuscript succeeds in presenting a conceptual and exploratory framework for analysing how signalling network topology can shape the qualitative landscape of adaptive responses under heterogeneous kinetic conditions. Its principal contribution lies in establishing a systematic platform for large-scale in silico exploration. At the same time, the current limitations in biological calibration, parameter grounding, and validation constrain the extent to which the conclusions can be interpreted as predictive or quantitatively representative of specific tumour contexts. Addressing these issues would further strengthen the connection between the theoretical landscape described here and experimentally observed resistance dynamics.

Reviewer #2 (Public review):

Summary:

This paper attempts to examine how rare, extreme events impact decision-making in rats. The paper used an extensive behavioural study with rats to evaluate how the probability and magnitude of outcomes impact preference. The paper, however, provides limited evidence for the conclusions because the design did not allow for the isolation of the rare, extreme events in choice. There are many confounding factors, including the outcome variance and presence of less-rare, and less-extreme outcome in the same conditions.

Strengths.

(1) The major strength of the paper is the significant volume of behavioural data with a reasonable sample size of 20 rats.

(2) The paper attempts to examine losses with rats (a notoriously tricky problem with non-human animals) by substituting time-outs as a proxy for losses. This allows for mixed gambles that have both gain and loss possible outcomes.

(3) The paper integrates both a behavioural and a modelling approach to get at the factors that drive decision-making.

(4) The paper takes seriously the question of what it means for an event to be rare, pushing to less frequent outcomes than usually used with non-human animals.

Weaknesses:

(1) The primary issue with this work is that the primary experimental manipulation fails to isolate the rare, extreme events in choice. As I understand the task, in all the conditions with a rare extreme event (e.g., 80 pellets with probability epsilon), there is also a less-rare, less-extreme event (e.g., 12 pellets with probability 5). In addition, the variance differs between the two conditions. So, any impact attributable to the rare, extreme event could be due to the less rare event or due difference in the variance (or other statistical moments, like skew or kurtosis). That the distributions can be shown to be different under specific assumption to value maximizing agents (e.g., with Jensen Gaps and Table 2) is not really relevant to what rats are sensitive and what drive their behaviour. The design here does not support the conclusions. Finally, by deliberately confounding rarity and extremity, the design does not allow for assessing the impact of either aspect on rat behaviour.

(2) The RL modelling work also fails to show a specific impact of the rare extreme event. As best as I can understand Eq 2, the model provides a free parameter that adds a bonus to the value of either the two options with high-variance gains (A and V in the paper) or to the two options with high-variance losses (F and V in the paper). Or equivalently to the ones with "Jackpots" vs the ones with "Black Swans" (see Point 1 above as to how these different aspects are all confounded in this design). This parameter seems to only depends on whether this option could have possibly yielded the rare, extreme outcome (i.e., based on the generative probability) and was not connected to its actual appearance. [This point is unclear as the text says this, but the rebuttal states otherwise; plus some options never received the REE, see Table S11]. That makes it a free parameter that just bumps up (or down) the probability of selecting a pair of options. That may be due to presence of the REE or the other rare event or just the variance difference. Moreover, in the case of the "black swan" or high-variance loss conditions, this seems very much like a loss aversion parameter, but an additive one instead of a multiplicative one. Is there a theoretical claim here that "extreme losses" need an additive loss-aversion parameter?

(3) The paper presented the methods and results with lots of neologisms and fairly obscure jargon (e.g., fragility, total REE sensitivity). That might it very hard to decipher exactly what was done and what was found. For example, on p. 4, the use of concave and convex was very hard to decipher; the text even has to repeat itself 3 times (i.e., "to repeat" and "in other words") and is still not clear. It would be much clearer (and probably accurate) to say that the options varied along the variance dimension, separately for gains and losses. Option A was low-variance gains and losses. Option B was low-variance losses and high-variance gains. Option C was high-variance losses and low-variance gains, and Option D was high-variance losses gains. That tells much more clearly what the animals experienced without the reader having to master a set of new terminologies around fragility and robustness, which brings a set of theoretical assumption unnecessarily into the description of the experimental design. Alternatively, if the authors are wary of using the term "variance" because other moments of the distribution also differ, they could use "high-value gains" or "high-value losses" or something else which does not obscure the experimental design with jargon. Again, this goes back to point 1 above, whereby the different options differ on so many dimensions (as is made even more apparent in the rebuttal) that the design cannot isolate the impact of the variables of interest.

(4) Were the probabilities shuffled or truly random (seem to be fixed sequences, so neither)? What were the experienced probabilities? Given the fixed sequences, these experienced ("ex-post") probabilities, could differ tremendously from the scheduled ("ex ante") probabilities. It's quite possible than an animal never experienced the rare, extreme event for a specific option. From Table S11, that is guaranteed to have happened in that 4 animals only ever experienced the "black swan" outcome once. It's even possible (if they only picked a specific option on the 10th/60th choices by chance), that they only ever experienced that rare extreme event. This point still cannot be known given the information provided, which does not break down outcomes by options. The Supplemental in Table S11 only gives overall numbers but does not indicate what the rats experienced for each choice/option-which is what matters here. A simple table that indicates for each of the 4 options, how often they were selected, and how often the animals experienced each of the 6-8 possible outcome would make it much clearer how closely the experience matched the planned outcomes. In addition, by restricting the rare outcome to either the 10th or 60th activations in a session, these are not random. Did the animals learn this association? The text states that they did not, but no evidence is provided.

(5) The choice data are generally presented in an overprocessed fashion with a sum and a difference (in both figures and tables). The basic datum (probability/frequency of selecting each of the 4 options) is not provided directly in the main text, even if it can theoretically be inferred from the sum and the difference. New right side of Table S4 is probably the most valuable piece in terms of explaining what rats did and should be highlighted a lot more. Inspection of that table reveals some interesting (and potentially worrying) results. Most notably, the vast majority of responding happens on the "anti-fragile" and "robust" option, often totalling around 90% of all selections, especially amongst the most common blue rats. Alas, those were all those the two options that were deliberately assigned to the two most preferred holes in the training phase (see p. 26). Does this reflect genuine preference for reward distributions or does this reflect a spatial hole bias? The assignment strategy makes this impossible to tell apart.

(6) There is insufficient detail provided on the inferential statistical tests (e.g., no degrees of freedom or effect sizes), and only limited information on exactly what tests were run and how (bootstrapping, but little detail). Without code or data (only summary information is provided in the supplement), this is difficult to evaluate. In addition, the studies seem not to pre-registered in any way, leaving many research degrees of freedom. Not all studies need to be pre-registered and sometimes discovery of new things requires exploratory work, but preregistration does provide additional safeguards against overemphasizing post-hoc detected patterns-a serious issue in behavioural science. Moreover, this promotes transparency in reporting results and analyses, allowing for a better assessment of the strength of evidence for a claim. For example, here, were any alternative analysis pipelines attempted? Also, there were many sub-groupings of the animals and subsequent comparisons between them which all seemed post-hoc. On what grounds were these divisions made-were other divisions examined as well?

(7) On p. 12 (Fig 4), there is an attempt to look at the impact of a rare, extreme event by plotting a measure of preference for the 10 trials before/after the rare, extreme event. In the human literature, the main impact of experiencing a rare, extreme event is what is known as the wavy recency effect (See Plonsky et al. 2015 in Psych Review for example, now cited). What this means is that there tends to there tends to be some immediate negative recency (e.g., avoiding a rare gain) followed by positive recency (e.g., chasing the rare gain). Typically, this refers to the specific option that yielded that outcome. First, as the other analyses do, the current analysis combines choice of the option that yielded the rare outcome with choice of other options, so that cannot directly assess the impact of the rare, extreme event on choice. Also, using a 10-trial window would thus obscure any impact of this rare, extreme event. There is mention of the very next trial, but an analysis that looks at the 10-trial time course trial-by-trial could reveal any impact that might be predicted from the human literature.

(8) As I understood the method (p. 31), the assignment of options to physical locations was not random or counterbalanced, but deliberately biased to have one of the options in the preferred location. This would seem to create a bias towards a particular option and a bias away from the other options, which confounds the preference data in subsequent analyses. Table S4 reinforces this concern where the vast majority of response are clustered in the two most preferred options from training.

(9) Are delays really losses? This is a big assumption. Magnitude and delay are different aspects of experience, which are not necessarily commensurable and can be manipulated independently. And, for the model, how were these delays transformed into outcomes for the model. Eq 1 skips over that. Is there an assumption of linearity? In addition, I was not wholly clear if the delays meant fewer trials in a session or if the delays merely extended the session and meant longer delays until the next choice period.

Other points:

(1) I think the authors still misunderstand the concept of "hot-stove effects". The idea is that the experience of a very bad outcome can lead to avoiding the situation again (i.e., not sampling that option) and can provide the appearance of oversensitivity to that bad outcome. Here, that might be more thought as "black-swan avoidance". Imagine if, to the rat, all options are equal in value, then some initial bad luck in encountering the black swan might make the animal avoid that option, even though with enough experience, then it would have been equal in value.

(2) I am still not convinced that the Jensen inequalities add to this paper in terms of understanding the rat behaviour. That may be more suited for a different paper about the statistical and mathematical properties of certain generative distributions, but not here given what rats actually choose and experience.

(3) Providing the data open access is very good. The code, however, should be equally available and not just upon request. Code needs to be available for assessment during peer review and for reproducibility checks. There are substantial enough problems with reproducibility in the field that code availability should be a minimum criterion for publication (see Miske et al., 2026 in Nature for the most recent large-scale evaluation of this problem).

(4) The paper still somewhat mischaracterizes the literature on rare events, posing it as a series of "exceptions", rather than recognizing that a huge chunk of the literature uses rare events rarer than 10%. Also, there is even existing terminology in that literature for exactly the situation that is being created here-rare treasures (aka jackpots here) and rare disasters (aka Black Swans here).

(5) Defining the observed behaviour in terms convexity, instead of stating choices more plainly obscures what is done/found. This is especially the case here because convex and concave mean different things when applied to gains/losses in terms of whether or not that option can lead to the REE. The use of the terms obscures rather than clarifies and probably is best left for the discussion (and maybe the intro) when mapping from theoretical distributions to the experiment at hand. In the paper, even the bottom of p.5 seems to incorrectly define "Total Sensitivity" as the combined proportion of selecting convex options in either domain, which does not map how convex is defined in Fig 1B or elsewhere in the text.

(6). Fig 1C is baffling. Why are probabilities drawn moving away from the origin? The standard scientific plotting convention is for numbers to grow when moving away from the origin. That would be vastly clearer. Also, the color coding is confusing. Green-red maps onto convex-concave, but that would naturally seem to indicate gains vs losses, not convex vs concave. And why are probabilities growing larger in both directions from the origin? Much more sensible to communicate the procedure would likely be a standard plot of magnitude vs probability.

(7) Discussion: I think the main difference between the human situations discussed and this experiment is that humans have not experienced those rare "black swan" outcomes. Rather, they hear about the disasters that are possible and do not incorporate that information, as discussed in the description-experience literature already cited in this paper (though not in that context).

Reviewer #1 (Public review):

I read this paper with great interest based on my experience in insect sciences. Previous concerns:

(1) The paper has an original biological question that is overly broad and mechanistically ambitious. The central biological question, namely how CLas infection enhances fecundity of Diaphorina citri via dopamine signaling, is clearly stated and well motivated by previous literature. However, my advice to the authors is that, while the general question is clear, the manuscript attempts to answer multiple mechanistic layers simultaneously. As a result, I feel that the biological narrative becomes diffuse, especially in later sections where DA, miRNA regulation, AKH signaling, and JH signaling are all proposed as parts of a single linear cascade. In summary, my key concern is that the paper often moves from correlation to causal hierarchy without fully disentangling whether these pathways act sequentially, in parallel, or redundantly. A more explicitly framed primary hypothesis (e.g., "DA-DcDop2 is necessary and sufficient for CLas-induced fecundity") may improve conceptual clarity.

(2) On the novelty of the data, I feel they are moderately novel, with substantial confirmatory components. If I am correct, the novel contributions include the identification of DcDop2 as the DA receptor responsive to CLas infection in D. citri, the discovery that miR-31a directly targets DcDop2, which is supported by luciferase assays and RIP, and thirdly, the integration of dopamine signaling into the already-described CLas-AKH-JH-fecundity framework. My advice to the authors is to focus more on the manuscript's novelty, which lies more in pathway integration than in discovering fundamentally new biological phenomena. This is appropriate for a mechanistic paper, but should be framed as an extension of existing models rather than a paradigm shift.

(3) On the conclusions, I recommend that the authors modify their statements a little. I feel that there are some overstated or insufficiently supported claims. For instance, the assertion that CLas "hijacks" the DA-DcDop2-miR-31a-AKH-JH cascade implies direct pathogen manipulation, but no CLas-derived effector or mechanism is identified. Also, that the model suggests a linear signaling hierarchy, but the data largely show correlation and partial dependency rather than strict epistasis. In third, the term "mutualistic interaction" may be too strong, as host fitness costs outside fecundity (e.g., longevity, immunity) are not evaluated. In conclusion, I confirm that the data support a functional association, but mechanistic causality and evolutionary interpretation are somewhat overstated.

Comments on revised version:

The authors provided a satisfactory revision.

Reviewer #1 (Public review):

(1) In this study, the authors aimed at characterizing Huntington's Disease (HD) - related microstructural abnormalities in the basal ganglia and thalami as revealed using Soma and Neurite Density Imaging (SANDI) indices (apparent soma density, apparent soma size, extracellular water signal fraction, extracellular diffusivity, apparent neurite density, fractional anisotropy and mean diffusivity).

(2) The study implements a novel biophysical diffusion model that extends up-to-date methodologies and presents a significant potential for quantifying neurodegenerative processes of the grey matter of the human brain in vivo. The authors comment on the usefulness of this technique in other pathologies, but they exemplify only with multiple sclerosis. Further development of this, building evidence should be provided.

(3) Study found that HD-related neurodegeneration in the striatum accounted significantly for striatal atrophy and correlated with motor impairments. HD was associated with reduced soma density, increased apparent soma size and extracellular signal fraction in the basal ganglia, but not in the thalami. Additionally, these affects were larger at manifest stage.

(4) The results of this work demonstrate the impact of HD on basal ganglia and thalami which can be further explored as a non-invasive biomarker of disease progression. Additionally, the study shows that SANDI can be used to explore grey matter microstructure in a variety of neurological conditions.

Comments on revised version.

I have no further comments. Thank you

Reviewer #1 (Public review):

Integrating large-field stimulation with a retinotopic atlas, this study introduces an fMRI-based method for measuring contrast sensitivity across the visual field. Retinotopy was assessed using pRF mapping and a calibrated Benson atlas. The authors validate their method by replicating known patterns of contrast sensitivity across eccentricities and visual field quadrants in healthy subjects, and demonstrate its potential clinical utility through case studies of both simulated and real visual field loss.

Comments on revisions:

I appreciate the addition of the quadrant-scotoma condition and the authors' clarification that the goal is to demonstrate individual-level detection sensitivity. The 95% CI argument is reasonable, and I am satisfied with framing the simulated-scotoma work as proof-of-concept.

Reviewer #1 (Public review):

Summary:

In this study, the authors aim to characterize how moment-to-moment fluctuations in arousal during wakefulness shape large-scale functional brain connectivity. Using pupil diameter as an index of arousal and high-field functional imaging, they seek to determine whether arousal-related modulation of connectivity is uniform across the brain or organized into structured patterns, and whether such patterns show hemispheric asymmetry. The work further aims to assess whether these organizational features generalize across resting-state and naturalistic viewing conditions.

Strengths:

The study addresses an important and timely question regarding how spontaneous variations in arousal influence whole-brain communication during wakefulness. The dataset is rich, combining high-field imaging with concurrent physiological measurements, and the analyses are ambitious in scope. A key strength is the attempt to move beyond region-based effects and to describe arousal-related modulation at the level of large-scale connectivity organization. The comparison across rest and movie viewing provides useful context and suggests a degree of consistency across behavioral states.

Weaknesses

All analyses are based on 7T ultra-high-field imaging. The manuscript does not address whether the reported arousal-related patterns, including the community structure and hemispheric asymmetries, are expected to be reproducible at standard 3T field strengths. It therefore remains unclear whether the findings depend critically on the use of high-field data or whether they would generalize to more widely available datasets, limiting the broader applicability of the results.

Reviewer #1 (Public review):

This is an excellent paper from Dr. Yokoyama and colleagues. The experiments are technically demanding, given the very low cell numbers and the challenges of working with implantation sites at gestational days 6.5, 10.5, and 14.5. Overall, the impact of TGF-β receptor II deficiency in the NK lineage on uterine trNK cell numbers and litter size is convincing, and the authors' conclusions are well supported by the data. Less convincing, however, is the claim that the decrease in trNK cells is compensated by an increase in cNK cells; rather, the absence of TGF-β receptor II appears to result in an overall reduction of NK/ILC1 cells.

Comments on revised version:

I thank the authors for addressing all my comments from my initial review.

Reviewer #1 (Public review):

Intron retention is observed in many long noncoding RNAs. The authors here used a powerful genome-wide screening strategy to identify proteins controlling intron retention in the long noncoding RNA PURPL. One of the top hits across multiple cell lines surprisingly, was U2AF2, which is well known to bind the polypyrimidine tract close to the 3' splice site to promote splicing. Nonetheless, U2AF2 is working in the opposite direction here. Convincing follow-up RT-PCR experiments confirmed that knocking down U2AF2 does indeed lead to reduced intron retention of PURPL. The authors then show that this intron retention event is functionally important for both the nuclear retention of PURPL as well as its ability to enhance cell proliferation.

The authors then used transcriptome-wide analyses to look for additional intron retention events affected by U2AF2. Among the ~250 genes with decreased intron retention (more splicing) upon U2AF2 knockdown was MALAT1, a well-established long noncoding RNA that normally localizes to nuclear speckles. Depletion of U2AF2 or removal of the MALAT1 2nd intron resulted in reduced speckle localization and cell migration, revealing a critical and fascinating role for this intron retention event. Overall, the authors have used a set of complementary approaches to clearly demonstrate a very intriguing role for U2AF2 in controlling intron retention and functionality of a set of long noncoding RNAs.

I feel the current work has revealed an important role of intron retention in controlling the localization and functionality of long noncoding RNAs, which is likely broad in scope and is likely regulated by cell state.

One experimental suggestion: The authors show that expressing intron-2 containing PURPL in PURPL-depleted cells is sufficient to induce faster proliferation, but a valuable comparison would be identifying the phenotype expressing spliced PURPL transcript.

Reviewer #1 (Public review):

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

Summary:

The authors generated mouse and zebrafish models for DeSanto-Shinawi Syndrome, caused by loss-of-function variants in the WAC gene. Using these vertebrate systems, they demonstrate conserved craniofacial and social-behavioral phenotypes that parallel human clinical features, along with deficits in GABAergic markers. They observe increased seizure susceptibility and male-biased brain volumetric changes in Wac mutant mice. Together, these findings begin to define the biological consequences of Wac haploinsufficiency and provide valuable resources for future mechanistic studies.

Strengths:

WAC is a high-confidence neurodevelopmental disorder gene and one of the genes identified by large-scale exome sequencing efforts, including the Satterstrom et al. (2020) autism spectrum disorder cohort. This study establishes the first vertebrate Wac models, addressing a major gap in the understanding of DeSanto-Shinawi Syndrome, and provides a framework for studying other syndromic forms of autism. The models generated will be impactful and useful to the community to study and understand DeSanto-Shinawi Syndrome.

The cross-species analysis is important and well executed, and reveals both conserved and divergent phenotypes. The behavioral and anatomical assays are rigorously executed and well-controlled, and the inclusion of RNA-sequencing analyses adds valuable insights into the mechanisms underlying brain function in Wac mutants. Notably, the RNA-seq data reveal upregulation of several clustered protocadherins, genes central to neuronal identity and cell-cell interactions, which are known to be regulated by dynamic developmental regulation of chromatin architecture. This observation provides an intriguing hint that could link Wac function to higher-order chromatin organization and neuronal connectivity.

Weaknesses:

The evidence is solid, though the study remains incomplete in its mechanistic depth and molecular interpretation. The authors compellingly describe behavioral, anatomical, and transcriptomic phenotypes associated with WAC loss, yet do not explore how WAC mechanistically regulates chromatin or transcription. Given prior evidence that WAC interacts with the RNF20/40 ubiquitin ligase complex and promotes histone H2B ubiquitination and transcriptional elongation, the paper would benefit from a discussion of these functions as a potential link between Wac haploinsufficiency and the observed changes in neuronal gene expression. Similarly, the authors mention WAC's WW and coiled-coil domains but do not consider how these domains could mediate nuclear interactions or recruitment of transcriptional cofactors that shape gene regulation and chromatin organization in neurons.

The transcriptomic analysis is rich but largely descriptive. Although the upregulation of clustered protocadherins is particularly intriguing, these findings are not validated or localized to specific neuronal populations. The study would be strengthened by independently validating the most significant RNA-seq changes, such as protocadherin gamma genes, using in situ hybridization methods to confirm the spatial and cellular specificity of expression changes.

Reviewer #1 (Public review):

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

Summary:

The behaviour of cells expressing constitutively active HRas is examined in mosaic monolayers, both in MCF10a breast epithelial and Beas2b bronchial epithelial cell lines, mimicking the potential initial phase of development of carcinoma. Single HRas-positive cells are excluded from MCF10a but not Beas2b monolayers. Most interestingly, however, when in groups, these cells are not excluded, but rather sharply segregated within a MCF10a monolayer. In contrast, they freely mix with wt Beas2b cells. Biophysical analysis identifies high tension at heterotypic interfaces between HRas and wild-type cells as the likely reason for segregation of MCF10a cells. The hypothesis is supported experimentally, as myosin inhibition abolishes segregation. The probable reason for lack of segregation in the bronchial epithelium is to be found in the different intrinsic properties of these cells, which form a looser tissue with lower basal actomyosin activity. The behaviour of single cells and groups is recapitulated in a vortex model based on the principle of differential interfacial tension, under the condition of high heterotypic interfacial tension.

Strengths:

Despite being long recognized as a crucial event during cancer development, segregation of oncogenic cells has been a largely understudied question. This nice work addresses the mechanics of this phenomenon through a straightforward experimental design, applying the biophysical analytical approaches established in the field of morphogenesis. Comparison between two cell types provides some preliminary clues on the diversity of effects in various cancers.

Reviewer #1 (Public review):

Summary:

The authors address the lack of validated tools for the detection and quantification of proteins associated with amyotrophic lateral sclerosis (ALS) through an extensive screening of 303 commercially available antibodies to 33 protein targets. Their ALS-Reproducible Antibody Platform (ALS-RAP) delivers a validated antibody toolbox for ALS research, which will provide an advantageous starting point for researchers in this field. Ayoubi R. et al. showcase the characterization workflow, presenting as an example the characterization of antibodies targeting Galectin-1, encoded by the LGALS1 gene. A selection of these antibodies was also used to profile protein levels across human induced pluripotent stem cell (iPSC)-derived and primary neurological cell types, and the findings support that the ALS disease mechanism involves both neuronal and glial cells.

Strengths:

The knockout (KO)-based approach is definitely the major strength of this study, providing a high level of confidence in the data collected in human induced pluripotent stem cell (iPSC)-derived and primary neurological cell types. The focus on renewable reagents (monoclonal and recombinant antibodies) is also important. The extensive characterization of this set of antibodies will benefit any scientist interested in any of the 33 target proteins, even in fields other than neuroscience.

The authors perform an interesting protein profiling study assessing 27 proteins, comparing RNA and protein expression data, and using two independent WB preparations of the same cell types.

The conclusions that can be drawn from this first assessment might not be final, but the data are compelling because they have been collected with reliable and validated antibodies.

Another strength of this work is the data dissemination strategy, which includes the Only Good Antibodies (OGA) platform, where YCharOS data are curated and presented in an easy and intuitive manner that facilitates antibody selection by the end user for WB, IP and IF applications.

Weaknesses:

The authors mentioned the development of single-chain variable fragment (scFv) recombinant antibodies raised by the SGC against the six proteins (ANXA11, OPTN, MATR3, PFN1, UBQLN2 and VCP) that had limited renewable antibodies that are commercially available. The development was optimized to generate antibodies particularly suitable for IP, and the clone selection process was carried out using IP coupled to mass spectrometry. Even though the generation of these novel reagents is not the focus of this work, the authors do not provide any data on this aspect.

The protein profiling study is limited to WB data, and the authors did not provide any explanation on why there was no integration with IP and IF data, not even for those targets that have validated antibodies. Also, not all the cell types have been screened by chemiluminescence-based detection and by fluorescence-based WB, and the authors do not elaborate on the reason for such a choice.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors reveal that the availability of extracellular asparagine (Asn) represents a metabolic vulnerability for the activation and differentiation of naive CD4+ T cells. To deplete extracellular Asn, they employed two orthogonal approaches: activating naive CD4+ T cells in either PEGylated asparaginase (PEG-AsnASE)-treated medium or custom-formulated RPMI medium specifically lacking Asn. Importantly, they demonstrate that Asn depletion not only impaired metabolic reprogramming associated with CD4+ T cell activation but also reduced CD4+ helper T cell lineage-specific cytokine production, thereby ameliorating the severity of experimental autoimmune encephalomyelitis.

The experiments presented here are comprehensive and well-designed, providing compelling evidence for the conclusions. The conclusions will be important to the field.

Comments on revised version:

The authors have sufficiently addressed my previous comments. The manuscript represents an excellent contribution to the field.

Reviewer #1 (Public review):

Summary:

In this study, the authors set out to define how arginine availability regulates lipid metabolism and to explore the implications of this relationship in pancreatic ductal adenocarcinoma (PDAC), a tumor type known to exist in an arginine-poor microenvironment. Using a combination of rigorous genetic and metabolomic approaches, they uncover a previously underappreciated role for arginine in maintaining lipid homeostasis. Importantly, they demonstrate that arginine deprivation sensitizes PDAC cells to ferroptosis through lipidome perturbations, which can be exploited therapeutically via co-treatment with aESA and ferroptosis inducers (FINs). These findings have meaningful implications for the field. They not only shed light on the metabolic vulnerabilities created by nutrient restriction in PDAC, but also suggest a practical avenue for combination therapies that exploit ferroptosis sensitivity. This is particularly relevant in the context of pancreatic cancer, which is notoriously resistant to conventional treatments. The methods employed are broadly applicable to other nutrient-stress contexts and may inspire similar investigations in other solid tumor types.

Strengths:

One of the major strengths of the study is the use of complementary and well-controlled approaches-including metabolomic profiling, genetic perturbations, and in vivo models-to support the central hypothesis. The experiments are thoughtfully designed and clearly presented, and the conclusions are, for the most part, well supported by the data. The findings provide mechanistic insight into nutrient-lipid crosstalk and identify a potential therapeutic strategy for targeting arginine-deprived tumors.

Comments on revised version:

The authors have substantially strengthened the revised manuscript and have addressed my prior concerns, and the evidence supports the central conclusions. This work provides meaningful insight into how nutrient limitation in the tumor microenvironment creates metabolic liabilities that may be therapeutically exploited, and it should be of interest to investigators studying cancer metabolism, pancreatic cancer, lipid biology, and ferroptosis.

Reviewer #1 (Public review):

Summary:

This study investigates how two closely related fish species differ in their processing of visual motion, with a focus on spatial and temporal integration underlying behavior. Using a series of behavioral assays combined with computational modeling, the authors identify clear species-specific differences in how visual information is integrated to guide movement.

Strengths:

A major strength of the work is the systematic and quantitative behavioral analysis, which reveals robust differences between species, including broader spatial integration and longer temporal persistence in medaka compared to zebrafish. The decomposition of behavior into distinct components provides a useful framework for interpreting these differences.

Weaknesses:

The computational modeling captures several key aspects of the observed temporal dynamics, particularly differences in response persistence. However, the modeling framework is primarily focused on temporal processing and does not incorporate spatial integration, which is a central finding of the study. In addition, some experimental observations, such as responses to short-duration stimuli and certain frequency-dependent features, are only partially reproduced. These limitations indicate that the link between the model and the full range of behavioral results remains incomplete.

Reviewer #1 (Public review):

Summary:

This paper presents rare and unique recordings of single neurons, LFPs, and SEEG data from human patients performing reading and listening tasks. They identify single neurons in temporal and ventral occipito-temporal cortex that respond specifically to spoken and written language, and primarily encode either phonological or orthographic features of the stimuli. They also identify neurons in the middle temporal and inferior frontal cortex that respond to both modalities, which they interpret as amodal language responses. In general, neuronal population firing rates are correlated with both micro- and macro- scale broadband gamma responses, though they observe some dissociations, particularly with the macro-scale. The results are interpreted to support a model of modality-specific to amodal processing throughout many distributed brain areas for language.

Strengths:

(1) The data are truly unique, providing a large-scale characterization of single neuron responses from the human brain during written and spoken language processing.

(2) The task and stimulus conditions allow for examination of both low-level (e.g., orthographic/phonological) and higher-level (e.g., syntactic) encoding.

(3) Showing relationships between single neuron and multi-scale LFP recordings from the same sites helps bridge neuronal and meso/macroscale literatures.

Weaknesses:

(1) My main comment about the paper is that it feels like a collection of somewhat random descriptions of a very small number of hand-picked single neurons. I think that the task and stimulus design shown in Figure 1A sets up some clear hypotheses that could be tested rigorously across the full neuronal population, but instead, the authors pick a few neurons and fit encoding models that don't take advantage of the contrasts. I agree that encoding models are a powerful approach, but with only 508 total words and what appears to be a limited set of variability across the various features, it's not clear to me that the stimuli, which were apparently designed as minimal pairs, provide enough power to find robust results. Perhaps this is why the majority of the results only show a very small number of units (most of which are actually buried in the supplement), but it's odd to me that they don't show the results of the minimal contrasts other than for length.

(2) Related to point (1), other than Figure 2H and Figure 6A-B, the results are only shown for a tiny number of units. This is great for demonstrating qualitatively what the effects look like, but there is no quantification of the findings across the population, which undermines the point in the abstract that 1000 neurons were recorded. This is acknowledged in some places, but as a reader, it leaves me wondering how seriously to take the interpretations if they seemingly cannot be replicated. I understand this is a challenge with human single neuron recordings, but as presented, the paper as a whole comes across as largely anecdotal.

(3) Some of the key claims rest on the idea that neurons were recorded from the superior temporal gyrus and fusiform gyrus. For the STG claim, I don't understand how this was done, or what specifically they mean by STG, since the microwire locations do not appear to be anywhere near the lateral surface. This makes sense given the profile of the Behnke-Fried electrodes, but if they want to claim that there are neurons from the STG, they need to be more specific and show where precisely these wires are. If they are more medial as it appears, they need to explain how they dissociated STG from Heschl's gyrus. Similarly, for the fusiform neurons, I can only see a couple of probes that appear to have their tips near where I would think this area is. Perhaps this is more of a visualization issue with Figure 1F, but overall, I am not convinced that the neurons are exactly where they say they are.

(4) Related to point (3), some of the authors have made strong claims in prior work about the precise coordinates of the VWFA, so it would help to know how many units are within this exact region. The ROIs marked in Figure 2 are quite large, and given results like Vinckier et al. 2007, it's important to know where along the hierarchy the recordings were actually performed. Similarly, given the framing in the intro around the VWFA as a key area, the idea that some of the best example neurons are from the right fusiform is a bit confusing. I don't think they can make the claims about visual hemifields since it does not appear that they recorded eye tracking to verify constant central fixation, and it may be a bit surprising to see such strong orthographic selectivity in the right hemisphere (though, as a result, it may suggest a more nuanced view of lateralization of reading at the single neuron.

(5) In many sections of the paper, there are vague and unquantified claims like "many neurons" or "a large number of units". This needs to be made explicit. It would also help to show where statistical threshold cutoffs are on plots like Figure 2H, since the "brain-score" is used to select units for many analyses.

(6) More detail on the TRF models is needed in the methods. At the very least, a complete list of the features in each group is necessary to evaluate claims about very broad sets of features like "syntax". It would also help to know how the features were coded, especially where there is a mixture of continuous and discrete features within the model.

(7) Depending on how exactly the features were defined, I'm skeptical of some of the claims, like position-specific "w". There are some obvious confounds that need to be controlled here, like whether word-initial "w" is strongly associated with shorter, higher frequency words (like "wh-" words). There are other examples, like whether specific forked letters tend to appear in certain syllables in English words. While it may be the case that these kinds of patterns are uniformly distributed, it needs to be established in this particular stimulus set.

(8) The claim that there is monotonic encoding of word length does not seem strongly supported in the data. In both PC1 and the single neuron examples, it seems like there may be a non-linear relationship, which could suggest that another correlated feature (e.g., word frequency) is involved.

Minor Points:

(1) What are "boundaries"? They are not described anywhere I could find, but they are a feature group that was used in the TRFs. )

(2) The caption for Figure 6C says MTG and insula, but the text says MTG and IFG. Similar to the above comment about STG and fusiform, it's not clear to me how they achieved single-unit recordings with Behnke-Fried probes in these areas.

(3) The somewhat less robust correlations between firing rate and BGA in macro vs micro contacts are potentially interesting. However, did they verify that the closest macro contact was always in the gray matter of the same gyrus as the microwire?

Reviewer #1 (Public Review):

The medial reticular formation (MRF) in the brainstem has long been implicated in the regulation of locomotion. One common - albeit very simple - model often presents the MRF as a major relay station receiving inputs from MLR circuits, among other brain regions, that together convey locomotor signals through efferent projections targeting the caudal brainstem and the spinal cord. Yet, the MRF is a particularly large brain area whose cellular complexity is far from understood. How molecularly distinct MRF ensembles contribute to the regulation of locomotor behaviors is largely unknown. Here, the authors apply focal activation of either glutamatergic, GABAergic, or serotonergic neurons throughout the MRF using a chemogenetic gain-of-function approach to uncover the putative modulatory properties of these neuronal ensembles during walking. Using kinematic analysis of mice limbs during self-paced over-ground walkway locomotion, the authors find that activation of GABAergic MRF neurons can selectively slow down walking, whereas activation of glutamatergic neurons can induce a specific "shuffle" limb trajectory, altogether revealing that distinct MRF populations may retain the capability to engage divergent walking signatures, whose behavioral relevance are not yet clear. In contrast, the activation of serotonergic neurons did not affect walking signatures as described for the other two subgroups but led to an increase of locomotor speed. Interestingly, MRF neurons in each regional activation "hotspots" appear to target different domains in the lumbar spinal cord, suggesting that distinct circuit mechanisms are at play for the slowmo vs shuffle effects.

Major points:

1. While the experiments are carefully done and the results are well analyzed and clearly presented in a series of beautiful figures, several aspects of the methodology remain very confusing. In particular, the initial choice for the injection coordinates is not justified and the authors don't leverage the mapping of spinal projection neurons to drive their chemogenetic screen. Similarly, the authors group very different injection schemes (unilateral or bilateral targeting of MRF neurons), that should be analyzed separately. The choice of Z score cutoff that dictates the in-depth analysis of the chemogenetic phenotypes appears arbitrary and is not grounded in a set of objective criteria.

2. One issue that arise from the work presented here is that we don't know if these MRF neurons are active during locomotion in normal, unperturbed conditions. Knowing the recruitment profile of these MRF neurons would clarify whether the chemogenetic activation boosts the firing of neurons that are already active during walking, or activate neurons that are otherwise silent. Disentangling between these possibilities may have a profound impact on the overall interpretation of the results.

3. The results should be discussed in the broader context of historic stimulation experiments, notably in cats and other species, as well as more recent circuit mapping approaches in rodents. For instance, the notion that focal stimulation of distinct area within the MRF can elicit or modify the pattern of locomotion is not really new, so is the notion that some of these modulations are phase-specific and can influence the duration of single muscle activation during stance or swing phases. This last point has for instance already been assessed through individual muscle recordings paired with MRF stimulation in cats. Perhaps better introducing these key studies and a thorough discussion of what the results presented in this manuscript bring in terms of novelty will help readers ground this work into a more comprehensive and larger body of work.

Reviewer #1 (Public review):

Summary:

The authors considered the mechanism underlying previous observations that H2A.Z is preferentially excluded from methylated DNA regions. They considered two non-mutually exclusive mechanisms. First, they tested the hypothesis that nucleosomes containing both methylated DNA and H2A.Z might be intrinsically unstable due to their structural features. Second, they explored the possibility that DNA methylation might impede SRCAP-C from efficiently depositing H2A.Z onto these DNA methylated regions.<br /> Their structural analyses revealed subtle differences between H2A.Z-containing nucleosomes assembled on methylated versus unmethylated DNA. To test the second hypothesis, the authors allowed H2A.Z assembly on sperm chromatin in Xenopus egg extracts and mapped both H2A.Z localization and DNA methylation in this transcriptionally inactive system. They compared these data with corresponding maps from a transcriptionally active Xenopus fibroblast cell line. This comparison confirmed the preferential deposition or enrichment of H2A.Z on unmethylated DNA regions, an effect that was much more pronounced in the fibroblast genome than in sperm chromatin. Furthermore, nucleosome assembly on methylated versus unmethylated DNA, along with SRCAP-C depletion from Xenopus egg extracts, provided a means to test whether SRCAP-C contributes to the preferential loading of H2A.Z onto unmethylated DNA.

Strengths:

The strength and originality of this work lie in its focused attempt to dissect the unexplained observation that H2A.Z is excluded from methylated genomic regions.

Weaknesses:

The study has two weaknesses. First, although the authors identify specific structural effects of DNA methylation on H2A.Z-containing nucleosomes, they do not provide evidence demonstrating that these structural differences lead to altered histone dynamics or nucleosome instability. Second, building on the elegant work of Berta and colleagues (cited in the manuscript), the authors implicate SRCAP-C in the selective deposition of H2A.Z at unmethylated regions. Yet the role of SRCAP-C appears only partial, and the study does not address how the structural or molecular consequences of DNA methylation prevent efficient H2A.Z deposition. Finally, additional plausible mechanisms beyond the two scenarios the authors considered are not investigated or discussed in the manuscript.

Comments on revisions:

The authors have addressed all previously raised concerns and propose a revised version of the manuscript. Notably, the abstract and discussion sections have been improved, and new experimental data have been incorporated. Collectively, these revisions enhance the rigor and clarity of the data interpretation and discussion.

Given these improvements, this reviewer believes that the manuscript could be published, particularly if this publication is accompanied by the critical points discussed in the rebuttal letter.

Reviewer #2 (Public review):

Summary:

This is a laudable effort to help dissect the contributions of type I and type III IFNs to the antiviral response in chicken and therefore represents an important piece of work, not least in the light of birds being a key carrier and worldwide distributor of influenza virus. The first part of the study characterises the generation of IFNAR and IFNLR KO chicken strains and describes basic differences. Four different viruses are then tested in chicken embryos, while the subsequent analysis of the antiviral response in vivo is performed with one influenza H3N1 strain.

Strengths:

Having these two KO chicken strains as a tool is a great achievement. The initial analysis is solid. Clear effect of IFNAR deficiency in in vivo infection, less so for IFNLR deficiency.

Weaknesses:

(1) The antibody induction by KLH immunisation: We still don't know whether or not this vaccination induces IFN responses in wt mice, so it is still not possible to judge whether the effects observed are due to steady-state differences or to differential effects of IFN induced during the vaccination phase. Pre-immune results are now shown and are indeed zero. As suggested, the whole figure 4 is now condensed into one or two panels by proper calculation of Ab titers - would these titres be significantly different? This as all of the other in vivo experiments have not been repeated if I understand the methods section correctly. I understand that there are three R restrictions that are tighter in some countries, and I accept that with the numbers used here, some statistical significance is reached, but this is for instance not the case for survival.

(2) The basic conundrum here and in later figures is now addressed by the authors in the discussion: Situations where IFN type 1 and 3 signalling deficiency each have an independent effect (i.e. fig.4d) suggest that they act by separate, unrelated mechanisms. However, all the literature about these IFN families suggest that they show almost identical signalling and gene induction downstream of their respective receptors. How can the same signalling, clearly active here downstream of the receptors for IFN type 1 or type 3, be non-redundant, i.e. why does the unaffected IFN family not stand in? The mouse studies, which showed a rather subtle phenotype when only one of the two IFN systems was missing, but a massive reduction in virus control in double KO mice, are discussed, but a clear-cut explanation for the differences has not been reached. Reasons could be a direct effect of IFNab on B cells and an indirect effect of IFNL through non-B cells, timing issues, and many other scenarios can be envisaged. The authors do not address this question experimentally, which limits the depth of analysis, they have however now included a discussion of this dilemma.

(3) In the one in vivo experiment performed with chickens, only one virus tested, more influenza strains should be included as well as non-influenza viruses. I appreciate that this is logistically difficult.

(4) The basic conundrum of point 2 applies equally to Fig. 6a, both KOs have a phenotype. Again, in 6d, both IFNs appear to be separately required for Mx induction. An explanation has been attempted, but more experiments, for instance looking at different time points to understand if we are dealing simply with different kinetics of the response, have not been attempted, despite the fact that such experiments are likely not covered by strict three R rules.

(5) The in vivo infection is the most interesting experiment, and the key outcome here is that IFN type 1 is crucial for anti-H3N1 protection in chickens, while type 3 is less impactful. However, this experiment suffers from the different time points when chickens were culled, so many parameters are impossible to compare (e.g. weight loss, histopathology). Some explanation is given as to the comparisons chosen here, but a more thorough analysis at several time points would have strengthened this study.

Comments on revised version:

In the rebuttal, the authors have gone to some length to add to the discussion of the experiments, and some aspects are better explained now than before. Many of these explanations remain speculative however, so the study remains inconclusive in several aspects. As no new data was added, my overall judgement of this study remains unchanged.

Reviewer #1 (Public review):

Summary:

Ducrocq et al. present research exploring the genetic link between simple multicellular group formation (ace2Δ/ace2Δ) and its interaction with cell-cycle progression mutants (e.g., cln3Δ/cln3Δ), demonstrating that this combination can provide fitness benefits during fluctuating resource conditions, resulting in a rapid increase in the fraction of multicellular cell-cycle mutants over unicellular yeast without selection for multicellular size. Because both the multicellular phenotype and the regulatory link enabling faster escape from the stationary phase are controlled by the ACE2 transcription factor, this work demonstrates that multicellular cluster formation can arise as a side effect of a completely independent fitness advantage unrelated to the benefits of group formation itself. As a "passenger phenotype," multicellularity could thus emerge for other selective reasons, potentially facilitating a later transition to more entrenched multicellularity if novel conditions arise that make multicellular group formation directly beneficial.

Importantly, while the literature generally assumes that multicellular group formation incurs a cell-level fitness cost, this work demonstrates that certain genetic - environmental interactions can confer fitness benefits even at the level of individual cells forming multicellular groups. This finding should inspire both theoretical and empirical work exploring multicellular group formation selected for benefits at the level of individual cells, rather than the benefits of forming a larger organismal size that most work has relied on so far.

Strengths:

This work is novel and exciting for research exploring the very first steps of the transition from unicellularity to simple multicellularity. The formation of multicellular groups is almost always assumed to come at a cell-level fitness cost due to reduced reproductive fitness compared to remaining unicellular, which generally needs to be outweighed by the benefits of multicellular group formation (e.g., large size to escape predation) for the multicellular phenotype to be stable. However, this study presents an interesting case of a genetic and environmental condition under which individual cells forming simple multicellular clusters can actually have higher reproductive fitness than solitary living yeast cells. This contrasts with previous snowflake yeast studies where the multicellular phenotype was primarily beneficial due to strong selection for large groups (rather than cell-level fitness gains).

The claims and interpretation of the results align well with the data presented. This is due to the careful and straightforward experimental design testing predictions with a clear, stepwise methodology. The authors rule out alternative explanations and provide support for the proposed link between the mutations (ace2, cln3, and others), their impact on faster exit from quiescence and earlier entry into reproduction in fresh media, and the resulting higher fitness in the snowflake yeast phenotype compared to unicellular yeast.

This experimental framework (combining cell-cycle mutants under the same multicellular background) is very much likely to be adopted by others in the community to explore downstream implications of these results in laboratory and environmental yeast isolates.

Weaknesses:

The authors show that the same multicellular phenotype with higher cell-level fitness due to faster exit from the stationary phase can also be observed with alleles found at other loci in non-laboratory yeast strains, implying that the results are likely not specific to a peculiar case genetically engineered in laboratory strains, but that similar phenotypes may be present in nature. However, this remains to be explored by examining the natural ecology of commercially available or wild yeast isolates and their genomes. This is not a weakness of this study per se, but rather a direction for future work. It does mean, however, that the relevance of these findings for early multicellularity in yeast, and even more so for nascent multicellularity in distinct taxa, remains to be explored in the future. Until then, it is difficult to make strong claims about how applicable these results would be for non-laboratory yeast and other taxa. Regardless, this work represents a very exciting finding.

Comments on revised version:

The authors addressed all concerns thoroughly.