Reviewer #1 (Public review):

It is widely accepted that the number of muscle stem cells (MuSCs) declines with aging, leading to diminished regenerative capacity. In this study, when MuSCs were labeled with YFP at a young age, the authors found that the YFP-positive MuSC population remained stable with aging. However, VCAM1 and Pax7 expression levels were reduced in the YFP-positive MuSCs. These VCAM1-negative/low cells exhibited limited proliferative potential and reduced regenerative ability upon transplantation into MuSC-depleted mice. Furthermore, Vcam1-/low MuSCs were highly sensitive to senolysis and represented the population in which Vcam1 expression could be restored by DHT. Finally, the authors identified CD200 and CD63 as markers capable of detecting the entire geriatric MuSC population, including Vcam1-/low cells. Although numerous studies have reported an age-related decline in MuSC numbers, this study challenges that consensus. Therefore, the conclusions require further careful validation.

Major comments:

(1) As mentioned above, numerous studies have reported that the number of MuSCs declines with aging. The authors' claim is valid, as Pax7 and Vcam1 were widely used for these observations. However, age-related differences have also been reported even when using these markers (Porpiglia et al., Cell Stem Cell 2022; Liu et al., Cell Rep 2013). When comparing geriatric Vcam1⁺ MuSCs with young MuSCs in this study, did the authors observe any of the previously reported differences? Furthermore, would increasing the sample size in Figure 1 reveal a statistically significant difference? The lack of significance appears to result from variation within the young group. In addition, this reviewer requests the presentation of data on MuSC frequency in geriatric control mice using CD200 and CD63 in the final figure.

(2) Can the authors identify any unique characteristics of Pax7-VCAM-1 GER1-MuSCs using only the data generated in this study, without relying on public databases? For example, reduced expression of Vcam1 and Pax7. The results of such analyses should be presented.

(3) In the senolysis experiment, the authors state that GER1-MuSCs were depleted. However, no data are provided to support this conclusion. Quantitative cell count data would directly address this concern. In addition, the FACS profile corresponding to Figure 4D should be included.

(4) Figure S4: It remains unclear whether DHT enhances regenerative ability through restoration of the VCAM1 expression in GER1-MuSCs, as DHT also acts on non-MuSC populations. Analyses of the regenerative ability of Senolysis+DHT mice may help to clarify this issue.

(5) Why are there so many myonuclear transcripts detected in the single-cell RNA-seq data? Was this dataset actually generated using single-nucleus RNA-seq? This reviewer considers it inappropriate to directly compare scRNA-seq and snRNA-seq results.

Comments on revisions:

Related to Comment#3: The percentage is also influenced by the number of other cell types. Therefore, to demonstrate cell removal, it is necessary to present the absolute number of cells. If the cells were removed and were not replenished from Vcam1+ cells, the absolute number of cells should be reduced.

Related to Comment#4: Without the DHT+Senolysis experiment proposed by this reviewer or related experiments, there is no evidence demonstrating that GERI-MuSCs functionally rejuvenate. The current data only show that VCAM1 expression is restored.

Related to Comment#8: Individual results from 3-4 biological replicates should be shown in Figure 4. It will help readers to recognize the variation of each sample.

Reviewer #2 (Public review):

Kim et al. investigate heterogeneity in aged muscle stem cells using a model that enables lifelong lineage tracing. The questions addressed in the paper are highly relevant to the fields of aging and stem cell biology, and the experimental approach overcomes some of the limitations of previous studies.

The study provides evidence for phenotypic and functional heterogeneity within the lineage-traced aged MuSC pool. However, the data as presented do not yet support the broader conclusions that MuSC abundance is maintained with age or that a previously unrecognized aged MuSC subpopulation has been identified. These claims would require stronger age-matched cohorts, absolute cell counts normalized to tissue mass, and direct comparison to previously described aged muscle stem cell states.

If the core observations were experimentally reinforced, this study could prompt the field to reassess muscle stem cell loss, heterogeneity, and age-associated changes in canonical marker expression in geriatric mice. However, because several of the central claims depend on analyses that are currently incomplete, the conceptual impact should be treated as provisional. The deposited bulk RNA-seq and scRNA-seq datasets should be useful for mapping these states to existing atlases and for re-analysis by groups interested in quiescent and senescent programs in geriatric muscle stem cells.

Reviewer #3 (Public review):

Summary:

The manuscript by Kim et al. describes a MuSC subpopulation that loses VCam expression in geriatric muscle and shows reduced ability to contribute to muscle regeneration. They propose that this population underlies the reported decline of MuSCs in aged mice, suggesting that these cells remain present in geriatric muscle but are overlooked due to low or absent VCam expression. The identification of a subpopulation that changes with aging would be compelling and of interest to the field.

Strengths:

The authors employ a wide range of assays, from in vitro to in vivo systems, to characterize Vcam-low/negative cells from geriatric muscle. The loss of Vcam appears strong in geriatric mice. They further identify CD63 and CD200 as potential surface markers that remain stable with age, thereby enabling the isolation of MuSCs across different age groups.

Weaknesses:

Some issues remain before establishing whether this population represents a true functional subset or explains the reported decline in MuSC numbers in aged mice. Stronger fate assessment of Vcam-low/negative cells is needed to assess their propensity for cell death and whether this contributes to the conclusions. Comparisons include young, middle-aged, and geriatric mice, but not aged (~24 months) mice, which would help comparisons to previous reports of age-related MuSC decline. The suggestion that the Vcam-low/negative population reflects a senescence-like state remains unclear, as these cells display limited canonical senescence markers, exhibit reversible cell-cycle exit, and yet are reported to be sensitive to senolytic treatment. Validation of CD63 and CD200 as reliable age-independent MuSC markers requires further testing, specifically using the Pax7-YFP tracing model and co-labeling in geriatric mice. Finally, the grouping patterns in some analyses suggest that the Vcam-low/negative fraction may be present in only a subset of geriatric mice, raising the possibility that it reflects health status or pathology rather than a consistent aging-associated phenotype.

Reviewer #1 (Public review):

Summary:

The manuscript by Xu et al. reported base-resolution mapping of RNA pseudouridylation in five bacterial species, utilizing recently developed BID-seq. They detected pseudouridine (Ψ) in bacterial rRNA, tRNA, and mRNA, and found growth phase-dependent Ψ changes in tRNA and mRNA. They then focused on mRNA and conducted comparative analysis of Ψ profiles across different bacterial species. Finally, they developed a deep learning model to predict Ψ sites based on RNA sequence and structure.

Strengths:

This is the first comprehensive Ψ map across multiple bacterial species, and systematically reveals Ψ profiles in rRNA, tRNA, and mRNA under exponential and stationary growth conditions. It provides a valuable resource for future functional studies of Ψ in bacteria.

Weaknesses:

Ψ is highly abundant on non-coding RNA such as rRNA and rRNA, while its level on mRNA is very low. The manuscript focuses primarily on Ψ on mRNA, which is prone to false positives. Many conclusions in the manuscript are speculative, based solely on the sequencing data, but not supported by additional experiments.

Reviewer #2 (Public review):

Summary:

In this study, Xu et al. present a transcriptome-wide, single-base resolution map of RNA pseudouridine modifications across evolutionarily diverse bacterial species using an adapted form of BID-Seq. By optimizing the method for bacterial RNA, the authors successfully mapped modifications in rRNA, tRNA, and, importantly, mRNA across both exponential and stationary growth phases. They uncover evolutionarily conserved Ψ motifs, dynamic Ψ regulation tied to bacterial growth state, and propose functional links between pseudouridylation and bacterial transcript stability, translation, and RNA-protein interactions. To extend these findings, they develop a deep learning model that predicts pseudouridine sites from local sequence and structural features.

Strengths:

The authors provide a valuable resource: a comprehensive Ψ atlas for bacterial systems, spanning hundreds of mRNAs and multiple species. The work addresses a gap in the field - our limited understanding of bacterial epitranscriptomics, by establishing both the method and datasets for exploring post-transcriptional modifications.

Weaknesses:

The main limitation of the study is that most functional claims (i.e. translation efficiency, mRNA stability, and RNA-binding protein interactions) are based on correlative evidence. While suggestive, these inferences would be significantly strengthened by targeted perturbation of specific Ψ synthases or direct biochemical validation of proposed RNA-protein interactions (e.g., with Hfq). Additionally, the GNN prediction model is a notable advance.

Reviewer #3 (Public review):

Summary:

This study aimed to investigate pseudouridylation across various RNA species in multiple bacterial strains using an optimized BID-seq approach. It examined both conserved and divergent modification patterns, the potential functional roles of pseudouridylation, and its dynamic regulation across different growth conditions.

Strengths:

The authors optimized the BID-seq method and applied this important technique to bacterial systems, identifying multiple pseudouridylation sites across different species. They investigated the distribution of these modifications, associated sequence motifs, their dynamics across growth phases, and potential functional roles. These data are of great interest to researchers focused on understanding the significance of RNA modifications, particularly mRNA modifications, in bacteria.

Reviewer #1 (Public review):

Summary:

This important study functionally profiled ligands targeting the LXR nuclear receptors using biochemical assays in order to classify ligands according to pharmacological functions. Overall, the evidence is solid, but nuances in the reconstituted biochemical assays and cellular studies and terminology of ligand pharmacology limit the potential impact of the study. This work will be of interest to scientists interested in nuclear receptor pharmacology.

Strengths:

(1) The authors rigorously tested their ligand set in CRTs for several nuclear receptors that could display ligand-dependent cross-talk with LXR cellular signaling and found that all compounds display LXR selectivity when used at ~1 µM.

(2) The authors tested the ligand set for selectivity against two LXR isoforms (alpha and beta). Most compounds were found to be LXRbeta-specific.

(3) The authors performed extensive LXR CRTs, performed correlation analysis to cellular transcription and gene expression, and classification profiling using heatmap analysis-seeking to use relatively easy-to-collect biochemical assays with purified ligand-binding domain (LBD) protein to explain the complex activity of full-length LXR-mediated transcription.

Comments on revisions:

The authors have addressed the comments from the prior round of review with care. I find the revised manuscript significantly strengthened.

Reviewer #2 (Public review):

Summary:

In this manuscript by Laham and co-workers, the authors profiled structurally diverse LXR ligands via a coregulator TR-FRET (CRT) assay for their ability to recruit coactivators and kick off corepressors, while identifying coregulator preference and LXR isoform selectivity.

The relative ligand potencies measured via CRT for the two LXR isoforms were correlated with ABCA1 induction or lipogenic activation of SRE depending on cellular contexts (i.e, astrocytoma or hepatocarcinoma cells). While these correlations are interesting, there is some leg room to improve the quantitative presentation of these correlations. Finally, the CRT signatures were correlated with the structural stabilization of the LXR: coregulator complexes. In aggregate, this study curated a set of LXR ligands with disparate agonism signatures that may guide the design of future nonlipogenic LXR agonists with potential therapeutic applications for cardiovascular disease, Alzheimer's and type 2 diabetes, without inducing mechanisms that promote fat/lipid production.

Strengths:

This study has many strengths, from curating an excellent LXR compound set, to the thoughtful design of the CRT and cellular assays. The design of a multiplexed precision CRT (pCRT) assay that detects corepressor displacement as a function of ligand-induced coactivator recruitment is quite impressive as it allows measurement of ligand potencies to displace corepressors in the presence of coactivators, which cannot be achieved in a regular CRT assay that looks at coactivator recruitment and corepressor dissociation in separate experiments.

Comments on revisions:

These weaknesses have been satisfactorily addressed by the authors in the revised preprint.

Reviewer #1 (Public review):

Summary:

This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Fig. 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Fig. 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Fig. 2). They present data suggesting that in 73% of SGCs BMP signaling is low (assessed by dad-lacZ) (Fig. 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Fig. 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Fig. 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Fig. 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what in seen in the ovarian stem cell niche. This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Fig. 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Fig. 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Fig. 2). They present data suggesting that in 73% of SGCs BMP signaling is low (assessed by dad-lacZ) (Fig. 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Fig. 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Fig. 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Fig. 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what in seen in the ovarian stem cell niche.

Strengths:

(1) Use of an excellent and established model for tumorous cells in a stem cell microenvironment

(2) Powerful genetics allow them to test various factors in the tumorous vs non-tumorous cells

(3) Appropriate use of quantification and statistics

Weaknesses:

(1) What is the frequency of SGCs in nos>flp; bam-mutant tumors? For example, are they seen in every germarium, or in some germaria, etc or in a few germaria.

This concern was addressed in the rebuttal. The line number is 106, not line 103.

(2) Does the breakdown in clonality vary when they induce hs-flp clones in adults as opposed to in larvae/pupae?

This concern was addressed in the rebuttal. However, these statements are no on lines 331-335 but instead starting on line 339. Please be accurate about the line numbers cited in the rebuttal. They need to match the line numbers in the revised manuscript.

(3) Approximately 20-25% of SGCs are bam+, dad-LacZ+. Firstly, how do the authors explain this? Secondly, of the 70-75% of SGCs that have no/low BMP signaling, the authors should perform additional characterization using markers that are expressed in GSCs (i.e., Sex lethal and nanos).

The authors did not perform additional staining for GSC-enriched protein like Sex lethal and nanos.

(4) All experiments except Fig. 1I (where a single germarium with no quantification) were performed with nos-Gal4, UASp-flp. Have the authors performed any of the phenotypic characterizations (i.e., figures other than figure 1) with hs-flp?

In the rebuttal, the authors stated that they used nos>flp for all figures except for Fig. 1I. It would be more convincing for them to prove in Fig. 1 than there is not phenoytpic difference between the two methods and then switch to the nos>FLP method for the rest of the paper.

(5) Does the number of SGCs change with the age of the female? The experiments were all performed in 14-day old adult females. What happens when they look at young female (like 2-day old). I assume that the nos>flp is working in larval and pupal stages and so the phenotype should be present in young females. Why did the authors choose this later age? For example, is the phenotype more robust in older females? or do you see more SGCs at later time points?

The authors did not supply any data to prove that the clones were larger in 14-day-old flies than in younger flies. Additionally, the age of "younger" flies was not specified. Therefore, the authors did not satisfactorily answer my concern.

(6) Can the authors distinguish one copy of GFP versus 2 copies of GFP in germ cells of the ovary? This is not possible in the Drosophila testis. I ask because this could impact on the clonal analyses diagrammed in Fig. 4A and 4G and in 6A and B. Additionally, in most of the figures, the GFP is saturated so it is not possible to discern one vs two copies of GFP.

In the rebuttal, the authors stated that they cannot differential one vs two copies of GFP. They used other clone labeling methods in Fig. 4 and 6. I think that the authors should make a statement in the manuscript that they cannot distinguish one vs two copies of GFP for the record.

(7) More evidence is needed to support the claim of elevated Dpp levels in bam or bgcn mutant tumors. The current results with dpp-lacZ enhancer trap in Fig 5A,B are not convincing. First, why is the dpp-lacZ so much brighter in the mosaic analysis (A) than in the no-clone analysis (B); it is expected that the level of dpp-lacZ in cap cells should be invariant between ovaries and yet LacZ is very faint in Fig. 5B. I think that if the settings in A matched those in B, the apparent expression of dpp-lacZ in the tumor would be much lower and likely not statistically significantly. Second, they should use RNA in situ hybridization with a sensitive technique like hybridization chain reactions (HCR) - an approach that has worked well in numerous Drosophila tissues including the ovary.

The HCR FISH in Fig.5 of the revised manuscript needs an explanation for how the mRNA puncta were quantified. Currently, there is no information in the methods. What is meant but relative dpp levels. I think that the authors should report in and unbiased manner "number" of dpp or gbb puncta in TFs. For the germaria, I think that they should report the number of puncta of dpp or gbb divide by the total area in square pixels counted. Additionally, the background fluorescence is noticeably much higher in bamBG/delta86 germaria, which would (falsely) increase the relative intensity of dpp and gbb in bam mutants. Although, I commend the authors for performing HCR FISH, these data are still not convincing to me.

(8) In Fig 6, the authors report results obtained with the bamBG allele. Do they obtain similar data with another bam allele (i.e., bamdelta86)?

The authors did not try any experiments with the bamdelta86 allele, despite this allele being molecularly defined, where the bamBG allele is not defined.

Reviewer #2 (Public review):

In the current version, Zhang et al. have made substantial improvements to the manuscript. It is now easier to read, and the data are more solid compared with the previous version, supporting their conclusion that tumor GSCs secrete stemness factors (BMPs and Dpp) to suppress the differentiation of neighboring wild-type GSCs. This study should benefit a broad readership across developmental biology, germ cell biology, stem cell biology, and cancer biology.

However, the following suggestions may further improve the clarity and rigor of the research content:

(1) Clarification of sample size (n).<br /> Each germarium can contain highly variable numbers of SGCs, sometimes reaching 50-100. When reporting "n" values, the authors are encouraged to also indicate the number of germaria analyzed. For example, in lines 126-128:<br /> "Notably, 74% of SGCs (n = 132) were GFP-negative, while the remaining 26% were GFP-positive (Figure 2B, C). This suggests that SGCs can be categorized into two distinct groups: those resembling GSCs (GSC-like) and those resembling cystoblasts (cystoblast-like)."<br /> Please clarify how many germaria were examined to obtain n = 132. In addition, it is unclear whether the authors intend to suggest that the GFP-negative SGCs are GSC-like or cystoblast-like; this point should be clarified.

(2) Improvement of Fig. 6 in situ hybridization images.<br /> The in situ hybridization images in Fig. 6 are not fully convincing. The control images, in particular, would benefit from higher resolution and enlarged views of the germarium region. In panel C, abundant signals are also present outside the germarium, which may complicate interpretation and should be clarified or controlled for.

Alternatively, the authors could strengthen the in situ analysis by using bam mutants or bam dpp / bam gbb double mutants as controls to better define signal specificity.

Reviewer #3 (Public review):

Zhang et al. investigated how germline tumors influence the development of neighboring wild-type (WT) germline stem cells (GSC) in the Drosophila ovary. They report that germline tumors generated by differentiation-arrested mutations (bam and bgcn) inhibit the differentiation of neighboring WT GSCs by arresting them in an undifferentiated state, resulting from reduced expression of the differentiation-promoting factor Bam. They find that these tumor cells produce low levels of the niche-associated signaling molecules Dpp and Gbb, which suppress bam expression and consequently inhibit the differentiation of neighboring WT GSCs non-cell-autonomously. Based on these findings, the authors propose that germline tumors mimic the niche to suppress the differentiation of the neighboring wild-type germline stem cells.

Strengths:

The study uses a well-established in vivo model to address an important biological question concerning the interaction between germline tumor cells and wild-type (WT) germline stem cells in the Drosophila ovary. If the findings are substantiated, this study could provide valuable insights that are applicable to other stem cell systems.

Weaknesses:

The authors have addressed some of my concerns in the revised submission. However, the data presented do not allow the authors to distinguish whether the failed differentiation of WT stem cells/germline cells results from "arrested differentiation due to the loss of the differentiation niche" or from "direct inhibition by tumor-derived expression of niche-associated molecules Dpp and Gbb". The critical supporting data, HCR in situ results, are not sufficiently convincing.

Reviewer #2 (Public review):

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.

We outline below the 3 points that, in our opinion, remain to be clarified.

(1) Effect of NAG on slender-form infections in tsetse flies<br /> The conclusion that "NAG has a negligible effect on slender infections in tsetse flies" based on Figure 1, cannot be fully supported in the absence of a positive control. A relevant positive control is well established in the literature, namely that NAG promotes Tsetse infection by stumpy forms. Without such a control, it is not possible to exclude technical issues (for example, an ineffective NAG treatment), which would yield results similar to those presented in Figure 1.

(2) Infection of non-teneral flies<br /> Because the experiments shown in Figure 1 (teneral flies) and Figure 2 (non-teneral flies) were not conducted in parallel or under identical conditions, it is important that the figure legends clearly state the parasite numbers used in each case. Specifically, infections of teneral flies were performed with 200 parasites/mL (approximately 4 parasites per bloodmeal), whereas non-teneral infections used 1 × 10⁶ parasites/mL (approximately 20,000 parasites per bloodmeal?). At present, this information is scattered across the Methods and Supplementary Tables 1 and 2, making it difficult for readers to immediately appreciate that the parasite load differs by roughly 5,000-fold between these conditions.

As previously shown by the authors (Schuster et al., 2021) and in the Rotureau laboratory (Tsagmo Ngoune et al.), and as generally expected, the initial parasite dose strongly influences infection outcomes in teneral flies. In this context, it would be informative to know whether the authors have attempted infections of non-teneral flies using lower parasite numbers (noting that Tsagmo Ngoune et al. used a maximum of 10,000 parasites) and what the infection rate was.<br /> Relatedly, the statement in line 370 appears to be an overgeneralization, as fly age was not directly tested under matched experimental conditions:

Line 370 - "Here, we unambiguously show that, in the absence of immunosuppressive treatment, slender forms can establish infections in tsetse flies, irrespective of the fly's age or sex."

(3) Transcriptomic analysis<br /> Supplementary Figure 8 lacks statistical analysis, which limits its interpretability. Two types of comparisons would be particularly helpful:<br /> (i) a comparison of PAD1/2 expression levels between slender and stumpy forms at 0 h; and<br /> (ii) for each gene, a comparison of the overall change in expression (from 0 to 72 h) between infections initiated with slender versus stumpy forms.<br /> In addition, the figure legend should clarify what "expression levels" refer to. TPM? Normalized counts?

Finally, for the benefit of the field, eLife could encourage publishing a collaborative study in which the Engstler and Rotureau laboratories exchange parasite lines and culture protocols (including media with and without methylcellulose) and perform tsetse fly infections in parallel in their respective laboratories. Such an approach could help resolve the remaining discrepancies and provide a valuable reference for the community.

Reviewer #1 (Public review):

Summary:

This study investigates the roles of the two tumor necrosis factor genes (tnfa and tnfb) in zebrafish during inflammatory responses. TNF is a central regulator of inflammation across vertebrates; however, while mammalian TNF signaling is well characterized, the functional divergence of duplicated TNF genes in teleosts remains less well understood. In this work, the authors generate novel zebrafish fluorescent reporter lines for tnfb and use them to perform comparative analyses of the spatial and temporal expression patterns of tnfa and tnfb during inflammation. They report that these paralogous genes are produced by distinct immune cell populations and exhibit different induction kinetics during inflammatory processes. Based on these observations, the authors propose that tnfa and tnfb may fulfill non-redundant roles in the zebrafish immune response.

Strengths:

The study addresses an important gap in understanding the functional divergence of TNF paralogs in teleosts. Given that gene duplication events are common in fish genomes, clarifying how duplicated cytokines partition their functions is valuable for both evolutionary immunology and zebrafish model research. The work makes effective use of the zebrafish model, which is particularly well suited for in vivo imaging of dynamic immune cell behaviors during inflammation. A key strength of the study is the integration of analyses of cell-type specificity, transcriptional regulation, and temporal expression dynamics. In particular, the live imaging experiments are compelling and provide clear visual evidence that tnfa and tnfb differ in both cellular sources and expression kinetics, which strengthens the claim that these paralogs may have diverged in their regulation and potentially their function. By distinguishing these aspects of the two cytokines, the study provides useful conceptual and methodological guidance for future investigations of inflammatory signaling in zebrafish.

Weaknesses:

(1) While the manuscript convincingly documents distinct expression patterns, the functional consequences of these differences remain unexplored. The conclusions regarding non-redundant roles would benefit from functional perturbation experiments. Relatedly, the authors propose that tnfa and tnfb may play different immunological roles, but the mechanistic basis underlying these differences is not addressed. For example, do the two cytokines engage different receptors or signaling pathways? Do they trigger distinct downstream transcriptional programs?

(2) Some imaging-based observations appear largely qualitative. Additional quantitative analyses, such as statistical comparisons of expression levels across time points or cell populations, would strengthen the robustness of the conclusions. For instance, in Figure 4, the expression levels of tnfa and tnfb reporter transgenes in immune cells should be quantitatively compared between control and amputated conditions.

(3) It would also be important to clarify whether the distinct maturation kinetics of the fluorescent reporters were taken into account when interpreting expression timing. Since GFP typically matures more rapidly than mCherry in vivo, the authors should comment on whether this difference could influence the apparent expression kinetics of tnfa versus tnfb.

Reviewer #2 (Public review):

Summary:

In this manuscript, van Dijk et al analyse the expression of the largely ignored paralogue of TNF in zebrafish, tnfb. They generate reporter transgenic lines and show that the reporter expression is consistent with endogenous mRNA expression in zebrafish larvae. Unlike its better-known paralogue tnfa, tnfb is constitutively expressed in mantle cells of neuromasts, and in a few leukocytes. It is also inducible in macrophages and some neutrophils upon wounding or detection of microbes, with faster kinetics than tnfa or il1b.

Strengths:

Generation and convincing validation of new transgenic reporter lines for tnfb with either green or red fluorescent proteins. Superb imaging and careful analysis of these lines crossed to complementary reporter transgenics, backed with in situ hybridization and qRT-PCR analysis of FACS-sorted cells. Excellent methods section.

Weaknesses:

Lack of functional analysis; these lines are a potentially valuable tool, but so far provide no clue regarding the role of tnfb. Is it a pro-inflammatory cytokine acting in synergy with tnfa, or is it an antagonist? What are its receptor(s)? What signalling pathways and downstream genes does it induce? Addressing at least some of these questions should greatly increase the impact of the paper.

Reviewer #1 (Public review):

Summary:

It has long been known that Drosophila embryonic ventral nerve cord neuroblasts incorporate both spatial and temporal transcription factor expression to generate 30 distinct neuroblasts and lineages per hemisegment. This manuscript aims to elucidate the mechanism by which this integration of spatial and temporal transcription factors occurs through "direct regulation" or "epigenetic regulation". Direct regulation is defined as both spatial and temporal factors binding to open chromatin and working together to dictate specific lineages. Epigenetic regulation is defined as a spatial factor priming the chromatin in a neuroblast-specific manner to allow for the integration of temporal factors to generate specific lineages. The authors conclude that there is a two-step model in which a spatial transcription factor code "primes" the chromatin in terms of accessibility and then recruits temporal factors to ensure lineage-specific enhancer activation.

Strengths:

The authors tested two models, "direct regulation" vs "epigenetic regulation" in a well-defined pool of neural stem cells during normal development.

Weaknesses:

The data in this study cannot clearly substantiate these two models.

Overall, there are a number of issues that are inconsistent and not supportive of the model proposed in this manuscript. Firstly, there is no evidence of pioneer factor activity in any of the NB lineages described - i.e., any changes in chromatin accessibility being shown over time. The authors must show chromatin conformation changes during the window of spatial transcription factor expression in order to convince the readers of this phenomenon. Secondly, the phenotypic data do not align with the sequencing data - the story would be more cohesive if the sequencing data and phenotypic data were in the same NB subtypes. On one hand, we are shown that Gsb misexpression induces loss of chromatin accessibility in NB 7-4, however in the widespread loss model, we are not shown a phenotype in these NB7-4 - which suggest that the chromatin accessibility at these sites (sites that have already been distinguished as SoIs for that NB subtype) does not play an important role in distinguishing NB 7-4 identity. However, the authors report loss of NB3-5 identity but have no evidence as to how the chromatin has changed (or if it has at all) in that subtype, leaving the readers to wonder how the loss of identity occurred.

Reviewer #2 (Public review):

Summary:

This article by Bhattacharya et al. investigates how neural stem cells (NSCs, NBs) in Drosophila integrate spatial and temporal cues to activate neuron-specific terminal selector (TS) genes. Prior to this work, it was understood that NSCs utilize spatial transcription factors (STFs) and temporal transcription factors (TTFs) to determine lineage identity and birth order, but the mechanisms of integration were not fully elucidated. The authors employed chromatin profiling techniques to analyze the binding of STFs and TTFs in two specific neuroblast lineages, NB5-6 and NB7-4. They found that Gsb (an STF) binds both accessible and less-accessible chromatin in NB5-6, while En (another STF) binds only to pre-accessible chromatin in NB7-4. The findings support an "STF code" where the combination of pioneer and non-pioneer spatial factors, along with temporal factors, triggers neuroblast-specific enhancer activation and determines lineage identity.

Strengths:

The experiments are well-executed, the interpretations are generally sound, and the figures are clear and elegant. However, some conclusions are drawn too broadly without essential functional data. Therefore, additional work is needed to more effectively convey the central message.

Weaknesses:

(1) Integration of TaDa and functional data on Gsb for the STF model

The authors demonstrate that TaDa profiling maps Gsb binding across the genome and identifies candidate chromatin-priming sites in NB5-6. Gsb LOF/GOF experiments reveal effects on NB identity. Combining TaDa data with LOF and GOF analyses indicates that Gsb influences NB5-6 specification by binding to both open and relatively closed chromatin, helping maintain NB5-6 identity while limiting NB3-5 fate.

However, the study does not establish a direct link between specific LOF/GOF phenotypes and particular genomic targets. For instance, analyzing Gsb occupancy at lineage-specific identity factors or terminal selector genes (such as Lbe, Ap, or Eya for NB5-6; and Ems, etc., for NB3-5) in wild-type and manipulated conditions (Gsb misexpression) would directly connect chromatin binding to the regulation of fate determinants. These investigations would strengthen the mechanistic connection between the correlative TaDa profiles and the observed identity changes, supporting the idea that Gsb functions as a context-dependent chromatin-priming factor within the STF code, rather than as a generic transcription factor.

(2) Gsb misexpression reveals bidirectional chromatin remodelling

Experiments with ectopic Gsb expression demonstrate bidirectional chromatin remodeling in NB7-4, showing decreases in accessibility at some binding sites and increases at others. While the authors show that Gsb can disrupt chromatin upon misexpression, interpreting its "pioneer-like" or chromatin-priming activity is complex due to several factors: the misexpression occurs in a non-native lineage, the direct versus indirect effects rely on whole-embryo Dam-Gsb peaks instead of NB7-4-specific binding, and heat-shock-induced chromatin changes are not fully accounted for. These issues make it challenging to definitively determine Gsb's role in chromatin priming.

A complementary approach would be to perform Gsb knockdown/loss-of-function in its native NB5-6 lineage and profile chromatin accessibility (TaDa or CATaDa). This would allow a cleaner, more physiologically relevant assessment of Gsb's contribution to priming, SoI establishment, and Hb recruitment. Such an experiment would strengthen the causal link between Gsb occupancy and chromatin state and clarify whether Gsb truly acts as a context-dependent pioneer in vivo, rather than producing indirect effects due to ectopic misexpression.

(3) En is not a pioneer factor

The authors conclude that Engrailed (En) is not a pioneer factor, based on the observation that En binding correlates with accessible chromatin and that En is not enriched at NB5-6-specific SOIs. However, this conclusion is not sufficiently supported by the functional data.

First, the absence of En binding at NB5-6-specific SOIs does not necessarily indicate an inability to engage closed chromatin. These regions were not selected for the presence of En consensus motifs, so their lack of occupancy may simply reflect the absence of En binding motifs rather than a lack of pioneering capacity. A systematic motif analysis at NB5-6-specific SOIs is needed to determine whether En binding sites are present but unoccupied.

Second, the claim that En lacks pioneer activity relies solely on a single steady-state TaDa/DamID occupancy assay at one developmental stage. Because pioneer factor interactions can be transient, low-affinity, and stage-specific, such binding may not be detected by TaDa, which also depends on local GATC density and methylation kinetics and may yield false negatives. Given these technical limitations, the absence of En binding at less accessible regions does not definitively rule out a priming role.

In the absence of direct functional assays (En LOF/GOF), the authors should explicitly acknowledge these technical and conceptual limitations and tone down the claim that "En lacks pioneer activity".

(4) Clarity of STF-code Model and Central Message

The manuscript begins by presenting two models, direct and epigenetic, but the central takeaway of the paper is not clear. Specifically, the nuanced roles of the spatial factors Gsb and En as chromatin-priming versus stabilizing/effector factors within an STF code, and the resulting division of labor, are not clearly illustrated. The distinction between Gsb as a chromatin-priming factor and En as a cofactor-dependent activator/stabilizer should be explicitly presented in a stepwise model for better clarity. The authors could strengthen this by providing a schematic with two sequential stages illustrating how neuroblast identity factors (STF code) change chromatin states to drive lineage-specific enhancer activation. The schematic can be shown from the neuroectoderm to individual NB lineages to make it more panoramic.

(5) Identification of Priming Factors in NB7-4

While the authors suggest that an unknown priming factor might be responsible for establishing sites of integration in NB7-4, they do not identify or explore potential candidates for this role. Further investigation into what factors might be involved in chromatin priming in NB7-4 could provide a more complete understanding of the mechanisms at play.

(6) Functional Validation of STF Code Components

The study proposes an STF code for each neuroblast lineage, but the specific components of these codes, beyond Gsb and En, are not fully explored. Identifying and validating additional factors that contribute to the STF code in each lineage could strengthen the conclusions.

Reviewer #1 (Public review):

Summary:

This study presents an interesting approach for finding electrophysiological models that match experimental patch-clamp data. The authors develop a new method for deriving optimized current clamp protocols by training a neural network on synthetic data. This optimized current clamp is then used on both computational training data and on experimental data to predict current gating and conductance parameters that correctly reconstruct the electrical phenotype.

Strengths:

(1) The fitting of gating variables through an optimized patch clamp protocol is interesting.

(2) The inclusion of experimental data is important, and the approach is shown to be effective in fitting them.

Weaknesses:

(1) Some clarity is necessary on the generation and selection of variable IPSC models. With such a large variation in so many parameters, I would expect some resulting parameters to generate non-realistic phenotypes, quiescent cells, etc. Are all 200,000 or 1,100,000 generated cells viable? Or are they selected somehow for realistic cell properties?

(2) The error shown in Figure 4 between different population sizes is not completely explained in the text - there seems to be a minimal difference between a population of 1,000 and 10,000, followed by a very good fit at 200,000. Is there a particular threshold that needs to be crossed where the error drops off? Related, how was the 200,000 number chosen?

(3) Related to the point above, the 1,100,000 population for fitting experimental data also needs a more complete explanation: how was this number chosen, and how does the error compare with the other population sizes shown in Figure 4?

(4) Why are the optimized current clamp protocols different between panels A and B in Figure 5? Are they somehow informed by experimental data?

(5) Figure 6D: Is the EAD risk in panel D specific to cell 1, 2, or the pooled variants of both?

(6) How sensitive is the fitting to minor parameter variation? Further, if one were to pick, let's say, the next-best fitting value, would that fall close to the best one? Is the solution found unique, or are there multiple sets with good fits?

Reviewer #2 (Public review):

Summary:

The authors present a computational framework for generating "cell-specific" digital twins of human iPSC-CMs from a single optimized voltage clamp recording. Using deep learning trained on > 1 million artificial cells, the authors demonstrate that the model can infer 52 biophysical parameters governing 6 major ionic currents, and the resulting digital twins can reproduce experimentally recorded action potentials.

Strengths:

The framework has clear potential for understanding cellular heterogeneity in iPSC-CMs, predicting individual drug responses, and reducing the experimental burden of multiple patch clamp protocols.

Weaknesses:

There are several concerns about the validation of the model and its clarity. First, the biological variability being modeled in this manuscript is not defined well. It is unclear whether the framework addresses cell-to-cell differences within a single differentiation batch, variability across iPSC lines, or donor-to-donor differences. This ambiguity makes it difficult to interpret what the "digital twin populations" actually represent biologically. Second, the main claim, "the digital twins enable drug testing and arrhythmia prediction that would be impractical experimentally", is not experimentally validated. For example, the E-4031 simulations predict EAD rates, but no direct experimental head-to-head comparison is provided to confirm that these predictions are accurate. Third, technical reproducibility and biological representativeness are not assessed. Single voltage clamp recordings are inherently noisy. Without knowing how much variability comes from the recording process (technical variation) vs true biological differences, it is difficult to judge whether observed "cell-specific" parameter differences are meaningful. In addition, the optimized protocol is claimed to be superior to conventional approaches, but again, no experimental comparison is shown.

The authors should address these concerns, with particular emphasis on clarifying the biological context and providing direct experimental validation. Below are detailed specific points:

(1) Ambiguous definition of iPSC-CM heterogeneity.

The authors model "typical iPSC-CM heterogeneity" by varying 52 parameters +/- 40% around a baseline model (Figure 1), generating > 1 million synthetic cells. However, the manuscript does not clearly state what biological variability this model is intended to capture. Is this modeling within-line, cell-to-cell variability (e.g., cells from the same dish or differentiation batch that differ due to stochastic gene expression or maturation state)? Or is this modeling between-line or between-donor variability (e.g., genetic background differences, reprogramming efficiency)? This distinction is critical for interpretation. If the goal is to understand why different cells in the same dish behave differently, then training data should reflect that. If the goal is to compare patient lines or disease models, the framework needs validation across multiple donors or lines.

For example, the experimental validation in Figure 5 uses a single iPSC line (iPS-6-9-9T.B), but how many differentiation batches or dishes were tested, or whether cells came from the same preparation are unclear. Another example is that the wide AP diversity in the training population (Figure 1A) is impressive, but there is no demonstration that real experimental cells actually fall within this assumption range of +/- 40%.

From a biological perspective, iPSC-CMs are known to be highly heterogeneous within lines (maturation state, metabolic differences, epigenetic variation, spatial differences within the same dish, etc) and between lines (different donor/genetic background). Thus, please explicitly state whether the +/- 40% variation is intended to model within-line or between-line heterogeneity, and justify this choice with wet experiment data (or reference to experimental literature on iPSC-CM variability). Please clarify how many dishes, differentiation batches, and time points post-differentiation were used for experimental recordings (Figures 5-6). If the framework is intended to generalize across lines from different donors, please test the model on multiple independent iPSC lines (from different donors).

(2) Biological representativeness of single-cell measurements.

The framework generates digital twins from single voltage clamp recordings. The patch clamp recordings in iPSC-CMs are subject to substantial technical variability. The manuscript does not address a fundamental question: "How representative are the measurements from a single cell on the dish (or line)?" In other words, if I measure one cell from a dish of a million cells, does that cell's digital twin tell me something about the dish as a whole, or just about that one cell? The manuscript presents Cell 1 and Cell 2 (Figures 5-6) as distinct individuals, but it's unclear whether these differences reflect true biological heterogeneity or simply sampling variability. I think the authors should perform replicate recordings on multiple cells (e.g., > 10 cells) from the same dish (same differentiation batch) and quantify how much the inferred parameters vary, and then compare between lines.

(3) No experimental validation of the main claim that in silico populations can replace wet experiments.

The most exciting claim in the manuscript is that digital twins enable drug testing and arrhythmia prediction "at scale" without requiring hundreds of patch clamp experiments. Specifically, the authors show that in silico populations derived from two experimental cells (Figure 6C) predict dose-dependent EAD incidence for the IKr blocker E-4031 (Figure 6D), with ~3% of cells showing EADs at 50 nM.

However, this prediction is not validated experimentally. If I actually patch 20-30 real iPSC-CMs and apply 50 nM E-4031, will ~3% of them show EADs, as the model predicts? Without this validation, I think the drug testing framework is purely hypothetical. The model may be internally consistent (e.g., Cell 1's twin behaves differently from Cell 2's twin), but there is no evidence that these in silico populations reflect real biological variability in drug response. Please provide experimental validation that justifies the prediction by digital twins.

(4) Experimental validation and head-to-head comparison of optimized protocol.

The authors claim that their deep learning-optimized voltage clamp protocol (Figure 3, Figure 4A) is superior to conventional approaches, but they have not validated this experimentally by doing a head-to-head comparison. The manuscript does not compare the optimized protocol to any published voltage clamp designs. If the optimized protocol is genuinely easier to implement and more informative than existing approaches, this would be a major practical advance. But without side-by-side comparison, it is impossible to judge whether the optimization made a real difference.

Reviewer #3 (Public review):

Summary:

This work uses a convolutional neural network to optimize a voltage clamp protocol to identify features and parameters from human pluripotent stem cell-derived cardiomyocytes.

Yang et al. introduce an innovative experimental framework that integrates computational modeling and deep learning to generate a digital twin of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs).

Strengths:

The major strength is the methodology used to bridge in silico prediction of cell behavior and mechanistic insights from the experimental dataset.

The approach used in this study represents a significant step toward precision medicine by enabling in silico prediction of cellular behavior and mechanistic insight from experimental datasets. The study addresses an important and timely challenge in stem cell-based and personalized medicine, and the authors compellingly leverage state-of-the-art methods alongside strong expertise in computational modeling and cardiac electrophysiology

Weaknesses:

While the overall approach is highly compelling and the potential impact is substantial, there are two areas where clarification and refinement, particularly in the phrasing and framing used throughout the manuscript, would further strengthen the work.

(1) While the overall goal of the study is compelling, the manuscript would benefit from clearer articulation of how the proposed framework is intended to be used in practice. In particular, it is not entirely clear whether the authors envision this approach as:

a) a method to extract population-level trends that, when paired with biological data, enhance statistical power and interpretability, or

b) a strategy capable of constructing a population-based model from limited single-cell recordings. If the latter is intended, additional guidance on the number of action potentials required per cell and the assumptions underlying this extrapolation would greatly clarify the scope and applicability of the method.

(2) The manuscript would also benefit from a clearer explanation of how electrophysiological heterogeneity observed in hPSC-CMs is linked to inter-patient variability. Although the authors state that this framework can be generalized to compare patient-specific hiPSC-CM lines, it remains unclear how this generalization is achieved, given the substantial sources of variability intrinsic to hiPSC-CMs (e.g., batch effects, reprogramming strategy, differentiation protocol, and maturation state). As acknowledged by the authors, addressing this level of variability likely requires large datasets; further clarification of how the proposed approach mitigates or accommodates these challenges would strengthen the translational claims.

Below are my suggestions that could help strengthen the claims in the manuscript:

(1) Adding a dedicated section describing the electrophysiological phenotype of the hPSC-CMs used in this study would help justify the choice of the underlying ionic model and the selection of the six ion currents analyzed. These currents are not only developmentally regulated but may also vary substantially across different hPSC-CM lines, which has implications for generalizability.

(2) If feasible, inclusion of patch-clamp data from an additional hPSC-CM line would significantly strengthen the claim that this framework can harmonize and generalize across datasets and cell sources.

(3) The authors note that the experimental cells exhibited high variability in action potential morphology. This is an important observation that directly supports the motivation for the study and should be explicitly presented, even if only in the supplementary materials.

(4) In the hERG-blocker experiments, further clarification is needed regarding the biological relevance of the reported 3% incidence of early afterdepolarizations (EADs). Additionally, an interrupted sentence in this section makes it unclear whether the goal is to demonstrate that the digital twin can capture rare arrhythmic risk events or whether the digital twin is necessary to determine whether this level of risk is clinically meaningful.

(5) The manuscript states that some action potentials were excluded from the experimental dataset. A brief explanation of the exclusion criteria, along with guidance on how to distinguish high-quality from low-quality recordings, would improve transparency and reproducibility.

Reviewer #1 (Public review):

Summary:

Here, Mattenburger et al use structural biology, biochemistry, and genetics to analyze the membrane-attacking end (spike/spike tip) of the contractile injection systems of two DNA phages (P2 and T4). Understanding how a phage tail mediates host recognition and injects DNA into the host is an important question. This manuscript is divided into two stories. First is a biochemical fractionation showing that the fused spike-spike tip protein of P2 (GpV) is translocated into the host periplasm. Second is a somewhat separate story about the spike tip protein of T4 (gp5.4), which is structurally characterized and shown to aid in infection of E. coli with truncated lipopolysaccharides (LPS). I find the suggestion that gp5.4 aids in penetration of the bacterial envelope the most compelling portion of the manuscript, but I find this conclusion to be insufficiently supported, and the presentation could be described as awkward. Further, while the experiments are generally elegant, I believe additional experiments and a discussion to fully connect the two stories of the manuscript would increase impact.

Strengths:

The manuscript is methodologically careful and adds nuance to our understanding of P2 and T4 spike function. The T4 gp5.4 structure is extensively characterized, with crystallography and cryo-EM support. Many experiments are elegant and clever, specifically the P2 periplasmic fractionation and the ex vivo gp5.4 phage reconstitution. If completely supported and explained, the finding that gp5.4 aids in penetration of the bacterial envelope rather than adsorption is compelling.

Weaknesses:

The novelty of the work is somewhat incremental, as phage injection is known to occur into the periplasm and gp5.4 is known to be part of the spike tip (Taylor et al, 2016). The finding that gp5.4 promotes penetration and DNA delivery in strains with truncated LPS is incompletely supported. The gp5.4am phage plaquing data are incompletely explained, and may generate a more modest effect for gp5.4 than is claimed. The P2 results, although well-performed, do not directly support the T4 experiments given the evolutionary divergence between these two phages. Lastly, the overall organization of the manuscript and writing is lacking as (1) the P2 results are presented within the T4 data, (2) many figures are presented out of order, and (3) there is no discussion to contextualize the results for the reader.

Reviewer #2 (Public review):

Summary:

The manuscript provides a very high-resolution crystal structure of the bacteriophage T4 spike gp5-gp5.4 complex and clear evidence of the importance of gp5.4 for the fitness of the phage and its necessity for successful infection of strains of Escherichia coli with truncated lipopolysaccharide. Evidence, or at least speculation, as to what bacterial compounds gp5.4 interacts with would have been welcome.

Strong points:

(1) Very high resolution detailed crystal structure of the gp5-gp5.4 complex.

(2) First proof of the importance of gp5.4 for bacteriophage T4 and by extension, of homologous proteins in other phages.

Weaker points:

(1) Localisation experiments were performed not with protein 5.4 but the homologous gpV from bacteriophage P2.

(2) The exact mechanism was not yet resolved, i.e. to which bacterial component gp5.4 binds.

Reviewer #3 (Public review):

Summary:

The paper describes the structure of gp5.4, the spike tip of phage T4. This structure was released in the PBD in 2013. The paper further investigates the role of this protein in virion assembly, stability, and infection by comparing the behaviour of the WT phage and a phage without the protein, resulting from an amber mutation in the phage genome. A competition assay between the WT and mutant phage shows a clear increase in the fitness of the WT. A further screening of a transposon bank allowed for the identification of a host strain that is resistant to the mutant phage while still sensitive to the WT phage.

Strengths:

(1) Beautiful structure, at very high resolution (1.15 Å).

(2) Very sophisticated microbiology experiments to allow mutant phage characterisation and dissect the role of the spike tip in phage fitness.

Weaknesses:

(1) The paper is very descriptive, and the lack of a general conclusion, not to say discussion, is frustrating. What do the findings of the paper bring to the knowledge of infection? What would be the fate of the spike and tip? A discussion in the context of the data available in the literature would greatly increase the interest of the paper.

(2) Why didn't the authors include the description of the structure of the homologous Pvc10 and PhiKV gp5.4 in complex with gp5ß, which they also solved a while ago?

(3) Because microbiology is sophisticated, special care should be taken to introduce the strains used (both E. coli and T4). E.g. it is still not clear to me what the difference is between the supF and the supD coli strains in terms of mutant phage produced (both should produce T4(5.4am)-gp5.4?).

(4) For the same reason, strains should always be called by the same name.

(5) In some sections, the conclusion seems lost in the description of controls (e.g. in the "The spike is translocated into the periplasmic space during infection" paragraph).

Appraisal:

The authors show that the sharp tip of the membrane-perforating tube of T4 contractile tail contributes to perforating the outer membrane. In particular, this protein is necessary in a host bearing mutated LPS.

Joint Public Review:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public review):

Disclaimer:

This reviewer is not an expert on MD simulations but has a basic understanding of the findings reported and is well-versed with mycobacterial lipids.

Summary:

In this manuscript titled "Dynamic Architecture of Mycobacterial Outer Membranes Revealed by All-Atom 1 Simulations", Brown et al describe outcomes of all-atom simulation of a model outer membrane of mycobacteria. This compelling study provided three key insights:

(1) The likely conformation of the unusually long chain alpha-branched, beta-methoxy fatty acids-mycolic acids in the mycomembrane to be the extended U or Z type rather than the compacted W-type.

(2) Outer leaflet lipids such as PDIM and PAT provide regional vertical heterogeneity and disorder in the mycomembrane that is otherwise prevented in a mycolic acid only bilayer.

(3) Removal of specific lipid classes from the symmetric membrane systems lead to significant changes in membrane thickness and resilience to high temperatures. (4) The asymmetric mycomembrane presents a phase transition from a disordered outer leaflet to an ordered inner leaflet.

Strengths:

The authors take a stepwise approach to increasing the membrane's complexity and highlight the limitations of each approach. A case in point is the use of supraphysiological temperatures of 333 K or higher in some simulations. Overall, this is a very important piece of work for the mycobacterial field and will likely help develop membrane-disrupting small molecules and provide important insights into lipid-lipid interactions in the mycomembrane.

Weaknesses:

The authors used alpha-mycolic acids only for their models. The ratios of alpha-, keto-, and methoxy-mycolic acids are well documented in the literature, and it may be worth including them in their model. Future studies can aim to address changes in the dynamic behavior of the MOM by altering this ratio, but including all three forms in the current model will be important and may alter the other major findings of the current study.

Reviewer #2 (Public review):

Summary:

The manuscript reports all-atom molecular dynamics simulations on outer membrane of Mycobacterium tuberculosis. This is the first all-atom MD simulation of MTb outer membrane and complements the earlier studies which used coarse-grained simulation.

Strengths:

The simulation of outer membrane consisting of heterogeneous lipids is a challenging task and the current work is technically very sound.

The observation about membrane heterogeneity and ordered inner leaflets vs disordered outer leaflets is a novel result from the study. This work will also facilitate other groups to work on all atom models of mycobacterial outer membrane for drug transport etc.

Comments on revisions:

I would like to thank the authors for addressing all the concerns and providing additional details to improve the clarity of presentation.

Reviewer #1 (Public review):

Summary:

Gosselin et al., develop a method to target protein activity using synthetic single-domain nanobodies (sybodies). They screen a library of sybodies using ribosome/ phage display generated against bacillus Smc-ScpAB complex. Specifically, they use an ATP hydrolysis deficient mutant of SMC so as to identify sybodies that will potentially disrupt Smc-ScpAB activity. They next screen their library in vivo, using growth defects in rich media as a read-out for Smc activity perturbation. They identify 14 sybodies that mirror smc deletion phenotype including defective growth in fast-growth conditions, as well as chromosome segregation defects. The authors use a clever approach by making chimeras between bacillus and S. pnuemoniae Smc to narrow-down to specific regions within the bacillus Smc coiled-coil that are likely targets of the sybodies. Using ATPase assays, they find that the sybodies either impede DNA-stimulated ATP hydrolysis or hyperactivate ATP hydrolysis (even in the absence of DNA). The authors propose that the sybodies may likely be locking Smc-ScpAB in the "closed" or "open" state via interaction with the specific coiled-coil region on Smc. I have a few comments that the authors should consider:

Major comments:

(1) Lack of direct in vitro binding measurements:<br /> The authors do not provide measurements of sybody affinities, binding/ unbinding kinetics, stoichiometries with respect to Smc-ScpAB. Additionally, do the sybodies preferentially interact with Smc in ATP/ DNA-bound state? And do the sybodies affect the interaction of ScpAB with SMC?<br /> It is understandable that such measurements for 14 sybodies is challenging, and not essential for this study. Nonetheless, it is informative to have biochemical characterization of sybody interaction with the Smc-ScpAB complex for at least 1-2 candidate sybodies described here.

(2) Many modes of sybody binding to Smc are plausible<br /> The authors provide an elaborate discussion of sybodies locking the Smc-ScpAB complex in open/ closed states. However, in the absence of structural support, the mechanistic inferences may need to be tempered. For example, is it also not possible for the sybodies to bind the inner interface of the coiled-coil, resulting in steric hinderance to coiled-coil interactions. It is also possible that sybody interaction disrupts ScpAB interaction (as data ruling this possibility out has not been provided). Thus, other potential mechanisms would be worth considering/ discussing. In this direction, did AlphaFold reveal any potential insights into putative binding locations?

(3) Sybody expression in vivo<br /> Have the authors estimated sybody expression in vivo? Are they all expressed to similar levels?

(4) Sybodies should phenocopy ATP hydrolysis mutant of Smc<br /> The sybodies were screened against an ATP hydrolysis deficient mutant of Smc, with the rationale that these sybodies would interfere this step of the Smc duty cycle. Does the expression of the sybodies in vivo phenocopy the ATP hydrolysis deficient mutant of Smc? Could the authors consider any phenotypic read-outs that can indicate whether the sybody action results in an smc-null effect or specifically an ATP hydrolysis deficient effect?

Significance:

Overall, this is an impressive study that uses an elegant strategy to find inhibitors of protein activity in vivo. The manuscript is clearly written and the experiments are logical and well-designed. The findings from the study will be significant to the broad field of genome biology, synthetic biology and also SMC biology. Specifically, the coiled coil domain of SMC proteins have been proposed to be of high functional value. The authors have elegantly identified key coiled-coil regions that may be important for function, and parallelly exhibited potential of the use of synthetic sybody/designed binders for inhibition of protein activity.

Reviewer #2 (Public review):

Summary:

Structural Maintenance of Chromosome proteins (SMCs), a family of proteins found in almost all organisms, are organizers of DNA. They accomplish this by a process known as loop extrusion, wherein double-stranded DNA is actively reeled in and extruded into loops. Although SMCs are known to have several DNA binding regions, the exact mechanism by which they facilitate loop extrusion is not understood but is believed to entail large conformational changes. There are currently several models for loop extrusion, including one wherein the coiled coil (CC) arms open, but there is a lack of insightful experimentation and analysis to confirm any of these models. The work presented aims to provide much-needed new tools to investigate these questions: conformation-selective sybodies (synthetic nanobodies) that are likely to alter the CC opening and closing reactions.

The authors produced, isolated, and expressed sybodies that specifically bound to Bacillus subtilis Smc-ScpAB. Using chimeric Smc constructs, where the coiled coils were partly replaced with the corresponding sequences from Streptococcus pneumoniae, the authors revealed that the isolated sybodies all targeted the same 4N CC element of the Smc arms. This region is likely disrupted by the sybodies either by stopping the arms from opening (correctly) or forcing them to stay open (enough). Disrupting these functional elements is suggested to cause the Smc-dependent chromosome organization lethal phenotype, implying that arm opening and closing is a key regulatory feature of bacterial Smc-ScpAB.

Significance:

The authors present a new method for trapping bacterial Smc's in certain conformations using synthetic antibodies. Using these antibodies, they have pinpointed the (previously suggested) 4N region of the coiled coils as an essential site for the opening and closing of the Smc coiled coil arms and that hindering these reactions blocks Smc-driven chromosomal organization. The work has important implications for how we might elucidate the mechanism of DNA loop extrusion by SMC complexes.

Reviewer #3 (Public review):

Summary:

Gosselin et al. use the sybody technology to study effects of in vivo inhibition of the Bacillus subtilis SMC complex. Smc proteins are central DNA binding elements of several complexes that are vital for chromosome dynamics in almost all organisms. Sybodies are selected from three different libraries of the single domain antibodies, using the "transition state" mutant Smc. They identify 14 such mutant sybodies that are lethal when expressed in vivo, because they prevent proper function of Smc. The authors present evidence suggesting that all obtained sybodies bind to a coiled-coil region close to the Smc "neck", and thereby interfere with the Smc activity cycle, as evidenced by defective ATPase activity when Smc is bound to DNA.<br /> The study is well done and presented and shows that the strategy is very potent in finding a means to quickly turn off a protein's function in vivo, much quicker than depleting the protein.

The authors also draw conclusions on the molecular mode of action of the SMC complex. The provide a number of suggestive experiments, but in my view mostly indirect evidence for such mechanism.

My main criticism is that the authors have used a single - and catalytically trapped form of SMC. They speculate why they only obtain sybodies from one library, and then only identify sybodies that bind to a rather small part of the large Smc protein. While the approach is definitely valuable, it is biassed towards sybodies that bind to Smc in a quite special way, it seems. Using wild type Smc would be interesting, to make more robust statements about the action of sybodies potentially binding to different parts of Smc.

Line 105: Alternatively, the other libraries did not produce good binders or these sybodies were 106 not stably expressed in B. subtilis. This could be tested using Western blotting - I am assuming sybody antibodies are commercially available. However, this test is not important for the overall study, it would just clarify a minor point.

Fig. 2B: is odd to count Spo0J foci per cells, as it is clear from the images that several origins must be present within the fluorescent foci. I am fine with the "counting" method, as the images show there is a clear segregation defect when sybodies are expressed, I believe the authors should state, though, that this is not a replication block, but failure to segregate origins.

Testing binding sites of sybodies to the SMC complex is done in an indirect manner, by using chimeric Smc constructs. I am surprised why the authors have not used in vitro crosslinking: the authors can purify Smc, and mass spectrometry analyses would identify sites where sybodies are crosslinked to Smc. Again, I am fine with the indirect method, but the authors make quite concrete statements on binding based on non-inhibition of chimeric Smc; I can see alternative explanations why a chimera may not be targeted.

Smc-disrupting sybodies affect the ATPase activity in one of two ways. Again, rather indirect experiments. This leads to the point Revealing Smc arm dynamics through synthetic binders in the discussion. The authors are quite careful in stating that their experiments are suggestive for a certain mode of action of Smc, which is warranted.

In line 245, they state More broadly, the study demonstrates how synthetic binders can trap, stabilize, or block transient conformations of active chromatin-associated machines, providing a powerful means to probe their mechanisms in living cells. This is off course a possible scenario for the use of sybodies, but the study does not really trap Smc in a transient conformation, at least this is not clearly shown.

Overall, it is an interesting study, with a well-presented novel technology, and a limited gain of knowledge on SMC proteins.

Significance:

The work describes the gaining and use of single-binder antibodies (sybodies) to interfere with the function of proteins in bacteria. Using this technology for the SMC complex, the authors demonstrate that they can obtain a significant of binders that target a defined region is SMC and thereby interfere with the ATPase cycle.

The study does not present a strong gain of knowledge of the mode of action of the SMC complex.

Reviewer #1 (Public review):

Summary:

Tkacik et al describe their efforts to reconstitute and biochemically characterize ARAF, BRAF, and CRAF proteins and measure their ability to be paradoxically activated by current clinical and preclinical RAF inhibitors. Paradoxical activation of MAPK signaling is a major clinical problem plaguing current RAF inhibitors, and the mechanisms are complex and relatively poorly understood. The authors utilize their preparations of purified ARAF, BRAF, and CRAF kinase domains to measure paradoxical activation by type I and type II inhibitors, utilizing MEK protein as the substrate, and show that CRAF is activated in a similar fashion to BRAF, whereas ARAF appears resistant to activation. These data are analyzed using a simple cooperativity model with the goal of testing whether paradoxical activation involves negative cooperativity between RAF dimer binding sites, as has been previously reported. The authors conclude that it does not. They also test activation of B- and CRAF isoforms prepared in their full-length autoinhibited states and show that under the conditions of their assays, activation by inhibitors is not observed. In a particularly noteworthy part of the paper, the authors show that mutation of the N-terminal acidic (NtA) motif of ARAF and CRAF to match that of BRAF enhances paradoxical activation of CRAF and dramatically restores paradoxical activation of ARAF, which is not activated at all in its WT form, indicating a clear role for the NtA motif in the paradoxical activation mechanism. Additional experiments use mass photometry to measure BRAF dimer induction by inhibitors. The mass photometry measurements are a relatively novel way of achieving this, and the results are qualitatively consistent with previous studies that tracked BRAF dimerization in response to inhibitors using other methods. Overall, the paper establishes that WT CRAF is paradoxically activated by the same inhibitors that activate BRAF, and that ARAF contains the latent potential for activation that appears to be controlled by its NtA motif. The biochemical activation data for BRAF are qualitatively consistent with previous work.

Strengths:

While previous studies have put forward detailed molecular mechanisms for paradoxical activation of BRAF, comparatively little is known about the degree to which ARAF and CRAF are prone to this problem, and relatively little biochemical data of any sort are available for ARAF. Seen in this light, the current work should be considered of substantial potential significance for the RAF signaling field and for efforts to understand paradoxical activation and design new inhibitors that avoid it.

Weaknesses:

There are, unfortunately, some significant flaws in the data analysis and fitting of the RAF activation data that render the primary conclusion of the paper about the detailed activation mechanism, namely that it does not involve negative cooperativity between active sites, unjustified. This claim is made repeatedly throughout the manuscript, including in the title. Unfortunately, their data analysis approach is overly simplistic and does not probe this question thoroughly. This is the primary weakness of the study and should be addressed. A full biochemical modeling approach that accurately captures what is happening in the experiment needs to be applied in order for detailed inferences to be drawn about the mechanism beyond just the observation of activation.

The authors' analysis of their RAF:MEK "monomer" paradoxical activation data (Figures 1, 3, and Tables 1, 2) suffers from two fundamental flaws that render the resulting AC50/IC50 and cooperativity (Hill) parameters essentially uninterpretable. Without explaining or justifying their choice, the authors use a two-phase cooperative binding model from GraphPad Prism to fit their activation/inhibition data. This model is intended to describe cooperative ligand binding to multiple coupled sites within a preformed receptor assembly, and does not provide an adequate description of what is happening in this complicated experiment. Specifically, it has two fundamental flaws when applied to the analysis in question:

(a) It does not account for ligand depletion effects that occur with high-affinity drugs, and that profoundly affect the shapes of the dose-response curves, which are what are being fit

The chosen model is one of a class of ligand-binding models that are derived by assuming that the free ligand concentration is effectively equal to the total ligand concentration. Under these conditions, binding curves have a characteristic steepness, and the presence of cooperativity can be inferred from changes in this steepness as described by a Hill coefficient. However, many RAF inhibitors, including most of the type II inhibitors in this study, bind to the dimerized forms of at least one of the RAF isoforms with ultra-high affinity in the picomolar range (particularly apparent in Figure 1 with LY inhibiting BRAF). Under these conditions, the model assumption is not valid. Instead, binding occurs in the high-affinity regime in which the drug titrates the receptor and effectively all the added drug molecules bind, so there is hardly any free ligand (see e.g. Jarmoskaite and Herschlag eLife 2020 for a full description of this "titration" regime). The shapes of the curves under these conditions reflect the total amount of RAF protein (and to some extent drug affinity), rather than the presence of cooperativity. Fitting dose response curves with the chosen model under these conditions will result in conflating binding affinity and protein concentration with cooperativity.

(b) It does not model the RAF monomer-dimer equilibrium, which is dramatically modulated by drug binding, rendering the results RAF-concentration dependent in a manner not accounted for by the analysis.

The chosen analysis model also fails to consider the monomer-dimer equilibrium of RAF. This has two ramifications. Since drug binding is coupled to dimerization to a very strong degree, the observed apparent affinities of drug binding (reflected in AC50 and IC50 values) are functions of the concentration of RAF molecules used in the experiment. Since dimerization affinities are likely different for ARAF, BRAF, and CRAF, the measured AC50 values also cannot be compared between isoforms. This concentration dependence is not addressed by the authors. A related issue is that the model assumes drug binding occurs to two coupled sites on preformed dimers, not to a mixture of monomers and dimers. "Cooperativity" parameters determined in this manner will reflect the shifting monomer-dimer equilibrium rather than the cooperativity within dimers. Additionally, the inhibition side of the activation/inhibition curves is driven by binding of the drug to the single remaining site on the dimer, not to two coupled sites, and so one cannot determine cooperativity values for this process in this manner.

As a result of both of these issues, the parameters reported in the tables do not correctly reflect cooperativity and cannot be used to infer the presence or absence of negative cooperativity between RAF dimer subunits. To address these major issues, the authors would need to apply a data analysis/fitting procedure that correctly models the biochemical interactions occurring in the sample, including both the monomer-dimer equilibrium and how this equilibrium is coupled to drug binding, such as that developed in e.g., Kholodenko Cell Reports 2015. Alternatively, the authors should remove the statements claiming a lack of negative cooperativity from the manuscript and alter the title to reflect this.

Some other points to consider

(1) The observation that ARAF is not activated by type II inhibitors is interesting. A detailed comparison of the activation magnitudes between inhibitors and between A-, B-, and CRAF is hampered by the arbitrary baseline signal in the assay, which arises from a non-zero FRET ratio in the absence of any RAF activity. The authors might consider background correcting their data using a calibration curve constructed using MEK samples of known degrees of phosphorylation, so that they can calculate turnover numbers and fold activation values rather than an increase over baseline. This will likely reveal that the activation effects are more substantial than they appear against the high background signal.

(2) The authors note that full-length autoinhibited 14-3-3-bound RAF monomers are not activated by type I and II inhibitors. However, since this process involves the formation of a RAF dimer from two monomers, the process would also be expected to be concentration dependent, and the authors have only investigated this at a single protein concentration. Since disassembly of the autoinhibited state must also occur before dimerization, it might be expected to be kinetically disfavored as well. Have the authors tested this?

(3) ATP concentration modulates activation. While this is an interesting observation, some of this analysis suffers from the same issue discussed above, of not considering high-affinity binding effects. For instance, LY is not affected by ATP concentration in their data (Figure 4D), but this is easily explained as being due to its very tight binding affinity, resulting in titration of the receptor and the shape of the inhibition curve reflecting the amount of RAF kinase in the experiment and not the effective Kd or IC50 value.

Reviewer #2 (Public review):

This manuscript by Tkacik et al. uses in vitro reconstituted systems to examine paradoxical activation across RAF isoforms and inhibitor classes. The authors conclude that paradoxical activation can be explained without invoking negative allostery and propose a general model in which ATP displacement from an "open monomer" promotes dimerization and activation. The biochemical work is technically sound, and the systematic comparison across RAF paralogs (along with mutational/functional analysis) across inhibitor classes is a strength.

However, the central mechanistic conclusions are overgeneralized relative to the experimental systems, and several key claims, particularly the dismissal of negative allostery and the proposed unifying model in Figure 6, are not directly supported by the data presented. Most importantly, the absence of RAS, membranes, and relevant regulatory context fundamentally limits the physiological relevance of several conclusions, especially regarding the current clinical type I.5 RAF inhibitors and paradoxical activation.

Overall, this is a potentially valuable biochemical study, but the manuscript would benefit from more restrained interpretation, clearer framing of scope, and revisions to the model and title to better reflect what is actually tested.

(1) A central issue is that the biochemical system lacks RAS, membranes, 14-3-3 and endogenous regulatory factors that are known to be required for paradoxical RAF and MAPK activation in cells. As previous work has repeatedly shown and the authors also acknowledge, paradoxical activation by RAF inhibitors is RAS-dependent in cells, and this dependence presumably explains why full-length autoinhibited RAF complexes are refractory to activation in the authors' assays.

Importantly, the absence of paradoxical activation by type I.5 inhibitors in this system is therefore not mechanistically informative. Type I.5 inhibitors (e.g., vemurafenib, dabrafenib, encorafenib), but not Paradox Breakers (e.g., plixorafenib), robustly induce paradoxical activation in cells because binding of the inhibitor to inactive cytosolic RAF monomer promotes a conformational change that drives RAF recruitment to RAS in the membrane, promoting dimerization. The inability of the type 1.5 inhibitor to suppress the newly formed dimers is the basis of the pronounced paradoxical activation in cells. In the absence of RAS and membrane recruitment, failure to observe paradoxical activation in vitro does not distinguish between competing mechanistic models.

As a result, conclusions regarding inhibitor class differences, and especially the generality of the proposed model, should be substantially tempered.

(2) The authors argue that their data argue against negative allostery as a central feature of paradoxical activation. However, the presented data do not directly test negative allostery, nor do they exclude it. The biochemical assays do not recreate the cellular context in which negative allostery has been inferred. Further, structural data showing asymmetric inhibitor occupancy in RAF dimers cannot be dismissed on the basis of alternative symmetric structures alone, particularly given the dynamic nature of RAF dimers in cells.

Most importantly, negative allostery was proposed to explain paradoxical activation by Type I.5 RAF inhibitors, yet these inhibitors do not paradoxically activate in the assays presented here. The absence of paradoxical activation in this system, therefore, cannot be used to argue against a mechanism that is specifically invoked to explain cellular behavior not recapitulated by the assay.

(3) The model presented in Figure 6 is conceptually possible but remains speculative. Key elements of the model, including RAS engagement, membrane recruitment, 14-3-3 rearrangements, and the involvement of cellular kinases and phosphatases, are explicitly absent from the experimental system. Accordingly, the model is not tested by the data presented and should not be framed as a validated or general mechanism. The figure and accompanying text should be clearly labeled as a working or conceptual model rather than a mechanistically supported conclusion.

(4) The manuscript states that type I.5 inhibitors do not induce paradoxical activation in the biochemical assay because their C-helix-out binding mode disfavors dimerization. While this is true in isolation, it overlooks the well-established fact that type I.5 inhibitors (with the exception of paradox breakers) clearly promote RAS-dependent RAF dimerization in cells. This distinction is critical and should be explicitly acknowledged when interpreting the in vitro findings.

(5) The title suggests a general mechanism for paradoxical activation across RAF isoforms and inhibitor classes, whereas the data primarily address type I and type II inhibitors acting on isolated kinase-domain monomers. A more accurate framing would avoid the term "general" and confine the conclusions to C-helix-in (type I/II) RAF inhibitors in a reduced biochemical context.

Reviewer #3 (Public review):

Summary:

Tkacik et al. systematically characterized all three RAF kinase isoforms in vitro with all three types of RAF inhibitors (Type I, I1/2, and II) to investigate the mechanism underlying paradoxical activation.

In this study, the authors reconstituted heterodimers of A-, B-, and C-RAF kinase domains bound to non-phosphorylable MEK1 (SASA), mimicking the monomeric auto-inhibited state of RAF. These "RAF monomers" were tested for MEK phosphorylation with an increasing concentration of all three types of RAF inhibitors (Type I, I1/2, and II). This study is reminiscent of a previous study of the same team measuring RAF kinase activity in the presence of all three types of inhibitors in the context of dimeric RAF isoforms stabilized by 14-3-3 proteins (Tkacik et al 2025 JBC). RAF monomers had little to no activity at low concentrations of inhibitors (consistent with their "monomeric state"). Addition of type I1/2 inhibitor did not induce paradoxical activation as, in this context, they do not induce RAF dimerization required for activation, as observed by MP. Addition of type I and type II inhibitors led to paradoxical activation consistent with the RAF dimerization induced by these inhibitors, as observed by MP. Interestingly, type II inhibitors induced activation only for B- and C-RAF and not A-RAF.

At high concentrations of type II inhibitors, kinase activity is inhibited with a strong or weak positive cooperativity for BRAF and CRAF, respectively. This observation is very similar to what the authors previously observed with their dimeric RAF system. Interestingly, when the NtA motif is modified by phosphomimetic mutations in A- and C-Raf, basal kinase activity is stronger, but most importantly, inhibitor-induced paradoxical activation is much stronger with both type I and II inhibitors. This demonstrates that mutation of the NtA motif of ARAF and CRAF sensitized them to paradoxical activation by type II inhibitors.

The authors also tested the effect of ATP in the paradoxical activation observed in their RAF "monomer" system. As previously published in their assay with 14-3-3 stabilized dimeric RAF, the authors observed an expected shift of the IC50 with Type I inhibitors, while Type II inhibitors seem to behave as a non-competitive inhibitor. The authors next reconstituted the MAP kinase pathway (with RAF monomers at the top of the phosphorylation cascade) to test paradoxical activation amplification. Again, Type I1/2 inhibitors did not induce paradoxical activation, while Type I and II inhibitors did. The authors tested the inhibitors with FL auto-inhibited RAF/MEK/14-3-3 complexes, where, contrary to the "RAF monomers" experiments, FL B- and C-RAF were not paradoxically activated but were inhibited by all three types of inhibitors.

Overall, Tkacik et al. tackle an important question in the field for which definitive experiments and thorough biochemical investigation to understand the molecular mechanisms for the inhibitor-induced paradoxical activation are still missing, and of high importance for future drug development.

Strengths:

The biochemical experiments here are rigorously executed, and the results obtained are highly informative in the field to decipher the intricate mechanisms of RAF activation and inhibitor-induced paradoxical activation.

Weaknesses:

The interpretation of the results in the context of the current state of the art is ambiguous and raises questions about the relevance of introducing a new model for inhibitor-induced paradoxical activation, particularly since the findings presented here do not clearly contradict established paradigms. I believe some clarification and precision are required.

Main comments:

(1) Figure 2:

The authors comment on the expected greater increase (for a cascade assay) in the magnitude of ERK phosphorylation compared to what was observed for MEK phosphorylation. However, this observation might be reflective of the stoichiometries used in the assay, with 40 times more MEK compared to RAF concentration (250nm vs 6nM), which might favour pERK vs pMEK.

- The authors should clarify their rationale for the protein concentration used in this assay and explain how protein stoichiometry was taken into account for the interpretation of their results.

- In addition, the authors should justify comparing pMEK and pERK TR-FRET values when different anti-phospho antibodies were used. Antibodies may have distinct binding affinities for their epitopes. Could this not lead to differences in FRET signal amplitudes that complicate direct comparison?

(2) Supplementary Figure 2:

The author mentioned that the inhibitors did not activate the FL auto-inhibited RAF complexes; however, they did inhibit the TR-FRET signal.

- Can the authors comment on the origin of the observed basal activity? Would the authors expect self-release of the RAF kinase protein from the auto-inhibited state in the absence of RAS, leading to dimerization and activation? Alternatively, do the inhibitors at low-concentration relieve the auto-inhibited state, thereby driving dimerization and activation?

- Did the author test the addition of RAS protein in their in vitro system to determine whether "soluble" RAS is sufficient to release the protective interactions with RBD/CRD/14-3-3 and lead to inhibitor-induced paradoxical activation of FL RAF?

(3) Figure 5B:

The authors said that the Kd values obtained from their MP assay are consistent with prior studies of RAF homodimerization and RAF:MEK heterodimerization. While this is true from the previous studies of RAF:MEK interaction by BLI (performed from the same team), the Kd of isolated RAF kinase homodimerization has been measured around ~30µM by AUC in the cited ref (24,27 & 37).

- The authors should discuss the discrepancy between their Kd of homodimerization and the reported Kd values in the literature. At the concentration used for MP, it is surprising to observe RAF dimerization while the Kd of homodimerization has been measured at ~30µM (in the absence of MEK).

- Would the authors expect the presence of MEK to influence the homodimerization affinity for the isolated KD?

(4) Conclusions:

Several times in the introduction and the conclusion, the authors suggest that the negative allostery model (where "inhibitor binding to one protomer of the dimer promotes an active but inhibitor-resistant conformation in the other") is a model that applies to all types of RAF inhibitors (I, I1/2, and II).

However, from my understanding and all the references cited by the authors, this model only applies to type I1/2 inhibitors, where indeed the aC IN conformation in the second (inhibitor-free) protomer of the RAF dimer might be incompatible with the type I1/2 inhibitors inducing aC OUT conformation. The type I and type II inhibitors are aC IN inhibitors and are expected to bind both protomers from RAF dimers with similar affinities. Therefore, the negative allostery model does not apply to the type I and type II inhibitors. The difference in the mechanism of action of inhibitors is even used to explain the difference in the concentration range in which inhibitor-induced activation is observed in cells. The description of the state of the art in this study is confusing and does not help to properly understand their argumentation to revise the established model for paradoxical RAF activation.

- Can the authors clarify their analysis of the state of the art on the different mechanisms of action for the paradoxical activation of RAF by the different types of RAF inhibitors?

5) Conclusions:

"Our results suggest that negative allostery (or negative cooperativity) is not a requisite feature of paradoxical activation. The type I and type II inhibitors studied here induce RAF dimers and exhibit paradoxical activation but do so without evidence of negative cooperativity, nor do they appear to inhibit intentionally engineered RAF dimers with negative cooperativity (25). Indeed, type II inhibitors exhibit apparent positive cooperativity while type I inhibitors are non-cooperative inhibitors of RAF dimers (25)."

- Can the authors explain how results on the paradoxical activation induced by type I and type II inhibitors inform or challenge a model that specifically applies to type I1/2 inhibitors?

The authors often refer to their previous study (reference 25), where they tested the inhibition of all three types of inhibitors with engineered RAF dimers. While I agree with the authors that in reference 25 the Type I and type II inhibitors inhibit RAF dimers without exhibiting negative cooperativity (as expected from the literature and the current model), the authors did observe some negative cooperativity for Type I1/2 inhibitors in their study most particularly for the type I1/2 PB (with hill slope ranging from -0.4 to -0.9, indicative of negative cooperativity).<br /> While the observations that type II inhibitors display positive cooperativity is both novel and very interesting, from what I understand the results from thakick et al 2025 and the current study appear more in line with the current paradigm in the field (which describe paradoxical activation with negative cooperativity for type I1/2 inhibitors and no negative cooperativity for the Type I and II inhibitors) rather than disapproving of the current model and supporting for a new model.

- In this context, can the authors clarify how their results challenge the current model for paradoxical activation?

(6) Conclusions:

The authors describe the JAB34 experiment from Poulikakos et al. 2010 to conclude that "While this experiment cleanly demonstrates inhibitor-induced transactivation of RAF dimers, it is important to recognize that the differential inhibitor sensitivity of the two subunits in this experiment is artificial - it is engineered rather than induced by inhibitor binding as the negative allostery model proposes."

Indeed, the JAB34 experiment demonstrated the inhibitor-induced transactivation, but the Poulikakos et al. 2010 study does not discuss differential inhibitor sensitivity. The negative allostery model was proposed later by poulikakos team in other papers (Yao et al 2015 and Karoulia et al, 2016), in which JAB34 was not used.

- Can the authors clarify how the JAB34 experiments question differential inhibitor sensitivity?

(7) Conclusions:

"Considering that the conformation required for binding of type I.5 inhibitors destabilizes RAF dimers, it is unclear how an inhibitor binding to one protomer would be able to transmit an allosteric change to the opposite protomer, if that inhibitor's binding causes the existing dimer to dissociate."

- The authors should comment on whether 14-3-3 proteins might overcome negative regulation by type I1/2 inhibitors, similar to what has been shown for ATP, which acts as a dimer breaker like type I1/2 inhibitors.

(8) Conclusions:

"Furthermore, the complex effects of type I.5 inhibitors on dimer stability and the clear resistance of active RAF dimers to these inhibitors complicates interpretation of inhibition data - weak or incomplete inhibition of an enzyme can be difficult to discern from true negative cooperativity (43). As we discuss below, the clear resistance of RAF dimers to type I.5 inhibitors is alone sufficient to explain their ineffective inhibition during paradoxical activation, without invoking negative allostery."

- The authors should explain how they reconcile this statement and their proposal of a new model that does not rely on negative allostery with their previous findings showing negative cooperativity for RAF dimer inhibition with type I1/2 inhibitors.

(9) Conclusions:

Here, the authors propose a new universal model to explain paradoxical activation of RAF by all types of RAF inhibitors:<br /> " Our findings here, in light of structural studies of RAF complexes and prior cellular investigations of paradoxical activation, lead us to a model for paradoxical activation that does not rely on negative allostery and is consistent with activation by diverse inhibitor classes. In this model, the open monomer complex is the target of inhibitor-induced paradoxical activation (Figure 6). Binding of ATP to the RAF active site stabilizes the inactive conformation of the open monomer, which disfavors dimerization. Displacement of ATP by an ATP-competitive inhibitor, irrespective of class, alters the relative N- and C-lobe orientations of the kinase to promote dimerization (30, 35). Once dimerized, inhibitor dissociation from one or both sides of the dimer would allow phosphorylation and activation of MEK."

From my understanding, the novelty of this new model is twofold: a) the open monomer is the target of the inhibitor-induced paradoxical activation and b) once dimerized, inhibitor dissociation from one or both sides of the dimer would allow phosphorylation and activation of MEK.

Novelty a) implies, as the authors stated, that "Inhibitor-induced activation and inhibition act on distinct species - activation on the open monomer and inhibition on the 14-3-3-stabilized dimer". The authors should explain what they mean by "activation of the open monomer", while only RAF dimers are catalytically active (except for BRAF V600E mutant)?

For novelty b), the authors should explain more clearly what experimental results support this new model.

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, shown below.]

In this study, the noncanonical amino acid acridon-2-ylalanine (Acd) was inserted at various positions within the human Hv1 protein using a genetic code expansion approach. The purified mutants with incorporated fluorophore were shown to be functional using a proton flux assay in proteoliposomes. FRET between native tryptophan and tyrosine residues and Acd were quantified using spectral FRET analysis. Predicted FRET efficiencies calculated from an AlphaFold model of the Hv1 dimer were compared to the corresponding experimental values. Spectral FRET analysis was also used to test whether structural rearrangements caused by Zn2+, a well-known Hv1 inhibitor, could be detected. The experimental data provide a good validation of the approach, but further expansion of the analysis will be necessary to differentiate between intra- and intersubunit structural features.

Interestingly, the observed rearrangements induced by Zn2+ were not limited to the protein region proximal to the extracellular binding site but extended to the intracellular side of the channel. This finding agrees with previous studies showing that some extracellular Hv1 inhibitors, such as Zn2+ or AGAP/W38F, can cause long-range structural changes propagating to the intracellular vestibule of the channel (De La Rosa et al. J. Gen. Physiol. 2018, and Tang et al. Brit J. Pharm 2020). The authors should consider adding these references.

Since one of the main goals of this work was to validate Acd incorporation and the spectral FRET analysis approach to detect conformational changes in hHv1 in preparation for future studies, the authors should consider removing one subunit from their dimer model, recalculating FRET efficiencies for the monomer, and comparing the predicted values to the experimental FRET data. This comparison could support the idea that the reported FRET measurements can inform not only on intrasubunit structural features but also on subunit organization.

Reviewer #2 (Public review):

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

This manuscript addresses the temporal patterns in how cholinergic signaling to the gut affects the lifespan of the worm C. elegans, which should make the manuscript of wide interest to those who study aging, as well as those who study the brain-gut axis in health and disease. The authors show that early acetylcholine (ACh) signaling to the intestine via the ACR-6 receptor shortens worm lifespan, which depends on the DAF-16/FOXO transcription factor. However, later ACh signaling to the intestine via the GAR-3 receptor extends lifespan, which in turn depends on the heat shock factor HSF-1. The authors also show a potential mechanism through which these two temporal patterns of ACh signaling might be coordinated to influence longevity in the worm, and possibly in other animals.

Strengths:

The authors observed that the functional ablation of acr-2-expressing cholinergic neurons in C. elegans (Pacr-2::TeTx) produced a lifespan curve that intersects the lifespan curve of a wild-type population. The first quartile of Pacr-2::TeTx worms shows a longer lifespan than the first quartile of wild-type worms, whereas the last quartile of Pacr-2::TeTx worms exhibits a shorter lifespan than wild type. These observations raised the hypothesis that cholinergic neurons have two opposing effects on longevity: an early longevity-inhibiting effect and a later longevity-promoting effect. Much of the data support the authors' conclusions.

The authors have also addressed the points raised in the previous review.

Reviewer #3 (Public review):

I very much enjoyed reading Lingxiu Xu et al.'s paper "Temporally controlled nervous system-to-gut signaling bidirectionally regulates longevity in C. elegans," where they investigate the mechanisms by which motor neurons regulate lifespan in C. elegans worms. In this paper, they first discover that interfering with synaptic release in cholinergic motor neurons affects lifespan. Using mutants and gene knockdowns they show that these effects are due to the neurotransmitter acetylcholine. They show that the effects these motor neurons on lifespan are opposite, depending on timed genetic interventions promoting synaptic release. If these interventions occur during development, lifespan is shortened, but if they occur starting on day 7 of adulthood, then lifespan is lengthened. They then show that the transcription factor daf-16 is required for the former effect, while the transcription factor hsf-1 is required for the latter one. In addition, these early and late effects, they find, required the acetylcholine receptors acr-6 and gar-3, respectively, and intestinal expression of these genes rescues the respective phenotypes. Interestingly, tagging the endogenous acr-6 and gar-3 genes with mCherry, they find that the ACR-6 and GAR-3 proteins are expressed in the intestine, ACR-6 during development and GAR-3 during adulthood. Based on these findings they propose a model where acetylcholine from motor neurons regulates lifespan by modulating different receptors expressed at different times. These receptors, in turn, affect lifespan in opposing ways via different transcription factors.

Comments on revisions:

I am grateful to the authors for their effort to address my comments and suggestions, and for the thoughtful discussion of their efforts to strengthen the claims supporting their model.

Reviewer #4 (Public review):

This is a very interesting study, where the authors discovered two neuroendocrine signaling circuits with opposite effects on organismal longevity elicited by motor neurons at different ages.

Interestingly, both systems employ the same neurotransmitter (that is, acetylcholine) and signal the intestine. However, one has effects on early life to shorten lifespan whereas the other system is activated in mid-life to extend lifespan. At the mechanistic level, this bidirectional regulation is possible through the recruitment of two different ACh receptors in the gut: ACR-6 and GAR-3. The authors found that ACR-6 expression in the intestine is restricted to early life, whereas GAR-3 expression in the gut is confined to mid-late life. Interestingly, ACR-6 modulates the transcription factor DAF-16, but GAR-3 regulates HSF-1.

The study combines different approaches, including inducible systems (AID) which are critical for the conclusions of the paper. The conclusions are well supported by the experiments and results. The data provide a potential mechanism for the temporal control of lifespan and shed light on the complex role of the nervous system in organismal aging. These results can have important implications to understand how organismal aging is regulated in a temporal manner by cell non-autonomous mechanisms.

The paper has significantly improved after addressing all the Reviewers' comments and I did not observe significant weaknesses in the study.

Reviewer #1 (Public review):

This manuscript presents a comprehensive and technically impressive study investigating the interplay between active (H3K4me1) and silencing (H3K27me3) chromatin states and gene expression during early zebrafish development. By applying an optimized single-cell multi-omics method (whole-organism T-ChIC) to profile histone modifications and transcriptomes simultaneously in thousands of cells from 4 to 24 hours post-fertilization, the work addresses a significant gap in understanding how epigenetic states are established and propagated during vertebrate embryogenesis.

There are several obvious strengths:

(1) Innovative Methodology: The adaptation and application of the T-ChIC protocol to a whole-organism, multiplexed time-course design is a major technical achievement. The generation of a high-quality, paired chromatin (H3K27me3 and H3K4me1) and full-length transcriptome dataset from the same single cells is a powerful resource for the field.

(2) Novel Biological Insights:

(2.1) It provides single-cell evidence for the promoter-anchored cis-spreading of H3K27me3 as a mechanism for gene silencing during differentiation, a process that appears largely lineage-agnostic.

(2.2) It demonstrates that global chromatin states (both active and repressive) are initially decoupled from transcriptional output in pluripotent cells and become correlated as cells mature, suggesting this coupling is a hallmark of identity formation.

(2.3) It develops a predictive model using TF expression and the H3K4me1 state at TF binding sites to infer lineage-specific activator/repressor functions and epigenetic regulation of TFs themselves, revealing novel roles for factors like zbtb16a and zeb1a.

There are also several weaknesses for further clarification:

(1) The study focuses on H3K27me3 and H3K4me1. Why these two specific histone modifications were chosen as the primary focus for this study on early fate commitment?

(2) There are some similar single-cell techniques available (histone modifications and transcription from the same single cell), what is the performance of T-ChIC when comparing to other methods？

Comments on revised version:

Other histone modifications and TFs, or even DNA methylation could be tested to see the robustness of T-ChIC.

Reviewer #2 (Public review):

Summary:

Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and the full-length transcriptome and applied the method to early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields.

Strengths:

This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish.

Weaknesses:

The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited.

Overall, the T-ChIC method is efficient and user-friendly, and the single-cell datasets for zebrafish early development are also valuable. Audiences in the field of epigenomic and embryonic development will benefit from this work.

Comments on revised version:

The authors have answered my previous concerns.

Reviewer #1 (Public review):

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):

Summary:

The authors aim to engineer a synthetic system for manipulating ATP homeostasis in budding yeast by expressing the microsporidian nucleotide transporter NTT1, thereby enabling ATP import from the extracellular environment. Using this system, they attempt to test whether intracellular ATP abundance causally regulates replicative lifespan and whether extracellular ATP sensing contributes independently to longevity pathways. The manuscript presents data from ATP biosensing, transcriptomics, mitochondrial perturbations, and microfluidic aging assays to build a dual-mechanism model linking ATP availability, MAPK signaling, mitochondrial function, and aging trajectories.

Strengths:

A major strength of the study is its creative application of xenotopic synthetic biology to directly manipulate ATP homeostasis-an ambitious approach that addresses an important and difficult question in aging biology. The use of complementary methods, including single-cell ATP reporters, microfluidic lifespan measurements, and RNA-seq, generates a rich experimental dataset with the potential to reveal multiple layers of ATP-dependent physiological regulation. The manuscript also raises interesting hypotheses regarding extracellular nucleotide sensing and HOG/MAPK pathway involvement, opening conceptual space for future exploration of ATP-based signaling in yeast.

Weaknesses:

Despite these strengths, the manuscript suffers from several critical weaknesses that undermine the central conclusions. Foremost, the intracellular ATP measurements contradict key interpretations: NTT1 expression lowers ATP levels, yet multiple sections assert or assume that NTT1 increases intracellular ATP via import. This unresolved contradiction propagates throughout the mechanistic model. The authors do not consider or experimentally address the more parsimonious explanation that NTT1 may be a bidirectional ATP transporter, which would unify many perplexing results. Several important analyses are missing (e.g., transcriptomic comparison of NTT1 cells with vs. without ATP), and key signaling claims lack proper validation (e.g., Hog1 quantification, AMPK controls). Additionally, inconsistencies in figures-such as incorrect scale bars, mismatched ATP measurements, and a conceptual model contradicted by the data-further detract from clarity. As a result, the manuscript does not yet convincingly achieve its stated aims, and the current evidence does not adequately support the proposed causal relationships between ATP homeostasis and lifespan.

Reviewer #2 (Public review):

Summary:

This work presents interesting findings where the addition of exogenous ATP extends the replicative lifespan of yeast cells in a way that seems uncorrelated with actual increased intracellular ATP levels or mitochondria. To be clear, the addition of ATP to yeast growth media increases the number of cell divisions per cell in yeast. Expression of the NTT1 ATP transporter gene increases intracellular ATP levels according to LCMS analysis, but the effect on replicative lifespan works without the NTT1 gene and without an intracellular increase in ATP (possibly with a decrease in intracellular ATP), so the effect appears to be independent of the effect on intracellular ATP levels or mitochondria, as mitochondria-less R0 yeast cells also have increased numbers of cell division when grown with extracellular ATP. The plots in Figure 5 make it seem like exogenous ATP addition lowers intracellular ATP for both the NTT1 cells and the wild-type cells, and that is not what the data in Figure 2d with LCMS shows.

As an aside, this seems like a better model for increased tumor cell growth in the presence of increased extracellular ATP, which happens in some cancers.

Restated, the data suggest they were successful in increasing intracellular ATP by LCMS, but not by queen reporter, and that the seemingly likely increased intracellular ATP was not causative, as cells that did not have an increase in intracellular ATP, but had the same exogenous ATP addition, also gained an increase in replicative lifespan. There could also be two distinct mechanisms extending replicative lifespan to the same degree in these two different strains. More measurements, controls, and analyses are needed to accurately determine what is happening with intracellular ATP levels with age. It is currently unknown if there is any correlation between ATP levels and replicative aging (with properly controlled longitudinal measurements).

Strengths:

Longitudinal imaging of single cells. Analyzed ATP levels with two approaches. Creative approach to use NTT1 transporter to increase intracellular ATP levels. Solid replicative lifespan data.

Weaknesses:

Mostly unclear about ATP levels with age and the relationship, or lack thereo,f between intracellular ATP levels and replicative lifespan. No idea what this effect depends on, but some ideas what it does not depend on (mitochondria or increased intracellular ATP). Experiments seem to lack biological controls (cells without gfp) for age related changes in autofluorescence (and pH that can affect gfp signal) for the fluorescent microscopy quantifying ATP with age using the QUEEN reporter (seems that way as written); conflicting evidence on ATP levels; lack of LC-MS measurements in old cells; no apparent correlation between ATP levels and replicative lifespan, but that could be wrong - just not apparent from the longitudinal data plots. The LCMS data seems better than the microscopy data on ATP because the microscopy approach seems to lack proper biological controls, and the selection of only the top 40% of pixels to quantify signal seems unjustified as written, and possibly prone to technical artifacts. Figure 2 B&C plots of ATP levels should show what the cells were normalized to. The figures also seem too diluted and should probably be combined or put in the supplements (hog1 western) if they do not relate to the lifespan effect. There seem to be some technical scientific editorial errors, like in Figure 7.

Reviewer #1 (Public review):

The new experiments on the HOX and XIC look strong. A limited (conservative) number of proteins are determined to be enriched at the respective loci. And the number of cells used is a good advancement for these kinds of methods.

Unfortunately, the warnings about mitochondrial to nuclear comparisons and validations do not appear to be taken seriously. It's not that "...there could be non-specific nuclear comparison." There are definitely non-specific enriched proteins. Minimizing false positives is the responsibility of those developing the method and generating the hit lists. I think you saying our probes go to where they are supposed to and label the proteins in that compartment is fine. But that is as far as that should go. Any non-validated protein hits in those comparisons need to be removed. It will contaminate the literature by having all the proteins in 1E, S4D-F, and S5 reported (even though it appears there is no tables reporting the new proteins claimed to be associated with that locus. Why is that?).

I think the line "...we have not made any claims about new proteins at specific loci." is the heart of the issue. What is the point of this method then? Isn't it to identify unknown proteins at a locus of interest? Without that, it's just generating a long list of proteins, where an unknown number of which are likely erroneous, and highlighting the ones you already knew to be there. Along those lines, it is not validation to show proteins that we already knew were at a locus are at the locus. Validation is developing a method to help find new things, then testing those new things to confirm the new method's fidelity.

The comparison of OMAP identified proteins to the several other methods that look at similar regions is not there. A Figure 1F is referred to in the rebuttal but is not in the manuscript. If you mean the Bioplex comparison, that is not the goal. The goal of this analysis to see how much overlap, if any, is being identified across methods. OMAP has so many proteins claimed to be associated with telomeres that are not tested or validated, it would be nice if other methods see similar ones.

Minor points: You have now done label free proteomics. A) Methodological details are needed. It is not clear if you mean MS1 or DIA based quant. B) Do you need all the language about how multiplexed proteomics is enabling this methods?

Labeling the all the enriched proteins in the volcano plots would be nice. I don't want to see just the "relevant" ones that support your claims. I want to see all the "new" ones your discovery method is claiming to discover.

Reviewer #2 (Public review):

Summary

The authors introduce DNA O-MAP, a method that combines oligo-based in situ hybridization with peroxidase-mediated proximity biotinylation to profile proteins and DNA-DNA interactions linked to targeted genomic regions. In the revised manuscript, they expand the method beyond repetitive elements by profiling non-repetitive gene clusters (HOXA and HOXB), studying inhibitor-induced chromatin remodeling, and differentiating homolog-specific proteomes on both the active and inactive X chromosome. These additions considerably broaden the scope of the work and indicate that DNA O-MAP is currently most effective for analyzing gene-cluster size or domain-level chromatin environments, rather than focusing on individual promoters or cis-regulatory elements.

Strengths

The study demonstrates that DNA O-MAP can be applied to both repetitive domains and non-repetitive genomic regions, including gene clusters spanning 80 kilobases and larger single-copy chromosomal intervals, rather than isolated cis-regulatory elements.

Orthogonal validation using ENCODE ChIP-seq data supports several differentially enriched proteins observed between the HOXA and HOXB gene clusters proteomes.

The ability to detect quantitative changes in local protein environments after chemical perturbation demonstrates the method's sensitivity at the level of extended genomic domains.

Homolog-resolved analysis of the active and inactive X chromosome provides an additional demonstration of biological specificity and technical flexibility at the megabase scale.

The revised manuscript appropriately frames DNA O-MAP as a method for interrogating local domain-level genomic environments, rather than exhaustively defining the protein composition of individual regulatory elements.

Weaknesses

As with all proximity labeling approaches, the effective resolution of DNA O-MAP is constrained by the spatial distance of peroxidase-mediated labeling rather than by genomic distance. Consequently, for gene-cluster-scale targets, enrichment extends beyond the targeted interval into surrounding chromosomal regions, potentially limiting the method's specificity at the level of individual promoters, enhancers, or gene bodies.

Specificity is demonstrated through comparative and internally controlled analyses rather than through a quantitative estimate of false discovery rate for locus specificity. Readers should therefore interpret individual protein enrichments as indicative of local chromatin environments rather than definitive evidence of direct binding to a specific regulatory element.

Orthogonal validation is necessarily selective and hypothesis-driven. A broader validation would be required before newly enriched proteins can be interpreted as bona fide region-resident factors.

Comparisons to prior locus-proteomics methods remain indirect and should be interpreted primarily in terms of demonstrated feasibility, scalability, and reduced cell-number requirements rather than absolute performance or resolution.

Reviewer #3 (Public review):

Significance of the Findings:

The study by Liu et al. presents a novel method, DNA-O-MAP, which combines locus-specific hybridisation with proximity biotinylation to isolate specific genomic regions and their associated proteins. The potential significance of this approach lies in its purported ability to target genomic loci with heightened specificity by enabling extensive washing prior to the biotinylation reaction, theoretically improving the signal-to-noise ratio when compared with other methods such as dCas9-based techniques. Should the method prove successful, it could represent a notable advancement in the field of chromatin biology, particularly in establishing the proteomes of individual chromatin regions-an extremely challenging objective that has not yet been comprehensively addressed by existing methodologies.

Strength of the Evidence:

The evidence presented by the authors is somewhat mixed, and the robustness of the findings appears to be preliminary at this stage. While certain data indicate that DNA-O-MAP may function effectively for repetitive DNA regions, a number of the claims made in the manuscript are either unsupported or require further substantiation. There are significant concerns about the resolution of the method, with substantial biotinylation signals extending well beyond the intended target regions (megabases around the target), suggesting a lack of specificity and poor resolution, particularly for smaller loci. Furthermore, comparisons with previous techniques are unfounded since the authors have not provided direct comparisons with the same mass spectrometry (MS) equipment and protocols. Additionally, although the authors assert an advantage in multiplexing, this claim appears overstated, as previous methods could achieve similar outcomes through TMT multiplexing. Therefore, while the method has potential, the evidence requires more rigorous support, comprehensive benchmarking, and further experimental validation to demonstrate the claimed improvements in specificity and practical applicability.

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 comprehensively addressed the comments raised in the previous round of review.]

Summary:

These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system, but more work is needed to validate these results, particularly in normal cells.

Overall, the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division.

Reviewer #2 (Public review):

Summary:

The manuscript submitted by Koch et al. describes a novel approach to collect budding yeast cells in metaphase I or metaphase II by synthetically activating the spinde checkpoint (SAC). The arrest is transient and reversible. This synchronization strategy will be extremely useful for studying meiosis I and meiosis II, and compare the two divisions. The authors characterized this so named syncSAC approach and could confirm previous observations that the SAC arrest is less efficient in meiosis I than in meiosis II. They found that downregulation of the SAC response through PP1 phosphatase is stronger in meiosis I than in meiosis II. The authors then went on to purify kinetochore-associated proteins from metaphase I and II extracts for proteome and phosphoproteome analysis. Their data will be of significant interest to the cell cycle community (they compared their datasets also to kinetochores purified from cells arrested in prophase I and -with SynSAC in mitosis).

Significance:

The technique described here will be of great interest to the cell cycle community. Furthermore, the authors provide data sets on purified kinetochores of different meiotic stages and compare them to mitosis. This paper will thus be highly cited, for the technique, and also for the application of the technique.

Reviewer #3 (Public review):

Summary:

In their manuscript, Koch et al. describe a novel strategy to synchronize cells of the budding yeast Saccharomyces cerevisiae in metaphase I and metaphase II, thereby facilitating comparative analyses between these meiotic stages. This approach, termed SynSAC, adapts a method previously developed in fission yeast and human cells that enables the ectopic induction of a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC components upon addition of the plant hormone abscisic acid (ABA). This is a valuable tool, which has the advantage that induces SAC-dependent inhibition of the anaphase promoting complex without perturbing kinetochores. Furthermore, since the same strategy and yeast strain can be also used to induce a metaphase arrest during mitosis, the methodology developed by Koch et al. enables comparative analyses between mitotic and meiotic cell divisions. To validate their strategy, the authors purified kinetochores from meiotic metaphase I and metaphase II, as well as from mitotic metaphase, and compared their protein composition and phosphorylation profiles. The results are presented clearly and in an organized manner.

Significance:

Koch et al. describe a novel methodology, SynSAC, to synchronize budding yeast cells in metaphase I or metaphase II during meiosis, as well and in mitotic metaphase, thereby enabling differential analyses among these cell division stages. Their approach builds on prior strategies originally developed in fission yeast and human cells models to induce a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC proteins upon addition of abscisic acid (ABA). The results from this manuscript are of special relevance for researchers studying meiosis and using Saccharomyces cerevisiae as a model. Moreover, the differential analysis of the composition and phosphorylation of kinetochores from meiotic metaphase I and metaphase II adds interest for the broader meiosis research community. Finally, regarding my expertise, I am a researcher specialized in the regulation of cell division.

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 comments raised in the previous round of review, shown below, through minor changes to the text without additional experiments.]

Summary:

Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle. They did so by screening publicly available data from the Cancer Dependency Map and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. They show in RPE1 cells that loss of FAMC53 leads to a DYRK1A + P53-dependent cell cycle arrest. Combined inactivation of FAM53C and DYRK1A in a TP53-null background caused S-phase entry with subsequent apoptosis. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.

Reviewer #2 (Public review):

The authors sought to identify new regulators of the G1/S transition by mining the Cancer Dependency Map (DepMap) co-dependency dataset. This analysis successfully identified FAM53C, a poorly characterized protein, as a candidate. The strength of the paper lies in this initial discovery and the subsequent biochemical work convincingly showing that FAM53C can directly interact with the kinase DYRK1A, a known cell cycle regulator.

The authors then present evidence, primarily from acute siRNA knockdown in RPE-1 cells, that loss of FAM53C induces a strong G1 cell cycle arrest. Their follow-up investigation proposes a model where FAM53C normally inhibits DYRK1A, thereby protecting Cyclin D from degradation and preventing p53 activation, to allow for G1/S progression. The authors have commendably addressed some concerns from the initial review: they have now demonstrated the G1 arrest using two independent siRNAs (an improvement over the initial pool), shown the effect in several additional cancer cell lines (U2OS, A549, HCT-116), and developed a more nuanced model that incorporates p53 activation, which helps to explain some of the complex data.

Reviewer #3 (Public review):

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53C-depleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off-target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Whether and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism.

Comments on the previous version:

In the revised version of the manuscript, authors addressed most of the critical points. They now include new data with depletion of FAM53C using single siRNAs that show small but significant enrichment of population of the G1 cells. This G1 arrest is likely caused by a combined effects on induction of p21 expression and decreased levels of cyclin D1. Authors observed that inhibition of DYRK1 rescued cyclin D1 levels in FAM53 depleted cells suggesting that FAM53C may inhibit DYRK1. This possibility is also supported by in vitro experiments. On the other hand, inhibition of DYRK1 did not rescue the G1 arrest upon depletion of FAM53C, suggesting that FAM53C may have also DYRK1-independent role in G1. Functional rescue experiments with cyclin D1 mutants and detection of DYRK1 activity in cells would be necessary to conclusively explain the function of FAM53C in progression through G1 phase but unfortunately these experiments were technically not possible. Knock out of FAM53C in iPSCs and in mice suggest that FAM53C may have additional functions besides the cell cycle control and/or that adaptation may have occurred in these model systems. Overall, the study implicated FAM53C in fine tuning DYRK1 activity in cells that may to some extent influence the progression through G1 phase. In addition, FAM53C may also have DYRK1 and cell cycle independent functions that remain to be addressed by future studies.

Reviewer #1 (Public review):

Summary:

The authors test the hypotheses, using an effort-exertion and an effort-based decision-making task, while recording brain dynamics with EEG, that the brain processes reward outcomes for effort differentially when they earned for themselves versus others.

Strengths:

The strengths of this experiment include what appears to be a novel finding of opposite signed effects of effort on the processing of reward outcomes when the recipient is self versus others. Also, the experiment is well-designed, the study seems sufficiently powered, and the data and code are publicly available.

Weaknesses:

There is some concern about the fact that participants report feeling less subjective effort, but also more disliking of tasks when they were earning rewards for others versus self. The concern is that participants worked with less vigor during self-versus-others trials and this may partly account for a key two-way Recipient x Effort interaction on the size of the Reward Positivity EEG component. Of note, participants took longer to complete tasks when working for others. While it is true that, in all cases, participants met the requisite task demands (they pressed the required number of buttons) they did so more sluggishly when earning rewards for others. The Authors argue that this reflects less motivation when working for others, which is a plausible explanation. The Authors also try to rule out this diminished vigor as a confounding explanation by showing that the two way interaction remains even when including reaction times (and also self-reported task liking) as a covariate. Nevertheless, it is possible that covariates do not fully account for the effects of differential motivation levels which would otherwise explain the two-way interaction. As such, I think a caveat is warranted regarding this particular result.

Reviewer #2 (Public review):

Summary:

Measurements of the reward positivity, an electrophysiological component elicited during reward evaluation, have previously been used to understand how self-benefitting effort expenditure influences processing of rewards. The present study is the first to complement those measurements with electrophysiological reward after-effects of effort expenditure during prosocial acts. The results provide solid evidence that effort adds reward value when the recipient of the reward is the self but discounts reward value when the beneficiary is another individual.

Strengths:

An important strength of the study is that amount of effort, the prospective reward, the recipient of the reward, and whether the reward was actually gained or not were parametrically and orthogonally varied. In addition, the researchers examined whether the pattern of results generalized to decisions about future efforts. The sample size (N=40) and mixed-effects regression models are also appropriate for addressing the key research questions. Those conclusions are plausible and adequately supported by statistical analyses.

Reviewer #1 (Public review):

Summary:

Del Rosario et al characterized the extent and cell types of sibling chimerism in marmosets. To do so, they took advantage of the thousands of SNPs that are transcribed in single-nucleus RNA-seq (snRNA-seq) data to identify the sibling genotype of origin for all sequenced cells across 4 tissues (blood, liver, kidney, and brain) from many marmosets. They found that chimerism is prevalent and widespread across tissues in marmosets, which has previously been shown. However, their snRNA-seq approach allowed them to identify precisely which cells were of sibling origin, and which were not. In doing so they definitively show that sibling chimerism across tissues is limited to cells of myeloid and lymphoid lineages. The authors then focus on a large sample of microglia sequenced across many brain regions to quantify: (1) variation in chimerism across brain regions in the same individual, and (2) the relative importance of genetic vs. environmental context on microglia function/identity. (1) Much like across different tissues in the same individual, they found that the proportion of chimeric microglia varies across brain regions collected from the same individuals (as well as differing from the proportion of sibling cells found in blood of the same animals), suggesting that cells from different genetic backgrounds may differ in their recruitment and/or proliferation across regions and local tissue contexts, or that this may be linked to stochastic bottleneck effects during brain development. (2) Their (admittedly smaller sample size) analyses of host-sibling gene expression showed that the local environment dominates genotype. All told, this thoughtful and thorough manuscript accomplishes two important goals. First, it all but closes a previously open question on the extent and cell origins of sibling chimerism. Second, it sets the stage for using this unique model system to examine, in a natural context, how genetic variation in microglia may impact brain development, function, and disease.

The conclusions of this paper are well supported by the data, and the authors exert appropriate care when extrapolating their results that come from smaller samples. However, there are a few concerns that should be addressed.

The "modest correlation" mentioned in lines 170-172 does not take into account the uncertainty in estimates of each chimeric cell proportion (although the plot shows those estimates nicely). This is particularly important for the macrophages, which are far less abundant. Perhaps a more appropriate way to model this would be in a binomial framework (with a random effect for individual of origin). Here, you could model sibling identity of each macrophage as a function of the proportion of sibling-origin microglia and then directly estimate the percent variance explained.

A similar (albeit more complicated because of the number of regions being compared) approach could be applied to more rigorously quantify the variation in chimerism across brain regions (L198-215; Fig 4). This would also help to answer the question of whether specific brain regions are more "amenable" to microglia chimerism than others.

While the sample size is small, it would be exciting to see if any microglia eQTL are driven by sibling chimerism across the marmosets.

L290-292: The authors should propose ways in which they could test the two different explanations proposed in this paragraph. For instance, a simulation-based modeling approach could potentially differential more stochastic bottleneck effects from recruitment-like effects.

While intriguing, the gene expression comparison (Fig 5) is extremely underpowered. It would be helpful to clarify this and note the statistical thresholds used for identifying DEGs (the black points in the figure).

Comments on revisions:

The authors have thoroughly addressed all my suggestions.

Reviewer #2 (Public review):

Summary:

This manuscript reports a novel and quite important study of chimerism among common marmosets. As the authors discuss, it has been known for years that marmosets display chimerism across a number of tissues. However, as the authors also recognize, the scope and details of this chimerism have been controversial. Some prior publications have suggested that the chimerism only involves cells derived from hematopoietic stem cells, while other publications have suggested more cell types can also be chimeric, including a wide range of cell types present in multiple organs. The present authors address this question and several other important issues by using snRNA-seq to track the expression of host and sibling-derived mRNAs across multiple tissues and cell types. The results are clear and provide convincing evidence that for the various organs analyzed, all chimeric cells are derived from hematopoietic cell lineages.

This work will have impact on studies using marmosets to investigate various biological questions, but will have biggest impact on neuroscience and studies of cellular function within the brain. The demonstration that microglia and macrophages from different siblings from a single pregnancy, with different genomes expressing different transcriptomes, are commonly present within specific brain structures of a single individual opens a number of new opportunities to study microglia and macrophage function as well as interations between microglia, macrophages and other cell types.

Strengths:

The paper has a number of important strengths. This analysis employs the first unambiguous approach providing a clear answer to the question of whether sibling-derived chimeric cells arise only from hematopoietic lineages or from a wider array of embryonic sources. That is a long-standing open question and these snRNA-seq data seem to provide a clear answer, at least for brain and liver and kidney. In addition, the present authors investigate quantitative variation in chimeric cell proportions across several dimensions, comparing the proportion of chimeric cells across individual marmosets, across organs within an individual and across brain regions within an individual. All these are significant questions, and the answers have important implications for multiple research areas. Marmosets are increasingly being used for a range of neuroscience studies, and a better understanding of the process that leads to chimerism of microglia and macrophages in the marmoset brain is a valuable and timely contribution. But this work also has implications for other lines of study such as defining embryological and development processes and the potential to track specific cell populations within genetically engineered marmosets. Third, the snRNA-seq data will be made available through Brain Initiative NeMO portal and the software used to quantify host vs. sibling cell proportions in different biosamples will be available through Github.

Comments on revisions:

Several minor weaknesses have been addressed by the authors in a revision of the original manuscript. Each of my concerns and perceived weaknesses regarding the initial submission have been satisfactorily addressed in the revision.

Reviewer #1 (Public review):

Summary:

This study aims to investigate the development of infants' responses to music by examining neural activity via EEG and spontaneous body kinematics using video-based analysis. The authors also explore the role of musical pitch in eliciting neural and motor responses, comparing infants at 3, 6, and 12 months of age.

Strengths:

A key strength of the study lies in its analysis of body kinematics and modeling of stimulus-motor coupling, demonstrating how the amplitude envelope of music predicts infant movement, and how higher musical pitch may enhance auditory-motor synchronization.

EEG data provide evidence for enhanced neural responses to music compared to shuffled auditory sequences. These findings ecourage further investigation of the proposed developmental trajectory of neural responses to music and their link to musical behavior in infants.

Comments on revisions:

I thank the authors for the considerable effort devoted to revising the manuscript and addressing the raised questions and comments. I particularly appreciate the additional analyses and the extended arguments included in the discussion. I believe that this paper represents a valuable contribution to the literature on music development.

One remaining comment concerns the evoked response observed in the shuffled condition, which I still find intriguing. Considering that the auditory events in the shuffled condition display a clear rise time, particularly for those events that were selected based on being preceded and followed by longer periods of silence, one would expect to observe an evoked response emerging from baseline. However, this pattern is not evident in the presented curves. The authors may further examine and discuss the shape and characteristics of these response patterns.

Reviewer #2 (Public review):

Summary:

Infants' auditory brain responses reveal processing of music (clearly different from shuffled music patterns) from the age of 3 months; however, they do not show related increase in spontaneous movement activity to music until the age of 12 months.

Strengths:

This is a nice paper, well designed, with sophisticated analyses and presenting clear results filling an important gap about early infant sensitivity, detection, and differentiation of musical sounds. The addition of EEG recordings (specifically ERPs) in response to music presentations at 3 different infant ages in the first postnatal year is important, and the manipulation of the music stimuli into shuffled, high and low pitch to capture differences in brain response processing and spontaneous movements is interesting. Further, the movement analysis based on Quantity of Movements (QoM) and movement subdivision into 10 distinct Principal Movements (PMs) is novel and creative.

Overall, results show that ERPs responses to music occurs earlier than QoM in early development, and that even at 12 months, motor responses to music remain coarse and not rhythmically aligned with the music tempo. This work increases our fundamental understanding of infants' early music perception in relation to auditory processing and motor response.

Comments on revisions:

The authors have addressed my questions in their revision. I have no other questions. Thanks again for the opportunity to read and evaluate this interesting work.

Reviewer #3 (Public review):

Summary

This study provides a detailed investigation of neural auditory responses and spontaneous movements in infants listening to music. Analyses of EEG data (event-related potentials and steady-state responses) first highlighted that infants at 3, 6 and 12 months of age and adults showed enhanced auditory responses to music than shuffled music. 6-month-olds also exhibited enhanced P1 response to high-pitch vs low-pitch stimuli, but not the other groups. Besides, whole body spontaneous movements of infants were decomposed into 10 principal components. Kinematic analyses revealed that the quantity of movement was higher in response to music than shuffled music only at 12 months of age. Although Granger causality analysis suggested that infants' movement was related to the music intensity changes, particularly in the high-pitch condition, infants did not exhibit phase-locked movement responses to musical events, and the low movement periodicity was not coordinated with music.

Strengths

This study investigates an important topic on the development of music perception and translation to action and danse. It targets a crucial developmental period that is difficult to explore. It evaluates two modalities by measuring neural auditory responses and kinematics, while cross-modal development is rarely evaluated. Overall, the study fills a clear gap in the literature.

Besides, the study uses state-of-the-art analyses. Detailed investigations were performed, as well as exploratory analyses in supplementary information. The discussion is rich in neurodevelopmental interpretations and comparisons with the literature. All steps are clearly detailed. The manuscript is very clear, well-written and pleasant to read. Figures are well-designed and informative. The authors' responses to previous reviews are also detailed and informative.

Comments on revisions:

The authors answered all my questions.

Reviewer #1 (Public review):

Summary:

This manuscript addresses an important methodological issue-the fragility of meta-analytic findings-by extending fragility concepts beyond trial-level analysis. The proposed EOIMETA framework provides a generalizable and analytically tractable approach that complements existing methods such as the traditional Fragility Index and Atal et al.'s algorithm. The findings are significant in showing that even large meta-analyses can be highly fragile, with results overturned by very small numbers of event recodings or additions. The evidence is clearly presented, supported by applications to vitamin D supplementation trials, and contributes meaningfully to ongoing debates about the robustness of meta-analytic evidence. Overall, the strength of evidence is moderate to strong.

Strengths:

(1) The manuscript tackles a highly relevant methodological question on the robustness of meta-analytic evidence.<br /> (2) EOIMETA represents an innovative extension of fragility concepts from single trials to meta-analyses.<br /> (3) The applications are clearly presented and highlight the potential importance of fragility considerations for evidence synthesis.

Reviewer #3 (Public review):

Summary and strengths:

In this manuscript, Grimes presents an extension of Ellipse of Insignificant (EOI) and Region of Attainable Redaction (ROAR) metrics to meta-analysis setting as metrics for fragility and robustness evaluation of meta-analysis. The author applies these metrics to three meta-analyses of Vitamin D and cancer mortality, finding substantial fragility in their conclusions. Overall, I think extension/adaption is a conceptually valuable addition to meta-analysis evaluation, and the manuscript is generally well-written.

Specific comments:

(1) The manuscript would benefit from a clearer explanation of in what sense EOIMETA is generalizable. The author mentions this several times, but without a clear explanation of what they mean here.

(2) The authors mentioned the proposed tools assume low between-study heterogeneity. Could the author illustrate mathematically in the paper how the between-study heterogeneity would influence the proposed measures? Moreover, the between-study heterogeneity is high in Zhang et al's 2022 study. It would be a good place to comment on the influence of such high heterogeneity on the results, and specifying a practical heterogeneity cutoff would better guide future users.

(3) I think clarifying the concepts of "small effect", "fragile result", and "unreliable result" would be helpful for preventing misinterpretation by future users. I am concerned that the audience may be confusing these concepts. A small effect may be related to a fragile meta-analysis result. A fragile meta-analysis doesn't necessarily mean wrong/untrustworthy results. A fragile but precise estimate can still reflect a true effect, but whether that size of true effect is clinically meaningful is another question. Clarifying the effect magnitude, fragility, and reliability in the discussion would be helpful.

Comments on revisions:

I am unable to find the author's responses to my previous round comments (Reviewer #3) in the revision package, though replies to the other reviewers are present. I will provide my updated feedback once these responses are available for review.

Reviewer #1 (Public review):

Summary:

In this manuscript, Guin and colleagues establish a microscopy-based CRISPR screen to find new factors involved in global chromatin organization. As a proxy of global chromatin organization they use centromere clustering in two different cell lines. They find 52 genes whose CRISPR depletion leads to centrome clustering defects in both cell lines. Using cell cycle synchronisation, they demonstrate that centromeres-redistribution upon depletion of these hits necessitates cell cycle progression through mitosis.

Strengths:

This manuscript explores the mechanisms of global chromatin organization, which is a scale of chromatin organization which remains poorly understood. The imaging based CRISPR screeen is very elegant and use of appropriate positive and negative controls reinforces the solidity of the findings.

Weaknesses:

The manuscript shows interesting observations but left a major question unanswered: what is the functional relevance of centromeres clustering?

Reviewer #2 (Public review):

The authors begin by highlighting the importance of genome organisation in cellular compartmentalisation and identity. They focus their study on centromeres - key chromosomal features required for segregation-and aim to identify proteins responsible for their spatial distribution in interphase nuclei. However, none of the experimental data addresses broader aspects of genome architecture, such as individual chromosome territories or A/B compartments. As such, the title of the article may be misleading and would benefit from being more specific, for example: "Identification of factors influencing centromere positioning in interphase."

Strengths:

One of the strengths of the paper is the comprehensive CRISPR-based screening and the comparative analysis between two distinct cell lines.

Including further investigation into factors that behave differently across these cell lines - particularly in relation to expression levels or the unique "inverted architecture" of RPE cells-would have added valuable depth.

Comments on revisions:

From the previous review:<br /> The Authors have undertook a very minimal revision of the paper. The Authors have addressed some of the comments raised by rewarding the text and being slightly more critical in the interpretation of their results and added previously published literature.<br /> They have provided more details on the characterisation of the new cell lines and added some statistical analyses.

However, I still believe that the title does not reflect the finding, as it is all about centromere position rather than "interphase genome architecture" as claimed.<br /> As I said in my previous comments, this will make a precedent and will cause mis-interpretations in the field.

Changes from the previous version:

While in the new manuscript the Authors have discussed that degradation of NUF2 and SPC24 caused some aberrant nuclear phenotypes, this is at odd with the first screening where these morphologies were used as part of the exclusion criteria. Some comments would be required.

Reviewer #3 (Public review):

Summary:

In this manuscript, Guin et al. use a CRISPR KO screen of ~1000 candidates in two human cell lines along with high-throughput image analysis to demonstrate that orderly progression through mitosis shapes centromere organization. They identify ~50 genes that perturb centromere clustering when depleted in both RPE1 and HCT116 cells and validate many of these hits using RNAi. They then use auxin-mediated acute depletion of four factors (NCAPH2, KI67, SPC24 and NUF2) to demonstrate that their effects on centromere clustering require passage through mitosis. They further suggest that lack of these factors during mitosis leads to disorganization of centromeres on the mitotic spindle and these effects persist in the subsequent interphase. Overall, the manuscript is clear, well-written, the experiments performed are appropriate and the data is interpreted accurately. In my opinion, the main strength of this manuscript is the discovery of several hits associated with altered centromere clustering. These hits will serve as a solid foundation for future work investigating the functional significance of centromere clustering in human cells. On the other hand, how the changes in centromere clustering relate to other aspects of interphase genome architecture (A/B compartments, chromosome territories etc) remains unclear and represents the main limitation of this manuscript.

Reviewer #1 (Public review):

In the revised manuscript, the authors refine their conclusions, narrow their interpretation, and add limited new analyses but have not added additional new data or made fundamental changes in the analyses of their data.

The central findings are that the SuM contains neurons that are activated by stressors (foot shock and social defeat). Chemogenetic activation of SuM and the neurons genetically tagged as active during foot shocks, which the authors define as Stress Activated Neurons, increases classic anxiety-like behaviors. The subiculum projects to the SuM, and terminals in the SuM from the ventral versus dorsal subiculum are differentially active during elevated plus-maze transitions. Chronic inhibition of vSub neurons that project to SuM mitigates CSDS-induced anxiety-like behaviors.

Due to limitations in the data and experimental design the findings are felt to remain incomplete. A central limitation is the discordance between the temporal resolution of the behavioral assays and the neural interventions used. This weakens support for the conclusions drawn about the causal roles the SuM and specific vSub projections to SuM (vSub→SuM) may play in anxiety and anxiety-like behaviors. The authors acknowledge this limitation but do not address it experimentally in the revised manuscript. Furthermore, the connection between chronic inhibition of vSub→SuM neurons for 10 days and the alleviation of CSDS-induced anxiety is incomplete. Separately, the use of foot shock and social defeat stressors in connection with SuM neurons, with limited exploration of the potential (or lack thereof) relation between the two groups, further limits the ability to draw conclusions from the data.

Although a number of interesting points are raised through the experiments the weakness noted will reduce the impact of the work in the field.

Reviewer #2 (Public review):

This manuscript investigates the neural mechanisms of anxiety and identifies the supramammillary nucleus (SuM) as a critical hub in mediating anxiety-related behaviors. The authors describe a population of neurons in the SuM that are activated by acute and chronic stress. While their activity is not required for fear memory recall, reactivation of these neurons after chronic stress robustly increases anxiety-like behaviors as well as physiological stress markers. Circuit analysis further shows that these stress-activated neurons are driven by inputs from the ventral, but not dorsal, subiculum, and inhibition of this pathway exerts an anxiolytic effect.

The study provides an elegant integration of techniques linking stress, neuronal ensembles, and circuit function, advancing our understanding of the neural substrates of anxiety. A particularly notable point is the selective role of these stress-activated neurons in anxiety, but not in associative fear memory, highlighting functional distinctions between neural circuits underlying anxiety and fear.

The recruited neuronal population is activated by acute and chronic stress, though the overlap across stress exposures is partial, suggesting that further studies will be important to define how these neurons respond under other stressors and conditions.

Overall, this work identifies SuM stress-activated neurons and their ventral subiculum inputs as central elements of anxiety circuitry, providing a valuable framework for future studies and potential targeted interventions for stress-related disorders.

Reviewer #3 (Public review):

Summary:

The authors aim to investigate the mechanisms of anxiety. The paper focuses on supramammillary nucleus (SuM) based on a fos screen and recordings showing that footshock and social defeat stress increases activity in this region. Using activity-dependent tagging, they show that reactivation of stress-activated neurons in SuM has an anxiety-like effect, reducing open-arm exploration in the elevated zero task. They then investigate the ventral subiculum as a potential source of anxiety-related information for SuM. They show that ventral subiculum (vSub) inputs to SuM are more strongly activated than dSub when mice explore open arms of the elevated zero. Finally, they show that DREADD-mediated inhibition of vSub-SuM projections alleviates stress-enhanced anxiety. Overall the results provide good evidence that SuM contains a stress-activated neuronal population whose later activity increases anxiety-like behavior. It further provides evidence that vSub projects to SuM are activated by stress and their inhibition alleviates some effects of stress.

Strengths:

Strengths of this paper include the use of convergent methods (e.g., fos plus electrode recordings, footshock and social defeat) to demonstrate that the SuM is activated by different forms of stress. The activity-dependent tagging experiment shows that footshock-activated SuM neurons are reactivated by social defeat but not sucrose is also compelling because it provides evidence that SuM neurons are driven by some integrative aspect of stress rather than by a simple sensory stimulus.

Weaknesses:

The strength of some evidence is judged to be incomplete. The paper provides good evidence that SuM contains stress-responsive neurons, and the activity of these neurons increases some measure of anxiety-like behavior. However, the evidence that the vSub-SuM projection "encodes anxiety" and that the SuM is a key regulator of anxiety is judged to be incomplete. I am not convinced that the identified SuM cells have a specific anxiety function. As the authors mention in the introduction, SuM regulates exploration and theta activity. Since theta potently regulates hippocampal function, there is the concern that SuM manipulations could have broad effects beyond anxiety-like behavior.

Reviewer #1 (Public review):

Summary:

GID/CTLH-type RING ligases are huge multi-protein complexes that play an important role in protein ubiquitylation. The subunits of its core complex are distinct and form a defined structural arrangement, but there can be variations in subunit composition, such as exchange of RanBP9 and RanBP10. In this study, van gen Hassend and Schindelin provide new crystal structures of (parts of) key subunits and use those structures to elucidate the molecular details of the pairwise binding between those subunits. They identify key residues that mediate binding partner specificity. Using in vitro binding assays with purified protein, they show that altering those residues can switch specificity to a different binding partner.

Strengths:

This is a technically demanding study that sheds light on an interesting structural biology problem in residue-level detail. The combination of crystallization, structural modeling and binding assays with purified mutant proteins is elegant and, in my eyes, convincing.

Weaknesses:

This study has no major weaknesses.

It will be very interesting to see follow-up studies that use the mutants generated here to dive deeper into the biology of RING ligases, or design new mutants of multi-subunit complexes with an analogous methodology.

Reviewer #2 (Public review):

Summary:

This is a very interesting study focusing on a remarkable oligomerization domain, the LisH-CTLH-CRA module. The module is found in a diverse set of proteins across evolution. The present manuscript focuses on the extraordinary elaboration of this domain in GID/CTLH RING E3 ubiquitin ligases, which assemble into a gigantic, highly ordered, oval-shaped megadalton complex with strict subunit specificity. The arrangement of LisH-CTLH-CRA modules from several distinct subunits is required to form the oval on the outside of the assembly, allowing functional entities to recruit and modify substrates in the center. Although previous structures had shown that data revealed that CTLH-CRA dimerization interfaces share a conserved helical architecture, the molecular rules that govern subunit pairing have not been explored. This was a daunting task in protein biochemistry that was achieved in the present study, which defines this "assembly specificity code" at the structural and residue-specific level.<br /> The authors used X-ray crystallography to solve high-resolution structures of mammalian CTLH-CRA domains, including RANBP9, RANBP10, TWA1, MAEA, and the heterodimeric complex between RANBP9 and MKLN. They further examined and characterized assemblies by quantitative methods (ITC and SEC-MALS) and qualitatively using nondenaturing gels. Some of their ITC measurements were particularly clever, and involved competitive titrations, and titrations of varying partners depending on protein behavior. The experiments allowed the authors to discover that affinities for interactions between partners is exceptionally tight, in the pM-nM range, and to distill the basis for specificity while also inferring that additional interactions beyond the LisH-CTLH-CRA modules likely also contribute to stability. Beyond discovering how the native pairings are achieved, the authors were able to use this new structural knowledge to reengineer interfaces to achieve different preferred partnerings.

Strengths:

Nearly everything about this work is exceptionally strong.<br /> -The question is interesting for the native complexes, and even beyond that has potential implications for design of novel molecular machines.<br /> -The experimental data and analyses are quantitative, rigorous, and thorough.<br /> -The paper is a great read - scholarly and really interesting.<br /> -The figures are exceptional in every possible way. They present very complex and intricate interactions with exquisite clarity. The authors are to be commended for outstanding use of color and color-coding throughout the study, including in cartoons to help track what was studied in what experiments. And the figures are also outstanding aesthetically.

Weaknesses:

There are no major weaknesses of note, and in the revision the authors addressed my minor suggestions for the text.

Reviewer #3 (Public review):

Summary:

Protein complexes, like the GID/CTLH-type E3 ligase, adopt a complex three-dimensional structure, which is of functional importance. Several domains are known to be involved in shaping the complexes. Structural information based on cryo-EM is available, but its resolution does not always provide detailed information on protein-protein interactions. The work by van gen Hassend and Schindelin provides additional structural data based on crystal structures.

Strengths:

The work is solid and very carefully performed. It provides high-resolution insights into the domain architecture, which helps to understand the protein-protein interactions on a detailed molecular level. They also include mutant data and can thereby draw conclusions on the specificity of the domain interactions. These data are probably very helpful for others who work on a functional level with protein complexes containing these domains.

Weaknesses:

The manuscript contains a lot of useful, very detailed information. This information is likely very helpful to investigate functional and regulatory aspects of the protein complexes, whose assembly relies on the LisH-CTLH-CRA modules. However, this goes beyond the scope of this manuscript.

Comments on revisions:

I am fine with the revised version of the manuscript.

Reviewer #1 (Public review):

Summary:

In this study, Diana et al. present a Monte Carlo-based method to perform spike inference from calcium imaging data. A particular strength of their approach is that they can estimate not only averages but also uncertainties of the modeled process. The authors focus on the quantification of spike time uncertainties in simulated data and in data recorded with high sampling rate in cebellar slices with GCaMP8f, and they demonstrate the high temporal precision that can be achieved with their method to estimate spike timing.

Strengths:

- The author provide a solid ground work for sequential Monte Carlo-based spike inference, which extends previous work of Pnevmatikakis et al., Greenberg et al. and others.

- The integration of two states (silence vs. burst firing) seems to improve the performance of the model.

- The acquisition of a GCaMP8f dataset in cerebellum is useful and helps make the point that high spike time inference precision is possible under certain conditions.

Weaknesses:

- Although the algorithm is compared (in the revised manuscript) to other models to infer individual spikes (e.g., MLSpike), these comparisons could be more comprehensive. Future work that benchmarks this and other algorithms under varying conditions (e.g., noise levels, temporal resolution, calcium indicators) would help assess and confirm robustness and useability of this algorithm.

- The mathematical complexity underlying the method may pose challenges for experimentalist who may want to use the methods for their analyses. While this is not a weakness of the approach itself, this highlights the need for further validation and benchmarking in future work, to build user confidence.

Comments on revisions:

Thank you for addressing the final comments, and congrats on this study!

Reviewer #2 (Public review):

Summary:

Methods to infer action potentials from fluorescence-based measurements of intracellular calcium dynamics are important for optical measurements of activity across large populations of neurons. The variety of existing methods can be separated into two broad classes: a) model-independent approaches that are trained on ground truth datasets (e.g., deep networks), and b) approaches based on a model of the processes that link action potentials to calcium signals. Models usually contains parameters describing biophysical variables, such as rate constants of the calcium dynamics and features of the calcium indicator. The method presented here, PGBAR, is model-based and uses a Bayesian approach. A novelty of PGBAR is that static parameters and state variables are jointly estimated using particle Gibbs sampling, a sequential Monte Carlo technique that can efficiently sample the latent embedding space.

Strengths:

A main strength of PGBAR is that it provides probability distributions rather than point estimates of spike times. This is different from most other methods and may be an important feature in cases when estimates of uncertainty are desired. Another important feature of PGBAR is that it estimates not only the state variable representing spiking activity, but also other variables such as baseline fluctuations and stationary model variables, in a joint process. PGBAR can therefore provide more information than various other methods. The information in the github repository is well-organized. The authors demonstrate convincingly that PGBAR can resolve inter-spike intervals in the range of 5 ms using fluorescence data obtained with a very fast genetically encoded calcium indicator at very high sampling rates (line scans at >= 1 kHz).

Weaknesses:

The accuracy of spike train reconstructions is not higher than that of other model-based approaches, and lower than the accuracy of a model-independent approach based on a deep network in a regime of commonly used acquisition rates.

Comments on revisions:

I have no further comments on the manuscript.

Reviewer #1 (Public review):

Summary:

The authors of Serantes et al. produced a well-designed set of experiments to address the mechanisms of olfactory disconnection during sleep. In contrast to other sensory modalities, olfaction is not filtered or potentially gated by the thalamus, potentially opening the door to unimodal sensory stimulation during sleep. Recent work (Schreck, 2022) used optogenetically activated Olfactory Sensory Neurons to show that local field potential and activity across the olfactory pathway, not only remained open during sleep but were potentially even accentuated under these brain states. However, their optogenetic manipulation is an artificial perturbation to the system that could override naturalistic early-gating mechanisms. In a set of careful experiments, Serantes et al. show that coupling between airflow and brain activity at the Olfactory Bulb is diminished under sleep and anesthetic brain states. In contrast to a peripheral gating mechanism proposed by Schreck, this lack of respiration-locked activity, measured with EEG and LFP, persists even in the presence of intense respiration and even when nasal airflow is artificially induced and controlled. Their results point to nonthalamic early sensory gating of olfactory information during sleep, which is independent of nasal airflow but dependent on internal brain states. Their work elicits questions about potentially undiscovered mechanisms at the level of the early sensory pathway.

Strengths:

The strengths of this paper lie in the level of control afforded by the multiple preps and the wide array of physiological recordings. Specifically, both their control of airflow with a dual tracheotomy and their control of internal states using both sleep and urethane anaesthesia have a cumulative impact on the results.

The paper is simple, well-written, well executed, has clear questions, describes the literature comprehensively, and points out conflicting results with precision and transparency. The same transparency and judgment should be used on their own results.

Another strength of the paper is the clear, unambiguous results. The effect sizes presented in the paper are sizable and convincing.

Weaknesses:

The paper's shortcomings include open questions and a lack of a full mechanistic understanding of the suggested internal gating process. There are some open questions about the relative importance of airflow sensing vs. odorant sensing. Recent work by Mahajan et al., Sci.Adv 2025 points to OSN as sensing both odorants and airflow to produce anemotaxis. Potentially, other cells could contribute to anemosensation as well, so that gated or non-gated information might depend on the ratio of airflow to odorant information. Perhaps, optogenetic stimulation of OSN acts as an unnatural sensory stimulation that can alter both olfaction and anemosensation.

Detailed ablation, pharmacological, and optogenetic experiments may be needed to elucidate the suggested mechanisms and determine the correct answer to the question posed by the authors.

Reviewer #2 (Public review):

Summary:

In this study, Serantes and colleagues analysed how sleep and anesthesia impact the processing of olfactory inputs, focusing on early sensory processing (occurring at the first or second synaptic contacts). First, they show that the transition to sleep has a major impact on breathing-dependent gamma activity. Second, they show that this decrease originates at the first synaptic contact and is independent of respiration itself. Third, they show a decrease in connectivity associated with neocortical slow waves. These results are very interesting and supported by a robust methodology. However, I have two major concerns regarding this work.

First, the authors fail to adequately contextualize their work. For example, the impact of sleep on respiration-locked gamma activity was reported several years ago and is, in fact, used in some laboratories to score sleep using data from the olfactory bulb.

Second, the authors should exercise much more caution when comparing the urethane anesthesia model with NREM/REM sleep cycles. There are very significant differences between the two. Yet, the title and abstract of the article mention only sleep and anesthesia. More concerningly, the results obtained under urethane anesthesia are uncritically generalized to sleep.

In conclusion, the first finding was already shown in previous studies, and the second and third results were obtained not during sleep but during an anesthetic state that only resembles certain aspects of sleep.

Strengths:

The authors deploy an interventional approach that allows them to determine with compelling evidence the relationship of the gamma activity time-locked to breathing and different aspects of breathing, proving in particular that the disconnection is independent of respiratory dynamics. They leveraged invasive recordings that allow them to pinpoint at which level the disconnection occurs.

Weaknesses:

(1) My first comment concerns how this work fits within the state of the art. The introduction of the article leaves out very important and highly relevant work.

(1a) First, "disconnection" is not a defining feature of sleep; "unresponsiveness" is. It is often assumed that this unresponsiveness (which can be directly measured, contrary to disconnection) is due to a form of disconnection, but there has been substantial work over the past decade showing that disconnection is not as extensive as initially expected. It is therefore incorrect, in my view, to state that "most models attribute sensory gating to thalamocortical mechanisms". Most models attribute sensory gating to a combination of thalamocortical and cortical mechanisms.

(1b) The rationale of the article appears unclear ("the olfactory system-bypassing the thalamus-offers a unique window into earlier stages of sensory disconnection"). If the idea is to investigate gating mechanisms before the thalamus, then any sensory modality would suffice, since even modalities that later relay through the thalamus involve pre-thalamic processing stages. I assume that the authors instead mean that, because olfactory information does not relay through the thalamus, gating mechanisms in the olfactory stream could occur very early. However, this also implies that focusing on olfactory processing would say little about other sensory modalities.

(1c) Key previous results have been completely overlooked. First, the impact of sleep on respiration-locked gamma activity was reported several years ago (Bagur et al., Plos Biology 2018). Second, important articles investigating olfactory processing during sleep have been overlooked (e.g., Arzi et al., Nature Neuroscience 2012; Arzi et al., Journal of Neuroscience 2014). I am not providing an exhaustive list here, but these articles are not only extremely relevant to the present study; they have also become classics in the sleep literature.

(2) For most of their findings (Figures 2 to 5), the authors used urethane anesthesia. They show that this pharmacological manipulation results in alternation between periods of high-amplitude delta waves (SWSt) and a desynchronized state (ASt). However, the parallel with NREM and REM sleep, respectively, is rough and insufficiently justified. Differences can already be noted by contrasting the short examples provided in the figures. While NREM and REM sleep differ in terms of muscle tone (EMG), no such difference is discernible between SWSt and ASt. In SWSt, the slow waves appear to overlap with fast activity at the cortical level (M1, S1), which is not typically the case during NREM sleep. In addition, because the time scale is not the same in Figures 1 and 2 (1 s vs 2 s), yet the slow waves appear to have similar durations, it is also possible that the slow waves generated during SWSt and NREM differ. To better support the proposed parallel between NREM and SWSt on the one hand, and ASt and REM on the other, the authors should provide a thorough comparison of these states (spectral features, properties of the slow waves, duration and frequency of each state, etc.). Without this, inferences from results obtained under urethane anesthesia to sleep are not warranted.

The authors acknowledge this issue in the Discussion ("These findings suggest that there is no functional equivalence between urethane-activated states and REM sleep"), but this caveat should be integrated from the very beginning (title, abstract, and introduction).

(3) In some graphs, the power spectrum is normalized. Under anesthesia, this normalization was performed "within each animal to the SWSt maximum for that signal". However, I could not find equivalent information for sleep. This is key information needed to correctly interpret the results shown in Figure 1.

(4) The authors should also clarify their criteria for concluding on the absence or presence of a given effect. For example, in the legend of Figure 1c, they write: "Note the presence of coherence during wakefulness, demonstrating the internalization of the respiratory signal, and its drop during sleep". Unless coherence is exactly zero, some degree of coherence is always "present". Figure 1 instead shows that coherence is modulated across frequencies during wakefulness, with peaks in the delta and theta ranges.

In Figure 2, they write: "PAC between respiration and OB gamma amplitude was present during ASt but disappeared during SWSt". Again, the authors should clarify what is meant by "disappeared", as they only tested for differences between ASt and SWSt.

Given that the authors implemented a strategy to test for above-chance coherence using surrogate datasets, they should consistently provide statistical tests showing which conditions or frequency bands exhibit coherence above chance in order to justify claims about the presence or absence of an effect.

(5) Likewise, comparisons across states should always be supported by statistical tests, for example, in Figure 4. In addition, despite the apparent absence of coherence during SWSt in Figures 4f and 4g (which again should be formally tested), Figure 4h shows an increase in coherence around 2 Hz, which suggests some degree of coherence between nasal airflow and the olfactory bulb.

(6) Figures should more clearly distinguish results based on a single "representative" animal from population averages. For example, were Figures 4g and 2h computed at the population level?

Reviewer #3 (Public review):

Summary:

Sleep is typified by a behavioural attenuation of responsiveness to external stimuli (higher arousal thresholds). There are various mechanisms through which sensory perception could be dampened, and while thalamic and cortical gate points have been well studied, the focus here is on peripheral ones - at the level of the olfactory bulb (OB). While something conceptually similar has been shown in insects, this paper represents an important contribution to understanding attenuation of sensory perception during rodent sleep and anaesthesia.

This paper shows that respiration-locked potentials and gamma activity in the olfactory bulb, which are important for olfactory coding, are diminished during sleep and when under anaesthesia compared to wake. Further, this state-dependent activity in OB is likely to be locally generated. Using a tracheotomy procedure aimed to dissociate nasal airflow from natural inhalations, authors demonstrate that local field potentials (LFPs) in the OB phase lock with artificially generated air pulses (delivered into the nasal cavity) during the active phase of anaesthesia but not during a more passive state. LFPs did not synchronise with respiratory signals during either anaesthesia state. Lastly, the authors showed that as delta power increased (typical of slow-wave-sleep), the coherence between nasal inhalation rhythms and OB LFP coherence decreased, indicating that as rats experienced something akin to slow-wave-sleep (during anaesthesia), disconnection from the external environment could be augmented. Taken together, the authors argue that the change in activity observed in the olfactory bulb during sleep and anaesthesia provides a non-permissive state for sensory processing and manifests as sensory dissociation

Strengths:

The manuscript is well-written, and the experiments are thorough. Experiments examining coupling of nasal respiration with OB potentials and delta activity are particularly interesting as they point to augmented sensory disconnection during a sleep phase typically associated with higher arousal thresholds.

Weaknesses:

(1) An experiment addressing the following points, is missing:

Does odour stimulation that wakes up a subject restore gamma activity and respiration-locked potentials?

Is OB/respiration desynchrony maintained when presented with a non-rousing stimulus?

Is waking upon stimulus delivery less likely as delta activity increases and coherence between OB/respiratory rhythms weakens?

(2) Many of the experiments are performed under anaesthesia, which I understand is for practical reasons. While authors are forthcoming about limitations of using anaesthesia in lieu of natural sleep states, I would have preferred to see more experiments performed on sleeping animals.

Reviewer #1 (Public Review):

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

Summary:

Ever-improving techniques allow the detailed capture of brain morphology and function to the point where individual brain anatomy becomes an important factor. This study investigated detailed sulcal morphology in the parieto-occipital junction. Using cutting-edge methods, it provides important insights into local anatomy, individual variability, and local brain function. The presented work advances the field and will stimulate future research into this important area.

Strengths:

Detailed, very thorough methodology. Multiple raters mapped detailed sulci in a large cohort. The identified sulcal features and their functional and behavioural relevance are then studied using various complementary methods. The results provide compelling evidence for the importance of the described sulcal features and their proposed relationship to cortical brain function.

Reviewer #2 (Public Review):

Summary:

After manually labelling 144 human adult hemispheres in the lateral parieto-occipital junction (LPOJ), the authors 1) propose a nomenclature for 4 previously unnamed highly variable sulci located between the temporal and parietal or occipital lobes, 2) focus on one of these newly named sulci, namely the ventral supralateral occipital sulcus (slocs-v) and compare it to neighbouring sulci to demonstrate its specificity (in terms of depth, surface area, gray matter thickness, myelination, and connectivity), 3) relate the morphology of a subgroup of sulci from the region including the slocs-v to the performance in a spatial orientation task, demonstrating behavioural and morphological specificity. In addition to these results, the authors propose an extended reflection on the relationship between these newly named landmarks and previous anatomical studies, a reflection about the slocs-v related to functional and cytoarchitectonic parcellations as well as anatomic connectivity and an insight about potential anatomical mechanisms relating sulcation and behaviour.

Strengths:

- To my knowledge, this is the first study addressing the variable tertiary sulci located between the superior temporal sulcus (STS) and intra-parietal sulcus (IPS).

- This is a very comprehensive study addressing altogether anatomical, architectural, functional and cognitive aspects.

- The definition of highly variable yet highly reproductible sulci such as the slocs-v feeds the community with new anatomo-functional landmarks (which is emphasized by the provision of a probability map in supp. mat., which in my opinion should be proposed in the main body).

- The comparison of different features between the slocs-v and similar sulci is useful to demonstrate their difference.

- The detailed comparison of the present study with state of the art contextualises and strengthens the novel findings.

- The functional study complements the anatomical description and points towards cognitive specificity related to a subset of sulci from the LPOJ

- The discussion offers a proposition of theoretical interpretation of the findings

- The data and code are mostly available online (raw data made available upon request).

Reviewer #3 (Public Review):

Summary:

72 subjects, and 144 hemispheres, from the Human Connectome Project had their parietal sulci manually traced. This identified the presence of previous undescribed shallow sulci. One of these sulci, the ventral supralateral occipital sulcus (slocs-v), was then demonstrated to have functional specificity in spatial orientation. The discussion furthermore provides an eloquent overview of our understanding of the anatomy of the parietal cortex, situating their new work into the broader field. Finally, this paper stimulates further debate about the relative value of detailed manual anatomy, inherently limited in participant numbers and areas of the brain covered, against fully automated processing that can cover thousands of participants but easily misses the kinds of anatomical details described here.

Strengths:

- This is the first paper describing the tertiary sulci of the parietal cortex with this level of detail, identifying novel shallow sulci and mapping them to behaviour and function.

- It is a very elegantly written paper, situating the current work into the broader field.

- The combination of detailed anatomy and function and behaviour is superb.

Reviewer #1 (Public review):

The manuscript investigates how neuropeptidergic signaling affects sleep regulation in Drosophila larvae. The authors first conduct a screen of CRISPR knock-out lines of genes encoding enzymes or receptors for neuropeptides and monoamines. As a result of this screen, the authors follow up on one hit, the hugin receptor, PK2-R1. They use genetic approaches including mutants and targeted manipulations of PK2-R1 activity in insulin-producing cells (IPCs) to increase total sleep amounts in 2nd instar larvae. Similarly, dilp3 and dilp5 null mutants and genetic silencing of IPCs show increases in sleep. The authors also show that hugin mutants and thermogenetic/optogenetic activation of hugin-expressing neurons caused reductions in sleep. Furthermore, they show through imaging-based approaches that hugin-expressing neurons activate IPCs. A key finding is that wash on of hugin peptides, Hug-γ and PK-2, in ex vivo brain preparations activates larval IPCs, as assayed by CRTC::GFP imaging. The authors then examine how the PK2-R1, hugin, and IPC manipulations affect adult sleep. Finally, the authors examine how Ca2+ responses through CRTC::GFP imaging in adult IPCs are influenced by the wash on of hugin peptides.

Strengths:

(1) This paper builds on previously published studies that examine Drosophila larval sleep regulation. Through the power of Drosophila genetics, this study yields additional insights into what role neuropeptides play in regulation of Drosophila larval sleep.

(2) This study utilizes several diverse approaches to examine larval and adult sleep regulation, neural activity, and circuit connections. The impressive array of distinct analyses provides new understanding into how Drosophila sleep-wake circuitry in regulated across the lifespan.

(3) The imaging approaches used to examine IPC activation upon hugin manipulation (either thermogenetic activation or wash on of peptides) demonstrate a powerful approach for examining how changes in neuropeptidergic signaling affect downstream neurons. These experiments involve precise manipulations as the authors use both in vivo and ex vivo conditions to observe an effect on IPC activity.

Weaknesses:

(1) There is limited discussion of why statistically significant differences are observed in some genetic and temperature controls. This discussion would better support the authors' conclusions.

(2) The functional connectivity of the huginPC-IPC circuit in larvae could be better supported by chemogenetics using real-time calcium imaging (GCaMP).

Comments on revisions:

I would like to thank the authors for the revisions. The inclusion of all sleep metrics, more detailed descriptions in the methods, & a more thorough comparison to other published articles has addressed most of my concerns.

Reviewer #3 (Public review):

Summary:

Sleep affects cognition and metabolism, evolving throughout development. In mammals, infants have fast sleep-wake cycles that stabilize in adults via circadian regulation. In this study, the author performed a genetic screen for neurotransmitters/peptides regulating sleep and identified the neuropeptide Hugin and its receptor PK2-R1 as essential components for sleep in Drosophila larvae. They showed that IPCs express Pk2-R1 and silencing IPCs resulted in significant increase in the sleep amount, which was consistent with the effect they observed in PK2-R1 knock out mutants. They also showed that Hugin peptides, secreted by a subset of Hugin neurons (Hug-PC), activate IPCs through the PK2-R1 receptor. This activation prompts IPCs to release insulin-like peptides (Dilps), which are implicated in the modulation of sleep. They showed that Hugin peptides induce a PK2-R1 dependent calcium (Ca²⁺) increase in IPCs, which they linked to the release of Dilp3, showing a connection between Hugin signaling to IPCs, Dilp3 release and sleep regulation. Additionally, the activation of Hug-PC neurons reduced sleep amounts, while silencing them had the opposite effect. In contrast to the larval stage, the Hugin/PK2-R1 axis was not critical for sleep regulation in Drosophila adults, suggesting that this neuropeptidergic circuitry has divergent roles in sleep regulation across different stages of development.

Strengths:

This study used an updated system for sleep quantification in Drosophila larvae and this method allowed precise measurement of larval sleep patterns which is essential for the understanding of sleep regulation.

The authors performed unbiased genetics screening and successfully identified novel regulators for larval sleep, Hugin and its receptor PK2-R1, making a substantial contribution to the understanding of neuropeptidergic control of sleep regulation.

They clearly demonstrated the mechanism by which Hugin expressing neurons influence sleep through the activation of IPCs via PK2-R1 with Ca2+ responses and can modulate sleep.

Based on the demonstrated activation of PK2-R1 by the human Hugin orthologue Neuromedin U, research on human sleep disorders may benefit from the discoveries from Drosophila since sleep regulating mechanisms are conversed across species.

Weaknesses:

Previously identified weaknesses have been largely addressed by the authors.

Reviewer #1 (Public review):

Summary:

In this manuscript, Henning et al. examine the impact of GABAergic feedback inhibition on the motion-sensitive pathway of flies. Based on a previous behavioral screen, the authors determined that C2 and C3, two GABAergic inhibitory feedback neurons in the optic lobes of the fly, are required for the optomotor response. Through a series of calcium imaging and disruption experiments, connectomics analysis, and follow-up behavioral assays, the authors concluded that C2 and C3 play a role in temporally sharpening visual motion responses. While this study employs a comprehensive array of experimental approaches, I have some reservations about the interpretation of the results in their current form. I strongly encourage the authors to provide additional data to solidify their conclusions. This is particularly relevant in determining whether this is a general phenomenon affecting vision or a specific effect on motion vision. Knowing this is also important for any speculation on the mechanisms of the observed temporal deficiencies.

Strengths:

This study uses a variety of experiments to provide a functional, anatomical, and behavioral description of the role of GABAergic inhibition in the visual system. This comprehensive data is relevant for anyone interested in understanding the intricacies of visual processing in the fly.

Weaknesses:

The most fundamental criticism of this study is that the authors present a skewed view of the motion vision pathway in their results. While this issue is discussed, it is important to demonstrate that there are no temporal deficiencies in the lamina, which could be the case since C2 and C3, as noted in the connectomics analysis, project strongly to laminar interneurons. If the input dynamics are indeed disrupted, then the disruption seen in the motion vision pathway would reflect disruptions in temporal processing in general and suggest that these deficiencies are inherited downstream. A simple experiment could test this. Block C2, C3, and both together using Kir2.1 and shibiere independently, then record the ERG. Alternatively, one could image any other downstream neuron from the lamina that does not receive C2 or C3 input.

Figure 6c. More analysis is required here, since the authors claim to have found a loss in inhibition (ND). However, the difference in excitation appears similar, at least in absolute magnitude (see panel 6c), for PD direction for T4 C2 and C3 block. Also I predict that C2&C3 block statistically different from C3 only, why? In any case, it would be good to discuss the clear trend in the PD direction by showing the distribution of responses as violin plots to better understand the data. It would be also good to have some raw traces to be able to see the differences more clearly, not only polar plots and averages.

The behavioral experiments are done with a different disruptor than the physiological ones. One blocks chemical synapses, the other shunts the cells. While one would expect similar results in both, this is not a given. It would be great if the authors could test the behavioral experiments with kir2.1 too.

Comments on revisions:

I have no further comments.

Reviewer #2 (Public review):

The work by Henning et al. explores the role of feedback inhibition in motion vision circuits, providing the first identification of inhibitory inheritance in motion-selective T4 and T5 cells of Drosophila. Among the strengths of this work is the verification of the GABAergic nature of C2 and C3 with genetic and immunohistochemical approaches. In addition, double-silencing C2&C3 experiments help to establish a functional role for these cells. The authors holistically use the Drosophila toolbox to identify neural morphologies, synaptic locations, network connectivity, neuronal functions and the behavioral output.

A limitation of the study is that the mediating neural correlates from C2&C3 to T4&T5 are not clarified, rather Mi1 is found to be one of them. In the future, the same set of silencing experiments performed for C2-Mi1 could be extended to C2 &C3-Tm1 or Tm4 to find the T5 neural mediators of this feedback inhibition loop. Future experiments might also disentangle the parallel or separate function of C2 and C3 neurons.

In summary, this work advances our current knowledge in Drosophila motion vision and sets the way for further exploring the intricate details of direction selective computations.

Comments on revisions:

A label for T5 is missing from Figure 5b. Thank you for addressing our concerns and considering each of our suggestions.

Reviewer #1 (Public review):

Summary:

In this study, authors employed comprehensive proteomics and transcriptomics analysis to investigate the systemic and organ-specific adaptations to IF in male and they found that shared biological signaling processes were identified across tissues, suggesting unifying mechanisms linking metabolic changes to cellular communication, which reveal both conserved and tissue-specific responses by which IF may optimize energy utilization, enhance metabolic flexibility, and promote cellular.

Strengths:

This study detected multiple organs including liver, brain and muscle and revealed both conserved and tissue-specific responses to IF.

Weaknesses:

(1) Why did the authors choose liver, brain and muscle but not other organs such as heart and kidney? The latter are proven to be the large consumer of ketones, which is also changed in the IF treatment of this study.

(2) The proteomics and transcriptomics analysis were only performed at 4 months. However, a strong correlation between IF and the molecular adaptions should be time points-dependent.

(3) The context lack section of "discussion", which shows the significance and weakness of the study.

(4) There is no confirmation for the proteomic and transcriptomic profiling. For example, the important changes in proteomics could be further identified by a Western blot.

Reviewer #2 (Public review):

Summary:

Fan and colleagues measure proteomics and transcriptomics in 3 organs (liver, skeletal muscle, cerebral cortex) from male C57BL/6 mice to investigate whether intermittent fasting (IF; 16h daily fasting over 4 months) produces systemic and organ-specific adaptations.

They find shared signaling pathways, certain metabolic changes and organ-specific responses that suggest IF might affect energy utilization, metabolic flexibility while promoting resilience at the cellular level.

Strengths:

The fact that there are 3 organs and 2 -omics approaches is a strength of this study.

Weaknesses:

Poor figures presentation and knowledge of the literature. One sex (male).

On resubmission the Authors' decision to discriminate the organ-specific from the organ-shared effects of intermittent fasting (IF) also enabled them to more precisely determine the lack of correspondence between transcriptomics and proteomics, i.e., not all transcripts lead to protein translation.

Reviewer #3 (Public review):

Summary:

Fan et al utilize large omics data sets to give an overview of proteomic and gene expression changes after 4 moths of intermittent fasting (IF) in liver, muscle and brain tissue. They describe common and district pathways altered under IF across tissues using different analysis approaches. Main conclusions presented are the variability in responses across tissues with IF. Some common pathways were observed, but there were notable distinctions between tissues.

Strengths:

(1) The IF study was well conducted and ran out to 4 months which was a nice long-term design.

(2) The multi omics approach was solid and additional integrative analysis was complementary to the illustrate the differential pathways and interactions across tissues.

(3) The authors did not over-step their conclusions and imply an overreached mechanism.

Weaknesses:

The weaknesses, which are minor, include use of only male mice and the early start (6 weeks) of the IF treatment. However, the authors have provided justification on why they chose male mice and the time points used in the study.

Reviewer #1 (Public review):

Summary:

This study investigated the immunogenicity of a novel bivalent EABR mRNA vaccine for SARS-CoV-2 that expresses enveloped virus-like particles in pre-immune mice as a model for boosting the population that is already pre-immune to SARS-CoV-2. The study builds on promising data showing a monovalent EABR mRNA vaccine induced substantially higher antibody responses than a standard S mRNA vaccine in naïve mice. In pre-immune mice, the EABR booster increased the breadth and magnitude of the antibody response, but for Omicron, the effects were modest and often not statistically significant. The authors provide compelling evidence to support this may be due to immune imprinting.

This study also builds on prior work with additional experiments to elucidate the mechanisms that contributed to the EABR increased immunogenicity in naive mice including evidence that the vaccine is inducing responses to more RBD epitopes and a potential role for heterodimer formation as a mechanism whereby bivalent vaccines induce cross-reactive B cell responses.

Strengths:

Evaluating a novel SARS-CoV-2 vaccine that was substantially superior in naive mice in pre-immune mice as a model for its potential in the pre-immune population.

Providing insight into a possible role of immune imprinting in shaping immune responses to updated booster immunizations.

Minor weaknesses:

(1) Overall, immune responses against Omicron variants were substantially lower than against the ancestral Wu-1 strain that the mice were primed with. The authors speculate this is evidence of immune imprinting. While parallel controls (mice immunized 3 times with just the bivalent EABR vaccine) were not tested, the authors point to prior published work showing Omicron S antigen is a strong immunogen. This indicates the lower immune responses to Omicron are likely due to immune imprinting (or original antigenic sin) and not due to S immunogen being inherently less immunogenic than the S protein from the ancestral Wu-1 strain.

(2) The authors reported statistically significant increase in antibody responses with the bivalent EABR vaccine booster when compared to the monovalent S mRNA vaccine but consistently failed to show significantly higher responses when compared to the bi-valent S mRNA vaccine suggesting that in pre-immune mice, the EABR vaccine has no apparent advantage over the bivalent S mRNA vaccine which is the current standard. There were, however, some trends indicating the group sizes were insufficiently powered to see a difference. The discussion acknowledges these limitations of their studies and potential limited benefits of the EABR strategy in pre-immune mice vs standard bivalent mRNA vaccine.

(3) The EABR S mRNA vaccine was superior to the conventional mRNA S vaccine in naïve mice but not in pre-immune mice. The authors expanded the discussion to propose a possible role for immune imprinting in this result which is supported by the data.

Reviewer #3 (Public review):

Summary:

The authors evaluated a novel bivalent (Wu1/BA.5 based) mRNA platform that uses the EABR strategy to produce enveloped virus-like particles for vaccination. These were tested as boosters in the context of pre-existing immunity in mice that received two prior immunizations with conventional Wu1 mRNA vaccines. The animal experimental timeline aimed at mimicking the vaccinations/booster schedule implemented during the COVID-19 pandemia. The authors tested and compared different booster strategies: (1) conventional Wu1 S protein encoding mRNA vaccine, (2) EABR Wu1 S protein encoding mRNA vaccine that produces enveloped virus-like particles, (3) conventional Wu1/BA.5 S protein encoding mRNA vaccine, and (4) EABR Wu1/BA.5 S protein encoding mRNA vaccine that produces enveloped virus-like particles. The EABR approach (monovalent or bivalent) enhanced the antibody response against Wu1 and Omicron subvariants. Interestingly, the bivalent EABR Wu1/BA.5 mRNA (strategy 4) generated polyclonal sera targeting multiple receptor-binding domain epitopes: these sera were more diverse than those generated with the other tested booster strategies (1 to 3).

Strengths:

The monovalent Wu1 S-EABR mRNA booster led to increase in antibody binding to tested Omicron variants (BA.5, BQ.1.1, XBB.1), while the bivalent Wu1/BA.5 S-EABR mRNA booster led to the highest Ab response against Omicron variants (BA.5, BQ.1.1, XBB.1) in pre-vaccinated mice.

Neutralization assays showed that the monovalent Wu1 S-EABR mRNA booster had the highest Wu1 neutralization activity and to a lesser extent the early BA.1 early Omicron variant. The monovalent Wu1 S-EABR mRNA booster and bivalent Wu1/BA.5 S-EABR mRNA booster had similar BA.5 neutralizing activity. Neutralizing activity of the different boosters was less pronounced with later Omicron variants BQ.1.1 and XBB.1. However, of the different boosters tested, the bivalent Wu1/BA.5 S-EABR mRNA booster induced the highest neutralizing titers. These results support that the EABR mRNA vaccine strategy helps improve neutralizing activity against different tested Omicron subvariants: a few (1 or 2) mRNA constructs expressing major antigens in enveloped virus-like particles likely provide a novel strategy to elicit an immune response that has the potential to neutralize subsequent variants.

The EABR enveloped virus-like particle strategy induces a more diverse antibody response, including epitopes not recognized by the other booster strategies: these new epitopes could play a role in neutralizing activity against new future variants.

Moreover, the bivalent Wu1/BA.5 S-EABR mRNA booster could potentially produce heterotrimeric S proteins to help activation of cross-reactive B cells and increase polyclass antibody responses.

Weaknesses:

When it comes to later Omicron variants (BQ.1.1 and XBB.1), there is a discrepancy between epitope binding response and neutralization titers: only a few binding antibodies have neutralizing activity with these later variants, showing a limitation of the EABR strategy.

The authors showed that the EABR mRNA strategy represents a novel antigen exposing strategy where antigens are produced at the cell surface and also at the surface of enveloped virus-like particles. This allows the production of novel antigens in addition to those that would be typically generated against cell surface exposed antigens. These novel antigens targeting new epitopes could potentially have neutralizing activity.

Using a bivalent EABR mRNA booster led to higher antibody titers and higher neutralizing activity. The challenge is to select the best antigen target/variant to support neutralizing activity against later virus variants.

Reviewer #1 (Public review):

Summary:

This study delineates a highly specific role for the pPVT in unconditioned defensive responses. The authors use a novel, combined SEFL and SEFR paradigm to test both conditioned and unconditioned responses in the same animal. Next, a c-fos mapping experiment showed enhanced PVT activity in the stress group when exposed to the novel tone. No other regions showed differences. Fiber photometry measurements in pPVT showed enhancement in response to the novel tone in the stressed but not non-stressed groups. Importantly, there were also no effects when calcium measurements were taken during conditioning. Using DREADDS to bidirectionally manipulate global pPVT activity, inhibition of the PVT reduced tone freezing in stressed mice while stimulation increased tone freezing in non-stressed mice.

Strengths:

A major strength of this research is the use of a multi-dimensional behavioral assay that delineates behavior related to both learned and non-learned defensive responses. The research also incorporates high-resolution approaches to measure neuronal activity and provide causal evidence for a role for PVT in a very narrow band of defensive behavior. The data are compelling, and the manuscript is well-written overall.

Weaknesses:

Figure 1 shows a small, but looks to be, statistically significant, increase in freezing in response to the novel tone in the no-stress group relative to baseline freezing. This observation was also noticed in Figures 2 and 7. The tone presented is relatively high frequency (9 kHz) and high dB (90), making it a high-intensity stimulus. Is it possible that this stimulus is acting as an unconditioned stimulus? In addition, in the final experiment, the tone intensity was increased to 115 dB, and the freezing % in the non-stressed group was nearly identical (~20%) to the non-stressed groups in Figures 1-2 and Figure 7. It seems this manipulation was meant as a startle assay (Pantoni et al., 2020). Because the auditory perception of mice is better at high frequencies (best at ~16 kHz), would the effect seen be evident at a lower dB (50-55) at 9 kHz? If the tone was indeed perceived as "neutral," there should be no freezing in response to the tone. This complicates the interpretation of the results somewhat because while the authors do admit the stimulus is loud, would a less loud stimulus result in the same effect? Could the interaction observed in this set of studies require not a novel tone, but rather a high-intensity tone that elicits an unconditioned response? Along these same lines, it appears there may be an elevation in c-fos in the PVT in the non-stress tone test group versus the no-stress home cage control, and overall it appears that tone increases c-fos relative to homecage. Could PVT be sensitive to the tone outside of stress? Would there be the same results with a less intense stimulus? I would also be curious to know what mice in the non-stressed group were doing upon presentation of the tone besides freezing. Were any startle or orienting responses noticed?

Comments on revisions:

Following revision, this reviewer felt all of the above concerns were addressed.

Reviewer #2 (Public review):

Summary:

Nishimura and colleagues present findings of a behavioral and neurobiological dissociation of associative and nonassociative components of Stress Enhanced Fear Responding (SEFR).

Strengths:

This is a strong paper that identifies the PVT as a critical brain region for SEFR responses using a variety of approaches, including immunohistochemistry, fiber photometry, and bidirectional chemogenetics. In addition, there is a great deal of conceptual innovation. The authors identify a dissociable behavior to distinguish the effects of PVT function (among other brain regions).

Weaknesses:

(1) The authors find a lack of difference between the Stress and No Stress groups in pPVT activity during SEFL conditioning with fiber photometry but an increase in freezing with Gq DREADD stimulation. How do authors reconcile this difference in activity vs function?

(2) Because the PVT plays a role in defensive behaviors, it would be beneficial to show fiber photometry data during freezing bouts vs exclusively presented during tone a shock cue presentations.

(3) Similar to the above point, were other defensive behaviors expressed as a result of footshock stress or PVT manipulations?

(4) Tone attenuation in Figure 8 seems to be largely a result of minimal freezing to a 115-dB tone. While not a major point of the paper, a more robust fear response would be convincing.

(5) In the open field test, the authors measure total distance. It would be beneficial to also show defensive behavioral (escape, freezing, etc) bouts expressed.

(6) The authors, along with others, show a behavioral and neural dissociation of footshock stress on nonassociative vs associative components of stress; however, the nonassociative components as a direct consequence of the stress seem to be necessary for enhancement of associative aspects of fear. Can authors elaborate on how these systems converge to enhance or potentiate fear?

(7) In the discussion, authors should elaborate on/clarify the cell population heterogeneity of the PVT since authors later describe PVT neurons as exclusively glutamatergic.

Comments on revisions:

Following revision, this reviewer felt all of the above concerns were addressed.

Reviewer #3 (Public review):

Summary:

The manuscript by Nishimura et al. examines the behavioural and neural mechanisms of stress-enhanced fear responding (SEFR) and stress-enhanced fear learning (SEFL). Groups of stressed (4 x shock exposure in a context) vs non-stressed (context exposure only) animals are compared for their fear of an unconditioned tone, and context, as well as their learning of new context fear associations. Shock of higher intensity led to higher levels of unlearned stress-enhanced fear expression. Immediate early gene analysis uncovered the PVT as a critical neural locus, and this was confirmed using fiber photometry, with stressed animals showing an elevated neural signal to an unconditioned tone. Using a gain and loss of function DREADDs methodology, the authors provide convincing evidence for a causal role of the PVT in SEFR.

Strengths:

(1) The manuscript uses critical behavioural controls (no stress vs stress) and behavioural parameters (0.25mA, 0.5mA, 1mA shock). Findings are replicated across experiments.

(2) Dissociating the SEFR and SEFL is a critical distinction that has not been made previously. Moreover, this dissociation is essential in understanding the behavioural (and neural) processes that can go awry in fear.

(3) Neural methods use a multifaceted approach to convincingly link the PVT to SEFR: from Fos, fiber photometry, gain and loss of function using DREADDs.

Weaknesses:

No weaknesses were identified by this reviewer; however, I have the following comments:

A closer examination of the Test data across time would help determine if differences may be present early or later in the session that could otherwise be washed out when the data are averaged across time. If none are seen, then it may be worth noting this in the manuscript.

Given the sex/gender differences in PTSD in the human population, having the male and female data points distinguished in the figures would be helpful. I assume sex was run as a variable in the statistics, and nothing came as significant. Noting this would also be of value to other readers who may wonder about the presence of sex differences in the data.

Comments on revisions:

Following revision, this reviewer felt all of the above comments were addressed.

Reviewer #1 (Public review):

It is well established that many potivirids (viruses in the Potiviridae family) particularly potyviruses (viruses in the Potyvirus genus) recruit (selectively) either eIF4E or eIF(iso)4E, while some others can use both of them to ensure a successful infection. CBSD caused by two potyvirids, i.e., ipomoviruses CBSV and UCBSV severely impedes cassava production in West Africa. In a previous study (PBI, 2019), Gomez and Lin (co-first authors), et al. reported that cassava encodes five eIF4E proteins including eIF4E, eIF(iso)4E-1, eIF(iso)4E-2, nCBP-1 and nCBP-2, and CBSV VPg interacts with all of them (Co-IP data). Simultaneous CRISPR/Cas9-mediated editing of nCBp-1 and -2 in cassava significantly mitigate CBSD symptoms and incidence. In this study, Lin et al further generated all five eIF4E family single mutants as well as both eIF(iso)4E-1/-2 and nCBP-1/-2 double mutants in a farmer-preferred casava cultivar. They found that both eIF(iso)4E and nCBP double mutants show reduced symptom severity and the latter is of better performance. Analysis of mutant sequences revealed one important point mutation L51F of nCBP-2 that may be essential for the interaction with VPg. The authors suggest that introduction of L51F mutation into all five eIF4E family proteins may lead to strong resistance. Overall I believe this is an important study enriching knowledge about eIF4E as a host factor/susceptibility factor of potyvirids and proposing new information for the development of high CBSD resistance in cassava. I suggest the following two major comments for authors to consider for improvement:

(1) As eIF(iso)4e-1/-2 or nCBP-1/-2 double mutans show resistance, why not try to generate a quadruple mutant? I believe it is technically possible through conventional breeding.

(2) I agree that L51F mutation may be important. But more evidence is needed to support this idea. For example. Authors may conduct quantitative Y2H assay on binding of VPg to each of eIF4E (L51F) mutants. Such data may

Comments on revisions:

(1) The authors explained it is technically challenging to generate quadruple mutant.<br /> (2) The authors have properly addressed my comment 2.<br /> I do not have more concerns.

Reviewer #2 (Public review):

Eukaryotic translation initiation factor 4E (eIF4E) acts as a key susceptibility factor for members of the Potyviridae family, and knockout of eIF4E family members enables the generation of corresponding virus-resistant germplasm. In this study, the authors performed systematic knockout experiments on the members of eIF(iso)4E and nCBP clades in cassava, which demonstrated that simultaneous knockout of the eIF4E-family genes nCBP-1 and nCBP-2 in the cultivar 60444 significantly attenuates Cassava Brown Streak Disease (CBSD) root symptoms and reduces viral titer. The authors further screened for CBP mutants without VPg-binding activity and identified the nCBP-2 L51F mutant, which loses the ability to interact with VPg. In the revised manuscript, the authors have addressed most of my previous questions and revised the relevant content accordingly. Overall, this study is a well-performed work, with extensive explorations carried out particularly in the gene knockout of members of eIF(iso)4E and nCBP. It provides an important value for investigating the functions of eIF(iso)4E and nCBP clade members in the development of disease-resistant germplasm, and the identified nCBP-2 L51F mutant also offers a crucial gene editing site target for the generation of virus-resistant cassava germplasm in future.

Reviewer #3 (Public review):

In the manuscript, the authors generated several mutant plants defective in the eIF4E family proteins and detected cassava brown streak viruses (CBSVs) infection in these mutant plants. They found that CBSVs induced significantly lower disease scores and virus accumulation in the double mutant plants. Furthermore, they identified important conserved amino acid for the interaction between eIF4E protein and the VPg of CBSVs by yeast two hybrid screening. The experiments are well designed, however, some points need to be clarified:

(1) The authors reported that the ncbp1 ncbp2 double mutant plants were less sensitive to CBSVs infection in their previous study, and all the eIF4E family proteins interact with VPg. In order to identify the redundancy function of eIF4E family proteins, they generated mutants for all eIF4E family genes, however, these mutants are defective in different eIF4E genes, they did not generate multiple mutants (such as triple, quadruple mutants or else) except several double mutant plants, it is hard to identify the redundant function eIF4E family genes.

(2) The authors identified some key amino acids for the interaction between eIF4E and VPg such as the L51, it is interesting to complement ncbp1 ncbp2 double mutant plants with L51F form of eIF4E and double check the infection by CBSVs.

Comments on revisions:

The reviewer understand Cassava is not a model plant, it is hard for the authors to generate multiple genetic mutant plants for experiments, so nothing was done to respond to the comments raised by the reviewer.

Reviewer #1 (Public review):

Summary:

The authors aimed to determine whether reward conditioning increases inhibitory regulation of Vglut1-expressing BLA→NAc neurons and whether this inhibition shapes motivated behaviors. They used whole-cell electrophysiology to measure conditioning-induced changes in synaptic inhibition and intrinsic excitability. Subsequently, they employed dual-recombinase chemogenetics to selectively inhibit this projection during behavioral tasks. The goal was to test whether suppressing the activity of Vglut1-expressing neurons would alter reward learning, valuation, and fear discrimination.

Strengths:

(1) The combination of electrophysical and behavioral assessments to dissect the function of Vglut1-expressing BLA→NAc neurons.

(2) The various behavioral assessments employed to determine the effect of silencing Vglut1-expressing BLA→NAc neurons.

Weaknesses:

(1) The introduction underscores the importance of molecular identity and population dynamics when studying the function of BLA→NAc neurons. Yet, the experiments and manuscript provide little to no information about the Slc17a7-expressing population under study. In fact, there is no evidence that the viral manipulations targeted this neuronal population (e.g., extent and specificity of viral transduction). Regarding population dynamics, evidence is meant to be provided by Experiment 1, but the results are difficult to interpret. The control mice were not exposed to the conditioning chambers, stimuli, or food rewards. These exposures may have been sufficient to produce the changes observed in the experimental mice (i.e., they may have had nothing to do with cue-reward learning). Further, the experiments provide no evidence that the observed effects result from prolonged conditioning, since there is no group receiving a single conditioning session.

(2) The dual-recombinase approach employed does not permit conclusions about the BLA→NAc pathway specifically, because the effects of silencing NAc-projecting BLA neurons could be driven by modulation of activity in other brain regions innervated by these same neurons through collateral projections. This limitation must be clearly acknowledged by the authors, and the manuscript should refrain from making definitive claims about the BLA→NAc pathway per se.

(3) The experimental parameters and measures used for cued-reward conditioning complicate any firm conclusions about the observed effects. The use of a 2-second cue provides a minimal temporal window to monitor cue-related behavior. This issue is masked in the data presented because what is labeled as "cued responses" includes responses that occur after the cue has terminated and overlap with those triggered by sucrose delivery itself. These post-cue responses cannot be classified as cue-reward responses since the cue is no longer present; they are reward-related responses. Perhaps the z-score calculation addresses this issue, but this is difficult to assess since the authors do not explain how this calculation was performed or what baseline period was used.

(4) Throughout the manuscript, there is conceptual confusion regarding the fundamental distinction between Pavlovian (cue-outcome) and instrumental (action-outcome) responses. It is unclear why the authors aimed to study both types of conditioning, but greater caution is necessary when interpreting the findings labeled as "instrumental conditioning." First, no evidence is provided that initiation port entries constitute an instrumental or goal-directed response rather than a Pavlovian approach behavior. Second, many of the conclusions are based on analyzing reward port entries-a Pavlovian conditioned response identical to that measured in the cued-reward conditioning task. This conflation undermines claims about instrumental learning.

(5) The data from the reward valuation and reversal learning experiments are difficult to interpret. The animals are not tested under extinction conditions (with the flavors present but without reward delivery), making it impossible to establish whether their behavior relies on learned associations or ongoing reinforcement. Further, the behavior generated by these procedures appears unreliable, with substantial inconsistencies across figures (compare Figure 4A with Figures 5B, C, G, H).

(6) The results from the auditory fear discrimination procedure are also difficult to interpret. No conditioning data are presented, and the "enhanced discrimination" could simply reflect reduced overall responding to the CS-. It is not clear how this selective impact on the CS- fits with the authors' conclusions about enhanced associative salience (noting that the meaning of the latter remains obscure).

(7) The manuscript contains several statements about behavioral outcomes that are not supported by statistical evidence. The list provided here is non-exhaustive, and the authors should carefully correct any conclusions that lack statistical support.<br /> a) Line 294 (Figure 2F): the control mice gradually reached a similar performance to the experimental mice.<br /> b) Lines 301-303 (Figures 3D-F): inhibition strengthened the temporal association between initiation and reward consumption.<br /> c) Lines 337-339 (Figure 4A): both groups increased their preference for 10% sucrose.

(8) The manuscript suffers from a lack of clarity and/or transparency about experimental parameters and data. Clarifications about the following would be necessary for the reader to confidently interpret the findings.<br /> a) Number of animals of each sex in each group.<br /> b) Number of animals excluded and justification.<br /> c) Analysis of sex differences.<br /> d) A clarification on the control group used in the electrophysiological experiment.<br /> e) Whether the same animals progress through multiple behavioral paradigms or if separate cohorts are used.<br /> f) All protocols should be described in the methods section.

Without clarifying the points made above, a reliable and fair assessment of the discussion is impossible.

Reviewer #2 (Public review):

Summary:

This study by Mercer et al. focused on Vglut1 neurons in the BLA that project to the NAc. They characterized reward conditioning-induced electrophysiological changes in these neurons, including a decrease in membrane excitability and an increase in inhibitory synaptic inputs onto them, and showed the consequences of reducing their activity in enhancing reward-seeking behaviors. Considering that Vglut1 neurons represent the majority of the BLA→NAc projecting neurons, the findings are important for potentially correcting some of the previous biases in understanding the role of BLA-to-NAc projection in reward processing, for example, the notion that this projection generally promotes reward seeking by conveying reward-associated cue information.

Strengths:

The paper is clearly written, with results strongly supporting the main conclusions for the most part.

There are a few weaknesses noted. For example:

(1) They used a retrograde recombinase strategy to drive DREADD expression in these cells; however, it is not known if they project exclusively to NAc or to other brain regions as well, and whether those other potential regions may mediate the DREADDs (Gi) effects on reward seeking. They also did not show which subregions of the NAc were innervated by these neurons.

(2) They did not assess potential changes in excitatory synaptic transmission onto these cells after reward conditioning, which leaves a gap in concluding a shift toward inhibition.

(3) They also did not report on whether the inhibition was specific to Vglut1 neurons.

(4) Some statistics appear missing (Figure 3D-F), not optimal (Figure 5CEF and HJK using separate t-tests rather than repeated measure ANOVA), not clear (Figure 2I on peak timing or port entry), or has low n number (Figure 1 Ephys, animal-based manipulations).

(5) They did not clarify why they used two different doses of the DREADDs ligand Compound 21 at 0.1 or 0.3 mg/kg for different experiments.

Reviewer #3 (Public review):

Summary:

This study by Mercer et al. investigates how inhibitory modulation of basolateral amygdala neurons expressing Vglut1 and projecting to the nucleus accumbens (Vglut1BLA→NAc) influences motivated behavior in both appetitive and aversive tasks. Using a combination of whole-cell electrophysiology, chemogenetic inhibition and behavioral tests, the authors demonstrate that (1) reward conditioning increases inhibitory synaptic input and reduces intrinsic excitability of Vglut1BLA→NAc neurons, (2) chemogenetic inhibition of these neurons enhances the number of conditioned approaches in a Pavlovian task and the number of nosepoke responses in an instrumental task, elevates reward valuation, and increases fear discrimination and (3) these effects are linked to salience assignment and associative strength, rather than altered learning or reversal flexibility. The work challenges the classical excitatory function usually reported about the BLA projection to the NAc and highlights an interesting and thought-provoking result. Nevertheless, the study does not address the potential effect of their manipulation on motoric impulsivity, nor did they provide a theoretical framework explaining this unorthodox yet interesting effect.

Strengths:

The study establishes the initial finding with a correlational approach that informs a causal study. They find convincingly that Pavlovian conditioning induces an increase in inhibitory inputs onto Vglut1BLA→NAc neurons that leads to reduced excitability. Causality is studied using a powerful dual recombinase chemogenetic strategy to selectively inhibit this population of Vglut1BLA→NAc neurons and determine the effect on different behavioral tasks. The use of different tasks provides convergence on their effect. This surprising finding provokes interest and will stimulate further investigation into the mechanisms underlying these effects.

Weaknesses:

Several important aspects of the evidence remain incomplete.

(1) First, an important aspect of the underlying processes at play remains to be investigated. In all behavioral tasks, the authors find that their manipulation increases responding that they interpret as a facilitation of learning. However, none of the appetitive tasks include a control stimulus that could address the specificity of their effect. Given that on the Pavlovian task, responding to the CS is almost 100%, I suspect that their manipulation may induce motoric impulsivity. This aspect would clearly benefit from additional controls.

(2) Second, I have several interrogations about the time-resolved probability of port entries (PSTHs).

a) There is a mismatch between the results presented in Figure 1. Panel D shows a peak of responses on the PSTH at ~2s on day 5 (my remark applies to all days), suggesting that the average should lie around this value. However, panel C reports a latency to respond at ~4sec. Could the authors double-check their PSTHs?

b) More generally, the fact that in the Pavlovian task all PSTHs show a peak at almost exactly 2 sec is quite surprising and raises questions about how they are constructed. Sure, the most salient event is the water drop occurring 2s after cue onset. Yet, if mice responded only to these drops, the peak response should occur at 2s+reaction time, which is not the case. Figure 2 shows that on the first acquisition day, responding is already centered around 2s and does not decrease with learning, except for treated animals.

(3) Several methodological flaws are present.

a) The authors need to report clearly the statistics. In most cases, the statistical test used is mentioned in the figure caption with a single P-value. Thus, on two-way ANOVAs, I do not know whether the P-value relates to the interaction, the main effects, or the post-hoc tests.

b) Another important issue is related to the average time-resolved z-score probability of port entries. The bin size used, the smoothing (that is much too strong), and the baseline period used to calculate the z-score are absent from the methods.

(4) This study reports that manipulating 70% of the glutamatergic projection to the NAc induces an effect opposed to what has been previously reported in many different studies. Such a surprising finding deserves a more elaborate discussion about the mechanism that could be at play.

Reviewer #1 (Public review):

This paper presents another excellent, sophisticated analysis from this group of brain-wide neural activity correlated with the tracking of belief about the generative state of a stochastic visual environment under volatile conditions. Whereas previous work focussed on the normative belief-updating dynamics mainly in brain areas related to motor planning, under conditions where the environmental state translates directly to a correct action, here, they abstract the belief-updating DV from a specific action by instead associating the environmental state to a stimulus-response mapping rule, to be used in a simple perceptual decision coming up after the environmental state cues. A decoding analysis shows that a remarkably large portion of the brain has activity correlated with the normatively evolving belief about environmental state and the evidence samples feeding into that belief. What the authors were trying to achieve, however, seems far more general than the above, namely, to study "the algorithmic and neural basis of higher-order internal decisions about behavioural context, formed under multiple sources of uncertainty", and I think that the loose implication of such grand notions (such phrasing brings to mind someone's choice to believe in God, to regulate their behaviour depending on whether they are on a rugby pitch or at church, etc, not how grating orientations link to left/right hand movements) muddies the value of the study. The authors thus may have overestimated the generality of the findings. I hope my impressions are a useful guide to focus the interpretations more.

Strengths:

One of the main strengths of the study is that it is a technical tour de force. As reflected in an unusually extensive methods section, the authors put an extraordinary amount of work into rigorous data collection and analysis, and all of it is described in excellent detail. The study also builds in a very valuable way on previous landmark studies on tracking of volatile environmental state linked to correct actions using MEG (Murphy et al 2021) and tracking of volatile stimulus-response mappings using fMRI (van den Brink et al 2023). Here, the environmental state is not directly linked to actions during the cues informing about the state, but instead linked to a stimulus-response mapping rule.

Weaknesses:

It is surprising, given this main innovation of abstracting the decision about visual position-distribution from particular actions, that the authors do not engage with the literature using EEG and fMRI to study such 'abstract,' 'motor-independent' or 'domain-general' (synonymous terms) decisions. The discussion, for example, mentions the curious lack of involvement of the frontal cortex, and the possibility of intermingled opposites being represented there; motor-independent EEG decision signals have been characterised by regressing against the absolute value of the differential belief-updating process for this very reason (e.g., see Pares-Pujolras et al 2025). Single-unit studies like Bennur & Gold (2011) have also found activity related to a decision about environmental state (non-volatile motion) even when that state does not yet translate directly to an action, and, like the current study, is instead specified in a later frame of the trial.

Another weakness, as mentioned above, is that of overgeneralisation. It is not clear how "higher-order, internal decisions" are generally defined, and terms more concretely grounded in the paradigm at hand (as in van den Brink et al (2023)), e.g., 'tracking of environmental state dictating a sensory-motor mapping rule,' would seem more useful. Since this task tracks a belief about a sensory feature and how it maps to motor actions, it may not be as surprising a revelation that a range of sensorimotor areas correlate with it, as compared to more general, truly internal decisions about behavioural context involving no sensory input (e.g., deciding one has become hungry). Similarly, the authors paint the belief-tracking process of Murphy et al (2021) as "lower-order" and the current one as higher-order, but both cases are the same in that a hidden binary generative state needs to be inferred on a continual basis from a series of discrete spatial positions presented visually. The only difference is that in the current case, the belief about the current binary state is not transformed directly into an immediate action choice but rather utilised to map a follow-up stimulus to its appropriate action. These decisions then happen one after the other in sequence, with a contingency, but I'm not sure this constitutes a 'high-level' and 'low-level' in the way implied by the authors.

The paper left me confused on the question of what these widespread decoding effects reflect - whether all areas directly compute and represent the normative DV in concert, or whether at least some areas reflect other processes that may correlate with the DV. Although the discussion mentions things like feedback modulation in V1, which seems to allow for the possibility that it is not directly involved in DV computation, the phrasing used ('encoding' and 'representation' and never 'secondary modulation') from Abstract to Results tends to imply direct involvement.

Related to this, it seems that the extensive model comparison was done for behaviour, but not for the activation in each area, which may have suggested some dissociations in role - for example, for areas that showed decoding of the evidence (LLR), at least some of them may more closely correspond to the related lower-level quantity of simply spatial position itself, or the higher-level quantity of the transformed belief update (the change in prior from before to after the current cue). There is a map of areas that correlate with the difference of new vs old prior (if I understand correctly - Figure 4D), but not of areas for which activity conforms better to this belief update than to the objective LLR or location. Aside from such model-defined quantities, a critical factor is spatial attention. The authors highlight that the correlated activation of visual regions may reflect feedback modulations akin to attention in nature, but it might actually reflect attention itself, since it is plausible that subjects would pay more attention to the upper field when it is more likely that the centre of the generative distribution is up there (i.e., belief leans upwards). It seems the data could provide insight into this: If the visual cortical effects reflect a spatial attention modulation towards the likely generative source (upper/lower), then the relationship with prior, coded so that upper and lower have opposite sign, should flip in ventral versus dorsal visual cortex. Figure 4A seems like it could be positioned to answer this, but I can't fully interpret it because the prior coding is not explicit in the methods - the relevant section (lines 989-1001) refers back to the normative model description (without pointing to specific equations), which does not say what states S1 and S2 mean (upper and lower? Correct and incorrect? The former is needed to test for this spatial-specificity expected of attention). Even if there are reasons not to perform extra analyses related to the above, the impressions could guide edits to clarify what the data can and cannot say about what these DV-decoding effects reflect. Finally, it could be acknowledged that because the environmental state (upper or lower field generative source) is directly linked to stimulus-response mapping, even decoding effects that are not spatially-specific could equally reflect a representation of either one of these.

The motivation for the decoding analysis running up to the response is not clear - what are the hypotheses here? Is the idea that if these areas truly represented the belief about the currently active context, then they should continue to do so during the response and beyond, since the next trial will begin in the same context as the previous ended? Or is this section tackling a different question? Is it that there is a potential confound in finding the significant decoding during the cue tokens, because it could be driven by the visual responses to the different spatial positions, and there are no such visual responses later at the response?

Reviewer #2 (Public review):

Summary:

Calder-Travis et al. investigate how people form decisions about abstract rules in environments that may change over time. They show that individuals adaptively accumulate information, adjusting how much weight they give new evidence depending on how surprising or uncertain the environment is. Using whole-brain recordings (MEG), they further report that signals reflecting beliefs about the current rule are broadly distributed, particularly in visual and parietal regions. They further argue that these belief-related signals cannot be reduced to representations of momentary sensory evidence alone.

Overall, the behavioral results convincingly demonstrate adaptive evidence accumulation consistent with the normative model. The neural data provide solid evidence for temporally structured belief-related signals that are broadly distributed across cortical regions. However, the evidence for sustained belief maintenance "across" cues and for full dissociation from gaze-related influences in visual cortex is less definitive. These issues temper, but do not undermine, the central conclusions.

Strengths:

A major strength of the study is the integration of normative modeling with temporally resolved neural data. The authors exploit the fine temporal scale of the recordings to examine belief updating across distinct task epochs, and they show that neural signals evolve in a manner consistent with the normative model that best captures behavior. This alignment between behavioral modeling and neural dynamics is carefully executed and conceptually coherent.<br /> Another strength is the authors' cautious interpretation of their findings. They explicitly acknowledge limitations in distinguishing between direct representation of a latent variable and neural modulation driven by that variable. This restraint strengthens the credibility of the conclusions and avoids overstatement.

Weaknesses:

(1) Evidence for sustained belief representation across cues

Behaviorally, the data clearly demonstrate accumulation across sequential cues. However, the neural analyses primarily focus on responses around individual samples (from pre-cue to late post-cue windows). While these analyses demonstrate belief updating following each sample, they do not fully establish whether belief representations are maintained continuously across cues.

Specifically, it remains unclear whether the neural representation of the prior belief is sustained from the late post-cue period of cue t-1 into the pre-cue period of cue t. Without explicit evidence of such continuity, it is difficult to conclude that the neural signals reflect a maintained belief state rather than repeated sample-locked updating processes. This distinction is important for interpreting the neural mechanism of accumulation.

(2) Interpretation of belief signals in the visual cortex

The claim that belief-related signals in the visual cortex cannot be explained by gaze position requires stronger support. The distribution of gaze positions across contexts appears largely non-overlapping, raising the possibility that context-related gaze biases could contribute to the observed neural effects.

In particular, the "gaze-inconsistent" analysis based on a median split may not fully dissociate belief from gaze if the absolute gaze positions remain systematically different between contexts. As currently presented, the evidence does not fully rule out the possibility that gaze-related modulation contributes to the belief-related signal in visual areas. This affects the strength of the interpretation regarding abstract belief representation in early sensory cortex.

(3) Clarity and transparency of task and model description

Several aspects of the task and modeling framework would benefit from clearer exposition. The description of the noise distribution in the context cue would be easier to interpret if the overlapping distributions were visualized explicitly, allowing readers to assess how much accumulation is required versus reliance on strong individual cues. Similarly, the main text would benefit from a clearer explanation of how change point probability and uncertainty are computed (not just in Methods), as these quantities are central to the analyses and interpretation.

In addition, temporal epochs (e.g., pre-cue, early post-cue, late post-cue) are not clearly defined with specific time ranges in the main text, making it difficult to compare across figures.

(4) Interpretation of neural dynamics

Several neural findings are intriguing but underinterpreted. For example, the absence of clear sensory evidence representation in early post-cue epochs in any regions (Figure 4B) is surprising and not discussed. The relative stability of belief-related signals in visual cortex compared to parietal regions (Figure 4E) is also unexpected and warrants interpretation. Additionally, the temporal dynamics of change point probability and uncertainty representations appear different from each other, but such a pattern was not described in detail.

Clarifying these points would strengthen the interpretability of the results and help readers understand the mechanistic implications.

Reviewer #3 (Public review):

Summary

In this study, the authors investigated how inference about the current task context is encoded in the cortex, using MEG measurements. Using the same behavioral task that was initially developed for an fMRI study to identify the loci of task context representation, the current results complement and extend the previous study by identifying the candidate regions that are important for the inference process, not just for encoding the end product. They reported widespread modulation of cortical activity by uncertainty in evidence and volatility of task context changes. In comparison, modulation correlated with the decision variable underlying the task context inference process was more restricted to the parietal and visual cortices, particularly in alpha-band activity.

Strengths:

(1) The normative model provides a solid computational foundation for disambiguating quantities related to decision variables from those related to task factors (e.g., uncertainty and volatility).

(2) The MEG technique allows examination of cortical activity that is modulated by the temporally evolving decision variable.

(3) Rigorous modeling efforts, including comparisons of well-reasoned alternative/reduced models and examinations of diagnostic features using participant-matched simulations.

Weaknesses:

(1) There are two major surprises in the results that raise concerns about how to interpret these data. The first is the absence of modulation of prefrontal cortical activities by prior or posterior. As the authors acknowledged, there are extensive single-neuron recording data (e.g., from the Miller group) demonstrating the presence of task rule modulation in the monkey PFC and prior representation in the PFC in the mouse study that they cited. The second surprise is that the strongest modulation of prior/posterior/evidence was almost always observed in the visual cortex, in contrast to the common embodied cognition assumption. A more elaborated discussion about these discrepancies would help contextualize the current results.

(2) It is not clear why the effects in Figures 2D and E dipped before responses, which is not expected from any of the models. This could potentially affect the interpretation of the MEG signals in late-post-cue or pre-response periods.

(3) The definitions of the different periods (e.g., early/late post-cue) are vague, making it hard to assess the functional relevance of the signals. For example, is the difference between the early pre-response map in Figure 5B and the late evidence map in Figure 4B due to completely non-overlapping time periods? A diagram of the timing definitions for different task periods would be helpful.

(4) Perhaps related to #2, it is puzzling that evidence encoding is absent in the visual cortex during the early post-cue period.

(5) The presentation and discussion of results related to correlated variability assume that the readers have already read their previous paper. A little more elaboration of the significance of this measurement would be helpful.

Reviewer #1 (Public review):

Summary:

The presented investigation aims to expand the sleep definition and its relationship with blood meal and/or circadian clock in the mosquito, Aedes aegypti. The authors exhausted the established sleep analytical paradigm and three behaviour toolkits: LAM10, EthoVision, and DART. They also investigated the potential underlying molecular mechanism by using dsRNA injection (LkR) and a KO mosquito (Cyc-/-).

Strengths:

The authors presented a very solid dataset showing posture changes and an increase in the arousal threshold of the mosquito after 10 minutes of immobility. This is a major clarification and extension to our understanding of insect sleep beyond Drosophila. Inclusion of analytical parameters such as bout length, waking activity and pDoze/Wake provide critical reminder for other investigators of the steps needed for defining sleep in a new species. The investigation, with its technical span in behaviour assays, therefore establishes a good standard for mosquito sleep analysis to the same quality seen in the landmark studies (Shaw et al 2000 and Hendricks et al 2000) for Drosophila sleep. The pioneering data showing a clear effect of blood meal and LkR reduction on locomotion and sleep provides an entry point for further investigations.

Weaknesses:

Despite the versatility of the behaviour and transgenic methods in this manuscript, there are two logical gaps in the conclusion, which are related to the effect of blood meal/BSA/LkR KD on A. aegypti sleep:<br /> (1) Conventionally, a coincidence of sleep increase and locomotion reduction would weaken the certainty of a sleep increase assessment. The authors implied this concurrence observed after blood meal is derived from internal "drowsy" neural state instead of physical "cripple", but they did not use their two high-resolution video tracking velocity or pDoze/Wake to clarify this.<br /> (2) The major molecular component underlying blood meal effect on sleep/locomotion is less certain, because the BSA solution used for feeding contains ATP, which itself is able to enter haemolymph and potentially exerts sleep/locomotion effect. Additionally, the basal or control sleep recording is done after sucrose feeding. It is, however, unclear from the method if this is 10% too? And if the observed sleep level increase after a blood meal is a result of sugar level reduction in the blood (~0.1%).

Reviewer #2 (Public review):

Zhang et al. investigate how blood feeding and dietary protein influence sleep in the mosquito Aedes aegypti. The authors first establish a behavioural definition of sleep using postural analysis and arousal threshold measurements, then demonstrate that both blood meals and a bovine serum albumin (BSA)-based protein diet increase sleep for several days. They further show that RNAi-mediated knockdown of the leucokinin receptor (Lkr) enhances sleep, implicating neuropeptide signalling in the regulation of postprandial sleep. The authors propose that elevated sleep persists well beyond the restoration of host-seeking behaviour, suggesting the existence of distinct "opportunistic" versus "determined" host-seeking phases.

Strengths

The central question is well-motivated, and the experimental approach is systematic. The use of multiple independent methods to characterise sleep - postural analysis, infrared activity monitoring, videography, and arousal threshold - provides converging evidence. The BSA feeding experiment is a particularly effective demonstration that dietary protein, rather than other blood components, is the key regulator of the sleep increase. The conservation of leucokinin signalling in sleep regulation between Drosophila and Ae. aegypti is a noteworthy finding that adds comparative depth.

Weaknesses

(1) Sleep definition.

The authors settle on a 10-minute immobility threshold, but their own data do not convincingly support this choice. The arousal threshold data (Figure 1G) show no significant difference between the 1-5 min and 6-10 min bins (P=0.246), with significance emerging only at the 11-15 min bin. The postural analysis likewise indicates that sleep-associated postures appear at ~20 min during the day and ~11 min at night. A 15-minute threshold would be better supported by the data as presented. The previous literature used 120 minutes for this species (Ajayi et al. 2022), making this a dramatic shift.

(2) Confound of reproduction and sleep.

The primary experimental paradigm measures sleep beginning at Day 4 post-blood feeding, immediately after oviposition. Animals have undergone gut distension, vitellogenesis, and oviposition, and what is being measured as "sleep" could reflect post-reproductive quiescence or recovery rather than diet-induced sleep per se. The BSA experiment partially addresses this, but since BSA also triggers vitellogenesis and egg production (as the authors note), the confound persists.

(3) Opportunistic vs. determined host-seeking hypothesis.

This framework is presented as a key conceptual contribution, but the paper contains no data on host-seeking behaviour. The authors infer two phases from the temporal mismatch between a 72-hour host-seeking suppression window (from prior studies) and elevated sleep through Day 5 (~120 hours). While this is an interesting hypothesis, it requires actual measurement of host-seeking alongside sleep to be substantiated, or at least the caveats need to be discussed more explicitly.

(4) Statistical approach.

The methods describe "one-way ANOVA, followed by Mann-Whitney tests with Welch's correction," which is an internally inconsistent combination: Mann-Whitney is non-parametric and does not use Welch's correction (which applies to t-tests). Throughout the figures, F-statistics (parametric) are reported alongside what appear to be non-parametric tests. The statistical framework needs to be clarified and made consistent. Exact sample sizes per group should also be stated explicitly in the methods for all experiments.

Reviewer #2 (Public review):

Summary:

Ito and Toyoizumi present a computational model of context-dependent action selection. They propose a "hippocampus" network that learns sequences based on which the agent chooses actions. The hippocampus network receives both stimulus and context information from an attractor network that learns new contexts based on experience. The model is consistent with a variety of experiments both from the rodent and the human literature such as splitter cells, lap cells, the dependence of sequence expression on behavioral statistics. Moreover, the authors suggest that psychiatric disorders can be interpreted in terms of over/under representation of context information.

My general assessment of the work is unchanged, and I still have some questions requesting methodological clarification

Strengths:

This ambitious work links diverse physiological and behavioral findings into a self-organizing neural network framework. All functional aspects of the network arise from plastic synaptic connections: Sequences, contexts, action selection. The model also nicely links ideas from reinforcement learning to a neuronally interpretable mechanisms, e.g. learning a value function from hippocampal activity.

Weaknesses:

The presentation, particularly of the methodological aspects, needs to be heavily improved. Judgment of generality and plausibility of the results is severely hampered but is essential, particularly for the conclusions related to psychiatric disorders. In its present form, it is impossible to judge whether the claims and conclusions made are justified. Also, the lack of clarity strongly reduces the impact of the work on the field.

Comments:

The authors have made strong efforts to improve on their description of the methods, however, it is still very hard to understand. As a result of some of their clarifications, new issues appeared that I was not able to extract in the previous version.

(1) Particularly I had problems figuring out how the individual dynamical systems are interrelated (sequences, attractor, action, learning). As I understand it now (and I still might be wrong) there is one discrete time dynamics, where in each time step one action takes place as well as the attractor and sequence dynamics are moved one step forward. Also, synaptic updates happen in every one of those time steps. The authors may verify or correct my interpretations and further improve on their description in the manuscript. It is also confusing that time in the figure panels is given in units of trials, where each trial may consist of (maybe different amounts of) multiple time steps. Are the thin horizontal red ad blue lines time steps?

(2) As a consequence of my new understanding of the model dynamics, I have become doubts about the interpretation of the attractor network as context encoding. Since the X population mainly serves to disambiguate sequence continuation, right before the action has to be taken (active for only two time steps in Figure 1C?) they could also be considered to encode task space (El-Gaby et al. 2024; doi: 10.1038/s41586-024-08145-x).

(3) Also technically, I wonder why the authors introduce the criterion of 50(!) time steps to allow the attractor to converge, if the state of the attractor network is only relevant in one time step to choose the appropriate continuation of the sequence of actions. Is attractor dynamics important at all? What would happen if just the input and output weights to the X population are kept and the recurrent weights are set 0?

(4) Figure 3E: How many time steps are the H cells active (red bars?) Figure 4J: What are the units of the time axis?

Reviewer #3 (Public review):

Summary:

This paper develops a model to account for flexible and context-dependent behaviors, such as where the same input must generate different responses or representations depending on context. The approach is anchored in the hippocampal place cell literature. The model consists of a module X, which represents context, and a module H (hippocampus), which generates "sequences". X is a binary attractor RNN, and H appears to be a discrete binary network, which is called recurrent but seems to operate primarily in a feedforward mode. H has two types of units (those that are directly activated by context, and transition/sequence units). An input from X drives a winner-take-all activation of a single unit H_context unit, which can trigger a sequence in the H_transition units. When a new/unpredicted context arises, a new stable context in X is generated, which in turn can trigger a new sequence in H. The authors use this model to account for some experimental findings, and on a more speculative note, propose to capture key aspects of contextual processing associated with schizophrenia and autism.

Strengths:

Context-dependency is an important problem. And for this reason, there are many papers that address context-dependency - some of this work is cited. To the best of my knowledge, the approach of using an attractor network to represent and detect changes in context is novel and potentially valuable.

Comments on revisions:

The authors have adequately addressed my concerns. Most importantly, the details of the implementation of the different components of the model are much more clearly described.

Reviewer #1 (Public review):

Summary:

The manuscript by Ito and Toyozumi proposes a new model for biologically plausible learning of context-dependent sequence generation, which aims to overcome the predefined contextual time horizon of previous proposals. The model includes two interacting models: an Amari-Hopfield network that infers context based on sensory cues, with new contexts stored whenever sensory predictions (generated by a second hippocampal module) deviate substantially from actual sensory experience, which then leads to hippocampal remapping. The hippocampal predictions themselves are context-dependent and sequential, relying on two functionally distinct neural subpopulations. On top of this state representation, a simple Rescola-Wagner-type rule is used to generate predictions for expected reward and to guide actions. A collection of different Hebbian learning rules at different synaptic subsets of this circuit (some reward-modulated, some purely associative, with occasional additional homeostatic competitive heterosynaptic plasticity) enables this circuit to learn state representations in a set of simple tasks known to elicit context-dependent effects.

Strengths:

The idea of developing a circuit-level model of model-based reinforcement learning, even if only for simple scenarios, is definitely of interest to the community. The model is novel and aims to explain a range of context-dependent effects in the remapping of hippocampal activity.

Weaknesses:

The link to model-based RL is formally imprecise, and the circuit-level description of the process is too algorithmic (and sometimes discrepant with known properties of hippocampus responses), so the model ends up falling in between in a way that does not fully satisfy either the computational or the biological promise. Some of the problems stem from the lack of detail and biological justification in the writing, but the loose link to biology is likely not fully addressable within the scope of the current results. The attempt at linking poor functioning of the context circuit to disease is particularly tenuous.

Reviewer #2 (Public review):

Summary:

Ito and Toyoizumi present a computational model of context-dependent action selection. They propose a "hippocampus" network that learns sequences based on which the agent chooses actions. The hippocampus network receives both stimulus and context information from an attractor network that learns new contexts based on experience. The model is consistent with a variety of experiments, both from the rodent and the human literature, such as splitter cells, lap cells, and the dependence of sequence expression on behavioral statistics. Moreover, the authors suggest that psychiatric disorders can be interpreted in terms of over-/under-representation of context information.

Strengths:

This ambitious work links diverse physiological and behavioral findings into a self-organizing neural network framework. All functional aspects of the network arise from plastic synaptic connections: Sequences, contexts, and action selection. The model also nicely links ideas from reinforcement learning to neuronally interpretable mechanisms, e.g., learning a value function from hippocampal activity.

Weaknesses:

The presentation, particularly of the methodological aspects, needs to be majorly improved. Judgment of generality and plausibility of the results is hampered, but is essential, particularly for the conclusions related to psychiatric disorders. In its present form, it is unclear whether the claims and conclusions made are justified. Also, the lack of clarity strongly reduces the impact of the work in the larger field.

More specifically:

(1) The methods section is impenetrable. The specific adaptations of the model to the individual use cases of the model, as well as the posthoc analyses of the simulations, did not become clear. Important concepts are only defined in passing and used before they are introduced. The authors may consider a more rigorous mathematical reporting style. They also may consider making the methods part self-contained and moving it in front of the results part.

(2) The description of results in the main text remains on a very abstract level. The authors may consider showing more simulated neural activity. It remains vague how the different stimuli and contexts are represented in the network. Particularly, the simulations and related statistical analyses underlying the paradigms in Figure 4 are incompletely described.

(3) The literature review can be improved (laid out in the specific recommendations).

(4) Given the large range of experimental phenomenology addressed by the manuscript, it would be helpful to add a Discussion paragraph on how much the results from mice and humans can be integrated, particularly regarding the nature of the context selection network.

(5) As a minor point, the hippocampus is pretty much treated as a premotor network. Also, a Discussion paragraph would be helpful.

Reviewer #3 (Public review):

Summary:

This paper develops a model to account for flexible and context-dependent behaviors, such as where the same input must generate different responses or representations depending on context. The approach is anchored in the hippocampal place cell literature. The model consists of a module X, which represents context, and a module H (hippocampus), which generates "sequences". X is a binary attractor RNN, and H appears to be a discrete binary network, which is called recurrent but seems to operate primarily in a feedforward mode. H has two types of units (those that are directly activated by context, and transition/sequence units). An input from X drives a winner-take-all activation of a single unit H_context unit, which can trigger a sequence in the H_transition units. When a new/unpredicted context arises, a new stable context in X is generated, which in turn can trigger a new sequence in H. The authors use this model to account for some experimental findings, and on a more speculative note, propose to capture key aspects of contextual processing associated with schizophrenia and autism.

Strengths:

Context-dependency is an important problem. And for this reason, there are many papers that address context-dependency - some of this work is cited. To the best of my knowledge, the approach of using an attractor network to represent and detect changes in context is novel and potentially valuable.

Weaknesses:

The paper would be stronger, however, if it were implemented in a more biologically plausible manner - e.g., in continuous rather than discrete time. Additionally, not enough information is provided to properly evaluate the paper, and most of the time, the network is treated as a black box, and we are not shown how the computations are actually being performed.

Reviewer #1 (Public review):

Summary:

In this compelling study, Howard et al. use deep mutational scanning to probe essentially all possible single amino acid substitutions in the TYK2 tyrosine kinase, and identify those that modulate signaling function and protein abundance. The methodological approach is elegant and thorough, and the results identify numerous examples of amino acid substitutions that have been previously reported to modulate TYK2 function, validating the approach.

Substitutions that are LOF with respect to IFN-a signaling but not protein abundance are particularly interesting and are widely dispersed across the protein. They include known functionally critical sites such as the active site and activation loop of the kinase domain, as well as the allosteric site within the regulatory pseudokinase domain, but also hundreds of other additional sites. The approach is then used to study the effects of substitutions on kinase inhibition using several JAK family inhibitors that target the pseudokinase domain. By assessing variant effects at both high and low drug concentrations, they are able to identify variants that mediate resistance or conversely potentiate inhibition, respectively. These map to distinct sites on the pseudokinase domain. Finally, the authors show that several TYK2 variants, most notably the P1104A substitution, previously shown to protect against autoimmune disease, correspond to substitutions that reduce protein abundance in their screen. Combining their DMS data with autoimmune phenotype and TYK2 genotype data uncovered a general dose relationship between autoimmunity and TYK2 abundance, and the authors propose that this might justify targeting TYK2 protein levels with degraders.

Strengths:

This is a nicely executed, well-written study with good figures and a clear presentation.

Weaknesses:

The only substantial critique I have is that while the paper makes a compelling case for the validity and power of the approach, the authors could perhaps go further in their interpretation of their data, particularly with regards to identifying functionally important sites and connecting them to putative allosteric sites and functionally relevant protein-protein interfaces in the context of what is known about JAK family kinase structure and function. An attempt is made to interpret the data in light of a composite structural model of full-length TYK2 engaged with the IFNAR1 receptor (Figure 2C), but much more could be said about this. Below, I list several examples where additional insight might be gleaned.

(1) The discussion of gain-of-function variants is limited. Given that tight regulation is a general theme of kinase signaling and gain-of-function mutations are a common disease mechanism, these mutations could be particularly interesting. Could the authors comment on patterns of gain versus loss? Are there gain-of-function signaling variants that work in a IFN-a dose dependent versus independent manner?

(2) The discussion of the signaling-specific variants (LOF in signaling but not abundance) is interesting but could be expanded. Can the authors comment on which regions of the pseudokinase/kinase interface, for instance, are affected, since this allosteric communication is a critical and unique aspect of JAK family protein function? Can something be said about what the 6 activation loop substitutions are doing?

(3) The cytokine signaling screen was performed at several different levels of IFN-α cytokine stimulation. The authors state that these data were used to identify quantitative variant effects (p7), but the cytokine dose response data are not widely discussed in the manuscript. Is it not possible that valuable information about the strength of substitution effects could be gleaned from this? One might expect that simple loss of function mutants that, e.g. completely destroy catalytic activity, will be LOF at all levels of stimulation, whereas mutations that have more nuanced "tuning" or allosteric effects on signaling might display LOF at low cytokine stimulation levels but be restored at high stimulation levels. Such information could be of potential functional importance and interest. Could the authors comment on this?

(4) In general, the variant data could be interpreted more specifically in light of the available detailed structural information about TYK2 and JAK kinases generally. For instance, could the resistance versus potentiation variants be interpreted in this context to hypothesize what they might be doing?

Reviewer #2 (Public review):

Howard et al. describe a set of deep mutational scanning (DMS) experiments applied to TYK2, which is a drug target implicated in autoimmune disease. By assaying protein abundance (stability) effects as well as immune signaling, the authors are able to disentangle variant effects that may be directly involved in protein activity (and therefore potentially druggable) from variant effects that are due to loss of protein or general structural instability. By performing these assays under multiple conditions, including the presence of various concentrations of small molecules, they develop a clear picture of which sites in TYK2 may be most relevant for intervention or targeting. Overall, the work represents a very compelling example of DMS for understanding protein biology and candidate drug mechanisms.

The work is very thorough, with multiple DMS assays described and compared/contrasted. This greatly enhances the impact and interpretability of any individual assay performed.

The authors have made improvements to the state of the art in terms of wet-lab assay design as well as the analysis of FACS-based deep mutational scans.

The potential mechanism of loss of protein abundance in TYK2 being protective for autoimmune disease is clear, but the estimates of the effect size in more physiologically relevant settings vary quite a bit and might be quite small. Are there examples that could be cited of other similar disease mechanisms where a 10% loss in abundance is associated with a clinical phenotype?

Reviewer #3 (Public review):

Summary:

In the paper "Deep mutational scanning reveals pharmacologically relevant insights into TYK2 signaling and disease", the authors perform a comprehensive deep mutational scan of the kinase TYK2, a protein of pharmacological interest due to its central role in multiple immune-related phenotypes. The study assesses two key functional phenotypes: protein abundance and IFN-α-dependent signaling. The signaling assays were conducted across a dose-response range under various inhibitor conditions, allowing for an in-depth characterization of TYK2 activity and regulation. Both the experimental design and data analysis were executed with rigor and transparency, yielding a dataset that appears highly reliable. The authors provide strong evidence and a scientifically grounded interpretation of their results.

The paper presents the results of a deep mutational scan based on two assays: an IFN-α-stimulated signaling assay and a protein abundance assay. These measurements are further supported by variant classifications from AlphaMissense and ClinVar, providing a framework for functional interpretation. Building on these data, the authors propose four potential pharmacological applications of their screening system at the end of the first results section.

First, they demonstrate that the combined analysis of abundance and IFN-α signaling identifies potential allosteric sites, focusing on variants with normal protein stability but reduced signaling activity. Through this approach, they detect two previously uncharacterized allosteric regions (Results Section 2).

Second, they explore how the screen can be used to predict variant-specific drug responses or resistance mechanisms (Results Section 3). This is achieved through assays involving two different inhibitors, which reveal both resistance- and potentiation-associated variants.

Third, they assess the relative functional consequences of ligand and inhibitor dosing by performing IFN-α and inhibitor dose-response experiments (1, 10, and 100 U/mL IFN-α; IC99 and IC75 inhibitor concentrations; Results Section 3).

Finally, the authors investigate how specific human variants, such as P1104A and I684S, may inform therapeutic modality selection (Results Section 4). Although these variants exhibit no detectable effect on IFN-α signaling within this experimental system, they substantially impact protein abundance. By integrating data from the UK Biobank, the authors further demonstrate that protective effects against autoimmune disease are associated with altered protein abundance rather than differences in IFN-α signaling, highlighting the distinct mechanistic basis of TYK2's clinical relevance.

Strengths:

Overall, we found this paper rigorous, well-written, and easy to follow. As such, we think this is an exceptional example of a deep mutational scanning manuscript, and this dataset will be invaluable to the field. We particularly appreciate that the authors could explore sensitivity to inhibitor concentration across multiple doses of the inhibitor.

Weaknesses:

Despite the authors' rigorous experimentation and thoughtful interpretation, the study leaves several important mechanistic questions unresolved, as is common in any study. While the data provide clear functional patterns, the underlying biophysical and biochemical explanations remain insufficiently explored. For instance, in point 1, the identification of two novel allosteric sites is intriguing, yet the paper does not elaborate on the structural basis or mechanistic rationale for their regulatory effects. In point 2, resistance and potentiation variants are described for two distinct inhibitors, but it remains unclear why certain variants respond specifically to one compound and not the other. In point 3, higher inhibitor concentrations appear to diminish allosteric interactions, though the reasons why some sites are affected while others are not are left unexplained. Finally, in point 4, the observation that protein abundance, but not IFN-α signaling, correlates with autoimmune protection is compelling but mechanistically ambiguous. These gaps do not detract from the technical excellence of the work; rather, they highlight opportunities for future studies to clarify the molecular and pharmacological mechanisms underlying TYK2 regulation and to deepen the translational insights drawn from this comprehensive mutational scan. We hope that the authors could provide more direction and mechanistic context in the discussion section to guide readers toward these next steps.

Joint Public Review:

Summary:

Inferring so-called "functional connectivity" between neurons or groups of neurons is important both for validating models and for inferring brain state. Under the assumption that brain dynamics is linear, the authors show that the error in estimating functional connectivity depends only on the eigenvalues of the covariance matrix of the observed data, and it is the small eigenvalues -corresponding to directions in which the variance of the brain activity is low - that lead to large estimation errors. Based on this, the authors show that to achieve low estimation error, it's important to excite the resonant frequencies and perturb well-connected hubs. The authors propose a practical iterative approach to estimate the functional connectivity and demonstrate faster convergence to the optimal estimate compared to passive observation.

Strengths:

The main contribution of the study is the derivation of an explicit expression for the error in functional connectivity that depends only on the covariance matrix of the observed data. If valid, this result can have a profound impact on the field. The study also motivates the current shift to closed-loop experiments by demonstrating the effectiveness of active learning in the system using perturbation, in comparison to passive estimation from resting-state activity. Finally, the relative simplicity of the model makes its practical applications straightforward, as the authors illustrate in the context of brain state classification and neural control.

Weaknesses:

The derivation of the main error term misses some important steps, which complicates peer review at this stage. In particular, factorisation of the covariance into noise and the inverse of the observation covariance matrix needs a more thorough justification. The cited sources do not contain the derivation for a noise term with full covariance, which is essential for deriving this error term.

The practical recommendation at the end of the paper also requires clearer guidance on how the design perturbations are constructed, and how many times and for how long the system is stimulated in each iteration of the experiment.

Finally, there is no analysis of model mis-specification. In particular, the true dynamics are unlikely to be linear; the noise is unlikely to be either Gaussian or uncorrelated across time; and the B matrix is unlikely to be known perfectly. We're not suggesting that the authors consider a more complex model, but it's important to know how sensitive their method is to model mismatch. If nothing can be done analytically, then simulations would at least provide some kind of guide.

Reviewer #1 (Public review):

Summary:

Using multi-region two-photon calcium imaging, the manuscript meticulously explores the structure of noise correlations (NCs) across mouse visual cortex and uses this information to make inferences about the organization of communication channels between primary visual cortex (V1) and higher visual areas (HVAs). Using visual responses to grating stimuli, the manuscript identifies 6 tuning groups of visual cortex neurons, and finds that NCs are highest among neurons belonging to the same tuning group whether or not they are found in the same cortical area. The NCs depend on the similarity of tuning of the neurons (their signal correlations) but are preserved across different stimulus sets - noise correlations recorded using drifting gratings are highly correlated with those measured using naturalistic videos. Based on these findings, the manuscript concludes that populations of neurons with high NCs constitute discrete communication channels that convey visual signals within and across cortical areas.

Strengths:

Experiments and analyses are conducted to a high standard and the robustness of noise correlation measurements is carefully validated. To control for potential influences of behaviour-related top-down modulation of noise correlations, the manuscript uses measurements of pupil dynamics as a proxy for behavioural state and shows that this top-down modulation cannot explain the stability of noise correlations across stimuli.

Weaknesses:

The interpretation of noise correlation measurements as a proxy from network connectivity is fraught with challenges. While the data clearly indicate the existence of distributed functional ensembles, the notion of communication channels implies the existence of direct anatomical connections between them, which noise correlations cannot measure.

The traditional view of noise correlations is that they reflect direct connectivity or shared inputs between neurons. While it is valid in a broad sense, noise correlations may reflect shared top-down input as well as local or feedforward connectivity. This is particularly important since mouse cortical neurons are strongly modulated by spontaneous behavior (e.g. Stringer et al, Science, 2019). Therefore, noise correlation between a pair of neurons may reflect whether they are similarly modulated by behavioral state and overt spontaneous behaviors. Consequently, noise correlation alone cannot determine whether neurons belong to discrete communication channels.

Reviewer #2 (Public review):

Summary:

This groundbreaking study characterizes the structure of activity correlations over millimeter scale in the mouse cortex with the goal of identifying visual channels, specialized conduits of visual information that show preferential connectivity. Examining the statistical structure of visual activity of L2/3 neurons, the study finds pairs of neurons located near each other or across distances of hundreds of micrometers with significantly correlated activity in response to visual stimuli. These highly correlated pairs have closely related visual tuning sharing orientation and/or spatial and/or temporal preference as would be expected from dedicated visual channels with specific connectivity.

Strengths:

The study presents best-in-class mesoscopic-scale 2-photon recordings from neuronal populations in pairs of visual areas (V1-LM, V1-PM, V1-AL, V1-LI). The study employs diverse visual stimuli that capture some of the specialization and heterogeneity of neuronal tuning in mouse visual areas. The rigorous data quantification takes into consideration functional cell groups as well as other variables that influence trial-to-trial correlations (similarity of tuning, neuronal distance, receptive field overlap, behavioral state). The paper demonstrates the robustness of the activity clustering analysis and of the activity correlation measurements. The paper shows convincingly that the correlation structure observed with grating stimuli is present in the responses to naturalistic stimuli. A simple simulation is provided that suggest that recurrent connectivity is required for the stimulus invariance of the results. The paper is well written and conceptually clear. The figures are beautiful and clear. The arguments are well laid out and the claims appear in large part supported by the data and analysis results (but see weaknesses).

Weaknesses:

An inherent limitation of the approach is that it cannot reveal which anatomical connectivity patterns are responsible for observed network structure. A methodological issue that does not seem completely addressed is whether the calcium imaging measurements with their limited sensitivity amplify the apparent dependence of noise correlations on the similarity of tuning. Although the paper shows that noise correlation measurements are robust to changes in firing rates / missing spikes, the effects of receptive field tuning dissimilarity are not addressed directly. The calcium responses of mouse visual cortical neurons are sharply tuned. Neurons with dissimilar receptive fields may show too little overlap in their estimated firing rates to infer noise correlations, which could lead to underestimation of correlations across groups of dissimilar neurons.

Reviewer #3 (Public review):

Summary:

Yu et al harness the capabilities of mesoscopic 2P imaging to record simultaneously from populations of neurons in several visual cortical areas and measure their correlated variability. They first divide neurons in 65 classes depending on their tuning to moving gratings. They found the pairs of neurons of the same tuning class show higher noise correlations (NCs) both within and across cortical areas. Based on these observations and a model they conclude that visual information is broadcast across areas through multiple, discrete channels with little mixing across them.<br /> NCs can reflect indirect or direct connectivity, or shared afferents between pairs of neurons, potentially providing insight on network organization. While NCs have been comprehensively studied in neurons pairs of the same area, the structure of these correlations across areas is much less known. Thus, the manuscripts present novel insights on the correlation structure of visual responses across multiple areas.

Strengths:

The measurements of shared variability across multiple areas are novel. The results are mostly well presented and many thorough controls for some metrics are included.

Weaknesses:

I have concerns that the observed large intra class/group NCs might not reflect connectivity but shared behaviorally driven multiplicative gain modulations of sensory evoked responses. In this case, the NC structure might not be due to the presence of discrete, multiple channels broadcasting visual information as concluded. I also find that the claim of multiple discrete broadcasting channels needs more support before discarding the alternative hypothesis that a continuum of tuning similarity explains the large NCs observed in groups of neurons.

Specifically:

Major concerns:

(1) Multiplicative gain modulation underlying correlated noise between similarly tuned neurons

(1a) The conclusion that visual information is broadcasted in discrete channels across visual areas relies on interpreting NC as reflecting, direct or indirect connectivity between pairs, or common inputs. However, a large fraction of the activity in the mouse visual system is known to reflect spontaneous and instructed movements, including locomotion and face movements, among others. Running activity and face movements are one of the largest contributors to visual cortex activity and exert a multiplicative gain on sensory evoked responses (Niell et al , Stringer et al, among others). Thus, trial-by-fluctuations of behavioral state would result in gain modulations that, due to their multiplicative nature, would result in more shared variability in cotuned neurons, as multiplication affects neurons that are responding to the stimulus over those that are not responding ( see Lin et al , Neuron 2015 for a similar point).

In the new version of the manuscript, behavioral modulations are explicitly considered in Figure S8. New analyses show that most of the variance of the neuronal responses is driven by the stimulus, rather than by behavioural variable. However, they new analyses still do not address if the shared noise correlation in cotuned neurons is also independent of behavioral modulations .

As behavioral modulations are not considered this confound affects the conclusions and the conclusion that activity in communicated unmixed across areas ( results in Figure 4), as it would result in larger NCs the more similar the tuning of the neurons is, independently of any connectivity feature. It seems that this alternative hypothesis can explain the results without the need of discrete broadcasting channels or any particular network architecture and should be addressed to support the main claims.

(2) Discrete vs continuous communication channels<br /> (2a) One of the author's main claims is that the mouse cortical network consists of discrete communication channels, as stated in teh title of the paper. This discreteness is based on an unbiased clustering approach on the tuning of neurons, followed by a manual grouping into six categories with relation to the stimulus space. I believe there are several problems with this claim. First, this clustering approach is inherently trying to group neurons and discretise neural populations. To make the claim that there are 'discrete communication channels' the null hypothesis should be a continuous model. An explicit test in favor of a discrete model is lacking, i.e. are the results better explained using discrete groups vs. when considering only tuning similarity? Second, the fact that 65 classes are recovered (out of 72 conditions) and that manual clustering is necessary to arrive at the six categories is far from convincing that we need to think about categorically different subsets of neurons. That we should think of discrete communication channels is especially surprising in this context as the relevant stimulus parameter axes seem inherently continuous: spatial and temporal frequency. It is hard to motivate the biological need for a discretely organized cortical network to process these continuous input spaces.

Finally, as stated in point 1, the larger NCs observed within groups than across groups might be due to the multiplicative gain of state modulations, due to the larger tuning similarity of the neurons within a class or group.

Reviewer #1 (Public review):

Summary:

The authors study criticality and drift in spontaneous activity observed in visual cortex of mice from existing data, and relate it to a model based on homeostatic plasticity. The main phenomena are power laws and an alignment across different neural representations that is maintained through drift.

Strengths:

The authors should be commended by making the effort of relating their model to experimental data. The mechanism that they propose has the advantage of being simple, and could unify various phenomena.

Weaknesses:

Introduction/abstract: General wording: the notion of reliability, which is key to the paper is not explicitly defined anywhere. The authors refer to some notion of information being preserved, but again, this is not clearly explained. A good example is the sentence "identical input signals exhibit significant variability but also share certain reliability across sessions". Depending on the definition of reliability, the sentence could be a contradiction. A similar issue appears when the authors talk about "restricted" representation. I get what they want to say, but it's not properly defined. "One example is the recent studies about stimulus-evoked..." The sentence explains that there are examples, but provides no citations! Also "One" and "exampleS"

Fig. 1: - The method to fit the power law is not detailed in the methods (just a vague reference to a package). This is a problem because some methods like least squares don't do well on power laws, and particularly for neuroscience due to low sampling (Wilting & Priesemann, Nat com.). - The "olive" curve is not "olive". Olive is dark green, and the color is purple. The problem appears in the subsequent figure.

Fig. 2: - The number of neurons is very small (19). This is very odd, since the original dataset has a lot of neurons. Also, the authors seem to pick age 97 and 102, but do not explain why those two points have any relevance. - If you run a correlation you need to explain what is the correlation (pearson, spearman?). It also matters where the variables are normalized or not, and there is no control for shuffling. - The authors mention "low dimensional", but don't explain what method they use (looks t-SNE to me). - The authors use the word "signal" while in the text they refer to the "mean activity". Are those the same? - "We reproduced previous results showing that low-dimensional embeddings of mean population response vectors for different signals remain similar across sessions" The blue and green clusters that the authors report as being close across sessions are not close. Red-green-grey seem to remain closer, but even that is quite a stretch. - Correlation across matrices is strange. Since the authors did not clarify the actual formula or method, the correlation of 0.5 in Fig. 2E could be simply due to the fact that all the variables are pre-selected to be positive (or above threshold). This would also have an important effect on the angle (Fig. G). In fact, it would explain how comes that the correlation does not decrease with Delta T (which is what would be expected from drift. - Whenever the authors run a statistical analysis, it would help to run a shuffled control.

Self-organised criticality emerges through homeostatic plasticity. - The authors refer a lot to reference 35, but it's not clear what is the difference between their work and that one. - The text provides a general overview and refers to the methods for details. Since most of the results are based on that mode, I suggest putting it in the main text (although this is an opinion, not a dealbreaker). - Especially, mention which populations are we talking about, what are the numbers of neurons in each, and how are they connected.

• Fig. 4 has a lot of the same weaknesses as Fig. 2. In fact, the results on E are very similar, despite the fact that the matrices in D are clearly not the same.

Enhanced Neural representation through self-organised criticality The phase transition seems to be an observation over a computational model, but I don't see much analysis. It would be nice to have some order parameter, although the plots are convincing without it. The authors do spend time talking about co-spiking and silent periods though, but don't actually plot this. The only reference is to S4, which actually only seems to cover the super-critical state.

Fig 6: - It might be true that the accuracy peaks at the critical point, but it's really hard to call it significant. The authors should run multiple models and assess significance. - I don't entirely see the point of C. What does it mean for the model? And although I assume it is on the same experimental data, the authors do not mention it.

Fig. 7: - Plot is squeezed, and has low resolution. - Since the authors didn't clarify whether they have II connections or not (some models use them, some don't), or whether their plasticity applies to inhibitory neurons, it is very hard to assess what are the differences between A and B.

References: There are a fair amount of works that studied computational models for criticality. I am particularly thinking of the works of Bruno del Papa "Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network". Experimentally, there are works showing that the so-called spontaneous activity is actually very reliable (if you record enough neurons). Nghia et al. "Nguyen, Nghia D., et al. "Cortical reactivations predict future sensory responses." Nature 625.7993 (2024): 110-118."

An important point missing in this work is that it assumes that spontaneous activity is somehow intrinsically generated. This is not necessarily true of cortical areas (where it could easily come from hippocampus).

Reviewer #2 (Public review):

This work attempts to reconcile the concepts of critical neural dynamics with short-term reliable responses and long-term drifting responses. This is an important question, because critical dynamics are typically associated with unpredictable population responses to perturbations. Instead, this paper demonstrates that recordings from the mouse visual cortex include typical avalanche statistics in their spontaneous state as well as clustered within-session responses to natural movies. The authors find that a spiking neural network with homeostatic plasticity on inhibitory coupling captures the correlation-based metrics observed in experiments and that this network self-organizes into a critical state.

Strengths:

The structure of the manuscript is clear, and the line of argumentation is easy to follow. The question raised is valid, and the model employed to answer it is adequate. While I am unsure if representation should be equated with reliable responses, I find the framework of reliable responses well-suited to compare experimental and numerical data.

Weaknesses:

• The claim that the presented model "self-organizes to the critical spontaneous state" is incompatible with Fig. 6 showing that the inhibitory timescale is a control parameter of the transition from subcritical to supercritical avalanche statistics.

• The notion of "drift" implies to me a gradual change on long timescales. This is demonstrated in Ref. [47] for a model including two different types of plasticity. Also, such a drift over time was observed in Ref. [11] Fig.3C. In the present work, we can see from Fig. 2E that the correlation drops immediately to a plateau. Instead, the model actually shows some decay of correlations, expected from the ongoing plasticity. This challenges the claim that the "model successfully reproduce[s] both representational drift and [...]". Instead, the model of [47] does reproduce representation drift.

• The claim that "spontaneous self-organized criticality serves as [...] functional mechanism for maintaining reliable information representation under continuously changing networks" is not justified by the above-raised points.

• From the methods, I understand that the dimensionality reduction in Fig.2C and Fig.4C is a result of independent t-SNE. Since t-SNE to my knowledge starts with a random projection of data to then optimize the embedding, the resulting orientation of independent runs cannot be compared such that statements like "rotation of low-dimensional representations as in Fig. 2C, where nodes (centers of the same-color clusters) change their positions across sessions (top panel and bottom panel), but their relative positions remain stable" are not possible.

Reviewer #3 (Public review):

Summary:

This study uses computational modeling of a spiking network of E-I with homeostatic inhibitory plasticity and aims to show that self-organized criticality that arises from the homeostatic mechanism can result in representational drift as well as reliable stimulus representation, because the geometric representation of stimuli remains restricted.

Strengths:

This paper provides a framework to link critical spontaneous state, homeostatic inhibitory plasticity, representational drift, and stimulus population response reliability

Weaknesses:

The study does not show a causal (or necessary/ sufficient) relationship between criticality at the spontaneous state, representational drift, and reliable stimulus presentation. The study only reports an observation that these features could co-exist. However, it does not show how the criticality of the spontaneous state could restrict the manifold for stimulus response.

Reviewer #2 (Public review):

Summary:

This manuscript compares transcription and translation in the spinal cord during the acute and chronic phases of neuropathic pain induced by surgical nerve injury. The authors chose to focus their investigation on translation in the chronic phase due to its greater impact on gene expression in the spinal cord compared to transcription.

(1) The study is significant because the molecular mechanisms underlying chronic pain remain elusive. The role of translational regulation in the spinal cord has not been investigated in neuroplasticity and chronic pain mouse models. The manuscript is innovative and technically robust. The authors employed several cutting-edge techniques such as Rio-seq, TRAP-seq, slice electrophysiology, and viral approaches. Despite the technical complexity, the manuscript is well-written. The authors demonstrated that inhibition of eIF4E alleviates pain hypersensitivity, that de novo protein synthesis is more pronounced in inhibitory interneurons, and that manipulating mTOR-eIF4E pathways alters mechanical sensitivity and neuroplasticity.

(2) Strengths: innovation (conceptual and technical levels), data support the conclusions.

Comments on revisions:

The authors did a great job addressing my comments.

Reviewer #4 (Public review):

Summary:

The significance of this study lies in its focus on translational regulation in the late phase of neuropathic pain, using both genetic and pharmacological approaches, with specific emphasis on parvalbumin-positive (PV⁺) inhibitory interneurons in the spinal cord. The authors are very responsive to all the reviewers' comments.

Strengths:

I did not review this manuscript in the first round. However, the authors have been highly responsive to the reviewers' comments and have substantially strengthened the study. They conducted new behavioral experiments that yielded informative negative results (Fig. 6A and 6B). These findings demonstrate that targeting translational control in PV neurons is sufficient to reverse SNI-induced reductions in PV neuron excitability, but insufficient to ameliorate behavioral phenotypes. This suggests that additional cell types and pathways contribute to late-phase neuropathic pain.

Weaknesses:

Only the withdrawal threshold was measured to assess neuropathic pain. Some studies only used female mice. However, the authors appropriately discuss the study's limitations in the final two paragraphs and have added experimental details to improve clarity. Overall, the manuscript has been significantly improved.

Reviewer #5 (Public review):

Summary:

This study investigates the molecular mechanisms underlying the maintenance of neuropathic pain, specifically focusing on the role of mRNA translation in the spinal cord. Using the Spared Nerve Injury (SNI) model, the authors demonstrate that while both transcription and translation are active in the early phase, the chronic phase (day 63) is uniquely characterized by a shift toward translational control. They identify spinal inhibitory neurons, particularly parvalbumin-positive interneurons, as key sites of this translational regulation.

Strengths:

Technical Rigor: The use of Ribo-seq and TRAP-seq allows for a high-resolution view of the "translatome," which more accurately reflects the functional protein output than standard mRNA-seq.Novelty: The study uncovers that reducing a single translation initiation factor (eIF4E) specifically in the CNS is sufficient to provide long-lasting relief from established chronic pain.Addressing Disinhibition: The electrophysiological evidence showing that increased translation in PV+ neurons reduces their excitability provides a clear mechanism for the "spinal disinhibition" typically seen in chronic pain.

Weaknesses:

Cell-Type Sufficiency: New experiments in the revision show that while inhibiting translation in PV+neurons restores their individual excitability, it is not sufficient on its own to reverse behavioral pain hypersensitivity. This suggests that the maintenance of chronic pain likely involves translational changes across a broader network of cell types, including other inhibitory neurons or non-neuronal cells like microglia. -This does not have to be resolved in the current study, but providing some framework to account for potential mechanisms might help the audience.

Reviewer #1 (Public review):

In this manuscript, Wafer and Tandon et al. present a thoughtful and well-designed genetic screen for regulators of adipose remodeling using zebrafish as a model system. The authors cross-referenced several human adipocyte-related transcriptomic and genetic association datasets to identify candidate genes, which they then functionally tested in zebrafish. Importantly, the authors devised an unbiased microscopy-based screening platform to document quantitative adipose phenotypes with whole animal imaging, while also employing rigorous statistical methods. From their screen, the authors identified 3 genes that resulted in robust adipose phenotypes out of a total of 25 that were tested. Overall, this work will be an important resource for the field because of the genes identified from the screen, the quantitative screening pipeline, and the rigorous phenotypic analysis.

Comments on revisions:

The authors have far exceeded my expectations with their revised manuscript. All my questions and concerns from the original manuscript have been addressed by the authors. The additional data and analysis in Figure 6 and Supplementary Figure 8 are compelling and have greatly improved the manuscript.

Reviewer #2 (Public review):

This manuscript by Wafer, Tandon et al., presents exciting new approaches for using the zebrafish CRISPR screening and imaging system to identify genes that are associated with hyperplastic and hypertrophic adipose morphology. This paper established valuable screening pipelines in zebrafish to identify genetic regulators that affect adipose tissue morphology by combining CRISPR with an imaging-based, comprehensive adipose spatial analysis platform. Starting from a human transcriptomic dataset with differentially expressed genes that separate small and large adipocytes, they eventually identified 3 genes that induce hyperplastic or hypertrophic phenotypes in zebrafish. From which, they focused on foxp1 gene, a transcription factor known to regulate tissue development. They discovered that the foxp1 mutant displays basal hypertrophic morphology and failed to undergo hypertrophic remodeling in response to a high-fat diet, suggesting a link between adipose tissue development and diet-induced remodeling response. Overall, this manuscript is extremely well-written, the data presented is quite compelling, and the identified novel genes that are associated with adipose tissue hyperplastic and hypertrophic morphology and diet-induced remodeling are very exciting.

Strength:

(1) Obesity remains a worldwide public health concern. The mechanisms underlying adipose tissue hypertrophic and hyperplastic adaptation remain unclear.

(2) This manuscript combined multiple omic datasets to identify candidate genes and performed a CRISPR-based screening to identify genes underlying adipose tissue development and adaptation. This new method will open opportunities that will facilitate our understanding and testing of new genetic mechanisms underlying the development of obesity.

(3) Using the screening approach, this paper successfully identified new genes that are associated with adipose tissue LD size change. More importantly, the paper provided further validation using a stable CRISPR line to show the phenotype in basal and HFD conditions.

(4) The experiments are extremely well-designed. Sample sizes are large. Statistical analysis is rigorous. Overall, this is a very high-quality study.

Author's response to the previous comments/weakness:

(1) In this revised manuscript, the authors provided new comprehensive spatial analyses of foxp1a and foxp1 b mutants in basal conditions as well as responding to high-fat feeding. The new data confirmed their initial findings and beautifully illustrated the spatiotemporal dynamics of the adipocytes in response to High-fat diet feeding.

(2) The authors have addressed all my comments, and I do not have further comments.

Reviewer #1 (Public review):

Mutations in CDHR1, the human gene encoding an atypical cadherin-related protein expressed in photoreceptors, are thought to cause cone-rod dystrophy (CRD). However, the pathogenesis leading do this disease is unknown. Previous work has led to the hypothesis that CDHR1 is part of a cadherin-based junction that facilitates the development of new membranous discs at the base of the photoreceptor outer segments, without which photoreceptors malfunction and ultimately degenerate. CDHR1 is hypothesized to bind to a transmembrane partner to accomplish this function, but the putative partner protein has yet to be identified.

The manuscript by Patel et al. makes an important contribution toward improving our understanding of the cellular and molecular basis of CDHR1 associated CRD. Using gene editing, they generate a loss of function mutation in the zebrafish cdhr1a gene, an ortholog of human CDHR1, and show that this novel mutant model has a retinal dystrophy phenotype, specifically related to defective growth and organization of photoreceptor outer segments (OS) and calyceal processes (CP). This phenotype seems to be progressive with age. Importantly, Patel et al, present intriguing evidence that pcdh15b, also known for causing retinal dystrophy in previous Xenopus and zebrafish loss of function studies, is the putative cdhr1a partner protein mediating the function of the junctional complex that regulates photoreceptor OS growth and stability.

This research is significant in that it:

(1) provides evidence for a progressive, dystrophic photoreceptor phenotype in the cdhr1a mutant and, therefore, effectively models human CRD; and

(2) identifies pcdh15b as the putative, and long sought after, binding partner for cdhr1a, further supporting the theory of a cadherin-based junction complex that facilitates OS disc biogenesis.

Comments on the revised version of the manuscript:

The authors adequately addressed previous comments related to lack of details on quantitative and statistical analyses and methods. In this regard, I believe the revised manuscript presents a stronger analysis of the data. I also appreciated the revised discussion section, which better contextualizes their new data with previous observations in different animal models.

The authors provided additional evidence in Fig 1C-H for the co-localization of pcdh15b and actin within CPs using immunolabeling with super resolution imaging. This data firmly supports their other observations. A similar approach tends to also show co-localization of actin and cdhr1a, although the authors suggest that the pattern of expression is less overlapping, which would be expected if cdhr1a is predominately expressed in the OS membranes whereas pcdh15b is predominantly expressed in the CP membranes. In my opinion the data presented to support this separation is still not that convincing. Moreover, the authors show that both cdhr1a and pcdh15b are expressed in CPs using immuno-TEM (Fig 1I). This is a difficult question to address experimentally, and it is, of course, still plausible that pcdh15b within the CP membrane and cdhr1a within the OS membrane are interacting in trans. However, I just don't think that the data unequivocally support mutually exclusive localization of these proteins as suggested by the authors and depicted in the model in Fig 1J.

Reviewer #2 (Public review):

Summary:

The goal of this study was to develop a model for CDHR1-based Con-rod dystrophy and study the role of this cadherin in cone photoreceptors. Using genetic manipulation, a cell binging assay, and high- resolution microscopy the authors find that like rods, cones localize CDHR1 to the lateral edge of outer segment (OS) discs and closely opposes PCDH15b which is known to localize to calyceal processes (CPs). Ectopic expression of CDHR1 and PCDH15b in K652 cells indicate these cadherins promote cell aggregation as heterophilic interactants, but not through homophilic binding. This data suggests a model where CDHR1 and PCDH15b link OS and CPs and potential stabilize cone photoreceptor structure. Mutation analysis of each cadherin results in cone structural defects at late larval stages. While pcdh15b homozygous mutants are lethal, cdhr1 mutants are viable and subsequently show photoreceptor degeneration by 3-6 months.

Strengths:

A major strength of this research is the development of an animal model to study the cone specific phenotypes associate with CDHR1-based CRD. The data supporting CDHR1 (OS) and PCDH15 (CP) binding is also a strength, although this interaction could be better characterized in future studies. The quality of the high-resolution imaging (at the light and EM levels) is outstanding. In general, results support the conclusions of the authors.

Weaknesses:

While the cellular phenotyping is strong, the functional consequences of CDHR1 disruption is not addressed. While this is not the focus of the investigation, such analysis would raise the impact of the study overall. This is particularly important given some of the small changes observed in OS and CP structure. While statistically significant, are the subtle changes biologically significant? Examples include cone OS length (Fig 4F, 6E) as well as other morphometric data (Fig 7I in particular). Related, for quantitative data and analysis throughout the manuscript, more information regarding the number of fish/eyes analyzed as well as cells per sample would provide confidence in the rigor. The authors should also not whether analysis was done in an automated and/or masked manner.

Comments on revisions:

Most of my concerns were addressed in this revised version.

Reviewer #3 (Public review):

Summary:

The manuscript by Patel et al investigates the hypothesis that CDHR1a on photoreceptor outer segments is the binding partner for PCDH15 on the calyceal processes, and the absence of either adhesion molecule results in separation between the two structures, eventually leading to degeneration. PCDH15 mutations cause Usher syndrome, a disease of combined hearing and vision loss. In the ear, PCDH15 binds CDH23 to form tip links between stereocilia. The vision loss in less understood. Previous work suggested PCDH15 is localized to the calyceal processes, but the expression of CDH23 is inconsistent between species. Patel et al suggest that CDHR1a (formerly PCDH21) fulfills the role of CDH23 in the retina.

The experiments are mainly performed using the zebrafish model system. Expression of Pcdh15b and Cdhr1a protein is shown in the photoreceptor layer through standard confocal and structured illumination microscopy. The two proteins co-IP and can induce aggregation in vitro. Loss of either Cdhr1a or Pcdh15, or both, results in degeneration of photoreceptor outer segments over time, with cones affected primarily.

The idea of the study is logical given the photoreceptor diseases caused by mutations in either gene, the comparisons to stereocilia tip links, and the protein localization near the outer segments. The work here demonstrates that the two proteins interact in vitro and are both required for ongoing outer segment maintenance. The major novelty for this paper would be the demonstration that Pcdh15 localized to calyceal processes interacts with Cdhr1a on the outer segment, thereby connecting the two structures. Unfortunately, the data as presented are inadequate proof of this model.

Strengths:

The in vitro data to support the ability of of Pcdh15b and Cdhr1a to bind is well done. The use of pcdh15b and cdhr1a single and double mutants is also a strength of the study, especially being that this would be the first characterization of a zebrafish cdhr1a mutant.

This is a large body of data.

Weaknesses:

(1) I have serious concerns about the quality of the imaging here. The premise that cdhr1a/pcdh15 juxtaposition is evidence for the two proteins mediating the connection between outer segments and calyceal processes requires very careful microscopy. The SIM images have two major issues - one being that the red and green channels are misaligned and the other being evidence of bleed through between the channels. This is obvious in Fig 2A but likely true across all the panels in Fig 2, and possibly applies to confocal images in Fig 1 as well. The co-labelling with actin shows very uneven, punctate staining for actin bundles.

(2) The newly added TEM and transverse sections include colored regions that obscure the imaging.

(3) The quantification should be done with averages from individual fish. Counting individual measurements as single data points artificially inflates the significance. Also, the cone subtypes are still lumped together for analysis despite their variable sizes.

(4) I highlighted previously that the measurement of calyceal processes was incorrect. The redrawn labels in Fig 7 are now more accurate, although still difficult to interpret. However, the quantification in Fig 7O is exactly the same. How can that be if the measurement region is now different?

(5) Lower magnification views would provide context for the TEM data.

(6) The statement describing the separation between calyceal processes and the outer segment in the mutants is still not backed up by the data.

(7) The authors state "from the fact that rod CPs are inherently much smaller than cone CPs". This is now referenced, but incorrectly. Also, the issue of pigment interference was not addressed.

(8) The images in panels B-F of the Supplemental Figure are uncannily similar, possibly even of the same fish at different focal planes.

Reviewer #1 (Public review):

Summary:

The authors provide a simple yet elegant approach to identifying therapeutic targets that synergize to prevent therapeutic resistance using cell lines, data-independent acquisition proteomics, and bioinformatic analysis. The authors identify several combinations of pharmaceuticals that were able to overcome or prevent therapeutic resistance in culture models of ovarian cancer, a disease with an unmet diagnostic and therapeutic need.

Strengths:

The manuscript utilizes state-of-the-art proteomic analysis, entailing data-independent acquisition methods, an approach that maximizes the robustness of identified proteins across cell lines. The authors focus their analysis on several drugs under development for the treatment of ovarian cancer and utilize straightforward thresholds for identifying proteomic adaptations across several drugs on the OVSAHO cell line. The authors utilized three independent and complementary approaches to predicting drug synergy (NetBox, GSEA, and Manual Curation). The drug combination with the most robust synergy across multiple cell lines was the inhibition of MEK and CDK4/6 using PD-0325901+Palbociclib, respectively. Additional combinations, including PARPi (rucaparib) and the fatty acid synthase inhibitor (TVB-2640). Collectively, this study provides important insight and exemplifies a solid approach to identifying drug syngery without large drug library screens.

Weaknesses:

The manuscript supports their findings by describing the biological function(s) of targets using referenced literature. While this is valuable, the number of downstream targets for each initial target is extensive, thus, the current work does not attempt to elucidate the mechanism of their drug synergy. Responses to drugs are quantified 72 hours after treatment and exclusively focused on cell viability and protein expression levels. The discovery phase of experimentation was solely performed on OVSAHO cell line. An additional cell line(s) would increase the impact of how the authors went about identifying synergistic targets using bioinformatics. Ovarian cancer is elusive to treatment as primary cancer will form spheroids within ascites/peritoneal fluids in a state of pseudo-senescence to overcome environmental stress. The current manuscript is executed in 2D culture, which has been demonstrated to deviate from 3D, PDX, and primary tumours in terms of therapeutic resistance (DOI: 10.3390/cancers13164208). Collectively, the manuscript is insufficient in providing additional mechanistic insight beyond the literature, and its interpretation of data is limited to 2D culture until further validated.

Comments on revisions:

The reviewer has no further recommendations for the authors.

Reviewer #2 (Public review):

Summary:

Franz and colleagues combined proteomics analysis of OVSAHO cell lines treated with 6 individual drugs. The quantitative proteomics data was then used for computational analysis to identify candidates/modules that could be used to predict combination treatments for specific drugs.

Strengths:

The authors present solid proteomics data and computational analysis to effectively repeat at the proteomics level analysis that have previously been done predominately with transcriptional profiling. Since most drugs either target proteins and/or proteins are the functional units of cells, this makes intuitively sense.

Weaknesses:

Considering the available resources of the involved teams, preforming the initial analysis in a single HGSC cells is certainly a weakness/limitation. During the revision additional cell lines were used for verification.

The data also shows how challenging it is to correctly predict drug combinations. In Table 2 (if I read it correctly) the majority of the drug combinations predicted for the initial cell line OVSAHO did not result in the predicted effect. It also shows how variable response was in the different HGSC cell lines used for combination treatment. The success rate will most likely continue to drop as more sophisticated models are being used (i.e., PDX). Human patients are even more challenging.

It would most likely be useful to more directly mention/discuss these caveats in the manuscript. This was added to the discussion during the revision. Overall the authors have responded to previous suggestions.

Reviewer #1 (Public review):

Summary:

This study proposes a simple and universal reinforcement-learning framework for understanding learning in complex motor tasks. Central to the framework is a policy-gradient algorithm, in which motor commands are updated not via the gradient of the reward with respect to policy parameters, but via the gradient of the policy itself, scaled by reward information. The authors demonstrate that this scheme can reproduce learning dynamics that have been reported in previous empirical studies.

Strengths:

The key contribution of this study lies in its application of a policy-gradient algorithm to describe motor learning processes. This idea is biologically plausible, as computing the gradient of the policy with respect to its parameters is likely to be substantially easier for the nervous system than computing the gradient of the reward with respect to policy parameters. The authors present three representative examples showing that this scheme can capture several aspects of motor learning dynamics. Notably, providing such a unified description across different tasks has been difficult for conventionally proposed learning frameworks, such as supervised learning.

Weaknesses:

While this scheme is valuable in that it captures certain aspects of learning dynamics, I find that its overall significance is limited for the following reasons.

(1) The empirical results examined in this study primarily demonstrate that motor learning drives performance toward the spatial task goal while reducing variability. Given that the policies are expressed using Gaussian distributions and that their parameters (i.e., the mean and covariance matrix) are updated during learning, it is not surprising that the proposed scheme can reproduce these results by fitting the parameters to the data.

(2) The proposed framework assumes that the motor learning system relies on the gradient of the policy with respect to its parameters. However, I am not convinced that this assumption is always appropriate, because in all three empirical studies examined here, explicit spatial error information is available. In such cases, the motor learning system could, in principle, compute the gradient of the error with respect to the policy parameters directly, without relying on a policy-gradient mechanism.

(3) Most importantly, it remains unclear how the proposed scheme advances our understanding of the underlying learning mechanisms beyond providing a descriptive account of the learning process. While the framework offers a compact mathematical description of learning dynamics, it is uncertain how it can yield novel mechanistic insights or testable predictions that distinguish it from existing learning models.

Reviewer #2 (Public review):

Summary:

In this study, Haith applies, and to some extent extends, the theoretical framework of policy gradient (PG) and the derived REINFORCE learning rules to human motor learning. This approach is coherent because human motor skill learning is characterized by improvements in both accuracy and precision (the inverse of variance), and REINFORCE provides update rules for both the mean and the variance of the motor commands.

Weaknesses:

The mean update (equation 4) is given in task space (i.e., angle and velocity for the skittle task), but the covariance update (equation 5) is given in eigenvector space. This formulation appears to have been provided for computational convenience, as it ensures that the variances are always positive by exponentiating the eigenvalues. However, this eigenspace formulation is somewhat artificial and complex (notably the update rule for the orientation of the covariance matrix) and seems far from biological reality. A simpler alternative, suggested by the author, is to provide the full covariance matrix, including crossed terms, and derive equations to update the diagonal variance terms and the cross-terms (perhaps after a transformation to keep all elements positive if needed). This would provide a simpler and more biologically plausible update to the covariance matrix terms, in the spirit of the original REINFORCE algorithm. The author suggests that he has derived the update rule for the cross terms, so this should be relatively easy to write and update, especially for the skittle learning rules. If the author wishes to keep their rules in simulations, then the two mathematical rules could be presented in the methods or a supplementary material section.

The discussion about binary rewards and the increase in variance in previous experiments is potentially interesting. However, I do not understand why variance cannot increase with the policy-gradient RL update? Surely, equation 5 can lead to both an increase and a decrease in variance depending on the reward prediction error and the noise (for example, suppose the noise at trial i is small and leads to a smaller reward than the baseline; variance would increase). It would be interesting to see detailed simulation results for the skittle task showing changes in both mean and variance across a few consecutive trials, with both increases and decreases in reward prediction errors. These results could then be compared in simulations with those of a task with discrete binary rewards.

Generalization is a major feature of human learning, but it is not discussed or studied here. In fact, in the de novo task simulations, there can be no generalization because the values are modeled as running averages for each target rather than derived from a critic network. Can the author discuss this point and, ideally, show generalization results in simulations, say in the skittle task?

The application of the model to reproduce the Shmuelof et al. data is, at the same time, justified (because one of their main results is an improvement in precision, which Policy Gradient directly addresses) and somewhat "forced," as the author approximates curved movements with a series of straight-line movements. The author therefore needs to specify multiple via points with PG updating and a reward function that also enforces smoothness. The justification for the Guigon 2023 model seems somewhat artificial because it mainly applies to slow movements. Can the author comment and discuss alternatives that do not require via points, drawing from the robotics literature if needed (Schaal's Dynamic Movement Primitives come to mind, for example).

Policy Gradient requires both a "noisy" and a clean "pass", making it non-biological in its simplest form. Legenstein et al. (2010) and Miconi (2017) provided biologically plausible forms for the mean update. Since Policy Gradient is proposed as a model of human motor learning, can the author discuss the biological plausibility of the proposed learning rules and possible biologically plausible extensions?

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.

Weaknesses:

My main concern is that the study's aim is a bit unclear. Modern benchmarking studies comparing physics-based docking with deep learning-based co-folding approaches (e.g., AF3, Boltz-2, Chai-1, and others) are increasingly expected to go beyond aggregate performance metrics. In addition to rigorous dataset construction, transparent methodology, and appropriate statistical evaluation, high-impact benchmarks typically provide actionable guidance on when each method class is most appropriate, reflecting their distinct inductive biases and practical constraints. Failure-mode analyses that link performance differences to protein flexibility, ligand chemistry, or binding-site characteristics are particularly valuable, as they move comparisons beyond "scoreboard" assessments toward mechanistic understanding. While full biological validation is not expected, qualitative interpretation grounded in physical and biological principles strengthens conclusions. Providing reproducible workflows or reference pipelines is not mandatory, but it is increasingly viewed as a best practice because it facilitates adoption and helps contextualize results for practitioners.

Reviewer #2 (Public review):

Summary:

The manuscript by Kim et al. evaluates the performance of three modern AI-based methods in predicting complex structures and binding affinities between proteins and chemical compounds. An honest 'prospective' evaluation is achieved by studying benchmark structures and chemical compounds that did not exist in the PDB at the time the AI structure prediction models (AlphaFold3, Chai-1, Boltz-2) were trained.

Strengths:

(1) The study addresses an important question in modern computational biology and drug discovery, and establishes the strengths and limitations of the three tools in solving various computational chemistry tasks, including compound pose prediction, active-inactive discrimination, and potency ranking.

(2) The conclusions are based on examination of four separate targets and respective compound datasets, where for one of the targets, the authors also obtained numerous X-ray structures to serve as experimental answers for the binding pose prediction task.

(3) The study reports relationships between structure prediction confidence, predicted energies (DOCK3.7), and affinity predictions (Boltz-2) with the geometric accuracy of compound pose prediction as well as the experimentally measured potency.

(4) One of the key findings is the limited ability of co-folding methods to predict conformational rearrangements, which does not correlate with their ability to predict binding poses of the compounds inducing these rearrangements.

(5) The findings could serve as useful guidelines for computational chemists in selecting appropriate software and scoring schemes for each task.

Weaknesses:

While I consider this a solid study, several aspects would need to be addressed to make it really strong:

(1) DOCK3.7 docking and scoring experiments were performed using one experimental structure of Mac1, selected from dozens of structures based on a criterion that is not sufficiently well justified. For sigma2 receptor, dopamine D4 receptor, and AmpC β-lactamase, it is not clear which structures or models were selected for docking at all. It is well known that geometry predictions, scoring, and active-inactive ROC AUCs are all strongly influenced by the selected structure. It would be important to attempt Mac1 docking using all available experimental Mac1 structures, or at least against representative structures in various conformations; it would also be quite insightful to compare results to docking of the same compound sets to AF3, Boltz-2 and Chai-1 predicted structures of Mac1. Same goes for the docking studies of sigma2, D4, and AmpC β-lactamase.

(2) For binding affinity predictions, as a control, authors should consider compound co-folding with an unrelated protein, or even with a pseudo-peptide that consists of a few random single amino acids - this would provide an honest baseline for such predictions.

(3) ROC curves Figure 3 and elsewhere should be shown, and AUCs quantified/reported on a log or square-root scaled x-axis, to emphasize early enrichment, which is the area of practical significance for these predictions. For example, Figure 3A currently suggests that the pose prediction performance of AF3 exceeds that of Boltz-2 whereas the early enrichment is clearly better for Boltz-2.

(4) 'Trained set' in figures and text should probably be 'training set'? Or otherwise explain this new term the first time it is introduced.

(5) Figure 1 illustrates a projection onto the first two principal components of a space that apparently had only one (scalar) metric for each compound pair (% maximum common substructure or Tanimoto coefficient); the authors need to better explain the principle behind this analysis and visualization.

Reviewer #3 (Public review):

Summary:

This study's core conclusions are well-supported by data. It is shown that 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:

(1) Unprecedented prospective design with 557 novel Mac1-ligand complexes ensures rigorous, independent evaluation of co-folding methods.

(2) Comprehensive comparison of 3 co-folding tools (AlphaFold3, Chai-1, Boltz-2) with DOCK3.7 across diverse targets and metrics enables nuanced performance assessment.

(3) 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:

(1) Limited generalization to diverse protein families (e.g., no ion channels/transporters).

(2) Ambiguity in the mechanism underlying co-folding's failure to predict rare conformational changes.

(3) Virtual screen comparison is unbalanced (docking-prioritized hit lists bias results).

Reviewer #1 (Public review):

Summary:

In this study, the authors propose that HSV-1 infection degrades the class I histone deacetylases HDAC1 and HDAC2. The MDM2 E3 ubiquitin ligase from the DNA damage response pathway is responsible for ubiquitinating these HDACs that are subsequently degraded via proteasomes. The authors hypothesize that HDAC degradation will cause hyperacetylation of viral chromatin and enable viral gene transcription.

Strengths:

The ubiquitination of HDAC1 & HDAC2 by Mdm2 and the mapping studies are clear.

Weaknesses:

(1) Degradation of HDACs is observed late, at least 12-24 h post-infection (1 PFU/cell). Viral genes have been transcribed by that point, and the virus has replicated its genome. The kinetics do not match the proposed model.

(2) The authors need to connect these findings with their story. As of now, these findings are correlative. For example, what is the impact of MDM2 depletion on viral gene expression and progeny virus production? Leptomycin B is not specific to the HDAC cytoplasmic translocation, and its effect on the infection could be due to its effect on ICP27.

(3) The time point when the inhibitors were added to the cultures has not been stated in any experiment. If inhibitors were added with the virus, viral gene expression would be blocked.

(4) The authors need to present late gene expression data in all the experiments where drugs have been used.

(5) Figure 1A, ICP4 is not detected up to 12 hours post-infection of HeLa cells with 1 PFU/cell. This cannot be true.

(6) Leptomycin B blocks nuclear/cytoplasmic shuttling of ICP27 that brings viral mRNAs to the cytoplasm to be translated. So, the effect of LMB is not specific to the HDACs.

(7) The key experiment is to use the degradation-resistant form of HDAC1 to evaluate its impact on viral gene transcription.

(8) In the experiment where Mdm2 was depleted, the authors need to demonstrate the effect on the infection. ICP4 expression is not enough. How about growth curves? After Mdm2 depletion, ICP4 expression increases, which may contradict the authors' findings. An analysis of alpha and gamma gene expression is important.

(9) Why did the authors analyze a liver HSV-1 infection and not a more relevant skin infection?

Reviewer #2 (Public review):

Summary:

The authors discovered that HDAC1/2 are degraded in HSV-1 and PRV infections. They attempted to establish a new mechanism by which HDAC1/2 are translocated to the cytoplasm to be degraded in HSV-1 infection, and the degradation causes changes in histone acetylation to affect the DDR pathway.

Strength:

(1) Interesting findings of HDAC1/2 degradation during HSV-1 and PRV infection, and it may impact more than the virology field.

(2) Significant work to identify the ubiquitin site in HDAC1/2 and K63 linkage.

Weaknesses:

(1) Insufficient evidence to support the mechanism described by the authors.

(2) Expansion of the conclusion to alphaherpesvirus without studying the intended mechanism in PRV infection.

Overall, there may be a correlation between HDAC1/2 level, ATM/ATR phosphorylation, and HDAC1 translocation during the HSV-1 infection. However, core evidence supporting the mechanism that a) HDAC1 export causes its degradation, b) degradation of HDAC1 causes histone acetylation changes and DRR activation has not been sufficiently demonstrated.

Reviewer #3 (Public review):

The authors state that infection of cells by the alphaherpesviruses HSV-1 or PRV leads to a proteosome-dependent reduction in levels of HDAC1 and HDAC2 and that this leads to chromatin hyperacetylation, a DNA damage response, and greater replication of these viruses. Previously, other authors reported no change in levels of HDAC1 and HDAC2 after HSV-1 infection of human cells, but this paper is neither cited nor commented on in this new submission. The experiments are poorly designed. For instance, most of the time points analysed are way beyond the time needed for HSV-1 replication and are therefore not biologically relevant. The infections are done with a dose of virus that does not ensure that all cells are infected synchronously, but rather infection spreads from cell to cell with multiple rounds of replication. Some essential controls are missing. Additionally, this reviewer feels that the data presented do not support the conclusions drawn. Currently, links are not established between a reduction in HDAC1/ 2 and other phenomena such as hyperacetylation of histones, a DDR, and altered virus replication. The paper does not identify which HSV or PRV protein(s) induce reduction in HDACs, nor how the HDACs mediate antiviral activity; what are the HSV-1 or PRV protein targets? Lastly, the paper is not well prepared, and it does not adequately refer to prior literature.

Reviewer #1 (Public review):

Summary

The authors use reduced-representation sequencing (GBS) across samples from the quaking aspen clonal stand Pando to identify putative somatic mutations, which were used to estimate clone age, and evaluate whether somatic variation shows spatial structure across the grove. This is a compelling and charismatic system to look at somatic mutation in plants. They report little sharing of putative somatic mutations as a function of distance and interpret this as evidence for weak mutation transmission or homogenization over time, potentially driven by rapid root growth and clonal spread dynamics. They use mutations to estimate clone age. The authors are generally upfront and commendably transparent about limitations in sequencing depth and mutation calling. The paper addresses an interesting research system, but struggles to overcome limitations in the suitability of the data.

Strengths.

This is a fantastic system and an interesting set of questions. The authors' GBS data does a great job distinguishing Pando from its neighbors, which is an important first step in studying the history of this clone.

The manuscript is upfront and highlights the need for improved data to refine inference, for example: "Higher-coverage whole-genome sequencing, and ideally single-cell sequencing of defined meristem lineages, will be needed to refine mutational and evolutionary parameter estimates in this iconic organism."

It also states that "either we are missing roughly 80% of true somatic mutations or only 20% of the mutations we detect are true positives."

I appreciate that the authors report an age estimate range that considers the breadth of potential false negatives and positives.

Weaknesses

I am still not sure whether the paper overcomes issues with the use of GBS for somatic mutation calling.

I found it difficult to reconcile the manuscript's description of the call set as "conservative" with the reported validation tests (calibrated by looking at retained variants detected in 2 of 8 technical replicates). How was this threshold determined? A mutation with 2/8 has quite low reproducibility, which could reflect either substantial false negatives under low depth (true variants frequently dropping out) or false positives that recur sporadically due to library - or sequencing-specific artifacts. Without stronger internal diagnostics or external validation, it is hard to determine which applies here.

The GBS sequence space and genomic distribution could be more clearly explained. According to the methods, "The total number of base pairs sequenced(129,194,577) was estimated using angsd, and reduced following the proportion of base pairs that we filtered out because of low coverage (48%)." What does the 129M basepairs represent? Is that 129M/genome length, or is it the number of aligned basepairs (i.e., 1M genome covered x129 depth)? In addition, summarizing where GBS loci fall across the genome, genic vs intergenic vs TE; repetitive vs unique, since these can have substantially different somatic mutation rates (Meyer et al. 2025). Without additional summary/descriptive statistics, it is hard to interpret both missingness and "rate".

Statistical concerns about some results. In the Figure 3 legend, the authors state that the sample-level relationship between shared variants and distance is significant: "Pearson correlation coefficient ... is −0.02, 95% CI = [−0.05, 0.00], which is significantly different from a randomized distribution (P < 0.001) (B)." However, as plotted in Figure 3B, the observed correlation (−0.02) appears to fall well within the bulk of the randomized distribution of correlation coefficients. If the reported P value is intended to be permutation-based (i.e., the tail probability under the randomized null), it is unclear how P could be < 0.001 given that the observed value does not appear extreme relative to the null.

The developmental program of plant stem cell layers is essential, but not discussed much. In a root-spreading clone, expectations about mutation sharing depend strongly on how new ramets arise developmentally (root-derived meristem initiation) and how layered meristems partition mutations across tissues (e.g., L1/L2/L3). I was surprised there was not a substantial discussion of the details about the layer specificity of somatic development and mutation accumulation in plants. Especially relating to mutations that would be shared between roots/shoots around potential layer-specific growth of roots. The current analysis seems to focus on comparisons within tissue types (e.g., leaves between ramets), but did not report informative tests between tissue and within-ramet (e.g., in heavily sampled trees, whether a ramet's root, shoot, leaves, share a subset of variants; whether neighboring ramets share root-lineage variants more than shoot-lineage variants). It would help to articulate expectations and clarify what the data can and cannot test. Relatedly, for "mutation rates," in aging material, it would be good to discuss which meristem layer(s) each tissue is likely sampling and how layer-specific mutation dynamics (e.g., reported differences between L1 vs L2 lineages) could influence rate and therefore age estimates (Goel et al. 2024, Amundson et al. 2025).

Developmental mosaicism makes expected allele fractions lower than discussed in the paper. The supplement states, "However, because the Pando clone is triploid, it reduces our expectation for fixation of a mutation to 0.33", but this ignores layer-specific stem cells in plant development. True that if calls are made against a haploid reference, then a new somatic mutation in a triploid background is expected around ~1/3 allele fraction - but only if fixed in 100% of cells. Layer-specificity (e.g., L1 vs L2 vs L3 restriction) or polyclonal founding events will push expected allele fractions substantially lower. Therefore, at ~12-14× depth (or min of 4x), these allele fractions translate into only a handful (or even 0) of alternate reads (<<33% is expectation).

Within-tree replicate consistency was unclear. The manuscript hints at multiple samples/replicates per tree (e.g., Figure S2), but it is not clear how often the same putative somatic variants are recovered across samples from the same ramet and tissue. A simple reproducibility summary would be extremely helpful: for variants called in one sample, what fraction are recovered in other samples from the same tree (by tissue), what variant allele fractions, and how do their spectra compare to mutations unique to a single sample?

The manuscript did not provide supplemental tables or mutation calls. Supplemental tables containing pre-filter and/or post-filter calls (or some other structured data file with flags indicating various quality metrics, REF vs ALT depths at minimum, REF call, and ALT call) would substantially improve transparency and ability to evaluate the work.

Reviewer #2 (Public review):

Summary:

The topic of the paper is intriguing as it sets out to age one of the potentially largest living organisms, a tree clone (Pando), using shallow genome resequencing of a large number of replicate samples. The key result is that the Pando clone is several tens of thousands of years old, which is of high-interest to plant genomics and evolutionary ecology.

Weaknesses:

Unfortunately, the claims are not matched by the available data and their analysis. Probably, the results can also not be resurrected using modified analyses, as the available data are not suited to reliably detect somatic genetic variation as a means to age-clonal plants.

In order to reliably age clones, one needs to consider the full process by which clone mates genetically diverge from one another over time, which starts with a plant's apical meristem (SAM). From this, all above-ground tissues such as twigs and branches, as well as leaves, are derived, which has been beautifully worked out now in oaks and many fruit trees (e.g., doi: 10.1101/2023.01.10.523380 ; 10.1101/2024.01.04.573414). For the accumulation and propagation of fixed somatic genetic variation, only the processes in the SAM matter. Hence, it does make little sense to look at tissue-specific mutations unless one is invoking non-cell division induced mutations through UV light. Those, however, would remain undetected with the present low-coverage sequencing as they cannot leave the mosaic status any more, as that tissue is essentially non-dividing.

Somatic genetic drift (https://www.nature.com/articles/s41559-020-1196-4) is the foundation for the fixation of somatic genetic variation and hence, for ageing (plant) clones. It requires quantitative modeling of the processes at the cell-line level when new modules, here, aspen trees are formed, in particular N (cell population size) and N0 (founder cell size).

Calibrations have to be made using the mutation and fixation rate at the somatic cell lineage level, ideally also with some empirical data. In trees such as aspen, it would be very easy to obtain calibration points of branch tips that have physically and thus genetically diverged upon a defined TCA to directly determine the rate of accumulation of somatic genetic variation by direct dendrochronology (i.e., counting tree rings).

Instead, in the present work, a mutation rate from another tree species is taken, which will introduce a lot of uncertainty into the estimates, given that tree SAMs divide at a very different pace (see doi 10.1093/evolut/qpae150). It is clear that a small difference in the assumed mutation rate, e.g., a higher one, would conversely reduce the age estimate considerably.

I am doubtful that a conventional phylogenetic model based on coalescence, such as the one employed here, can be utilized, as it assumes a sexually recombining population and hence variable sites. A model simulation on an asexually evolving population would be needed to check this.

In order to reliably call somatic genetic variation, a decent coverage of short-read sequences is needed, definitely > 15x, which was achieved in the present dataset. This is particularly relevant as a fixation in one of the three haploid chromosome sets would just amount to a read frequency of only 0.33. A coverage of only 4x reads per called site seems very low to me; in other words, the filtering steps do not seem to be very rigorous to me. It is also difficult to follow the logic of several ad hoc adjustments that were made to compensate for the low coverage of sequencing, in particular, the common panel and the replicate identical samples. Why chose 80% in the latter?

There are alternative, non-sequencing-based ways to double-check the accuracy of somatic SNP calls (e.g., described here https://www.nature.com/articles/s41559-020-1196-4), which could have been employed at least once to evaluate the error rates for the specific sequencing strategy.

I also suggest that for any future study, reference to mutation callers developed for cancer somatic mutation detection should be employed, which are now increasingly used both in clonal plants and trees for that purpose.

What worries me is that there is a poor correlation between physical and genetic distance. This lack of correlation among spatial and genetic structure, for example, the star-like phylogeny presented in Figure 6d, indicates a large fraction of false positives rather than some special, as yet unexplained processes of local mutation accumulation that the authors claim to have discovered.

Finally, the work is not properly embedded into the current literature. For example, recent developments of molecular clocks were not considered, such as the development of a dedicated somatic genetic clock that precisely addresses this question (https://www.nature.com/articles/s41559-024-02439-z). Also, older but nevertheless significant work that aged aspen clones using microsatellite markers is not mentioned (http://dx.doi.org/10.1111/j.1365-294X.2008.03962.x).

Reviewer #1 (Public review):

Summary:

The manuscript by Vasquez-Correa and colleagues describes the expression pattern of the ocelli (simple eye) gene regulatory network in ants. They correlate the expression pattern of these genes with the presence and absence of ocelli in different classes and species of ants. The presence of ocelli is a polyphenic trait in ants - understanding the molecular and developmental underpinnings of polyphenic traits is of significant interest to evolutionary biologists, developmental biologists, and ecologists. The authors propose that the presence of the latent expression of the ocellar network in classes of ants that do not display ocelli in the adults may underlie the re-evolution of ocelli within the ant lineage.

Strengths:

The strengths of the manuscript are that it is well written, the images are of the highest quality, and the data support the conclusions of the authors.

Weaknesses:

One improvement that could be made is to include imaginal discs of the queen ants as well as scanning electron images of the ocelli of the queen ant to match the pupal stage images of the worker and soldier ants. A second improvement is to attempt a gene knockdown using RNAi or similar methods to ensure that the genes that are being studied are, in fact, responsible for ocelli development in the ant.

Reviewer #2 (Public review):

Summary:

The manuscript titled "Latent gene network expression underlies partial re-evolution of a polyphenic trait in the worker caste of ants" by Vasquez-Correa et al. aimed to study genetic mechanisms underlying developmental plasticity, especially binary polyphenism in queen vs worker ant castes. This is an interesting question regarding the extent to which phenotypic traits were altered, lost or regained, and how molecular pathways (upstream vs. downstream) can facilitate this process.

In ants, reproductive castes (queens and males) develop wings as well as 3 ocelli for mating flights and other activities, while worker castes are wingless, and in some species, they have either no or a reduced number of ocelli. The phylogenetic analysis showed that in the Camponotini ant clade, the one-ocellus phenotype re-evolved in three species independently. The authors analyzed the conserved developmental pathways between Drosophila (well-established) and ants using HCR (a high-quality in situ hybridization technique). They found that although upstream genes for the development of ocelli (otd and hh) showed similar expression between castes, downstream genes (toy, eya, and so) had reduced or no expression in workers of C. floridanus, and this differential expression may lead to partial or complete loss of ocelli. Consistently, workers develop rudimentary tissues, suggesting that they initiate the ocellus developmental process but somehow stop it before adulthood.

Strengths:

Evo-devo approaches to reveal conserved molecular pathways of ocellus development. High-quality HCR provided convincing evidence of the expression of key genes in ocelli, eyes and antenna throughout larval development.

Using HCR, the authors showed differential expression of downstream genes in males vs. soldiers vs. minor workers of C. floridanus, which might explain phenotypic differences between castes.

Weaknesses:

Although the molecular pathway is conserved, the mechanism underlying the lack of ocelli in workers remains unclear. In C. floridanus, it could be explained by the evidence of no expression of certain developmental genes, but in other species, e.g. Polyrachis rastellata, is their expression intact, or reduced? There is no control male.

In addition, HCR in species with partial re-evolution (if their genomes have been sequenced) would be useful to understand the mechanism. For example, there might be differential spatial expression between medial and lateral ocelli.

Reviewer #3 (Public review):

Summary:

This paper examines the loss and re-evolution of specific organs during the evolution of ants. The authors show that these organs, the ocelli, disappear and are re-evolved in different ant species and in different ant castes within these species. The authors show that this is linked to dto a conserved GRN discovered in Drosophila, that appears to underlie the development of the ocelli, and demonstrate that this GRN appears to remain active in the developing heads of ants that have no ocelli- implying that it is the evolutionary latency of this GRN that allows loss and subsequent evolution.

Strengths:

This manuscript has outstanding imaging of a very difficult developing organ, and the key data, fluorescence in situ hybridisation, is done well and clearly shows what the authors wish to demonstrate. The methods are well described and underpin the whole work.

The authors convincing demonstatrate that gene expression patterns imply the conservation of the ocellus gene regulatory network from Drosophila to ants. They further show that this network is present even in ants that don't produce an adult ocellus, but do show that in those species, loss of a developing nascent ocellus (which they identify) occurs at the same time as an interruption in the expression of the key genes in the GRN. All of this data is beautifully presented and explained.

Weaknesses:

There is one key weakness in that there are no functional students that indicate that the GRN actually does make the ocellus, though the expression patterns are convincing. This applies to loss of the ocellus as well. It would be nice to see that transient loss of the ocelli GRN might lead to loss of ocelli in ant species that have them. These are very difficult things to achieve, as the key genes have earlier developmental roles, such that CRISPR knockouts would not be interpretable, and transient RNAi in the head capsules of developing pupal ants would be challenging.

Reviewer #1 (Public review):

Summary:

This manuscript identifies the orphan kinesin KIN-G as a substrate of Polo-like kinase (TbPLK) in Trypanosoma brucei and demonstrates that phosphorylation of Thr301 inhibits KIN-G microtubule binding and disrupts its cellular function. Using a combination of in vitro kinase assays, phosphosite mapping, microtubule binding and gliding assays, and in vivo complementation with phosphomimetic and phosphodeficient mutants, the authors link TbPLK-mediated regulation of KIN-G to defects in centrin arm integrity, FAZ elongation, Golgi organization, flagellum positioning, and division plane placement. The study provides a mechanistic advance in understanding how TbPLK regulates centrin arm biogenesis and integrates KIN-G into the growing regulatory network controlling hook complex and FAZ assembly. Overall, the work is technically strong, internally consistent, and builds logically on previous studies from this group and others.

Strengths:

A major strength of the manuscript is the clear mechanistic link between phosphoryltion of Thr301 and loss of microtubule binding activity. The use of phosphomimetic (T301D) and phosphodeficient (T301A) mutants in an RNAi-rescue framework provides a clean and convincing demonstration of functional relevance in vivo. The integration of biochemical assays with detailed cell biological phenotyping (centrin arm length, FAZ elongation, basal body segregation, and cytokinesis markers) is particularly effective and makes the central conclusion robust. The observed phenotypic cascade from centrin arm defects to FAZ and division plane abnormalities is also well aligned with existing models of trypanosome morphogenesis.

Weaknesses:

My (more or less main) concern relates to the interpretation of the Golgi phenotype. The conclusion that phosphorylation of KIN-G "impairs Golgi biogenesis" is currently based on fluorescence microscopy using TbGRASP and Sec13 markers and on quantification of the number and distribution of Golgi/ERES puncta in binucleated cells. While these data convincingly demonstrate altered Golgi/ERES number and spatial organization, they do not distinguish between true defects in Golgi biogenesis or duplication and alternative possibilities such as fragmentation, vesiculation, or mislocalization of Golgi membranes. Given the central role of Golgi-centrin arm organization in the proposed model, ultrastructural analysis (for example, by EM or electron tomography) would greatly strengthen this aspect of the study by providing direct evidence for structural alterations of the Golgi and its association with the centrin arm and ERES. Such data would elevate this part of the manuscript from a descriptive fluorescence phenotype to a true structural cell biological insight. I appreciate that this experiment goes beyond the current dataset, but it would substantially enhance the mechanistic depth of the Golgi-related conclusions and strengthen the causal chain linking centrin arm defects to Golgi abnormalities. However, I have to confess, the inclusion of such data would make this reviewer particularly enthusiastic about the work. If this is not feasible, I would recommend tempering the wording of "Golgi biogenesis" to a more conservative description, such as altered Golgi organization or duplication, and explicitly acknowledging the limitations of fluorescence-based analysis for this conclusion.

An additional conceptual point concerns the dual role of TbPLK in centrin arm regulation. TbPLK is known to promote centrin arm biogenesis through phosphorylation of TbCentrin2, yet in this study, TbPLK phosphorylation of KIN-G negatively regulates centrin arm assembly. This dual positive and negative regulatory role is intriguing but could be discussed more explicitly. The manuscript would benefit from a clearer conceptual framework addressing how phosphorylation of KIN-G might serve as a temporal or spatial switch to restrain KIN-G activity at specific stages of centrin arm assembly.

Finally, a schematic model summarizing the proposed regulatory pathway from TbPLK phosphorylation of KIN-G to centrin arm assembly, FAZ elongation, division plane placement, and Golgi organization would aid the reader.

Reviewer #2 (Public review):

Summary:

The authors identify KIN-G as an in vitro substrate for phosphorylation by TbPLK and show that several of the in vitro P-ated sites, including T310, overlap with P-ation sites seen in live cells. The authors further show that PLK-mediated P-ation inhibits KIN-G binding to microtubules in vitro, as does a KIN-G-T301D mutant, and that expression of a KIN-G-T301D Phospho-mimic in T. brucei phenocopies KIN-G RNAi knockdowns, producing defects in cell division, morphogenesis of the centrin arm, FAZ and other cellular structures, as well as a misplaced cytokinesis furrow.

Understanding cytoskeletal rearrangements that drive cell division in T. brucei is an important and unresolved problem, so the work addresses important questions that are of great interest. PLK and KIN-G have previously been shown to be important for cell division and morphogenesis of cytoskeletal structures that drive cell division in T. brucei. The current work advances our understanding by suggesting a potential mechanism by which PLK and KIN-G might participate, namely through PLK-dependent P-ation to control KIN-G MT binding activity.

Strengths:

The authors use a rigorous combination of biochemistry, phosphoproteomics, cell biology, and mutant analysis to support their conclusion that PLK-mediated P-ation of KIN-G negatively regulates KIN-G microtubule binding, and this may explain the observation that a KIN-G T301 phosphomimic mutant blocks cell division and perturbs biogenesis of cytoskeletal structures that drive cell division and morphogenesis. Combining rigorous and informative in vitro studies with mutant analysis in live cells is a great strength. The work is solid and important, though a few pieces are needed to fully connect the in vitro findings with the in vivo observations, as detailed below.

Weaknesses:

Overall, I find this work to be solid and to provide an important addition to our understanding of mechanisms controlling cell division in T. brucei. The biochemistry, in particular, is rigorous and convincingly demonstrates PLK can P-ate KIN-G, altering its MT-binding ability. Analysis of phospho-mutants of KIN-G in live T. brucei supports the conclusion that P-ation of KIN-G at T301 negatively affects KIN-G function in vivo. I think, however, that the results fall short of supporting the title, because, although the data convincingly show that PLK can phosphorylate KIN-G at T301 in vitro, and that T301 is P-ated in vivo, they do not formally demonstrate (nor even test) whether PLK is the kinase responsible for this phosphorylation in vivo (experiments to address this seem quite feasible). I also do not see where the authors try to reconcile the absence of phenotype for KIN-G-T301A with the implied importance of KIN-G phosphorylation by PLK in cell division, which calls into question the need for P-ation of KIN-G-T301 in cell division. Suggestions for addressing these concerns are provided below.

My two main questions are:

(1) What is the biological relevance of KIN-G P-ation at T301?

a) The authors report no defect for the KIN-G-T301A mutant, so what then is the need for T301 P-ation, if the cell gets along fine without it? One step toward addressing this would be to ask what fraction of KIN-G shows P-ation at T301. Although published studies indicate P-ation at T301, it isn't known what percentage of KIN-G in the cell is P-ated. One might anticipate, for example, that T301-P is a small minority of the population in asynchronous cultures and that T301 P-ation increases at specific cell cycle stages.

b) Published work links PLK to cell division, FAZ elongation, etc... The current work suggests that one role of PLK is to P-ate KIN-G at T301. In contrast, however, the current work also indicates that P-ation of KIN-G at T301 is unnecessary for normal cell division, FAZ elongation, etc....

c) Some experiments or at least commentary on points a and b above would strengthen the paper.

(2) Is PLK the kinase that P-ates Kin-G T301 in vivo?

a) The authors show PLK P-ates T301 (and other residues) in vitro, and that T-301 is P-ated in vivo. To bring the analysis full circle, it would be informative to examine KIN-G P-ation in a PLK mutant or upon inhibition of PLK with published inhibitors. This seems to be a very doable experiment with the tools available.

Reviewer #3 (Public review):

Summary:

Here, the authors investigate the role of the Trypanosoma brucei polo-like kinase TbPLK in the function of flagellum-associated cellular structures in trypanosomes. They set out to test the hypothesis that a key substrate of TbPLK is the kinesin protein KIN-G, and that TbPLK phosphorylation of KIN-G regulates its functions in cells.

Strengths:

Using in vitro biochemistry with purified proteins, the authors convincingly demonstrate that TbPLK phosphorylates KIN-G at 29 sites. Moreover, they convincingly show that phosphorylation at one site, T301, impairs the binding of purified KIN-G to purified microtubules. Using immunofluorescence-based imaging approaches, they also show that TbPLK colocalizes with KIN-G at centrin arms during the early S-phase of the cell cycle. Centrin arms are structures that are located near the basal body and flagellum and are important for new flagellum biogenesis, Golgi positioning, and cell division. To evaluate the function of KIN-G phosphorylation in cells, they depleted KIN-G by RNAi, simultaneously expressed phospho-mimetic (T301D) and phospho-ablative mutant proteins, and used immunofluorescence to examine the impact on flagellum-associated cellular structures. They show that expression of the phospho-mimetic mutant KIN-G-T301D causes the following defects: reduced cell proliferation, disruption of centrin arm and Golgi biogenesis, impairment of FAZ elongation and flagellum positioning, and misplacement of the cell division plane. The data convincingly support the conclusion that KIN-G phosphorylation on T301 plays an important role in regulating the cellular functions of this kinesin motor protein.

Weaknesses:

Some of the broader conclusions are not directly supported by the data. For example, the title states "Polo-like kinase phosphorylation of the orphan kinesin KIN-G negatively regulates centrin arm biogenesis in Trypanosoma brucei," but the data do not directly address the specific role of TbPLK in phosphorylating KIN-G in cells. Moreover, some of the more specific conclusions in the paper, for example, that "phosphorylation of KIN-G" causes various cellular defects, are a bit of an overstatement. The supporting data rely on the expression of a phospho-mimetic mutant of KIN-G. Presumably, phosphorylation in cells is a normal part of KIN-G regulation, and it is not just phosphorylation, but rather hyperphosphorylation that is being mimicked by the mutant. Some rewording of the specific conclusions is warranted, and the broader conclusion would be better supported with additional experimental evidence.

Reviewer #1 (Public review):

Summary:

Severe childhood malaria is associated with three main overlapping syndromes: impaired consciousness (IC), respiratory distress (RD), and severe malaria anaemia (SMA). One central feature of severe malaria, driven by host and parasite factors, is the sequestration of parasitized red blood cells in vascular beds, leading to impaired tissue perfusion and lactic acidosis. The causing agent, the parasite ligand PfEMP1, is expressed on the surface of infected red blood cells, where it binds to a broad range of different endothelial receptors. Accumulation of parasite-infected erythrocytes in the host's microvasculature has been repeatedly confirmed for cerebral malaria, but there are scarce data on the extent of sequestration in the other severe malaria syndromes. However, the absence of effective adjunctive therapies for severe malaria implies that our understanding of its pathogenesis remains incomplete. Thus, by comparing var gene expression from a large Kenyan cohort (n=372 severe cases; n=340 non-severe cases), this study addresses a critical knowledge gap regarding the role of PfEMP1 across distinct severe malaria syndromes. The substantial sample size, phenotypic stratification, and use of two complementary methods (DBLa-tag sequencing and RT-qPCR), along with data about the parasite's ability to form rosettes and antibody level assessments, provide a strong setup. Var gene expression data - either proportions of different DBLa-tags classified by the number of cysteine residues and presence of particular motifs or relative expression RT-qPCR data from a set of primer pairs targeting conserved regions of var groups or particular domains - is associated with (a) severe malaria syndromes, (b) variant expression homogeneity, (c) rosetting ability, and (d) mortality using independent linear regression models, spearman ranks correlations, or logistic regression models. In summary, the study confirms that A-type and DC8-containing gene expression correlate with IC, that RD is associated with rosetting, and that SMA is linked to a high variant expression homogeneity (VEH) of var-A expression, which may indicate a longer infection duration. However, some findings remain inconclusive. For example, when analyzing pure syndromes, several associations changed: DC8 expression was also found to be significantly enriched in SMA (with multiple primer pairs) and RD, not exclusively with IC. Additionally, rosetting was associated with DC8 expression but not with IC, even though IC itself is linked to DC8 expression. Overall, the findings are significant and supported by a large dataset, though the reported evidence remains largely associative rather than mechanistic.

Strengths:

As the authors stated themselves, one of the key unresolved questions is whether severity-causing parasites are biologically different from parasites responsible for asymptomatic infections. This study is among the first to address this question using data from a large, phenotypically stratified cohort. The use of two complementary methods (DBLa-tag sequencing and RT-qPCR), together with data on the parasites' ability to form rosettes and assessments of antibody levels, provides an excellent experimental framework.

Weaknesses:

Even when assessing var gene expression using two different approaches - DBLα-tag sequencing and RT-qPCR targeting pre-defined variants - only a glimpse of the parasites' actual biology is captured. Moreover, a well-known confounder in gene expression studies of P. falciparum field isolates is variation in parasite age (hours post-invasion) or synchronicity, both of which significantly influence var gene expression. The methods employed in this study, unfortunately, do not allow for controlling or correcting for these factors. Then, the old classification system of DBLa-tag data developed by Bull et al is certainly still valid; however, more recent advances in bioinformatic tool development now allow for a more in-depth exploration of DBLa-tag datasets. Tools such as Varia (doi: 10.1186/s12859-022-04573-6), cUPS (https://doi.org/10.1371/journal.ppat.1012813), and upsML (doi: https://doi.org/10.1101/2025.05.19.654848) enable the prediction of DBLa-tag-connected PfEMP1 domains and the var group affiliations.

As A-type var gene expression has already been associated with severity, most expression studies (including this one) have a selection bias towards A- and B/A-type var genes. Here, A- and B/A-types are covered by 8 primer pairs (gpA1, gpA2, 4x DC8, DC13, DC4), whereas high polymorphic B-types are targeted by only 2 primer pairs (b1, DC9) and C-types only by a single primer (c2). Thus, any association with A-type expression is more likely to be observed, although evidence is accumulating that parasites are preferably expressing B-type var genes at the onset of blood stage infection in naïve/less immune individuals; this is also consistent with the observation of the authors that VEH is positively associated with immunity (measured as anti-IE) and negatively associated with temperature.<br /> I am not an expert in biostatistics, but to my understanding, independently performed regressions should be corrected for multiple testing.

Overall, the authors largely achieved their aims, identifying specific var groups associated with different severity syndromes. However, due to the complexity of var gene data and the interdependence of parameters, the resulting picture is not entirely clear. Some opposite results between different analyses may also be difficult for the reader to interpret. Nevertheless, this study can be considered a pioneering effort, providing valuable insights into the complex interplay of var gene expression across different severity syndromes and offering useful data for the field. Follow-up studies will be important to validate these findings and further dissect the mechanisms linking parasites gene expression to clinical outcomes.

Reviewer #2 (Public review):

Summary:

The manuscript presents results of a study using two complementary approaches (RT-qPCR and DBL) to analyze the putative relationship between var gene transcription (and hence, PfEMP1 expression) and clinical presentation among Kenyan children with Plasmodium falciparum malaria. Binary rosetting (yes/no) data are used in a similar way. The study includes samples collected over a period of almost 20 years from about 700 children presenting with either severe (impaired consciousness [IC], respiratory distress [RD], severe anemia [SA]) or non-severe malaria. During the study period, the study area experienced a remarkable drop in P. falciparum transmission intensity.

Strengths:

The study stands on the shoulders of many similar studies of this kind, both by the authors and by other research teams, and the inferences made largely confirm those made previously. The current study has analytical rigor and a large sample size. Disentangling the multiple parameters of the above-mentioned relationship is of obvious and crucial importance to an improved understanding of P. falciparum malaria pathogenesis and of the targets and mechanisms of protective immunity to the disease. The present study is a valuable effort towards that. The study is well-structured, and the figures are clear.

Weaknesses:

It is somewhat unclear to this reviewer to what extent the samples and data analyzed and reported here are new (i.e., not used/analyzed in previous studies). If there is substantial overlap with earlier studies, this is a weakness because of the risk of circular inferences. The Discussion section would benefit from less repetition of the results section and a more in-depth discussion of the findings obtained relative to the existing literature. Better inclusion of key primary references is recommended.

Reviewer #3 (Public review):

Summary:

In this manuscript, Ndugwa et al. attempt to link specific severe malaria manifestations with particular var gene expression patterns. This is an important question, and the dataset the authors have assembled over decades is impressive. However, greater clarity in the descriptions and statistics would, in my view, help this reviewers, and readers in general develop a more precise understanding of the significance of the findings.

Strengths:

The study addresses a critically important question in malaria pathogenesis, and the dataset is extensive and represents a significant long-term effort by the authors.

Weaknesses:

The Results section often lacks clarity: clinical group definitions (NS, non-IC, non-SMA, mild vs. moderate) are sometimes ambiguous, and key methodological details, including the VEH index calculation, RT-qPCR quantification, antibody detection methods, and rosetting assays, are either missing from the results text or poorly explained in the figure legends. Additionally, figure presentation requires improvement, with inconsistent reporting of sample sizes, undefined colors, and p-values that overlap with data points rather than being clearly displayed above them.

Reviewer #1 (Public review):

Summary:

The authors aimed to overcome the limitations of whole-tumor-cell vaccines, specifically the weak immunogenicity and rapid clearance often associated with them. They leveraged the unique properties of senescent tumor cells (STCs), which remain metabolically active and secrete chemokines, as a source of antigens. However, to counteract the secretion of the immunosuppressive lipid prostaglandin E2 (PGE2), which is part of the senescence-associated secretory phenotype (SASP), they engineered a hydrogel vaccine formulation (STCs+CLX-Lipo@Gel) containing STCs and liposomal celecoxib (a COX2 inhibitor).

Strengths:

(1) The study is conceptually strong in its approach to leveraging the SASP to improve immunotherapy responses. By selectively inhibiting COX2/PGE2 while preserving the secretion of recruitment chemokines (like CCL2 and CCL5) in the SASP, the authors successfully turn a potentially deleterious cellular state into a therapeutic asset.

(2) Mechanistic Insight: The manuscript provides detailed evidence regarding the mechanism of action. The authors convincingly show that the vaccine restores activity in the NK-DC axis. Specifically, they demonstrate that reducing PGE2 levels enhances NK cell activation (upregulation of NKG2D and NKp46) and promotes the secretion of CCL5 and XCL1 by NK cells, which subsequently recruits cDC1 dendritic cells.

(3) The therapeutic potential is tested across multiple models, including a subcutaneous melanoma model, a difficult-to-treat melanoma brain metastasis model, and an orthotopic pancreatic cancer model. The consistent efficacy across these distinct physiological contexts suggests broad applicability.

Weaknesses:

(1) While the authors successfully inhibit PGE2, the SASP is a complex cocktail of factors. The discussion regarding the long-term presence of these "live" senescent cells is somewhat limited. Although the hydrogel retains cells locally, the potential for other chronic inflammatory factors to eventually promote tumorigenesis or tissue damage in the surrounding niche warrants careful consideration when translating this approach to patients and may require additional preclinical testing.

(2) The study posits that STCs serve as an antigen reservoir. However, the manuscript would benefit from a clearer distinction between whether the immune system is recognizing senescence-specific neoantigens or simply shared tumor antigens that are being presented more effectively due to the adjuvant effect. The authors briefly touch upon neoantigens in the discussion, but the experimental data primarily measure general anti-tumor responses.

Impact:

This work bridges material science and immunology, offering a practical solution to the immunosuppressive barriers of cell-based vaccines. It provides a platform that could potentially be adapted for various solid tumors.

Reviewer #2 (Public review):

Summary:

Wang et al. examined an engineered whole-tumor-cell vaccine based on senescent tumor cells co-encapsulated with liposomal celecoxib in a chitosan hydrogel. The authors propose that prolonged persistence of senescent cells, combined with COX2/PGE2 inhibition, restores NK-DC crosstalk, enhances cDC1 recruitment, and ultimately drives robust CD8⁺ T-cell-mediated antitumor immunity. The study is nicely executed and clearly presented, with extensive in vitro and in vivo validation across multiple tumor models, including melanoma brain metastases and orthotopic PDAC. While the overall concept is timely and of potential interest, several mechanistic conclusions are based primarily on correlative evidence and would benefit from additional functional experiments to strengthen causal interpretation and translational relevance.

Strengths:

(1) Strong conceptual framework

(2) Impressive breadth of in vivo models.

(3) Clear immunological readouts.

(4) Innovative combination of senescence biology and biomaterials.

Weaknesses:

(1) Mechanistic conclusions rely heavily on correlation.

(2) Lack of functional immune cell depletion studies.

(3) Limited exploration of long-term safety and antigenic specificity.

Major Critiques:

(1) The authors emphasize the expansion and activation of cDC1 as a key mechanism linking innate and adaptive immunity, yet it does not directly test whether cDC1 is required for the observed CD8⁺ T-cell responses and tumor control.

The authors should perform experiments using Batf3-deficient mice or any other cDC1-depletion strategies to provide important mechanistic validation. If such experiments are not feasible, this limitation should be more clearly acknowledged and discussed.

(2) The authors note that senescence may generate neoantigens distinct from those present in proliferating tumor cells, but the extent to which STC-induced immunity cross-reacts with non-senescent tumor cells is not fully addressed. While it is appreciated that tumor challenge experiments are included, the author should perform a more explicit analysis of antigenic overlap that would strengthen the translational relevance of the approach. For example, they can compare senescence induced by different stimuli or directly assess immune recognition of non-senescent tumor targets, which would help clarify whether the vaccine primarily exploits senescence-specific antigens or broadly shared tumor antigens.

(3) Hydrogel encapsulation clearly extends STC persistence in vivo; however, the study provides limited information on the eventual clearance of these cells and the potential implications of prolonged SASP exposure. Given general concerns regarding chronic inflammation associated with senescent cells, additional discussion of long-term local and systemic responses would be helpful. If extended safety analyses are beyond the scope of the current study, the authors should acknowledge the limitation.

(4) The immunological effects are attributed to COX2/PGE2 inhibition, but it remains unclear whether these effects are specific to celecoxib or could reflect formulation-dependent or off-target mechanisms. The authors may perform additional experiments employing an alternative COX2 inhibitor, genetic COX2 suppression, or PGE2 rescue, which could further support the specificity of the COX2/PGE2-dependent mechanism.

Reviewer #1 (Public review):

Summary:

In the work from Qiu et al. a workflow aimed at obtaining the stabilization of a simple small protein against mechanical and chemical stressors is presented.

Strengths:

The workflow makes use of state-of-the-art AI-driven structure generation and couples it with more classical computational and experimental characterizations in order to measure its efficacy.

The work is well presented and results are thorough and convincing.

The Methods description is quite precise, and some important details were added during review.

Weaknesses:

The pulling velocity is quite high but in accordance with this observation the results were only used for comparative analyses.

Following the review process the authors have shown that the minimum distance between each protein from its periodic images was consistently above 1 nm, yet towards the end of some simulations the value crosses the non-bonded interaction cut-off distance.

Comments on revisions:

The authors did a good job in addressing the reviews.

Reviewer #2 (Public review):

Summary:

Qiu, Jun et. al., developed and validated a computational pipeline aimed at stabilizing α-helical bundles into very stable folds. The computational pipeline is a hierarchical computational methodology tasked to generate and filter a pool of candidates, ultimately producing a manageable number of high-confidence candidates for experimental evaluation. The pipeline is split into two stages. In stage I, a large pool of candidate designs is generated by RFdiffusion and ProteinMPNN, filtered down by a series of filters (hydropathy score, foldability assessed by ESMFold and AlphaFold). The final set is chosen by running a series of steered MD simulations. This stage reached unfolding forces above 100pN. In stage II, targeted tweaks are introduced - such as salt bridges and metal ion coordination - to further enhance the stability of the α-helical bundle. The constructs undergo validation through a series of biophysical experiments. Thermal stability is assessed by CD, chemical stability by chemical denaturation, and mechanical stability by AFM.

Strengths:

A hierarchical computational approach that begins with high-throughput generation of candidates, followed by a series of filters based on specific goal-oriented constraints, is a powerful approach for a rapid exploration of the sequence space. This type of approach breaks down the multi-objective optimization into manageable chunks and has been successfully applied for protein design purposes (e.g., the design of protein binders). Here, the authors nicely demonstrate how this design strategy can be applied to successfully redesign a moderately stable α-helical bundle into an ultrastable fold. This approach is highly modular, allowing the filtering methods to be easily swapped based on the specific optimization goals or the desired level of filtering.

Weaknesses:

Assessing the change in stability relative to the WT α-helical bundle is challenging because an additional helix has been introduced, resulting in a comparison between a three-helix bundle and a four-helix bundle. Consequently, the appropriate reference point for comparison is unclear. A more direct and informative approach would have been to redesign the sequence of the original α-helical bundle of the human spectrin repeat R15, allowing for a more straightforward stability comparison.

The three constructs chosen are 60-70% identical to each other, either suggesting over-constrained optimization of the sequence, or a physical constraint inherent to designing ultrastable α-helical bundles. It would be interesting to explore whether choosing a different combination of filters would enable ultrastable α-helical bundles constructs with a more varied sequence content.

While the use of steered MD is an elegant approach to picking the top N most stable designs, its computational cost may become prohibitive as the number of designs increases or as the protein size grows, especially since it requires simulating a water box that can accommodate a fully denatured protein.

Comments on revisions:

The authors have done a good job of addressing the comments.

Reviewer #3 (Public review):

Summary:

Qiu et al., present a hierarchical framework that combine AI and molecular dynamic simulation to design α-helical protein with enhanced thermal, chemical and mechanical stability. Strategically chemical modification by incorporating additional α-helix, site-specific salt bridges and metal coordination further enhanced the stability. The experimental validation using single-molecule force spectroscopy and CD melting measurements provide fundamental physical chemical insights into the stabilization of α-helices. Together with the group's prior work on super-stable β strands (https://www.nature.com/articles/s41557-025-01998-3), this research provides a comprehensive toolkit for protein stabilization. This framework has broad implications for designing stable proteins capable of functioning under extreme conditions.

Strengths:

The study represents a complete frame work for stabilizing the fundamental protein elements, α-helices. A key strength of this work is the integration of AI tools with chemical knowledge of protein stability.<br /> The experimental validation in this study is exceptional. The single-molecule AFM analysis provided a high-resolution look at the energy landscape of these designed scaffolds. This approach allows for the direct observation of mechanical unfolding forces (exceeding 200 pN) and the precise contribution of individual chemical modifications to global stability. These measurements offer new, fundamental insights into the physicochemical principles that govern α-helix stabilization.

Weaknesses:

(1) While the initial manuscript lacked a detailed explanation for the stabilizing effect of the additional helix, the revised version now includes a clear structural basis for this improvement. The authors successfully attribute the increased unfolding force threshold to the reinforcement of the hydrophobic core and enhanced cooperative interactions, supported by relevant literature correlations between helix bundle size and stability.

(2) The author analyzed both thermal stability and mechanical stability. It would be helpful for the author to discuss the relationship between these two parameters in the context of their design. Since thermal melting probes equilibrium stability (ΔG), while mechanical stability probes the unfolding energy barriers along pulling coordinate. While the integrative design approach successfully improved both stability types, a deeper exploration of how the specific structural modifications influence the unfolding energy barrier relative to the overall equilibrium stability would further strengthen the mechanistic impact of the work.

(3) While the current study demonstrates a dramatic increase in global stability, the analysis focuses almost exclusively on the unfolding (melting) process. However, thermodynamic stability is a function of both folding (kf) and unfolding (ku) rates. The author have clarified that the observed ultrastability likely originates from a significantly reduced unfolding rates, a hypothesis consistent with the unfolding force. Direct measurements of the kinetics would provide deeper insights.

(4) The authors chose the spectrin repeat R15 as the starting scaffold for their design. R15 is a well-established model known for its "ultra-fast" folding kinetics, with folding rates (kf ~105s), near three orders of magnitude faster than its homologues like R17 (Scott et.al., Journal of molecular biology 344.1 (2004): 195-205). Measuring the folding rates of newly designed proteins would provide additional insights into the design.

Comments on revisions:

I think the author have addressed comments.

Reviewer #1 (Public review):

Summary:

The authors present a novel investigation of movement vigor of individuals completing a synchronous extension-flexion task. Participants were placed into groups of two (so-called "dyads") and asked to complete shared movements (connected via a virtual loaded spring) to targets placed at varying amplitudes. The authors attempted to quantify what, if any, adjustments in movement vigor individual participants made during the dyadic movements, given the combined or co-dependent nature of the task. This is a novel, timely question of interest within the broader field of human sensorimotor control.

Participants from each dyad were labeled as "slow" (low vigor) or "fast" (high vigor), and their respective contributions to the combined movement metrics assessed. The authors presented four candidate models for dyad interactions: (a) independent motor plans (i.e., co-activity hypothesis), (b) individual-led motor plans (i.e., leader-follower hypothesis), (c) generalization to a weighted average motor plan (i.e., weighted adaptation hypothesis), and (d) an uncertainty-based model of dynamic partner-partner interaction (i.e., interactive adaptation hypothesis). The final model allowed for dynamic changes in individual motor plans (and therefore, movement vigor) based on partner-partner interactions and observations. After detailed observations of interaction torque and movement duration (or, vigor), the authors concluded that the interactive adaptation model provided the best explanation of human-human interaction during self-paced dyadic movements.

Strengths:

The experimental setup (simultaneous wrist extension-flexion movements) has been thoroughly vetted. The task was designed particularly well, with adequate block pseudo-randomization to ensure general validity of the results. The analyses of torque interaction, movement kinematics, and vigor are sound, as are the statistical measures used to assess significance. The authors structured the work via a helpful comparison of several candidate models of human-human interaction dynamics, and how well said models explained variance in the vigor of solo and combined movements.

The authors adequately addressed several concerns that I raised in my initial review of the work, including clarity regarding analyses of movement vigor and inclusion of additional analyses of reaction time. The results are supported by both parametric and non-parametric statistical methods.

The research question is timely and extends current neuroscientific understanding of sensorimotor control, particularly in social contexts. This work answers several new, important questions about control of vigor during volitional movements, and in doing so it motivates future research into the topic.

Weaknesses:

My chief concern about the study is the relatively low number of dyad data points (n=10). The authors recruited 20 participants, but the primary conclusions are based on dyad-specific interactions (i.e., analyses of "fast" vs "slow" participants in each pair). However, it is important to note that most of the effects upon which the conclusions rest are associated with relatively large effect sizes.

Reviewer #2 (Public review):

Summary:

This study examines how individual movement vigor is integrated into a shared, dyadic vigor when two individuals are physically coupled. Participants performed wrist-reaching movements toward targets at different distances while mechanically linked via a virtual elastic band, and dyads were formed by pairing participants with different baseline vigor profiles. Under interaction conditions, movements converged to coordinated patterns that could not be explained by simple averaging, indicating that each dyad behaved as a single functional unit. Notably, under coupling, movement durations for both partners were shorter than in the solo condition, arguing against the view that each individual simply executed an independent movement plan. Furthermore, dyadic vigor was primarily predicted by the slower partner's vigor rather than by the faster partner's, suggesting that neither a leader-follower strategy nor a weighted averaging account fully explains the observed behavior. The authors propose a computational model in which both partners adapt to the emerging interaction dynamics ("interactive adaptation strategy"), providing a coherent explanation of the behavioral observations.

Strengths:

The study is carefully designed and addresses an important question about how individual movement vigor is integrated during joint action. The experimental paradigm allows systematic manipulation of interaction strength and partner asymmetry. The behavioral results show clear and robust patterns, particularly the shortening of movement durations under elastic coupling (KL and KH condition) and the asymmetrical contribution of the slower partner's vigor to dyadic vigor. The computational model captures the main behavioral patterns well and provides a principled framework for interpreting dyadic vigor not as a simple combination of two independent motor plans, but as an emergent property arising from mutual adaptation. Conceptually, the study is notable in extending the notion of vigor from an individual attribute to a dyad-level construct, opening a new perspective on coordinated movement and motor decision-making.

Weaknesses:

The revised manuscript now clearly explains why the proposed computational model successfully accounts for the observed dyadic behavior. In particular, the mechanisms by which uncertainty associated with the slower partner and time-related costs of the faster partner jointly shape dyadic vigor are now clear. I have no further comments to add.

Reviewer #3 (Public review):

Summary:

This study provides novel insights into how individuals regulate the speed of their movements both alone and in pairs, highlighting consistent differences in movement vigor across people and showing that these differences can adapt in dyadic contexts. The findings are significant because they reveal stable individual patterns of action that are flexible when interacting with others, and they suggest that multiple factors, beyond reward sensitivity, may contribute to these idiosyncrasies. The evidence is generally strong, supported by careful behavioral measurements and appropriate modeling.

The authors have addressed all of my previous comments. I appreciate the clarification of abbreviations, terminology, and key concepts, the expansion of the discussion, and the adjustments to some of the statistical analyses in response to both my earlier comments and those of Reviewer 1.

Reviewer #1 (Public review):

Summary:

The authors aimed to extend a prior fiber photometry analysis process they developed by incorporating the new ability to determine instantaneous, within trial, relationships between the photometry signal and continuously changing variables. They present solid evidence via simulations and example use cases from published datasets highlighting that their approach can capture instantaneous relationships. Overall, while they make a compelling case that this approach is less biased and more insightful, the implementation for many experimentalists remains challenging enough and may limit widespread adoption by the community.

Strengths:

This work builds on prior efforts to analyze photometry signals in a less biased and more statistically sound way. This work incorporates a very important aspect by avoiding the need to summarize individual trials with singular behavioral variables and instead allows for interactions with continuously changing variables to be investigated. The knowledge and expertise of the authors and the presentation provide strong validity and strength to the work. Examples from prior studies in the field are a necessary and important component of the work.

Weaknesses:

While use cases are provided from prior data, a clearer presentation of how common implementations in the field are performed (i.e. GLM) and how one could alternatively use the cFLMM approach would help. Otherwise, most may continue using common approaches of Pearson's correlations and GLMs.

Reviewer #2 (Public review):

The paper presents a regression-based approach for analysing fiber photometry data termed Concurrent Functional Mixed Models (cFLMMs). The approach works by fitting linear mixed effect models separately to each time point in trial aligned data, then applying smoothing to the model coefficients (betas), and computing confidence intervals. The method extends the authors previous work on using FLMMs for photometry data analysis by allowing for the inclusion of predictors whose value changes across timepoints within a trial, rather than just from trial to trial. As fiber photometry is a rapidly expanding field, developing principled methods to analyse photometry data is valuable, particularly as the authors have released an R package that implements their method to facilitate their use by other groups. The basic FLMM approach for using mixed effects models to analyse trial aligned photometry data, detailed by the authors in their previous manuscript (Loewinger et al. 2025, doi: 10.7554/eLife.95802) appears valuable. The aim of incorporating variables that change within trial into this framework is interesting, and the technical implementation appears to be rigorous. However, I have some reservations as to whether the way in which variables that change within trial have been integrated into the analysis framework is likely to be widely useful, and hence how impactful the additional functionality of cFLMM relative to the previously published FLMM will be.

In the original FLMM approach, where predictors change only from trial-to-trial, fitting separate regressions at each timepoint generates a timeseries of betas is for each predictor, indicating when and how the predictor explained variance across the trial. This makes a lot of sense and is widely used in neuroscience data analysis. In extending this approach to incorporate variables that change within trial, the authors have used the same method of fitting separate regression models at each timepoint, to obtain a timeseries of betas for each predictor. It is less clear that this approach makes sense for variables that change within trial. This is because the resulting betas only capture how variation in the predictor across trials at a given timepoint explains variance in the signal, but does not capture effects of variation in the predictor across timepoints within trials. This partitioning of variance in the predictor into a between-trial component whose effect on the signal is modelled, and a within-trial component whose effect on the signal is not, is artificial in many experiment designs, and may yield hard to interpret results.

Consider e.g. the experimental condition considered in Figure 3, taken from Machen et al. 2025 (doi: 10.1101/2025.03.10.642469) in which mice ran down a linear track to collect rewards. In analysing such data, one might want to know how neural activity covaried with the animal's position, but as this variable changes strongly within trial but will have a similar time-course across trials, the cFLMM analysis approach will not work to quantify these effects. This is because variance attributed to position would not capture how neural activity covaried with changes in the animals position within trial, but rather how neural activity covaried with changes in the animals position from trial-to-trial at a given timepoint, which could occur due to e.g. trial-to-trial differences in latency to start moving or running speed. As such, although significant effects of 'position' might be observed, they would not capture covariation between position and neural activity in a straightforwardly interpretable way.

It is therefore not obvious to me that incorporating variables that change within trial into an analysis framework that runs separate regressions at each timepoint in trial aligned data is likely to be widely useful. If scientific questions require understanding how neural activity covaries as a function of variables that change both within and across trials, an alternative approach would be to run a single regression analysis across all timepoints, and capture the extended temporal responses to discrete behavioural events by using temporal basis functions convolved with the event timeseries. This provides a very flexible framework for capturing covariation of neural activity both with variables that change continuously such as position, and discrete behavioural events such as choices or outcomes, while also handling variable event timing from trial-to-trial.

One way that cFLMM is used in the manuscript is to handle variable timing of trial events in trial aligned data. In the Machen et al. data, the time when the animal reaches the reward varies from trial to trial, and this is represented in the cFLMM analysis by a binary variable which changes value at this timepoint. From the resulting beta coefficient timeseries (Figure 3C) it is not straightforward to understand how neural activity changed as the subject approached and then received the reward. A simpler approach to quantify this, which I think would have yielded more interpretable coefficient timeseries would have been to align activity across trials on when the subject obtained the reward, rather than on the start of the trial, allowing e.g. the effect of reward type to be visualised as a function of time relative to reward delivery, and hence to see the differential effects during approach vs consumption. More broadly, handling variable trial timing in analyses like FLMM which use trial aligned data, can be achieved either by separately aligning the data to different trial events of interest or by time warping the signal to align multiple important timepoints across trials. It is not obvious that using cFLMM with binary indicator variables that indicate when task states changed will yield a clearer picture of neural activity than these methods.

It may be that I am missing some key strengths of cFLMM relative to the other approaches I have outlined, or that there are applications where this approach to implementing within-trial variable changes is a natural formalism. However my impression is that while cFLMM represent a technical advance, it is not clear how widely useful the model formalism will be.

Reviewer #3 (Public review):

Summary:

This work is an extension of their previous study (Loewinger et al 2025) describing a statistical framework for the analysis of photometry data using functional linear mixed models with joint confidence intervals, together with an open-source tool implemented in R. The present study extends it by adding the possibility of using 'concurrent' variables (variables that change within a trial) as regressors, for example, capturing the change of speed at each timepoint in the trial. The main claim is that using 'concurrent' regressors can identify associations between signal and behavior that could not be captured by 'non-concurrent' regressors (the value for a regressor on a specific trial is the same for each timepoint), which could lead to misleading conclusions. While the motivation for using time-varying covariates is useful and supported by previous literature (using fixed-effects models, although not cited in this manuscript), the reanalysis of previous studies does not clearly prove the benefit of using concurrent regressors as opposed to non-concurrent, and some of the results are difficult to interpret.

Strengths:

• The motivation for using time-varying covariates is well supported by previous literature using them on fixed-effects models, and here the authors are extending it to mixed-effects models.<br /> • The authors have included this new functionality in their previous open-source R package.

Weaknesses:

• The main weakness of this study is that it is not clear what the conceptual or methodological advance of this work is. As it is written, the manuscript focuses on showing how concurrent regressors offer interpretation advantages over non-concurrent regressors. While the benefit of such time-varying regressors is supported by previous literature (e.g., Engelhard et al., 2020), it is not clear whether the examples provided in the current study clearly support the advantage of one over the other, especially in the reanalysis of Machen et al. (2025), where the choice of regressors is confusing. In this specific example, if the question is about speed and reward type, why variables such as latency to reward or a binary 'reward zone vs corridor' (RZ) regressors are used instead of concurrent velocity (or peak velocity - in the case of the non-concurrent model)? Furthermore, if timing from trial start to reward collection is variable, why not align to reward collection, which would help in the interpretation of the signal and comparison between methods? Furthermore, while for the non-concurrent method, the regressors' coefficients are shown, for the concurrent one, what seems to be plotted are contrasts rather than the coefficients. The authors further acknowledge the interpretational difficulties of their analysis.<br /> • Because the relation between behavioral variables and neuronal signal is not instantaneous, previous literature using fixed effects uses, for example, different temporal lags, splines, and convolutional kernels; however, these are not discussed in the manuscript.<br /> • From the methods, it seems that in the concurrent version of fastFMM, both concurrent and non-concurrent regressors can be included, but this is not discussed in the manuscript.<br /> • The methodological advance is not clearly stated, apart from inputting into fastFMM a 3D matrix of regressors x trial x timepoint, instead of a 2D matrix of regressors x trial.<br /> • This manuscript is neither a clear demonstration of the need for concurrent variables, nor a 'tutorial' of how to use fastFMM with the added extension.

Reviewer #1 (Public review):

This study examines how two types of RNA polymerases organize themselves within the nucleus of C. elegans cells, building directly on the same group's prior publication and largely functioning as a companion to that earlier work. While the observation that the two polymerases occupy distinct but neighboring locations at the same genomic region adds nuance to our understanding of gene cluster regulation, the manuscript would benefit from more clearly delineating which findings are new versus continuations of previously published work. Protein localization, gene expression effects, and genomic mapping data appear to overlap substantially with the earlier paper.

The condensate claims would also benefit from additional experimental support. Demonstrating fusion events and concentration-dependent assembly are now standard expectations in the field. Additionally, one measurement reported appears inconsistent with a condensate model, warranting further discussion.

Some findings would benefit from more interpretive context. Why does polymerase clustering fluctuate with the cell cycle? What are the functional implications of ATTF-6 being required for one polymerase's foci but not the others?

The elevated-temperature experiments are intriguing but difficult to interpret, as the temperature used is well-established as a broad stress trigger in this organism. Acknowledging this and considering additional controls would help clarify whether the observed effects are specific to foci behavior.

Finally, the manuscript would be strengthened by adding quantification to some figures and revising the model diagram to better reflect what the current data support.

Reviewer #2 (Public review):

Summary:

The researchers analyzed GFP-tagged RNA Pol II and RNA Pol III catalytic subunits RPB-1 and RPC-1, and showed that they form foci in early embryo nuclei that overlap with the 5S rDNA loci and foci by ATTF-6-RFP. They showed foci are round, dissolve upon hexanediol incubation, and are detected during S phase, removed during, and re-established after mitosis. The researchers performed FRAP and showed fast exchange of polymerases, unlike ATTF-6. They show that, unlike RNA Pol III, RNA Pol II foci are dependent on ATTF-6 and temperature sensitive. The researchers propose that the two polymerases form distinct foci with different biochemical dependencies. This study shows that, although closely located within a gene cluster, the regulation of RNA Pol II and RNA Pol III is independent.

Strengths:

The researchers provide high-quality images that support the main results. The researchers' use of auxin-inducible and RNAi depletion work is validated in the same embryos by fluorescent analysis of the target protein.

Weaknesses:

Although the researchers propose the hypothesis that the RNA Pol II and RNA Pol III form distinct condensates, alternative hypotheses are not presented, and the criteria by which the other possibilities are ruled out are not discussed.

Reviewer #3 (Public review):

Wang et al demonstrate that RNA polymerase II and RNA polymerase III form distinct nuclear foci at the 5S rDNA-SL1 gene cluster in C. elegans. By ChIP, Pol II is highly enriched at the SL1 gene, whereas Pol III is enriched at the 5S rRNA gene. Both polymerase foci are spherical, show rapid exchange in FRAP experiments, and assemble in a cell-cycle-dependent manner, predominantly during S phase. The transcription factors ATTF-6 and SNPC-4 are required for the formation of Pol II foci but are dispensable for Pol III foci. Pol II foci, but not Pol III foci, are temperature-sensitive and dissolve upon heat stress; dissolution correlates with a strong reduction of SL1 transcription, whereas 5S rRNA levels remain largely unaffected.

Overall, this is a clean, well-organized, and well-controlled study, and I only have two comments.

(1) Roundness measurements, FRAP, and sensitivity to 1,6-hexanediol are indicative but not sufficient to show that these foci are condensates. They could, for example, also be scaffolded /chromatin-anchored assemblies (see https://pubmed.ncbi.nlm.nih.gov/36526633/). Please either provide better evidence or rephrase/tone down the condensate statements.

(2) Image quantification is only provided for Figure 5, but should also be reported for Figures 6 and 7. In addition to the foci number, also, e.g., intensity over background (similar to partition coefficient) should be quantified.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #2 (Public review):

This manuscript, "Nerve Injury-Induced Protein 2 preserves lysosomal membrane integrity to suppress ferroptosis", identifies a previously unrecognized function of NINJ2 as a regulator of lysosomal membrane integrity and iron homeostasis, thereby suppressing ferroptosis. The authors demonstrate that NINJ2 localizes to lysosomes, interacts with LAMP1, limits lysosomal membrane permeabilization (LMP), stabilizes ferritin, and protects cells from ferroptotic cell death. They further extend these mechanistic findings to human cancer datasets, showing co-overexpression and positive correlation of NINJ2 with ferritin genes in iron-addicted cancers.

Overall, the study is conceptually interesting, technically solid, and integrates cell biology, iron metabolism, and ferroptosis in a coherent framework. The work expands the functional repertoire of the Ninjurin family beyond plasma membrane rupture and inflammation, which will be of interest to researchers in cell death, lysosome biology, and cancer metabolism.

Strengths:

(1) The identification of NINJ2 as a lysosome-associated protein that suppresses ferroptosis represents a meaningful advance beyond its previously described roles in inflammation, pyroptosis, and tumorigenesis.

(2) The work distinguishes NINJ2 functionally from NINJ1, reinforcing the idea that structurally related Ninjurins have divergent membrane-related roles.

(3) The study presents a logically connected pathway:<br /> NINJ2 loss → LMP → labile iron increase → ferritin degradation → ferroptosis sensitization, which is well supported by the data.

(4) The link between LAMP1, ferritin turnover, and ferroptosis is particularly compelling and timely given recent interest in lysosomal contributions to ferroptotic signaling.

(5) The authors use confocal microscopy, proximity ligation assays, biochemical IPs, iron measurements, protein half-life analyses, ferroptosis assays, and TCGA-based analyses, providing convergent evidence for their model.

(6) Use of two distinct cell lines (MCF7 and Molt4) strengthens generalizability.

(7) The integration of cancer expression datasets linking NINJ2 with ferritin expression in hepatocellular and breast carcinomas enhances translational relevance.

(8) Assigning NINJ2 a lysosomal protective function, distinct from NINJ1-mediated plasma membrane rupture, is novel.

(9) Linking NINJ2 to ferroptosis regulation via lysosomal iron handling, rather than canonical GPX4 or system Xc⁻ pathways, is also novel, along with proposing a NINJ2-LAMP1-ferritin axis as a buffering mechanism against iron-driven lipid peroxidation.

(10) These insights are not incremental; they reframe how NINJ2 may function at the intersection of membrane biology, iron metabolism, and regulated cell death.

Areas for improvement:

While the study is strong, several issues should be addressed for mechanistic depth and general relevance.

(1) Although NINJ2 is shown to interact with LAMP1 and LAMP1 knockdown rescues ferritin levels, it remains unclear whether the NINJ2-LAMP1 interaction is required for lysosomal protection. The authors could:<br /> a) Map the NINJ2 domain required for LAMP1 interaction and test whether an interaction-deficient mutant fails to protect against LMP and ferroptosis.<br /> b) Rescue NINJ2 KO cells with wild-type versus mutant NINJ2 to establish causality.

(2) The conclusion that NINJ2 suppresses ferroptosis relies primarily on RSL3 and Erastin sensitivity. A direct assessment of ferroptosis would hence the study, such as:<br /> a) Include ferroptosis rescue experiments using ferrostatin 1 or liproxstatin 1.<br /> b) Assess lipid peroxidation directly (e.g., C11 BODIPY staining) to strengthen the ferroptosis claim.

(3) The manuscript discusses lysosomal ferritin degradation but does not directly examine NCOA4, a central mediator of ferritinophagy. It would be good to:<br /> a) Test whether NCOA4 knockdown rescues ferritin loss and ferroptosis sensitivity in NINJ2 KO cells.<br /> b) This would clarify whether NINJ2 acts upstream of canonical ferritinophagy pathways or via an alternative mechanism.

(4) The study is entirely cell-based, despite references to inflammatory and tumor phenotypes in Ninj2-deficient mice. While not strictly required, even limited in vivo validation (e.g., ferroptosis markers or iron accumulation in existing Ninj2 KO tissues) would substantially strengthen the manuscript.

(5) Finally, most imaging data (e.g., Galectin 3/LAMP1 colocalization, PLA signals) and immunoblot data are presented qualitatively. The authors should provide the qualifications of Western blots and other measurements.

Reviewer #1 (Public review):

Summary:

This study aims to test whether human mate choice is influenced by HLA similarity while accounting for genome-wide relatedness, using the Himba as an evolutionarily relevant small-scale society population, unique among most HLA-mate choice studies. By comparing self-chosen ("love") and arranged marriages and using NGS-based 8-locus HLA class I and II sequences and genome-wide SNP data, the authors ask whether partners who freely choose each other are more HLA-dissimilar than those paired through social arrangements or random pairs. They further extend their work by examining functional differences in peptide-binding divergence among pairs and predicted pathogen recognition in potential offspring.

Strengths:

This study has many strengths. The most obvious is their ability to test for HLA-based mate choice in the Himba, a non-European, non-admixed, small-scale society population, the type of population that has been missing, in my opinion, from the majority of HLA mate choice studies. While Hedrick and Black (1997) used a similarly evolutionarily relevant remote tribe of native South Americans, they only considered 2 class I loci (HLA-A and HLA-B) at the first typing field (serological allele group) and did not have data for genome-wide relatedness. The Himba are also unique among previously studied populations because they have both socially arranged and self-chosen partnerships, so the authors could test if freely-chosen partners had lower MHC-similarity than assigned or randomly chosen partners.

Another key strength of the study was the relatively large sample size (HLA allele calls from 366 individuals, 102 unrelated) and 219 individuals with HLA data, whole genome SNP data, and involved in a partnership.

The study was also unique among HLA-mate choice studies for comparing peptide binding region protein divergence (calculated as the Grantham distance between amino acid sequences) among partner types and randomly generated pairs. This was also the first time I have seen a study use peptide binding prediction analysis of relevant human pathogens for potential offspring among partners to test if there would be a pathogen-relevant fitness benefit of partner selection.

Weaknesses:

My main concerns relate to the reliance on imputed HLA haplotypes and on IBD-based metrics in a region of the genome where both approaches are known to be problematic.

First, several key results depend on HLA haplotypes inferred through imputation rather than directly observed sequence data. The authors trained HIBAG imputation models on Himba SNP data across the full 5 Mb HLA region using paired HLA allele calls from target capture sequencing (L251-253). However, the underlying SNP data were generated by mapping reads to a 1000 Genomes Yoruba reference, meaning that both SNP discovery and subsequent imputation depend on the haplotypes represented in that reference panel. As a result, the imputation framework is likely biased toward common haplotypes shared between the Himba and Yoruba populations, while rare or Himba-specific HLA alleles are less likely to be imputed accurately or at all. This limitation has been noted previously for HLA imputation, particularly for novel or low-frequency variants and for populations that are poorly represented in reference panels. While the authors compare (first-field) imputed alleles to sequenced alleles to assess imputation accuracy, this validation step itself may be biased toward the same common haplotypes that are easiest to impute. This becomes especially problematic if IBD is inferred using imputed haplotypes, because haplotype sharing would then primarily reflect common, reference-supported haplotypes, while true population-specific variation would be effectively invisible. In this scenario, downstream estimates of IBD sharing may be inflated for common haplotypes and deflated for rare ones, potentially biasing conclusions about haplotype sharing, selection, and mate choice at the HLA region.

Second, the interpretation of excess identity-by-descent (IBD) sharing in the HLA region is difficult given the well-documented genomic properties of this locus. The classical HLA region is highly gene-dense, structurally complex, and characterized by extreme heterogeneity in recombination rates, with pronounced hot- and cold-spots (Miretti et al. 2005; de Bakker et al. 2006, reviewed in Radwan et al. 2020). Elevated IBD in such regions can arise from low recombination, background selection, or demographic processes such as bottlenecks, all of which can mimic signals of recent positive selection. While the authors suggest fluctuating or directional selection, extensive haplotype sharing is also consistent with long-term balancing selection at the MHC (Albrechtsen et al. 2010) or recent demographic history in this population.

Beyond these main issues, there are several additional concerns that affect interpretation. Sample sizes and partnership counts are sometimes unclear; some figures would benefit from clearer scaling (Figure 1) and annotation (Figures S6 and S7), and key methodological choices (e.g., treatment of DRB copy number variation, no recombination correction in IBD calling) require further explanation. Finally, some conclusions, particularly those invoking optimality or specific selective mechanisms, are not directly tested by the analyses presented and would benefit from more cautious framing.

Reviewer #2 (Public review):

Summary:

Evidence for the influence of MHC on mate choice in humans is challenging, as social structures and norms often confound the power of studying populations. This study uses an unusual, diverse, but relatively isolated population that allows a direct comparison of arranged and chosen partners to determine if MHC diversity is increased when choice drives mate choice. Overall, the authors use a range of genetic analyses to determine individual relationships alongside different measures of MHC diversity and potential selection pressures. The overall finding that there is no heterozygous dissimilarity difference between arranged and chosen partners. There is evidence of positive selection that may be a stronger driver, or at least it may mask other selection forces.

Strengths:

A rare opportunity to study human mate choice and genetic diversity. An excellent range of data and analysis that is well applied, and all results point to the same conclusion.

Overall, this is a very well-written and concise paper when considering the significant amount of data and excellent analysis that has been undertaken.

Weaknesses:

(1) For the type of samples and data available, none are obvious.

(2) Although this paper is clearly focused on humans, I was expecting more discussion around the studies that have been undertaken in animals. It is likely that between populations and species, there are different pressures that have driven the MHC evolution, but also mate choice.

(3) The peptide presentation based on pathogen genomes is interesting but usually not significant. I wondered if another measure of MHC haplotype diversity to complement this would be the overall repertoire of peptides that could be presented, pathogen-based or otherwise. There is usually significant overlap in the peptides that can be presented, for example, between HLA-A and HLA-B, and this may reveal more significant differences between the alleles and haplotype frequencies.

Reviewer #3 (Public review):

The study investigates MHC-related mate choice in humans using a sample of couples from a small-scale sub-Saharan society. This is an important endeavour, as the vast majority of previous studies have been based on samples from complex, highly structured societies that are unlikely to reflect most of human evolutionary history. Moreover, the study controls for genome-wide diversity, allowing for a test of the specificity of the MHC region, as theoretically predicted. Finally, the authors examine potential fitness benefits by analysing predicted pathogen-binding affinities. Across all analyses, no deviations from random pairing are detected, suggesting a limited role for MHC-related mate choice in a relatively homogeneous society. Overall, I find the study to be carefully executed, and the paper clearly written. Nevertheless, I believe the paper would benefit if the following points were considered:

(1) The authors claim (p. 2, l. 85) that their study is the first to employ a non-European small-scale society. I believe this claim is incorrect, as Hendrick and Black (1997) investigated MHC similarity among couples from South American indigenous populations.

(2) Regarding the argument that in complex societies, mating with a random individual would already result in sufficient MHC dissimilarity (p. 2, 78), see the paper from Croy et al. 2020, which used the largest sample to date in this research area.

(3) Dataset. As some relationships are parallel, I assume that certain individuals entered the dataset multiple times. This should be explicitly reported in the Methods. If I understand the analyses correctly, this non-independence was addressed by including individual identity as a random effect in the model - the authors should confirm whether this is the case. I am also wondering to what extent so-called "discovered partnerships" may affect the results. Shared offspring may be the outcome of short or transient affairs and could have a different social status compared with other informal relationships. Would the observed patterns change if these partnerships were excluded from the analyses?

(4) How many pairs were due to relatedness closer than 3rd degree? In addition, why was 4th degree relatedness used as a threshold in some of the other analyses?

(5) I was surprised by the exclusion of HIV, given that Namibia has a very high prevalence of HIV in the general population (e.g., Low et al. 2021).

(6) It appears that age criteria were applied when generating random pairs (p. 8, l. 350). Could the authors please specify what they consider a realistic age gap, and on what basis this threshold was chosen? As these are virtual couples used solely to estimate random variation within the population, it is not entirely clear why age constraints are necessary. Would the observed patterns change if no age criteria were applied?

(7) I think it would be helpful for readers if the Results section explicitly stated that real couples did not differ from randomly generated pairs. At present, only the comparison between chosen and arranged pairs is reported.

(8) I appreciate the separate analyses of pathogen-binding properties for MHC class I and class II, given their functional distinctiveness. For the same reason, I would welcome a parallel analysis of MHC sharing conducted separately for class I and class II loci.

(9) I think the Discussion would benefit from a more detailed comparison with previous studies. In addition, the manuscript does not explicitly address limitations of the current study, including the relatively limited sample size given the extensive polymorphism in the MHC region.

References:

Hedrick, P. W., & Black, F. L. (1997). HLA and mate selection: no evidence in South Amerindians. The American Journal of Human Genetics, 61(3), 505-511.

Croy, I., Ritschel, G., Kreßner-Kiel, D., Schäfer, L., Hummel, T., Havlíček, J., ... & Schmidt, A. H. (2020). Marriage does not relate to major histocompatibility complex: A genetic analysis based on 3691 couples. Proceedings of the Royal Society B, 287(1936), 20201800.

Low, A., Sachathep, K., Rutherford, G., Nitschke, A. M., Wolkon, A., Banda, K., ... & Mutenda, N. (2021). Migration in Namibia and its association with HIV acquisition and treatment outcomes. PLoS One, 16(9), e0256865.

Reviewer #1 (Public review):

Summary:

Hsiung et al. investigated whether the effects of autophagy gene knockdown on the lifespan of long-lived C. elegans mutants depend on experimental conditions. The authors first compiled published data on autophagy-dependent lifespan regulation in daf-2 and wild-type backgrounds, highlighting that prior results are notably inconsistent and likely context-dependent. They then systematically tested the lifespan effects of RNAi knockdown of six autophagy genes (atg-2, atg-4.1, atg-9, atg-13, atg-18, and bec-1) in wild-type (N2), daf-2 (reduced insulin/IGF-1 signalling), and glp-1 (germlineless) animals, while varying temperature, daf-2 allele, FUDR concentration, and bacterial infection status.

The key findings are as follows. In wild-type animals, lifespan suppression by most autophagy gene knockdowns was more pronounced at 20{degree sign}C than at 25{degree sign}C, where little or no effect was observed. In daf-2 mutants, stronger lifespan suppression was seen in the weaker daf-2(e1368) allele at 20{degree sign}C, but not in the stronger daf-2(e1370) allele, and effects were largely absent at 25{degree sign}C. In glp-1 mutants, four of six gene knockdowns suppressed lifespan to a greater extent than in N2, though again in a temperature-dependent manner. FUDR at a high concentration (800 µM) abolished the life-shortening effects of most knockdowns and, in the case of atg-9 and atg-13, led to lifespan extension. Kanamycin treatment to eliminate bacterial proliferation did not fully account for the lifespan effects, suggesting that increased susceptibility to infection is not the primary mechanism. The authors also tested the programmed aging hypothesis that autophagy promotes lifespan reduction through biomass repurposing, but found no changes in vitellogenin levels upon knockdown of any of the six genes.

Altogether, among all genes tested, atg-18 knockdown produced the strongest and most consistent lifespan suppression across nearly all conditions, including both daf-2 and glp-1 backgrounds. The authors probed whether atg-18 acts through the FOXO transcription factor DAF-16 by examining dauer formation and ftn-1 expression, but found no evidence for this, suggesting a DAF-16-independent mechanism.

Strengths:

The primary strength of this work lies in its systematic and comprehensive approach to dissecting how experimental variables influence the outcome of autophagy-lifespan epistasis tests. The compilation of prior data alongside the authors' own multi-condition dataset is a genuinely useful resource for the field. The study raises a timely and important point about condition selection bias, which is relevant not only to autophagy research but to C. elegans aging studies more broadly. The finding that atg-18 behaves distinctly from other autophagy genes across all conditions is noteworthy and opens avenues for future mechanistic work.

Weaknesses:

Despite its breadth, the study has several weaknesses that limit the strength of some conclusions.

(1) Variability in control lifespan data. The N2 lifespan values under ostensibly identical conditions (e.g., GFP RNAi at 20{degree sign}C) differ substantially across experiments (compare Tables S2, S5, S6, S7, and S9). Since N2 serves as the baseline for calculating whether the effect is greater in long-lived mutants via Cox proportional hazard (CPH) analysis, this variability in controls directly affects the reliability of those comparisons.

(2) Limited biological replication. Most experiments were performed with only two biological replicates. In several cases, the two replicates yield contradictory outcomes: one showing significant lifespan suppression and the other showing no effect or even extension. The authors combine these into cumulative datasets for analysis, which, while not incorrect in principle, may obscure genuine irreproducibility. Given that the central message of the paper concerns variability and condition dependence, additional replication would have substantially strengthened confidence in the reported results.

(3) Low sample sizes in individual trials. A number of lifespan assays were conducted with only 40-50 worms per replicate, and in some cases, as few as 30. Such sample sizes are below the standard commonly used in the C. elegans aging field and are likely to contribute to the variability observed.

(4) RNAi efficacy measured only in N2 at 20{degree sign}C. The authors demonstrated that atg-2 and atg-4.1 RNAi did not significantly reduce target mRNA levels, which may explain their weaker lifespan effects. However, these same RNAi treatments significantly affected lifespan in several other conditions (e.g., daf-2(e1368) at 20{degree sign}C, glp-1 at 20{degree sign}C and 25{degree sign}C, and N2 with 15 µM FUDR). Measuring RNAi efficacy across different genetic backgrounds and conditions would be needed to properly interpret these variable results.

(5) Incomplete mechanistic exploration. The investigation of why atg-18 knockdown has uniquely strong effects was limited to DAF-16. Given published evidence that atg-18 may regulate HLH-30/TFEB, a master transcriptional regulator of autophagy and lysosomal biogenesis, testing whether atg-18 specifically affects HLH-30 nuclear localisation or activity could have provided valuable mechanistic insight and would distinguish atg-18 from the other genes tested.

Reviewer #2 (Public review):

Summary:

This study examines how genes involved in cellular recycling (autophagy) influence lifespan under different experimental conditions. The findings help clarify why previous studies have reported conflicting results about whether blocking autophagy shortens or extends lifespan. The work will be of interest to researchers studying aging and cellular stress responses, particularly those using model organisms.

Strengths:

The findings are valuable, as they help resolve inconsistencies within a specific subfield of aging research. The evidence presented is solid, as the data broadly support the primary claims of the study. In addition, the discussion is thorough and thoughtfully integrates the findings within the broader context of the field.

Weaknesses:

Additional functional validation would further strengthen the conclusions.