Reviewer #2 (Public review):

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

Comments:

(1) Introduction section

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

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

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

(2) Methods section

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

The study provides an in-depth phenotyping of a novel zebrafish larval model of ADHD. This topic is interesting, and the model and the approach are relevant and well-justified. While the paper has a massive amount of high-quality data, the general structure and presentation of this material lack focus and a clearly articulated rationale.

Strengths:

The paper is methodologically sound, well-presented, and well- illustrated. It has a clear logical rationale and reasonable experimental design.

Weaknesses:

The amount of high-quality data is impressive, yet the general structure and presentation of this material lack focus and a clearly articulated rationale.

(1) First, it is unclear why VR is necessary here. It needs a better explanation in both the abstract and the intro section of the manuscript.

(2) Second, data need to be better presented (most important things first, least important - shorter or move to the Supplementary materials). Currently, it is too much to be clear and easy to follow.

(3) Discussion needs to better state the novelty and the significance of these findings. What does the study offer that is new? Why was it important to perform? What big questions does it address?

(4) The authors should better discuss the study limitations and future directions of research.

(5) There should be a stronger conclusion with a take-home message to emphasize what new information the study brings and why it is important.

(6) The overall style of the paper should be improved. Currently, it reads like a dry bulleted CRO report, not a usual scholarly paper.

(7) Optimize the text flow. Currently, the overall flow of the discussion needs to be smoother - it now reads as a selection of bulleted paragraphs, with few connections between them.

Reviewer #1 (Public review):

Summary:

The authors report the results of a tDCS brain stimulation study (verum vs sham stimulation of left DLPFC; between-subjects) in 46 participants, using an intense stimulation protocol over 2 weeks, combined with an experience-sampling approach, plus follow-up measures after 6 months.

Strengths:

The authors are studying a relevant and interesting research question using an intriguing design, following participants quite intensely over time and even at a follow-up time point. The use of an experience-sampling approach is another strength of the work.

Comments on revised version.

With the last round of revisions, the authors have now addressed my concerns.

Reviewer #4 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

A growing body of evidence indicates that Alzheimer's disease is not simply a disease of neurons accumulating toxic protein aggregates, but one in which the immune system, both its resident brain component and its circulating peripheral arm, plays an active and sustained role. Understanding how these two immune compartments interact with one another and with diseased neural tissue has been hampered by the fact that the mouse immune system differs fundamentally from the human one in ways likely to matter for disease progression. The authors set out to address this gap by building a modular laboratory model that brings together three human cell types in a three-dimensional setting: brain organoids derived from human stem cells to provide a neural substrate, stem cell-derived brain immune cells (microglia) to represent the resident immune compartment, and circulating immune cells (CD8-positive T cells) harvested from human blood to represent the peripheral adaptive immune response. By exposing this tri-cellular system to a toxic form of amyloid protein, the hallmark aggregating molecule of Alzheimer's disease, the authors aimed to dissect, step by step, how microglia respond to amyloid stress, what inflammatory signals they release as a consequence, and whether those signals are sufficient to attract T cells into the neural environment. They further aimed to test whether blocking the molecular receptors that guide T cell movement could interrupt this process, with the broader goal of positioning the platform as a tool for human-relevant drug screening.

Strengths

The conceptual architecture of the platform is one of its clearest strengths. The decision to add immune components in a stepwise, modular fashion, first characterising the neural response to amyloid, then adding microglia, then adding T cells, makes it possible to attribute observed changes to specific cellular contributions in a way that a more complex all-at-once model would not allow. This staged design is well thought-through, and its logic is clearly communicated. The combination of single-cell transcriptional profiling, calcium imaging for real-time functional readouts, transwell migration assays, and protein secretion measurements gives the study a genuinely multi-modal character that goes beyond what purely transcriptomic or purely imaging-based approaches can offer. The observation that T cells failed to migrate toward amyloid-treated organoids in the absence of microglia is a clean and conceptually important result, clearly supporting the idea that the resident immune response acts as an intermediary between amyloid pathology and the recruitment of peripheral immune cells. The identification of specific chemokine receptor pathways mediating T cell movement and the demonstration that pharmacological blockade of those receptors reduces migration and provide a degree of mechanistic resolution useful for thinking about future therapeutic strategies.

Weaknesses

Despite these strengths, several aspects of the work as presented substantially limit the confidence one can place in its conclusions.

The most consequential issue concerns the origin of the cells used in the model. The three cellular components: the brain organoids, the microglia, and the T cells are derived from genetically unrelated individuals. The T cells, in particular, come from healthy blood donors unrelated to the stem cell lines used to generate the neural tissue. This means the immune cells and the tissue they are interacting with carry different molecular identity markers (the proteins that the immune system uses to distinguish self from non-self). In this setting, any T cell activation or directed movement could reflect a generic rejection-like response to foreign tissue rather than a disease-relevant, chemokine-directed recruitment process. This is not a subtle concern: it represents a fundamental ambiguity at the heart of the model's central finding, and it is not acknowledged anywhere in the manuscript. For the transwell migration data to be interpretable as a model of Alzheimer's disease rather than of immune incompatibility, the authors would need to demonstrate that migration is driven by the specific chemokine environment and not by the genetic mismatch between cells, for example, using cells from the same donor or from matched donors, or by showing that blocking identity-marker recognition does not alter migration.

A related concern is that the T cells used are from healthy individuals, whereas T cells from people with Alzheimer's disease are known to differ in their activation state, surface receptor expression, and functional behaviour. The platform cannot yet claim to model the specific T cell biology of Alzheimer's disease until disease-relevant T cells are incorporated.

Beyond this foundational issue, the study frequently describes findings in causal terms that the experimental design does not support. The resident immune cells are said to "drive" T cell recruitment and "establish" a feedback loop. These are strong mechanistic claims. The evidence presented indicates that when microglia are present, more T cells migrate, and that blocking T cells receptors reduces migration. What is missing is direct evidence that the specific molecules measured, particularly the chemokines CCL4 and CCL5, are the agents responsible, as opposed to other signals also present in the conditioned environment. No experiment directly neutralises these chemokines to test whether their removal is sufficient to abolish T cell recruitment. Without such an experiment, the receptor-blocking data show only that the receptors matter, not that the measured ligands are the ones activating those receptors.

The abstract describes one particular molecule, CXCL10, as a contributor to T cell recruitment, but the data in the paper itself show no significant change in CXCL10 levels between conditions. This discrepancy between the abstract and the results is misleading to readers who may not read the figures in detail.

The single-cell sequencing data, which form the basis for claims about changes in cell populations following amyloid treatment or microglia addition, are presented without validation of the cell type labels against established reference datasets from human brain tissue. The proportional shifts in cell populations between conditions (Figures 1H and 3E) are described as significant findings but are shown without any statistical test appropriate for this type of compositional data. Comparisons of cell-type proportions derived from single-cell sequencing require specialised statistical approaches that account for the interdependence of proportions and the variability between samples; standard tests are not appropriate here, and none are applied.

There is also an unresolved inconsistency in the age at which the organoids were analysed by single-cell sequencing: the text states day 90, while the figure legend states day 60, and the methods section contains a passage describing experimental conditions (including a cholesterol treatment and a drug called semaglutide) that are entirely unrelated to this study and appear to have been copied from a different manuscript. These issues raise concerns about the rigour of the manuscript preparation and should be corrected.

Finally, the sample sizes underpinning several key conclusions are small (typically three to four organoids per group), particularly for the protein-secretion measurements used to identify the inflammatory signals responsible for T cell recruitment. While organoid studies are inherently limited in scale, the strength of the mechanistic claims made here would benefit from larger sample size or independent experimental replication.

Conclusion:

The authors have built a platform that is conceptually well-conceived and generates data consistent with a role for microglia in bridging amyloid pathology and T cell recruitment. In that sense, they have made meaningful progress toward their stated aims. However, the platform, as described, cannot yet deliver the human-specific mechanistic insight it claims to provide, primarily because the non-autologous configuration of the model introduces an uncontrolled variable that confounds the interpretation of the immune interaction data. The claim to have provided "the first human-specific mechanistic demonstration" of microglial activation as a bridge between amyloid pathology and adaptive immune recruitment is not supported by the evidence presented. The data are consistent with this interpretation but do not establish it.

The general approach, building increasingly complex human neural-immune models by adding components in a controlled, stepwise manner, is a valuable direction for the field and one that other groups working on neuroinflammation will find useful to consider. The combination of live calcium imaging and transcriptional profiling in the same experimental system is a practical contribution that demonstrates the kind of multi-modal readout this class of model can support. If the autologous confound is resolved in future iterations and if the mechanistic claims are grounded in more direct experimental evidence, this type of platform could become a genuinely useful tool for investigating human neuroimmune biology and for screening candidate therapeutic compounds in a human-relevant context. As currently presented, however, readers and researchers considering adopting this approach should be aware that the immune interaction data may reflect genetic mismatches between cell sources rather than disease-specific biology, and that the causal conclusions drawn from the chemokine and migration data go beyond what the experiments can support.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

(1) Figure 1H is missing statistical analyses.

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

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

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

Reviewer #1 (Public review):

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

This manuscript by Rudich ZD et al. systematically profiled the transcriptomic changes in nine long-lived C. elegans mutants and presented a careful and informative comparative analysis of these aging-related changes. In addition to these valuable datasets and bioinformatics analyses, the authors performed a large-scale RNAi screen to assess the role of the differentially expressed genes (DEGs) in these mutants and identify several potential targets to promote healthy aging. Moreover, the authors have provided a user-friendly website to examine genes of interest in those longevity mutants from their datasets.

Strengths:

Compared to previous transcriptomic analyses of these mutants in different reports, this study minimized the technical variations and benefitted from the advances in RNA-Seq technology and bioinformatics tools. Therefore, it should provide a more consistent and comprehensive view of the molecular mechanisms underlying the longevity of these mutants. The datasets in this manuscript are valuable to other researchers in the biology of aging.

Weaknesses:

Meanwhile, since these mutants have been extensively studied, the advance of this study in unknown ageing mechanisms remains limited.

Comments on revised version.

The authors addressed the concerns successfully.

Reviewer #2 (Public review):

Summary:

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

Significance:

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

Comments on revised version:

The authors addressed my concerns successfully.

Reviewer #1 (Public review):

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

This 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.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Reviewer #3 (Public review):

This study makes clever use of generative AI to create stimuli that are pixel-for-pixel identical but which have radically different meanings depending on their orientation, to investigate the perception of animacy while retaining control over low-level image features (so-called 'anagram' stimuli).

The authors present seven elegantly designed experiments in a commendably compact format.

Experiments 1 and 2 involved a working memory paradigm in which participants had to spot which of five objects in an array changed after a pause. Importantly, the changed object was an anagram stimulus that in one orientation matched the animacy/inanimacy of the changed object, and in the other orientation was the opposite (e.g., a rabbit is replaced by either a dog or a boot, where the dog and boot stimuli are actually identical, just rotated by 90 degrees). They found a difference in accuracy depending on whether the animacy of the objects matched.

Experiments 3 and 4 used a visual search task in which the participants had to localize the target, and the distractors were anagrams that either matched the target in terms of animacy or did not. There was a significant cost in terms of response time when the animacy of the target was the same as that of the distractors. Experiments 5 and 6 also used a similar visual search design, except that the task was to determine if the target was present or absent from the display, and the distractors again either matched or differed from the target in terms of animacy. Again, the authors found slower responses when the distractor arrays matched the animacy of the target than when they differed.

An obvious potential concern about the studies is addressed by Experiment 7. It is unclear if the observed effects are related to the specific orientations of the target and distractor stimuli selected in each condition. For example, it could be that all the animate versions of the anagrams involved tall and skinny shapes, while all the inanimate versions involved wide and short objects, due to the 90-degree rotational difference between the two versions of the stimuli. To control for this, the authors repeated the visual search experiment but with convex-hull silhouettes of each of the stimuli. In other words, all targets and distractors from each trial were replaced by a black splotch with approximately the same overall outline (envelope) as the corresponding stimulus. Importantly, in contrast to the anagram stimuli, the silhouettes had had no meaningful semantic interpretation, and their animacy did not change depending on their orientation.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

Some claims require additional evidence or clarification.

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence how dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strength:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuit interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of the role of dopamine in cognitive functions. Additional experiments investigating the contribution of MBON-α1 to the circuit, connectomic analysis together with a dissection of dopamine receptor function further strengthen the proposed circuit motif and its biological relevance.

Weaknesses:

While the study is well designed, presents compelling findings and has been further strengthened by additional experiments, some aspects remain unclear. The role of DPM neurons in memory consolidation seems not yet fully resolved, as different genetic approaches yield variable results. Furthermore, some manipulations impair memory without affecting sleep fragmentation - or vice versa, suggesting that the observed memory deficits cannot be explained solely by impaired sleep-dependent consolidation. It would also have been interesting to discuss potential mechanisms by which dopamine receptor-mediated cAMP signaling could lead to a reduction in Ca²⁺ signals. I am confident that these questions can be addressed in future studies.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopaminergic circuits interact to regulate memory consolidation in Drosophila and may reveal general principles underlying the neural regulation of memory.

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 updated the labelling of Figure 7. As all of the reviewer comments have now been addressed, I believe that this version of the manuscript can now be put forward as the Version of Record.]

Summary:

This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.

To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously presented evidence, at a timescale amenable to measuring neural responses with fMRI. They asked participants to choose between bundles or 'lotteries' of food times, which they revealed sequentially and slowly to the participant across time. This allowed modelling of the haemodynamic response to each new observation in the lottery, separately for previously accumulated and currently presented evidence.

Using this approach, they find that regions of the brain supporting valuation (vmPFC and ventral striatum) have responses reflecting gaze-weighted valuation of the currently presented item, where as regions previously associated with evidence accumulation (preSMA and IPS) have responses reflected gaze-weighted modulation of previously accumulated evidence.

A major strength of the current paper is the design of the task, nicely allowing the researchers to examine evidence accumulation across time despite using a technique with poor temporal resolution. The dissociation between currently presented and previously accumulated evidence in different brain regions in GLM1 (before gaze-weighting), as presented in Figure 5, is already compelling. The result that regions such as preSMA response positively to |AV| (absolute difference in accumulated value) is particularly interesting, as it would seem that the 'decision conflict' account of this region's activity might predict the exact opposite result. Additionally, the behaviour has been well modelled at the end of the paper when examining temporal weighting functions across the multiple samples.

In response to reviewer comments, the authors have explicitly tested for the effects of gaze-weighting over and above any main effect of value, and convincingly shown that these effects are both present in the main regions of interest - namely |SV| and gaze-weighted |SV| in the vmPFC, alongside |AV| and |AV_gaze| in the pre-SMA. This provides clear evidence in support of the notion of gaze-weighting of value signals in these regions.

Reviewer #2 (Public review):

Summary:

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

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

Weaknesses:

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

Reviewer #1 (Public review):

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

Summary:

This manuscript reports the discovery and characterization of the first bifunctional degrader of tankyrase. Notably, the tankyrase degrader exhibits stronger β-catenin inhibition and tumor growth suppression compared to conventional tankyrase inhibitors. Mechanistically, while tankyrase inhibitors stabilize tankyrase and promote Axin puncta formation-thereby impairing β-catenin degradation-the degrader avoids this effect, resulting in deeper suppression of β-catenin signaling. These findings suggest that targeted degradation of tankyrase offers a novel therapeutic strategy for β-catenin-driven cancers. Overall, this is a compelling study with significant translational potential.

Strengths:

(1) The manuscript presents a rigorous and well-executed study on a timely and impactful topic.

(2) The biochemical and cellular characterization of the tankyrase degrader is thorough, and the comparative analysis with tankyrase inhibitors is insightful.

(3) The finding that tankyrase stabilization by inhibitors may interfere with Axin function is novel and significant. It aligns with earlier observations (e.g., Huang 2009) that transient tankyrase overexpression can stabilize β-catenin independently of PAR domain activity.

(4) The use of TNKS1/2 knockout cells expressing catalytically inactive tankyrase to demonstrate β-catenin inhibitory activity of the tankyrase degrader is elegant.

(5) The finding that the tankyrase degrader has superior anti-proliferative effects in colorectal cancer models has important therapeutic implications.

Comments on previous version:

I had a favorable opinion of the manuscript in the first round of review. I don't have additional comments on the revised manuscript. The manuscript looks fine to me.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on previous version:

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

Reviewer #3 (Public review):

In this manuscript, Wang et al employ a chemical biology approach to investigate the differences between the enzymatic and scaffolding roles of tankyrase during Wnt β-catenin signalling. It was previously established that, in addition to its enzymatic activity, tankyrase 1/2 also plays a scaffolding function within the destruction complex, a property conferred by SAM-domain-dependent polymerization (PMID: 27494558). It is also known that TNKS1/2 is an autoregulated protein and that its enzymatic inhibition leads to accumulation of total TNKS proteins and stabilization of Axin punctae (through the scaffolding function of TNKS1/2), leading to rigidification of the DC and decreased β-catenin turnover. The authors surmised that this could, in part, explain the limited efficacy of TNKS1/2 catalytic inhibition for the treatment of colorectal cancers. To test this hypothesis, they evaluated a series of PROTAC molecules promoting the degradation of TNKS1/2 to block both the catalytic and scaffolding activities. They show that IWR1-POMA (their most active molecule) promotes more efficient suppression of beta-catenin-mediated transcription and is more active in inhibiting colorectal cancer cell and CRC patient-derived organoids growth. Mechanistically, the authors used FRAP to demonstrate that catalytic inhibitors of TNKS led to a reduced dynamic assembly of the DC (rigidification), whereas IWR1-POMA did not affect the dynamics.

Overall, this is an interesting study describing the design and development of a PROTAC for TNKS1/2 that could have increased efficacy where catalytic inhibitors have displayed limited activity. Knowing the importance of the scaffolding role of TNKS1/2 within the destruction complex, targeting both the catalytic and scaffolding roles certainly makes sense. The manuscript contains convincing evidence of the different mechanisms of the PROTAC vs catalytic inhibitors. Some additional efforts to quantify several of the experiments and to indicate the reproducibility and statistical analysis would strengthen the manuscript. Ultimately, it would have been great to evaluate the in vivo efficacy of IWR1-POMA in an in vivo CRC assay (APCmin mice or using PDX models); however, I realize that this is likely beyond the scope of this manuscript.

Reviewer #4 (Public review):

From the Reviewing Editor:

This important study reports the development of the first PROTACs targeting the ADP-ribosyltransferases tankyrase 1 and 2, with the goal of inhibiting Wnt/β-catenin signaling more completely than is possible with catalytic tankyrase inhibitors. The work addresses a significant limitation of existing tankyrase inhibitors: although catalytic inhibition stabilizes AXIN1/2 and suppresses Wnt signaling, it also stabilizes tankyrase itself, potentially enhancing non-catalytic scaffolding functions and promoting accumulation of degradasome-like puncta.

The evidence is convincing. The authors use appropriate and well-validated approaches, including chemical biology, cellular assays, and proteomic profiling, to show that PROTAC-mediated degradation of tankyrase avoids tankyrase accumulation while still stabilizing AXIN and inhibiting Wnt/β-catenin signaling. The data support the conclusion that degradation of tankyrase can separate pathway inhibition from the confounding effects of stabilized tankyrase protein and may therefore offer advantages over conventional catalytic inhibitors.

A strength of the study is the clear mechanistic comparison between tankyrase degradation and catalytic inhibition. The manuscript provides convincing evidence that the PROTAC and catalytic inhibitors act through distinct mechanisms, with the PROTAC targeting both catalytic and scaffolding roles of tankyrase. The study is well conducted and clearly presented, and the authors have addressed most concerns raised during review.

A remaining limitation is that the therapeutic potential of the compound is not tested in vivo, for example in APC-mutant colorectal cancer models, APCmin mice, or patient-derived xenografts. Such experiments would strengthen claims about practical efficacy, although they are not essential for the main mechanistic conclusions of the manuscript.

Overall, this is an important and insightful contribution. It advances the tankyrase and Wnt signaling fields by providing a new chemical strategy to suppress tankyrase function more completely than catalytic inhibition alone, and it offers a useful framework for future therapeutic exploration of tankyrase degradation.

Reviewer #1 (Public review):

Summary:

This study identifies a mechanism responsible for the accumulation of the MET receptor in invadopodia, following stimulation of Triple-negative breast cancer (TNBC) cells with HGF. HGF-driven accumulation and activation of MET in invadopodia causes the degradation of the extracellular matrix promoting cancer cell invasion, a process here investigated using gelatine-degradation and spheroid invasion assays.

Mechanistically, HGF stimulates the recycling of MET from RAB14-positive endodomes to invadopodia, increasing their formation. At invadopodia, MET induces matrix degradation via direct binding with the metallo protease MT1-MMP.

The delivery of MET from the recycling compartment to invadopodia is mediated by RCP which facilitates the colocalization of MET to RAB14 endosomes. On this compartment, HGF induces the recruitment of the motor protein KIF16B promoting the tubulation of the RAB14-MET recycling endosomes to the cell surface.

This pathway is critical for the HGF-driven invasive properties of TNBC cells as it is impaired upon silencing of RAB14.

Strengths:

The study is well organized and executed using state of the art technology. The effects of MET recycling in the formation of functional invadopodia are carefully studied taking advantage of mutant forms of the receptor that are degradation-resistant or endocytosis-defective.

Data analyses are rigorous and appropriate controls are used in most of the assays to assess the specificity of the scored effects. Overall, the quality of the research is high.<br /> The conclusions are well supported by the results and the data and methodology are of interest for a wide audience of cell biologists.

Previous Weaknesses:

The role of the MET receptor in invadopodia formation and cancer cell dissemination has been intensively studied in many settings including Triple Negative breast cancer cells. The novelty of the present study mostly consists in the detailed molecular description of the underlying mechanism based on HGF-driven MET recycling. The question of whether the identified pathway is specific for TNBC cells or represents a general mechanism of HGF-mediated invasion detectable in other cancer cells is not addressed or at least discussed.

Comments on revised version:

The authors have partially replied to my previous concerns.

Reviewer #2 (Public review):

Summary:

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

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

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

Comments on revised version:

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

(1) Inappropriate experimental design for studying MET trafficking

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

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

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

(2) Insufficient validation of antibody specificity in immunofluorescence

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

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

(3) Questionable MET localization in TIRF microscopy

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

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

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

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

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

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

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

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

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

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

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

(6) Overinterpretation of the data

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

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

Conclusion:

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

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

Reviewer #1 (Public review):

Summary:

Festa et al. provide a detailed analysis of the outcome of spike-timing-dependent plasticity acting on inhibitory synapses for distinct shapes of the kernel that governs how pre- and postsynaptic spike times induce synaptic changes. The authors investigate symmetric and asymmetric kernels, providing a theoretical description of the ingredients that give rise to rate- or covariance-dominated plasticity based on a simplified two-neuron circuit. These analyses are confirmed via simulations of large recurrent networks with random excitatory connectivity. For excitatory connections arranged in a one-dimensional ring, the authors show that two distinct classes of inhibitory neurons (distinguished by their plasticity rules) form an effective Mexican-hat weight profile. Furthermore, the authors show that external inhibition of one of the inhibitory neuron types gives rise to the phenomenon of surround modulation.

Strengths:

The analytical description of the two-neuron circuit is robust and accurately captures the qualitative evolution of inhibitory weights in the recurrent network with random excitatory connectivity. The emergence of the Mexican hat from the combination of distinct inhibitory synaptic plasticity rules acting on different neuron types is an important result that reveals how such connectivity can be learned in biologically plausible networks. All the analyses are well done, and the simulation results are convincing, which supports a robust interpretation of the findings.

Weaknesses:

The two-neuron circuit model is a good choice for the analytics, but it may have hidden a covariance effect of the "rate-dominated" symmetric spike-based kernel that would appear when several inhibitory neurons, each sharing a different spike correlation with the postsynaptic neuron, converge onto it. The rate homeostasis achieved by the rate-dominated model arises from adjusting inhibitory weights according to their initial correlation with the output neuron, so that after learning, the weights are distributed such that these correlations are cancelled out (Vogels et al., 2011). In other words, even the rate-dominated rule is covariance-driven under the hood: with a single inhibitory input, the two-neuron circuit cannot expose this, but with several differently correlated inputs, the covariance dependence should reappear.

It is unclear whether the distribution of inhibitory weights has stabilised after 25 minutes of simulation time (Figure 3C), given that a considerable proportion of (mutual) weights reach the maximum allowed weight while (unidirectional) weights appear to vanish. Without a maximum-weight bound, and given sufficiently long simulations, the weights might diverge to infinity or decay to zero, so the apparent stationarity may be imposed by the bound rather than reflecting a true steady state. This could also be a finite-size effect, given the small number of excitatory connections per neuron.

The connections from excitatory neurons to the two inhibitory populations are different in the ring model (exc to PV is wider than exc to SST according to Table 3), and it is not clear whether this width difference, rather than the plasticity rules themselves, is responsible for the emergence of the Mexican hat.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

Reviewer #1 (Public review):

Summary:

This manuscript by Laura Korobkova and Brian Dias describes an interesting study of the role of GABAergic neurons in the zona incerta (ZI) in incentive motivation for reward.

The authors report that DREADD inhibition of ZI neurons reduced the effort breakpoint in a progressive ratio task, which measures the intensity of incentive motivation to obtain food rewards. In other tests, chemogenetic inhibition did not alter food consumption or memory.

Conversely, DREADD excitation of ZI neurons increased incentive motivation in the progressive ratio task, expressed as a higher breakpoint for food rewards.

Korobkova and Dias report that prior stress exposure to a series of stressors (e.g., forced swim & water submersion, restraint, mild footshock) by itself reduced the breakpoint for food reward under vehicle, though it did not impair the ability to learn an instrumental response. However, DREADD excitation of ZI neurons in previously stressed mice increased the breakpoint to normal levels equivalent to the never-stressed group. This important finding indicates the ability of ZI stimulation to rescue the incentive motivational deficit induced by prior stress.

In fiber photometry studies using vGAT-CRE mice to specifically identify GABA neurons, Korobkova and Dias report that ZI GABA neurons are excited by sensory signals, including neutral cues. However, after reward conditioning, ZI GABA neurons increase their activation to the CS+ cue that predicts reward, but not to the CS- cue that doesn't. ZI neurons also respond in an instrumental reward task during both lever press and reward delivery. The authors conclude that ZI neurons respond to sensory stimuli, but specifically code the motivational significance of reward-related stimuli.

In optogenetic studies, the authors find that ZI GABA neuron stimulation during a reward CS+ enhances motivated responding to obtain reward, particularly in females, but not stimulation outside the CS+. This suggests the ZI stimulation in females may specifically enhance the incentive salience of the CS+, namely the cue's ability to trigger an increase in 'wanting' for the reward. However, that effect was not found here in males.

Altogether, this is a fine contribution to the literature, and the authors deserve congratulations on their study and manuscript.

Strengths:

This is a powerful and creative set of studies that clarifies the roles of ZI neurons in sensory processing and especially in incentive motivation for rewards. The use of multiple methods and test situations to triangulate on reward motivation functions gives a well-rounded perspective on ZI function. The discovery of incentive motivation roles for ZI neurons is intriguing and improves understanding of ZI, which traditionally has been a relatively understudied brain structure. The finding that ZI stimulation may rescue stress-induced deficits in motivation is especially notable and may have therapeutic implications.

Weaknesses:

Minor: This version of the manuscript focuses the introduction and discussion specifically on ZI GABA neurons. The ZI may be primarily GABAergic, but also contains other neurons, and DREADD studies may have used the hSyn promoter, which would impact all types of ZI neurons. Other studies here did more specifically target GABA neurons using vGAT Cre mice and specific targeting. The manuscript might be slightly improved by distinguishing in the discussion a bit more clearly which effects implicate GABA neurons specifically, and which effects might include other neurons too, to more clearly parse out the relative roles of GABA vs broader neuronal populations in ZI.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #3 (Public review):

Summary:

The authors investigated the role of the zona incerta in motivation and cue-reward associations. Using chemogenetic and optogenetic manipulations of the ZI, they altered motivation in cued and uncued variants of the progressive ratio task and rescued deficits in motivation induced by chronic stress. They further use fiber photometry to demonstrate that the ZI tracks the formation of cue-reward associations.

Strengths:

(1) The authors fill an important gap in the literature linking sensory input to motivation via the zona incerta.

(2) The authors demonstrate that ZI tracks cue value rather than just tracking sensory input.

(3) The authors demonstrate that the ZI excitation rescues stress-induced suppression of motivation.

(4) The authors perform several important control tasks, demonstrating that their findings are not a result of alterations in locomotor activity, food consumption, or memory.

Weaknesses:

In Figure 1D and E (inhibitory vs excitatory DREADDS), the control groups in the Gi group appear to have more elevated breakpoints than the control groups in the Gq group, although a statistical comparison between the two is not reported. It is not clear if this is because the two groups were given a different reinforcement schedule, this should be made clearer.

In Figure 1E, it is important to note that although the authors found a significant planned comparison between Gq VEH and Gq CNO, the interaction was not significant, nor were comparisons to mice injected with control virus. Thus, activation of ZI GABA neurons appears to be a relatively weak effect.

In Figure 5, the authors see what is likely a significant difference in lever presses during acclimation between the Gi and GFP groups, which they state is an expected difference. However, it is difficult to see why this would be expected. While Gi:CNO manipulation yielded lower breakpoints in Figure 1D, it did not yield lower FR1 responding for food in Fig S3 (although this was FR1 for food dispenser visits rather than lever press). One reason I ask is that the authors highlight the differences in CS+/CS- between groups, but the biggest difference between groups appears to be in acclimation, which may be driving the group x block interaction.

In Figure 6, the authors demonstrate that optogenetic stimulation during cue light increases the breakpoint in females, but not in males. They suggest that this may be because the males did not sufficiently discriminate the cue light before optogenetic manipulation began. If this were the case, then the authors would need to use "cue discrimination" as a factor to determine if it is a better predictor than sex.

The authors' work demonstrates that chemogenetic inhibition of GABAergic ZI cells reduces uncued motivation for reward but enhances cued responses under extinction. The authors state that this is a paradoxical finding that suggests that the ZI operates within a redundant motivation network. However, a critical difference between the two tasks is that one measures motivation for food while the other measures persistent responding under food extinction, which are not the same process. Thus, a simpler explanation is that ZI inhibition reduces motivation and impairs extinction.

Reviewer #1 (Public review):

Summary:

This paper uses three different datasets to study the relationship between the standard deviation of dynamic brain state time series (state engagement variability or SEV) and measures of cognition. Results show associations between SEV and cognitive measures, with stronger associations in patients than controls (at least for inhibition).

Strengths:

Strengths include the use of innovative dynamic approaches to study cognition and the validations across three independent datasets.

Weaknesses:

With a highly innovative approach, it can be challenging to provide enough context for the reader to understand and interpret the results. In particular, the paper would benefit from:

(1) More detail on the brain state calculation, multiple comparison control, and added benchmarking of the novel summary SEV measure.

(2) Guidance on the interpretation of relatively low prediction performance, negative t-statistics, and more broadly regarding the justification for the multi-step approach going from 4 brain states to 1 SEV to a network of edges.

(3) Removal of the moment-to-moment alignment results given the circularity of the edge time series extraction with overlapping contributions to SEV and cognitive control time series.

(4) Adjustment of text to avoid causal interpretations and to reduce the emphasis on transdiagnostics.

Major Points:

While the brain states were developed in prior work, SEV is a new metric and therefore warrants careful benchmarking in terms of test-retest reliability, sensitivity to scan length/quality, and associations with demographic variables like age and sex (which do not appear to be controlled for in analyses).

Although the external validation approach is appreciated, the prediction performance is pretty low (predicted-observed correlation 0.17-0.3). It would be good to also report other metrics of performance, such as balanced accuracy.

The steps in the paper are somewhat convoluted by going from 4 brain states to 1 SEV, back to specific FC networks. This makes the paper a bit complex and difficult to interpret. It would be helpful to provide a clear justification for these steps and/or a figure to orient the readers.

Many results are reported in the manuscript, and it is unclear whether/what multiple comparisons control was adopted where.

The moment-to-moment change section tries to test whether inter-individual variation in SEV maps onto cognitive control, which is very interesting. However, both measures were operationalized using edge-timeseries calculated from the same data with shared inputs (as shown in Figure 4B). As such, the 'alignment' (i.e., correlation) between resulting time series appears somewhat circular given that it is likely driven by the shared inputs. More broadly, edge timeseries were summed across edges (and subtracted between edges with positive and negative CPM associations), which further complicates their interpretability in the context of 'cognitive control'. I would recommend removing this section or using behavioral data to quantify cognitive control.

The descriptions of how brain states were derived are unclear. In line 466, what do 'these fMRI data' refer to? Was the least-squares regression performed across subjects (given that it results in one beta value per time point)? Was this performed as a multiple regression and - if so - what was the collinearity between brain state inputs?

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

The paper submitted by Renard et al. seeks to capture the moment when learning occurs and to identify the associated changes in neuronal activity within cortical circuits. Specifically, the study aims to test whether sensory representations in the cortex reorganize on the same timescale over which behavioral changes first emerge.

To address this question, the authors developed a new behavioral paradigm in which mice were first trained on an auditory detection task and then introduced to whisker stimulation, which they learned to associate with reward. This design allowed mice to form a new whisker-reward association within a single behavioral session, enabling the authors to track learning-associated neuronal changes during the course of the experiment.

Using pharmacological and optogenetic interventions, the authors first show that learning depends on the whisker somatosensory cortex. They then combined the task with longitudinal two-photon calcium imaging to examine real-time changes in neuronal representations that accompany improvements in task performance over trials within a session and across days. By applying a range of analytical approaches, they show that learning induces a rapid reorganization of sensory cortical representations over tens of trials, on the timescale of minutes. They further propose that spontaneous reactivation of neurons during the task may contribute to these representational changes during learning.

Strengths:

(1) Overall, the experiments are thoughtfully designed, well controlled, and clearly presented. The conclusions are generally well supported by the data. The manuscript is clearly written, and the Discussion acknowledges potential caveats while outlining future directions.

(2) A major strength of the study is the design of a new learning paradigm in which head-fixed mice rapidly form a new sensory-motor association within a single session, on the timescale of minutes. This offers a unique opportunity to track real-time changes in neuronal dynamics associated with learning during a single recording experiment.

(3) Taking advantage of this behavioral design, the authors show that learning induces rapid reorganization of sensory cortical representations. They also report an increase in spontaneous reactivation of neurons that gained stimulus responsiveness during training, and propose that these reactivations may contribute to rapid representational reorganization. These findings provide important insights into the neural dynamics associated with learning.

Weaknesses:

(1) The authors propose that spontaneous reactivation mediates rapid reorganization of neuronal representations and thereby supports rapid task learning. However, as they also acknowledge in the Discussion, the present study does not directly test a causal role for these reactivations in facilitating representational changes or behavioral improvement.

(3) The authors show reorganization of neuronal representations even on the first day of training with the new whisker task. However, because there is no explicit control for natural representational drift, it remains unclear to what extent these changes reflect learning-related reorganization rather than spontaneous day-to-day drifts in neuronal responses.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

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

The methods are generally well documented and easy to follow.

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public review):

This is a methodologically sound manuscript and provides reasonably interpretable results. While being appropriate, they do not seem to bring entirely novel concepts; nevertheless, most of my comments concern the calibration of the interpretive claims rather than the quality of the data.

Strengths:

(1) Longitudinal within-subject imaging:<br /> Tracking the same layer 2/3 neurons across learning allows the bidirectional effect (enhancement in R+, suppression in R-) to be measured within identified cells rather than inferred across cohorts.

(2) Appropriate behavioural controls:<br /> The R+/R- design controls for repeated sensory exposure, and maintaining rewarded auditory trials in both groups controls for engagement and arousal, arguing against disengagement as the source of the R- effect.

(3) Convergent causal manipulations:<br /> Muscimol and optogenetic inactivation both abolish acquisition and include an adjacent control region (fpS1); the temporally restricted optogenetic result partially addresses the concern (Hong et al., 2018) that sustained inactivation may destabilise downstream circuits.

(4) Convergent analyses:<br /> Single-cell learning modulation indices, population similarity measures, and a trial-resolved decoder projection onto a naïve-to-expert axis provide consistent evidence that representational change is concurrent with behavioural acquisition.

(5) Projection-specific resolution:<br /> Retrograde labelling shows learning-related changes in wS2-projecting, but not wM1-projecting neurons, consistent with preferential routing of task-relevant signals through the wS1 to wS2 pathway.

(6) Mechanistically motivated reactivation analysis:<br /> Relating rapid, reward-dependent plasticity to spontaneous reactivations on a timescale of minutes is an original use of the single-session paradigm.

Weaknesses and points requiring clarification

(1) The stimulus is not strictly novel: Passive whisker stimulations were delivered on pre-training Days -2 and -1, so what changes on Day 0 is the stimulus-reward contingency rather than the stimulus itself. This resembles contingency reassignment with reversal-like properties (and possible habituation or latent inhibition) rather than de novo learning, and the licking response is already established during auditory training. The framing should be qualified accordingly.

(2) Barrel cortex dependence should be stated more narrowly: The data show that acute wS1 suppression prevents acquisition of this task, not that whisker detection in general requires barrel cortex; cortical dependence varies with task and manipulation (Hong et al., 2018; Ryan et al., 2022 vs Miyashita and Feldman, 2013). The near-threshold explanation would require psychometric or stimulus-intensity data.

(3) The passive block carries confounds: It is acquired after task disengagement, when satiety, arousal, and reward history differ across groups and days. The authors should report within-block response adaptation and the robustness of the main results to early versus late passive trials. Additionally, could passive presentation of the stimulus without reward delivery lead to devaluation of the stimulus, leading to additional behaviour and plasticity changes which are not addressed?

(4) Some statistics appear to be neuron-level rather than animal-level:<br /> Very small p-values (e.g., the LMI-participation correlation r = 0.24, p on the order of 10^-41) suggest thousands of non-independent neurons treated as independent samples, risking pseudoreplication. Central claims should rest on hierarchical or animal-level statistics with effect sizes.

(5) The cosine-similarity decrease in R- animals needs clarification:<br /> Because cosine similarity is scale-invariant, uniform suppression would leave it largely unchanged; the observed decrease therefore implies heterogeneous suppression, reduced signal-to-noise, or increased variability, and the favoured interpretation should be stated.

(6) The decoder requires cautious interpretation:<br /> Training on passive trials and applying to active Day 0 trials could introduce a behavioural-state domain shift. The meaning of positive and negative values in Figure 4C should be defined, and near-zero early projections reflect the classifier boundary rather than a biological baseline.

(7) The reactivation analysis is the least conclusive and is susceptible to circularity:<br /> The template and the LMI are both derived from the passive whisker response, predisposing responsive neurons to register as reactivation participants, and the Day 0 template is obtained after learning.

Leave-one-cell-out and pre-learning templates, cell-specific templates, and tests of whether reactivations predict subsequent trial responses would strengthen the claim; causal disruption would ultimately be required.

(8) Figure, sample-size, and specificity points:<br /> The positive LMI shift in R+ animals is less visible than the R- shift in Figure 3F; the optogenetic cohort is small (n = 6 per group); and confirming that auditory detection was preserved during wS1 inactivation would establish whisker-specificity.

(9) The comparison to prior work is overly broad:<br /> Banerjee et al. (2020) and Chéreau et al. (2020) are reversal learning and discrimination paradigms and may not be equated with simple whisker detection; the defensible novelty claim is the trial-resolved tracking within the first session and its concurrence with online reactivations.

Reviewer #1 (Public review):

The authors address a difficult and well-known problem in systems/computational neuroscience: how to estimate the magnitude of "information-limiting" noise. Existing approaches (direct Fisher-information estimation, decoding + Cramer-Rao, and large-N extrapolation) are data-hungry and unstable, which has left the field with conflicting empirical estimates across systems.

The central proposal - "split-trial analysis" - is simple and appealing. The recorded population is randomly partitioned into two non-overlapping halves; a decoder (continuous case) or classifier (binary case) is trained on each half using the same trials; and the covariance of the two halves' decoding errors is used to estimate the variance of the information-limiting noise. There is a clean mathematical derivation to support this conclusion (although there are a couple of mathematical errors in the methods section that should be fixed to avoid confusion on the part of the reader).

They benchmark the method in simulation against three prior methods (Moreno-Bote et al. 2014; Rumyantsev et al. 2020; Kafashan et al. 2021) and report substantially better sample efficiency, lower bias, and greater robustness. They then apply the method to three datasets: (1) mouse head-direction cells (Ajabi et al.), (2) mouse V1 (Stringer et al.), and (3) macaque PFC during a saccade task (Bartolo et al.).

This is a strong and timely contribution. The core idea is elegant, and the method appears to be more practical than existing alternatives in the finite-data regime that real experiments occupy. The three applications are well chosen, and each yields a non-trivial, biologically interpretable result. I am strongly supportive of the potential of this paper.

That said, the paper makes several strong empirical claims - most notably that prior V1 estimates were substantial overestimates, and that PFC information-limiting noise is temporally redundant - and the central estimator rests on an independence assumption whose finite-N validity is only partially characterized. Before these claims can be considered well supported, I would like the authors to address the following:

Major Points:

(1) The method relies on a key independence assumption that may not always be satisfied in the regime of finite neurons and trials. The author's main idea is to decompose the residuals of two decoders as follows:

X1 = delta + phi1<br /> X2 = delta + phi2

The covariance is equal to the scale of information limiting noise, Var[delta], plus three terms:

Cov[X1, X2] = Var[delta] + Cov[delta, phi1] + Cov[delta, phi2] + Cov[phi1, phi2].

We can define phi1 as the part of X1 that is orthogonal to delta and likewise define phi2 as the part of X2 that is orthogonal to delta; thus, the cross terms evaluate to zero, and we are left with:

Cov[X1, X2] = Var[delta] + Cov[phi1, phi2]

Now the authors introduce an assumption that Cov[phi1, phi2] = 0. This leaves us with Cov[X1, X2] = Var[delta], but the question is: when is it justified to assume that Cov[phi1, phi2] = 0? For example, it is possible that

phi1 = c(N) * z + e1<br /> phi2 = c(N) * z + e2

where z is another shared noise dimension that is not information limiting and e1 and e2 are truly independent. Here, c(N) is a constant that goes to zero as the number of neurons used to train the decoder, N, goes to infinity. Thus, in the limit of having very large neural populations at hand for the analysis, the author's assumption of Cov[phi1, phi2] = 0 can be justified. If the authors agree with this analysis, it would be nice to (a) flesh it out and include it in the methods / supplementary notes, and (b) to analyze in simulation how good this approximation is in finite N regimes. I suspect that the assumption works in finite N regimes if noise is low-dimensional, but that if there are many additional dimensions of correlation (i.e. many z's above), you will need a very large number of neurons before Cov[phi1, phi2] approaches zero.

Along these lines, another worthwhile analysis would be to report outcomes when the neural populations are sub-sampled further. Intuitively, it should fail once you subsample to only a handful of neurons, e.g. 3, but I'm curious where the breaking point is and whether the decline is graceful.

(2) In point 1, I raised the question of how the method behaves with a finite number of neurons. Another worry is that there is a finite number of trials. In particular, if you train two decoders on the same trials, I would worry that non-information-limiting fluctuations in those trials would induce correlations in the decoders that then would show up as correlations on the held-out test set. A more conservative approach would be to split trials into three disjoint subsets: a training set for decoder A, a training set for decoder B, and a common test set used to compute Cov[X1, X2].

As a concrete example, suppose that on the particular trials used for training, the animal happened to be more aroused when theta = 1 and less aroused when theta = 0, and that arousal added a fluctuation on top of the neural response. This arousal-related signal is not information-limiting - it would average away given enough trials - but because both decoders are fit to these same trials, each one adjusts its weights to partially discount the same spurious high-arousal/low-arousal trend. Their weights are now distorted in a correlated way, so when both are applied to the shared test set, their errors covary, and the method reads this shared-training artifact as information-limiting noise.

I think this dynamic should be acknowledged in the text and clarified in more detail. Ideally, simulations could be done to estimate how many trials are needed to average out this sort of confound, and similar to the suggestion in point 1 above, I would be interested in seeing what happens when the authors sub-sample trials before running their analysis. Together with point 1, the feedback is that I'd like to see more about "how many neurons and how many trials" are needed in order to trust your results. Similarly, are there diagnostics or resampling methods (e.g. bootstrapping) that could be helpful for a practitioner to know if they have enough neurons/trials?

(3) Unless I've fundamentally misunderstood something, there is an error on page 17 in the methods. There we find sigma2 = Var[delta] = ... = Cov[phi1, phi2], but I believe this is meant to be Cov[X1, X2]. Indeed, the method assumes that Cov[phi1, phi2] = 0, as discussed in point 1.

Additionally, on page 4, the authors introduce the main quantity as Cov[\hat{theta}_1, \hat{theta}_2] instead of Cov[X1, X2]. However, if theta is changing from trial to trial, then these two quantities are not technically equal to each other, so it would be more accurate to write down the conditioning on theta. That is, assuming conditionally unbiased decoders, Cov[X1, X2] = Cov[\hat{theta}_1, \hat{theta}_2 | theta] for a fixed theta.

More generally, I found it hard to wrap my head around the underlying math on my first read through the paper. The polarization identity, 1/4 * (Var(X1 + X2) - Var(X1 - X2)), seems like a very roundabout way to derive the method. This identity is very helpful for the deconvolution extension, but I would have thought that a simpler and more straightforward derivation would have just used the expansion, Cov[X1, X2] = Var[delta] + Cov[delta, phi1] + Cov[delta, phi2] + Cov[phi1, phi2], as I did in point 1. I suggest the authors revise the mathematical presentation for clarity.

Minor Points

(1) A very nice feature of the authors' method is that they make no parametric assumption on the distribution of noise. This is in contrast to Kanitscheider et al. [12]'s finite-sample bias correction using the inverse-Wishart distribution of $\hat\Sigma^{-1}$, which is derived under an assumption of multivariate Gaussianity. I think it is worth adding a sentence to highlight this feature of the model.

(2) Statistical inference claims (across sessions and population sizes) are supported by reported s.d.'s but no formal tests or confidence-interval-based comparisons. Given that several claims are comparative (split-trial < naive; V1 < prior reports; PFC stable over windows), please add appropriate uncertainty quantification (e.g., bootstrap CIs over sessions) and, where a difference is claimed, a test or effect size.

Reviewer #2 (Public review):

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

Comments:

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

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

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

Reviewer #3 (Public review):

Summary:

In this manuscript, Le and Wei proposed a new method to identify differential correlations in real recordings (and simulations) that is based on splitting the simultaneously recorded population of neurons into two disjoint subpopulations. The method is based on evaluating the correlation between the decoded stimulus for each sub-population across trials. The authors validate their method on simulations and find the magnitude of differential correlations on three different publicly available datasets.

Strengths:

We think that this is a solid and relevant study for the computational neuroscience community, especially for the originality of the method and the fact that it seems to bypass the problem of very large populations to identify differential correlations. Overall, the results are novel and significant, and it addresses an important gap in the field. The main results are presented clearly and are easy to follow.

Weaknesses:

However, we believe that there are some additional analyses and clarifications that should be made to increase the clarity and impact of this study. In general, we believe that the authors should make a better effort to explain how their novel method depends on the number of trials and the number of neurons. More specifically:

Major

(1) The authors should show a realistic case for the covariance matrix in Figure 1. Currently, they are showing only Poisson noise (Figure 1c-e), only gain + Poisson (Figure 1f-h), and only differential correlations + Poisson (Figure 1i-k). They should show these same plots with a biologically realistic non-differential correlation structure (limited-range correlations, see Kanitscheider PNAS 2015). Perhaps even show the case for limited-range + gain + differential correlations. They should do the same for Figure 2.

(2) Throughout the manuscript, the role of population size (N) on the method is a bit confusing. Figures 1 and 2 give the impression that N is not particularly important, which is counterintuitive and surprising. We understand that that is one of the strengths of the split-trial method, but the authors should explain in much more detail in the results and methods the role of population size on their novel method. Why is large N crucial for the other methods, but not for them? There is a little bit of population-size dependency on Figures 3-5, especially on Figure 4g. The authors should explain in more detail those effects.

(3a) For dataset [27], the stimulus density was ~12 samples per deg for uniform sampling and ~1000 samples per deg for dense sampling. Figure S12 shows an overestimate of information-limiting noise when the number of trials used was significantly downsampled, which is, first of all, in disagreement with simulation results showing "when only a small number of trials are available to infer a large d-prime, split-trial analysis exhibits an under-estimation". It is true that we are not strictly in a binary classification task setting, but we are wondering if the authors have any justification for this result for [27].

(3b) Related to this point, the estimated info-limiting noise was 0.26 deg with all neurons and 0.6 deg with downsampling (we guess that is the first value of red lines in Figure S12). The only difference here, if we understand correctly, is the number of trials used. Otherwise, it's exactly the same neural responses used for estimation. So, a similar magnitude should be expected. If the latter is due to an insufficient number of trials used, would the same problem apply to the uniform sampling dataset? In other words, if there were more trials recorded with uniformly sampled stimuli, would the authors expect to see a further and significant decrease of sigma as well?

Reviewer #1 (Public review):

Summary:

The manuscript by Nagvekar et al. studies engulfing macrophages in the killifish brain upon aging. It first describes the development of a transgenic knock-in killifish line overexpressing a secreted fluorescent protein in neurons. This becomes a tool for isolating myeloid cells that are capable of endocytosis or phagocytosis of the fluorescent protein, which seem to comprise the majority of the myeloid cells within the young adult brain. The paper then demonstrates the similarities of what they call "engulfing macrophages" to brain myeloid cell types of other species and investigates changes to this population upon aging. Overall, the study combines multiple complementary technologies to support their data, that are nicely presented and well described in the legends, while the textual description remains very concise. The findings are of interest to scientists studying brain aging, and microglia/macrophages.

Major comments:

(1) Although the authors describe and analyze their data from the viewpoint of engulfing macrophages, the paper would benefit from a broader perspective and a comparison to other studies on microglia in different species. Along this line, the title does not really seem to cover the data presented here very well, and the introduction lacks a proper explanation of terminology on microglia/brain macrophages and their known roles, cell types versus cell states and the current state of the art in fish versus other model species in the context of aging.

(2) The result that nearly all myeloid cells in the killifish brain are of the engulfing macrophage type is somewhat surprising. This appears to differ from other studies in for instance zebrafish (e.g. ref 80, that describes the heterogeneity of the myeloid cells in detail). There are two questions we like to raise: (Q1) What is the evidence towards this homogeneity? and (Q2) Could there be a technical bias?

Regarding (Q1): What is the evidence towards this homogeneity? The markers used are overlapping with markers for microglia. It would be helpful to clarify how canonical microglia populations are represented in the dataset. What is the heterogeneity of the oScarletHIGH cells? On several plots (Fig1f, Fig2d, Fig4a) this population of cells seems more heterogeneous than described. Are there different cell states or types? What is the percentage of myeloid cells that is oScarletLOW? To what extent do these cells compare transcriptionally to the oScarletHIGH cells?<br /> a. Fig1f-i depict an enriched oScarletHIGH group alongside oScarletLOW cells. This representation is a bit misleading since it seems to indicate that really all myeloid cells are of the engulfing macrophage type whereas it is the majority, but not all.<br /> b. Line 52: The authors describe that the oScarletHIGH cell group is "enriched for signatures characteristic of macrophage functions". This finding is logical, as the isolation procedure of this population of cells was based on the endocytic and phagocytic properties of the cells. This result appears more consistent with a validation of the isolation strategy than with definitive evidence for myeloid cell identity.

Regarding (Q2): Could there be a technical bias? An alternative explanation that may warrant discussion is whether aspects of the experimental pipeline (cell dissociation, FACS, scRNA-seq) could influence myeloid cell states. For instance, it is conceivable that dissociation induces a reactive program that enhances uptake of fluorescent protein, potentially enriching for oScarletHIGH cells. As the authors use a similar experimental setup to prove uptake of dextran and ovalbumin, such a technical artefact may merit consideration. As this would influence the major conclusions of the paper, the authors might want to address this comment with additional experimental controls, such as single-nuclei RNA-seq on control young and aged brains to profile the natural myeloid population when not submitted to a cell dissociation and FACS procedure.

(3) The authors compare the oScarletHIGH cell transcriptomes to mouse and killifish datasets. Both the mouse (Barr et al) and killifish (Nagvekar, this paper) dataset are from enriched immune cells (mouse= CD45+ cells, and the 3 cell types selected from that). Why did the authors not compare to the whole mouse CD45+ dataset? Including zebrafish (Rovira et al, 2025) here would strengthen the evolutionary comparison. I also feel that the additional comparison with young killifish (Ayana et al) might not be that solid since this dataset was initially not enriched and has a significantly lower number of myeloid cells, and thus much less power. The old age time point in that study contained more myeloid cells and might be interesting to include for cell type comparison. There are other, perhaps more unbiased ways of comparing cell types across species, for instance SAMap, developed by co-author Bo Wang. Did the authors consider using this or other methods?

(4) Regarding the comparison with the aged brain:<br /> a. Figure 4: It would be nice to include the same comparisons as for young fish (cfr Fig.1 panels F-I).<br /> The percentage of oScarletHIGH cells in the aged condition is 8% (Fig1-suppl1) compared to 4% at young age. On the other hand, a lower number of cells was isolated at old age compared to young age (Figure4a). Can the authors elaborate on this difference? Later on, it is stated that the engulfing capacity declines with aging, but could this be linked to the lower or potentially biased recovery of cells?

b. Figure4a: Transcriptional differences are stated between young and old (line 226), can a relevant selection be shown in e.g. a dot plot or heatmap?<br /> The UMAP clustering does seem to indicate batch effects on panels a and g. Can the authors provide sub clustering and show that young and old/ FACS sorted high and low cover similar cell types/states? The PCA plot (panel f) and marker analysis is not fully convincing, as PC1 and 2 alone do not suffice to explain all the variance in these cells, and the markers are common ones for many microglia/macrophage cell types (and thus likely to be expressed similarly).

c. Figure 5: It would be informative to include the corresponding aged condition for panels c and e.

Significance:

General assessment

Strengths: This manuscript introduces a valuable new transgenic tool to isolate and characterize myeloid cells in the brain of the fast-aging killifish (Nothobranchius furzeri), an emerging model organism in aging research. The study combines multiple complementary approaches, including transgenesis, FACS, histology, and single-cell transcriptomics, to investigate brain immune populations and their changes upon aging. The cross-species comparison and aging analyses provide useful datasets and candidate markers for the field of neuroimmunology and comparative brain aging. Overall, the data are clearly presented, the experiments are logically structured, and the manuscript provides a useful resource for future studies on brain immune cells in teleosts.

Limitations/points for improvement: The major limitation of the study concerns a potential technical bias introduced by the experimental pipeline (cell dissociation, FACS isolation, and transcriptomic profiling), which may have influenced the observed predominance and transcriptional state of the oScarletHIGH/engulfing macrophage population. At present, it remains difficult to fully exclude whether the protocol itself contributes to the apparent homogeneity of the myeloid compartment or induces a shared reactive state. Because this issue affects some of the central conclusions, the manuscript would benefit either from additional controls (e.g., dissociation-independent approaches such as single-nuclei RNA-seq) or from a more cautious interpretation and discussion of this possibility in the text.

Advance: The fast-aging killifish is becoming an important vertebrate model for studying aging, yet the brain immune compartment in this species remains relatively underexplored. This manuscript provides both a novel experimental tool and a transcriptomic resource for studying myeloid cells in the killifish brain. To my knowledge, the study is among the first to profile engulfing/endocytic myeloid populations in the context of brain aging in this model organism and to compare these cells across species. The advance is primarily technical and descriptive/resource-generating, while also offering conceptual insight into how brain myeloid populations may change during aging and how they compare evolutionarily across vertebrates. Although the mechanistic interpretation would benefit from additional validation, the study clearly extends current knowledge and provides a framework for future work on neuroimmune aging in fish.

Audience: The manuscript will primarily be of interest to a specialized basic research audience, including researchers in neuroimmunology, brain aging, microglia/macrophage biology, and comparative neuroscience. It will also be relevant to scientists using killifish or other emerging vertebrate models for aging research. Beyond the immediate field, the study may be of broader interest to researchers investigating immune-brain interactions and the evolutionary conservation of myeloid cell states across species. The transgenic line and transcriptomic datasets are likely to serve as a useful resource for future comparative and functional studies.

Reviewer #2 (Public review):

Summary:

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

Major comments:

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

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

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

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

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

Significance:

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

Reviewer #3 (Public review):

Summary:

Rahul Nagvekar et al. generated a novel genetic model (SP-oScarlet) to label brain macrophages via their engulfment activity in the naturally short-lived African turquoise killifish. They found that these brain phagocytes exhibit transcriptional features resembling mammalian BAMs/MDMs and provided evidence that their engulfment capacity declines with age. The model and topic are interesting, but some of the central conclusions require more precise calibration to match the strength of the supporting evidence.

Major comments:

The SP-oScarlet model enriches cells based on phagocytic capacity - by design, any phagocytic cell, including microglia, can be labeled. Only 0.5% of oScarlet^LOW cells were myeloid cells, confirming that this method captures virtually the entire myeloid population. The transcriptional resemblance to BAMs/MDMs is therefore a post hoc characterization of brain phagocytes broadly, rather than evidence for a selectively labeled subset. The authors show examples of apoeb⁺ cells near vasculature (Fig. 3b), but do not provide a comprehensive quantification of the full spatial distribution of oScarlet^HIGH cells. Importantly, neither the SP-oScarlet macrophages nor previously published wild-type killifish brain macrophages could be transcriptionally separated into three subgroups analogous to mammalian microglia, BAMs, and MDMs by PCA. This suggests that fish brain macrophages may not exist as subpopulations that correspond with their mammalian counterparts. The authors should therefore describe these cells as brain myeloid cells that exhibit BAM/MDM-like transcriptional characteristics, rather than implying they are a population equivalent to mammalian BAMs/MDMs.

The age-related decline in oScarlet fluorescence in oScarlet^HIGH cells in vivo could reflect either reduced phagocytic capacity of macrophages, or reduced oScarlet secretion by neurons, as the authors have discussed (Fig. 5a). The ex vivo assay addresses this by standardizing substrate concentration, which is a strength, but an in vivo functional assessment would provide a more physiologically relevant complement. The authors have already established the methodology for in vivo substrate injection (Fig. 3a, dextran). A similar experiment comparing substrate uptake in young and old fish would circumvent potential artifacts of the ex vivo approach, such as enzymatic dissociation altering surface receptor availability, and would directly test whether engulfment declines in the native brain environment.

Significance:

General assessment: This study presents a novel genetic model (SP-oScarlet) for visualizing chronic engulfment by brain macrophages in a short-lived vertebrate. The finding that killifish brain phagocytes exhibit BAM/MDM-like transcriptional features is interesting. Leveraging the killifish's naturally short lifespan, the authors further provide functional evidence that brain macrophage engulfment capacity declines with age. However, the authors should exercise caution when defining these cells as a distinct population specialized for engulfment of material from the brain extracellular space, since the SP-oScarlet model labels nearly the entire myeloid population in the brain.

Advance: This study establishes a novel genetic model for chronic, in vivo visualization of engulfment in a vertebrate brain. The conceptual insight that killifish brain phagocytes transcriptionally resemble BAMs/MDMs rather than classical microglia is novel and may reflect evolutionary differences in brain clearance strategies.

Audience: This research will interest a broad audience across developmental biology, genetics, neuroimmunology, aging research, and evolutionary biology.

Reviewer #3 (Public review):

Summary:

Due to the low SNR of cryo-EM micrographs necessitated by radiation damage, determining the structure of proteins smaller than 50 kDa is exceedingly challenging, such that only a handful have been solved to date. This work aims to improve the reconstruction of small proteins in single-particle cryo-EM by using high-resolution 2D template matching, an algorithm previously used to locate and align macromolecules in situ, to align and reconstruct small proteins. This approach uses an existing macromolecular structure, either experimentally determined or predicted by AlphaFold, to simulate a noise-free 3D reference and generates whitened projections, crucially including high-spatial-frequency information, to align particles by the orientation with maximal cross-correlation. They demonstrate the success of this approach by generating a 3D reconstruction from an existing dataset of a 41.3 kDa protein kinase that had previously evaded attempts at high-resolution structure determination. To alleviate concerns that this is purely from template bias, they demonstrate clear density at two regions that were not present in the template: 6 residues in an alpha helix and an ATP in the ligand binding pocket. The latter is particularly important for its implications in determining structures of ligand-bound proteins for drug discovery. They also produce a composite omit map from 36 partial-deletion reconstructions spanning the entire protein, demonstrating a reconstruction can be obtained without template bias. Additionally, the authors provide an update to the classic calculation in Henderson 1995 to predict the minimum molecular mass of a protein that can be solved by single-particle cryo-EM.

Strengths:

I am in no doubt that this technique can be used to gain valuable insights into the structures of small proteins, and this is an important advancement for the field. It is complementary to single-particle cryo-EM and provides an extra tool for the experimentalist that may work better in certain cases. For cases where only a small region of the structure is of interest, such as in drug screening, this method provides a simple workflow to screen many structures.

The claim that using high-spatial frequency information is essential for aligning small proteins is a valuable insight. A recent pre-print published at a similar time to this manuscript used high-resolution information in standard ab-initio reconstruction to generate a high-resolution reconstruction from the same dataset, supporting the claims made in the manuscript.

The theoretical section outlined in the appendix is also theoretically sound. It uses the same logic as Henderson, but applies more up-to-date knowledge, such as incorporating dose-weighting and altering the cross-correlation based noise estimation. This update is valuable for understanding factors preventing us from reaching the theoretical limit.

Weaknesses:

This method is a complementary technique to determine the structure of small macromolecules to existing methods such as Blush regularization and HR-HAIR. Although the authors have demonstrated convincingly that their method selects a stack of high-quality particles, it is less clear whether it performs better than RELION when using the same stack of particles, particularly in the ATP binding pocket. As the authors discuss, systematic benchmarks comparing these methods over more targets than the one presented here, will be important for determining the utility of this method.

The method presented here also introduces template bias. Omit maps are used to reduce template bias by removing the region of interest from the template. Producing a full reconstruction through a composite omit map is computationally expensive and can introduce artifacts at boundaries. Therefore, unless this method outperforms modern SPA methods, its major use case will likely be restricted to ligand binding studies rather than full 3D reconstructions.

Reviewer #1 (Public review):

Hanako and colleagues demonstrated that glycolipid MPIase is essential for the TAT system, and they successfully reconstituted the TAT system in vitro for the first time. This will facilitate the understanding of the mechanism of the TAT system.

My major points are listed below for the authors to consider:

(1) The authors successfully reconstituted the TAT system using the purified TatA/B/C, but the translocation efficiency was much lower than that of native INV. The authors partly attributed this to the reason that "MPIase recovery would be too low to detect the TAT activity" in the Discussion part. So, what would happen to the translocation efficiency if you added more MPIase to the reconstituted system? How about the abundance of MPIase from the INV and reconstituted proteoliposomes?

(2) Why were only TatC levels measured in Figure 2C, whereas the expression levels of TatA were not detected? Also, from my observation, the amount of TatC in the third lane is lower than that in the previous two lanes.

(3) The authors should explain why the TatA/B/C ratios in Figure 3C (1:1:1) and Figure 3D (10:1:1) are inconsistent.

(4) ~30% of the fluorescence was recovered in the membrane fraction (Figure 4A) both in the functional TAT signal sequence (RR) and in the inactivating mutant signal sequence (KK), which suggests that MPIase acts as a relatively broad recognition factor. Given that MPIase does not discriminate between RR and KK, why do un-translocated substrates remain in the cytoplasm rather than non-specifically adhering to the membrane when MPIase is depleted in vivo?

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #1 (Public review):

Summary:

The authors study how the migration of distal visceral endoderm (DVE) cells in early mouse embryos becomes channeled towards one direction and the corresponding movement of the epiblast on which the DVE cells migrate. To this end, they develop an analysis pipeline of an in toto live data set previously obtained by the authors, which includes superpixel motion tracking of the visceral endoderm surface and subregions thereof. They find that a morphological asymmetry of the ectoplacental cone is indicative of anterior-posterior axis orientation. Even during the phases prior to and after collective migration, DVE cell speed was larger than in the surrounding tissue. The crossover from the pre-migratory to the migratory phase relies on the alignment of DVE cell motion. During the migration phase, counter-rotating vortices appeared in the emVE as expected when a rigid body moves through an incompressible fluid. Furthermore, DVE migration exhibits what the authors term a ratchet-like behavior, where the cells alternate between bursts of collective migration and periods of essentially no net motion. This behavior could be reproduced in vertex-model simulations, where DVE cells were subjected to a constant external force in an otherwise passive environment of cells. The observed intermittent behavior results from building up stress in the surrounding tissue that is released through cell rearrangements involving T1 transitions. These findings are in line with experimental results, although in embryos, T1 transitions are not as abundant as in the simulations and are largely confined to the region ahead of the DVE. Finally, the authors report a distally directed planar motion in the anterior epiblast underlying the visceral endoderm and thus opposite to the motion of the DVE. Cell migration in the posterior epiblast was slower and more random than in the anterior.

Strengths:

The authors provide a detailed analysis of the cell migration patterns in the embryo and show through vertex-model simulations that some of the observed features are really consequences of the properties of incompressible fluids.

Weaknesses:

Naming the intermittent dynamics of DVE cells as ratchet-like seems inappropriate, as it is rather reminiscent of stick-slip dynamics.. Quantitatively, the simulations do not provide much more insight beyond providing the flow profile of the (complex) fluid behavior of the tissue surrounding the DVE. It would be interesting to identify mechanisms that underlie migration alignment of DVE cells and to study in detail the T1 transitions - why are they confined to certain regions of the tissue? Furthermore, the theoretical analysis should be extended so that it also considers the dynamics of epiblast cells.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors investigate the dynamics of distal visceral endoderm (DVE) migration during early anterior-posterior axis formation in the mouse embryo. Using long-term light-sheet imaging combined with geodesic projections and quantitative motion analysis, they characterize DVE migration at both the cellular and tissue levels. The study identifies three distinct phases of DVE migration, describes the intermittent "stop-and-go" nature of DVE movement, and quantifies coordinated tissue behaviors within the visceral endoderm. The authors further report a previously unrecognized posterior movement of the underlying epiblast that occurs concomitantly with anterior DVE migration. Finally, they develop a two-dimensional vertex model to investigate the mechanical basis of the observed intermittent migration, proposing that cycles of stress accumulation and T1-mediated stress relaxation within the surrounding visceral endoderm account for the observed dynamics

Strengths:

Overall, this is a very interesting study combining state-of-the-art live imaging with an impressive quantitative image analysis framework. The imaging quality is excellent, and the authors provide one of the most detailed quantitative descriptions of visceral endoderm (VE) dynamics to date. In particular, the combination of whole-embryo light-sheet imaging, geodesic projections and quantitative analysis provides a rich dataset that will undoubtedly be valuable for the community. The model is also informative and provides a mechanistic hypothesis for the start and stop motion of the VE.

Weaknesses:

(1) Clarification of the Superpixel-based image analysis

The image analysis pipeline is impressive but could be explained more clearly for readers unfamiliar with the authors' previous work. In particular, the manuscript relies extensively on superpixel tracking, but it remains unclear what advantages this approach offers over more conventional Lagrangian particle image velocimetry (PIV). Since this paper should be self-contained, it would be helpful if the authors briefly explained the rationale for choosing superpixel tracking rather than referring readers to their previous eLife publication.

Related to this point, the manuscript appears to use two different levels of coarse-graining. Motion is initially estimated from thousands of superpixels (1000-5000 according to the Methods), whereas the quantitative analyses are ultimately averaged over only 32 spatial sectors. The relationship between these two levels of representation is not entirely clear and would benefit from clarification. Why use such a dense superpixel seeding, which seems oversampled, if the intent is to eventually bin the result?

Relatedly, how was the number of superpixels chosen? What is their effective size relative to the size of a VE or epiblast cell? This information is important because the analysis appears to be oversampled. This is particularly evident in Movie S14/Figure 7, where numerous superpixels appear to span a single epiblast cell. At this spatial scale, the measured motion is likely to include intracellular or subcellular movements, such as interkinetic nuclear migration or transient cell-shape changes, rather than pure tissue displacement. This may be somewhat misleading, as the visual impression is that the tissue itself is moving, whereas in some instances this reflects cellular/subcellular fluctuations. A discussion of the spatial scale of the superpixel analysis, together with a demonstration that the conclusions are robust to the degree of coarse-graining, would greatly strengthen the manuscript, especially regarding he movement of the epiblast (see point 4).

(2) Use of the term "ratchet-like"

We would recommend avoiding the term ratchet-like and instead using start-stop or stop-and-go migration throughout the manuscript. While these terms describe the same observed behavior, ratchet-like implicitly suggests an irreversible mechanism underlying the motion, whereas the present study primarily documents an intermittent migration pattern. In my opinion, stop-and-go is a more descriptive and mechanistically neutral terminology, leaving the mechanistic interpretation to the modelling section.

(3) Mechanistic interpretation of the stop-and-go behavior

The vertex model constitutes the principal mechanistic component of the study and provides an interesting explanation for intermittent DVE migration through stress accumulation followed by T1-mediated stress relaxation. However, the comparison between the model and the experimental data reveals an important discrepancy. As acknowledged by the authors, the model predicts a broader distribution of T1 transitions than observed experimentally, whereas in vivo T1 events appear largely confined to the embryonic visceral endoderm ahead of the migrating DVE.

This discrepancy suggests that an important aspect of junctional mechanics may be missing from the current formulation. Have the authors considered whether an asymmetric constitutive description, in which junctions remodel more readily under compression than under tension, could better account for the observed spatial restriction of T1 events? Such constitutive asymmetry may provide a more biologically realistic mechanism for intermittent migration while preserving the overall framework proposed here.

Overall, we find the modelling direction promising, but at present the model appears somewhat premature or overly simplified relative to the experimental observations. The simulations convincingly demonstrate that T1-mediated stress relaxation can generate intermittent migration, but they do not yet quantitatively, if not qualitatively, reproduce the spatial distribution of T1 events observed in vivo. Since the authors have segmented some samples, could all the cells then provide a movie with T1 annotated? That would be helpful to get an intuition on the level of performance of the model compared to experimental data.

Related to this point, the stop-and-go behavior shown in Figure S7 is not immediately obvious. It would be helpful to display the instantaneous DVE velocity together with the timing of T1 transitions, allowing the proposed correlation to be appreciated more directly. In addition, in Figure 5E, the lower panel appears to be labelled "DVE position", whereas the text suggests that DVE velocity is intended. This should be clarified.

(4) Motion of the epiblast

The observation of coordinated epiblast motion is intriguing. However, it would be helpful if the authors quantified the magnitude of the net displacement. From the movies, the overall displacement appears relatively modest, perhaps on the order of one cell diameter. Is this indeed the case?

More generally, we have some concerns regarding the quantification and representation of epiblast motion. As discussed above, the superpixel analysis appears to operate at a subcellular scale, with many superpixels spanning the apico-basal extent of individual epiblast cells. Consequently, the measured motion may partly reflect transient cell deformations, for example during mitosis or interkinetic nuclear migration, rather than displacement of the tissue itself. Finally, we wonder whether the flattened representation is the most appropriate way to present the epiblast data. Such projections are clearly helpful for analyzing the whole VE motion over a curved epithelial surface. However, the epiblast motion described here is essentially linear, and it is therefore less obvious how the flattening affects the apparent displacement. It would be helpful if the authors could also present the epiblast movement in the original, non-flattened imaging data (e.g. using an optical transverse section through the embryo). At present, the motion is only shown either as a geodesic projection or as a flattened transverse view, such that the reader never directly observes the movement in its native three-dimensional geometry.

Reviewer #1 (Public review):

Summary:

Shpektor et al. propose a link between how humans learn abstract and hierarchical structures to support memory (for example, remembering the event of the first landing on the moon) and the medial temporal lobe (MTL) and grid cells in particular. Given that there is solid work on how grid cells in different modules jointly encode position in rodents, providing evidence for the existence of a similar code in humans in the non-spatial domain and in relation to memory formation, would constitute a valuable finding.

The authors first examine a small human intracranial dataset to demonstrate that sequence position is decodable in MTL population codes. They then examine behavioral data from two larger groups of participants who passively viewed content presented in a hierarchical sequence and show that errors in recall of positions within that sequence qualitatively match hierarchical predictions. The task design enabled distinct signatures of memory representations at different levels of hierarchy. While there were no multivariate patterns in MTL or any brain region that matched these patterns reliably, a follow-up analysis in MTL revealed a gradient along the anterior-posterior axis, such that lower levels of the hierarchy tended to have representational peaks in more anterior regions of the MTL, which was consistent across the two fMRI datasets.

Major strengths of the study include the novelty of the experimental paradigm and data.

In particular, single cell recording in MTL from a small number of human participants during sequence learning and testing a larger group of human participants on a sequence amenable to hierarchical structure learning, and collecting fMRI data during retrieval.

Furthermore, the paper tackles an important question and does so from both directions, using inspirations from both biology and computational science to navigate it.

The primary weaknesses of the paper are a lack of compelling support for the overarching claim about hierarchical representation and a lack of clarity and consistency about exactly what those hierarchical representations should and do look like. My concerns regarding these weaknesses are described below, and I believe that most, if not all, of them could be addressed through additional analysis and paper revisions.

In the first part of the paper, the authors provide single-cell recordings in MTL, and they report the existence of cells that are sensitive to position (more so than to picture). However, they don't elaborate on this result with a model for an abstract sequence code. This is an issue because one possible explanation for the sequential position decoding is that neurons just fire at the presentation of the first image and decay at different rates, or ramp up toward action or feedback. One might be able to decode the position in sequence from these cells' activity, but can hardly call this an abstract code of position in a sequence. However, the authors don't provide further investigation into what the single-cell result might suggest and move on to a completely different fMRI experiment in the second part of the paper. Being able to decode sequence position does not, in my view, necessarily imply an abstract positional code - and I felt that further analysis of the single unit data would be required to identify what representations gave rise to that decoding ability.

The most compelling evidence that participants were encoding temporal order hierarchically came from behavioral data in the second part of the paper. However, these results were not presented clearly enough to evaluate their reliability and specificity. Figure 2i shows histograms of errors across participants with arrows pointing to bars that apparently correspond to errors of different levels of hierarchy. There are three colored bars, corresponding to errors of one unit at the first, second, or third levels of hierarchy. The first level is not diagnostic of hierarchy, but the other two colored bars appear higher than the colors nearby them. However, my understanding is that these bars correspond to situations with the same tone - which seems like an obvious reason that two positions might be confused, which in my view would weaken the argument for hierarchical encoding. Furthermore, there is no display of variability in the plot or indication of individual differences, so it is hard to tell whether the histogram is dominated by a few participants who made a lot of errors or is reflective of a general tendency across participants.

The fMRI analyses, while creative, raise questions regarding interpretability. The authors report no representations of hierarchical position at any level, either in MTL or across the whole brain, which would typically be taken as a lack of evidence for the representations existing. Follow-up analyses revealed that what shadows of representations do exist seem to line up along the anterior-posterior gradient. But what does that mean if we can't be sure that the representations are really there? Typically, we tally up evidence supporting an overarching claim by testing multiple predictions that are all consistent with the same story - but in this case, it seems that not all such test results are consistent.

In many cases, it was difficult to judge the strength of evidence due to somewhat minimal reporting on the exact hypotheses tested and test statistics.

On a high level, I found the overarching story linking the two datasets together to be somewhat tenuous. While I understand that science rarely rolls out as a coherent story, presenting the authors' valuable experiments in this fashion makes it harder for the reader to digest the information and reach a conclusion. The relevance of the first section of the paper to the second is not immediately apparent. Each section provides somewhat incomplete evidence for a set of claims on its own - but my view was that combining the two studies led to more questions than answers - since the paradigms and measurements are so different.

In conclusion, the authors propose an interesting account of how memories are formed in the human brain, by building an abstract and hierarchical code. The paper identifies a few separate findings that are suggestive of hierarchical abstract memory encoding in the MTL - yet I believe that more work would need to be done to irrefutably support that claim.

Reviewer #2 (Public review):

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

Shpektor et al. investigate how hierarchical sequence structure is represented in the entorhinal cortex (EC) and medial temporal lobe (MTL) using a combination of single-unit recordings and fMRI. In the single-unit recordings, they find abstract representations of ordinal position within short sequences in both the EC and the hippocampus. Next, they use two fMRI datasets to examine representations of hierarchical sequence structure in EC. They find that these representations (1) are organized along a posterior-to-anterior hierarchy, with finer sequence structure represented in posterior EC and coarser structure in anterior EC, and (2) generalize across sensory features, suggesting an abstract representation of sequence position. The authors take these findings as evidence of a non-spatial hierarchical coordinate system in the human EC, analogous to grid cells in rodents.

Strengths:

The methodological approach presented in this study is commendable, combining single-unit recordings in the MTL with two fMRI datasets. The finding of hierarchical and abstract sequence representations in the EC is compelling and is replicated across these datasets and modalities. The manuscript addresses important questions about how the MTL abstracts across experiences that share hierarchical structure, a topic of considerable current interest. As such, the work is likely to be of broad interest to researchers studying these processes in both rodents and humans.

Weaknesses:

In my view, the main weaknesses concern the interpretation of the results, as well as several areas where additional analyses and methodological clarification would strengthen the manuscript. My point-by-point comments are as follows:

(1) I found the evidence for hierarchical and abstract sequence-position representations interesting. However, I am less convinced by the stronger claim that these findings demonstrate a coordinate system analogous to grid-cell coding. The current results appear to provide stronger support for abstract sequence-position coding than for grid-like coding per se. In particular, it is not clear to me that hierarchical sequence representations necessarily imply a grid-like representational format or a coordinate system. Many neural systems exhibit gradients of representational scale along the anterior-posterior axis, both within and across brain regions, without being considered grid-like. I would encourage the authors to clarify why it should be interpreted specifically in terms of a coordinate system rather than more general hierarchical sequence representations. The manuscript would benefit either from a more explicit justification of this link to grid-cell coding or from a more cautious framing of the conclusions.

(2) Relatedly, the emphasis on grid-cell-like coding naturally centers the story on entorhinal cortex (EC). Yet, the single-neuron results indicate that the hippocampus contained a comparable number of position-selective cells. In addition, a large body of literature has implicated the hippocampus in hierarchical representations of memories, sequences, and relational structure. For completeness, I encourage the authors to repeat the key fMRI analyses within the hippocampus, rather than focusing exclusively on EC.

(3) I have some concerns regarding the amount of information available to distinguish representations at different levels of the sequence hierarchy. As I understand the design, each 113-tone sequence was associated with only eight images, meaning there were approximately 14 tones between successive image events. It would be helpful to provide additional detail regarding how image coordinates were assigned and selected, how many observations contributed to each hierarchical level, and how much statistical power was available to distinguish representations at different scales.

(4) I was also uncertain about the potential influence of visual similarity in Dataset 1. My understanding is that the images were not entirely unique but instead consisted of rotated versions of the same images. If so, this visual similarity could potentially complicate the interpretation of representational structure. It would therefore be useful to clarify whether repeated images occurred within the same or different locations in the hierarchy and to provide analyses demonstrating that the reported effects cannot be explained by visual similarity. This seems particularly important given that the corresponding effects in Dataset 2 were weaker.

(5) The rationale for using a custom orderness metric could be explained more clearly. It would be helpful to understand why a custom metric was preferred over rank-order measures such as Kendall's tau or Spearman's rho. I would be interested in seeing whether the orderness results replicate using one of these more conventional metrics.

(6) I had difficulty reconciling the finding that sequence representation effects are stronger across rather than within sequences. Intuitively, I would have expected representations within a sequence to reflect both shared hierarchical position and sensory experience, thus yielding stronger within-sequence effects than across sequences. The opposite pattern seems somewhat counterintuitive. I would appreciate additional discussion of this pattern and what it implies about the nature of the underlying representation. It would also be informative to know whether similar effects are observed elsewhere in the brain, and why EC might preferentially express a purely abstract representation more strongly than representations that additionally share sensory features.

(7) The authors' theory is that hierarchical representations of sequences in EC are used as a scaffold for memory, yet the current paper does not link their behavioural results to their neural ones. I think making such a link would greatly strengthen the results presented here. For example, is displacement error or sequence memory related to ordered representations of the sequence structure?

(8) I thought the manuscript would benefit from a broader discussion of prior work on (1) sequence representations and (2) hierarchical representations in the hippocampus and related regions. As it stands, the manuscript does a good job of situating its findings within the literature on grid cells in the EC but gives comparatively little attention to the literature on sequence representations in the hippocampus. Placing the current findings within this broader body of work would help clarify which aspects of the results are specific to a grid-like interpretation and which may instead reflect more general principles of hierarchical representation in the MTL or across the brain.

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate whether glycogen phosphorylase represents a molecular target of benzoylphenylurea insecticides and evaluate the physiological consequences of suppressing glycogen phosphorylase activity in the diamondback moth Plutella xylostella. The authors combine recombinant protein biochemistry, enzyme inhibition assays, RNA interference, structural modelling, metabolite profiling, gene expression analyses, and physiological measurements to determine whether diflubenzuron directly inhibits glycogen phosphorylase and whether suppression of this enzyme is sufficient to impair insect development. Based on these experiments, the authors conclude that diflubenzuron does not directly inhibit glycogen phosphorylase and that insects tolerate substantial suppression of this enzyme through compensatory metabolic responses.

Strengths:

This study addresses an important question in insect toxicology by systematically evaluating glycogen phosphorylase as a potential insecticidal target. The authors combine complementary biochemical, molecular, physiological, and structural approaches, including recombinant enzyme characterization, inhibitor assays, RNA interference, metabolite profiling, structural modelling, and measurements of fitness-related traits. This integrative approach provides a comprehensive evaluation of the biological consequences of glycogen phosphorylase suppression. In particular, the biochemical evidence that diflubenzuron does not inhibit glycogen phosphorylase, together with the observation that strong suppression of glycogen phosphorylase produces only transient physiological effects without measurable impacts on development or reproduction, provides strong support for the conclusion that glycogen phosphorylase is unlikely to represent an effective standalone insecticidal target.

Weaknesses:

The main limitation concerns the proposed mechanism underlying metabolic compensation. The observed increases in gluconeogenic gene expression, changes in metabolite abundance, and reductions in total protein are consistent with activation of compensatory metabolism, but are insufficient to directly demonstrate increased gluconeogenic flux or establish that amino acid-derived carbon is incorporated into newly synthesized glucose. Similarly, although the analyses of glycogen-associated enzymes strengthen the discussion of alternative metabolic pathways, changes in gene expression alone do not demonstrate that these pathways contribute to glycogen utilization in vivo.

Some mechanistic interpretations therefore extend beyond the data presented. For example, decreases in total protein are interpreted as evidence of protein catabolism fuelling gluconeogenesis, yet they do not directly demonstrate amino acid mobilization or incorporation into glucose. Likewise, increased expression of gluconeogenic genes is interpreted as evidence of increased pathway activity, although transcriptional changes do not necessarily reflect metabolic flux. Finally, the absence of major developmental defects following glycogen phosphorylase suppression is attributed primarily to metabolic compensation, but an alternative explanation is not fully considered. Such explanation could be that glycogen phosphorylase is not rate-limiting for glucose homeostasis under the nutrient-rich experimental conditions, where dietary carbohydrates are continuously available. Consequently, the proposed compensatory mechanism remains plausible and well supported by indirect evidence, but several aspects would benefit from more cautious interpretation.

Overall, the authors successfully achieve their primary objective of evaluating glycogen phosphorylase as a candidate insecticidal target. The study provides useful biochemical and physiological evidence that this enzyme is unlikely to represent an effective target for insecticide development in P. xylostella, while highlighting the importance of metabolic plasticity when assessing metabolic targets. The experimental approaches and datasets presented here should be valuable to researchers studying insect metabolism, insecticide mode of action, and target validation, although the precise mechanisms underlying the proposed metabolic compensation remain an important subject for future investigation.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The revision clarifies terminology, more carefully distinguishes element intactness from demonstrated transpositional activity, and better acknowledges the roles of lineage-specific loss and localized horizontal transfer alongside vertical inheritance.]

Summary:

This manuscript provides a comprehensive systematic analysis of envelope-containing Ty3/gypsy retrotransposons (errantiviruses) across metazoan genomes, including both invertebrates and ancient animal lineages. Using iterative tBLASTn mining of over 1,900 genomes, the authors catalog 1,512 intact retrotransposons with uninterrupted gag, pol, and env open reading frames. They show that these elements are widespread-present in most metazoan phyla, including cnidarians, ctenophores, and tunicates-with active proliferation indicated by their multicopy status. Phylogenetic analyses distinguish "ancient" and "insect" errantivirus clades, while structural characterization (including AlphaFold2 modeling) reveals two major env types: paramyxovirus F-like and herpesvirus gB-like proteins. Although bot envelope types were identified in previous analyses two decades ago, the evolutionary provenance of these envelope genes was almost rudimentary and anecdotal (I can say this because I authored one of these studies). The results in the present study support an ancient origin for env acquisition in metazoan Ty3/gypsy elements, with subsequent vertical inheritance and limited recombination between env and pol domains. The paper also proposes an expanded definition of 'errantivirus' for env-carrying Ty3/gypsy elements outside Drosophila.

Strengths:

(1) Comprehensive Genomic Survey:

The breadth of the genome search across non-model metazoan phyla yields an impressive dataset covering evolutionary breadth, with clear documentation of search iterations and validation criteria for intact elements.

(2) Robust Phylogenetic Inference:

The use of maximum likelihood trees on both pol and env domains, with thorough congruence analysis, convincingly separates ancient from lineage-specific elements and demonstrates co-evolution of env and pol within clades.

(3) Structural Insights:

AlphaFold2-based predictions provide high-confidence structural evidence that both env types have retained fusion-competent architectures, supporting the hypothesis of preserved functional potential.

(4) Novelty and Scope:

The study challenges previous assumptions of insect-centric or recent env acquisition and makes a compelling case for a Pre-Cambrian origin, significantly advancing our understanding of animal retroelement diversity and evolution. THIS IS A MAJOR ADVANCE.

(5) Data Transparency:

I appreciate that all data, code, and predicted structures are made openly available, facilitating reproducibility and future comparative analyses.

Original Major Weaknesses:

(1) Functional Evidence Gaps:

The work rests largely on sequence and structure prediction. No direct expression or experimental validation of envelope gene function or infectivity outside Drosophila is attempted, which would be valuable to corroborate the inferred roles of these glycoproteins in non-insect lineages. At least for some of these species, there are RNA-seq datasets that could be leveraged.

(2) Horizontal Transfer vs. Loss Hypotheses:

The discussion argues primarily for vertical inheritance, but the somewhat sporadic phylogenetic distributions and long-branch effects suggest that loss and possibly rare horizontal events may contribute more than acknowledged. Explicit quantitative tests for horizontal transfer, or reconciliation analyses, would strengthen this conclusion. It's also worth pointing out that, unlike retrotransposons that can be found in genomes, any potential related viral envelopes must, by definition, have a spottier distribution due to sampling. I don't think this challenges any of the conclusions, but it must be acknowledged as something that could affect the strength of this conclusion

(3) Limited Taxon Sampling for Certain Phyla:

Despite the impressive breadth, some ancient lineages (e.g., Porifera, Echinodermata) are negative, but the manuscript does not fully explore whether this reflects real biological absence, assembly quality, or insufficient sampling. A more systematic treatment of negative findings would clarify claims of ubiquity. However, I also believe this falls beyond the scope of this study.

(4) Mechanistic Ambiguity:

The proposed model that env-containing elements exploit ovarian somatic niches is plausible but extrapolated from Drosophila data; for most taxa, actual tissue specificity, lifecycle, or host interaction mechanisms remain speculative and, to me, a bit unreasonable.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Original Weaknesses:

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

Reviewer #3 (Public review):

Summary and Significance:

In this work, Cary and Hayashi address the important question of when, in evolution, certain mobile genetic elements (Ty3/gypsy-like non-LTR retrotransposons) associated with certain membrane fusion proteins (viral glycoprotein F or B-like proteins), which could allow these mobile genetic elements to be transferred between individual cells of a given host. It is debated in the literature whether the acquisition of membrane fusion proteins by non-LTR retrotransposons is a rather recent phenomenon that separately occurred in the ancestors of certain host species or whether the association with membrane fusion proteins is a much more ancient one, pre-dating the Cambrian explosion. Obviously, this question also touches upon the origin of the retroviruses, which can spread between individuals of a given host but seem restricted to vertebrates. Based on convincing data, Cary and Hayashi argue that an ancient association of non-LTR retrotransposons with membrane fusion proteins is most probable.

Strengths:

The authors take the smart approach to systematically retrieve apparently complete, intact, and recently functional Ty3/gypsy-like non-LTR retrotransposons that, next to their characteristic gag and pol genes, additionally carry sequences that are homologous to viral glycoprotein F (env-F) or viral glycoprotein B (env-B). They then construct and compare phylogenetic trees of the host species and individual encoded proteins and protein domains, where 3D-structure calculations and other features explain and corroborate the clustering within the phylogenetic trees. Congruence of phylogenetic trees and correlation of structural features is then taken as evidence for an infrequent recombination and a long-term co-evolution of the reverse transcriptase (encoded by the pol gene) and its respective putative membrane fusion gene (encoded by env-F or env-B). Importantly, the env-F and env-B containing retrotransposons do not form a monophyletic group among the Ty3/gypsy-like non-LTR retrotransposons, but are scattered throughout, supporting the idea of an originally ancient association followed by a random loss of env-F/env-B in individual branches of the tree (and rather rare re-associations via more recent recombinations).

Reviewer #1 (Public review):

[Editors' note: The authors addressed reviewer comments well, further strengthening the conclusions of the study.]

Summary:

A whole-organism drug screen was performed to identify molecules that decrease Apolipoprotein B (ApoB) as a target for agents to reduce atherosclerosis. Kelpsch et al. used a zebrafish reporter line, LipoGlo, which is a fusion of the Nano-luciferase protein to the ApoB protein as a proxy for the presence of ApoB-containing lipoproteins (B-lps) in larval stages. The LipoGlo line was screened against a well-characterized drug library and identified 49 hits from their primary screen. Follow-up studies further refined this list to 19 molecules that reproducibly reduced B-lps significantly. The authors focused their studies on enoxolone, a licorice root extract, and showed that larvae treated with this agent can reduce the production of B-lps. As enoxolone has been reported to suppress Hepatocyte Nuclear factor 4a (HNF4a), the authors investigated whether loss-of-hnf4a or pharmacological inhibition of hnf4a in zebrafish also produced similar phenotypes as enoxolone treatment. Their studies showed that this was the case. Transcriptomic studies after enoxolone treatment resulted in altered expression of genes involved in cholesterol biosynthesis and in glucose/insulin signaling pathways. This study highlights the utility of a zebrafish whole-organism chemical screen for modifiers of B-lps production and/or its clearance. A significant finding is that enoxolone inhibits hnf4a in zebrafish to reduce B-lps production and supports targeting HNF4a as a therapeutic means to reduce the emergence of atherosclerosis.

Strengths:

The authors performed a whole-organism chemical screen with over 3000 agents. Such screens are challenging, and the authors used strict criteria for determining hits. The conclusions of this study are well supported by the presented data.

Comment on revised version:

The authors have addressed all my comments.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Reviewer #3 (Public review):

Summary:

In "A‬‭ whole-animal‬‭ phenotypic‬‭ drug‬‭ screen‬‭ identifies‬‭ suppressors‬‭ of‬‭ atherogenic‬ lipoproteins", Kelpsch et al seek to identify new, chemically targetable pathways that regulate ApoB function and could ultimately serve as treatments for elevated lipid disorders and/or cardiovascular disease. Given the interconnected nature of lipid regulation in the whole organism with interdependent organs and secreted components (i.e. lipoproteins), they use the vertebrate model zebrafish to screen a large library of ~3000 compounds for their ability to lower the important ApoB-containing lipoproteins. They find 49 hits with 19 compounds passing a higher level of scrutiny, and focus on the role of enoxolone in modulating B-Ip levels at least partly through the HNF4alpha transcription factor and, putatively, through downstream cholesterol/lipid biosynthetic pathways.

Strengths:

The study uses a well-validated in vivo stain (LipoGlo) for measuring lipoproteins in the context of a developing whole organism with a quantitative read-out on a high-throughput platform, allowing for screening of thousands of compounds altering the complex metabolic/physiologic functions necessary for lipoprotein production.

The use of genetic mutant HNF4alpha to assign the mechanism of action to the prime candidate compound studied (enoxolone) is a powerful approach for this challenging aspect of chemical genetics studies.

Reviewer #1 (Public review):

This is an interesting study on the nature of representations across the visual field. The question of how peripheral vision differs from foveal vision is a fascinating and important one. The majority of our visual field is extra-foveal, yet our sensory and perceptual capabilities decline in pronounced and well-documented ways away from the fovea. Part of the decline is thought to be due to spatial averaging ('pooling') of features. Here, the authors contrast two models of such feature pooling with human judgments of image content. They use much larger visual stimuli than in most previous studies, and some sophisticated image synthesis methods to tease apart the prediction of the distinct models.

More importantly, in so doing, the researchers thoroughly explore the general approach of probing visual representations through metamers-stimuli that are physically distinct but perceptually indistinguishable. The work is embedded within a rigorous and general mathematical framework for expressing equivalence classes of images and how visual representations influence these. They describe how image-computable models can be used to make predictions about metamers, which can then be compared to make inferences about the underlying sensory representations. The main merit of the work lies in providing a formal framework for reasoning about metamers and their implications, for comparing models of sensory processing in terms of the metamers that they predict, and for mapping such models onto physiology. Importantly, they also consider the limits of what can be inferred about sensory processing from metamers derived from different models.

Overall, the work is of a very high standard and represents a significant advance over our current understanding of perceptual representations of image structure at different locations across the visual field. The authors do a good job of capturing the limits of their approach I particularly appreciated the detailed and thoughtful Discussion section and the suggestion to extend the metamer-based approach described in the MS with observer models. The work will have an impact on researchers studying many different aspects of visual function including texture perception, crowding, natural image statistics and the physiology of low- and mid-level vision.

The main weaknesses of the original submission relate to the writing. A clearer motivation could have been provided for the specific models that they consider, and the text could have been written in a more didactic and easy to follow manner. The authors could also have been more explicit about the assumptions that they make.

Comments on revised version.

The authors have now fully addressed my concerns and I think the paper is a valuable contribution. In future studies within the same research program I would appreciate seeing further consideration of how metamerism at different stages of visual processing interact to determine behaviour in tasks. For example, there are presumably interesting impacts of feedback that may modify feature spaces, thereby rendering aspects of appearance that were previously metameric perceptually discriminable.

Reviewer #2 (Public review):

Summary:

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

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

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

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

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

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

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

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

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

Comments on revised version.

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

Reviewer #1 (Public review):

Leukemia-driving NUP98 oncofusion proteins form chromatin-associated biomolecular condensates in the nucleus, and these structures are important for oncogenic transformation. Most NUP98 fusions do not contain domains that mediate the recognition of specific DNA elements. Instead, they entail domains that are important for chromatin regulation. For instance, the NUP98::KDM5A fusion features a fusion of the NUP98 N-terminus with the third PHD domain of the histone demethylase KDM5A. As PHD domains are critical for the recognition of methylated histones without any sequence specificity, it is not clear what controls the condensation and chromatin binding of NUP98::KDM5A, leading to the induction of oncogenic transcriptional programs.

In this work, the authors use a combination of cellular and in vitro studies to show that biomolecular condensation of NUP98::KDM5A is dependent on H3K4me3 binding. Their model proposes that concentration-dependent chromatin-associated condensation of NUP98::KDM5A depends on local densities of H3K4me3 on chromatin and the levels of the fusion oncoprotein. In line with this, the analysis of gene expression data from NUP98::KDM5A-positive AML cells shows a positive correlation between differentially expressed genes and H3K4me3 levels.

This is an interesting manuscript that aims to dissect the molecular mechanisms underlying biomolecular condensation of the NUP98::KDM5A oncoprotein. The work is solid, and the results are well explained and presented in a logical order. However, the study suffers from several weaknesses that if addressed would improve the study.

Major points:

(1) All cellular experiments are performed in settings of transient transfection of NUP98::KDM5A in non-hematopoietic cell types. These conditions are not physiologically relevant, as these cells do not depend on the fusion oncogene. Therefore, any claims about concentration-dependent effects on condensation need to be validated in AML cells that are driven by NUP98::KDM5A. While this may not be possible in primary patient-derived cells, several groups have published AML models of NUP98::KDM5A-driven AML that could be used.

(2) The results presented in Figure 4 are not entirely supportive of the mechanism. It is known that active gene expression correlates with high H3K4me3 levels; therefore, the correlations shown by the authors are expected. Yet, the authors do not discuss the fact that many H3K4me3-positive genomic regions do not show NUP98::KDM5A binding. This should be elaborated on in the discussion section.

(3) While the focus of the manuscript is on NUP98::KDM5A, this oncofusion is part of a family of >30 fusions that join the NUP98 N-terminus to a variety of factors with roles in epigenetic control and transcription. While the repertoire of NUP98 fusion partners is diverse with regard to functional domains, they all induce a conserved set of target genes that is characteristic of this leukemia subtype. How can this be achieved in the context of NUP98 fusion proteins that do not contain a PHD domain, such as NUP98::NSD1 or NUP98::HOXA9? Please discuss this.

Reviewer #2 (Public review):

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

The authors used a panel of cell models to determine whether CDK4/6 overexpression resulted in resistance to the EGFR inhibitor Osimertinib, and the mechanisms underlying the resistance.

(1) Major Concerns (highest priority):

There is a lack of detail about the methodology in the results section/figure legends, which makes it difficult to interpret the data. Sometimes, adequate information is also not included in the methods themselves. For example, Figure 1A: how many doses did each mouse receive? How long after dosing were animals sacrificed? Figures 1E and 2A: is this RNA-seq analysis?

Using a second EGFR inhibitor for some of the key experiments would increase the rigor of the studies shown.

(2) Nice to have experiments:

Using CRISPR KO of CDK4 in the CDK4-amplified HCC827 and testing response to Osi and presence of replication stress would also increase the rigor of the studies.

The authors show that in their patient data, some cell cycle regulators which are amplified in NSCLC at similar rates to CDK4/6, such as CCNE1, had no increase in FGA. Overexpressing CCNE1 and testing Osi response in their cell models would be a nice test of their proposed mechanism that it is the genomic instability and FGA that are driving resistance. This wouldn't need to be done in vivo, but could be done using cell culture-based methods.

Similarly, testing the overexpression of some of the proposed target genes, such as STEAP1 and AGR2, on the therapeutic response to Osi in cell culture would also be a nice test of the mechanism proposed.

Reviewer #2 (Public review):

Summary:

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

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

Major Comments:

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

Polymyxins are the last line of drugs to treat gram-negative bacteria-induced multi-drug resistance; however, they cause nephrotoxicity in 60% of patients. In this work, the authors have studied the structure-interaction relationship (SIR) of polymyxins with hPepT2 using computational and experimental methods. Moreover, it is observed that the electrostatic interactions coordinate the hPepT2-Polymyxin interactions; hence, an alanine scanning strategy is used to understand the interactions and derive the polymyxin variants.

Computational methods such as molecular modeling, coarse-grained and all-atom MD simulations, and interaction studies are performed, while the results are validated in the mouse model, which is a great strategy to prove the hypothesis.

Strengths:

A clear understanding of the hPepT2-Polymyxin interactions and the role of electrostatic interactions is one of the very important strengths of the paper. In addition, this work proposes a great pipeline for using computational approaches and experimental validation methods to guide the development of newer antibiotics.

Overall, the study proposes novel polymyxin analogues with reduced or no nephrotoxicity, thereby providing a promising foundation for the rational development of safer lipopeptide antibiotics.

Weaknesses:

This work is very well executed and presented; however, addressing the following concerns might improve the presentation of the work:

(1) The introduction is well articulated; however, including a paragraph on the known inhibitors might be helpful in understanding the current status. In addition, it might also help to introduce Dabs, FADDI variants, Gly-sar and MIPS.

(2) The following details of modeling with AlphaFold2 should be included: how the final structure was selected, what the RMSD and structure alignment of the template are, and the final selected structure. A section on modeling with all the parameter details might be useful for reproducing the structure. In addition, specify how the alanine scanning was performed alongside the structure prediction of polymyxins.

(3) In the all-atom MD simulation method, detailing several parameters might help in reproducing the results: simulation time for each system, water model, system composition, protonation state, box type and dimensions, salt ions and concentration, membrane parameters and ligand parameterization methods. Also, the following details on energy minimization might be useful: minimization algorithm, number of steps for minimization and structure restraints in place.

(4) On page 6, line 210, the MIC is used for the first time; although MIC is given in the abbreviation list, the first occurrence should have a complete name. A one-line explanation of MIC in the introduction or wherever suitable might be better but is not mandatory.

(5) Similarly, Gly-sar is first mentioned on page 8, line 301, but its complete name is only mentioned later on page 10, line 368. This can be addressed if a short description is included in the introduction section.

(6) For coarse-grained MD simulation, why were 2 replicates performed? Most studies perform 3 replicates, which are also good in terms of statistics and error bar calculations. In addition, the authors should specify whether an independent minimization is done for each of the two replicates or whether the minimization step is common for both.

(7) For MD simulation results, giving simulation movies in supplementary results might be a better way to show how the trajectories behaved.

(8) The description of visualisation software such as VMD or PyMol is missing. The authors should specify if any visualization tool is used.

(9) For the mouse model study, the authors claim that FADDI-795 has no observable nephrotoxicity; however, the n=3 shows that a very small number of mouse models were used to make the assumption. In addition, the number of mice used in each experiment is not explicitly mentioned in the methods section.

(10) In Table 2, the column 8 header is not visible.

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #3 (Public review):

Summary:

Jiang et al. described findings aimed at interrogating the interactions of the antibiotic polymyxin B with human kidney proteins that mediate nephrotoxicity. Their findings using both computational molecular dynamics simulations and experimental approaches illustrate the importance of aspartic acid residues (D215) in mediating the antibiotic uptake into the cells, and upon mutagenesis with Alanine, the effects are less pronounced. Further, they could modify the antibiotic units interacting with proteins into less toxic peptides with retained antibacterial properties.

Strengths:

I was impressed by this text, which advances the knowledge of how the antibiotic causes human nephrotoxicity and how this could be exploited into less problematic antibiotic peptides.

Weaknesses:

Interactions of Polymyxin B with kidney proteins were not demonstrable in vivo, and with reliable technologies such as X-ray or NMR.

Reviewer #1 (Public review):

Summary:

A prevailing view is that translation of 5' capped mRNAs, i.e. mRNAs that are translated via ribosome scanning, is inhibited by highly structured 5' untranslated regions (5' UTRs). Despite having a common, structured 5' UTR, the mRNAs produced by the SARS-CoV-2 virus are efficiently translated. In this study, the authors identified a DRACH motif in stem-loop 3 (SL3), suggesting a potential site of m6A methylation of A74 by the enzyme METTL3. Given that such m6A modifications are known to disrupt RNA structure formation, the authors tested the hypothesis that this may be the basis underlying the efficient translation of these mRNAs. Mutational approaches complemented by METTL3 siRNA knockdown were employed to support this hypothesis. Additional experiments showed that this is required for efficient association of a reporter mRNA with polysomes (indicative of active translation), and suggest that the 5' UTR is more highly structured when methylation is abrogated.

Strengths:

The data clearly indicate that N6 methylation of A74 is required for efficient translation of SARS-CoV-2 mRNAs.

Weaknesses:

While the evidence supports the authors' central hypothesis, there are two issues that should be addressed. The first is that all of the approaches are indirect. All of the evidence for the presence of mRNA structural elements is based on computational and genetic analyses. We now know that there is something there, but we still do not know what it is. The authors need to use a biochemical approach to actually map the structural elements of the 5' UTR and determine how such structure(s) are changed by loss of methylation. The second hinges on the assumption that these mRNAs are translated via canonical ribosome scanning. RNA viruses are well-known to use a variety of other mechanisms, e.g. internal ribosome entry signals and ribosome tethering, to promote efficient translation. Alternatives to ribosome scanning should be considered.

Reviewer #2 (Public review):

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

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Aly et al investigate the potential for a single N6-methyladenosine RNA modification in the context of the 5' UTR sequence of SARS-CoV-2 to regulate translation of a downstream luciferase reporter transfected into cells. They show using meRIP (m6A RNA IP) that this site is methylated in the plasmid-driven transcript, and convincingly show it mediates reporter translational efficiency using knockdown of the m6A methyltransferase METTL3 and mutation of the modified UTR site together with analysis of the transcript's association with polyribosomes. They suggest that the benefit to translation conferred by the modification is through its effect on the secondary structure of the 5' UTR, based on an RT-PCR-based assay in control and METTL3 knockdown cells linking RT processivity to translation (luciferase) output. They also extend their conclusions to two cellular mRNA 5' UTRs, also reported to contain a single m6A modification, and show METTL3-dependent changes in RNA structure stability, hinting at a broader significance of this mechanism of m6A control of gene expression.

The conclusions of the paper are mostly well supported by the data presented, though validation of knockdown of METTL3 (and reader proteins) is absent.

A major limitation of the work is the exclusive use of the reductionist artificial reporter system in uninfected cells. Though the 5' UTR site they identify is methylated in the context of a transcript generated in the nucleus (where the m6A installing complex is mainly localized, and believed to act exclusively in uninfected cells), how frequently this site is modified, if at all, on viral RNAs generated within cytoplasmic membrane-bound replication organelles. Similarly, whether the translation regulation by a single m6A modification identified here occurs within the context of an infected cell, in which there are many changes to the RNA and translational regulatory landscape, also remains to be tested.

How this work can be reconciled with others that have concluded either little potential for translational regulation by 5' UTR modification (Guca et al 2024; PMID: 38244546) or that an eIF3-mediated mechanism is responsible (Meyer et al, 2015 PMID: 26593424) is not addressed in the discussion.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors investigated the effect of chronic activation of dopamine neurons using chemogenetics. Using Gq-DREADDs, the authors chronically activated midbrain dopamine neurons and observed that these neurons, particularly their axons, exhibit increased vulnerability and degeneration, resembling the pathological symptoms of Parkinson's disease. Baseline calcium levels in midbrain dopamine neurons were also significantly elevated following the chronic activation. Lastly, to identify cellular and circuit-level changes in response to dopaminergic neuronal degeneration caused by chronic activation, the authors employed spatial genomics (Visium) and revealed comprehensive changes in gene expression in the mouse model subjected to chronic activation. In conclusion, this study presents novel data on the consequences of chronic hyperactivation of midbrain dopamine neurons.

Strengths:

This study provides direct evidence that the chronic activation of dopamine neurons is toxic and gives rise to neurodegeneration. In addition, the authors achieved the chronic activation of dopamine neurons using water application of clozapine-N-oxide (CNO), a method not commonly employed by researchers. This approach may offer new insights into pathophysiological alterations of dopamine neurons in Parkinson's disease. The authors also utilized state-of-the-art spatial gene expression analysis, which can provide valuable information for other researchers studying dopamine neurons. Although the authors did not elucidate the mechanisms underlying dopaminergic neuronal and axonal death, they presented a substantial number of intriguing ideas in their discussion, which are worth further investigation.

Weaknesses:

Many claims raised in this paper are only partially supported by the experimental results. So, additional data are necessary to strengthen the claims. The effects of chronic activation of dopamine neurons are intriguing; however, this paper does not go beyond reporting phenomena. It lacks a comprehensive explanation for the degeneration of dopamine neurons and their axons. While the authors proposed possible mechanisms for the degeneration in their discussion, such as differentially expressed genes, these remain experimentally unexplored.

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

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

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

Weaknesses:

Major concerns:

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

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

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

Reviewer #3 (Public Review):

Summary:

In this manuscript, Rademacher and colleagues examined the effect on the integrity of the dopamine system in mice of chronically stimulating dopamine neurons using a chemogenetic approach. They find that one to two weeks of constant exposure to the chemogenetic activator CNO leads to a decrease in the density of tyrosine hydroxylase staining in striatal brain sections and to a small reduction of the global population of tyrosine hydroxylase positive neurons in the ventral midbrain. They also report alterations in gene expression in both regions using a spatial transcriptomics approach. Globally, the work is well done and valuable and some of the conclusions are interesting. However, the conceptual advance is perhaps a bit limited in the sense that there is extensive previous work in the literature showing that excessive depolarization of multiple types of neurons associated with intracellular calcium elevations promotes neuronal degeneration. The present work adds to this by showing evidence of a similar phenomenon in dopamine neurons. In terms of the mechanisms explaining the neuronal loss observed after 2 to 4 weeks of chemogenetic activation, it would be important to consider that dopamine neurons are known from a lot of previous literature to undergo a decrease in firing through a depolarization-block mechanism when chronically depolarized. Is it possible that such a phenomenon explains much of the results observed in the present study? It would be important to consider this in the manuscript. The relevance to Parkinson's disease (PD) is also not totally clear because there is not a lot of previous solid evidence showing that the firing of dopamine neurons is increased in PD, either in human subjects or in mouse models of the disease. As such, it is not clear if the present work is really modelling something that could happen in PD in humans.

Comments on the introduction:

The introduction cites a 1990 paper from the lab of Anthony Grace as support of the fact that DA neurons increase their firing rate in PD models. However, in this 1990 paper, the authors stated that: "With respect to DA cell activity, depletions of up to 96% of striatal DA did not result in substantial alterations in the proportion of DA neurons active, their mean firing rate, or their firing pattern. Increases in these parameters only occurred when striatal DA depletions exceeded 96%." Such results argue that an increase in firing rate is most likely to be a consequence of the almost complete loss of dopamine neurons rather than an initial driver of neuronal loss. The present introduction would thus benefit from being revised to clarify the overriding hypothesis and rationale in relation to PD and better represent the findings of the paper by Hollerman and Grace.

It would be good that the introduction refers to some of the literature on the links between excessive neuronal activity, calcium, and neurodegeneration. There is a large literature on this and referring to it would help frame the work and its novelty in a broader context.

Comments on the results section:

The running wheel results of Figure 1 suggest that the CNO treatment caused a brief increase in running on the first day after which there was a strong decrease during the subsequent days in the active phase. This observation is also in line with the appearance of a depolarization block.

The authors examined many basic electrophysiological parameters of recorded dopamine neurons in acute brain slices. However, it is surprising that they did not report the resting membrane potential, or the input resistance. It would be important that this be added because these two parameters provide key information on the basal excitability of the recorded neurons. They would also allow us to obtain insight into the possibility that the neurons are chronically depolarized and thus in depolarization block.

It is great that the authors quantified not only TH levels but also the levels of mCherry, co-expressed with the chemogenetic receptor. This could in principle help to distinguish between TH downregulation and true loss of dopamine neuron cell bodies. However, the approach used here has a major caveat in that the number of mCherry-positive dopamine neurons depends on the proportion of dopamine neurons that were infected and expressed the DREADD and this could very well vary between different mice. It is very unlikely that the virus injection allowed to infect 100% of the neurons in the VTA and SNc. This could for example explain in part the mismatch between the number of VTA dopamine neurons counted in panel 2G when comparing TH and mCherry counts. Also, I see that the mCherry counts were not provided at the 2-week time point. If the mCherry had been expressed genetically by crossing the DAT-Cre mice with a floxed fluorescent reported mice, the interpretation would have been simpler. In this context, I am not convinced of the benefit of the mCherry quantifications. The authors should consider either removing these results from the final manuscript or discussing this important limitation.

Although the authors conclude that there is a global decrease in the number of dopamine neurons after 4 weeks of CNO treatment, the post-hoc tests failed to confirm that the decrease in dopamine number was significant in the SNc, the region most relevant to Parkinson's. This could be due to the fact that only a small number of mice were tested. A "n" of just 4 or 5 mice is very small for a stereological counting experiment. As such, this experiment was clearly underpowered at the statistical level. Also, the choice of the image used to illustrate this in panel 2G should be reconsidered: the image suggests that a very large loss of dopamine neurons occurred in the SNc and this is not what the numbers show. A more representative image should be used.

In Figure 3, the authors attempt to compare intracellular calcium levels in dopamine neurons using GCaMP6 fluorescence. Because this calcium indicator is not quantitative (unlike ratiometric sensors such as Fura2), it is usually used to quantify relative changes in intracellular calcium. The present use of this probe to compare absolute values is unusual and the validity of this approach is unclear. This limitation needs to be discussed. The authors also need to refer in the text to the difference between panels D and E of this figure. It is surprising that the fluctuations in calcium levels were not quantified. I guess the hypothesis was that there should be more or larger fluctuations in the mice treated with CNO if the CNO treatment led to increased firing. This needs to be clarified.

Although the spatial transcriptomic results are intriguing and certainly a great way to start thinking about how the CNO treatment could lead to the loss of dopamine neurons, the presented results, the focussing of some broad classes of differentially expressed genes and on some specific examples, do not really suggest any clear mechanism of neurodegeneration. It would perhaps be useful for the authors to use the obtained data to validate that a state of chronic depolarization was indeed induced by the chronic CNO treatment. Were genes classically linked to increased activity like cfos or bdnf elevated in the SNc or VTA dopamine neurons? In the striatum, the authors report that the levels of DARP32, a gene whose levels are linked to dopamine levels, are unchanged. Does this mean that there were no major changes in dopamine levels in the striatum of these mice?

The usefulness of comparing the transcriptome of human PD SNc or VTA sections to that of the present mouse model should be better explained. In the human tissues, the transcriptome reflects the state of the tissue many years after extensive loss of dopamine neurons. It is expected that there will be few if any SNc neurons left in such sections. In comparison, the mice after 7 days of CNO treatment do not appear to have lost any dopamine neurons. As such, how can the two extremely different conditions be reasonably compared?

Comments on the discussion:

In the discussion, the authors state that their calcium photometry results support a central role of calcium in activity-induced neurodegeneration. This conclusion, although plausible because of the very broad pre-existing literature linking calcium elevation (such as in excitotoxicity) to neuronal loss, should be toned down a bit as no causal relationship was established in the experiments that were carried out in the present study.

In the discussion, the authors discuss some of the parallel changes in gene expression detected in the mouse model and in the human tissues. Because few if any dopamine neurons are expected to remain in the SNc of the human tissues used, this sort of comparison has important conceptual limitations and these need to be clearly addressed.

A major limitation of the present discussion is that it does not discuss the possibility that the observed phenotypes are caused by the induction of a chronic state of depolarization block by the chronic CNO treatment. I encourage the authors to consider and discuss this hypothesis. Also, the authors need to discuss the fact that previous work was only able to detect an increase in the firing rate of dopamine neurons after more than 95% loss of dopamine neurons. As such, the authors need to clearly discuss the relevance of the present model to PD. Are changes in firing rate a driver of neuronal loss in PD, as the authors try to make the case here, or are such changes only a secondary consequence of extensive neuronal loss (for example because a major loss of dopamine would lead to reduced D2 autoreceptor activation in the remaining neurons, and to reduced autoreceptor-mediated negative feedback on firing). This needs to be discussed.

There is a very large, multi-decade literature on calcium elevation and its effects on neuronal loss in many different types of neurons. The authors should discuss their findings in this context and refer to some of this previous work. In a nutshell, the observations of the present manuscript could be summarized by stating that the chronic membrane depolarization induced by the CNO treatment is likely to induce a chronic elevation of intracellular calcium and this is then likely to activate some of the well-known calcium-dependent cell death mechanisms. Whether such cell death is linked in any way to PD is not really demonstrated by the present results.

The authors are encouraged to perform a thorough revision of the discussion to address all of these issues, discuss the major limitations of the present model, and refer to the broad pre-existing literature linking membrane depolarization, calcium, and neuronal loss in many neuronal cell types.

Reviewer #1 (Public review):

The authors conducted a comprehensive benchmarking and evaluation of co-folding platforms, including AlphaFold3, Boltz-2, Chai-1, and the docking algorithm Dock3.7, which employs a physics-based scoring function that incorporates van der Waals interactions, electrostatics, and ligand desolvation energies. The system of interest was the SARS-CoV-2 NSP3 macrodomain (Mac1), an increasingly popular antiviral target, and the ligand sets comprised 557 unseen ligand poses (keeping the training for these co-folding platforms in mind). Additionally, the authors investigated whether the co-folding models could distinguish true ligands from non-binding small molecules. The study is thorough, with extensive statistical support and consensus across multiple metrics (chemoinformatics for quantifying ligand similarity and efficacy). The questions that the authors aim to address are whether the co-folding models struggle with memorization, whether they can distinguish between a true and a false binder, whether they replicate experimental binding affinities and efficacy, and how they compare to the physics-based docking algorithm (Dock3.7).

Strengths:

Overall, this is a scientifically solid paper.

The work is highly detailed and well executed, featuring thorough data analysis and statistical assessment.

Comments on revised version:

The authors have adequately addressed my concerns.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Appraisal of Aims Achieved:

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

Field Impact:

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

Reviewer #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 provides 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.

Appraisal of Aims and Conclusions:

The authors successfully achieve their aim of presenting a cohesive, multi-layered framework for postnatal retinal maturation, aligning functional physiological data with structural and transcriptomic timelines. The data robustly supports the correlation between retinal waves, microglial activity, and vascular remodeling and also identifies apoptotic RGCs as the potential "pacemakers" of Stage II waves.

Impact, Utility, and Community Asset:

This work will significantly impact developmental neurobiology by reframing programmed cell death as an active, instructive driver of neural network patterning and angiogenesis, rather than a passive clearance process. Methodologically, the integration of large-scale MEA recordings and live calcium imaging with scRNA-seq sets an excellent benchmark for multimodal developmental studies. Furthermore, the transcriptomic datasets mapping microglial phenotypes and vascular remodeling will serve as a highly valuable reference repository for the broader visual neuroscience community.

Additional Context for Readers:

To fully appreciate this study, readers should view it through the lens of neurovascular unit assembly. While Stage II cholinergic waves are traditionally studied purely in the context of visual circuit refinement, this work adds vital context by showing they also regulate the surrounding metabolic ecosystem. It effectively demonstrates that early electrical activity, programmed cell death, and vascular scaffolding do not occur in isolation, but are deeply interdependent processes.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

This work addresses a question of practical importance that had never been systematically analysed in the cryo-ET field: when collecting tilt-series data, what is the optimal angular step size between successive tilt images? Due to the upper limit in electron exposure (100 - 150 e⁻/Ų), this question is important, since finer angular sampling improves attainable reconstruction resolution (Crowther criterion) but reduces the signal-to-noise ratio of each individual image, potentially compromising both image quality and the ability to computationally align successive frames. To address this, the authors designed a thorough benchmarking study comparing five tilt increments (1{degree sign}, 2{degree sign}, 3{degree sign}, 5{degree sign}, and 10{degree sign}) while keeping the total dose and tilt range constant. They evaluated the consequences at every stage of the cryo-ET workflow - from raw image quality and tilt-series alignment, through template matching for ribosome detection, to high-resolution subtomogram averaging - with the goal of providing the community with an evidence-based recommendation for data acquisition.

The manuscript is well written, and the experimental design is carefully thought out. The work provides valuable practical insights into cryo-ET data acquisition by demonstrating that balancing two competing demands - sufficient dose per individual tilt image and fine angular sampling - is essential to achieve high-quality tomographic reconstructions. The identification of a practical optimum at 3{degree sign} tilt increment is the key contribution of the work. It will be interesting to see in the future whether this optimum shifts for smaller molecular targets, and how emerging tilt interpolation strategies such as cryoTIGER may interact with the choice of experimental angular increment.

Comments on revised version.

Well done! I really like the manuscript and from my point of view it's an excellent piece of work and super useful for the community. Thank you so much for the meticulous work!

Reviewer #2 (Public review):

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

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

Comments on revised version.

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

Reviewer #1 (Public review):

Different studies have proposed distinct mechanisms by which succinate dehydrogenase (SDH)-deficient cells escape aspartate limitation, highlighting metabolic heterogeneity across experimental systems. In this study, the authors address these previously conflicting observations by longitudinally tracking the adaptation of multiple SDHB-knockout clones derived from the same parental cell line.

The authors identify two distinct adaptive mechanisms: complex I suppression with predominantly GOT1-dependent aspartate synthesis, and preservation of complex I activity with increased PC-GOT2-dependent aspartate synthesis. They further define shared and unique dependencies associated with these adaptive states, providing a rationale for potential therapeutic targeting strategies.

Overall, this is a strong study in cancer metabolism, integrating complementary longitudinal and mechanistic approaches, including long-term adaptation, isotope tracing, genetic perturbation, metabolomics, and functional cell growth assays. Although the study provides substantial mechanistic insight, several limitations remain.

(1) MPC is proposed as a shared dependency of both adaptive states. Testing whether MPC inhibition suppresses SDH-deficient tumor growth in vivo would substantially strengthen the therapeutic relevance.

(2) The distinction between complex I-intact and complex I-suppressed states is based mainly on the expression of two complex I subunits and the oxygen consumption. More direct assays of complex I activity or assembly are needed. Early-passage SDHB-knockout cells should also be included as controls in the OCR experiments.

(3) The two adaptive states appear to rely differentially on glucose- versus glutamine-derived aspartate synthesis. Testing the sensitivity of EP and LP clones to glucose or glutamine deprivation would further support this metabolic distinction.

(4) Since SDH is described as a tumor suppressor, the authors should clarify why SDHB loss initially inhibits hPheo1 cell proliferation.

(5) The study focuses on SDHB loss, and it remains unclear whether similar adaptive mechanisms arise following loss of other SDH subunits, including SDHA, SDHC, or SDHD, across different biological contexts. This limitation should be discussed explicitly.

Reviewer #2 (Public review):

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

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

Major points:

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

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

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

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

Reviewer #1 (Public review):

Summary:

This study describes motor cortical activity patterns during food handling in mice, investigating whether the hand/s used is reflected in distinct neural activity. The experiments focus on forelimb M1 and M2 (fM1, fM2) and an oral-manual region LOM. The main findings are that fM1 and fM2 have largely similar relationships with forelimb control, and LOM neurons are more broadly tuned. These conclusions are reached using a variety of analyses spanning straightforward firing rate analyses, selectivity metrics, PCA, and GLM decoding methods to assess tuning generalizability. The study's significance is strengthened by including analyses of bimanual control, and in this sphere, there are descriptive data and analyses that aficionados of cortical control of dexterous behaviors will find useful. The use of unimanual control is useful as a point of comparison here, but less novel overall. There are a number of places where the descriptions of what is being analyzed, what is being concluded, and data reporting should be strengthened and clarified. Additionally, the study could be greatly improved by consolidating figures and the analyses shown, since many are redundant. Many of the analyses need clearer reporting of means and effect sizes in the text, rather than just statistical outcomes. Overall, at this juncture, the study presents analyses of a unique dataset that may seed future investigations of mechanisms of bimanual coordination.

Strengths:

There are relatively few studies that compare neural activity across bimanual and unimanual control. This study uses a naturalistic food handling task to explore neural relationships to forelimb kinematics under these conditions. The uniqueness of the task and analysis target is a strength of the study.

The authors remain fairly conservative and make few strong claims in the study, which may be warranted given the diversity of tuning profiles they observed.

Weaknesses:

There are a number of statistical tests that were accompanied by too little information to critically evaluate. Means and effect sizes needed to be better reported; some details of analyses were difficult to parse, making the strength of the conclusions difficult to evaluate.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

Reviewer #3 (Public review):

Summary:

Barrett et al. compare the responses of different parts of the mouse primary and secondary motor cortex in the context of a task where the animals manipulate and eat food using either or both hands. They find that roughly half the activity is conserved when reaching with one hand vs. the other hand, or with both. Similarity of activity was somewhat higher in the "lateral oral and manual" (LOM) part of the motor cortex, consistent with notions of a more generalized oromanual function there.

Strengths:

This work aims at addressing two worthwhile questions in a mouse model of motor control: (1) what specializations do we have for controlling feeding movements, and (2) how are the arms and hands coordinated with one another? The authors develop a simple but innovative apparatus to block either hand during food handling, track the behavior at high temporal fidelity, and record a sizable neural dataset. The analyses come from numerous angles to take good advantage of the data, and succeed in showing multiple lines of evidence for greater invariance in LOM than in the forelimb parts of M1 and M2.

Weaknesses:

There are several limitations of the current study. Most importantly, the behavior presents an inherent challenge: there is only one type of movement for each of the three conditions (contra hand, ipsi hand, and bimanual). This is entirely reasonable from the perspective that this is the ethological behavior when feeding, but it limits what analyses are possible. In particular, it precludes disentangling the neural relationship with many correlated aspects of behavior, and limits identifying population-level features of the neural activity meaningfully. This means that there are a number of alternative possible sources of the neuron-level area differences found here, and the population-level features may not be reliable. Second, the behavior tracking was used at a relatively coarse level, and thus the relationships to various behavioral variables were left less distinguishable than they might have been. Finally, there may be an issue with the coordinates of what is being called forelimb M1 here, which may include some hindlimb M1.

Reviewer #1 (Public review):

Summary:

The study is methodologically solid and introduces a compelling regulatory model. However, several mechanistic aspects and interpretations require clarification or additional experimental support to strengthen the conclusions.

Strengths:

(1) The manuscript presents a compelling structural and biochemical analysis of human glutamine synthetase, offering novel insights into product-induced filamentation.

(2) The combination of cryo-EM, mutational analysis, and molecular dynamics provides a multifaceted view of filament assembly and enzyme regulation.

(3) The contrast between human and E. coli GS filamentation mechanisms highlights a potentially unique mode of metabolic feedback in higher organisms.

Comment on revised version.

The authors have addressed all of my comments and concerns. The revisions have substantially improved the quality of the manuscript. I have no further questions or concerns.

Reviewer #2 (Public review):

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

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

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

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

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

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

Symmetry concern

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

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

Filament abundance and physiological relevance

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

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

K52/C53 interface mutations

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

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

Functional linkage between filamentation and kinetics is weak.

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

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

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

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

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

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

Ligand pose and hydrogen bonding.

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

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

Glutamate has not been excluded.

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

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

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

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

Specific comments

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

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

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

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

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

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

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

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

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

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

Glutamate comparison:

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

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

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

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

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

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

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

Reviewer #3 (Public review):

In this manuscript, the authors propose a product-dependent negative-feedback mechanism of human glutamine synthetase, whereby the product glutamine facilitates filament formation, leading to reduced catalytic specificity for ammonia. Using time-resolved cryo-EM, the authors demonstrate filament formation under product-rich conditions. Multiple high-quality structures, including decameric and di-decameric assemblies, were resolved under different biochemical states and combined with MD simulations, revealing that the conformational space of the active site loop is critical for the GS catalysis. The study also includes extensive steady-state kinetic assays, supporting the view that glutamine regulates GS assembly and its catalytic activity. Overall, this is a detailed and comprehensive study. However, I would advise that a few points be addressed and clarified.

Comments on revised version.

The revision addresses several reviewer concerns: the authors add sharpened maps, ligand-density panels, symmetry expansion/focused classification, biochemical blank/substrate/TCEP controls, and E305-loop focused classification. The E305-loop part is stronger now: turnover decamer recovers partial E-flap density in few classes, while turnover filament does not.

My only remaining comment is that - as also the authors agree on the need to integrate density with biochemical data and that local resolution/averaging complicates modeling - I would advise softening the claim regarding glutamine from "glutamine binds" to "density consistent with glutamine/product-associated density". In general, it would be best to avoid overstating atomic certainty at the filament interface and the safest framing is the observed interface density is compatible with glutamine but not independently conclusive.

Reviewer #3 (Public review):

Summary:

Triandafillou and colleagues report a single-cell resolved spatial atlas of gene expression of 26 gastruloids. While previous work had analyzed either single-cell gene expression or spatially coarse-grained patterns of gene expression (van den Brink et al, 2020) the authors here use multiplexed sequential RNA FISH (seqFISH) to create the first gastruloid atlas which is simultaneously spatially and cellularly resolved. This atlas adds to a growing list of resources cataloging gastruloid development (see also Suppinger et al 2023).

To analyze this dataset, the authors also describe a novel analytical framework. Their analysis centers around the 'L-score', which measures the degree to which pairs of genes are either coexpressed or mutually exclusive. While this metric is similar to calculating correlations in gene expressions, it has important differences (including that it can in principle be asymmetric, although the authors symmetrize much of their analysis). In addition to the gene-centric L-metric analysis, the authors also analyze cells in their dataset according to the cell type entropy (an information-theoretical measure of confidence in cell type assignment) and the 'exposure index' (a measure of the similarity of nearest cellular neighbors).

Using this framework, the authors focus analysis of two major features of development. The first is the differentiation of the bipotent neuromesodermal progenitor (NMP) cells in the posterior of the gastruloid into either presomitic mesoderm (PSM) or spinal cord SC lineages. They use L-metric analysis to compare overlap in marker genes used to separate NMP, PSM, and SC fates. They highlight that L-metric analysis can recover spatial patterns of gene expression (without explicit spatial information) and discern subtle features of marker genes beyond simple binning of cell types (e.g. that Epha5 expression in anterior NMPs may predict future SC differentiation).

The second is the formation of endothelial (spatial) clusters within the gastruloid. The authors highlight two subtypes of endothelial clusters: (1) smaller clusters within the somitic anterior region, and (2) larger clusters associated with endoderm. While the authors discern some subtle differences in gene expression between these two clusters, their different spatial patterns suggest a potential physiological difference that would not be captured in traditional droplet microfluidic-based scRNAseq pipelines.

Overall, this manuscript is a sophisticated and technically sound study that will provide a valuable beachhead for future studies of developmental patterning in gastruloids and organoids.

Strengths:

The major strengths of this study are the overall technical sophistication of the data set and analysis, as well as its potential generalizability to other developmental systems (both in vitro and in vivo). The data are extensively analyzed and reasonably interpreted, and this atlas makes good use of the variability in gastruloid development to extract statistical structure of developmental processes. The L-score offers a parameter-free tool to analyze transcriptomic datasets that could overcome pitfalls of other approaches.

Weaknesses:

The major limitations of this study are the depth and novelty of the developmental processes studied. The authors provide very convincing proof-of-concept that their data set can recover known features of gastruloid development, including NMP differentiation and endothelial development. However, further analysis and/or investigation would be required to discover new principles of gastruloid development and patterning.

Comments on revised manuscript:

In their revised manuscript, Triandafillou et al have made substantial updates including analysis of variability with their 26 gastruloid datasets; formalization of the L-score (formerly L-metric) and clarification on its interpretation; and validation of their gastruloid samples (e.g. Hox gene colinearity). They have also clarified and sharpened language throughout the manuscript. With these additions further bolster the usefulness of this study as a resource for the gastruloid field, they do not provide major advances in understanding gastruloid development.

Reviewer #1 (Public review):

[Editor's Note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have responded to the comments raised in the previous round of 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.

Comments on previous version:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Concerns from the previous round of review:

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

This paper by Esmaeili and co-authors presents a connectome prediction study to predict episodic memory and relate prediction errors to other phonotypic variables.

Strengths:

(1) A primary and external validation dataset.

(2) Novel use of prediction errors (i.e., brain-cognitive gap).

(3) A wide range of data was investigated.

Reviewer #1 (Public review):

[Editors' note: The revised manuscript addressed the concerns of both reviewers, who have concluded that the manuscript is convincing and important. The manuscript can move towards the Version of Record.]

Summary:

This study is built on the emerging knowledge of trained immunity, where innate immune cells exhibit enhanced inflammatory responses upon challenged by a prior insult. Trained immunity is now a very fast-evolving field and has been explored in diverse disease conditions and immune cell types. Earhart and the team approached the topic from a novel angle and was the first to explore a potential link to the complement system.

The study focused on the central complement protein C3 and investigated how its signalling may modulate immune training in alveolar macrophages. The authors first performed in vivo experiments in C57BL mouse models to observe the presence of enhanced inflammation and C3a in BAL fluid following immune training. These changes were then compared with those from C3-deficient mice, which confirmed the involvement of C3a. This trained immunity was further validated in ex vivo experiments using primary alveolar macrophage, which was blunted in C3-deficiency, and, intriguingly, rescued by adding exogenous C3 protein, but not C3a. The genetic-based findings were supported by pharmacological experiments using the C3aR antagonist SB290157. Mechanistically, transcriptomic analyses suggested the involvement of metabolism-linked, particularly glycolytic, genes, which was in agreement with an upregulation of glycolytic flux in WT but not C3-deficient macrophages.

Collectively, these data suggest that C3, possible through engaging with C3aR, contributes to trained immunity in alveolar macrophages.

Strengths:

The conclusions reached were well supported by in vivo and ex vivo experiments, encompassing both genetic-knockout animal models and pharmacological tools.

The transcriptomic and cell metabolism studies provided valuable mechanistic insights.

Weaknesses:

For the in vivo experiments, the histopathological and other inflammatory markers (Fig 1.) were not directly linked to alveolar macrophages by experimental evidence. Other innate immune cells (e.g. dendritic cells, neutrophils) and endothelial cells could also be involved in immune training and contribute to the pathological outcomes. These cells were not examined or mentioned in the study.

For the ex vivo experiments assessing immune training in alveolar macrophages, only the release of selected inflammatory factors were measured. Macrophage activities constitute multiple aspects (e.g. phagocytosis, ROS production, microbe killing), which should also be considered to better depict the effect of trained immunity.

The proposed mechanism of C3 getting cleaved intracellularly then binding to lysosomal C3aR need to be further supported by experimental evidence.

There was an absence of any validation in human-based models.

Comments on the revised version.

The revised manuscript now encompasses a much wider scope and stronger evidence.

The authors have included the re-analysis of a recently published dataset of human volunteers who received aerosolized BCG exposure compared to saline. Although not proven causality, this data helped strengthen the human relevance of the findings presented in this research and directly rationalized the decision to focus on Ams. The persistence of elevated C3/C3aR1 expression to day 7 further supports the idea that complement‑associated reprogramming is not merely an acute inflammatory phenomenon. Whilst it may be outside of the scope of this current study, it would be helpful to clarify in future studies whether other complement components (C5, factor B, factor D) were also modulated in the dataset, to contextualize whether the response is uniquely centered on C3/C3aR1 or part of a broader complement activation program.

The authors have also expanded the functional characterization of trained alveolar macrophages by including phagocytosis and ROS generation measurements. It is intriguing that HKPA training did not markedly alter the phagocytosis and ROS production by alveolar macrophages relative to the control group, however, C3 deficiency significantly dampened these responses in both trained and untrained groups. This reduction is in congruence with the cytokine release data, but there could be other factors involved.

I appreciate the careful revision and much more expansive mechanistic interpretation regarding intracellular C3aR, and that further studies are underway to better understand the cell type-specific, subcellular localization of C3a-C3aR in alveolar macrophages.

Overall, the revised data interpretation and discussion significantly improved in balance and contextualization of the findings.

Reviewer #2 (Public review):

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

Comments on revised version.

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

Reviewer #1 (Public review):

The investigators elegantly utilized single-cell co-assay of RNA and ATAC seq to unveil the heterogeneous gene regulatory networks in Ewing sarcoma. The authors should be commended on their ability to identify multiple unique modules of gene regulation of Ewing sarcoma utilizing complex computational methods between numerous Ewing sarcoma cell lines. Additionally, they complimented their single cell findings with xenografts as well as primary Ewing sarcoma patient tumors - validating the intratumoral heterogeneous gene regulatory networks of Ewing sarcoma. More importantly, they have revealed that exogenous TGF-B may modify these distinct epigenetic and transcriptional signatures within Ewing sarcoma tumors. Overall, the manuscript highlights an important discovery of the heterogenous gene regulatory programming of Ewing sarcoma and further highlights the role that TGFB plays within the tumor microenvironment of Ewing sarcoma. There are some areas of ambiguity that require clarification to increase the impact of the manuscript.

Comments on the latest revision:

The responses to my review were appropriate and my comments were all addressed.

Reviewer #2 (Public review):

Summary:

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

Comments on revised version.

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

Reviewer #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: Definitions and terminology have been made more precise. Additional analysis confirms the conclusions previously stated and clarifies concerns about the computational tractability of the method.]

Summary:

The manuscript puts forward a statistical method to more accurately report the significance of correlations within data. The motivation for this study is two-fold. First, the publication of biological studies demands the report of p-values, and it is widely accepted that p-values below the arbitrary threshold of 0.05 give the authors of such studies justification to draw conclusions about their data. Second, many biological studies are limited by the number of replicate samples that are feasible, with replicates of less than 5 typical. The authors report a statistical tool that uses a permute-match approach to calculate p-values. Notably, the proposed method reduces p-values from around 0.2 to 0.04 as compared to a standard permutation test with a small sample size. The approach is clearly explained, including detailed mathematical explanations and derivations. The advantage of the approach is also demonstrated through analysis of computer-generated synthetic data with specified correlation and analysis of previously published data related to fish schooling. The authors make a clear case that this method is an improvement over the more standard approach currently used and also demonstrate the impact of this methodology on the ability to obtain p-values that are the standard for biological research. Overall, this paper is very strong. While the subject matter seems somewhat specialized, I would make the case that this will be an important study that has broad general interest to readers. The findings are very general and applicable to many research contexts. Experimentalists also want to report accurate p-values in their work and better understand how these values are calculated. Although I believe the previous statement is true, I am not sure that many research groups doing biological work are reading specialized statistics journals regularly. Therefore, a useful and broadly applicable statistical tool is well placed in this journal.

Strengths:

The proposed method is broadly applicable to many realistic datasets in many experimental contexts.

The power of this method was demonstrated with both real experimental data and "synthetic" data. The advantages of the tool are clearly reported. The zebrafish data is a great example dataset.

The method solves a real-life problem that is frequently encountered by many experimental groups in the biological sciences.

The writing of the paper is surprisingly clear, given the technical nature of the subject matter. I would not at all consider myself a statistician or mathematician, but I found the text easy to follow. The authors did an impressive job guiding the reader through material that would often be difficult to grasp. The introduction was also well-written and clearly motivated the goals of the study.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Reviewer #1 (Public review):

In this study, Otgonbaatar and colleagues investigate the stability of Armadillo (Arm) during Drosophila development using a creative tandem fluorescent protein timer approach via endogenous tagging of Arm. The tagging strategy allows for newly synthesised and longer-term stabilised Arm pools to be distinguished from one another. Specifically, the authors address the functional relevance of and mechanism behind the stabilisation of junctional Arm during dorsal closure.

The authors show that Arm is stabilised at the leading edge during dorsal closure. Using a sophisticated optogenetics approach, which allows for acute perturbations, they show that stabilised Arm is functionally required for dorsal closure. Increasing Wg (by overexpression) did not affect dorsal closure or Arm stability, in contrast to Axin overexpression, which reduces Wg/Arm signalling. In line with canonical signalling control of Arm levels being critical, stabilisation of Arm by N-terminal mutations disrupted dorsal closure. However, the same deletion is also expected to affect interaction with alpha-catenin. Co-localisation with E-cadherin and actin suggests a junctional role of leading-edge localised Arm. Optogenetic targeting of alpha-catenin points towards a key role of adherence junctions in dorsal closure. Allele replacement with mutant variants of Arm to affect adherence junction complex assembly further indicates an important contribution of coupling between Arm and alpha-catenin. Using overexpression approaches, the authors suggest that Dsh and Jnk contribute to dorsal closure.

This microscopy- and optogenetics-based study is generally well-conducted and provides strong evidence for stabilised Arm during dorsal closure, as well as its functional importance. This is an important discovery relevant to morphogenesis and potentially mechanotransduction. From a technical perspective, the validated beta-catenin timer provides a valuable tool for the field. The timer has revealed that Arm stabilisation does not coincide with Wg stripes, suggesting a Wg-independent stabilisation mechanism that may instead depend on adherence junction assembly, especially the interaction of Arm with alpha-catenin. However, as N-terminal deletion within Arm and Axin overexpression also disrupted dorsal closure, substantial ambiguity remains. Can suppression of the beta-catenin degradation machinery be ruled out as a regulatory mechanism? An expansion of ArmTimer mutant variants could contribute to testing the authors' conclusion further. Structural insights into junctional interactions involving Arm (e.g., 10.1074/jbc.M114.554709) could, for example, be used for further functional exploration by mutagenesis. The direct mechanistic impact of JNK and its potential link to Dsh in dorsal closure remains less compelling.

In summary, this is a highly relevant and important study, potentially pointing to a novel stabilisation mechanism of beta-catenin in development. Further corroboration of the mechanism, to test whether it is indeed distinct from canonical signalling, would be needed to support the conclusions.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

In the paper, the authors propose a new RNA velocity method, TSvelo, which predicts the transcription rate linearly based on the expression of RNA levels of transcription factors. This framework is an extension of its recent work TFvelo by including unspliced reads and designing a coherent neuralODE framework. Improved performance was demonstrated in six diverse datasets.

Strengths:

Overall, this method introduces innovative solutions to link cell differentiation and gene regulation, with a balance between model complexity (neuralODE) and interpretability (raw gene space).

Comments on revised version:

I thank the authors for further revision, and I do not have any other concerns. I believe it is an important contribution to this field of trajectory inference and gene regulation.

Reviewer #3 (Public review):

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

Comments on revised version:

The Authors addressed my 2 follow-up comments suitably.

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

Reviewer #1 (Public review):

Summary:

In this manuscript, Seegren and colleagues demonstrate that in a mouse model of neonatal E. coli meningitis, loss of toll-like receptor 4 (TLR4) in VE-cadherin+ endothelial cells and a subset of meningeal fibroblasts leads to a marked decrease in transcriptional dysregulation across multiple leptomeningeal cell types, a decrease in vascular permeability, and a decrease in macrophage abundance. In contrast, loss of macrophage TLR4 had less pronounced effects. Using cultured wildtype and TLR4-knockout endothelial cells, the authors further demonstrate that TLR4 signaling leads to reversible internalization of the tight junction protein claudin-5, establishing a potential mechanism of increased vascular permeability. Authors also show that claudin-5 internalization is independent of NF-κB. Finally, the authors use RNA-sequencing of wildtype and TLR4-knockout endothelial cells to define the TLR4-dependent cell-autonomous transcriptional response to E. coli.

Comments on revised version.

The authors have considerably improved and strengthened the work through the addition of new experimental data, new data analyses, and modifications to their interpretation. Notably, the authors used additional Cre-reporter mice to clarify that Cdh5-CreER is active in endothelial cells and some meningeal fibroblasts, and thus revised nomenclature and interpretation to acknowledge that the Tlr4fl/-;Cdh5-CreER cKO (Tlr4-VEKO) is not exclusively endothelial. The authors also demonstrated that Tlr4-VEKO does not affect peripheral E.coli burden, but acknowledge that changes to periphery-derived signals (e.g., cytokines) may contribute to observed leptomeningeal phenotypes.

The authors added PCA plots to show similarity in gene expression shifts across biological replicates (mice). This provides support for the claim that Tlr4-VEKO attenuates infection-associated transcriptional changes. With respect to differential expression analysis, I agree with authors that characteristics of individual cells (e.g. heterogeneity) are of interest. I remain concerned, however, that the formal differential analysis strategy appears to consider cells as independent experimental units, which they are not because a single cell cannot be randomly assigned to an experimental group (control or cKO, uninfected or infected). The mouse is the correct experimental unit for a comparison across these groups because it can be randomized. I appreciate that many of the gene expression changes appear consistent across mice (e.g. Figure 1 - Figure supplement 7) and that there are clear infection- and genotype-associated phenotypes in other assays. I would simply caution that the authors' analysis strategy likely leads to a larger number of type I errors (false positives) than is generally accepted; a mixed (hierarchical) model or pseudo-bulk approach would be more appropriate for future studies.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on revised version.

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

Reviewer #3 (Public review):

Summary:

This study investigates the molecular underpinnings of immune responses in the leptomeninges in neonatal bacterial meningitis. Bacterial meningitis is a major disease burden, particularly for neonates, and it has previously been noted that the meningeal immune environment in infants is permissive to opportunistic infection (Kim et al., Sci Immunol, 2023). There is less known about the contribution of the stromal compartment to meningeal immune responses. Seegren et al. interrogate the role of leptomeningeal endothelium in host defense in E. coli infected neonatal mice using mouse genetic tools to delete the LPS receptor Tlr4 from either endothelial cells/stromal cells (using Cdh5-CreER) or myeloid cells (using LysM-Cre). The authors use snRNAseq, cleared cortical mounts, and in vitro work to define the impact of E. coli infection on leptomeningeal endothelial cells. This study uses a range of innovative techniques to probe the role of the stromal compartment in meningitis. With additional experiments to confirm the specificity of their Cre models, this strengthens the interpretation of the study significantly. The only major weakness is the inability to confirm TLR4 knockout in myeloid cells.

Strengths:

This study makes excellent use of cleared cortical mounts to examine the biology of the leptomeninges, in particular, changes to the endothelium, with unprecedented detail. In combination with high-quality sequencing data provide new insights into the impact of meningitis on the leptomeninges. The data presented by the authors is of very high quality.

The authors have also done substantial work to address my two major comments regarding 1) the specificity of their Cre systems and 2) peripheral impacts of the interventions.

(1) The authors identified and acknowledged some impacts in the leptomeningeal stroma (the relatively high level of recombination in ECs vs FBs presumably reflects a single low dose being given, where other groups have done more aggressive tamoxifen regimens that drive recombination in FBs as well). Given the incomplete recombination in the leptomeningeal FBs, I agree with their conclusion that it is probably endothelial driven. Acknowledging the contributions of other myeloid cells with the L. The Cre-NLS experiments with nuclear markers provided excellent data and had beautiful staining.

(2) The authors did not observe differences in bacterial burden in peripheral organs in either CKO model, suggesting that CNS impacts are not downstream of peripheral bacterial control.

Weaknesses:

(1) The inducible Cre lines used by the authors target peripheral tissues as well as CNS tissues. Although this is mollified by the lack of impact on peripheral disease burden.

(2) The authors were not able to confirm TLR4 knockout in myeloid cells, and this caveat is acknowledged. The lack of response in TLR4 VEKO mice strongly suggests successful conditional knockout.

(3) The cell line model (bEnd.3) is a relatively low fidelity model of BBB endothelial cells. The authors acknowledge this, and it is likely that endothelial cell responses to LPS are highly conserved.

(4) It is perhaps not surprising that Tlr4 is required for meningitis responses with E. coli. However, it is unclear if these findings can be generalised to other, more common, meningitis infections (streptococcal/pneumococcal).

Reviewer #1 (Public review):

Summary:

The authors sequence the transcriptome of three sensory neurons from D. melanogaster to study the cell-cell and animal-animal variability in these cells, with a focus on cell adhesion molecules. The work reports useful cell-specific transcriptomics datasets that will be of great interest to those studying cell types, transcriptomes, neuronal development, and cell surface proteomes. The authors also report large numbers of knockdown data (gene-by-gene or in combinations) and report neuronal wiring and behavioral phenotypes. The manuscript is highly descriptive of the system studied - in a good way, but often over-speculates in rationale or conclusions.

Strengths:

The manuscript is data-rich. The single-cell transcriptomics datasets, not trivial to collect, are a major strength of the work and will prove useful to the field. Also, the biased expression of Dscam is interesting, even though the authors cannot pursue the mechanism or a function for this.

Weaknesses:

The study lacks depth (i.e., mechanism) in explaining observations.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

The authors present evidence that during acetaminophen (APAP)-induced liver injury, mid-zone hepatocytes activate an integrated stress response (ISR) program via Atf4 and Chop, leading to induction of Btg2. This program suppresses proliferation in the early phase of injury, prioritizing hepatocyte survival before regeneration begins. The study uses spatial transcriptomics, immunohistochemistry, CUT&RUN, and AAV overexpression to support this model.

Strengths:

(1) Innovative use of spatial transcriptomics to capture zonal differences in hepatocyte stress responses.

(2) Identification of a mid-zone specific ISR signature and candidate downstream regulator Btg2

(3) Functional experiments with Atf4-Chop-Btg2 modulation provide causal evidence linking ISR activation to proliferation inhibition.

(4) Conceptually significant model that hepatocytes actively balance survival and regeneration dynamically in a zone-specific manner.

(5) Multiple models validation of the finding

(6) The functional link of such zone2 phenotype is added.

Reviewer #2 (Public review):

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

Major points:

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

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

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

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

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

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

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

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

Comments on revised version.

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

Reviewer #1 (Public review):

In this study, Hossain et al. investigated the role of Interleukin-2-inducible T cell kinase (ITK) in autoimmune lung injury, demonstrating that ITK-deficient (Itk-/-) mice are protected against pristane-induced pulmonary hemorrhage (PH). The authors suggest that this protection correlates with a significant remodeling of the T cell compartment in Itk-/- mice, including increased frequency of memory-like CD4+ and CD8+ T cells (CD44⁺CD62L⁺) as well as higher frequency of Treg populations. Furthermore, adoptive transfer of ITK-deficient Treg isolated from injured ITK-deficient mice confers protection against pulmonary hemorrhage in WT recipients.

Strengths:

The adoptive transfer of wild-type and Itk-/- Treg populations demonstrates that ITK-deficient Treg can actively rescue pre-existing lung injury and reverse systemic secondary metrics like proteinuria in wild-type recipients, providing proof-of-concept validation for the therapeutic utility of the ITK-Treg axis.

Weaknesses:

A primary limitation of this manuscript is its omission of foundational literature from the Schwartzberg and Littman laboratories, which originally established the indispensable role of IL-2-inducible T-cell kinase (ITK) in proximal T-cell receptor (TCR) signaling dynamics and thymic lineage commitment. Because classic studies demonstrate that ITK is a critical regulator of thymic T cell development and cellular proliferation (PMID: 8777721, 10213685), the authors' claim that "these findings indicate that ITK deficiency skews the T cell compartment toward a memory-like state, establishing a distinct immune baseline that may favor protective and regulatory responses over pathogenic inflammation" is not substantiated by evidence and requires more robust validation.

The exclusive reliance on splenic immunophenotyping is a major limitation, as it fails to capture the local cellular dynamics within the primary organs of injury (the lung and kidney). Evaluating canonical and non-canonical Treg expansion solely in the spleen overlooks the distinct functional programming of tissue-resident subsets. The authors should extend their characterization of regulatory T cell compartments directly to the lungs and draining lymphoid structures.

More importantly, the authors overlook key historical publications that explicitly established ITK as a negative "rheostat" or gatekeeper for regulatory T cell (Treg) differentiation. Specifically, Huang et al. (PMID: 25063868) previously demonstrated that Treg abundance is inversely correlated with ITK expression, and that ITK activity serves as a vital negative tuner of IL-2-driven Foxp3⁺ Treg expansion. Since it is already well-established that suppressing or deleting ITK promotes Treg accumulation and function, and that these cells are intrinsically vital to suppressing systemic autoimmunity, it is unclear how these findings expand upon our existing mechanistic understanding of ITK regulatory biology.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

(2) The murine phenotype seems robust.

Weaknesses:

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

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

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

Reviewer #3 (Public review):

Summary:

Hossain et al. investigate the role of ITK as a central regulator of autoimmune lung injury. They used ITK-deficient mice and the pristane-induced pulmonary hemorrhage (PH) model to show that ITK deficiency confers protection against PH. The adoptive cell transfer experiment suggests a possible role for altered Treg cells in ITK-deficient mice in regulating the inflammatory response in the lungs of pristane-injected mice. This study shows that targeting the ITK axis may be beneficial by reducing systemic inflammatory injury that contributes to poor outcomes in PH.

Strengths:

This study highlights the importance of ITK in regulating pulmonary hemorrhage. The enrichment of Treg cells is known to confer protection in autoimmunity-mediated alveolar damage. However, ITK's involvement in regulating Treg cell function is interesting and could be explored as a novel therapeutic approach for chronic inflammation.

Weaknesses:

The novelty of this study lies in the association between ITK-deficient Tregs and pulmonary hemorrhage in autoimmunity. The weakness of the manuscript is the lack of sufficient experiments to support the claim that ITK-deficient mice show protection specifically mediated by Treg cells, and to demonstrate that ITK-deficient Treg cells are more efficient than WT Treg cells in regulating other immune cells that drive pulmonary damage. The authors performed all the experiments in ITK global knockout mice, in which not only T cells but all other cell types are deficient in ITK. Furthermore, they have not performed any functional analysis to demonstrate the functional differences between WT Treg and ITK-deficient Treg cells, undermining the novelty of this study.

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 made minor revisions to address the comments raised in the previous round of review.]

Summary:

This manuscript by Alonso-Caraballo et al, is a novel piece of work that examines the impact of oxycodone self-administration on neural plasticity within paraventricular thalamic (PVT) to nucleus accumbens shell (Shell) pathway - two regions shown to play a key role in cue-induced drug seeking on their own, and whether this plasticity varies based on abstinence period and biological sex.

Strengths:

The authors show using a clinically relevant long-access model of opioid self-administration promotes dependence and acute withdrawal in both male and female rats. During subsequent cue-induced relapse tests at 1 or 14-days following the conclusion of self-administration, data show that while both male and females demonstrate drug-seeking behavior at both time points, females show a further elevation in responding on day 14 versus day 1 that is not observed in the males. When accounting for past work showing elevations in drug seeking in males after 30 days, these data indicate that craving-induced relapse for opioids may develop faster and may be more pronounced in females compared to males.

These behavioral findings were paralleled by use of ex vivo acute slice electrophysiology and circuit-specific ex vivo optogenetics to examine the impact of oxycodone self-administration on synaptic strength within the paraventricular thalamus (PVT) to nucleus accumbens shell (NAcSh) pathway(s). Data support a time-dependent but sex independent strengthening of glutamatergic signaling at PVT-to-NAcSh medium spiny neurons (MSNs) that is only present following a relapse test at 14 days post abstinence in males versus females, providing the first evidence that opioid self-administration and/or cue-induced drug-seeking augments this pathway. Using an extensive set of physiological measures, the authors show that this increased synaptic strength reflects a upregulation of presynaptic release probability. Further, this upregulation of excitatory signaling aligned temporally with an increase in MSN excitability, as assessed by increases in action potential firing frequency. Finally, the authors provide the first evidence that similar to other inputs to the NAcSh, PVT projections innervate both MSN as well as local interneurons, promoting a GABA-A specific feedforward inhibitory circuit. Interestingly, unlike direct excitatory inputs to MSNs, no changes were observed ostensibly within this feedforward circuit, highlighting a selective enhancement of excitatory drive and output of MSNs with protracted abstinence.

Overall, these data highlight a potential role for heightened synaptic strength within the PVT-NAcSh pathway in cue-induced relapse behavior during protracted abstinence and identify a potential therapeutic target during abstinence to reduce relapse risk in abstaining individuals.

Weaknesses:

Overall, the experimental approach and data provided appear rigorous and support their overall conclusions and achieve their goal of understanding how opioid self-administration impacts synaptic strength within the PVT-NAcSh pathway. Although not undermining these data, there are a few potential weaknesses that reduce the impact of the work. For example, the inability to directly assess whether cue-induced drug-seeking is in fact augmented compared to daily intake during self-administration in the maintenance face only permits the authors to denote that reexposure to cues and the context is sufficient to promote active lever pressing without demonstrating whether seeking behavior is in fact elevated further during a cue test. This is notably understandable as drug available sessions were 6-hours versus a 1hour relapse test. Importantly, it is clearly demonstrated that drug seeking is higher on average in female mice after 14 days versus 1 day.

With regard to interpretation of electrophysiology findings, the lack of inclusion of an abstinence only group does not permit interpretations to parse out whether observed increases in synaptic strength (or the lack of) reflect abstinence or an interaction between abstinence period and re-exposure to the operant chamber, as slices were taken 30-45 min post relapse test. While much literature has shown that drug induced adaptations in the NAc requires a post drug period for plasticity to measurably emerge, studies have also shown that re-exposure to heroin-associated cues following abstinence seemingly "reverses" increases in cell excitability in prelimbic-NAc pyramidal neurons (Kokane et al., 2023) and that depotentiation of morphine-induced increases in synaptic strength in the NAc shell can be depotentiated by drug re-exopsure -- an effect also observed with cocaine re-exposure (Madayag et al., 2019). Notably, the lack of effect at 14 but not 1 day supports the likelihood that the relapse test does not in fact influence the plasticity within the PVT-NAcSh circuit.

While the lack of effect on AMPAR:NMDAR ratio and rectification indices do support the notion that enhanced EPSC amplitudes in input-output curves do not reflect a change in AMPAR subunit expression (i.e., increased GluA2-lacking receptors that exhibit inward rectification at depolarized potential) nor a change in postsynaptic sensitivity to glutamate, without direct assessment of AMPAR-specific and NMDAR-specific input-output curves, it doesn't definitively exclude the possibility that both AMPA and NMDA receptor currents are being upregulated, thus negating an observable change in postsynaptic strength.

Overall, these findings provide novel insight into how the PVT-NAcSh pathway is altered by opioid self-administration and whether this is unique based on abstinence period and sex. Importantly, these were the primary objectives stated by the author. Data highlight a potential role for the observed adaptations in relapse behavior and identify a potential therapeutic target during abstinence to reduce relapse risk in abstaining individuals. However, it should be noted that no causal link is demonstrated without experiments to reduce/prevent relapse.

Comments on previous revisions:

The authors addressed previous concerns brought up, specifically by clarifying data interpretation as well as text modifications related to potential caveats of these interpretations.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Impact:

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

Reviewer #3 (Public review):

Summary:

Alonso-Caraballo et al. use behavioral testing and ex vivo patch-clamp electrophysiology combined with circuit-specific optogenetic stimulation of PVT terminals to examine how oxycodone self-administration and abstinence duration shape cue-induced relapse and PVT-NAcSh synaptic transmission in male and female rats. In the revision, the authors reanalyzed intrinsic excitability using nested hierarchical GLMMs, acknowledged the low power in the male prolonged-abstinence group, and expanded the discussion of relevant PVT-NAc literature. These changes improve the manuscript. That said, most of the revisions are textual and the main experimental gap remains. Both sexes show increased oxycodone seeking compared to saline at 14 days, but only females show a time-dependent incubation from 1 to 14 days, and the PVT-NAcSh synaptic strengthening is the same in both sexes. Nothing in the revision brings those two observations closer together. The excitability data also come from NAcSh MSNs with no confirmation of PVT connectivity, which limits what circuit-specific conclusions can be drawn. The study is a solid characterization of abstinence-related synaptic changes in this pathway, but some of the conclusions still go further than the data allow.

Strengths:

The behavioral characterization is thorough and well-executed, covering self-administration, somatic withdrawal, and cue-induced relapse across two abstinence durations in both sexes. The sex-specific escalation in oxycodone seeking from 1 to 14 days in females but not males is a clear and compelling finding. The use of circuit-specific ex vivo optogenetics to isolate PVT terminal inputs onto NAcSh neurons is a genuine methodological strength, and the demonstration of feedforward inhibitory recruitment through local GABAergic interneurons adds meaningful novelty to the circuit characterization. The reanalysis of intrinsic excitability using nested hierarchical GLMMs appropriately accounts for the non-independence of cells recorded within the same animal and is a real improvement over the original approach. The expanded discussion of prior PVT-NAc work, particularly the more accurate treatment of Keyes et al. (2020) and Paniccia et al. (2024), better situates the findings within the existing literature.

Reviewer #1 (Public review):

Summary:

In this study, Tittelmeier et al. explored the role of sphingolipid metabolism in maintaining endolysosomal membrane integrity and its downstream effects on tau aggregation and toxicity, using both worms and human cell models. The authors showed that knockdown of sphingolipid metabolism genes reduced endolysosomal membrane fluidity, as revealed by FRAP and C-Laurdan imaging, leading to increased vesicle rupture. Furthermore, tau aggregates accumulated in endolysosomes and exacerbated membrane rigidity and damage, promoting seeded tau aggregation, likely by enabling tau seed escape into the cytosol. Importantly, unsaturated fatty acid supplementation restored membrane fluidity, suppressed tau propagation, and alleviated neurotoxicity in C. elegans. These findings provide insight into how lipid dysregulation contributes to tau pathology and highlight membrane fluidity restoration as a potential therapeutic avenue for Alzheimer's disease.

Strengths:

The study addresses the connection between sphingolipid metabolism, endolysosomal membrane integrity, and tau pathology, which is a relevant topic in the context of Alzheimer's disease and related tauopathies.

The use of both C. elegans and human cell models provides cross-species perspectives that help frame the findings in a broader biological context.

The combination of FRAP and C-Laurdan dye imaging offers a biophysical approach to investigate changes in membrane properties, which is a technically interesting aspect of the study.

The observation that unsaturated fatty acid supplementation can modulate membrane fluidity and influence tau-related phenotypes adds an element of potential therapeutic interest.

The study presents multiple experimental approaches to address the proposed mechanism, and efforts were made to examine both membrane behavior and tau aggregation dynamics.

Comments on revised version:

I thank the authors for their thorough revisions and detailed responses. All of my previous concerns have been satisfactorily addressed, and I have no further comments.

Reviewer #2 (Public review):

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

Comments on revised version:

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

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

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

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

Reviewer #3 (Public review):

Summary:

The authors set off with an analysis of the lysosomal integrity upon knockdown of genes of the sphingolipid metabolic pathway that they identified in a previous work of an RNA screen using a new C.elegans Tau model. They then used cell culture and C.elegans experiments to study the link between lysosomal rupture and Tau propagation.

Strengths:

The authors use two complementary model systems and used probes to assess membrane rigidity that allow a quick assessment of the membrane dynamics and offer the opportunity to treat the cells with lipids, RNAi. Tau seeds etc.

Comments on revised version:

The authors have addressed the majority of my critical comments and thus I support the manuscript.

They have still not analysed the knockdown efficiencies of their RNAi experiments. But this is their choice.

The other publication establishing their Tau model is meanwhile published and there is no disconnect anymore between the model their analysis builds on.

Reviewer #2 (Public review):

Summary:

The JAK-STAT pathway (JSP) exhibits cell-type-specific functional heterogeneity in breast cancer. This study investigates the JSP in breast cancer and its response to anti-PD‑1 immunotherapy. JSP displays distinct cell‑type heterogeneity: it promotes malignant phenotypes and immunosuppression in tumor cells, while enhancing cytotoxicity and reducing exhaustion in T cells. Elevated JSP expression correlates with improved immunotherapy responses, especially in triple‑negative breast cancer. These findings highlight the paradoxical roles of JSP, indicating that broad inhibition may compromise anti‑tumor immunity.

Strengths:

The major strengths of this study include the comprehensive characterization JSP heterogeneity across epithelial, tumor, and T cells in breast cancer. The identification of JSP and STAT4 as predictive biomarkers for immunotherapy response, particularly in triple‑negative breast cancer, provides clinically relevant insights for patient stratification.

Comments on revised version.

The corresponding content has been revised.

Reviewer #3 (Public review):

Summary:

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

Strengths:

Significant therapeutics implications

Weaknesses:

Limited molecular mechanisms

Comments on revised version:

The authors have addressed my comments

Reviewer #1 (Public review):

Summary

In this study, the authors have performed tissue-specific ribosome pulldown to identify gene expression (translatome) differences in the anterior vs posterior cells of the C. elegans intestine. They have performed this analysis in fed and fasted states of the animal. The data generated will be very useful to the C. elegans community, and the role of pyruvate shown in this study will result in interesting follow-up investigations.

However, several strong claims made in the study are solely based on in silico predictions and are not supported by experimental evidence.

Comments on revised version.

The authors have been responsive to the comments, but have not added new experiments in this manuscript that would have clarified and improved some of the mentioned shortcomings of the study.

There are 3 comments that the authors should address:

(1) In their response to reviewers, the authors agree that "the Pges-1deltaB promoter is not absolutely restricted to INT1 and that weak GFP expression can also be detected in INT2." They also mention that "because Pges-1deltaB is an engineered promoter derived from the intestine-specific Pges-1 promoter, this low-level INT2 expression is not unexpected." However, in line 93 of the revised manuscript, the authors claim that "Pges-1deltaB is strictly expressed in INT1 cells". This discrepancy should be fixed. They should instead describe this in line 93 as "Pges-1deltaB expression is very strongly enriched in INT1 cells, but low-level expression in INT2 was also detected".

(2) In response to reviewers, the authors explained that "Our model is that fasting induces INS-7 secretion by lowering intracellular pyruvate in INT1 cells. Under this framework, blocking mitochondrial pyruvate breakdown would be expected to reduce pyruvate utilization and thus maintain intracellular pyruvate, preventing the drop in pyruvate that normally occurs during fasting. This would explain why these manipulations suppress fasting-induced INS-7 secretion." However, the effect of blocking import of pyruvate from cytosol into mitochondria (via knockdown of mitochondrial pyruvate carrier genes mpc-1 and mpc-2) does not agree with their proposed model. Blocking mitochondrial import of pyruvate should maintain cytosolic pyruvate levels and thus prevent the drop in pyruvate that normally occurs during fasting. In such a scenario, we would expect to see no increase in INS-7 secretion during fasting, which is opposite to the result in Fig.7D. If the pyruvate sensor is in the cytosol, we would expect that the mpc-1/2 RNAi treated animals would be unable to increase INS-7 secretion upon starvation. If the pyruvate sensor is in the mitochondrial matrix, we would expect that the mpc-1/2 RNAi treated animals would have higher INS-7 secretion than vector RNAi control animals in fed conditions. How do the authors explain this discrepancy between their observed results and their proposed model? Why does blocking mitochondrial import of pyruvate affect only refeeding-induced reduction in INS-7 secretion but not fasting-induced increase in INS-7 secretion? Is it possible that instead of responding to absolute intracellular concentrations of pyruvate, the pyruvate sensor increases INS-7 secretion upon detecting a relative drop in the mitochondrial levels of pyruvate (or its downstream metabolite)? This should be described in the text to better interpret the mpc-1/2 RNAi results.

(3) Line 493: The authors refer to 'Table S4', which is not included in the manuscript.

Reviewer #3 (Public review):

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

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

Comments on revised version:

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

Reviewer #2 (Public review):

Summary:

This manuscript investigates how neural cell development is affected in Lowe syndrome. Using neural cultures differentiated from human iPSCs carrying either a LS mutation or a genetically engineered mutation in OCRL, the authors show a depletion of mitochondrial DNA and decrease in mitochondrial activities that correlate with an increased formation of astrocytes at the expense of neurons. Similar effects on mitochondria and on astrocyte development were observed in a LS mouse model. Moreover, these mutant brain cells are less likely to be ciliated and show a reduction in Sonic hedgehog signalling.

Strengths/Weaknesses:

The study derives strength from the analyses of two different models of Lowe syndrome, both reaching similar conclusions. However, the observed changes in mitochondrial defects, neuronal/astrocytic development and primary cilia are only correlated, with no attempt to investigate a causal relationship. Moreover, the mouse model is only analysed at the adult stage providing no insights into the development of the defects. Different brain regions are analysed with immunostainings and qRT-PCR making it challenging to draw clear correlations between these findings. The quality of the corresponding figures is often poor and the selection of markers is frequently inappropriate. Taken together, these limitations complicate the interpretations of the data and significantly limit the conclusions that can be drawn from the study.

Although the study remains incomplete as main claims are only partially supported it can be used as a starting point for future functional studies into the link between mitochondrial defects and primary cilia in neural development.

Comments on revised version:

I am afraid the revised manuscript does little to address the concerns I raised in my initial review. The authors have primarily revised the text, removed over-interpretations and discussed critical points as limitations of the study. This gives the impression that key concerns have merely been rationalised, particularly as only a few new experiments are presented. My main concerns therefore remain:

(1) The authors present three different phenotypes (altered neural differentiation, mitochondria dysfunction, alterations in primary cilia and ciliary Shh signalling) but a link between these phenotypes is not investigated. No mechanistic experiments are presented. Instead, the authors try to address the lack of a mechanism through refined wording but still use formulations that imply a direct link between these phenotypes. For example, their rebuttal letter finishes with the statement that the manuscript "provides a multi-model, cross-species framework linking mitochondrial dysfunction, ciliary signaling, and altered neural differentiation in Lowe syndrome". Similar formulations are used in the text.

(2) The authors still claim that ciliary Shh signalling is reduced but ignore the fact that Shh mRNA in iN cells and Shh protein in the IOB mouse are significantly decreased. This reduction represents the most likely explanation for the reduced levels of Gli1 and Ptc1 mRNAs (Shh target genes), rather than dysfunction of cilia. In order to test for cilia dysfunction, the authors need to use experiments in which they quantify the response of control and OCRL mutant cells to exogenously added Shh protein or Shh agonists. Moreover, the increased Gli1 protein expression in the IOB mouse contradicts the reduced levels of Gli1 mRNA.

(3) The analyses of the IOB mice are only done in 2 months old adult animals, nevertheless claims are made that changes in cell proportions are consequences of altered cell fate decisions. Alterations in proliferation and cell death are not addressed by experiments.

Reviewer #1 (Public review):

The authors of this study developed a method to quantify calvarial bone marrow from MRI head scans, enabling study of its composition in large datasets of adults, usually collected to study the brain. Bone marrow intensity can be semi-quantitatively measured in T1-weighted MRI scans due to the greater signal intensity of fat than watery red marrow. This is an ingenious use of the MRI-produced information for other important phenotypes, such as bone structure and marrow content. Different head types were tested for complying to the model, which is notable.

The model was also successfully validated using several publicly available MRI resources - real data - in (1) dataset consisting of 30 individuals that were scanned 10 times each at 3-day intervals, and (2) the monozygotic (MZ) twin data from the Human Connectome Project cohort. Then the authors applied this validated method to head-MRI scans from the UK Biobank (n=33,042) to extract information on spatial distribution of bone marrow adiposity (BMA) in the calvaria, allowing a GWAS to identify associated genes.

The authors revealed high heritability and identified 41 genetic loci significantly associated with the BMA trait, including six sex-specific loci. Of note, statistics estimate that 99% of BMA trait-influencing variants are shared with BMD (497 of 500 variants), which may mean these results demonstrate the biological relevance to bone health. Some of the BMA genes were found related to the Wnt pathway, including WNT16, WNT4, NXN; this is a "positive control", since the Wnt/β-catenin signaling pathway was suggested as an important determinant of BMA. Also, associations in genes (BMP4, DLX5, LGR4, LRP4, SFRP4) that are known to specifically influence adiposity, are encouraging. Integrating mapped genes with bone marrow single-cell RNA-seq data revealed patterns of adipogenic lineage differentiation and lipid loading.

The study also investigated genetic overlap between BMA and twelve (or 13) "brain and body" traits, and identified significant genetic correlations with BMI, cognitive ability and Parkinson's disease.

In sum, since MRI head scans present a hitherto unexplored opportunity to address unresolved aspects of bone marrow biology, this study is both timely and innovative.

Comments on revised version:

The authors responded most of this reviewer's comments. Their explanations are convincing. Yet, upon re-reading the revised version of this paper, I still have concerns about the clarity of mostly analysis presentation, e.g.:

Line 130-133: the sentence is still unclear: "To obtain the BM signal intensity for an individual datapoint of the calvarium, we ... averaged these BM intensities to get the (average?) BM intensity for that datapoint. Then, we averaged (again?) these datapoint intensities across the calvarium to produce the global BMA measure for the scan."

Also, I still cannot understand whether the "overlap between the true and predicted bone marrow ...below 0.7" is concerning or not, - whether this threshold of 0.7 is arbitrary.

Genetic correlation: pls. make sure it's clear that the Rg was calculated using SNP "effect sizes".

Reviewer #2 (Public review):

Summary:

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

Major strengths:

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

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

Major limitations:

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

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

Likely impact and utility:

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

Reviewer #3 (Public review):

Summary:

This paper addresses a fundamental gap in bone biology: our near-complete ignorance of the in vivo dynamics of calvarial bone marrow adiposity (BMA) at population scale. The authors developed an elegant artificial neural network trained on simulated data to automatically localize and quantify the bone marrow layer within standard T1-weighted MRI head scans; scans originally acquired to study the brain but harboring rich, unexploited information about adjacent bone. Applying this method to over 33,000 individuals from the UK Biobank, they accomplished three things that had never been done before: (1) they precisely quantified the sex-dimorphic age trajectory of calvarial BMA, including the dramatic post-menopausal rise and the protective role of hormone replacement therapy; (2) they performed the first well-powered GWAS of this trait, identifying 41 genome-wide significant loci including six sex-specific ones, with SNP heritability of 31.5%; and (3) they revealed significant genetic correlations and overlap between BMA and traits including bone mineral density, Parkinson's disease, and general cognitive ability, a finding made all the more intriguing by the recently described direct vascular channels connecting calvarial bone marrow to the meninges. Integration of GWAS genes with single-cell RNA-sequencing data from mesenchymal lineage cells further illuminated which genes govern lineage commitment to the adipogenic pathway versus lipid loading in mature adipocytes.

Comments on revised version.

The reviews raised substantive points across three domains, and the authors engaged with every one of them seriously and thoroughly.

On the validation of T1-weighted MRI as a measure of BMA: Reviewer 2 raised the strongest concern, arguing that T1-weighted signal intensity had never been formally validated as a quantitative fat-fraction measure in the calvarium. The authors responded with both a principled scientific argument and new data. They assembled existing literature demonstrating that T1-weighted signal is an established semi-quantitative proxy for marrow fat in multiple skeletal sites (Loevner et al. 2002, Shen et al. 2013, Zhang et al. 2020), and provided additional comparative analyses against quantitative T1 relaxation maps, multiple intensity normalization strategies (KDE, WhiteStripe, GMM, FCM, Z-score), DEXA-derived bone mineral density, and osteoporosis status. The biological coherence of their findings, recapitulating known sex and age profiles, identifying genes already established in cell and animal models of BMA biology, and estimating heritabilities consistent with twin data constitutes powerful, convergent evidence for construct validity. Their point that a semi-quantitative measure of a highly variable, well-demarcated biological signal can outperform a perfectly precise measure of a poorly defined entity is methodologically sound and well-argued.

On sex differences and the role of Hyperostosis frontalis interna: Reviewer 1 raised the clinically astute concern that Hyperostosis frontalis interna (HFI), a condition of inner table thickening prevalent in up to 49% of postmenopausal women, could confound calvarial BMA measurements and drive apparent sex differences. The authors performed a dedicated new analysis, stratifying BMA-BMD associations by sex and age group. They demonstrated that (1) the BMA-BMD association is robust in both males and females, (2) it remains stable across age groups, and (3) the neural network trained on simulations incorporating wide anatomical variation including inner table thickness is inherently resistant to moderate inner table thickening. Given that HFI is restricted to the frontal bone, which represents only a fraction of the calvarial surface, and that severe cases are rare (ICD-10 prevalence ~0.02% in the UK Biobank), the authors make a convincing case that this does not materially bias their results. Their suggestion that the method could itself be used in future work to study the genetic architecture of HFI is a nice forward-looking addition.

On genetic correlation interpretation and cross-trait pleiotropy: Reviewer 1 asked for clarification of the vertical versus horizontal pleiotropy distinction and for formal Mendelian randomization to support the possible causal effect of BMA on cognition. The authors appropriately clarified the conceptual framework in the revised text and, rather than overstating a causal claim without the supporting analysis, responsibly softened the language to "may be consistent with the hypothesis that BMA could have a causal effect on cognition." This is scientifically honest and appropriate.

On mouse scRNAseq and its relevance to humans: The authors acknowledged that the results section had not explicitly stated the mouse origin of the scRNAseq data, corrected this, and provided a well-justified rationale for the relevance of mouse mesenchymal lineage data to human BMA biology, which is a well-established and widely accepted model system in this field.

On GWAS replication: The claim that the study lacked replication was addressed by clarifying the a priori separation of discovery (white British, n=33,042) and replication (non-white British, n=4,958) samples, with 62% of significant discovery SNPs and 95% of lead SNPs replicating in the correct direction.

Overall Assessment:

This is a technically innovative, scientifically rigorous, and biologically meaningful paper. The method is genuinely novel, the sample size is among the largest ever applied to this phenotype, the genetic findings are well-powered and well-replicated, and the integration across imaging, genetics, and single-cell transcriptomics is exemplary. The authors have engaged with every substantive reviewer criticism in good faith, producing new analyses where appropriate and defending, and convincingly, with findings that were challenged without adequate basis. The revised manuscript is strengthened throughout.

This paper opens a new window quite literally, through the skull - into bone marrow biology at a scale and resolution that has never been achieved before.

Reviewer #1 (Public review):

Summary:

The authors demonstrate the stereoselective role of D-serine in 1C metabolism showing that D-serine competes with L-serine and inhibits mitochondrial L-serine transport. They observe expression of 1C metabolites in their metabolomics approach in primary cortical neurons treated with L-serine, D-serine and mixture of both. Their conclusions are based on the reduction in levels of glycine, polyamines and their intermediates and formate. Single cell RNA sequencing of N2a cells showed that cells treated with D-serine enhanced expression of genes associated with mitochondrial functions such as respiratory chain complex assembly and mitochondrial functions with downregulation of genes related to amino acid transport, cellular growth and neuron projection extension. Their work demonstrates that D-serine inhibits tumor cell proliferation and induces apoptosis in neural progenitor cells highlighting the importance of D-serine in neurodevelopment.

Strengths:

D-amino acids do not merely function as ligands at receptors but have underlying roles in signaling and metabolism. These roles are just beginning to be uncovered. The authors elucidate the metabolic role of D-serine in the context of neuronal maturation by its suppression of mitochondrial L-serine availability for SHMT2 and 1C flux. This is the strength of the manuscript. The implications for the metabolic role of D-serine in neurons is a highlight and underlines its roles in neuronal metabolism.

Weaknesses:

These are some minor issues that come up on critical assessment of the manuscript and is only intended to strengthen the manuscript. The comments below are based on the revisions made by the authors including the justification of their approach and rebuttal.

(1) Kinetic assessment of D-serine versus L-serine: The authors have made reference to prior work by Miyamoto et al. and justify their rationale. This is acceptable.

(2) Molecular Dynamics simulations while a good first step in modeling interactions at the active site, relies on force fields. The authors state that any elaborate study into longer simulations is beyond the scope and their simulations data are supported by other experimental work. This is justified.

(3) The use of N2a cell line is also justified to reflect the proliferative nature of immature neurons.

(4) With regards to caspase 3 comment, the whole blot is convincing and shows cleaved caspase-3 band at approx. 15 kDa.

(5) Scale Bars are clearly visible and Fig S6 which was earlier S5 is legible. If possible, the authors can include an magnified inset in the merged image to show the clear activation of caspase-3.

(6) Issue of phosphatidyl serine standard in LC-MS is justified by the use of L-serine standard due to lack of availability.

(7) The authors mention about enantiomeric shift of serine metabolism during neural development which appears to be a discussion of prior published data from Hubbard et al 2013, Burk et al 2020, and Bella et al 2021 in Supplementary Figure panels 8 A-E.<br /> The authors justify by citing references to the work which may be acceptable and also the current norms of publication. This reviewer felt contrary to the fact, however it is left to the editors to make a decision on this.

(8) The discussion section has been substantially revised and now reads well.

(9) The relevant references have been cited. In doing so, the work integrates and elucidates a mechanistic and functional role of D-serine in neurons.

(10) Figure S7A in the revised manuscript shows the specificity of D-serine in the cleaved caspase-3 assay which is informative.

Comments on revised version.

This reviewer is satisfied by the effort made by the authors based on the prior comments raised.

Reviewer #2 (Public review):

Summary:

This study by Suzuki et al. reports an interesting stereo-selective role of D-serine in regulating one-carbon metabolism during neurodevelopment to adapt the functional transition, probably through the competition with mitochondrial transport of L-serine. The authors provide a multi-layered set of evidence, including metabolomics, enzyme assays, mitochondrial transport competition and functional assays in immature/neural progenitor cells, to build up a conceptual integration of D-serine as both a neurotransmitter and a metabolic regulator in central neural system, which raises a broad potential interest to the neuroscience and metabolism communities.

Strengths:

This work provides a conceptual advance that D-serine is not only serves as a traditional neurotransmitter in central neural system but also critically contributes to metabolic regulation of neural cells. The authors performed solid metabolomic assays to validate the suppressive effect of D-serine on one-carbon metabolic pathway, providing some evidence that D-serine competitively inhibits mitochondrial serine transport, but not directly impairs SHMT2 enzymatic activity. All these data indicate a critical role of D-serine synthesis during neural maturation and suggest a potential translational strategy for targeting serine metabolism in neural tumors.

Comments on revised version.

My previous concerns have been appropriately addressed or discussed in this revised version of manuscript. I have to say that, at this stage, I have no further questions.

Reviewer #3 (Public review):

Summary:

This manuscript presents a comprehensive and well-executed investigation into the metabolic role of D-serine in the central nervous system. The authors provide solid evidence that D-serine competitively inhibits mitochondrial L-serine transport, thereby impairing one-carbon metabolism. This stereoselective mechanism reduces glycine and formate production, suppresses cellular proliferation, and induces apoptosis in immature neural cells and glioblastoma stem cells. Developmental analyses further reveal a physiological enantiomeric shift in serine metabolism during neurogenesis, aligning with the transition from proliferation to maturation. Overall, the study bridges developmental neurobiology, cancer metabolism, and amino acid transport, uncovering a previously unrecognized metabolic function of D-serine beyond its role in neurotransmission.

Strengths:

(1) The discovery that D-serine inhibits one-carbon metabolism by competing for mitochondrial L-serine transport-rather than through enzymatic inhibition or receptor-mediated signaling-represents a significant and previously underappreciated mechanism. This finding has broad implications for understanding metabolic regulation during neurodevelopment and offers potential relevance for targeting metabolic vulnerabilities in cancer.

(2) The authors integrate metabolomics, mitochondrial transport assays, molecular dynamics simulations, genetic and pharmacologic perturbations, transcriptomics, and both in vitro and ex vivo models. The breadth of experimental approaches, combined with the coherence of the findings across systems, provides strong support for the central conclusions and enhances the overall impact of the study.

(3) The temporal shift in D-/L-serine levels during neurodevelopment is elegantly linked to the transition from proliferative to mature neuronal states. The selective vulnerability of neural progenitors and tumor cells-contrasted with the resistance of mature neurons-highlights a biologically meaningful and potentially targetable metabolic distinction.

Weaknesses:

(1) While the authors attribute D-serine's metabolic effects to competition with mitochondrial L-serine transport, the specific identity of the transporter(s) mediating this process remains undefined. This represents a meaningful mechanistic gap, as the central conclusion depends on D-serine limiting mitochondrial L-serine availability to inhibit one-carbon metabolism.

(2) The effective concentrations of D-serine used in vitro (IC₅₀ ≈ 1-2 mM) exceed typical brain levels (~0.3 mM). While the authors acknowledge this, a more focused discussion on whether higher local D-serine concentrations could arise in specific microenvironments-such as synaptic compartments, tumor niches, or pathological states-would help contextualize the in vitro findings and strengthen their physiological relevance. For example, disruptions in D-serine clearance or altered expression of serine racemase and transporters in disease contexts could lead to localized accumulation. Moreover, differences between extracellular and intracellular D-serine pools-and the mechanisms governing their regulation-may further influence its metabolic impact in vivo.

(3) While the manuscript focuses on neural stem/progenitor cells and neural tumors, it remains unclear whether the anti-proliferative effects of D-serine are specific to neural lineages or extend to other highly proliferative non-neural cell types. A brief discussion addressing this point would help clarify the scope of D-serine's metabolic impact and whether its mechanism of action reflects a unique vulnerability in neural cells or a more general feature of proliferative metabolism. This distinction is particularly relevant for assessing the broader therapeutic potential of targeting mitochondrial L-serine transport.

Reviewer #1 (Public review):

Summary:

The authors aim to understand how changes in the balance between excitatory and inhibitory interactions influence the stability and reorganization of network connections. To address this question, they extend a coupled-phase-oscillator model by adding plasticity rules. The central finding is that stronger inhibitory interactions lead to relatively stable and desynchronized network dynamics, whereas weaker inhibitory interactions produce a bistable regime in which intermediate-strength connections fluctuate while stronger connections are preserved.

Strengths:

This study offers a simple theoretical framework for linking network state, coupling stability, and reorganization. The model produces clear qualitative results, showing that different dynamical regimes are associated with different balances of excitatory and inhibitory interactions. This could be useful as a conceptual starting point for considering how network states may regulate the stability and flexibility of connections. The manuscript also explores several model parameters.

Weaknesses:

The evidence is incomplete in supporting the biological interpretations. The model is a highly simplified coupled-phase-oscillator system and does not directly represent spiking activity, membrane potentials, synaptic currents, conduction delays, cellular excitability, or detailed biological plasticity mechanisms. Although the authors clarify that the model units are not actual neurons or synapses, the discussion often interprets the results in terms of neuronal inhibition, synaptic stability, sleep-related reorganization, and preservation of strong biological connections. This creates a gap between the abstract model and the biological conclusions. In particular, the manuscript does not sufficiently discuss what biological oscillatory activity the modeled phases are intended to represent, such as population-level activity reflected in electroencephalography or local field potentials. In several places, the manuscript appears to assume that neurons can generally be treated as oscillators, but this is not always a valid assumption. The authors should more clearly distinguish between rhythmic or phase-like activity at the population level and the dynamics of individual neurons, and should frame the model more cautiously as a phenomenological description of collective synchronization rather than a mechanistic model of spiking neuronal circuits.

There are also important methodological limitations. Although the manuscript presents the model equations, parameter values, time step, simulation duration, and coupling update rules, several other essential details are not clearly specified, including the number of simulation runs, the procedure for setting initial conditions, and the numerical method used to solve the ordinary differential equations. Critically, technical details such as the integration scheme, solver settings, initialization procedure, and random seed handling are essential for reproducibility. Because the main findings depend on the interaction between phase dynamics and adaptive coupling, even small implementation differences could affect the reported dynamical regimes and coupling fluctuations.

A further concern is the presentation of the mathematical formulation. Several equations appear to contain notation errors and inconsistencies, making it difficult to follow the exact model definition. The authors should carefully revise the mathematical notation throughout the manuscript to ensure that the model can be understood and reproduced unambiguously.

Overall, the study provides a useful but limited theoretical account of how network dynamics may regulate coupling stability and reorganization. The results support the internal behavior of the proposed model, but the broader biological claims are not yet fully convincing. The likely impact of the work is therefore mainly conceptual: it may stimulate further modeling studies, but additional methodological detail, stronger justification of the modeling assumptions, and comparison with more biologically grounded models would be needed before the conclusions can be applied confidently to neuronal circuit dynamics or sleep-related synaptic reorganization.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the impact of plasticity mechanisms in an excitation-inhibition (EI) network model on the emergence of synchronization patterns that the authors associate with different sleep phases. The model consists of an EI Kuramoto network in which recurrent excitatory couplings evolve according to Hebbian and homeostatic adaptation rules. Through numerical simulations, the authors analyze how these plasticity mechanisms modify both the collective dynamics and the structure of the coupling matrix.

Strengths:

The topic addressed in the manuscript is timely and potentially relevant, as understanding the interplay between synaptic adaptation and collective neural dynamics remains an important challenge in theoretical neuroscience.

Weaknesses:

In its current form, the work suffers from substantial conceptual, methodological, and technical limitations that significantly weaken the conclusions.

From a biological perspective, the model is highly abstract and qualitative. The connection between the model variables and the physiological processes that the authors aim to describe remains unclear. Consequently, the manuscript does not provide sufficient evidence to support biologically meaningful conclusions regarding sleep dynamics. In my opinion, the work is more naturally positioned within the framework of theoretical or computational dynamical systems than within the scope of a biology-oriented journal.

From a mathematical and dynamical-systems perspective, the analysis is incomplete, and several important technical aspects are either missing or inadequately addressed. In particular, the characterization of the dynamical regimes is often imprecise, the numerical evidence is not sufficiently robust, and little effort is made to interpret the results within the broader context of synchronization theory, adaptive networks, or collective dynamics.

More specifically:

(1) The biological interpretation of the model variables is ambiguous throughout the manuscript. At several points, the authors suggest that individual oscillators should not be interpreted as neurons but rather as abstract biological units (lines 78-82, 86-89, 418-420). However, other parts of the manuscript refer to the coupling matrix entries, particularly $J_{ee}$, as synaptic weights (e.g., line 118). These two interpretations are not obviously compatible. If the oscillators represent coarse-grained or abstract units, the biological meaning of the adaptive couplings should be carefully justified. More generally, the manuscript lacks a clear discussion of what aspects of neural circuits are captured by the model and which aspects are intentionally neglected.

(2) More fundamentally, the manuscript inherits the well-known limitations associated with interpreting Kuramoto oscillators as neural elements. Kuramoto phase oscillators provide a minimal description of synchronization phenomena, but they do not explicitly represent membrane dynamics, firing rates, spiking activity, synaptic currents, or realistic neuronal timescales.

Under certain assumptions, Kuramoto-like models can be rigorously derived from more detailed neuronal models through phase-reduction techniques (see, for instance, Chapter 10 of Izhikevich's \textit{Dynamical Systems in Neuroscience}). However, the authors do not employ such a reduction procedure, nor do they establish a formal connection between their model variables and the dynamics of neuronal populations. As a consequence, the biological interpretation of the model remains unclear.

The authors should therefore explicitly discuss these limitations and carefully justify why the synchronization patterns observed in such a highly reduced model can be related to neural sleep states. At present, the biological interpretation appears considerably stronger than what the model itself can support, for instance, the claims in lines 321-322 or 330-337. In particular, it remains unclear whether the reported dynamical regimes should be interpreted as genuine mechanisms underlying sleep rhythms or merely as generic synchronization phenomena arising in adaptive oscillator networks.

(3) The numerical methodology raises serious concerns regarding the robustness of the reported results. According to the Methods section, simulations are performed using only $N=100$ oscillators and integration times of approximately 50 time units. Such choices may be sufficient for illustrative purposes but are generally inadequate for drawing conclusions about asymptotic collective behavior in adaptive dynamical systems. Finite-size fluctuations can strongly affect synchronization measures, and adaptive networks are well known to exhibit extremely long transients, metastability, and slow convergence processes. No systematic finite-size analysis is provided, nor is there any demonstration that the reported states persist for larger system sizes or longer integration times.

(4) The use of the term "bistable regime" to describe the dynamics shown in Figure 1C is incorrect. A bistable regime refers to a parameter region in which multiple attractors coexist and the asymptotic state depends on the initial condition. The figure instead presents a single trajectory displaying oscillatory dynamics. No evidence is provided for the coexistence of attractors, nor are multiple initial conditions explored. Furthermore, the displayed time series are too short to determine whether the observed dynamics correspond to a stable limit cycle, quasiperiodic motion, intermittent behavior, or a long transient approaching another attractor. The authors should perform a proper dynamical characterization of this regime. Similar collective oscillatory states have been extensively studied in synchronization and adaptive-network models and should be discussed in relation to the existing literature.

In particular, several time series shown in the manuscript (e.g., Figures 1C and 2F) exhibit trends that suggest the possibility of unresolved transient dynamics. The authors should demonstrate convergence of the reported regimes by substantially extending simulation times and by performing finite-size analyses. Simulations with at least one order of magnitude more units ($N\gtrsim 1000$) and integration times sufficient to establish asymptotic behavior would be expected in a study whose main claims rely on collective dynamical phenomena.

(5) The manuscript lacks several standard tools routinely employed in the analysis of nonlinear dynamical systems. The conclusions are largely based on visual inspection of time series and order parameters. However, no bifurcation analysis, stability analysis, phase-space characterization, attractor reconstruction, or systematic exploration of parameter dependence is provided. As a consequence, many of the identified "phases" or "regimes" remain only qualitatively described. A more rigorous dynamical-systems treatment would substantially strengthen the work and would help distinguish genuine asymptotic states from finite-size or transient phenomena.

(6) A substantial fraction of the results appears to extend the authors' previous work by incorporating plastic adaptation mechanisms. While incremental advances are acceptable, the manuscript would benefit from a broader theoretical context. The discussion is heavily centered on previous studies by the same authors, whereas there exists an extensive literature on synchronization, adaptive networks, neural mass models, balanced EI systems, and sleep-related oscillations that is largely absent from the discussion. The novelty and significance of the present contribution would be easier to assess if the results were more carefully compared with alternative theoretical approaches.

Reviewer #1 (Public review):

Summary:

The authors present a nanobody-based pulse-labeling system to track yeast NPCs. Transient expression of a nanobody targeting Nup84 (fused to NeonGreen or an affinity tag) permits selective visualization and biochemical capture of NPCs. Short induction effectively labels NPCs, and the resulting purifications match those from conventional Nup84 tagging. Crucially, when induction is repressed, dilution of the labeled pool through successive cell cycles allows the visualization of "old" NPCs (and potentially individual NPCs) providing a powerful view of NPC lifespan and turnover without permanently modifying a core scaffold protein.

Strengths:

(1) A brief expression pulse labels NPCs, and subsequent repression allows dilution-based tracking of older (and possibly single) NPCs over multiple cell cycles.

(2) The affinity-purified complexes closely match known Nup84-associated proteins, indicating specificity and supporting utility for proteomics.

Weakness:

Reliance on GAL induction introduces metabolic shifts (raffinose → galactose → glucose) that could subtly alter cell physiology or the kinetics of NPC assembly. As acknowledged by the authors, alternative induction systems (e.g., β-estradiol-responsive GAL4-ER-VP16) could be implemented as a way to avoid carbon-source changes.

Comments on revised version.

The authors have thoughtfully addressed all of my concerns. In particular, they have updated the proteomic analysis in Figure 1I, showing that they recover most NPC components (including basket Nups), including non-NPC proteins as controls, and providing all data as a supplementary table. These changes strengthen the authors conclusion and improve transparency. I have no further recommendations and congratulate the authors for their exciting work.

Reviewer #2 (Public review):

Summary:

This preprint describes a practical and useful approach for labeling and tracking NPCs in situ, using a fluorescently conjugated nanobody that binds directly to the core scaffold nucleoporin Nup84 with nanomolar affinity. Useful applications including timelapse imaging, affinity purification, and proximity labeling are envisioned.

Strengths:

Clever use of a fluorescently conjugated nanobody that binds directly to the core scaffold nucleoporin Nup84 with nanomolar affinity.

Reviewer #3 (Public review):

Summary:

Submitted to the Tools and Resources series, this study reports on the use of a single-domain antibody targeting the nucleoporin Nup84 to probe and track NPCs in budding yeast. The authors demonstrate their ability to rapidly label or pull down NPCs by inducing the expression of a tagged version of the nanobody (Fig. 1).

Strengths:

This tool's main strength is its versatility as an inexpensive, easy-to-set-up alternative to metabolic labelling or optical switching. This same rationale could, in principle, be applied to the study of other multiprotein complexes using similar strategies, provided that single-chain antibodies are available.

Weaknesses:

This approach has no inherent weaknesses, but it would be useful to verify in the future that this pulse labelling strategy can also be used to detect assembly intermediates, structural variants, or damaged NPCs, e.g. NPC clusters formed in some nucleoporin mutants.

Overall, the data clearly shows that Nup84 nanobodies are a valuable tool for imaging NPC dynamics and investigating their interactomes through affinity purification.

Comments on revised version.

None at this stage.

Reviewer #1 (Public review):

This manuscript by Rudich ZD et al. systematically profiled the transcriptomic changes in nine long-lived C. elegans mutants and presented a careful and informative comparative analysis of these aging-related changes. In addition to these valuable datasets and bioinformatics analyses, the authors performed a large-scale RNAi screen to assess the role of the differentially expressed genes (DEGs) in these mutants and identify several potential targets to promote healthy aging. Moreover, the authors have provided a user-friendly website to examine genes of interest in those longevity mutants from their datasets.

Strengths:

Compared to previous transcriptomic analyses of these mutants in different reports, this study minimized the technical variations and benefitted from the advances in RNA-Seq technology and bioinformatics tools. Therefore, it should provide a more consistent and comprehensive view of the molecular mechanisms underlying the longevity of these mutants. The datasets in this manuscript are valuable to other researchers in the biology of aging.

Weaknesses:

Meanwhile, since these mutants have been extensively studied, the advance of this study in unknown ageing mechanisms remains limited.

Comments on revised version.

In the revised manuscript, the authors have addressed most of my concerns. In the text of this manuscript, the authors should still include more discussion on why osm-5 and daf-2 are categorized into two different groups.

Reviewer #2 (Public review):

Summary:

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

Significance:

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

Comments on revised version:

The authors addressed my concerns successfully.

Reviewer #1 (Public review):

Summary:

This study aims to clarify MATR3's function and molecular mechanism in oocyte growth and maturation, explore its association with OMA and its potential as a diagnostic and therapeutic target using specific knockout mouse models, human OMA samples and multi-omics technologies. And it has fully achieved preset objectives with results strongly supporting conclusions. Specifically, it addresses the gap in the synergistic mechanism of epigenetic and secretory signals regulated by RNA-binding proteins (RBPs) in oocyte growth and enriches the molecular etiological spectrum of oocyte maturation disorders. It is the first time to reveal the conservative function of MATR3 in multiple species, providing a paradigm for cross-species research on RBPs in the field of reproductive biology. And it provides a new candidate target for OMA, a clinically refractory infertility disease, and is expected to promote the optimization of assisted reproductive technology and the development of precision medicine.

Strengths:

The strengths of this study are significant and prominent. First, the research system is comprehensive, integrating knockout mouse models, in vitro knockdown models, multi-species (mouse, porcine and human) verification, combined with scRNA-seq, LACE-seq, CO-IP and other multi-omics and molecular biology technologies, forming a complete and progressive evidence chain. Second, the mechanism analysis is in-depth, clarifying the dual molecular mechanisms of MATR3 regulating the transcriptional synthesis and secretion of GDF9 through "recruiting KDM3B to regulate H3K9me2 demethylation" and "directly binding to Rdx mRNA", with a clear logical closed loop. Third, the clinical correlation is close. It is the first time to find abnormal nuclear localization of MATR3 in oocytes of OMA patients, providing new clues for clinical disease mechanism research, and verifying the downstream function of GDF9 through rescue experiments, effectively enhancing the translational value of the results.

Weaknesses:

This study included only one OMA patient's oocyte sample. Without clinical screening for MATR3 mutations or abnormal expression, establishing a causal relationship between MATR3 and OMA remains difficult.

Reviewer #2 (Public review):

Summary:

This study investigates the role of MATR3 in oocyte development and folliculogenesis using conditional knockout mouse models together with in vitro follicle culture and molecular analyses. The authors aim to determine whether MATR3 regulates oocyte maturation and follicle development and to explore potential mechanisms linking MATR3 function to transcriptional and epigenetic regulation in growing oocytes.

Strengths:

A major strength of the work is the use of a conditional knockout mouse model combined with complementary in vitro follicle culture approaches, which together provide a useful framework for examining gene function during oocyte development. The study also attempts to integrate cellular phenotypes with molecular analyses of transcriptional activity and epigenetic markers.

Weaknesses:

Several weaknesses limit the strength of the conclusions. These include insufficient validation of key experimental manipulations (such as the efficiency of MATR3 knockdown in siRNA experiments), limited quantification or statistical analysis for some datasets, inconsistencies between the text and presented data in certain figures, and incomplete methodological descriptions that make it difficult to fully evaluate reproducibility.

Comments on revised version.

Thank you for submitting the revised manuscript. I believe the revisions have substantially improved the quality and clarity of the study, and the authors have addressed the major concerns raised during the initial review.

Reviewer #3 (Public review):

Summary:

The study aims to elucidate the dual molecular mechanisms of the RNA-binding protein MATR3 in oocyte growth and maturation. The authors propose that MATR3, highly expressed in growing oocytes (GOs), regulates oocyte quality through two pathways: epigenetically, by recruiting KDM3B to remove the repressive H3K9me2 mark at the Gdf9 locus to activate transcription; and post-transcriptionally, by binding Rdx mRNA to maintain microvillus structure for GDF9 secretion. This mechanism ensures oocyte-granulosa cell communication and female fertility. The study also explores the link between MATR3 and human oocyte maturation arrest (OMA).

Strengths:

The study proposes an innovative dual-mechanism model encompassing "epigenetic transcriptional activation and cytoskeletal regulation," which not only expands the functional understanding of RNA-binding proteins in chromatin regulation but also reveals the coordination between nuclear transcription and organelle structure. By integrating scRNA-seq and LACE-seq, the authors constructed a comprehensive regulatory network for MATR3, identifying both key targets and numerous potential molecules, thereby providing rich resources for future mechanistic studies. Furthermore, the inclusion of oocyte samples from human OMA patients directly links the basic findings to clinical reproductive disorders. Despite the limited sample size, this approach demonstrates strong translational potential.

Weaknesses:

The partial phenotypic improvement achieved by exogenous GDF9 supplementation suggests that the downstream effector pathways may involve a more complex network regulation, implying that the current interpretation of GDF9 central role could be further explored. Regarding the developmental abnormalities of granulosa cells in the conditional knockout model, their pathological origins require in-depth analysis to determine whether they represent primary alterations or secondary adaptive responses resulting from the loss of oocyte signaling.

Reviewer #1 (Public review):

Summary:

This study identifies three redundant pathways-glycine cleavage system (GCS), serine hydroxymethyltransferase (GlyA), and formate-tetrahydrofolate ligase/FolD-that feed the one-carbon tetrahydrofolate (1C-THF) pool essential for Listeria monocytogenes growth and virulence. Reactivation of the normally inactive fhs gene rescues 1C-THF deficiency, revealing metabolic plasticity and vulnerability for potential antimicrobial targeting.

Strengths:

(1) Novel evolutionary insight-Reversible reactivation of a pseudogene (fhs) shows adaptive metabolic plasticity, relevant for pathogen evolution.

(2) They systematically combine targeted gene deletions with suppressor screening to dissect the folate/one-carbon network (GCS, GlyA, Fhs/FolD).

Reviewer #3 (Public review):

Summary:

In this study, Freier et al., demonstrate that 3 distinct metabolic pathways are critical for the synthesis of 1C-THF, a metabolite that is crucial for the growth and virulence of Listeria monocytogenes. Using an elegant suppressor screen, they also demonstrate the hierarchical importance of these metabolic pathways with respect to the biosynthesis of 1C-THF.

Strengths:

This study uses elegant bacterial genetics to confirm that 3 distinct metabolic pathways are critical for 1C-THF synthesis in L. monocytogenes and lack of either one of these pathways compromises bacterial growth and virulence. The study uses a combination of in vitro growth assays, macrophage-CFU assays and murine infection models to demonstrate this.

Comments on revisions:

The revised manuscript is improved, and the additional genetic experiments provide further support for the proposed metabolic model. However, the central conclusion is not fully established without direct measurement of 1C-THF levels. While I appreciate the authors' explanation regarding the technical limitations, quantitative metabolite measurements (e.g., by mass spectrometry) would have provided much stronger evidence linking the genetic perturbations to altered 1C-THF pools.

Reviewer #1 (Public review):

[Editors' note: This revised version of your article has been assessed by the Reviewing Editor without further input from the original reviewers. The comments raised by the original reviewers in the earlier round of review have been addressed. The study findings are quite insightful and important, and the evidence is strong, convincing, and a substantial addition to the evidence base.]

A well-designed and preregistered simulation study investigating whether replication-success metrics can be applied to assess animal-to-human translation. The study is comprehensive, uses realistic parameter settings, and provides valuable insights into how different metrics behave under varied conditions.

Strengths:

(1) Methodologically rigorous and transparently preregistered.

(2) Comprehensive simulation design covering a wide range of plausible scenarios.

(3) Clear description of metrics and decision rules.

(4) Valuable contribution to understanding the limitations of applying replication metrics to translation questions.

Reviewer #2 (Public review):

Summary:

The authors attempt to address the issue of high rates of translation failure from animal studies to humans in the literature, where promising results in animal studies fail when conducting human clinical trials. Using parameters from a previous meta-analysis on prenatal amino acid supplementation and the effects it has on maternal blood pressure, the authors assessed the performance of the metrics used and whether they can quantify translation success. Performing a simulation study, the authors compared nine translation success metrics and found that no one method was uniformly optimal. The authors list several limitations of the study, such as comparability of effect sizes between animal and human studies, different goals of animal studies versus human studies, and the focus of the study on one aspect (statistics of translation) is part of a broader, more complex decision-making process before proceeding to human trials. The authors recommend using multiple metrics in combination while taking into consideration their strengths and weaknesses to assess the translation of animal studies to human outcomes. The paper achieves the aim of providing a model with several metrics to evaluate translation success from animal studies to humans.

Strengths:

(1) Utilizing 9 different translation success metrics in combination provides strong flexibility in evaluating whether results in animal studies can translate to humans. This would allow researchers to evaluate translation success using multiple different metrics according to the context of the study.

(2) The authors accommodated for the limited sample size in animal studies, which are typically underpowered, and also caution that special attention should be given to heterogeneity when interpreting translation results.

(3) Overall, this approach has the potential to be applied to other biomedical studies, provided the limitations for each of the metrics are considered. It would provide a useful tool in assessing translation from animals to humans, in addition to other factors such as safety, pharmacokinetics, etc.

Weaknesses:

While the study has several strengths, there are some limitations.

(1) Preclinical animal study sizes tend to be much smaller than human studies, which results in underpowered results. The authors adjusted for this by pooling animal study data. However, high heterogeneity in the animal studies can affect translation results.

(2) The study focuses only on evaluating the statistical component of translation, which is only one aspect of the decision-making process to move on to human trials. The study does not take into account safety and toxicological profiles, pharmacokinetics, or genetics, which are important considerations that influence the overall effect in humans.

Reviewer #3 (Public review):

Summary:

This paper focused on how to navigate the complex decision-making process of whether to go into human trials. This is a critical topic considering the well-documented challenges in replicating and translating findings. While these are two distinct topics (i.e., replication and translation), they are related, and the authors simulated many conditions to assess the utility of replication assessment metrics.

Strengths:

A major strength of the study is the detailed approach to identifying relevant conditions and metrics, and to providing rich results that outline the strengths and weaknesses of each metric. Any simulation study is challenged by trying to identify the most relevant variables of interest, and this study provided sound justification for its chosen variables of interest. While this study does not make a strong recommendation (which I see as a strength), it does provide a comprehensive overview of the various metrics and conditions that were investigated.

Conclusion:

This paper provides a much-needed investigation and discussion of how decisions are made when assessing whether to go into human trials. This is an important topic that productively challenges the status quo, considering documented challenges in replication and translation in biomedical research.

Reviewer #1 (Public review):

In this manuscript, Clausner and colleagues use simultaneous EEG and fMRI recordings to clarify how visual brain rhythms emerge across layers of early visual cortex. They report that gamma activity correlates positively with feature-specific fMRI signals in superficial and deep layers. By contrast, alpha activity generally correlated negatively with fMRI signals, with two a higher frequency within the alpha reflecting feature-specific fMRI signals. This feature-specific alpha code indicates an active role of alpha oscillations in visual feature coding, providing compelling evidence that the functions of alpha oscillations go beyond cortical idling or feature-unspecific suppression.

The study is very interesting and timely. Methodologically, it is state of the art. The findings on a more active role of alpha activity that goes beyond the classical idling or suppression accounts is in line with recent findings and theories. In sum, this paper makes a very nice contribution to the literature. In particular, it provides a novel characterization of how oscillatory signals orchestrate the coding of visual contents in the visual cortex and provides a starting point for further research examining how this oscillatory coding changes across visual contents and tasks.

Reviewer #2 (Public review):

The authors address a long-standing controversy regarding the functional role of neural oscillations in cortical computations and layer-specific signalling. Several studies have implicated gamma oscillations in bottom-up processing, while lower-frequency oscillations have been associated with top-down signalling. Therefore, the question the authors investigate is both timely and theoretically relevant, contributing to our understanding of feedforward and feedback communication in the brain. This paper presents a novel and complicated data acquisition technique, the application of simultaneous EEG and fMRI, to benefit from both temporal and spatial resolution. A sophisticated data analysis method was executed in order to understand the underlying neural activity during a visual oddball task. The authors defined both feature-specific and feature-unspecific contrasts, further subdivided by EEG power regressors, to examine how orientation information is signalled across cortical layers. Feature specific contrast was established via comparing trials where stimulus orientation (respectively) was left with those where the stimulus orientation was right. Further specifying it depending on EEG power regressors as congruent where stimulus orientation of EEG regressor matches voxel preference or incongruent (stimulus orientation of EEG regressor does not match voxel preference).

Figures are well-designed and appropriately represent the results, which seem to support the overall conclusions. However, some of the claims (particularly those regarding the contribution of gamma oscillations) feel somewhat overstated, as the results offer indeed some significant evidence. On the other hand, the lower-frequency findings are compelling, the functional specificity observed within the alpha frequency band is a particularly interesting result and further highlights the importance of distinguishing feature specificity in order to reveal more nuanced characteristics of neuroimaging data.

Overall, main findings are very interesting, and mainly in line with our current understanding of feedback and feedforward signalling. The paper is well-written, addresses a relevant and timely research question, introduces a novel and elegant analysis approach, and presents interesting findings.

The evidence for gamma involvement in the observed effects is selective: no significant gamma-related clusters were found for the feature-unspecific BOLD signal (Figure 5C,F), with significant effects emerging only in positively responding voxels and only for the contrast between congruent and incongruent conditions in the feature-specific BOLD response. The authors address this in the Discussion, noting that the stimulus may have elicited a weaker gamma response overall, and the contrast of EEG congruent vs. incongruent is necessary in order to achieve the largest contrast-to-noise ratio.

Authors reported negative relationship between the alpha frequency band and the feature specific BOLD signal increases (for congruent condition, Figure 5A,D). Furthermore, testing for the functional specificity between lower vs. upper alpha (Figure 5B,E), the authors included statistical test on the mixed effects model coefficients, and found significant interaction between alpha frequencies in the congruent condition. This interaction was mainly driven by the upper alpha band (which was later confirmed with simple effects analysis) and revealed stronger negative relationship of upper alpha and the BOLD signal for the subtraction of congruent over incongruent conditions. These are exciting results, which further advocate for a more active role of upper alpha band involvement (relative to lower alpha band) in processing visual features.

Expanding on this, the authors have also conducted an exploratory analysis of the relationship between the behavioural findings and underlying neural activity for non-oddball trials (Figure S12 in Supplementary Figures). This confirmed a positive relationship between task performance and alpha frequency, suggesting that high behavioural accuracy is reflected by a stronger modulation of high-frequency alpha power.

This study provides a valuable and exciting contribution to the literature on oscillatory dynamics and laminar fMRI.

Comments on revised version.

Thank you for the thorough revision and for addressing the comments so carefully. The new figures are super beautiful and make the results considerably easier to interpret, they are a real improvement to the paper.

Reviewer #1 (Public review):

Summary:

The article is testing the relative advantages of plant lineages with differing ploidy and admixture across environmental gradients. The results show that intraspecific variation in ploidy and admixture between lineages impacts plant traits that may enable persistence and range expansion.

Strengths:

Suitable marker panel size and convincing results that include attempts to analyse mixed ploidy level data, which is a challenge.

Weaknesses:

(1) Inadequate explanation of allele dosage for ploidy levels, some of which do not match the allele counts expected for genome copy number.

(2) The setup and sample sizes of the common garden experiments are very unclear. The numbers implied are extremely low to draw robust conclusions.

(3) Unclear how allele dosage is determined. Given it's so central to many analyses, it would be useful to see how this is done rather than use a citation.

Reviewer #2 (Public review):

Summary:

This manuscript describes a combination of species distribution mapping experimental data from common garden and physiological experiments to project the future distribution of genetic subgroups with the widespread grass Phragmites australis. Overall, the sample sizes seem appropriate for the questions being asked, and the key results regarding projected change in distribution of the focal lineages are well supported. However, at this point, it is difficult to evaluate the broader impact of the work on the field or the utility of the data for the broader community outside of those studying the focal species, P. austrina.

Strengths:

A key strength of the paper is the use of common garden and physiological experiments in conjunction with species distribution modeling. The experiments provide a mechanistic basis for the correlations between interspecific lineage and climatic data, suggesting that the distributional patterns are more likely to result from genetic differences rather than limited dispersal among regions. I would, in fact, emphasize the experimental validation of modeling efforts even more in the introduction.

Weaknesses:

I see two weaknesses with the framing of the ms and the presentation of the results. First, no data support the claims that polyploidy has any causal effect. The ploidy levels are, in fact, completely confounded with other genetic differences, so it is not possible to eliminate genetic variation, independent of ploidy, as the causative factor. As the authors note, ploidy was not manipulated in the reported experiments. Thus, the focus on polyploidy in the introduction and elsewhere distracts from the novel and informative experiments that were conducted. Second, the manuscript indicates that intraspecific variation is critical for the evolutionary potential of a species to respond to environmental change, but intraspecific variation is seldom considered in species distribution models. To me, an assessment of evolutionary potential requires estimates of heritable genetic variation and responses to selection. The sample sizes presented here are modest to estimate heritabilities, but the manuscript could be framed with this perspective in mind. However, instead, the manuscript performs species distribution modeling on a small number of sub-specific lineages, essentially treating them as homogeneous "species" - thus the analysis commits the same oversimplification that the manuscript highlights, but does so at a finer evolutionary scale than species. Not acknowledging this simplification (or better, examining phenotypic variation within the genetically defined lineages) hinders what would otherwise be a strength of the manuscript.

The title suggests that asymmetric introgression and thermal tolerance are the most important findings of the work. However, the introduction contains no explanation of the potential importance of gene flow (other than to say that asymmetric gene flow was suggested by some preliminary analyses), and the discussion offers only a limited explanation of either the potential mechanisms underlying the asymmetric gene flow or its importance for the long-term evolution of the species. Similarly, the novelty of combining experiments and species distribution modeling is scarcely mentioned, and there is no exploration of the connection between tolerance alleles and gene flow. Could introgression of heat tolerance alleles alter the spread of the hybridizing lineages, for example? A greater emphasis on these general population genetic parameters could potentially highlight the broader impact of this work.

Reviewer #1 (Public review):

Summary:

This manuscript describes an investigation of peptide analogue agonists selective for the human Y4 receptor for pancreatic polypeptide over Y1, Y2 and Y5 receptors. After studies of mutated Y4R in transiently transfected COS-7 cells, binding models were calculated. Then, screening of a virtual library identified three non-peptidergic (albeit somewhat peptide-like) compounds with potential agonist activity that were subsequently confirmed and furthermore were found to have receptor interactions similar to the peptide analogues. This study provides fundamental new information that improves understanding of the Y4R structure and mechanism of activation by the native agonist and the selective peptide analogues. The non-peptide agonists have potential for future pharmacotherapy.

Strengths:

All of the experiments seem to be well performed, using state-of-the-art methods. The manuscript is quite comprehensive and has used a broad range of methods. The conclusions are convincingly supported by the experimental results.

Weaknesses:

The mutagenesis was almost exclusively based on the replacement of potentially interesting amino acid residues with alanine. Replacement with other residues, based on modelling and docking, could have refined the model further. Neither molecular dynamics nor cryo-EM was used to study the agonists' interactions with the Y4 receptor and these are therefore likely next steps in the characterization of the Y4R mechanism of activation.

Reviewer #2 (Public review):

Summary:

Pelczyk et al. investigated the binding site of the neuropeptide Y Y4 receptor with the aim of identifying novel small-molecule agonists. The authors first assessed small cyclic peptides as tool compounds and then identified interactions between peptides and receptor residues, which were confirmed by single-point mutagenesis combined with functional assays for intracellular signalling. It is interesting that a peptide receptor can be activated by the relatively small cyclic peptides used in the study. The authors identified both common and peptide-specific interactions. The identified interactions guided ultra-large library screening, which yielded 53 compounds, 3 of which were confirmed as Y4R-specific agonists in an IP-one accumulation assay.

Strengths:

The combination of techniques (docking, mutagenesis and functional assays) strongly supports the identification and evaluation of small molecules as agonists at the neuropeptide Y Y4 receptor. Functional assays highlight residues that are important for the binding of all tested peptides, as well as residues with peptide-specific importance.

The structure-activity relationship component of the study nicely highlights which components of the peptide are important for binding to the different members of the neuropeptide Y receptor family.

Weaknesses:

It would have been great to see concentration-response curves for the three identified small-molecule agonists, as this would have stengthened the case for these agonists.

Reviewer #1 (Public review):

Summary:

In this manuscript, Flamholz and colleagues use metagenomic sequencing to profile the microbiome of individuals with sickle cell disease (SCD), the most common genetic blood disorder in the world. To build on previous studies that found dysbiosis in SCD, this manuscript aims to examine whether changes in either bacterial species or bacteriophages correlate with inflammatory hallmarks of the disease. The authors claim that sickle cell dysbiosis does not correlate with inflammatory hallmarks of the disease, but instead, aged neutrophil numbers and bacteriophages do. Appropriate control subjects and additional analyses are needed to support that conclusion.

Strengths:

The primary strength of this paper is the investigation into disease-associated changes in bacteriophages. This is an entirely novel idea in the sickle cell field, and based on the current results, may be an important, under-recognized disease hallmark. It is unclear, however, if phages are "the chicken or the egg" in terms of sickle cell inflammatory profiles; do these increases in phage number simply result from other disease processes, or are they in any way contributing to disease pathophysiology?

Weaknesses:

A primary weakness of the manuscript is the fact that the majority of individuals included in the control group maintain sickle cell trait (HbAS genotype). Although typically asymptomatic, it is unclear if this genotype is associated with microbial changes that would not be observed in a true control group (HbAA genotype). This is a significant limitation that may limit the ability to draw conclusions from the current data set.

Another key weakness is the lack of beta diversity assessment. Although decreased alpha diversity is observed in individuals with SCD, and specific bacterial taxa are differentially abundant following multivariate analyses, there is no overall comparison of bacterial community composition between individuals with SCD and controls. Prior to drawing conclusions about the relationship (or lack thereof) between the SCD microbiome and inflammatory markers, it is important to know if this study did indeed find disease-associated changes in microbiome composition.

It is unclear which individuals were used for aged neutrophil (AN) and molecular data assessments. For example, were children who were still receiving penicillin prophylaxis included in these specific assessments? Given the authors' previous work demonstrating that antibiotic treatment decreases AN pathology, it seems critical to limit all AN/molecular analyses to older subjects who are not on daily penicillin treatment (if possible).

A minor weakness is the continued use of "disease" vs. "healthy" indicators as primary microbiome metrics that are used for molecular correlations. The lack of metric specificity - and lack of discussion regarding which diseases were used to generate these indicators (how similar/different are they to sickle cell?) - could be said to make these metrics essentially meaningless.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

The evidence for this proposed immune-related mechanism is incomplete. The study is cross-sectional and largely based on associations, so it cannot determine whether these virus-like elements drive immune activation, reflect immune activation, or are linked indirectly through disease severity, treatment history, or other clinical factors. The main limitations are the single-center design, modest sample size for some immune measurements, limited ability to control for treatment and disease heterogeneity, and the need for clearer multiple-testing correction in the correlation analyses. In particular, stronger adjustment for available clinical factors such as hydroxyurea use, transfusion history, pain admissions, genotype, and other markers of disease burden would help readers judge how specific the microbial and viral findings are to sickle cell disease itself.

Overall, the authors largely achieve their descriptive aim of identifying gut microbial differences associated with sickle cell disease. The evidence is solid for the presence of broad microbial community differences, but incomplete for the stronger conclusion that virus-like elements form a pathophysiological immune axis. The work will likely be useful to researchers studying the microbiome, inflammation, and sickle cell disease, especially as a hypothesis-generating dataset. Its impact would be strengthened by more cautious interpretation, stronger control of clinical confounders, clearer statistical correction, and future longitudinal or experimental studies to test causality.

Reviewer #3 (Public review):

Summary:

In this manuscript, Flamholz et al. sought to determine whether consistent and significant interactions exist between the gut microbiome and disease pathology in sickle cell disease (SCD). By sequencing and analysing metagenomes from faecal samples collected from 98 SCD patients and 46 control subjects, they identified community-level shifts in both the bacterial and proviral gut microbiome of SCD patients. They further reported correlations between the proviral microbiome and multiple blood cytokines, whereas similar associations were not observed for the bacterial microbiome. Based on these findings, the authors propose the existence of a viral-immune axis in SCD pathophysiology and targetable functional alterations in the gut microbiome.

Strengths:

This work includes the largest SCD cohort analysed to date, enabling analysis with relatively strong statistical power. In addition to profiling the bacterial microbiome, the study also examines the gut proviral microbiome, thereby providing a more comprehensive investigation of the topic. The newly generated metagenomic dataset will also be valuable for further meta-analysis by the wider community. Overall, the authors have largely achieved their aims.

Weaknesses:

However, this study represents a single-centre cross-sectional investigation, and most findings remain correlative in nature. In particular, the claim that the study identifies targetable functional alterations in the gut microbiome for disease treatment may be somewhat overstated. Although the reported functional module changes in SCD patients are intriguing, additional mechanistic and/or longitudinal evidence would be required before these features can realistically be considered targetable.

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 satisfactorily addressed the comments raised in the previous round of review with textual revisions.]

Summary:

The manuscript by Singh et al. presents an application of MOA-seq to better define transcriptional control underlying the hypoxia response in human endothelial cells. This group's previously described MOA-seq technique allows for precise, identity-agnostic mapping of occupied sites of DNA-binding proteins across the epigenome and over time. Here, they applied MOA-seq to HUVECs under normal oxygen conditions or variable lengths of hypoxia treatment, comparing changes in occupancy over time and associating these changes with corresponding transcriptome alterations. This approach revealed thousands of dynamically occupied sites comprising 10 major kinetic clusters that appear to define distinct subsets and phases of the hypoxia response. Analysis of DNA motifs in these dynamically occupied regions captured the known major roles of HIF1A in the hypoxia response and also implicated new HIF1A-associated regulators. Importantly, they also identified many potential HIF1A-independent candidate TFs that act at HREs, which has been an outstanding question in the field. Additionally, this study identified ~7K additional sites not previously defined as regulatory elements by ENCODE.

Strengths:

Overall, this study is well executed and described, providing new biological insights as well as a rich data resource for the field. As MOA-seq was previously developed for use in plants, this work demonstrates the application of this method in mammalian cells and highlights its utility in identifying new potential regulatory sites not captured by DNase-seq or ATAC-seq. The conclusions made by the authors are well supported by the results, with the caveat that extensive use of DNA motif identification and ontology analyses invariably leads to some uncertainty regarding factor identity and gene network properties.

Reviewer #2 (Public review):

Summary:

Singh et al. apply MOA-seq to map transcription factor occupancy genome-wide in HUVECs across a hypoxia time course. The study provides a well-validated, high-resolution view of cistrome dynamics and identifies both HIF1A-associated and independent regulatory programs.

Major comments from the first round of review:

Methodological validation is strong. MOA-seq's ability to map protein-bound DNA at near-nucleotide resolution without factor-specific antibodies is a genuine advance, and the cross-validation against independent ChIP-seq and ENCODE datasets is convincing. As noted, future work with additional biological replicates could further strengthen confidence in the smaller kinetic clusters.

Imaging-based validation would strengthen the key biological claims. The kinetic clustering and pathway enrichments are computationally inferred. Orthogonal approaches, for example, live-cell fluorescence imaging of HIF1A nuclear translocation to confirm the proposed temporal binding waves, would provide independent experimental support.

Reviewer #2 (Public review):

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

Summary:

This paper is an exciting follow-up to two recent publications in eLife: one from the same lab, reporting that slender forms can successfully infect tsetse flies (Schuster, S et al., 2021), and another independent study claiming the opposite (Ngoune, TMJ et al., 2025). Here, the authors address four criticisms raised against their original work: the influence of N-acetyl-glucosamine (NAG), the use of teneral and male flies, and whether slender forms bypass the stumpy stage before becoming procyclic forms.

Strengths:

We applaud the authors' efforts in undertaking these experiments and contributing to a better understanding of the T. brucei life cycle. The paper is well-written and the figures are clear.

Comments on revisions:

We thank the authors for the revised manuscript and for considering our comments.

Reviewer #1 (Public review):

Summary:

This manuscript reports the discovery and characterization of the first bifunctional degrader of tankyrase. Notably, the tankyrase degrader exhibits stronger β-catenin inhibition and tumor growth suppression compared to conventional tankyrase inhibitors. Mechanistically, while tankyrase inhibitors stabilize tankyrase and promote Axin puncta formation-thereby impairing β-catenin degradation-the degrader avoids this effect, resulting in deeper suppression of β-catenin signaling. These findings suggest that targeted degradation of tankyrase offers a novel therapeutic strategy for β-catenin-driven cancers. Overall, this is a compelling study with significant translational potential.

Strengths:

(1) The manuscript presents a rigorous and well-executed study on a timely and impactful topic.

(2) The biochemical and cellular characterization of the tankyrase degrader is thorough, and the comparative analysis with tankyrase inhibitors is insightful.

(3) The finding that tankyrase stabilization by inhibitors may interfere with Axin function is novel and significant. It aligns with earlier observations (e.g., Huang 2009) that transient tankyrase overexpression can stabilize β-catenin independently of PAR domain activity.

(4) The use of TNKS1/2 knockout cells expressing catalytically inactive tankyrase to demonstrate β-catenin inhibitory activity of the tankyrase degrader is elegant.

(5) The finding that the tankyrase degrader has superior anti-proliferative effects in colorectal cancer models has important therapeutic implications.

Comments on revised version:

I had a favorable opinion of the manuscript in the first round of review. I don't have additional comments on the revised manuscript. The manuscript looks fine to me.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Comments on revised version:

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

A few minor points remain:

I appreciate the authors acknowledge that testing the physical properties of the degradasome puncta is necessary to explore whether they indeed represent condensates. The term "condensates" implies liquid-liquid phase separation (rightly or wrongly). However, this question has not yet been resolved in the case of degradasomes. I therefore suggest the term "condensates" to be avoided. A simple morphological description as "puncta" may suffice.

I thank the authors for including the additional data comparing tankyrase binding by IWR and IWR-POMA. I agree that using the BRET signal of IWR-POMA is informative. Adding the IC50 values directly to the figure panels (S3E, S3G) would help the reader to quickly assess binding. The comparison between these two panels is insightful.

Regarding the use of the terms TNKS, TNKS1 and TNKS2, if the authors would like to use the name "TNKS" to refer to both paralogues collectively, can this please be specified early in the manuscript to limit confusion with the official gene name "TNKS", which of course only refers to one paralogue?

Reviewer #3 (Public review):

In this manuscript, Wang et al employ a chemical biology approach to investigate the differences between the enzymatic and scaffolding roles of tankyrase during Wnt β-catenin signalling. It was previously established that, in addition to its enzymatic activity, tankyrase 1/2 also plays a scaffolding function within the destruction complex, a property conferred by SAM-domain-dependent polymerization (PMID: 27494558). It is also known that TNKS1/2 is an autoregulated protein and that its enzymatic inhibition leads to accumulation of total TNKS proteins and stabilization of Axin punctae (through the scaffolding function of TNKS1/2), leading to rigidification of the DC and decreased β-catenin turnover. The authors surmised that this could, in part, explain the limited efficacy of TNKS1/2 catalytic inhibition for the treatment of colorectal cancers. To test this hypothesis, they evaluated a series of PROTAC molecules promoting the degradation of TNKS1/2 to block both the catalytic and scaffolding activities. They show that IWR1-POMA (their most active molecule) promotes more efficient suppression of beta-catenin-mediated transcription and is more active in inhibiting colorectal cancer cell and CRC patient-derived organoids growth. Mechanistically, the authors used FRAP to demonstrate that catalytic inhibitors of TNKS led to a reduced dynamic assembly of the DC (rigidification), whereas IWR1-POMA did not affect the dynamics.

Overall, this is an interesting study describing the design and development of a PROTAC for TNKS1/2 that could have increased efficacy where catalytic inhibitors have displayed limited activity. Knowing the importance of the scaffolding role of TNKS1/2 within the destruction complex, targeting both the catalytic and scaffolding roles certainly makes sense. The manuscript contains convincing evidence of the different mechanisms of the PROTAC vs catalytic inhibitors. Some additional efforts to quantify several of the experiments and to indicate the reproducibility and statistical analysis would strengthen the manuscript. Ultimately, it would have been great to evaluate the in vivo efficacy of IWR1-POMA in an in vivo CRC assay (APCmin mice or using PDX models); however, I realize that this is likely beyond the scope of this manuscript.

Reviewer #4 (Public review):

From the Reviewing Editor:

This important study reports the development of the first PROTACs targeting the ADP-ribosyltransferases tankyrase 1 and 2, with the goal of inhibiting Wnt/β-catenin signaling more completely than is possible with catalytic tankyrase inhibitors. The work addresses a significant limitation of existing tankyrase inhibitors: although catalytic inhibition stabilizes AXIN1/2 and suppresses Wnt signaling, it also stabilizes tankyrase itself, potentially enhancing non-catalytic scaffolding functions and promoting accumulation of degradasome-like puncta.

The evidence is convincing. The authors use appropriate and well-validated approaches, including chemical biology, cellular assays, and proteomic profiling, to show that PROTAC-mediated degradation of tankyrase avoids tankyrase accumulation while still stabilizing AXIN and inhibiting Wnt/β-catenin signaling. The data support the conclusion that degradation of tankyrase can separate pathway inhibition from the confounding effects of stabilized tankyrase protein and may therefore offer advantages over conventional catalytic inhibitors.

A strength of the study is the clear mechanistic comparison between tankyrase degradation and catalytic inhibition. The manuscript provides convincing evidence that the PROTAC and catalytic inhibitors act through distinct mechanisms, with the PROTAC targeting both catalytic and scaffolding roles of tankyrase. The study is well conducted and clearly presented, and the authors have addressed most concerns raised during review.

A remaining limitation is that the therapeutic potential of the compound is not tested in vivo, for example in APC-mutant colorectal cancer models, APCmin mice, or patient-derived xenografts. Such experiments would strengthen claims about practical efficacy, although they are not essential for the main mechanistic conclusions of the manuscript.

Overall, this is an important and insightful contribution. It advances the tankyrase and Wnt signaling fields by providing a new chemical strategy to suppress tankyrase function more completely than catalytic inhibition alone, and it offers a useful framework for future therapeutic exploration of tankyrase degradation.

Reviewer #1 (Public review):

Summary:

This "Tools and Resources" submission describes a platform for the modeling of stimulus-response relationships in the retina. It includes a repository for experimental data sets with standardized programmatic access, and a suite of software for constructing stimulus-response models and evaluating them.

Strengths:

(1) The paper is well written.

(2) The platform could serve an integrative function by connecting different research programs and offering a common baseline for evaluating stimulus-response models.

(3) The finding that there is "substantial explainable variance remains uncaptured by current models" is a useful insight to motivate further work and measure progress.

Weaknesses:

(1) The modeling supported by the package focuses on predictive accuracy at the cost of less interpretability.

(2) The article needs to make a stronger argument that this style of modeling is fruitful, especially when applied to the retina.

Main comments:

(1) Abstract machine learning vs mechanistic models. The "Core + Readout" architecture advocated here seems to be divorced from all the neurobiological detail that is already known in the retina. It mostly aims at prediction, not interpretation. Such a black-box modeling framework is useful in brain regions where we know very little about connectivity, or mechanisms, or even about the primary function being performed there, like in the mammalian cortex. In those cases, any model that can deliver a prediction is a step forward, even if it does not connect to biological mechanisms. But that's decidedly not the situation in the retina, where so many mechanistic details are known: from consensus cell types, to synaptic detail, to single-neuron biophysics, to circuit motifs. How can one connect this ML modeling approach with the extensive mechanistic knowledge available in retinal neuroscience? And can the combination somehow lead to a better understanding? The authors seem to recognize this tension (e.g. line 215ff and 370ff) but don't give it much weight. A stronger case needs to be made here for how this kind of modeling will advance the field.

(2) The "gradient field" approach. Figure 4c illustrates a case of this dissonance. The gradient field of the response increases with contrast in multiple directions. This is obvious a priori (see line 274) from the more mechanistic model we already have of this On-Off cell. These are the W3 cells described in www.pnas.org/cgi/doi/10.1073/pnas.1211547109. The circuit-based model from that paper, with rectifying on and off subunits from bipolar cells, gives a much more compact explanation for what the neuron does. Because each of the subunits has a spatio-temporal receptive field, this model can predict the entire dynamics to arbitrary stimuli, rather than just 2 dimensions of static stimuli as in the present analysis. So what is the value added here? Again, a stronger case needs to be made that these "Core + Readout" modeling activities enhance understanding.

(3) The "most exciting input" approach (Line 193ff):

- Presumably, some power constraint must be put on the stimulus? Otherwise, increasing the contrast will make it more exciting. What are these constraints?

- Presumably, this optimal stimulus is computed from the model based on non-optimal stimuli? What are the assumptions going into that?

- The most exciting stimulus is not necessarily the most useful characterization. Near its maximal firing rate, the neuron doesn't discriminate stimuli much, because the slope there is zero (line 237). Instead (or in addition), one would like to know along which stimulus axis the neuron is most sensitive. See e.g. discussion in Dayan & Abbott 2000, Figure 3.11.

Reviewer #2 (Public review):

Summary

openretina is a Python package for training and applying convolutional neural network-based models of retinal ganglion cell responses. The package integrates dataloading, model training, and evaluation in a unified framework built on PyTorch Lightning and Hydra, and ships with pre-trained model checkpoints and publicly available datasets (whitenoise, natural scenes) spanning multiple species (marmoset, mouse, axolotl, salamander) and recording modalities (multielectrode array recordings or 2-p calcium imaging). Beyond predictive modelling, openretina includes a suite of in silico analysis tools for probing learned representations, including maximally exciting input synthesis, discriminatory stimulus optimisation, and model weight visualisation. The broader openretina initiative aims to establish a community-driven platform for computational retina research, lowering barriers to entry and facilitating cross-dataset model benchmarking. This is a valuable contribution given the longstanding fragmentation of datasets, codebases, and analysis practices across retina laboratories.

Strengths:

The tool has several strengths. By providing a framework built on deep learning infrastructure, the package substantially lowers the barrier to entry for researchers without extensive machine learning backgrounds. The inclusion of pre-trained model checkpoints across multiple species and recording modalities will allow users to apply state-of-the-art models. The in silico toolkit - and in particular the MEI synthesis pipeline - has already demonstrated its scientific potential, with prior work using optimised stimuli to discover a previously uncharacterised RGC type confirmed experimentally, illustrating what becomes possible when these tools are made broadly accessible. The current modular Core + Readout architecture is a well-suited architecture for modeling retina responses. The HDF5-based data standard provides a sensible common format for contributing new datasets. Overall, the initiative is well-motivated, the engineering is competent, and the vision of a collaborative, community-driven platform for retina modelling is one that the retina community would benefit from.

Weaknesses:

(1) The in silico tools provided are valuable, but users should interpret their outputs in light of the performance of the underlying models. The predictive performances of current models and datasets in the package are far from performance ceilings.

(2) The authors appropriately note that optimised stimuli reveal what a neuron responds to but not how the computation is implemented. I would encourage readers to keep this distinction in mind when using the weight visualization tools as well - convolutional filters in a shared, unconstrained core do not map onto retinal circuit elements, and should be treated as model descriptors rather than circuit proxies. For example, RGCs of the same type may appear to sample inputs from two different filters, which should have been a single filter. Or a single RGC may be sampling from two filters, which under more constrained conditions could be approximated with a single filter. These are degeneracies in the CNN modeling framework that should be kept in mind when drawing circuit-level interpretations.

(3) The datasets currently distributed with the package vary in recording quality, and users should be aware that model performance may not only reflect architectural limitations but may also be limited by noise and data artifacts, including spike sorting errors.

(4) As the platform grows and community-contributed datasets are added, explicit data quality standards will be essential. I encourage the authors to develop dataset standards to ensure that their resource provides access to highly curated datasets, which I believe is an important step in having high-fidelity models whose functional interpretations can be trusted.

Reviewer #3 (Public review):

Summary:

This manuscript presents openretina, a Python-based platform designed to facilitate collaborative retinal modeling across datasets, laboratories, species, and recording modalities. The package provides standardized model architectures, evaluation metrics, and analysis tools, while also integrating several publicly available retinal datasets. The authors further demonstrate the platform through examples of in silico analyses and model benchmarking.

Strengths:

(1) Emphasis on standardization and reproducibility. Retinal modeling has become increasingly dependent on deep learning approaches, yet datasets and evaluation procedures remain fragmented across laboratories. By providing a unified framework, the authors lower barriers to entry and create opportunities for more systematic comparisons of models and datasets.

(2) The manuscript is clearly written, and the examples effectively illustrate the range of analyses supported by the platform.

(3) The benchmarking results are useful, particularly because they reveal substantial remaining gaps between current model performance and explainable variance ceilings.

Weaknesses:

Not a weakness per se, but rather a limitation, is that the manuscript focuses on software infrastructure rather than new biological or computational insights. While this is appropriate for a resource paper, some of the scientific examples, such as the gradient-field analysis of ON-OFF cells, function more as demonstrations than as rigorous validations of novel hypotheses. It might be useful to add a few sentences discussing potential scientific projects that can be immediately facilitated by the openretina (the current text in the Discussion focuses more on advancements in the technical/social aspects of science that will be supported by openretina).

Overall, this is a valuable and timely resource that is likely to benefit the retinal and computational neuroscience communities.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors apply the AsLOV2 domain to control the localisation and the exposure of two peptides (PMI and PMI-M3) that compete with Mdm2/MdmX for binding to p53, thus freeing p53 from these negative regulators and allowing its levels to rise. The authors follow an established strategy in optogenetics, which is to combine two layers of regulation for tighter control: (1) caging the peptide into the Ja helix of AsLOV2; 2) sequestration of the peptide away from its site of action using the LOVTRAP system.

Strengths:

The authors show that a reporter is activated when cells are exposed to light. A strength is in the lower background that was achieved after adding the second layer of regulation.

Weaknesses:

This study claims to be focused on the control of endogenous p53; however, endogenous p53 levels are not quantified. Moreover, endogenous p53 target genes are also not analysed. Only a synthetic reporter is quantified, which has been placed in the genome of HCT116 cells after the creation of a stable cell line. Microscopy images show only one or a maximum of two cells. Finally, the authors claim their strategy is a general one that can be applied to control other peptides, but they do not show this generality in this paper.

Reviewer #2 (Public review):

The authors developed Opto-MDMi, an optogenetic system for light-controlled activation of endogenous p53. The main idea is to target the p53-MDM2/MDMX regulatory interaction using PMI inhibitory peptides. This is a nice strategy because it avoids overexpression of p53, which can have adverse effects that might confound the study of p53 activity. The authors first tested a LOVTRAP-based localization strategy, which showed some efficacy but also showed basal activation. They then developed a LOV2-PMI peptide-caging module to control the activity of the PMI peptide itself, testing for interactions first in vitro and then in vivo. Finally, they combined the two systems into a dual-lock design, where LOVTRAP controls localization and LOV2-PMI controls peptide activity. This combination led to somewhat more potent stimulation of p53 activity.

Another useful aspect of the paper is the detailed description of the development and testing of the LOV2-PMI peptide-caging module, which may aid in the design of other LOV2-based peptide-caging designs.

Strengths

Overall, the paper is novel and rigorous, and the claims are supported by the data. The optoMDMi tool seems ready for implementation, for example, to manipulate and study the role of p53 signaling dynamics. A few points of clarification would strengthen the work.

Weaknesses

The authors develop many tool variants, but there is some lack of clarity over how all of these tools compare to each other, and which ones interested users should use. The work would also be strengthened by showing modulation of endogenous p53 in more than one cell line.