Reviewer #1 (Public review):

Summary:

The authors investigate how the Drosophila TNF receptor-associated factor Traf4 - a multifunctional adaptor protein with potential E3 ubiquitin ligase activity - regulates JNK signaling and adherens junctions (AJs) in wing disc epithelium. When they overexpress Traf4 in the posterior compartment of the wing disc, many posterior cells express the JNK target gene puckered (puc), apoptose, and are basally extruded from the epithelium. The authors term this process "delamination", but I think that this is an inaccurate description, especially since they can suppress the "delamination" by blocking programmed cell death (by concomitantly overexpressing p35). Through Y2H assays using Traf4 as a bait, they identified the Bearded family proteins E(spl)m4 (and to a lesser extent E(spl)m2), as Traf4 interactors. They use Alphafold to model computationally the interaction between Traf4 and E(spl)m4. They show that co-overexpression of Traf4 with E(spl)m4 in the posterior domain of the wing disc reduces death of posterior cells. They generate a new, weaker hypomorphic allele of Traf4 that is viable (as opposed to the homozygous lethality of null Traf4 alleles). There is some effect of these mutations on wing margin bristles; fewer wing margin bristle defects are seen when E(spl)m4 is overexpressed, suggesting opposite effects of Traf4 and E(spl)m4. Finally, they use the Minute model of cell competition to show that Rp/+ loser clones have greater clone area (indicating increased survival) when they are depleted for Traf4 or when they overexpress E(spl)m4. Only the cell competition results are quantified. Because most of the data in the preprint are not quantified, it is impossible to know how penetrant the phenotypes are. The authors conclude that E(spl)m4 binds the Traf4 MATH/TRAF domain, disrupts Traf4 trimerization, and selectively suppresses Traf4-mediated JNK and caspase activation without affecting its role in AJ destabilization. However, I believe that this is an overstatement. First, there is no biochemical evidence showing that Traf4 binds E(spl)m4 and that E(spl)m4 disrupts Traf4 trimerization. Second, the data on AJs is weak and not quantified; additionally, cells that are being basally extruded lose contact with neighboring cells, hence changes in adhesion proteins. Related to this, the authors, in my opinion, inaccurately describe basal extrusion of dying cells from the wing disc epithelium as delamination.

Strengths:

(1) The authors use multiple approaches to test the model that overexpressed E(spl)m4 inhibits Traf4, including genetics, cell biological imaging, yeast two-hybrid assays, and molecular modeling.

(2) The authors generate a new Traf4 hypomorphic mutant and use this mutant in cell competition studies, which supports the concept that E(spl)m4 (when overexpressed) can antagonize Traf4.

Weaknesses:

(1) Conflation of "delamination" with "basal extrusion of apoptotic cells": Over-expression of Traf4 causes apoptosis in wing disc cells, and this is a distinct process from delamination of viable cells from an epithelium. However, the two processes are conflated by the authors, and this weakens the premise of the paper.

(2) Dependence on overexpression: The conclusions rely heavily on ectopic expression of Traf4 and E(spl)m4. Thus, the physiological relevance of the interaction remains inferred rather than demonstrated.

(3) Lack of quantitative rigor: Except for the cell competition studies, phenotypic descriptions (e.g., number of apoptotic cells, puc-LacZ intensity) are qualitative; additional quantification, inclusion of sample size, and statistical testing would strengthen the conclusions.

(4) Limited biochemical validation: The Traf4-E(spl)m4 binding is inferred from Y2H and in silico models, but no co-immunoprecipitation or in vitro binding assays confirm direct interaction or the predicted disruption of trimerization.

(5) Specificity within the Bearded family: While E(spl)m2 shows partial binding and Tom shows none, the mechanistic basis for this selectivity is not deeply explored experimentally, leaving questions about motif-context contributions unresolved.

Reviewer #2 (Public review):

Summary:

This manuscript analyzes the contribution of Traf4 to the fate of epithelial cells in the developing wing imaginal disc tissue. The manuscript is direct and concise and suggests an interesting and valuable hypothesis with dual functions of Traf4 in JNK pathway activation and cell delamination. However, the text is partially speculative, and the evidence is incomplete as the main claims are only partially supported. Some results require validation to support the conclusions.

Strengths:

(1) The manuscript is direct and concise, with a well-written and precise introduction.

(2) It presents an interesting and valuable hypothesis regarding the dual role of Traf4 in JNK pathway activation and cell delamination.

(3) The study addresses a relevant biological question in epithelial tissue development using a genetically tractable model.

(4) The use of newly generated Traf4 mutants adds novelty to the experimental approach.

(5) The manuscript includes multiple experimental strategies, such as genetic manipulation and imaging, to explore Traf4 function.

Weaknesses:

(1) The evidence supporting key claims is incomplete, and some conclusions are speculative.

(2) The use of GFP-tagged Traf4 lacks validation regarding its functional integrity.

(3) Orthogonal views and additional imaging data are needed to confirm changes in apicobasal localization and cell delamination.

(4) Experimental conditions and additional methods should be further detailed.

(5) The interaction between Traf4 and E(spl)m4 remains speculative in Drosophila.

(6) New mutants require deeper analysis and validation.

(7) The elimination of Traf4 mutant clones may be due to cell competition, which requires further experimental clarification.

(8) The role of Traf4 in cell competition is contradictory and needs to be resolved.

Reviewer #3 (Public review):

Summary:

This is an important and well-conceived study that identifies the Bearded-type small protein E(spl)m4 as a physical and genetic interactor of TRAF4 in Drosophila. By combining classical genetics, yeast two-hybrid assays, and AlphaFold in silico modeling, the authors convincingly demonstrate that E(spl)m4 acts as an inhibitor of TRAF4-mediated induction of JNK-driven apoptosis in developing larval imaginal wing discs, while not affecting TRAF4's role in adherence junction remodeling.

Based primarily on modeling, the authors propose that the specificity of E(spl)m4 towards TRAF4-mediated signaling arises from its interference with TRAF4 trimerization, which is likely required for the activation of the JNK signaling arm but not for the maintenance of adherence junctions and stability of E-cadherin/β-catenin complex.

Overall, this study is of broad interest to cell and developmental biologists. It also holds potential biomedical relevance, particularly for strategies aimed at modulating TRAF protein activities to dissect and modulate canonical versus non-canonical signaling functions.

Strengths:

(1) The work identifies the Bearded-type small protein E(spl)m4 as a physical and genetic interactor of TRAF4 in Drosophila, extending the understanding of E(spl)m4 beyond its established functions in Notch signaling.

(2) The study is experimentally solid, well-executed, and written, combining classical genetics with protein-protein interaction assays and modeling to reveal E(spl)m4 as a new regulator of TRAF4 signaling.

(3) The genetic and biochemical data convincingly show the ability of E(spl)m4 overexpression to inhibit TRAF4-induced JNK-dependent apoptosis, while leaving the TRAF4 role in adherens junction remodeling unaffected.

(4) The findings have important implications for the regulation of cell signaling and apoptosis and may guide pharmacological targeting of TRAF proteins.

Weaknesses:

The study is overall strong; however, several aspects could be clarified or expanded to strengthen the proposed mechanism and data presentation:

(1) The proposed mechanism that E(spl)m4 inhibits TRAF4 activation of JNK signaling by affecting TRAF4 trimerization relies mainly on modeling. Experimental evidence would strengthen this claim. For example, a native or non-denaturing SDS-PAGE could be used to assess TRAF4 oligomerization states in the absence or presence of E(spl)m4 overexpression, testing whether E(spl)m4 interferes with high-molecular-weight TRAF4 assemblies.

(2) The study depends largely on E(spl)m4 overexpression, which may not reflect physiological conditions. It would be valuable to test, or at least discuss, whether loss-of-function or knockdown of E(spl)m4 modulates the strength or duration of JNK-mediated signaling, potentially accelerating apoptosis. Such data would reinforce the model that E(spl)m4 acts as a physiological modulator of TRAF4-JNK signaling in vivo.

(3) The authors initially identify both E(spl)m4 and E(spl)m2 as TRAF4 interactions, but subsequently focus on E(spl)m4. It would be helpful to clarify or discuss the rationale for prioritizing E(spl)m4 for detailed functional analysis.

(4) E(spl)m4 overexpression appears to protect RpS3 loser clones (Figure 6H-K), yet caspase-3-positive cells are still visible in mosaic wing discs. Please comment on the nature of these Caspase 3-positive cells, whether they are cell-autonomous to the clone or non-autonomous (Figure 6K)?

(5) This is a clear, well-executed, and conceptually strong study that significantly advances understanding of TRAF4 signaling specificity and its modulation by the Bearded-type protein E(spl)m4.

Reviewer #1 (Public review):

Nielsen et al have identified a new disease mechanism underlying hypoplastic left heart syndrome due to variants in ribosomal protein genes that lead to impaired cardiomyocyte proliferation. This detailed study starts with an elegant screen in stem cell derived cardiomyocytes and whole genome sequencing of human patients and extends to careful functional analysis of RP gene variants in fly and fish models. Striking phenotypic rescue is seen by modulating known regulators of proliferation including the p53 and Hippo pathways. Additional experiments suggest that cell type specificity of the variants in these ubiquitously expressed genes may result from genetic interactions with cardiac transcription factors. This work positions RPs as important regulators of cardiomyocyte proliferation and differentiation involved in the etiology of HLHS, and point to potential downstream mechanisms.

The revised manuscript has been extended, facilitating interpretation and reinforcing the authors' conclusions.

Reviewer #2 (Public review):

Tanja Nielsen et al. presents a novel strategy for identification of candidate genes in Congenital Heart Disease (CHD). Their methodology, which is based on comprehensive experiments across cell models, drosophila and zebrafish models, represents an innovative, refreshing and very useful set of tools for identification of disease genes, in a field which are struggling with exactly this problem.

The authors have applied their methodology to investigate the pathomechanisms of Hypoplastic Left Heart Syndrome (HLHS) - a severe and rare subphenotype in the large spectrum of CHD malformations. Their data convincingly implicates ribosomal proteins (RPs) in growth and proliferation defects of cardiomyocytes, a mechanism which is suspected to be associated with HLHS.

By whole genome sequencing analysis of a small cohort of trios (25 HLHS patients and their parents) the authors investigated a possible association between RP encoding genes and HLHS.

Although the possible association between defective RPs and HLHS needs to be verified, the results suggest a novel disease mechanism in HLHS, which is a potentially substantial advance in our understanding of HLHS and CHD. The conclusions of the paper are based on solid experimental evidence from appropriate high- to medium-throughput models, while additional genetic results from an independent patient cohort is needed to verify an association between RP encoding genes and HLHS in patients.

Reviewer #1 (Public review):

The study analyzes the gastric fluid DNA content identified as a potential biomarker for human gastric cancer. However, the study lacks overall logicality, and several key issues require improvement and clarification. In the opinion of this reviewer, some major revisions are needed:

(1) This manuscript lacks a comparison of gastric cancer patients' stages with PN and N+PD patients, especially T0-T2 patients.

(2) The comparison between gastric cancer stages seems only to reveal the difference between T3 patients and early-stage gastric cancer patients, which raises doubts about the authenticity of the previous differences between gastric cancer patients and normal patients, whether it is only due to the higher number of T3 patients.

(3) The prognosis evaluation is too simplistic, only considering staging factors, without taking into account other factors such as tumor pathology and the time from onset to tumor detection.

(4) The comparison between gfDNA and conventional pathological examination methods should be mentioned, reflecting advantages such as accuracy and patient comfort.

(5) There are many questions in the figures and tables. Please match the Title, Figure legends, Footnote, Alphabetic order, etc.

(6) The overall logicality of the manuscript is not rigorous enough, with few discussion factors, and cannot represent the conclusions drawn.

Comments on revisions:

The authors have addressed all concerns in the revision.

Reviewer #2 (Public review):

Summary

The authors aimed to evaluate whether total DNA concentration in gastric fluid (gfDNA) collected during routine endoscopy could serve as a diagnostic and prognostic biomarker for gastric cancer. Using a large cohort (n=941), they reported elevated gfDNA in gastric cancer patients, an unexpected association with improved survival, and a positive correlation with immune cell infiltration.

Strengths

The study benefits from a substantial sample size, clear patient stratification, and control of key clinical confounders. The method is simple and clinically feasible, with preliminary evidence linking gfDNA to immune infiltration.

Weaknesses

(1) While the study identifies gfDNA as a potential prognostic tool, the evidence remains preliminary. Unexplained survival associations and methodological gaps weaken support for the conclusions.

(2) The paradoxical association between high gfDNA and better survival lacks mechanistic validation. The authors acknowledge but do not experimentally distinguish tumor vs. immune-derived DNA, leaving the biological basis speculative.

(3) Pre-analytical variables were noted but not systematically analyzed for their impact on gfDNA stability.

Comments on revisions:

To enhance the completeness and credibility of this research, it is essential to clarify the biological origin of gastric fluid DNA and validate these preliminary findings through a prospective, longitudinal study design.

Reviewer #1 (Public review):

The authors of this study set out to address a central question in the psycholinguistics literature: does the human brain's ability to predict upcoming language come at a cognitive cost, or is it an automatic, "free" process? To investigate this, they employed a dual-task paradigm where participants read texts word-by-word while simultaneously performing a secondary task (an n-back task on font color) designed to manipulate cognitive load. The study examines how this external cognitive load, along with the effects of aging, modulates the impact of word predictability (measured by surprisal and entropy) on reading times. The central finding is that increased cognitive load diminishes the effects of word predictability, supporting the conclusion that language prediction is a resource-dependent process.

A major strength of the revised manuscript is its comprehensive and parallel analysis of both word surprisal and entropy. The initial submission focused almost exclusively on surprisal, which primarily reflects the cost of integrating a word into its context after it has been perceived. The new analysis now thoroughly investigates entropy as well, which reflects the uncertainty and cognitive effort involved in predicting the next word before it appears. This addition provides a much more complete and theoretically nuanced picture, allowing the authors to address how cognitive load affects both predictive and integrative stages of language processing. This is a significant improvement and substantially increases the paper's contribution to the field.

Furthermore, the authors have commendably addressed the initial concerns regarding the robustness of their replication findings. The first version of the manuscript presented replication results that were inconsistent, particularly for key interaction effects. In the revision, the authors have adopted a more focused and appropriately powered modeling approach for the replication analysis. This revised analysis now demonstrates a consistent effect of cognitive load on the processing of predictable words across both the original and replication datasets. This strengthens the evidence for the paper's primary claim.

The initial review also raised concerns that the results could be explained by general cognitive factors, such as task-switching costs, rather than the specific demands on the language prediction system. While the complexity of cognitive load in a dual-task paradigm remains a challenge, the authors have provided sufficient justification in their revisions and rebuttal to support their interpretation that the observed effects are genuinely tied to the process of language prediction.

Reviewer #2 (Public review):

Summary:

This paper considers the effects of cognitive load (using an n-back task related to font color), predictability, and age on reading times in two experiments. There were main effects of all predictors, but more interesting effects of load and age on predictability. The effect of load is very interesting, but the manipulation of age is problematic, because we don't know what is predictable for different participants (in relation to their age). There are some theoretical concerns about prediction and predictability, and a need to address literature (reading time, visual world, ERP studies).

There is a major concern about the effects of age. See the results (155-190): this depends what is meant by word predictability. It's correct if it means the predictability in the corpus. But it may or may not be correct if it refers to how predictable a word is to an individual participant. The texts are unlikely to be equally predictable to different participants, and in particular to younger vs. older participants, because of their different experience. To put it informally, the newspaper articles may be more geared to the expectations of younger people. But there is also another problem: the LLM may have learned on the basis of language that has largely been produced by young people and so its predictions are based on what young people are likely to say. Both of these possibilities strike me as extremely likely. So it may be that older adults are affected more by words that they find surprising, but it is also possible that the texts are not what they expect, or the LLM predictions from the text are not the ones that they would make. In sum, I am not convinced that the authors can say anything about the effects of age unless they can determine what is predictable for different ages of participants. I suspect that this failure to control is an endemic problem in the literature on aging and language processing and needs to be systematically addressed.

Overall, I think the paper makes enough of a contribution with respect to load to be useful to the literature. But for discussion of age, we would need something like evidence of how younger and older adults would complete these texts (on a word-by-word basis) and that they were equally predictable for different ages. I assume there are ways to get LLMs to emulate different participant groups, but I doubt if we could be confident about their accuracy without a lot of testing. But without something like this, I think making claims about age would be quite misleading.

The authors respond to my summary comment by saying that prediction is individual and that they account for age-related effects in their models. But these aren't my concerns. Rather:

(1) The texts (these edited newspaper articles) could be more predictable for younger than older adults. If so, effects with older adults could simply be because people are less likely to predict less than more predictable words.

(2) The GPT-2 generated surprisal scores may correspond more closely to younger than older adult responses -- that is, its next word predictions may be more younger- than older-adult-like.

In my view, the authors have two choices: they could remove the discussion of age-related effects, or they could try to address BOTH (1) and (2).

As an aside, consider what we would conclude if we drew similar conclusions from a study in which children and adults read the same (children's) texts, but we didn't test what was predictable to each of them separately.

The paper is really strong in other respects and if my concern is not addressed, the conclusions about age might be generally accepted.

Reviewer #1 (Public review):

Summary:

Wojnowska et al. report structural and functional studies of the interaction of Streptococcus pyogenes M3 protein with collagen. They show through X-ray crystallographic studies that the N-terminal hypervariable region of M3 protein forms a T-like structure, and that the T-like structure binds a three-stranded collagen-mimetic peptide. They indicate that the T-like structure is predicted by AlphaFold3 with moderate confidence level in other M proteins that have sequence similarity to M3 protein and M-like proteins from group C and G streptococci. For some, but not all, of these related M and M-like proteins, AlphaFold3 predicts, with moderate confidence level, complexes similar to the one observed for M3-collagen. Functionally, the authors show that emm3 strains form biofilms with more mass when surfaces are coated with collagen, and this effect can be blocked by an M3 protein fragment that contains the T-structure. They also show the co-occurrence of emm3 strains and collagen in patient biopsies and a skin tissue organoid. Puzzlingly, M1 protein has been reported to bind collagen, but collagen inhibits biofilm in a particular emm1 strain but that same emm1 strain colocalizes with collagen in a patient biopsy sample. The implications of the variable actions of collagen on biofilm formation are not clear.

Strengths:

The paper is well written and the results are presented in a logical fashion.

Weaknesses:

A major limitation of the paper is that it is almost entirely observational and lacks detailed molecular investigation. Insufficient details or controls are provided to establish the robustness of the data.

Comments on revisions:

The authors' response to this reviewer's Major issue #1 is inadequate. Their argument is essentially that if they denature the protein, then there is no activity. This does not address the specificity of the structure or its interactions.

They went only part way to addressing this reviewer's Major issue #2. While Figure 8 - supplement 3 shows 1D NMR spectra for M3 protein (what temperature?), it does not establish that stability is unaltered (to a significant degree).

This reviewer's Major issue #3 is one of the major reasons for considering this study to be observational. This reviewer agrees that structural biology is by its nature observational, but modern standards require validation of structural observations. The authors' response is that a mechanistic investigation involving mutant bacterial strains and validation involving mutated proteins is beyond their scope. Therefore, the study remains observational.

Major issue 4 was addressed suitably, but brings up the problematic point that the emm1 2006 strain colocalizes quite well with collagen in a patient biopsy sample but not in other assays. This calls into question the overall interpretability of the patient biopsy data.

The authors have not provided a point-by-point response. Issues that were indicated to be minor previously were deemed to be minor because this reviewer thought that they could easily be addressed in a revision. It appears that the authors have ignored many of these comments, and these issues are therefore now considered to be major issues. For example, no errors are given for Kd measurements, Table 2 is sloppy and lacks the requested information, negative controls are missing (Figure 10 - figure supplement 1), and there is no indication of how many independent times each experiment was done.

And "C4-binding protein" should be corrected to "C4b-binding protein."

Reviewer #2 (Public review):

Streptococcus pyogenes, or group A streptococci (GAS) can cause diseases ranging skin and mucosal infections, plasma invasion, and post-infection autoimmune syndromes. M proteins are essential GAS virulence factors that include an N-terminal hypervariable region (HVR). M proteins are known to bind to numerous human proteins; a small subset of M proteins were reported to bind collagen, which is thought to promote tissue adherence. In this paper, authors characterize M3 interactions with collagen and its role in biofilm formation. Specifically, they screened different collagen type II and III variants for full-length M3 protein binding using an ELISA-like method, detecting anti-GST antibody signal. By statistical analysis, hydrophobic amino acids and hydroxyproline found to positively support binding, whereas acidic residues and proline negatively impacted binding. The authors applied X-ray crystallography to determine the structure of the N-terminal domain (42-151 amino acids) of M3 protein (M3-NTD). M3-NTD dimmer (PDB 8P6K) forms a T-shaped structure with three helices (H1, H2, H3), which are stabilized by a hydrophobic core, inter-chain salt bridges and hydrogen bonds on H1, H2 helices, and H3 coiled coil. The conserved Gly113 serves as the turning point between H2 and H3. The M3-NTD is co-crystalized with a 24-residue peptide, JDM238, to determine the structure of M3-collagen binding. The structure (PDB 8P6J) shows that two copies of collagen in parallel bind to H1 and H2 of M3-NTD. Among the residues involved binding, conserved Try96 is shown to play a critical role supported by structure and isothermal titration calorimetry (ITC). The authors also apply a crystal-violet assay and fluorescence microscopy to determine that M3 is involved in collagen type I binding, but not M1 or M28. Tissue biopsy staining indicates that M3 strains co-localize with collagen IV-containing tissue, while M1 strains do not. The authors provide generally compelling evidence to show that GAS M3 protein binds to collagen, and plays a critical role in forming biofilms, which contribute to disease pathology. This is a very well-executed study and a well-written report relevant to understanding GAS pathogenesis and approaches to combatting disease; data are also applicable to emerging human pathogen Streptococcus dysgalactiae. One caveat that was not entirely resolved is if/how different collagen types might impact M3 binding and function. Due to the technical constrains, the in vitro structure and other binding assays use type II collagen whereas in vivo, biofilm formation assays and tissue biopsy staining use type I and IV collagen; it was unclear if this difference is significant. One possibility is that M3 has an unbiased binding to all types of collagens, only the distribution of collagens leads to the finding that M3 binds to type IV (basement membrane) and type I (varies of tissue including skin), rather than type II (cartilage).

Comments on revisions:

We are glad to see that the authors addressed our prior comments on M3 binding to different types of collagens in discussion section; adding a prediction of M3 binding to type I collagen (Figure 8-figure supplement 1B and 1C) is helpful to fill in the gap. Although it would be nice to experimentally fill in the gap by putting all types of collagens into one experiment (For example, like Figure 9A, use different types of human collagens to test biofilm formation; or Figure 10, use different types of human collagens to compete for biofilm formation), this appears to be beyond the scope of this paper. Meanwhile, the changes they have made are constructive.

The authors have addressed the majority of our prior comments.

Reviewer #3 (Public review):

Summary:

In this well-written manuscript, Unitt and colleagues propose a new, hierarchical nomenclature system for the pathogen Neisseria gonorrhoeae. The proposed nomenclature addresses a longstanding problem in N. gonorrhoeae genomics, namely that the highly recombinant population complicates typing schemes based on only a few loci and that previous typing systems, even those based on the core genome, group strains at only one level of genomic divergence without a system for clustering sequence types together. In this work, the authors have revised the core genome MLST scheme for N. gonorrhoeae and devised life identification numbers (LIN) codes to describe the N. gonorrhoeae population structure.

Strengths:

The LIN codes proposed in this manuscript are congruent with previous typing methods for Neisseria gonorrhoeae like cgMLST groups, Ng-STAR, and NG-MAST. Importantly, they improve upon many of these methods as the LIN codes are also congruent with the phylogeny and represent monophyletic lineages/sublineages. Additionally, LIN code cluster assignment is fixed, and clusters are not fused as is common in other typing schemes.

The LIN code assignment has been implemented in PubMLST allowing other researchers to assign LIN codes to new assemblies and put genomes of interest in context with global datasets, including in private datasets.

Weaknesses:

The authors have defined higher resolution thresholds for the LIN code scheme. However, they do not investigate how these levels correspond to previously identified transmission clusters from genomic epidemiology studies. This will be an important focus of future work, but it may be beyond the scope of the current manuscript.

Comments on revisions:

The authors have addressed my previous comments. I have no additional recommendations.

Reviewer #1 (Public review):

Summary:

The authors set out to evaluate the regulation of interferon (IFN) gene expression in fish, using mainly zebrafish as a model system. Similar to more widely characterized mammalian systems, fish IFN is induced during viral infection through the action of the transcription factor IRF3 which is activated by phosphorylation by the kinase TBK1. It has been previously shown in many systems that TBK1 is subjected to both positive and negative regulation to control IFN production. In this work, the authors find that the cell cycle kinase CDK2 functions as a TBK1 inhibitor by decreasing its abundance through recruitment of the ubiquitinylation ligase, Dtx4, which has been similarly implicated in the regulation of mammalian TBK1. Experimental data are presented showing that CDK2 interacts with both TBK1 and Dtx4, leading to TBK1 K48 ubiqutinylation on K567 and its subsequent degradation by the proteasome.

Strengths:

The strengths of this manuscript are its novel demonstration of the involvement of CDK2 in a process in fish that is controlled by different factors in other vertebrates and its clear and supportive experimental data.

Weaknesses:

The weaknesses of the study include the following. 1) It remains unclear how CDK is regulated during viral infection and how it specifically recruits E3 ligase to TBK1. The authors find that its abundance increases during viral infection, an unusual finding given that CDK2 levels are often found to be stable. How this change in abundance might affect cell cycle control was not explored. 2) The implications and mechanisms for a relationship between the cell cycle and IFN production will be a fascinating topic for future studies. In particular, it will be critical to determine if CDK2 catalytic activity is required. An experiment with an inhibitor suggests that this novel action of CDK2 is kinase independent, but the lack of controls showing the efficacy of the inhibitor prevents a firm conclusion. It will also be critical to determine if there is a role for cyclins in this process or if there is competition for binding between TBK1 and cyclin and, if so, if this has an impact on the cell cycle. Likewise, an impact of CDK2 induction by virus infection on normal cell cycling will be important to investigate.

Reviewer #2 (Public review):

Summary:

In this paper, the authors describe a novel function involving the cell cycle protein kinase CDK2, which binds to TBK1 (an essential component of the innate immune response) leading to its degradation in a ubiquitin/proteasome-dependent manner. Moreover, the E3 ubiquitin ligase, Dtx4, is implicated in the process by which CDK2 increases the K48-linked ubiquitination of TBK1. This paper presents intriguing findings on the function of CDK2 in lower vertebrates, particularly its regulation of IFN expression and antiviral immunity.

Strengths:

(1) The research employs a variety of experimental approaches to address a single question. The data are largely convincing and appear to be well executed.

(2) The evidence is strong and includes a combination of in vivo and in vitro experiments, including knockout models, protein interaction studies, and ubiquitination analyses.

(3) This study significantly impacts the field of immunology and virology, particularly concerning the antiviral mechanisms in lower vertebrates. The findings provide new insights into the regulation of IFN expression and the broader role of CDK2 in immune responses. The methods and data presented in this paper are highly valuable for the scientific community, offering new avenues for research into antiviral strategies and the development of therapeutic interventions targeting CDK2 and its associated pathways.

Reviewer #1 (Public review):

The authors investigated the potential role of IgG N-glycosylation in Haemorrhagic Fever with Renal Syndrome (HFRS), which may offer significant insights for understanding molecular mechanisms and for the development of therapeutic strategies for this infectious disease.

Reviewer #2 (Public review):

This work sought to explore antibody responses in the context of hemorrhagic fever with renal syndrome (HFRS) - a severe disease caused by Hantaan virus infection. Little is known about the characteristics or functional relevance of IgG Fc glycosylation in HFRS. To address this gap, the authors analyzed samples from 65 patients with HFRS spanning the acute and convalescent phases of disease via IgG Fc glycan analysis, scRNAseq, and flow cytometry. The authors observed changes in Fc glycosylation (increased fucosylation and decreased bisection) coinciding with a 4-fold or greater increased in Haantan virus-specific antibody titer. The study also includes exploratory analyses linking IgG glycan profiles to glycosylation-related gene expression in distinct B cell subsets, using single-cell transcriptomics. Overall, this is an interesting study that combines serological profiling with transcriptomic data to shed light on humoral immune responses in an underexplored infectious disease. The integration of Fc glycosylation data with single-cell transcriptomic data is a strength.

Reviewer #1 (Public review):

Summary:

The microbiota of Dactylorhiza traunsteineri, an endangered marsh orchid, forms complex root associations that support plant health. Using 16S rRNA sequencing, we identified dominant bacterial phyla in its rhizosphere, including Proteobacteria, Actinobacteria, and Bacteroidota. Deep shotgun metagenomics revealed high-quality MAGs with rich metabolic and biosynthetic potential. This study provides key insights into root-associated bacteria and highlights the rhizosphere as a promising source of bioactive compounds, supporting both microbial ecology research and orchid conservation.

Strengths:

The manuscript presents an investigation of the bacterial communities in the rhizosphere of D. traunsteineri using advanced metagenomic approaches. The topic is relevant, and the techniques are up-to-date; however, the study has several critical weaknesses.

Weaknesses:

(1) Title: The current title is misleading. Given that fungi are the primary symbionts in orchids and were not analyzed in this study (nor were they included among other microbial groups), the use of the term "microbiome" is not appropriate. I recommend replacing it with "bacteriome" to better reflect the scope of the work.

(2) Line 124: The phrase "D. traunsteineri individuals were isolated" seems misleading. A more accurate description would be "individuals were collected", as also mentioned in line 128.

(3) Experimental design: The major limitation of this study lies in its experimental design. The number of plant individuals and soil samples analyzed is unclear, making it difficult to assess the statistical robustness of the findings. It is also not well explained why the orchids were collected two years before the rhizosphere soil samples. Was the rhizosphere soil collected from the same site and from remnants of the previously sampled individuals in 2018? This temporal gap raises serious concerns about the validity of the biological associations being inferred.

(4) Low sample size: In lines 249-251 (Results section), the authors mention that only one plant individual was used for identifying rhizosphere bacteria. This is insufficient to produce scientifically robust or generalizable conclusions.

(5) Contextual limitations: Numerous studies have shown that plant-microbe interactions are influenced by external biotic and abiotic factors, as well as by plant age and population structure. These elements are not discussed or controlled for in the manuscript. Furthermore, the ecological and environmental conditions of the site where the plants and soil were collected are poorly described. The number of biological and technical replicates is also not clearly stated.

(6) Terminology: Throughout the manuscript, the authors refer to the "microbiome," though only bacterial communities were analyzed. This terminology is inaccurate and should be corrected consistently.

Considering the issues addressed, particularly regarding experimental design and data interpretation, significant improvements to the study are needed.

Reviewer #2 (Public review):

Summary:

The authors aim to provide an overview of the D. traunsteineri rhizosphere microbiome on a taxonomic and functional level, through 16S rRNA amplicon analysis and shotgun metagenome analysis. The amplicon sequencing shows that the major phyla present in the microbiome belong to phyla with members previously found to be enriched in rhizospheres and bulk soils. Their shotgun metagenome analysis focused on producing metagenome assembled genomes (MAGs), of which one satisfies the MIMAG quality criteria for high-quality MAGs and three those for medium-quality MAGs. These MAGs were subjected to functional annotations focusing on metabolic pathway enrichment and secondary metabolic pathway biosynthetic gene cluster analysis. They find 1741 BGCs of various categories in the MAGs that were analyzed, with the high-quality MAG being claimed to contain 181 SM BGCs. The authors provide a useful, albeit superficial, overview of the taxonomic composition of the microbiome, and their dataset can be used for further analysis.

The conclusions of this paper are not well-supported by the data, as the paper only superficially discusses the results, and the functional interpretation based on taxonomic evidence or generic functional annotations does not allow drawing any conclusions on the functional roles of the orchid microbiota.

Weaknesses:

The authors only used one individual plant to take samples. This makes it hard to generalize about the natural orchid microbiome.

The authors use both 16S amplicon sequencing and shotgun metagenomics to analyse the microbiome. However, the authors barely discuss the similarities and differences between the results of these two methods, even though comparing these results may be able to provide further insights into the conclusions of the authors. For example, the relative abundance of the ASVs from the amplicon analysis is not linked to the relative abundances of the MAGs.

Furthermore, the authors discuss that phyla present in the orchid microbiome are also found in other microbiomes and are linked to important ecological functions. However, their results reach further than the phylum level, and a discussion of genera or even species is lacking. The phyla that were found have very large within-phylum functional variability, and reliable functional conclusions cannot be drawn based on taxonomic assignment at this level, or even the genus level (Yan et al. 2017).

Additionally, although the authors mention their techniques used, their method section is sometimes not clear about how samples or replicates were defined. There are also inconsistencies between the methods and the results section, for example, regarding the prediction of secondary metabolite biosynthetic gene clusters (BGCs).

The BGC prediction was done with several tools, and the unusually high number of found BGCs (181 in their high-quality MAG) is likely due to false positives or fragmented BGCs. The numbers are much higher than any numbers ever reported in literature supported by functional evidence (Amos et al, 2017), even in a prolific genus like Streptomyces (Belknap et al., 2020). This caveat is not discussed by the authors.

The authors have generated one high-quality MAG and three medium-quality MAGs. In the discussion, they present all four of these as high-quality, which could be misleading. The authors discuss what was found in the literature about the role of the bacterial genera/phyla linked to these MAGs in plant rhizospheres, but they do not sufficiently link their own analysis results (metabolic pathway enrichment and biosynthetic gene cluster prediction) to this discussion. The results of these analyses are only presented in tables without further explanation in either the results section or the discussion, even though there may be interesting findings. For example, the authors only discuss the class of the BGCs that were found, but don't search for experimentally verified homologs in databases, which could shed more light on the possible functional roles of BGCs in this microbiome.

In the conclusions, the authors state: "These analyses uncovered potential metabolic capabilities and biosynthetic potentials that are integral to the rhizosphere's ecological dynamics." I don't see any support for this. Mentioning that certain classes of BGCs are present is not enough to make this claim, in my opinion. Any BGC is likely important for the ecological niche the bacteria live in. The fact that rhizosphere bacteria harbour BGCs is not surprising, and it doesn't tell us more than is already known.

References:

Belknap, Kaitlyn C., et al. "Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria." Scientific reports 10.1 (2020): 2003

Amos GCA, Awakawa T, Tuttle RN, Letzel AC, Kim MC, Kudo Y, Fenical W, Moore BS, Jensen PR. Comparative transcriptomics as a guide to natural product discovery and biosynthetic gene cluster functionality. Proc Natl Acad Sci U S A. 2017 Dec 26;114(52):E11121-E11130.

References:

Belknap, Kaitlyn C., et al. "Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria." Scientific reports 10.1 (2020): 2003

Amos GCA, Awakawa T, Tuttle RN, Letzel AC, Kim MC, Kudo Y, Fenical W, Moore BS, Jensen PR. Comparative transcriptomics as a guide to natural product discovery and biosynthetic gene cluster functionality. Proc Natl Acad Sci U S A. 2017 Dec 26;114(52):E11121-E11130.

Yan Yan, Eiko E Kuramae, Mattias de Hollander, Peter G L Klinkhamer, Johannes A van Veen, Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere, The ISME Journal, Volume 11, Issue 1, January 2017, Pages 56-66

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, though some clarifications would further enhance interpretability.

Strengths:

(1) The manuscript tackles a highly relevant methodological question on the robustness of meta-analytic evidence.

(2) EOIMETA represents an innovative extension of fragility concepts from single trials to meta-analyses.

(3) The applications are clearly presented and highlight the potential importance of fragility considerations for evidence synthesis.

Weaknesses:

(1) The rationale and mathematical details behind the proposed EOI and ROAR methods are insufficiently explained. Readers are asked to rely on external sources (Grimes, 2022; 2024b) without adequate exposition here. At a minimum, the definitions, intuition, and key formulas should be summarized in the manuscript to ensure comprehensibility.

(2) EOIMETA is described as being applicable when heterogeneity is low, but guidance is missing on how to interpret results when heterogeneity is high (e.g., large I²). Clarification in the Results/Discussion is needed, and ideally, a simulation or illustrative example could be added.

(3) The manuscript would benefit from side-by-side comparisons between the traditional FI at the trial level and EOIMETA at the meta-analytic level. This would contextualize the proposed approach and underscore the added value of EOIMETA.

(4) Scope of FI: The statement that FI applies only to binary outcomes is inaccurate. While originally developed for dichotomous endpoints, extensions exist (e.g., Continuous Fragility Index, CFI). The manuscript should clarify that EOIMETA focuses on binary outcomes, but FI, as a concept, has been generalized.

Reviewer #2 (Public review):

Summary:

The study expands existing analytical tools originally developed for randomized controlled trials with dichotomous outcomes to assess the potential impact of missing data, adapting them for meta-analytical contexts. These tools evaluate how missing data may influence meta-analyses where p-value distributions cluster around significance thresholds, often leading to conflicting meta-analyses addressing the same research question. The approach quantifies the number of recodings (adding events to the experimental group and/or removing events from the control group) required for a meta-analysis to lose or gain statistical significance. The author developed an R package to perform fragility and redaction analyses and to compare these methods with a previously established approach by Atal et al. (2019), also integrated into the package. Overall, the study provides valuable insights by applying existing analytical tools from randomized controlled trials to meta-analytical contexts.

Strengths:

The author's results support his claims. Analyzing the fragility of a given meta-analysis could be a valuable approach for identifying early signs of fragility within a specific topic or body of evidence. If fragility is detected alongside results that hover around the significance threshold, adjusting the significance cutoff as a function of sample size should be considered before making any binary decision regarding statistical significance for that body of evidence. Although the primary goal of meta-analysis is effect estimation, conclusions often still rely on threshold-based interpretations, which is understandable. In some of the examples presented by Atal et al. (2019), the event recoding required to shift a meta-analysis from significant to non-significant (or vice versa) produced only minimal changes in the effect size estimation. Therefore, in bodies of evidence where meta-analyses are fragile or where results cluster near the null, it may be appropriate to adjust the cutoff. Conducting such analyses-identifying fragility early and adapting thresholds accordingly-could help flag fragile bodies of evidence and prevent future conflicting meta-analyses on the same question, thereby reducing research waste and improving reproducibility.

Weaknesses:

It would be valuable to include additional bodies of conflicting literature in which meta-analyses have demonstrated fragility. This would allow for a more thorough assessment of the consistency of these analytical tools, their differences, and whether this particular body of literature favored one methodology over another. The method proposed by Atal et al. was applied to numerous meta-analyses and demonstrated consistent performance. I believe there is room for improvement, as both the EOI and ROAR appear to be very promising tools for identifying fragility in meta-analytical contexts.

I believe the manuscript should be improved in terms of reporting, with clearer statements of the study's and methods' limitations, and by incorporating additional bodies of evidence to strengthen its claims.

Reviewer #3 (Public review):

Summary and strengths:

In this manuscript, Grimes presents an extension of the Ellipse of Insignificant (EOI) and Region of Attainable Redaction (ROAR) metrics to the 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/adaptation 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.

Reviewer #1 (Public review):

The authors used fluorescence microscopy, image analysis, and mathematical modeling to study the effects of membrane affinity and diffusion rates of MinD monomer and dimer states on MinD gradient formation in B. subtilis. To test these effects, the authors experimentally examined MinD mutants that lock the protein in specific states, including Apo monomer (K16A), ATP-bound monomer (G12V) and ATP-bound dimer (D40A, hydrolysis defective), and compared to wild-type MinD. Overall, the experimental results support the conclusions that reversible membrane binding of MinD is critical for the formation of the MinD gradient, but the binding affinities between monomers and dimers are similar.

The modeling part is a new attempt to use the Monte Carlo method to test the conditions for the formation of the MinD gradient in B. subtilis. The modeling results provide good support for the observations and find that the MinD gradient is sensitive to different diffusion rates between monomers and dimers. This simulation is based on several assumptions and predictions, which raises new questions that need to be addressed experimentally in the future.

Reviewer #3 (Public review):

This important study by Bohorquez et al examines the determinants necessary for concentrating the spatial modulator of cell division, MinD, at the future site of division and the cell poles. Proper localization of MinD is necessary to bring the division inhibitor, MinC, in proximity to the cell membrane and cell poles where it prevents aberrant assembly of the division machinery. In contrast to E. coli, in which MinD oscillates from pole-to-pole courtesy of a third protein MinE, how MinD localization is achieved in B. subtilis-which does not encode a MinE analog-has remained largely a mystery. The authors present compelling data indicating that MinD dimerization is dispensable for membrane localization but required for concentration at the cell poles. Dimerization is also important for interactions between MinD and MinC, leading to the formation of large protein complexes. Computational modeling, specifically a Monte Carlo simulation, supports a model in which differences in diffusion rates between MinD monomers and dimers lead to concentration of MinD at cell poles. Once there, interaction with MinC increases the size of the complex, further reinforcing diffusion differences. Notably, interactions with MinJ-which has previously been implicated in MinCD localization, are dispensable for concentrating MinD at cell poles although MinJ may help stabilize the MinCD complex at those locations.

Comments on revisions:

I believe the authors put respectable effort into revisions and addressing reviewer comments, particularly those that focused on the strengths of the original conclusions. The language in the current version of the manuscript is more precise and the overall product is stronger.

Reviewer #1 (Public review):

Summary:

Outstanding fundamental phenomenon (migrasomes) en route to become transitionally highly significant.

Strengths:

Innovative approach at several levels: Migrasomes, discovered by DR. Yu's group, are an outstanding biological phenomenon of fundamental interest and now of potentially practical value.

Weaknesses:

I feel that the overemphasis on practical aspects (vaccine), however important, eclipses some of the fundamental aspects that may be just as important and actually more interesting. If this can be expanded, the study would be outstanding.

Comments on revisions: This reviewer feels that the authors have addressed all issues.

Reviewer #2 (Public review):

Summary:

The authors report describes a novel vaccine platform derived from a newly discovered organelle called a migrasome. First, the authors address a technical hurdle for using migrasomes as a vaccine platform. Natural migrasome formation occurs at low levels and is labor intensive, however, by understanding the molecular underpinning of migrasome formation, the authors have designed a method to make engineered migrasomes from cultures cells at higher yields utilizing a robust process. These engineered migrasomes behave like natural migrasomes. Next, the authors immunized mice with migrasomes that either expressed a model peptide or the SARS-CoV-2 spike protein. Antibodies against the spike protein were raised that could be boosted by a 2nd vaccination and these antibodies were functional as assessed by an in vitro pseudoviral assay. This new vaccine platform has the potential to overcome obstacles such as cold chain issues for vaccines like messenger RNA that require very stringent storage conditions.

Strengths:

The authors present very robust studies detailing the biology behind migrasome formation and this fundamental understanding was used to from engineered migrasomes, which makes it possible to utilize migrasomes as a vaccine platform. The characterization of engineered migrasomes is thorough and establishes comparability with naturally occurring migrasomes. The biophysical characterization of the migrasomes is well done, including thermal stability and characterization of the particle size (important characterizations for a good vaccine).

Weaknesses:

With a new vaccine platform technology, it would be nice to compare them head-to-head against a proven technology. The authors would improve the manuscript if they made some comparisons to other vaccine platforms such as a SARS-CoV-2 mRNA vaccine or even an adjuvanted recombinant spike protein. This would demonstrate a migrasome based vaccine could elicit responses comparable to a proven vaccine technology. Additionally, understanding the integrity of the antigens expressed in their migrasomes could be useful. This could be done by looking at functional monoclonal antibody binding to their migrasomes in a confocal microscopy experiment.

Updates after revision:

The revised manuscript has additional experiments that I believe improve the strength of evidence presented in the manuscript and address the weaknesses of the first draft. First, they provide a comparison to the antibody responses induced by their migrasome based platform to recombinant protein formulated in an adjuvant and show the response is comparable. Second, they provide evidence that the spike protein incorporated into their migrasomes retains structural integrity by preserving binding to monoclonal antibodies. Together, these results strengthen the paper significantly and support the claims that the novel migrasome based vaccine platform could be a useful in the vaccine development field.

Reviewer #1 (Public review):

Summary

This work performed Raman spectral microscopy for E. coli cells with 15 different culture conditions. The author developed a theoretical framework to construct a regression matrix which predicts proteome composition by Raman data. Specifically, this regression matrix is obtained by statistical inference from various experimental conditions. With this model, the authors categorized co-expressed genes and illustrate how proteome stoichiometry is regulated among different culture conditions. Co-expressed gene clusters were investigated and identified as homeostasis core, carbon-source dependent, and stationary phase dependent genes. Overall, the author demonstrates a strong and comprehensive data analysis scheme for the joint analysis of Raman and proteome datasets.

Strengths and major contributions

Major contributions: (1) Experimentally, the authors contributed Raman datasets of E. coli with various growth conditions. (2) In data analysis, the authors developed a scheme to compare proteome and Raman datasets. Protein co-expression clusters were identified, and their biological meaning were investigated.

Discussion and impact for the field

Raman signature contains both proteomic and metabolomic information and is an orthogonal method to infer the composition biomolecules. This work is a strong initiative for introducing the powerful technique to systems biology and provide a rigorous pipeline for future data analysis. The regression matrix can be used for cross-comparison among future experimental results on proteome-Raman datasets.

Comments on revisions:

The authors addressed all my questions nicely. In particular, the subsampling test demonstrated that with enough "distinct" physiological condition (even for m=5) one could already explore the major mode of proteome regulation and Raman signature. The main text has been streamlined and the clarity is improved. I have a minor suggestion:

(i) For equation (1), it is important to emphasize that the formula works for every j=1,...,15, and the regression matrix B is obtained by statistical inference by summarizing data from all 15 conditions.

Reviewer #1 (Public review):

Summary:

The authors recorded neural activity using laminar probes while mice engaged in a global/local visual oddball paradigm. The focus of the article is on oscillatory activity, and found activity differences in theta, alpha/beta, and gamma bands related to predictability and prediction error.

I think this is an important paper, providing more direct evidence for the role of signals in different frequency bands related to predictability and surprise in the sensory cortex.

Comments:

Below are some comments that may hopefully help further improve the quality of this already very interesting manuscript.

(1) Introduction:

The authors write in their introduction: "H1 further suggests a role for θ oscillations in prediction error processing as well." Without being fleshed out further, it is unclear what role this would be, or why. Could the authors expand this statement?

(2) Limited propagation of gamma band signals:

Some recent work (e.g. https://www.cell.com/cell-reports/fulltext/S2211-1247(23)00503-X) suggests that gamma-band signals reflect mainly entrainment of the fast-spiking interneurons, and don't propagate from V1 to downstream areas. Could the authors connect their findings to these emerging findings, suggesting no role in gamma-band activity in communication outside of the cortical column?

(3) Paradigm:

While I agree that the paradigm tests whether a specific type of temporal prediction can be formed, it is not a type of prediction that one would easily observe in mice, or even humans. The regularity that must be learned, in order to be able to see a reflection of predictability, integrates over 4 stimuli, each shown for 500 ms with a 500 ms blank in between (and a 1000 ms interval separating the 4th stimulus from the 1st stimulus of the next sequence). In other words, the mouse must keep in working memory three stimuli, which partly occurred more than a second ago, in order to correctly predict the fourth stimulus (and signal a 1000 ms interval as evidence for starting a new sequence).

A problem with this paradigm is that positive findings are easier to interpret than negative findings. If mice do not show a modulation to the global oddball, is it because "predictive coding" is the wrong hypothesis, or simply because the authors generated a design that operates outside of the boundary conditions of the theory? I think the latter is more plausible. Even in more complex animals, (eg monkeys or humans), I suspect that participants would have trouble picking up this regularity and sequence, unless it is directly task-relevant (which it is not, in the current setting). Previous experiments often used simple pairs (where transitional probability was varied, eg, Meyer and Olson, PNAS 2012) of stimuli that were presented within an intervening blank period. Clearly, these regularities would be a lot simpler to learn than the highly complex and temporally spread-out regularity used here, facilitating the interpretation of negative findings (especially in early cortical areas, which are known to have relatively small temporal receptive fields).

I am, of course, not asking the authors to redesign their study. I would like to ask them to discuss this caveat more clearly, in the Introduction and Discussion, and situate their design in the broader literature. For example, Jeff Gavornik has used much more rapid stimulus designs and observed clear modulations of spiking activity in early visual regions. I realize that this caveat may be more relevant for the spiking paper (which does not show any spiking activity modulation in V1 by global predictability) than for the current paper, but I still think it is an important general caveat to point out.

(4) Reporting of results:

I did not see any quantification of the strength of evidence of any of the results, beyond a general statement that all reported results pass significance at an alpha=0.01 threshold. It would be informative to know, for all reported results, what exactly the p-value of the significant cluster is; as well as for which performed tests there was no significant difference.

(5) Cluster test:

The authors use a three-dimensional cluster test, clustering across time, frequency, and location/channel. I am wondering how meaningful this analytical approach is. For example, there could be clusters that show an early difference at some location in low frequencies, and then a later difference in a different frequency band at another (adjacent) location. It seems a priori illogical to me to want to cluster across all these dimensions together, given that this kind of clustering does not appear neurophysiologically implausible/not meaningful. Can the authors motivate their choice of three-dimensional clustering, or better, facilitating interpretability, cluster eg at space and time within specific frequency bands (2d clustering)?

Reviewer #2 (Public review):

Summary:

Sennesh and colleagues analyzed LFP data from 6 regions of rodents while they were habituated to a stimulus sequence containing a local oddball (xxxy) and later exposed to either the same (xxxY) or a deviant global oddball (xxxX). Subsequently, they were exposed to a controlled random sequence (XXXY) or a controlled deterministic sequence (xxxx or yyyy). From these, the authors looked for differences in spectral properties (both oscillatory and aperiodic) between three contrasts (only for the last stimulus of the sequence).

(1) Deviance detection: unpredictable random (XXXY) versus predictable habituation (xxxy)

(2) Global oddball: unpredictable global oddball (xxxX) versus predictable deterministic (xxxx), and

(3) "Stimulus-specific adaptation:" locally unpredictable oddball (xxxY) versus predictable deterministic (yyyy).

They found evidence for an increase in gamma (and theta in some cases) for unpredictable versus predictable stimuli, and a reduction in alpha/beta, which they consider evidence towards the "predictive routing" scheme.

While the dataset and analyses are well-suited to test evidence for predictive coding versus alternative hypotheses, I felt that the formulation was ambiguous, and the results were not very clear. My major concerns are as follows:

(1) The authors set up three competing hypotheses, in which H1 and H2 make directly opposite predictions. However, it must be noted that H2 is proposed for spatial prediction, where the predictability is computed from the part of the image outside the RF. This is different from the temporal prediction that is tested here. Evidence in favor of H2 is readily observed when large gratings are presented, for which there is substantially more gamma than in small images. Actually, there are multiple features in the spectral domain that should not be conflated, namely (i) the transient broadband response, which includes all frequencies, (ii) contribution from the evoked response (ERP), which is often in frequencies below 30 Hz, (iii) narrow-band gamma oscillations which are produced by large and continuous stimuli (which happen to be highly predictive), and (iv) sustained low-frequency rhythms in theta and alpha/beta bands which are prominent before stimulus onset and reduce after ~200 ms of stimulus onset. The authors should be careful to incorporate these in their formulation of PC, and in particular should not conflate narrow-band and broadband gamma.

(2) My understanding is that any aspect of predictive coding must be present before the onset of stimulus (expected or unexpected). So, I was surprised to see that the authors have shown the results only after stimulus onset. For all figures, the authors should show results from -500 ms to 500 ms instead of zero to 500 ms.

(3) In many cases, some change is observed in the initial ~100 ms of stimulus onset, especially for the alpha/beta and theta ranges. However, the evoked response contributes substantially in the transient period in these frequencies, and this evoked response could be different for different conditions. The authors should show the evoked responses to confirm the same, and if the claim really is that predictions are carried by genuine "oscillatory" activity, show the results after removing the ERP (as they had done for the CSD analysis).

(4) I was surprised by the statistics used in the plots. Anything that is even slightly positive or negative is turning out to be significant. Perhaps the authors could use a more stringent criterion for multiple comparisons?

(5) Since the design is blocked, there might be changes in global arousal levels. This is particularly important because the more predictive stimuli in the controlled deterministic stimuli were presented towards the end of the session, when the animal is likely less motivated. One idea to check for this is to do the analysis on the 3rd stimulus instead of the 4th? Any general effect of arousal/attention will be reflected in this stimulus.

(6) The authors should also acknowledge/discuss that typical stimulus presentation/attention modulation involves both (i) an increase in broadband power early on and (ii) a reduction in low-frequency alpha/beta power. This could be just a sensory response, without having a role in sending prediction signals per se. So the predictive routing hypothesis should involve testing for signatures of prediction while ruling out other confounds related to stimulus/cognition. It is, of course, very difficult to do so, but at the same time, simply showing a reduction in low-frequency power coupled with an increase in high-frequency power is not sufficient to prove PR.

(7) The CSD results need to be explained better - you should explain on what basis they are being called feedforward/feedback. Was LFP taken from Layer 4 LFP (as was done by van Kerkoerle et al, 2014)? The nice ">" and "<" CSD patterns (Figure 3B and 3F of their paper) in that paper are barely observed in this case, especially for the alpha/beta range.

(8) Figure 4a-c, I don't see a reduction in the broadband signal in a compared to b in the initial segment. Maybe change the clim to make this clearer?

(9) Figure 5 - please show the same for all three frequency ranges, show all bars (including the non-significant ones), and indicate the significance (p-values or by *, **, ***, etc) as done usually for bar plots.

(10) Their claim of alpha/beta oscillations being suppressed for unpredictable conditions is not as evident. A figure akin to Figure 5 would be helpful to see if this assertion holds.

(11) To investigate the prediction and violation or confirmation of expectation, it would help to look at both the baseline and stimulus periods in the analyses.

Reviewer #3 (Public review):

Summary:

In their manuscript entitled "Ubiquitous predictive processing in the spectral domain of sensory cortex", Sennesh and colleagues perform spectral analysis across multiple layers and areas in the visual system of mice. Their results are timely and interesting as they provide a complement to a study from the same lab focussed on firing rates, instead of oscillations. Together, the present study argues for a hypothesis called predictive routing, which argues that non-predictable stimuli are gated by Gamma oscillations, while alpha/beta oscillations are related to predictions.

Strengths:

(1) The study contains a clear introduction, which provides a clear contrast between a number of relevant theories in the field, including their hypotheses in relation to the present data set.

(2) The study provides a systematic analysis across multiple areas and layers of the visual cortex.

Weaknesses:

(1) It is claimed in the abstract that the present study supports predictive routing over predictive coding; however, this claim is nowhere in the manuscript directly substantiated. Not even the differences are clearly laid out, much less tested explicitly. While this might be obvious to the authors, it remains completely opaque to the reader, e.g., as it is also not part of the different hypotheses addressed. I guess this result is meant in contrast to reference 17, by some of the same authors, which argues against predictive coding, while the present work finds differences in the results, which they relate to spectral vs firing rate analysis (although without direct comparison).

(2) Most of the claims about a direction of propagation of certain frequency-related activities (made in the context of Figures 2-4) are - to the eyes of the reviewer - not supported by actual analysis but glimpsed from the pictures, sometimes, with very little evidence/very small time differences to go on. To keep these claims, proper statistical testing should be performed.

(3) Results from different areas are barely presented. While I can see that presenting them in the same format as Figures 2-4 would be quite lengthy, it might be a good idea to contrast the right columns (difference plots) across areas, rather than just the overall averages.

(4) Statistical testing is treated very generally, which can help to improve the readability of the text; however, in the present case, this is a bit extreme, with even obvious tests not reported or not even performed (in particular in Figure 5).

(5) The description of the analysis in the methods is rather short and, to my eye, was missing one of the key descriptions, i.e., how the CSD plots were baselined (which was hinted at in the results, but, as far as I know, not clearly described in the analysis methods). Maybe the authors could section the methods more to point out where this is discussed.

(6) While I appreciate the efforts of the authors to formulate their hypotheses and test them clearly, the text is quite dense at times. Partly this is due to the compared conditions in this paradigm; however, it would help a lot to show a visualization of what is being compared in Figures 2-4, rather than just showing the results.

Reviewer #1 (Public review):

Summary:

This study develops and validates a neural subspace similarity analysis for testing whether neural representations of graph structures generalize across graph size and stimulus sets. The authors show the method works in rat grid and place cell data, finding that grid but not place cells generalize across different environments, as expected. The authors then perform additional analyses and simulations to show that this method should also work on fMRI data. Finally, the authors test their method on fMRI responses from entorhinal cortex (EC) in a task that involves graphs that vary in size (and stimulus set) and statistical structure (hexagonal and community). They find neural representations of stimulus sets in lateral occipital complex (LOC) generalize across statistical structure and that EC activity generalizes across stimulus sets/graph size, but only for the hexagonal structures.

Strengths:

(1) The overall topic is very interesting and timely and the manuscript is well written.

(2) The method is clever and powerful. It could be important for future research testing whether neural representations are aligned across problems with different state manifestations.

(3) The findings provide new insights into generalizable neural representations of abstract task states in entorhinal cortex.

Weaknesses:

(1) There are two design confounds that are not sufficiently discussed.

(1.1) First, hexagonal and community structures are confounded by training order. All subjects learned the hexagonal graph always before the community graph. As such, any differences between the two graphs could be explained (in theory) by order effects (although this is unlikely). However, because community and hexagonal structures shared the same stimuli, it is possible that subjects had to find ways to represent the community structures separately from the hexagonal structures. This could potentially explain why there was no generalization across graph size for community structures.

(1.2) Second, subjects had more experience with the hexagonal and community structures before and during fMRI scanning. This is another possible reason why there was no generalization for the community structure.

(2) The authors include the results from a searchlight analysis to show specificity of the effects for EC. A more convincing way (in my opinion) to show specificity would be to test for (and report the results) of a double dissociation between the visual and structural contrast in two independently defined regions (e.g., anatomical ROIs of LOC and EC). This would substantiate the point that EC activity generalizes across structural similarity while sensory regions like LOC generalize across visual similarity.

Reviewer #2 (Public review):

Summary:

Mark and colleagues test the hypothesis that entorhinal cortical representations may contain abstract structural information that facilitates generalization across structurally similar contexts. To do so, they use a method called "subspace generalization" designed to measure abstraction of representations across different settings. The authors validate the method using hippocampal place cells and entorhinal grid cells recorded in a spatial task, then show perform simulations that support that it might be useful in aggregated responses such as those measured with fMRI. Then the method is applied to an fMRI data that required participants to learn relationships between images in one of two structural motifs (hexagonal grids versus community structure). They show that the BOLD signal within an entorhinal ROI shows increased measures of subspace generalization across different tasks with the same hexagonal structure (as compared to tasks with different structures) but that there was not evidence for the complementary result (ie. increased generalization across tasks that share community structure, as compared to those with different structures). Taken together, this manuscript describes and validates a method for identifying fMRI representations that generalize across conditions and applies it to reveal that entorhinal representations that emerge across specific shared structural conditions.

Strengths:

I found this paper interesting both in terms of its methods and its motivating questions. The question asked is novel and the methods employed are new - and I believe this is the first time that they have been applied to fMRI data. I also found the iterative validation of the methodology to be interesting and important - showing persuasively that the method could detect a target representation - even in the face of random combination of tuning and with the addition of noise, both being major hurdles to investigating representations using fMRI.

Weaknesses:

The primary weakness of the paper in terms of empirical results is that the representations identified in EC had no clear relationship to behavior, raising questions about their functional importance.

The method developed is a clearly valuable tool that can serve as part of a larger battery of analysis techniques, but a small weakness on the methodological side is that for a given dataset, it might be hard to determine whether the method developed here would be better or worse than alternative methods.

Reviewer #3 (Public review):

Summary:

The article explores the brain's ability to generalize information, with a specific focus on the entorhinal cortex (EC) and its role in learning and representing structural regularities that define relationships between entities in networks. The research provides empirical support for the longstanding theoretical and computational neuroscience hypothesis that the EC is crucial for structure generalization. It demonstrates that EC codes can generalize across non-spatial tasks that share common structural regularities, regardless of the similarity of sensory stimuli and network size.

Strengths:

At first glance, a potential limitation of this study appears to be its application of analytical methods originally developed for high-resolution animal electrophysiology (Samborska et al., 2022) to the relatively coarse and noisy signals of human fMRI. Rather than sidestepping this issue, however, the authors embrace it as a methodological challenge. They provide compelling empirical evidence and biologically grounded simulations to show that key generalization properties of entorhinal cortex representations can still be robustly detected. This not only validates their approach but also demonstrates how far non-invasive human neuroimaging can be pushed. The use of multiple independent datasets and carefully controlled permutation tests further underscores the reliability of their findings, making a strong case that structural generalization across diverse task environments can be meaningfully studied even in abstract, non-spatial domains that are otherwise difficult to investigate in animal models.

Weaknesses:

While this study provides compelling evidence for structural generalization in the entorhinal cortex (EC), several limitations remain that pave the way for promising future research. One issue is that the generalization effect was statistically robust in only one task condition, with weaker effects observed in the "community" condition. This raises the question of whether the null result genuinely reflects a lack of EC involvement, or whether it might be attributable to other factors such as task complexity, training order, or insufficient exposure possibilities that the authors acknowledge as open questions. Moreover, although the study leverages fMRI to examine EC representations in humans, it does not clarify which specific components of EC coding-such as grid cells versus other spatially tuned but non-grid codes-underlie the observed generalization. While electrophysiological data in animals have begun to address this, the human experiments do not disentangle the contributions of these different coding types. This leaves unresolved the important question of what makes EC representations uniquely suited for generalization, particularly given that similar effects were not observed in other regions known to contain grid cells, such as the medial prefrontal cortex (mPFC) or posterior cingulate cortex (PCC). These limitations point to important future directions for better characterizing the computational role of the EC and its distinctiveness within the broader network supporting learning and decision making based on cognitive maps.

Reviewer #1 (Public review):

The authors present exciting new experimental data on the antigenic recognition of 78 H3N2 strains (from the beginning of the 2023 Northern Hemisphere season) against a set of 150 serum samples. The authors compare protection profiles of individual sera and find that the antigenic effect of amino acid substitutions at specific sites depends on the immune class of the sera, differentiating between children and adults. Person-to-person heterogeneity in the measured titers is strong, specifically in the group of children's sera. The authors find that the fraction of sera with low titers correlates with the inferred growth rate using maximum likelihood regression (MLR), a correlation that does not hold for pooled sera. The authors then measure the protection profile of the sera against historical vaccine strains and find that it can be explained by birth cohort for children. Finally, the authors present data comparing pre- and post- vaccination protection profiles for 39 (USA) and 8 (Australia) adults. The data shows a cohort-specific vaccination effect as measured by the average titer increase, and also a virus-specific vaccination effect for the historical vaccine strains. The generated data is shared by the authors and they also note that these methods can be applied to inform the bi-annual vaccine composition meetings, which could be highly valuable.

Thanks to the authors for the revised version of the manuscript. A few concerns remain after the revision:

(1) We appreciate the additional computational analysis the authors have performed on normalizing the titers with the geometric mean titer for each individual, as shown in the new Supplemental Figure 6. We agree with the authors statement that, after averaging again within specific age groups, "there are no obvious age group-specific patterns." A discussion of this should be added to the revised manuscript, for example in the section "Pooled sera fail to capture the heterogeneity of individual sera," referring to the new Supplemental Figure 6.

However, we also suggested that after this normalization, patterns might emerge that are not necessarily defined by birth cohort. This possibility remains unexplored and could provide an interesting addition to support potential effects of substitutions at sites 145 and 275/276 in individuals with specific titer profiles, which as stated above do not necessarily follow birth cohort patterns.

(2) Thank you for elaborating further on the method used to estimate growth rates in your reply to the reviewers. To clarify: the reason that we infer from Fig. 5a that A/Massachusetts has a higher fitness than A/Sydney is not because it reaches a higher maximum frequency, but because it seems to have a higher slope. The discrepancy between this plot and the MLR inferred fitness could be clarified by plotting the frequency trajectories on a log-scale.

For the MLR, we understand that the initial frequency matters in assessing a variant's growth. However, when starting points of two clades differ in time (i.e., in different contexts of competing clades), this affects comparability, particularly between A/Massachusetts and A/Ontario, as well as for other strains. We still think that mentioning these time-dependent effects, which are not captured by the MLR analysis, would be appropriate. To support this, it could be helpful to include the MLR fits as an appendix figure, showing the different starting and/or time points used.

(3) Regarding my previous suggestion to test an older vaccine strain than A/Texas/50/2012 to assess whether the observed peak in titer measurements is virus-specific: We understand that the authors want to focus the scope of this paper on the relative fitness of contemporary strains, and that this additional experimental effort would go beyond the main objectives outlined in this manuscript. However, the authors explicitly note that "Adults across age groups also have their highest titers to the oldest vaccine strain tested, consistent with the fact that these adults were first imprinted by exposure to an older strain." This statement gives the impression that imprinting effects increase titers for older strains, whereas this does not seem to be true from their results, but only true for A/Texas. It should be modified accordingly.

Reviewer #2 (Public review):

This is an excellent paper. The ability to measure the immune response to multiple viruses in parallel is a major advancement for the field, that will be relevant across pathogens (assuming the assay can be appropriately adapted). I only had a few comments, focused on maximising the information provided by the sera. These concerns were all addressed in the revised paper.

Reviewer #3 (Public review):

The authors use high throughput neutralisation data to explore how different summary statistics for population immune responses relate to strain success, as measured by growth rate during the 2023 season. The question of how serological measurements relate to epidemic growth is an important one, and I thought the authors present a thoughtful analysis tackling this question, with some clear figures. In particular, they found that stratifying the population based on the magnitude of their antibody titres correlates more with strain growth than using measurements derived from pooled serum data. The updated manuscript has a stronger motivation, and there is substantial potential to build on this work in future research.

Comments on revisions:

I have no additional recommendations. There are several areas where the work could be further developed, which were not addressed in detail in the responses, but given this is a strong manuscript as it stands, it is fine that these aspects are for consideration only at this point.

Reviewer #1 (Public review):

Summary:

This study provides evidence that neuropeptide signaling, particularly via the CRH-CRHBP pathway, plays a key role in regulating the precision of vocal motor output in songbirds. By integrating gene expression profiling with targeted manipulations in the song vocal motor nucleus RA, the authors demonstrate that altering CRH and CRHBP levels bidirectionally modulate song variability. These findings reveal a previously unrecognized neuropeptidergic mechanism underlying motor performance control, supported by molecular and functional evidence.

Strengths:

Neural circuit mechanisms underlying motor variability have been intensively studied, yet the molecular bases of such variability remain poorly understood. The authors address this important gap using the songbird (Bengalese finch) as a model system for motor learning, providing experimental evidence that neuropeptide signaling contributes to vocal motor variability. They comprehensively characterize the expression patterns of neuropeptide-related genes in brain regions involved in song vocal learning and production, revealing distinct regulatory profiles compared to non-vocal related regions, as well as developmental, revealing distinct regulatory profiles compared to non-vocal regions, as well as developmental and behavioral dependencies, including altered expression following deafening and correlations with singing activity over the two days preceding sampling. Through these multi-level analyses spanning anatomy, development, and behavior, the authors identify the CRH-CRHBP pathway in the vocal motor nucleus RA as a candidate regulator of song variability. Functional manipulations further demonstrate that modulation of this pathway bidirectionally alters song variability.

Overall, this work represents an effective use of songbirds, though a well-established neuroethological framework uncovers how previously uncharacterized molecular pathways shape behavioral output at the individual level.

Weaknesses:

(1) This study uses Bengalese finches (BFs) for all experiments-bulk RNA-seq, in situ hybridization across developmental stages, deafening, gene manipulation, and CRH microinfusion-except for the sc/snRNA-seq analysis. BFs differ from zebra finches (ZFs) in several important ways, including faster song degradation after deafening and greater syllable sequence complexity. This study makes effective use of these unique BF characteristics and should be commended for doing so.

However, the major concern lies in the use of the single-cell/single-nucleus RNA-seq dataset from Colquitt et al. (2021), which combines data from both ZFs and BFs for cell-type classification. Based on our reanalysis of the publicly available dataset used in both Colquitt et al. (2021) and the present study, my lab identified two major issues:

(a) The first concern is that the quality of the single-cell RNA-seq data from BFs is extremely poor, and the number of BF-derived cells is very limited. In other words, most of the gene expression information at the single-cell (or "subcellular type") level in this study likely reflects ZF rather than BF profiles. In our verification of the authors' publicly annotated data, we found that in the song nucleus RA, only about 18 glutamatergic cells (2.3%) of a total of 787 RA_Glut (RA_Glut1+2+3) cells were derived from BFs. Similarly, in HVC, only 53 cells (4.1%) out of 1,278 Glut1+Glut4 cells were BF-derived. This clearly indicates that the cell-subtype-level expression data discussed in this study are predominantly based on ZF, not BF, expression profiles.

Recent studies have begun to report interspecies differences in the expression of many genes in the song control nuclei. It is therefore highly plausible that the expression patterns of CRHBP and other neuropeptide-signaling-related genes differ between ZFs and BFs. Yet, the current study does not appear to take this potential species difference into account. As a result, analyses such as the CellChat results (Fig. 2F and G) and the model proposed in Fig. 6G are based on ZF-derived transcriptomic information, even though the rest of the experimental data are derived from BF, which raises a critical methodological inconsistency.

(b) The second major concern involves the definition of "subcellular types" in the sc/snRNA-seq dataset. Specifically, the RA_Glut1, 2, and 3 and HVC_Glu1 and 4 clusters-classified as glutamatergic projection neuron subtypes-may in fact represent inter-individual variation within the same cell type rather than true subtypes. Following Colquitt et al. (2021), Toji et al. (PNAS, 2024) demonstrated clear individual differences in the gene expression profiles of glutamatergic projection neurons in RA.

In our reanalysis of the same dataset, we also observed multiple clusters representing the same glutamatergic projection neurons in UMAP space. This likely occurs because Seurat integration (anchor-based mutual nearest neighbor integration) was not applied, and because cells were not classified based on individual SNP information using tools such as Souporcell. When classified by individual SNPs, we confirmed that the RA_Glut1-3 and HVC_Glu1 and 4 clusters correspond simply to cells from different individuals rather than distinct subcellular types. (Although images cannot be attached in this review system, we can provide our analysis results if necessary.)

This distinction is crucial, as subsequent analyses and interpretations throughout the manuscript depend on this classification. In particular, Figure 6G presents a model based on this questionable subcellular classification. Similarly, the ligand-receptor relationships shown in Figure 2G - such as the absence of SST-SSTR1 signaling in RA_Glut3 but its presence in RA_Glut1 and 2-are more plausibly explained by inter-individual variation rather than subcellular-type specificity.

Whether these differences are interpreted as individual variation within a single cell type or as differences in projection targets among glutamatergic neurons has major implications for understanding the biological meaning of neuropeptide-related gene expression in this system.

(2) Based on the important finding that "CRHBP expression in the song motor pathway is correlated with singing," it is necessary to provide data showing that the observed changes in CRHBP and other neuropeptide-related gene expression during the song learning period or after deafening are not merely due to differences in singing amount over the two days preceding brain sampling.

Without such data, the following statement cannot be justified: "Regarding CRHBP expression in the song motor pathway increases during song acquisition and decreases following deafening."

(3) In Figure 5B, the authors should clearly distinguish between intact and deafened birds and show the singing amount for each group. In practice, deafening often leads to a reduction in both the number of song bouts and the total singing time. If, in this experiment, deafened birds also exhibited reduced singing compared to intact birds, then the decreased CRHBP expression observed in HVC and RA (Figures 3 and 4) may not reflect song deterioration, but rather a simple reduction in singing activity.

As a similar viewpoint, the authors report that CRHBP expression levels in RA and HVC increase with age during the song learning period. However, this change may not be directly related to age or the decline in vocal plasticity. Instead, it could correlate with the singing amount during the one to two days preceding brain sampling. The authors should provide data on the singing activity of the birds used for in situ hybridization during the two days prior to sampling.

Reviewer #2 (Public review):

Summary:

The results presented here are a useful extension of two of their previous papers (Colquitt et al 2021, Colquitt et al 2023), where they used single-cell transcriptomics to characterize the inhibitory and excitatory cell types and gene expression patterns of the song circuit, comparing them to mammalian and reptilian brains, and characterized the effect of deafening on these gene expression patterns. In this paper, they focus on the differential expression of various neuropeptidergic systems in the songbird brain. They discover a role for the CRHBP gene in song performance and causally show its influence on song variability.

Strengths:

The authors leverage the advantages of the 'nucleated' structure of the songbird neural circuitry and use a robust approach to compare neuropeptidergic gene expression patterns in these circuits. Their analysis of the expression patterns of the CRHBP gene in different cell types supports their conclusion that interneurons are particularly amenable to this modulation. Their use of a knockdown strategy along with pharmacological manipulation provides strong support for a causal role of neuropeptidergic modulation on song behaviour. These results have important implications as they bring into focus neuropeptide modulation of the song-motor circuit and pave the way for future studies focussing on how this signalling pathway regulates plasticity during song learning and maintenance.

Weaknesses:

While the results demonstrating the bidirectional modulation of CRH and CRHBP on song performance shed light on their role in song plasticity, it would be important to show this in juvenile finches during sensorimotor learning. We also don't get a clear picture of the 'causal' role of this signalling pathway on the song pre-motor area, HVC, as the knockdown and pharmacological manipulation studies were done in RA, whereas we see a modulation of CRHBP expression during deafening and song learning in both RA and HVC. Given the role of interneurons in the HVC in song acquisition (e.g., Vallentin et al. 2016, Science), it would have been interesting to see the results of HVC-specific manipulation of this neuropeptidergic pathway and/or how it affects the song learning process. Perhaps a short discussion of this would help to give the readers some perspective. Finally, a more direct demonstration of the neurophysiological effect of the signalling pathway would also strengthen our understanding of precisely how these modulate the song circuit plasticity, which I understand might be beyond the scope of this study.

Technical/minor:

In the Methods section, several clarifications would be beneficial. For instance, the description of the design matrices would benefit from being presented in a more general statistical form (e.g., linear model equations) rather than using R syntax. This would make the modeling approach more accessible to readers unfamiliar with software-specific syntax. In addition, while some variables (e.g., cdr_scale, frac_mito_scale) are briefly defined, others (e.g., tags, cut3,nsongs_last_two_days_cut3) could be more clearly described. This applies to the descriptions of both the gene set enrichment analysis and the neuropeptide-receptor analysis, which rely heavily on package-specific terminology (e.g., fgseaMultilevel, computeCommunProb), making it difficult for readers to understand the conceptual or statistical basis of the analyses. It would improve clarity if the authors provided a complete list of variable definitions, types (categorical or continuous), and any scaling/transformations applied would enhance clarity and reproducibility.

Reviewer #3 (Public review):

Summary:

The stable production of learned vocalizations like human language and birdsong requires auditory feedback. What happens in the brain areas that generate stable vocalizations as performance deteriorates is not well understood. Using a species of songbird, the current study investigates individual cells within the evolutionarily-conserved brain regions that generate learned vocalizations to describe that the complement of neuropeptide (short proteins) signals may be a key feature of behavioral change. Because neuropeptides are important across species, these findings may help explain diminishing stability in learned behaviors even in humans.

Strengths:

The experiments are solid and follow a strong progression from description through manipulation. The songbird model is appropriate and powerful to inform on generalizable biological mechanisms of precisely learned behaviors, including human speech.

Weaknesses:

While it is always possible to perform more experiments, most of the weaknesses are in the presentation of the project, not in the evidence or analysis, which are leading-edge and appropriate. Generally, the ability to follow the findings and to independently assess rigor would be enhanced with increased explicit mention of the statistical thresholds and subjective descriptions. In addition, two prior pieces of relevant work seem to be omitted, including one performing deafening, gene expression measures, and behavioral assessment in zebra finches, and another describing neuropeptide complements in zebra finch singing nuclei based largely on mass spectrometry. The former in particular should be related to the current findings.

Reviewer #1 (Public review):

Summary:

The authors aim to investigate the mechanisms underlying Kupffer cell death in metabolic-associated steatotic liver disease (MASLD). The authors propose that KCs undergo massive cell death in MASLD and that glycolysis drives this process. However, there appears to be a discrepancy between the reported high rates of KC death and the apparent maintenance of KC homeostasis and replacement capacity.

Strengths:

This is an in vivo study.

Weaknesses:

There are discrepancies between the authors' observations and previous reports, as well as inconsistencies among their own findings.

Before presenting the percentage of CLEC4F⁺TUNEL⁺ cells, the authors should have first shown the number of CLEC4F⁺ cells per unit area in Figure 1. At 16 weeks of age, the proportion of TUNEL⁺ KCs is extremely high (~60%), yet the flow cytometry data indicate that nearly all F4/80⁺ KCs are TIMD4⁺, suggesting an embryonic origin. If such extensive KC death occurred, the proportion of embryonically derived TIMD4⁺ KCs would be expected to decrease substantially. Surprisingly, the proportion of TIMD4⁺ KCs is comparable between chow-fed and 16-week HFHC-fed animals. Thus, the immunostaining and flow cytometry data are inconsistent, making it difficult to explain how massive KC death does not lead to their replacement by monocyte-derived cells.

These data suggest that despite the reported high rate of cell death among CLEC4F⁺TIMD4⁺ KCs, the population appears to self-maintain, with no evidence of monocyte-derived KC generation in this model, which contradicts several recent studies in the field.

Moreover, there is no evidence that TIMD4⁺CLEC4F⁺ KCs increase their proliferation rate to compensate for such extensive cell death. If approximately 60% of KCs are dying and no monocyte-derived KCs are recruited, one would expect a much greater decrease in total KC numbers than what is reported.

It is also unexpected that the maximal rate of KC death occurs at early time points (8 weeks), when the mice have not yet gained substantial weight (Figure 1B). Previous studies have shown that longer feeding periods are typically required to observe the loss of embryo-derived KCs.

Furthermore, it is surprising that the HFD induces as much KC death as the HFHC and MCD diets. Earlier studies suggested that HFD alone is far less effective than MASH-inducing diets at promoting the replacement of embryonic KCs by monocyte-derived macrophages.

In Figure 2D, TIMD4 staining appears extremely faint, making the results difficult to interpret. In contrast, the TUNEL signal is strikingly intense and encompasses a large proportion of liver cells (approximately 60% of KCs, 15% of hepatocytes, 20% of hepatic stellate cells, 30% of non-KC macrophages, and a proportion of endothelial cells is also likely affected). This pattern closely resembles that typically observed in mouse models of acute liver failure. Given this apparent extent of cell death, it is unexpected that ALT and AST levels remain low in MASH mice, which is highly unusual.

No statistical analysis is provided for Figure 5D, and it is unclear which metabolites show statistically significant changes in Figure 5C.

In addition, there is no evaluation of liver pathology in Clec4f-Cre × Chil1flox/flox mice. It remains possible that the observed effects on KC death result from aggravated liver injury in these animals. There is also no evidence that Chil1 deficiency affects glucose metabolism in KCs in vivo.

Finally, the authors should include a more direct experimental approach to modulate glycolysis in KCs and assess its causal role in KC death in MASH.

Reviewer #2 (Public review):

Summary:

In this manuscript, He et al. set out to investigate the mechanisms behind Kupffer Cell death in MASLD. As has been previously shown, they demonstrate a loss of resident KCs in MASLD in different mouse models. They then go on to show that this correlates with alterations in genes/metabolites associated with glucose metabolism in KCs. To investigate the role of glucose metabolism further, they subject isolated KCs in vitro to different metabolic treatments and assess cleaved caspase 3 staining, demonstrating that KCs show increased Cl. Casp 3 staining upon stimulation of glycolysis. Finally, they use a genetic mouse model (Chil1KO) where they have previously reported that loss of this gene leads to increased glycolysis and validate this finding in BMDMs (KO). They then remove this gene specifically from KCs (Clec4fCre) and show that this leads to increased macrophage death compared with controls.

Strengths:

As we do not yet understand why KCs die in MASLD, this manuscript provides some explanation for this finding. The metabolomics is novel and provides insight into KC biology. It could also lead to further investigation; here, it will be important that the full dataset is made available.

Weaknesses:

Different diets are known to induce different amounts of KC loss, yet here, all models examined appear to result in 60% KC death. One small field of view of liver tissue is shown as representative to make these claims, but this is not sufficient, as anything can be claimed based on one field of view. Rather, a full tissue slice should be included to allow readers to really assess the level of death. Additionally, there is no consistency between the markers used to define KCs and moMFs, with CLEC4F being used in microscopy, TIM4 in flow, while the authors themselves acknowledge that moKCs are CLEC4F+TIM4-. As moKCs are induced in MASLD, this limits interpretation. Additionally, Iba1 is referred to as a moMF marker but is also expressed by KCs, which again prevents an accurate interpretation of the data. Indeed, the authors show 60% of KCs are dying but only 30% of IBA1+ moMFs, as KCs are also IBA1+, this would mean that KCs die much more than moMFs, which would then limit the relevance of the BMDM studies performed if the phenotype is KC specific. Therefore, this needs to be clarified. The claim that periportal KCs die preferentially is not supported, given that the majority of KCs are peri-portal. Rather, these results would need to be normalised to KC numbers in PP vs PC regions to make meaningful conclusions. Additionally, KCs are known to be notoriously difficult to keep alive in vitro, and for these studies, the authors only examine cl. Casp 3 staining. To fully understand that data, a full analysis of the viability of the cells and whether they retain the KC phenotype in all conditions is required. Finally, in the Cre-driven KO model, there does not seem to be any death of KCs in the controls (rather numbers trend towards an increase with time on diet, Figure 6E), contrary to what had been claimed in the rest of the paper, again making it difficult to interpret the overall results. Additionally, there is no validation that the increased death observed in vivo in KCs is due to further promotion of glycolysis.

Reviewer #3 (Public review):

This manuscript provides novel insights into altered glucose metabolism and KC status during early MASLD. The authors propose that hyperactivated glycolysis drives a spatially patterned KC depletion that is more pronounced than the loss of hepatocytes or hepatic stellate cells. This concept significantly enhances our understanding of early MASLD progression and KC metabolic phenotype.

Through a combination of TUNEL staining and MS-based metabolomic analyses of KCs from HFHC-fed mice, the authors show increased KC apoptosis alongside dysregulation of glycolysis and the pentose phosphate pathway. Using in vitro culture systems and KC-specific ablation of Chil1, a regulator of glycolytic flux, they further show that elevated glycolysis can promote KC apoptosis.

However, it remains unclear whether the observed metabolic dysregulation directly causes KC death or whether secondary factors, such as low-grade inflammation or macrophage activation, also contribute significantly. Nonetheless, the results, particularly those derived from the Chil1-ablated model, point to a new potential target for the early prevention of KC death during MASLD progression.

The manuscript is clearly written and thoughtfully addresses key limitations in the field, especially the focus on glycolytic intermediates rather than fatty acid oxidation. The authors acknowledge the missing mechanistic link between increased glycolysis and KC death. Still, several interpretations require moderation to avoid overstatement, and certain experimental details, particularly those concerning flow cytometry and population gating, need further clarification.

Strengths:

(1) The study presents the novel observation of profound metabolic dysregulation in KCs during early MASLD and identifies these cells as undergoing apoptosis. The finding that Chil1 ablation aggravates this phenotype opens new avenues for exploring therapeutic strategies to mitigate or reverse MASLD progression.

(2) The authors provide a comprehensive metabolic profile of KCs following HFHC diet exposure, including quantification of individual metabolites. They further delineate alterations in glycolysis and the pentose phosphate pathway in Chil1-deficient cells, substantiating enhanced glycolytic flux through 13C-glucose tracing experiments.

(3) The data underscore the critical importance of maintaining balanced glucose metabolism in both in vitro and in vivo contexts to prevent KC apoptosis, emphasizing the high metabolic specialization of these cells.

(4) The observed increase in KC death in Chil1-deficient KCs demonstrates their dependence on tightly regulated glycolysis, particularly under pathological conditions such as early MASLD.

Weaknesses:

(1) The novelty is questionable. The presented work has considerable overlap with a study by the same lab, which is currently under review (citation 17), and it should be considered whether the data should not be presented in one paper.

(2) The authors report that 60% of KCs are TUNEL-positive after 16 weeks of HFHC diet and confirm this by cleaved caspase-3 staining. Given that such marker positivity typically indicates imminent cell death within hours, it is unexpected that more extensive KC depletion or monocyte infiltration is not observed. Since Timd4 expression on monocyte-derived macrophages takes roughly one month to establish, the authors should consider whether these TUNEL-positive KCs persist in a pre-apoptotic state longer than anticipated. Alternatively, fate-mapping experiments could clarify the dynamics of KC death and replacement.

(3) The mechanistic link between elevated glycolytic flux and KC death remains unclear.

(4) The study does not address the polarization or ontogeny of KCs during early MASLD. Given that pro-inflammatory macrophages preferentially utilize glycolysis, such data could provide valuable insight into the reason for increased KC death beyond the presented hyperreliance on glycolysis.

(5) The gating strategy for monocyte-derived macrophages (moMFs) appears suboptimal and may include monocytes. A more rigorous characterization of myeloid populations by including additional markers would strengthen the study's conclusions.

(6) While BMDMs from Chil1 knockout mice are used to demonstrate enhanced glycolytic flux, it remains unclear whether Chil1 deficiency affects macrophage differentiation itself.

(7) The authors use the PDK activator PS48 and the ATP synthase inhibitor oligomycin to argue that increased glycolytic flux at the expense of OXPHOS promotes KC death. However, given the high energy demands of KCs and the fact that OXPHOS yields 15-16 times more ATP per glucose molecule than glycolysis, the increased apoptosis observed in Figure 4C-F could primarily reflect energy deprivation rather than a glycolysis-specific mechanism.

(8) In Figure 1C, KC numbers are significantly reduced after 4 and 16 weeks of HFHC diet in WT male mice, yet no comparable reduction is seen in Clec4Cre control mice, which should theoretically exhibit similar behavior under identical conditions.

Reviewer #1 (Public review):

Summary:

This study addresses the emerging role of fungal pathogens in colorectal cancer and provides mechanistic insights into how Candida albicans may influence tumor-promoting pathways. While the work is potentially impactful and the experiments are carefully executed, the strength of evidence is limited by reliance on in vitro models, small patient sample size, and the absence of in vivo validation, which reduces the translational significance of the findings.

Strengths:

(1) Comprehensive mechanistic dissection of intracellular signaling pathways.

(2) Broad use of pharmacological inhibitors and cell line models.

(3) Inclusion of patient-derived organoids, which increases relevance to human disease.

(4) Focus on an emerging and underexplored aspect of the tumor microenvironment, namely fungal pathogens.

Weaknesses:

(1) Clinical association data are inconsistent and based on very small sample numbers.

(2) No in vivo validation, which limits the translational significance.

(3) Species- and cell type-specificity claims are not well supported by the presented controls.

(4) Reliance on colorectal cancer cell lines alone makes it difficult to judge whether findings are specific or general epithelial responses.

Reviewer #2 (Public review):

The authors in this manuscript studied the role of Candida albicans in Colorectal cancer progression. The authors have undertaken a thorough investigation and used several methods to investigate the role of Candida albicans in Colorectal cancer progression. The topic is highly relevant, given the increasing burden of colon cancer globally and the urgent need for innovative treatment options.

However, there are some inconsistencies in the figures and some missing details in the figures, including:

(1) The authors should clearly explain in the results section which patient samples are shown in Figure 1B.

(2) What do a, ab, b, b written above the bars in Figure 1F represent? Maybe authors should consider removing them, because they create confusion. Also, there is no explanation for those letters in the figure legend.

(3) The authors should submit all the raw images of Western blot with appropriate labels to indicate the bands of protein of interest along with molecular weight markers.

(4) The authors should do the quantification of data in Figure 2d and include it in the figure.

(5) In Figure 2h, the authors should indicate if the quantification represents VEGF expression after 6h or 12h of C. albicans co-culture with cells.

(6) In Figure 2i, quantification of VEGF should be done and data from three independent experiments should be submitted. The authors should also mention the time point.

Reviewer #1 (Public review):

This study presents an exploration of PPGL tumour bulk transcriptomics and identifies three clusters of samples (labeled as subtypes C1-C3). Each subtype is then investigated for the presence of somatic mutations, metabolism-associated pathway and inflammation correlates, and disease progression.

The proposed subtype descriptions are presented as an exploratory study. The proposed potential biomarkers from this subtype are suitably caveated and will require further validation in PPGL cohorts together with mechanistic study.

The first section uses WGCNA (a method to identify clusters of samples based on gene expression correlations) to discover three transcriptome-based clusters of PPGL tumours using a new cohort of n=87 PPGL samples from various locations in the body.

The second section inspects a previously published snRNAseq dataset, assigning the published samples to subtypes C1-C3 using a pseudo-bulk approach.

The tumour samples are obtained from multiple locations in the body, summarised in Fig1A. It will be important to see further investigation of how the sample origin is distributed among the C1-C3 clusters, and whether there is a sample-origin association with mutational drivers and disease progression.

Comments on revisions:

In SupplFile3 (pdf) - please correct the table format. The contents are obscured due to the narrowness of the table columns.

Deposit the new RNAseq data (N=87 cases, N=5 controls) in an appropriate repository; see "Data on human genotypes and phenotypes" at https://elife-rp.msubmit.net/html/elife-rp_author_instructions.html#dataavailability

Reviewer #2 (Public review):

Summary:

A study that furthers the molecular definition of PPGL (where prognosis is variable) and provides a wide range of sub-experiments to back up the findings. One of the key premises of the study is that identification of driver mutations in PPGL is incomplete and that compromises characterisation for prognostic purposes. This is a reasonable starting point on which to base some characterisation based on different methods.

Strengths:

The cohort is a reasonable size, and a useful validation cohort in the form of TCGA is used. Whilst it would be resource-intensive (though plausible given the rarity of the tumour type) to perform RNAseq on all PPGL samples in clinical practice, some potential proxies are proposed.

Weaknesses:

Performance of some of the proxy markers for transcriptional subtype is not presented.

Limited prognostic information available.

Comments on revisions:

Having reviewed the responses to my comments and associated revisions, I am satisfied that they have been addressed.

Reviewer #1 (Public review):

This paper examines how geometric regularities in abstract shapes (e.g., parallelograms, kites) are perceived and processed in the human brain. The manuscript contains multimodal data (behavior, fMRI, MEG) from adults and additional fMRI data from 6-year-old children. The key findings show that (1) processing geometric shapes lead to reduced activity in ventral areas in comparison to complex stimuli and increased activity in intraparietal and inferior temporal regions, (2) the degree of geometric regularity modulates activity in intraparietal and inferior temporal regions, (3) similarity in neural representation of geometric shapes can be captured early by using CNN models and later by models of geometric regularity. In addition to these novel findings, the paper also includes a replication of behavioral data, showing that the perceptual similarity structure amongst the geometric stimuli used can be explained by a combination of visual similarities (as indexed by feedforward CNN model of ventral visual pathway) and geometric features. The paper comes with openly accessible code in a well-documented GitHub repository and the data will be published with the paper on OpenNeuro.

In the revised version of this manuscript, the authors clarified certain aspects of the task design, added critical detail to the description of the methods, and updated the figures to show unsmoothed data and variability across participants. Importantly, the authors thoroughly discussed potential task effects (for the fMRI data only) and added additional analyses that indicate that the effects are unlikely to be driven by linguistic labels/name availability of the stimuli.

Comments on the revision:

Thank you for carefully addressing all my concerns and especially for clarifying the task design.

Reviewer #2 (Public review):

Summary

The current study seeks to understand the neural mechanisms underlying geometric reasoning. Using fMRI with both children and adults, the authors found that contrasting simple geometric shapes with naturalistic images (faces, tools, houses) led to responses in the dorsal visual stream, rather than ventral regions that are generally thought to represent shape properties. The author's followed up on this result using computational modeling and MEG to show that geometric properties explain distinct variance in the neural response than what is captured by a CNN.

Strengths

These findings contribute much-needed neural and developmental data to the ongoing debate regarding shape processing in the brain and offer additional insights into why CNNs may have difficulty with shape processing. The motivation and discussion for the study is appropriately measured, and I appreciate the authors' use of multiple populations, neuroimaging modalities, and computational models in explore this question.

Weaknesses

The presence of activation in aIPS led the authors to interpret their results to mean that geometric reasoning draws on the same processes as mathematical thinking. However, there is only weak and indirect evidence in the current study that geometric reasoning, as its tested here, draws on the same circuits as math.

Reviewer #3 (Public review):

Summary:

The authors report converging evidence from behavioral studies as well as several brain-imaging techniques that geometric figures, notably quadrilaterals, are processed differently in visual (lower activation) and spatial (greater) areas of the human brain than representative figures. Comparison of mathematical models to fit activity for geometric figures shows the best fit for abstract geometric features like parallelism and symmetry. The brain areas active for geometric figures are also active in processing mathematical concepts even in blind mathematicians, linking geometric shapes to abstract math concepts. The effects are stronger in adults than in 6-year-old Western children. Similar phenomena do not appear in great apes, suggesting that this is uniquely human and developmental.

Strengths:

Multiple converging techniques of brain imaging and testing of mathematical models showing special status of perception of abstract forms. Careful reasoning at every step of research and presentation of research, anticipating and addressing possible reservations. Connecting these findings to other findings, brain, behavior, and historical/anthropological to suggest broad and important fundamental connections between abstract visual-spatial forms and mathematical reasoning.

Weaknesses:

I have reservations of the authors' use of "symbolic." They seem to interpret "symbolic" as relying on "discrete, exact, rule-based features." Words are generally considered to symbolic (that is their major function), yet words do not meet those criteria. Depictions of objects can be regarded as symbolic because they represent real objects, they are not the same as the object (as Magritte observed). If so then perhaps depictions of quadrilaterals are also symbolic but then they do not differ from depictions of objects on that quality. Relatedly, calling abstract or generalized representations of forms a distinct "language of thought" doesn't seem supportable by the current findings. Minimally, a language has elements that are combined more or less according to rules. The authors present evidence for geometric forms as elements but nowhere is there evidence for combining them into meaningful strings.

Further thoughts

Incidentally, there have been many attempts at constructing visual languages from visual elements combined by rules, that is, mapping meaning to depictions. Many written languages like Egyptian hieroglyphics or Mayan or Chinese, began that way; there are current attempts using emoji. Apparently, mapping sound to discrete letters, alphabets, is more efficient and was invented once but spread. That said, for restricted domains like maps, circuit diagrams, networks, chemical interactions, mathematics, and more, visual "languages" work quite well.

The findings are striking and as such invite speculation about their meaning and limitations. The images of real objects seem to be interpreted as representations of 3D objects as they activate the same visual areas as real objects. By contrast, the images of 2D geometric forms are not interpreted as representations of real objects but rather seemingly as 2D abstractions. It would be instructive to investigate stimuli that are on a continuum from representational to geometric, e. g., real objects that have simple geometric forms like table tops or boxes under various projections or balls or buildings that are rectangular or triangular. Objects differ from geometric forms in many ways: 3D rather than 2D, more complicated shapes; internal features as well as outlines. The geometric figures used are flat, 2-D, but much geometry is 3-D (e. g. cubes) with similar abstract features. The feature space of geometry is more than parallelism and symmetry; angles are important for example. Listing and testing features would be fascinating.

Can we say that mathematical thinking began with the regularities of shapes or with counting, or both? External representations of counting go far back into prehistory; tallies are frequent and wide-spread. Infants are sensitive to number across domains as are other primates (and perhaps other species). Finding overlapping brain areas for geometric forms and number is intriguing but doesn't show how they are related.

Categories are established in part by contrast categories; are quadrilaterals and triangles and circles different categories? As for quadrilaterals, the authors say some are "completely irregular." Not really; they are still quadrilaterals, if atypical. See Eleanor Rosch's insightful work on (visual) categories. One wonders about distinguishing squashed quadrilaterals from squashed triangles.

What in human experience but not the experience of close primates would drive the abstraction of these geometric properties? It's easy to make a case for elaborate brain processes for recognizing and distinguishing things in the world, shared by many species, but the case for brain areas sensitive to abstracting geometric figures is harder. The fact that these areas are active in blind mathematicians and that they are parietal areas suggest that what is important is spatial far more than visual. Could these geometric figures and their abstract properties be connected in some way to behavior, perhaps with fabrication, construction or use of objects? Or with other interactions with complex objects and environments where symmetry and parallelism (and angles and curvature--and weight and size) would be important? Manual dexterity and fabrication also distinguish humans from great apes (quantitatively not qualitatively) and action drives both visual and spatial representations of objects and spaces in the brain. I certainly wouldn't expect the authors to add research to this already packed paper, but raising some of the conceptual issues would contribute to the significance of the paper.

Reviewer #1 (Public review):

Summary:

This paper presents three experiments. Experiments 1 and 3 use a target detection paradigm to investigate the speed of statistical learning. The first experiment is a replication of Batterink, 2017, in which participants are presented with streams of uniform-length, trisyllabic nonsense words and asked to detect a target syllable. The results replicate previous findings, showing that learning (in the form of response time facilitation to later-occurring syllables within a nonsense word) occurs after a single exposure to a word. In the second experiment, participants are presented with streams of variable length nonsense words (two trisyllabic words and two disyllabic words), and perform the same task. A similar facilitation effect was observed as in Experiment 1. In Experiment 3 (newly added in the Revised manuscript), an adult version of the study by Johnson and Tyler is included. Participants were exposed to streams of words of either uniform length (all disyllabic) or mixed length (two disyllabic, two trisyllabic) and then asked to perform a familiarity judgment on a 1-5 scale on two words from the stream and two part-words. Performance was better in the uniform length condition.

The authors interpret these findings as evidence that target detection requires mechanisms different from segmentation. They present results of a computational model to simulate results from the target detection task, and find that a bigram model can produce facilitation effects similar to the ones observed by human participants in Experiments 1 and 2 (though this model was not directly applied to test whether human-like effects were also produced to account for the data in Experiment 3). PARSER was also tested and produced differing results from those observed by humans across all three experiments. The authors conclude that the mechanisms involved in the target detection task are different from those involved in the word segmentation task.

Strengths:

The paper presents multiple experiments that provide internal replication of a key experimental finding, in which response times are facilitated after a single exposure to an embedded pseudoword. Both experimental data and results from a computational model are presented, providing converging approaches for understanding and interpreting the main results. The data are analyzed very thoroughly using mixed effects models with multiple explanatory factors. The addition of Experiment 3 provides direct evidence that the profile of performance for familiarity ratings and target detection differ as a function of word length variability.

Weaknesses:

(1) The concept of segmentation is still not quite clear. The authors seem to treat the testing procedure of Experiment 3 as synonymous with segmentation. But the ability to more strongly endorse words from the stream versus part-words as familiar does not necessarily mean that they have been successfully "segmented", as I elaborated on in my earlier review. In my view, it would be clearer to refer to segmentation as the mechanism or conceptual construct of segmenting continuous speech into discrete words. This ability to accurately segment component words could support familiarity judgments but is not necessary for above-chance familiarity or recognition judgments, which could be supported by more general memory signals. In other words, segmentation as an underlying ability is sufficient but not necessary for above-chance performance on familiarity-driven measures such as the one used in experiment 3.

(2) The addition of experiment 3 is an added strength of the revised paper and provides more direct evidence of dissociations as a function of word length on the two tasks (target detection and familiarity ratings), compared to the prior strategy of just relying on previous work for this claim. However, it is not clear why the authors chose not to use the same stimuli as used in experiment 1 and 2, which would have allowed for more direct comparisons to be made. It should also be specified whether test items in the UWL and MWL were matched for overall frequency during exposure. Currently, the text does not specify whether test words in the UWL condition were taken from the high frequency or low frequency group; if they were taken from the high frequency group this would of course be a confound when comparing to the MWL condition. Finally, the definition of part-words should also be clarified,

(3) The framing and argument for a prediction/anticipation mechanism was dropped in the Revised manuscript, but there are still a few instances where this framing and interpretation remain. E.g. Abstract - "we found that a prediction mechanism, rather than clustering, could explain the data from target detection." Discussion page 43 "Together, these results suggest that a simple prediction-based mechanism can explain the results from the target detection task, and clustering-based approaches such as PARSER cannot, contrary to previous claims."

Minor (4) It was a bit unclear as to why a conceptual replication of Batterink 2017 was conducted, given that the target syllables at the beginning and end of the streams were immediately dropped from further analysis. Why include syllable targets within these positions in the design if they are not analyzed?

(5) Figures 3 and 4 are plotted on different scales, which makes it difficult to visually compare the effects between word length conditions.

Reviewer #2 (Public review):

Summary:

The valuable study investigates how statistical learning may facilitate a target detection task and whether the facilitation effect is related to statistical learning of word boundaries. Solid evidence is provided that target detection and word segmentation rely on different statistical learning mechanisms.

Strengths:

The study is well designed, using the contrast between the learning of words of uniform length and words of variable length to dissociate general statistical learning effects and effects related to word segmentation.

Weaknesses:

The study relies on the contrast between word length effects on target detection and word learning. However, the study only tested the target detection condition and did not attempt to replicate the word segmentation effect. It is true that the word segmentation effect has been replicated before but it is still worth reviewing the effect size of previous studies.

The paper seems to distinguish prediction, anticipation, and statistical learning, but it is not entirely clear what each terms refers to.

Comments on revisions:

The authors did not address my concerns...they only replied to reviewer 1.

Reviewer #1 (Public review):

This study investigates how ant group demographics influence nest structures and group behaviors of Camponotus fellah ants, a ground-dwelling carpenter ant species (found locally in Israel) that build subterranean nest structures. Using a quasi-2D cell filled with artificial sand, the authors perform two complementary sets of experiments to try to link group behavior and nest structure: first, the authors place a mated queen and several pupae into their cell and observe the structures that emerge both before and after the pupae eclose (i.e., "colony maturation" experiments); second, the authors create small groups (of 5, 10, or 15 ants, each including a queen) within a narrow age range (i.e., "fixed demographic" experiments) to explore the dependence of age on construction. Some of the fixed demographic instantiations included a manually induced catastrophic collapse event; the authors then compared emergency repair behavior to natural nest creation. Finally, the authors introduce a modified logistic growth model to describe the time-dependent nest area. The modification introduced parameters that allow for age-dependent behavior, and the authors use their fixed demographic experiments to set these parameters, and then apply the model to interpret the behavior of the colony maturation experiments. The main results of this paper are that for natural nest construction, nest areas, and morphologies depend on the age demographics of ants in the experiments: younger ants create larger nests and angled tunnels, while older ants tend to dig less and build predominantly vertical tunnels; in contrast, emergency response seems to elicit digging in ants of all ages to repair the nest.

The experimental results are convincing, providing new information and important insights into nest and colony growth in a social insect species. A model, inspired by previous work but modified to capture experimental results, is in reasonable agreement with experiments and is more biologically relevant than previous models.

Reviewer #2 (Public review):

I enjoyed this paper and its examination of the relationship between overall density and age polyethism to reduce the computational complexity required to match nest size with population. I had some questions about the requirement that growth is infinite in such a solution, but these have been addressed by the authors in the responses and updated manuscript. I also enjoyed the discussion of whether collective behaviour is an appropriate framework in systems in which agents (or individuals) differ in the behavioural rules they employ, according to age, location, or information state. This is especially important in a system like social insects, typically held as a classic example of individual-as-subservient to whole, and therefore most likely to employ universal rules of behaviour. The current paper demonstrates a potentially continuous age-related change in target behaviour (excavation), and suggests an elegant and minimal solution to the requirement for building according to need in ants, avoiding the invocation of potentially complex cognitive mechanisms, or information states that all individuals must have access to in order to have an adaptive excavation output.

The authors have addressed questions I had in the review process and the manuscripts is now clear in its communication and conclusions.

The modelling approach is compelling, also allowing extrapolation to other group sizes and even other species. This to me is the main strength of the paper, as the answer to the question of whether it is younger or older ants that primarily excavate nests could have been answered by an individual tracking approach (albeit there are practical limitations to this, especially in the observation nest setup, as the authors point out). The analysis of the tunnel structure is also an important piece of the puzzle, and I really like the overall study.

Reviewer #1 (Public review):

In this manuscript, the authors aimed to identify the molecular target and mechanism by which α-Mangostin, a xanthone from Garcinia mangostana, produces vasorelaxation that could explain the antihypertensive effects. Building on prior reports of vascular relaxation and ion channel modulation, the authors convincingly show that large-conductance potassium BK channels are the primary site of action. Using electrophysiological, pharmacological, and computational evidence, the authors achieved their aims and showed that BK channels are the critical molecular determinant of mangostin's vasodilatory effects, even though the vascular studies are quite preliminary in nature.

Strengths:

(1) The broad pharmacological profiling of mangostin across potassium channel families, revealing BK channels - and the vascular BK-alpha/beta1 complex - as the potently activated target in a concentration-dependent manner.

(2) Detailed gating analyses showing large negative shifts in voltage-dependence of activation and altered activation and deactivation kinetics.

(3) High-quality single-channel recordings for open probability and dwell times.

(4) Convincing activation in reconstituted BKα/β1-Caᵥ nanodomains mimicking physiological conditions and functional proof-of-concept validation in mouse aortic rings.

Weaknesses are minor:

(1) Some mutagenesis data (e.g., partial loss at L312A) could benefit from complementary structural validation.

(2) While Cav-BK nanodomains were reconstituted, direct measurement of calcium signals after mangostin application onto native smooth muscle could be valuable.

(3) The work has an impact on ion channel physiology and pharmacology, providing a mechanistic link between a natural product and vasodilation. Datasets include electrophysiology traces, mutagenesis scans, docking analyses, and aortic tension recordings. The latter, however, are preliminary in nature.

Reviewer #2 (Public review):

Summary:

In the present manuscript, Cordeiro et al. show that α-mangostin, a xanthone obtained from the fruit of the Garcinia mangostana tree, behaves as an agonist of the BK channels. The authors arrive at this conclusion through the effect of mangostin on macroscopic and single-channel currents elicited by BK channels formed by the α subunit and α + β1sununits, as well as αβ1 channels coexpressed with voltage-dependent Ca2+ (CaV1,2) channels. The single-channel experiments show that α-mangostin produces a robust increase in the probability of opening without affecting the single-channel conductance. The authors contend that α-mangostin activation of the BK channel is state-independent and molecular docking and mutagenesis suggest that α-mangostin binds to a site in the internal cavity. Importantly, α-mangostin (10 μM) alleviates the contracture promoted by noradrenaline. Mangostin is ineffective if the contracted muscles are pretreated with the BK toxin iberiotoxin.

Strengths:

The set of results combining electrophysiological measurements, mutagenesis, and molecular docking reveals α-mangostin as a potent activator of BK channels and the putative location of the α-mangostin binding site. Moreover, experiments conducted on aortic preparations from mice suggest that α-mangostin can aid in developing drugs to treat a myriad of diverse diseases involving the BK channel.

Weaknesses:

Major:

(1) Although the results indicate that α-mangostin is modifying the closed-open equilibrium, the conclusion that this can be due to a stabilization of the voltage sensor in its active configuration may prove to be wrong. It is more probable that, as has been demonstrated for other activators, the α-mangostin is increasing the equilibrium constant that defines the closed-open reaction (L in the Horrigan, Aldrich allosteric gating model for BK). The paper will gain much if the authors determine the probability of opening in a wide range of voltages, to determine how the drug is affecting (or not), the channel voltage dependence, the coupling between the voltage sensor and the pore, and the closed-open equilibrium (L).

(2) Apparently, the molecular docking was performed using the truncated structure of the human BK channel. However, it is unclear which one, since the PDB ID given in the Methods (6vg3), according to what I could find, corresponds to the unliganded, inactive PTK7 kinase domain. Be as it may, the apo and Ca2+ bound structures show that there is a rotation and a displacement of the S6 transmembrane domain. Therefore, the positions of the residues I308, L312, and A316 in the closed and open configurations of the BK channel are not the same. Hence, it is expected that the strength of binding will be different whether the channel is closed or open. This point needs to be discussed.

Minor:

(1) From Figure 3A, it is apparent that the increase in Po is at the expense of the long periods (seconds) that the channel remains closed. One might suggest that α-mangostin increases the burst periods. It would be beneficial if the authors measured both closed and open dwell times to test whether α-mangostin primarily affects the burst periods.

(2) In several places, the authors make similarities in the mode of action of other BK activators and α-mangostin; however, the work of Gessner et al. PNAS 2012 indicates that NS1619 and Cym04 interact with the S6/RCK linker, and Webb et al. demonstrated that GoSlo-SR-5-6 agonist activity is abolished when residues in the S4/S5 linker and in the S6C region are mutated. These findings indicate that binding of the agonist is not near the selectivity filter, as the authors' results suggest that α-mangostin binds.

(3) The sentence starting in line 452 states that there is a pronounced allosteric coupling between the voltage sensors and Ca2+ binding. If the authors are referring to the coupling factor E in the Horrigan-Aldrich gating model, the references cited, in particular, Sun and Horrigan, concluded that the coupling between those sensors is weak.

Reviewer #3 (Public review):

Summary:

This research shows that a-mangostin, a proposed nutraceutical, with cardiovascular protective properties, could act through the activation of large conductance potassium permeable channels (BK). The authors provide convincing electrophysiological evidence that the compound binds to BK channels and induces a potent activation, increasing the magnitude of potassium currents. Since these channels are important modulators of the membrane potential of smooth muscle in vascular tissue, this activation leads to muscle relaxation, possibly explaining cardiovascular protective effects.

Strengths:

The authors present evidence based on several lines of experiments that a-mangostin is a potent activator of BK channels. The quality of the experiments and the analysis is high and represents an appropriate level of analysis. This research is timely and provides a basis to understand the physiological effects of natural compounds with proposed cardio-protective effects.

Weaknesses:

The identification of the binding site is not the strongest point of the manuscript. The authors show that the binding site is probably located in the hydrophobic cavity of the pore and show that point mutations reduce the magnitude of the negative voltage shift of activation produced by a-mangostin. However, these experiments do not demonstrate binding to these sites, and could be explained by allosteric effects on gating induced by the mutations themselves.

Reviewer #1 (Public review):

Summary:

This study identifies three redundant pathways-glycine cleavage system (GCS), serine hydroxymethyltransferase (GlyA), and formate-tetrahydrofolate ligase/FolD-that feed the one-carbon tetrahydrofolate (1C-THF) pool essential for Listeria monocytogenes growth and virulence. Reactivation of the normally inactive fhs gene rescues 1C-THF deficiency, revealing metabolic plasticity and vulnerability for potential antimicrobial targeting

Strengths:

(1) Novel evolutionary insight - reversible reactivation of a pseudogene (fhs) shows adaptive metabolic plasticity, relevant for pathogen evolution.

(2) They systematically combine targeted gene deletions with suppressor screening to dissect the folate/one-carbon network (GCS, GlyA, Fhs/FolD).

Weaknesses:

(1) The study infers 1C-THF depletion mostly genetically and indirectly (growth rescue with adenine) without direct quantification of folate intermediates or fluxes. Biochemical confirmation, LC-MS-based metabolomics of folates/1C donors, or isotopic tracing would strengthen mechanistic claims.

(2) In multiple result sections, the authors report data from technical triplicates but do not mention independent biological replicates (e.g., Figure 2C, Figure 4A-B, Figure 6D). In addition, some results mention statistical significance but without a detailed description of the specific statistical tests used or replicates, such as Figure 2A-C, Figure 2E, and Figure 2G-I.

Reviewer #2 (Public review):

Summary:

The manuscript by Freier et al examines the impact of deletion of the glycine cleavage system (GCS) GcvPAB enzyme complex in the facultative intracellular bacterial pathogen Listeria monocytogenes. GcvPAB mediates the oxidative decarboxylation of glycine as a first step in a pathway that leads to the generation of N5, N10-methylene-Tetrahydrofolate (THF) to replenish the 1-carbon THF (1C-THF) pool. 1C-THF species are important for the biosynthesis of purines and pyrimidines as well as for the formation of serine, methionine, and N-formylmethionine, and the authors have previously demonstrated that gcvPAB is important for bacterial replication within macrophages. A significant defect for growth is observed for the gcvPAB deletion mutant in defined media, and this growth defect appears to stem from the sensitivity of the mutant strain to excess glycine, which is hypothesized to further deplete the 1C-THF pool. Selection of suppressor mutations that restored growth of gcvPAB deletion mutants in synthetic media with high glycine yielded mutants that reversed stop codon inactivation of the formate-tetrahydrofolate ligase (fhs) gene, supporting the premise that generation of N10-formyl-THF can restore growth. Mutations within the folk, codY, and glyA genes, encoding serine hydroxymethyltransferase, were also identified, although the functional impact of these mutations is somewhat less clear. Overall, the authors report that their work identifies three pathways that feed the 1C-THF pool to support the growth and virulence of L. monocytogenes and that this work represents the first example of the spontaneous reactivation of a L. monocytogenes gene that is inactivated by a premature stop codon.

Strengths:

This is an interesting study that takes advantage of a naturally existing fhs mutant Listeria strain to reveal the contributions of different pathways leading to 1C-THF synthesis. The defects observed for the gcvPAB mutant in terms of intracellular growth and virulence are somewhat subtle, indicating that bacteria must be able to access host sources (such as adenine?) to compensate for the loss of purine and fMet synthesis. Overall, the authors do a nice job of assessing the importance of the pathways identified for 1C-THF synthesis.

Weaknesses:

(1) Line 114 and Figure 1: The authors indicate that the gcvPAB deletion forms significantly fewer plaques in addition to forming smaller plaques (although this is a bit hard to see in the plaque images). A reduction in the overall number of plaques sounds like a bacterial invasion defect - has this been carefully assessed? The smaller plaque size makes sense with reduced bacterial replication, but I'm not sure I understand the reduction in plaque number.

(2) Do other Listeria strains contain the stop codon in fhs? How common is this mutation? That would be interesting to know.

(3) Based on the observation that fhs+ ΔgcvPAB ΔglyA mutant is only possible to isolate in complex media, and fhs is responsible for converting formate to 1C-THF with the addition of FolD, have the authors thought of supplementing synthetic media with formate and assessing mutant growth?

Reviewer #3 (Public review):

Summary:

In this study, Freier et al. demonstrate that 3 distinct metabolic pathways are critical for the synthesis of 1C-THF, a metabolite that is crucial for the growth and virulence of Listeria monocytogenes. Using an elegant suppressor screen, they also demonstrate the hierarchical importance of these metabolic pathways with respect to the biosynthesis of 1C-THF.

Strengths:

This study uses elegant bacterial genetics to confirm that 3 distinct metabolic pathways are critical for 1C-THF synthesis in L. monocytogenes, and the lack of either one of these pathways compromises bacterial growth and virulence. The study uses a combination of in vitro growth assays, macrophage-CFU assays, and murine infection models to demonstrate this.

Weaknesses:

(1) The primary finding of the study is that the perturbation of any of the 3 metabolic pathways important for the synthesis of 1C-THF results in reduced growth and virulence of L. monocytogenes. However, there is no evidence demonstrating the levels of 1C-THF in the various knockouts and suppressor mutants used in this study. It is important to measure the levels of this metabolite (ideally using mass spectrometry) in the various knockouts and suppressor mutants, to provide strong causality.

(2) The story becomes a little hard to follow since macrophage-CFU assays and murine infection model data precede the in vitro growth assays. The manuscript would benefit from a reorganization of Figures 2,3, and 4 for better readability and flow of data.

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.

Weaknesses:

(1) The descriptions of some observations lack detail, which limits understanding of some key concepts.

(2) The presence of endogenous NR ligands within cells may confound the correlation of ligand activity of cellular assays to biochemical assay data.

(3) The normalization of biochemical assay data could confound the classification of graded activity ligands.

(4) The presence of >1 coregulator peptide in the biplex (n=2 peptides) CRT (pCRT) format will bias the LBD conformation towards the peptide-bound form with the highest binding affinity, which will impact potency and interpretation of TR-FRET data.

(5) Correlation graphical plots lack sufficient statistical testing.

(6) Some of the proposed ligand pharmacology nomenclature is not clear and deviates from classifications used currently in the field (e.g., hard and soft antagonist; weak vs. partial agonist, definition of an inverse agonist that is not the opposite function to an agonist).

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 leeway 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.

Weaknesses:

I did not identify any major weaknesses.

Reviewer #1 (Public review):

Summary:

This study presents a high-throughput screening platform to identify nanobodies capable of recruiting chromatin regulators and modulating gene expression. The authors utilize a yeast display system paired with mammalian reporter assays to validate candidate nanobodies, aiming to create a modular resource for synthetic epigenetic control.

Strengths:

(1) The overall screening design combining yeast display with mammalian functional assays is innovative and scalable.

(2) The authors demonstrate proof-of-concept that nanobody-based recruitment can repress or activate reporter expression.

(3) The manuscript contributes to the growing toolkit for epigenome engineering.

Weaknesses:

(1) The manuscript does not investigate which endogenous factors are recruited by the nanobodies. While repression activity is demonstrated at the reporter level, there is no mechanistic insight into what proteins are being brought to the target site by each nanobody. This limits the interpretability and generalizability of the findings. Related to this, Figure S1B reports sequence similarity among complementarity-determining regions (CDRs) of nanobodies that scored highly in the DNMT3A screen. However, it remains unclear whether this similarity reflects convergence on a common molecular target or is coincidental. Without functional or proteomic validation, the relationship between sequence motifs and effector recruitment remains speculative.

(2) The epigenetic consequences of nanobody recruitment are also left unexplored. Despite targeting epigenetic regulators, the study does not assess changes such as DNA methylation or histone modifications. This makes it difficult to interpret whether the observed reporter repression is due to true chromatin remodeling or secondary effects.

Reviewer #2 (Public review):

Summary:

Wan, Thurm et al. use a yeast nanobody library that is thought to have diverse binders to isolate those that specifically bind to proteins of their interest. The yeast nanobody library collection in general carries enormous potential, but the challenge is to isolate binders that have specific activity. The authors posit that one reason for this isolation challenge is that the negative binders, in general, dampen the signal from the positive binders. This is a classic screening problem (one that geneticists have faced over decades) and, in general, underscores the value of developing a good secondary screen. Over many years, the authors have developed an elegant platform to carry out high-throughput silencing-based assays, thus creating the perfect secondary screen platform to isolate nanobodies that bind to chromatin regulators.

Strengths:

Highlights the enormous value of a strong secondary screen when identifying binders that can be isolated from the yeast nanobody library. This insight is generalizable, and I expect that this manuscript should help inspire many others to design such approaches.

Provides new cell-based reagents that can be used to recruit epigenetic activators or repressors to modulate gene expression at target loci.

Weaknesses:

The authors isolate DNMT3A and TET1/2 enzymes directly from cell lysates and bind these proteins to beads. It is not clear what proteins are, in fact, bound to beads at the end of the IP. Epigenetic repressors are part of complexes, and it would be helpful to know if the IP is specific and whether the IP pulls down only DNMT3A or other factors. While this does not change the underlying assumptions about the screen, it does alter the authors' conclusions about whether the nanobody exclusively recruits DNMT3A or potentially binds to other co-factors.

Using IP-MS to validate the pull-down would be a helpful addition to the manuscript, although one could very reasonably make the case that other co-factors get washed away during the course of the selection assay. Nevertheless, if there are co-factors that are structural and remain bound, these are likely to show up in the MS experiment.

Reviewer #1 (Public review):

In this work, Zhang et al, through a series of well-designed experiments, present a comprehensive study exploring the roles of the neuropeptide Corazonin (CRZ) and its receptor in controlling the female post-mating response (PMR) in the brown planthopper (BPH) Nilaparvata lugen and Drosophila melanogaster. Through a series of behavioural assays, micro-injections, gene knockdowns, Crispr/Cas gene editing, and immunostaining, the authors show that both CRZ and CrzR play a vital role in the female post-mating response, with impaired expression of either leading to quicker female remating and reduced ovulation in BPH. Notably, the authors find that this signaling is entirely endogenous in BPH females, with immunostaining of male accessory glands (MAGs) showing no evidence of CRZ expression. Further, the authors demonstrate that while CRZ is not expressed in the MAGs, BPH males with Crz knocked out show transcriptional dysregulation of several seminal fluid proteins and functionally link this dysregulation to an impaired PMR in BPH. In relation, the authors also find that in CrzR mutants, the injection of neither MAG extracts nor maccessin peptide triggered the PMR in BPH females. Finally, the authors extend this study to D. melanogaster, albeit on a more limited scale, and show that CRZ plays a vital role in maintaining PMR in D. melanogaster females with impaired CRZ signaling, once again leading to quicker female remating and reduced ovulation. The authors must be commended for their expansive set of complementary experiments. The manuscript is also generally well written. Given the seemingly conserved nature of CRZ, this work is a significant addition to the literature, opening several avenues for testing the molecular and neurobiological mechanisms in which CRZ triggers the PMR.

However, there are some broad concerns/comments I had with this manuscript. The authors provide clear evidence that CRZ signaling plays a major role in the PMR of D. melanogaster, however, they provide no evidence that CRZ signaling is endogenous, as they did not check for expression in the MAGs of D. melanogaster males. Additionally, while the authors show that manipulating Crz in males leads to dysregulated seminal fluid expression and impaired PMR in BPH, the authors also find that CRZ injection in males in and of itself impairs PMR in BPH. The authors do not really address what this seemingly contradictory result could mean. While a lot of the figures have replicate numbers, the authors do not factor in replicate as an effect into their models, which they ideally should do.

Finally, while the discussion is generally well-written, it lacks a broader conclusion about the wider implications of this study and what future work building on this could look like.

Reviewer #2 (Public review):

Summary:

The work presented by Zhang and coauthors in this manuscript presents the study of the neuropeptide corazonin in modulating the post-mating response of the brown planthopper, with further validation in Drosophila melanogaster. To obtain their results, the authors used several different techniques that orthogonally demonstrate the involvement of corazonin signalling in regulating the female post-mating response in these species.

They first injected synthetic corazonin peptide into female brown planthoppers, showing altered mating receptivity in virgin females and a higher number of eggs laid after mating. The role of corazonin in controlling these post-mating traits has been further validated by knocking down the expression of the corazonin gene by RNA interference and through CRISPR-Cas9 mutagenesis of the gene. Further proof of the importance of corazonin signalling in regulating the female post-mating response has been achieved by knocking down the expression or mutagenizing the gene coding for the corazonin receptor.

Similar results have been obtained in the fruit fly Drosophila melanogaster, suggesting that corazonin signalling is involved in controlling the female post-mating response in multiple insect species.<br /> Notably, the authors also show that corazonin controls gene expression in the male accessory glands and that disruption of this pathway in males compromises their ability to elicit normal post-mating responses in their mates.

Strengths:

The study of the signalling pathways controlling the female post-mating response in insects other than Drosophila is scarce, and this limits the ability of biologists to draw conclusions about the evolution of the post-mating response in female insects. This is particularly relevant in the context of understanding how sexual conflict might work at the molecular and genetic levels, and how, ultimately, speciation might occur at this level. Furthermore, the study of the post-mating response could have practical implications, as it can lead to the development of control techniques, such as sterilization agents.

The study, therefore, expands the knowledge of one of the signalling pathways that control the female post-mating response, the corazonin neuropeptide. This pathway is involved in controlling the post-mating response in both Nilaparvata lugens (the brown planthopper) and Drosophila melanogaster, suggesting its involvement in multiple insect species.

The study uses multiple molecular approaches to convincingly demonstrate that corazonin controls the female post-mating response.

Weaknesses:

The data supporting the main claims of the manuscript are solid and convincing. The statistical analysis of some of the data might be improved, particularly by tailoring the analysis to the type of data that has been collected.

In the case of the corazonin effect in females, all the data are coherent; in the case of CRISPR-Cas9-induced mutagenesis, the analysis of the behavioural trait in heterozygotes might have helped in understanding the haplosufficiency of the gene and would have further proved the authors' point.

Less consistency was achieved in males (Figure 5): the authors show that injection of CRZ and RNAi of crz, or mutant crz, has the same effect on male fitness. However, the CRZ injection should activate the pathway, and crz RNAi and mutant crz should inhibit the pathway, yet they have the same effect. A comment about this discrepancy would have improved the clarity of the manuscript, pointing to new points that need to be clarified and opening new scientific discussion.

Reviewer #1 (Public review):

In this study, the authors investigated a specific subtype of SST-INs (layer 5 Chrna2-expressing Martinotti cells) and examined its functional role in motor learning. Using endoscopic calcium imaging combined with chemogenetics, they showed that activation of Chrna2 cells reduces the plasticity of pyramidal neuron (PyrN) assemblies but does not affect the animals' performance. However, activating Chrna2 cells during re-training improved performance. The authors claim that activating Chrna2 cells likely reduces PyrN assembly plasticity during learning and possibly facilitates the expression of already acquired motor skills.

There are many major issues with the study. The findings across experiments are inconsistent, and it is unclear how the authors performed their analyses or why specific time points and comparisons were chosen. The study requires major re-analysis and additional experiments to substantiate its conclusions.

Major Points:

(1a) Behavior task - the pellet-reaching task is a well-established paradigm in the motor learning field. Why did the authors choose to quantify performance using "success pellets per minute" instead of the more conventional "success rate" (see PMID 19946267, 31901303, 34437845, 24805237)? It is also confusing that the authors describe sessions 1-5 as being performed on a spoon, while from session 6 onward, the pellets are presented on a plate. However, in lines 710-713, the authors define session 1 as "naïve," session 2 as "learning," session 5 as "training," and "retraining" as a condition in which a more challenging pellet presentation was introduced. Does "naïve session 1" refer to the first spoon session or to session 6 (when the food is presented on a plate)? The same ambiguity applies to "learning session 2," "training session 5," and so on. Furthermore, what criteria did the authors use to designate specific sessions as "learning" versus "training"? Are these definitions based on behavioral performance thresholds or some biological mechanisms? Clarifying these distinctions is essential for interpreting the behavioral results.

(1b) Judging from Figures 1F and 4B, even in WT mice, it is not convincing that the animals have actually learned the task. In all figures, the mice generally achieve ~10-20 pellets per minute across sessions. The only sessions showing slightly higher performance are session 5 in Figure 1F ("train") and sessions 12 and 13 in Figure 4B ("CLZ"). In the classical pellet-reaching task, animals are typically trained for 10-12 sessions (approximately 60 trials per session, one session per day), and a clear performance improvement is observed over time. The authors should therefore present performance data for each individual session to determine whether there is any consistent improvement across days. As currently shown, performance appears largely unchanged across sessions, raising doubts about whether motor learning actually occurred.

(1c) The authors also appear to neglect existing literature on the role of SST-INs in motor learning and local circuit plasticity (e.g., PMID 26098758, 36099920). Although the current study focuses on a specific subpopulation of SST-INs, the results reported here are entirely opposite to those of previous studies. The authors should, at a minimum, acknowledge these discrepancies and discuss potential reasons for the differing outcomes in the Discussion section.

(2a) Calcium imaging - The methodology for quantifying fluorescence changes is confusing and insufficiently described. The use of absolute ΔF values ("detrended by baseline subtraction," lines 565-567) for analyses that compare activity across cells and animals (e.g., Figure 1H) is highly unconventional and problematic. Calcium imaging is typically reported as ΔF/F₀ or z-scores to account for large variations in baseline fluorescence (F₀) due to differences in GCaMP expression, cell size, and imaging quality. Absolute ΔF values are uninterpretable without reference to baseline intensity - for example, a ΔF of 5 corresponds to a 100% change in a dim cell (F₀ = 5) but only a 1% change in a bright cell (F₀ = 500). This issue could confound all subsequent population-level analyses (e.g., mean or median activity) and across-group comparisons. Moreover, while some figures indicate that normalization was performed, the Methods section lacks any detailed description of how this normalization was implemented. The critical parameters used to define the baseline are also omitted. The authors should reprocess the imaging data using a standardized ΔF/F₀ or z-score approach, explicitly define the baseline calculation procedure, and revise all related figures and statistical analyses accordingly.

(2b) Figure 1G - It is unclear why neural activity during successful trials is already lower one second before movement onset. Full traces with longer duration before and after movement onset should also be shown. Additionally, only data from "session 2 (learning)" and a single neuron are presented. The authors should present data across all sessions and multiple neurons to determine whether this observation is consistent and whether it depends on the stage of learning.

(2c) Figure 1H - The authors report that chemogenetic activation of Chrna2 cells induces differential changes in PyrN activity between successful and failed trials. However, one would expect that activating all Chrna2 cells would strongly suppress PyrN activity rather than amplifying the activity differences between trials. The authors should clarify the mechanism by which Chrna2 cell activation could exaggerate the divergence in PyrN responses between successful and failed trials. Perhaps, performing calcium imaging of Chrna2 cells themselves during successful versus failed trials would provide insight into their endogenous activity patterns and help interpret how their activation influences PyrN activity during successful and failed trials.

(2d) Figure 1H - Also, in general, the Cre⁺ (red) data points appear consistently higher in activity than the Cre⁻ (black) points. This is counterintuitive, as activating Chrna2 cells should enhance inhibition and thereby reduce PyrN activity. The authors should clarify how Cre⁺ animals exhibit higher overall PyrN activity under a manipulation expected to suppress it. This discrepancy raises concerns about the interpretation of the chemogenetic activation effects and the underlying circuit logic.

(3) The statistical comparisons throughout the manuscript are confusing. In many cases, the authors appear to perform multiple comparisons only among the N, L, T, and R conditions within the WT group. However, the central goal of this study should be to assess differences between the WT and hM3D groups. In fact, it is unclear why the authors only provide p-values for some comparisons but not for the majority of the groups.

(4a) Figure 4 - It is hard to understand why the authors introduce LFP experiments here, and the results are difficult to interpret in isolation. The authors should consider combining LFP recordings with calcium imaging (as in Figure 1) or, alternatively, repeating calcium imaging throughout the entire re-training period. This would provide a clearer link between circuit activity and behavior and strengthen the conclusions regarding Chrna2 cell function during re-training.

(4b) It is unclear why CLZ has no apparent effect in session 11, yet induces a large performance increase in sessions 12 and 13. Even then, the performance in sessions 12 and 13 (~30 successful pellets) is roughly comparable to Session 5 in Figure 1F. Given this, it is questionable whether the authors can conclude that Chrna2 cell activation truly facilitates previously acquired motor skills?

(5) Figure 5 - The authors report decreased performance in the pasta-handling task (presumably representing a newly learned skill) but observe no difference in the pellet-reaching task (presumably an already acquired skill). This appears to contradict the authors' main claim that Chrna2 cell activation facilitates previously acquired motor skills.

(6) Supplementary Figure 1 - The c-fos staining appears unusually clean. Previous studies have shown that even in home-cage mice, there are substantial numbers of c-fos⁺ cells in M1 under basal conditions (PMID 31901303, 31901303). Additionally, the authors should present Chrna2 cell labeling and c-fos staining in separate channels. As currently shown, it is difficult to determine whether the c-fos⁺ cells are truly Chrna2 cells⁺.

Overall, the authors selectively report statistical comparisons only for findings that support their claims, while most other potentially informative comparisons are omitted. Complete and transparent reporting is necessary for proper interpretation of the data.

Reviewer #2 (Public review):

Summary:

In this manuscript, Malfatti et al. study the role of Chrna2 Martinotti cells (Mα2 cells), a subset of SST interneurons, for motor learning and motor cortex activity. The authors trained mice on a forelimb prehension task while recording neuronal activity of pyramidal cells using calcium imaging with a head-mounted miniscope. While chemogenetically increasing Mα2 cell activity did not affect motor learning, it changed pyramidal cell activity such that activity peaks became sharper and differently timed than in control mice. Moreover, co-active neuronal assemblies become more stable with a smaller spatial distribution. Increasing Mα2 cell activity in previously trained mice did increase performance on the prehension task and led to increased theta and gamma band activity in the motor cortex. On the other hand, genetic ablation of Mα2 cells affected fine motor movements on a pasta handling task while not affecting the prehension task.

Strengths:

The proposed question of how Chrna2-expressing SST interneurons affect motor learning and motor cortex activity is important and timely. The study employs sophisticated approaches to record neuronal activity and manipulate the activity of a specific neuronal population in behaving mice over the course of motor learning. The authors analyze a variety of neuronal activity parameters, comparing different behavior trials, stages of learning, and the effects of Mα2 cell activation. The analysis of neuronal assembly activity and stability over the course of learning by tracking individual neurons throughout the imaging sessions is notable, since technically challenging, and yielded the interesting result that neuronal assemblies are more stable when activating Mα2 cells.

Overall, the study provides compelling evidence that Mα2 cells regulate certain aspects of motor behaviors, likely by shaping circuit activity in the motor cortex.

Weaknesses:

The main limitation of the study lies in its small sample sizes and the absence of key control experiments, which substantially weaken the strength of the conclusions.

Core findings of this paper, such as the lack of effect of Mα2 cell activation on motor learning, as well as the altered neuronal activity, rely ona sample size of n=3 mice per condition, which is likely underpowered to detect differences in behavior and contributes to the somewhat disconnected results on calcium activity, activity timing, and neuronal assembly activity.

More comprehensive analyses and data presentation are also needed to substantiate the results. For example, examining calcium activity and behavioral performance on a trial-by-trial basis could clarify whether closely spaced reaching attempts influence baseline signals and skew interpretation.

The study uses cre-negative mice as controls for hM3Dq-mediated activation, which does not account for potential effects of Cre-dependent viral expression that occur only in Cre-positive mice.

This important control would be necessary to substantiate the conclusion that it is increased Mα2 cell activity that drives the observed changes in behavior and cortical activity.

Reviewer #1 (Public review):

Summary:

This study investigates how human temporal voice areas (TVA) respond to vocalizations from nonhuman primates. Using functional MRI during a species-categorization task, the authors compare neural responses to calls from humans, chimpanzees, bonobos, and macaques while modeling both acoustic and phylogenetic factors. They find that bilateral anterior TVA regions respond more strongly to chimpanzee than to other nonhuman primate vocalizations, suggesting that these regions are sensitive not only to human voices but also to acoustically and evolutionarily related sounds.

The work provides important comparative evidence for continuity in primate vocal communication and offers a strong empirical foundation for modeling how specific acoustic features drive TVA activity.

Strengths:

­(1) Comparative scope: The inclusion of four primate species, including both great apes and monkeys, provides a rare and valuable cross-species perspective on voice processing.

­(2) Methodological rigor: Acoustic and phylogenetic distances are carefully quantified and incorporated into the analyses.

­(4) Neuroscientific significance: The finding of TVA sensitivity to chimpanzee calls supports the view that human voice-selective regions are evolutionarily tuned to certain acoustic features shared across primates.

­(4) Clear presentation: The study is well organized, the stimuli well controlled, and the imaging analyses transparent and replicable.

­(5) Theoretical contribution: The results advance understanding of the neural bases of voice perception and the evolutionary roots of voice sensitivity in the human brain.

Weaknesses:

­(1) Acoustic-phylogenetic confound: The design does not fully disentangle acoustic similarity from phylogenetic proximity, as species co-vary along both dimensions. A promising way to address this would be to include an additional model focusing on the acoustic features that specifically differentiate bonobo from chimpanzee calls, which share equal phylogenetic distance to humans.

­(2) Selectivity vs. sensitivity: Without non-vocal control sounds, the study cannot determine whether TVA responses reflect true selectivity for primate vocalizations or general auditory sensitivity.<br /> ­<br /> (3) Task demands: The use of an active categorization task may engage additional cognitive processes beyond auditory perception; a passive listening condition would help clarify the contribution of attention and task performance.

­(4) Figures and presentation: Some results are partially redundant; keeping only the most representative model figure in the main text and moving others to the Supplementary Material would improve clarity.

Reviewer #2 (Public review):

Summary:

This study investigated how the human brain responds to vocalizations from multiple primate species, including humans, chimpanzees, bonobos, and rhesus macaques. The central finding - that subregions of the temporal voice areas (TVA), particularly in the bilateral anterior superior temporal gyrus, show enhanced responses to chimpanzee vocalizations - suggests a potential neural sensitivity to calls from phylogenetically close nonhuman primates.

Strengths:

The authors employed three analytical models to consistently demonstrate activation in the anterior superior temporal gyrus that is specific to chimpanzee calls. The methodology was logical and robust, and the results supporting these findings appear solid.

Weakness:

The interpretation of the findings in this paper regarding the evolutionary continuity of voice processing lacks sufficient evidence. A simple explanation is that the observed effects can be attributed to the similarity in low-level acoustic features, rather than effects specific to phylogenetically close species. The authors only tested vocalizations from three non-human primate species, other than humans. In this case, the species specificity of the effect does not fully represent the specificity of evolutionary relatedness.

Reviewer #3 (Public review):

Summary:

Ceravolo et al. employed functional magnetic resonance imaging (fMRI) to examine how the temporal voice areas (TVA) in the human brain respond to vocalizations from different nonhuman primate species. Their findings reveal that the human TVA is not only responsible for human vocalizations but also exhibits sensitivity to the vocalizations of other primates, particularly chimpanzee vocalizations sharing acoustic similarities with human voices, which offers compelling evidence for cross-species vocal processing in the human auditory system. Overall, the study presents intellectually stimulating hypotheses and demonstrates methodological originality. However, the current findings are not yet solid enough to fully support the proposed claims, and the presentation could be enhanced for clarity and impact.

Strengths:

The study presents intellectually stimulating hypotheses and demonstrates methodological originality.

Weaknesses:

(1) The analysis of the fMRI data does not account for the participants' behavioral performance, specifically their reaction times (RTs) during the species categorization task.

(2) The figure organization/presentation requires significant revision to avoid confusion and redundancy.

Reviewer #1 (Public review):

In this manuscript, the authors used a coarse-grained DNA model (cgNA+) to explore how DNA sequences and CpG methylation/hydroxymethylation influence nucleosome wrapping energy and the probability density of optimal nucleosomal configuration. Their findings indicate that both methylated and hydroxymethylated cytosines lead to increased nucleosome wrapping energy. Additionally, the study demonstrates that methylation of CpG islands increases the probability of nucleosome formation.

The major strength of this method is that the model explicitly includes the phosphate group as DNA-histone binding site constraints, enhancing CG model accuracy and computational efficiency and allowing comprehensive calculations of DNA mechanical properties and deformation energies.

The revised version has addressed the concerns raised previously, significantly strengthening the study.

Reviewer #2 (Public review):

Summary:

This study uses a coarse-grained model for double stranded DNA, cgNA+, to assess nucleosome sequence affinity. cgNA+ coarse-grains DNA on the level of bases and accounts also explicitely for the positions of the backbone phosphates. It has been proven to reproduce all-atom MD data very accurately. It is also ideally suited to be incorporated into a nucleosome model because it is known that DNA is bound to the protein core of the nucleosome via the phosphates.

It is still unclear whether this harmonic model parametrized for unbound DNA is accurate enough to describe DNA inside the nucleosome. Previous models by other authors, using more coarse-grained models of DNA, have been rather successful in predicting base pair sequence dependent nucleosome behavior. This is at least the case as long as DNA shape is concerned whereas assessing the role of DNA bendability (something this paper focuses on) has been consistingly challenging in all nucleosome models to my knowledge.

It is thus of major interest whether this more sophisticated model is also more successful in handling this issue. As far as I can tell the work is technically sound and properly accounts for not only the energy required in wrapping DNA but also entropic effects, namely the change in entropy that DNA experiences when going from the free state to the bound state. The authors make an approximation here which seems to me to be a reasonable first step.

Of interest is also that the authors have the parameters at hand to study the effect of methylation of CpG-steps. This is especially interesting as this allows to study a scenario where changes in the physical properties of base pair steps via methylation might influence nucleosome positioning and stability in a cell-type specific way.

Overall, this is an important contribution to the questions of how sequence affects nucleosome positioning and affinity. The findings suggest that cgNA+ has something new to offer. But the problem is complex, also on the experimental side, so many questions remain open. Despite of this, I highly recommend publication of this manuscript.

Strengths:

The authors use their state-of-the-art coarse grained DNA model which seems ideally suited to be applied to nucleosomes as it accounts explicitly for the backbone phosphates.

Weaknesses:

The authors introduce penalty coefficients c_i to avoid steric clashes between the two DNA turns in the nucleosome. This requires c_i-values that are so high that standard deviations in the fluctuations of the simulation are smaller than in the experiments.

Reviewer #3 (Public review):

Summary:

In this study, authors utilize biophysical modeling to investigate differences in free energies and nucleosomal configuration probability density of CpG islands and nonmethylated regions in the genome. Toward this goal, they develop and apply the cgNA+ coarse-grained model, an extension of their prior molecular modeling framework.

Strengths:

The study utilizes biophysical modeling to gain mechanistic insight into nucleosomal occupancy differences in CpG and nonmethylated regions in the genome.

Weaknesses:

Although the overall study is interesting, the manuscripts need more clarity in places. Moreover, the rationale and conclusion for some of the analyses are not well described.

Comments on revised version:

The authors have addressed my concerns.

Reviewer #1 (Public review):

Summary:

This study examines letter-shape knowledge in a large cohort of children with minimal formal reading instruction. The authors report that these children can reliably distinguish upright from inverted letters despite limited letter naming abilities. They also show a visual-search advantage for upright over inverted letters, and this advantage correlates with letter-shape familiarity. These findings suggest that specialized letter-shape representations can emerge with very limited letter-sound mapping practice.

Strengths:

This study investigates whether children can develop letter-shape knowledge independently of letter-sound mapping abilities. This question is theoretically important, especially in light of functional subdivisions within the visual word form area (VWFA), with posterior regions associated with letter/orthographic shape and anterior regions with linguistic features of orthography (Caffarra et al., 2021; Lerma-Usabiaga et al., 2018). The study also includes a large sample of children at the very beginning of formal reading instruction, thereby minimizing the influence of explicit instruction on the formation of letter-shape knowledge.

Weakness:

A central concern is that a production task (naming) is used to index letter-name knowledge, whereas letter-shape knowledge is assessed with recognition. Production tasks impose additional demands (motor planning, articulation) and typically yield lower performance than recognition tasks (e.g., letter-sound verification). Thus, comparisons between letter-shape and letter-name knowledge are confounded by task type. The authors' partial-correlation and multiple-regression analyses linking familiarity (but not production) to the upright-search advantage are informative; however, they do not resolve the recognition-versus-production mismatch. Consequently, the current data cannot unambiguously support the claim that letter-shape representations are independent of letter-name knowledge.

Reviewer #2 (Public review):

Summary:

In this study, the authors propose that there are two types of letter knowledge: knowledge about letter sound and knowledge about letter shape. Based on previous studies on implicit statistical learning in adults and babies, the authors hypothesized that passive exposure to letters in the environment allows early readers to acquire knowledge of letter shapes even before knowledge of letter-sound association. Children performed a set of experiments that measures letter shape familiarity, letter-sound association performance, visual processing of letters, and a reading-related cognitive skill. The results show that even the children who have little to no knowledge of letter names are familiar with letter shapes, and that this letter shape familiarity is predictive of performance in visual processing of letters.

Strengths:

The authors' hypothesis is based on widely accepted findings in vision science that repeated exposure to certain stimuli promotes implicit learning of, for example, statistical properties of the stimuli. They used simple and well-established tasks in large-scale experiments with a special population (i.e., children). The data analysis is quite comprehensive, accounting for any alternative explanations when needed. The data support at least a part of their hypothesis that the knowledge of letter shapes is distinct from, and precedes, the knowledge of letter-sound association, and is associated with performance in visual processing of the letters. This study shed light on a rather overlooked aspect of letter knowledge, i.e., letter shapes, challenging the idea that letters are learned only through formal instruction and calling for future research on the role of passive exposure to letters in reading acquisition.

Weaknesses:

Although the authors have successfully identified the knowledge of letter shapes as another type of letter knowledge other than the knowledge of letter-sound association, the question of whether it drives the subsequent reading acquisition remains largely unanswered, despite it being strongly implied in the Introduction. The authors collected a RAN score, which is known to robustly predict future reading fluency, but it did not show a significant partial correlation with familiarity accuracy (i.e., familiarity accuracy is not necessary to predict RAN score). The authors discussed that the performance in visual processing of letters might capture unique variance in reading fluency unexplained by RAN scores, but currently, this claim seems speculative.

Since even children without formal literacy instruction were highly familiar with letter shapes, it would be reasonable to assume that they had obtained the knowledge through passive exposure. However, the role of passive exposure was not directly tested in the study.

Given the superimposed straight lines in Figure 2, I assume the authors computed Pearson correlation coefficients. Testing the statistical significance of the Pearson correlation coefficient requires the assumption of bivariate normality (and therefore constant variance of a variable across the range of the other). According to Figure 2, this doesn't seem to be met, as the familiarity accuracy is hitting the ceiling. The ceiling effect might not be critical in Figure 2, since it tends to attenuate correlation, not inflate it. But in Figures 3 and 4, the authors' conclusion depends on the non-significant partial correlation. In fact, the authors themselves wrote that the ceiling effect might lead to a non-significant correlation even if there is an actual effect (line 404).

Reviewer #3 (Public review):

Summary:

This study examined how young children with minimal reading instruction process letters, focusing on their familiarity with letter shapes, knowledge of letter names, and visual discrimination of upright versus inverted letters. Across four experiments, kindergarten and Grade 1 children could identify the correct orientation of letters even without knowing their names.

Strengths:

This study addresses an important research gap by examining whether children develop letter familiarity prior to formal literacy instruction and how this skill relates to reading-related cognitive abilities. By emphasizing letter familiarity alongside letter recognition, the study highlights a potentially overlooked yet important component of emergent literacy development.

Weaknesses:

The study's methods and results do not effectively test its stated research goals. Reading ability was not directly measured; instead, the authors inferred its relationship with reading from correlations between letter familiarity and reading-related cognitive measures, which limits the validity of their conclusions. Furthermore, the analytical approach was rather limited, relying primarily on simple and partial correlations without employing more advanced statistical methods that could better capture the underlying relationships.

Major Comments:

(1) Limited Novelty and Unclear Theoretical Contribution:

The authors aim to challenge the view that children acquire letter shape knowledge only through formal literacy instruction, but similar questions regarding letter familiarity have already been explored in previous research. The manuscript does not clearly articulate how the present study advances beyond existing findings or why examining letter familiarity specifically before formal instruction provides new theoretical insight. Moreover, if letter familiarity and letter recognition are treated as distinct constructs, the authors should better justify their differentiation and clarify the theoretical significance of focusing on familiarity as an independent component of emergent literacy.

(2) Overgeneralization to Reading Ability:

Although the study measured several literacy-related cognitive skills and examined correlations with letter familiarity, it did not directly assess children's reading ability, as participants had not yet received formal literacy instruction. Therefore, the conclusion that letter familiarity influences reading skills (e.g., Line 519: "Our results are broadly consistent with previous work that has highlighted print letter knowledge as a strong predictor of future reading skills") is not fully supported and should be clarified or revised. To draw conclusions about the impact on reading ability, a longitudinal study would be more appropriate, assessing the relationship between letter familiarity and reading skills after children have received formal literacy instruction. If a longitudinal study is not feasible, measuring familial risk for dyslexia could provide an alternative approach to infer the potential influence of letter familiarity on later reading development.

(3) Confusing and Limited Analytical Approach with Potential for More Sophisticated Modeling:

The study employs a confusing analytical approach, alternating between simple correlational analyses and group-based comparisons, which may introduce circularity - for example, defining high vs. low familiarity groups partly based on performance differences in upright versus inverted letters and then observing a visual search advantage for upright letters within these groups. Moreover, the analyses are relatively simple: although multiple linear regression is mentioned, the results are not fully reported. These approaches may not fully capture the complex relationships among letter familiarity, recognition, visual search performance, RAN, and other covariates. More sophisticated modeling, such as mixed-effects models to account for repeated measures, structural equation modeling to examine latent constructs, or multivariate approaches jointly modeling familiarity and recognition effects, could provide a clearer understanding of the unique contribution of letter shape familiarity to early literacy outcomes. In addition, a large number of correlations were conducted without correction for multiple comparisons, which may increase the risk of false positives and raise concerns about the reliability of some significant findings.

Reviewer #1 (Public review):

In this manuscript, Domingo et al. present a novel perturbation-based approach to experimentally modulate the dosage of genes in cell lines. Their approach is capable of gradually increasing and decreasing gene expression. The authors then use their approach to perturb three key transcription factors and measure the downstream effects on gene expression. Their analysis of the dosage response curve of downstream genes reveals marked non-linearity.

One of the strengths of this study is that many of the perturbations fall within the physiological range for each cis gene. This range is presumably between a single-copy state of heterozygous loss-of-function (log fold change of -1) and a three-copy state (log fold change of ~0.6). This is in contrast with CRISPRi or CRISPRa studies that attempt to maximize the effect of the perturbation, which may result in downstream effects that are not representative of physiological responses.

Another strength of the study is that various points along the dosage-response curve were assayed for each perturbed gene. This allowed the authors to effectively characterize the degree of linearity and monotonicity of each dosage-response relationship. Ultimately, the study revealed that many of these relationships are non-linear, and that the response to activation can be dramatically different than the response to inhibition.

To test their ability to gradually modulate dosage, the authors chose to measure three transcription factors and around 80 known downstream targets. As the authors themselves point out in their discussion about MYB, this biased sample of genes makes it unclear how this approach would generalize genome-wide. In addition, the data generated from this small sample of genes may not represent genome-wide patterns of dosage response. Nevertheless, this unique data set and approach represents a first step in understanding dosage-response relationships between genes.

Another point of general concern in such screens is the use of the immortalized K562 cell line. It is unclear how the biology of these cell lines translates to the in vivo biology of primary cells. However, the authors do follow up with cell-type-specific analyses (Figures 4B, 4C, and 5A) to draw correspondence between their perturbation results and the relevant biology in primary cells and complex diseases.

The conclusions of the study are generally well supported with statistical analysis throughout the manuscript. As an example, the authors utilize well-known model selection methods to identify when there was evidence for non-linear dosage response relationships.

Gradual modulation of gene dosage is a useful approach to model physiological variation in dosage. Experimental perturbation screens that use CRISPR inhibition or activation often use guide RNAs targeting the transcription start site to maximize their effect on gene expression. Generating a physiological range of variation will allow others to better model physiological conditions.

There is broad interest in the field to identify gene regulatory networks using experimental perturbation approaches. The data from this study provides a good resource for such analytical approaches, especially since both inhibition and activation were tested. In addition, these data provide a nuanced, continuous representation of the relationship between effectors and downstream targets, which may play a role in the development of more rigorous regulatory networks.

Human geneticists often focus on loss-of-function variants, which represent natural knock-down experiments, to determine the role of a gene in the biology of a trait. This study demonstrates that dosage response relationships are often non-linear, meaning that the effect of a loss-of-function variant may not necessarily carry information about increases in gene dosage. For the field, this implies that others should continue to focus on both inhibition and activation to fully characterize the relationship between gene and trait.

Comments on revisions:

Thank you for responding to our comments. We have no further comments for the authors.

Reviewer #2 (Public review):

Summary:

This work investigates transcriptional responses to varying levels of transcription factors (TFs). The authors aim for gradual up- and down-regulation of three transcription factors GFI1B, NFE2 and MYB in K562 cells, by using a CRISPRa- and a CRISPRi line, together with sgRNAs of varying potency. Targeted single-cell RNA sequencing is then used to measure gene expression of a set of 90 genes, which were previously shown to be downstream of GFI1B and NFE2 regulation. This is followed by an extensive computational analysis of the scRNA-seq dataset. By grouping cells with the same perturbations, the authors can obtain groups of cells with varying average TF expression levels. The achieved perturbations are generally subtle, not reaching half or double doses for most samples, and up-regulation is generally weak below 1.5-fold in most cases. Even in this small range, many target genes exhibit a non-linear response. Since this is rather unexpected, it is crucial to rule out technical reasons for these observations.

Strengths:

The work showcases how a single dataset of CRISPRi/a perturbations with scRNA-seq readout and an extended computational analysis can be used to estimate transcriptome dose-responses, a general approach that likely can be built upon in the future.<br /> Moreover, the authors highlight tiling of sgRNAs +/-1000bp around TSS as a useful approach. Compared with conventional direct TSS-targeting (+/- 200 bp), the larger sequence window allows placing more sgRNAs. Also it requires little prior knowledge of CREs, and avoids using "attenuated" sgRNAs which would require specialized sgRNA design.

Weaknesses:

The experiment was performed in a single replicate and it would have been reassuring to see an independent validation of the main findings, for example through measuring individual dose-response curves .

Much of the analysis depends on the estimation of log-fold changes between groups of single cells with non-targeting controls and those carrying a guide RNA driving a specific knockdown. Generally, biological replicates are recommended for differential gene expression testing (Squair et al. 2021, https://doi.org/10.1038/s41467-021-25960-2). When using the FindMarkers function from the Seurat package, the authors divert from the recommendations for pseudo-bulk analysis to aggregate the raw counts (https://satijalab.org/seurat/articles/de_vignette.html). Furthermore, differential gene expression analysis of scRNA-seq data can suffer from mis-estimations (Nguyen et al. 2023, https://doi.org/10.1038/s41467-023-37126-3), and different computational tools or versions can affect these estimates strongly (Pullin et al. 2024, https://doi.org/10.1186/s13059-024-03183-0 and Rich et al. 2024, https://doi.org/10.1101/2024.04.04.588111). Therefore it would be important to describe more precisely in the Methods how this analysis was performed, any deviations from default parameters, package versions, and at which point which values were aggregated to form "pseudobulk" samples.

Two different cell lines are used to construct dose-response curves, where a CRISPRi line allows gene down-regulation and the CRISPRa line allows gene upregulation. Although both lines are derived from the same parental line (K562) the expression analysis of Tet2, which is absent in the CRISPRi line, but expressed in the CRISPRa line (Fig. S1F, S3A) suggests clonal differences between the two lines. Similarly, the UMAP in S3C and the PCA in S4A suggest batch effects between the two lines. These might confound this analysis, even though all fold changes are calculated relative to the baseline expression in the respective cell line (NTC cells). Combining log2-fold changes from the two cell lines with different baseline expression into a single curve (e.g. Fig. 3) remains misleading, because different data points could be normalized to different base line expression levels.

The study estimates the relationship between TF dose and target gene expression. This requires a system that allows quantitative changes in TF expression. The data provided does not convincingly show that this condition is met, which however is an essential prerequisite for the presented conclusions. Specifically, the data shown in Fig. S3A shows that upon stronger knock-down, a subpopulation of cells appear, where the targeted TF is not detected any more (drop-outs). Also in Fig. 3B (top) suggests that the knock-down is either subtle (similar to NTCs) or strong, but intermediate knock-down (log2-FC of 0.5-1) does not occur. Although the authors argue that this is a technical effect of the scRNA-seq protocol, it is also possible that this represents a binary behavior of the CRISPRi system. Previous work has shown that CRISPRi systems with the KRAB domain largely result in binary repression and not in gradual down-regulation as suggested in this study (Bintu et al. 2016 (https://doi.org/10.1126/science.aab2956), Noviello et al. 2023 (https://doi.org/10.1038/s41467-023-38909-4)).

One of the major conclusions of the study is that non-linear behavior is common. It would be helpful to show that this observation does not arise from the technical concerns described in the previous points. This could be done for instance with independent experimental validations.

Did the authors achieve their aims? Do the results support the conclusions?:

Some of the most important conclusions, such as the claim that non-linear responses are common, are not well supported because they rely on accurately determining the quantitative responses of trans genes, which suffers from the previously mentioned concerns.

Discussion of the likely impact of the work on the field, and the utility of the methods and data to the community:

Together with other recent publications, this work emphasizes the need to study transcription factor function with quantitative perturbations. The computational code repository contains all the valuable code with inline comments, but would have benefited from a readme file explaining the repository structure, package versions, and instructions to reproduce the analyses, including which input files or directory structure would be needed.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

While it seems to provide convincing results, there are multiple technical concerns for the authors to clarify and double-check.

(1) The authors should clarify and discuss the TF-target map: here, the TF-target genes map is predefined by the TF binding's ChIP-seq data. This annotation is largely incomplete and mostly compiled from a set of bulk tissues. Therefore, for a certain population, the TF-target relation may change. This requires clarification and discussion, possibly exploring how to address this in the model. In addition, a regulon database could be added, e.g., DoRothEA?

(2) The authors should clarify how example genes are selected. This is particularly unclear in Figure 2d.

(3) The authors should clarify confidence in the statement in lines 179-180, that ANXA4 should initially decrease. This is particularly concerning, as TSvelo didn't capture the cell cycle transitions well during the initial part.

(4) A support reference should be added for the statement in line 260 that "neuron migrations are inside-out manner". There is no reference supporting this, and this statement is critical for the model assessment.

(5) The comparison to scMultiomics data is particularly interesting, as MultiVelo uses ATAC data to predict the transcription rate. It would be very insightful to add a direct comparison of the estimated transcription rate between using ATAC and directly using TFs' RNA expressions.

(6) In Figure 6g, it should be clarified how the lineage was determined. Did the authors use the LARRY barcodes, predicted cell fate, or any other methods? Here, the best way is probably using the LARRY barcodes for individual clones.

Reviewer #2 (Public review):

Summary:

Li et al. propose TSvelo, a computational framework for RNA velocity inference that models transcriptional regulation and gene-specific splicing using a neural ODE approach. The method is intended to improve trajectory reconstruction and capture dynamic gene expression changes in scRNA-seq data. However, the manuscript in its current form falls short in several critical areas, including rigorous validation, quantitative benchmarking, clarity of definitions, proper use of prior knowledge, and interpretive caution. Many of the authors' claims are not fully supported by the evidence.

Major comments:

(1) Modeling comments

(a) Lines 512-513: How does the U-to-S delay validate the accuracy of pseudotime? Using only a single gene as an example is not sufficient for "validation."

(b) Lines 512-518: The authors propose a strategy for selecting the initial state, but do not benchmark how accurate this selection procedure is, nor do they provide sufficient rationale. While some genes may indeed exhibit U-to-S delay during lineage differentiation, why does the highest U-to-S delay score indicate the correct initiation states? Please provide mathematical justification and demonstrate accuracy beyond using a single gene example. Maybe a simulation with ground truth could help here, too.

(c) Equation (8): The formulation looks to be incorrect. If $$W \in \mathbb{R}^{G\times G}$$ and $$W' - \Gamma' \in \mathbb{R}^{K\times K}$$, how can they be aligned within the same row? Please clarify.

(d) The use of prior knowledge graphs from ENCODE or ChEA to constrain regulation raises concerns. Much of the regulatory information in these databases comes from cell lines. How can such cell-line-based regulation be reliably applied to primary tissues, as is done throughout the manuscript? Additional experiments are needed to test the robustness of TSvelo with respect to prior knowledge.

(e) Lines 579-580: How is the grid search performed? More methodological details are required. If an existing method was used, please provide a citation.

(2) Application on pancreatic endocrine datasets

(a) Lines 140-141: What is the definition of the final pseudotime-fitted time t or velocity pseudotime?

(b) Lines 143-144: The use of the velocity consistency metric to benchmark methods in multi-lineage datasets is incorrect. In multi-lineage differentiation systems, cells (e.g., those in fate priming stages) may inherently show inconsistency in their velocity. Thus, it is difficult to distinguish inconsistency caused by estimation error from that arising from biological signals. Velocity consistency metrics are only appropriate in systems with unidirectional trajectories (e.g., cell cycling). The abnormally high consistency values here raise concerns about whether the estimated velocities meaningfully capture lineage differences.

(c) The improvement of TSvelo over other methods in terms of cross-boundary direction correctness looks marginal; a statistical test would help to assess its significance.

(d) Lines 177-178: Based on the figure, TSvelo does not appear to clearly distinguish cell types. A quantitative metric, such as Adjusted Rand Index (ARI), should be provided.

(e) Lines 179-183: The claim that traditional methods cannot capture dynamics in the unspliced-spliced phase portrait is vague. What specific aspect is not captured-the fitted values or something else? Evidence is lacking. Please provide a detailed explanation and quantitative metrics to support this claim.

(3) Application to gastrulation erythroid datasets

(a) Lines 191-194: The observation that velocity genes are enriched for erythropoiesis-related pathways is trivial, since the analysis is restricted to highly variable genes (HVGs) from an erythropoiesis dataset. This enrichment is expected and therefore not informative.

(b) Lines 227-228: It remains unclear how TSvelo "accurately captures the dynamics." What is the definition of dynamics in this context? Figure 3g shows unspliced/spliced vs. fitted time plots and phase portraits, but without a quantitative definition or measure, the claim of superiority cannot be supported. Visualization of a single gene is insufficient; a systematic and quantitative analysis is needed.

(4) Application to the mouse brain and other datasets

(a) Lines 280-281: The authors cannot claim that velocity streams are smoother in TSvelo than in Multivelo based solely on 2D visualization. Similarly, claiming that one model predicts the correct differentiation trajectory from a 2D projection is over-interpretation, as has been discussed in prior literature see PMID: 37885016.

(b) Lines 304-306: Beyond transcriptional signal estimation, how is regulation inferred solely from scRNA-seq data validated, especially compared with scATAC-seq data? Are there cases where transcriptome-based regulatory inference is supported by epigenomic evidence, thereby demonstrating TSvelo's GRN inference accuracy?

(c) The claim that TSvelo can model multi-lineage datasets hinges on its use of PAGA for lineage segmentation, followed by independent modeling of dynamics within each subset. However, the procedure for merging results across subsets remains unclear.

Reviewer #3 (Public review):

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

While the approach has the potential to be of interest for the field, and may present improvements compared to existing approaches, there are some major limitations that should be addressed, particularly concerning the benchmark (see major comment 1).

Major comments:

(1) My main criticism concerns the benchmarking: real data lack a ground truth, and are absolutely not ideal for comparing methods, because one can only speculate what results appear to be more plausible.<br /> A solid and extensive simulation study, which covers various scenarios and possibly distinct data-generating models, is needed for comparing approaches. The authors should check, for example, the simulation studies in the BayVel approach (Section 4, BayVel: A Bayesian Framework for RNA Velocity Estimation in Single-Cell Transcriptomics). Clearly, all methods should be included in the simulation.

(2) Related to the above: since a ground truth is missing, the real data analyses need to be interpreted with caution. I recommend avoiding strong statements, such as "successfully captures the correct gene dynamics", or "accurately infer", in favour of milder statements supported by the data, such as "... aligns with the biological processes described" (as in page 12), or "results are compatible with current biological knowledge", etc...

(3) Many methods perform RNA velocity analyses. While there is a brief description, I think it'd be useful to have a schematic summary (e.g., via a Table) of the main conceptual, mathematical, and computational characteristics of each approach.

(4) Related to the above: I struggled to identify the main conceptual novelty of TSvelo, compared to existing approaches. I recommend explaining this aspect more extensively.

(5) A computational benchmark is missing; I'd appreciate seeing the runtime and memory cost of all methods in a couple of datasets.

(6) I think BayVel (mentioned above) should be added to the list of competing methods (both in the text and in the benchmarks). The package can be found here: https://github.com/elenasabbioni/BayVel_pkgJulia .

Reviewer #1 (Public review):

Summary:

Stemming from the previous research on the adaptation of methylotrophic microbes in the phyllosphere environment, this paper tested a novel hypothesis on the molecular and cellular mechanisms by which yeast uses biomolecular condensates as unique niches for the regulation of methanol-induced mRNAs. While a few in vivo experiments were conducted in the phyllosphere, more assays were carried out on plates to mimic various stress conditions, diminishing the reliability of the conclusions in supporting the main hypothesis.

Strengths:

This study addressed an interesting and important biological question. Some of the experiments were conducted methodically and carefully. The visualization of both the biomolecular condensates and the mRNAs was helpful in addressing the questions. The results are expected to be useful in paving the way for the future study to directly test its main hypothesis. The results of this study could also have a general implication for the adaptation of a huge population of microbes in the enormous space of the phyllosphere on Earth.

Weaknesses:

The results were often over- and misinterpreted. Given mthat any hypotheses were tested indirectly on plates, the correlative results could only be used to carefully suggest the likelihood of the hypotheses. For example, a single edc3 mutant was used to represent a P-body-defective strain, although it is well known that EDC3 is a critical component in mRNA decapping; hence, the mutant should display a pleiotropic phenotype, rather than a mere reduced P-body phenotype. Using a similar reductionist approach, the study went on to employ a series of plate assays to argue that the conditions were mimicking the phyllosphere, which could be misleading under these circumstances. Furthermore, the low percentage of the colocalization between P-bodies and mimRNA granules and the similar results from negative control mRNAs do not convincingly support the idea that mimRNAs are sequestered between two biomolecular condensates, and P-bodies could serve as regulatory hubs. Given that the abundance of mimRNA granules was positively correlated with the transcript abundance of mimRNAs, and P-body abundance did not change too much under methanol induction, the results could not support an active mimRNA sequestration mechanism from mimRNA granules to P-bodies with a proportional increase of the overlap between the two condensates. More direct experiments conducted in the phyllosphere using multiple P-body defective yeast strains should strengthen the manuscript, assuming all the results turned out to be supportive.

Reviewer #2 (Public review):

Summary:

This article aims to elucidate the potential roles of P-bodies in yeast adaptation to complex environmental conditions, such as the plant leaf phyllosphere. The authors demonstrated that yeast mutants defective in one of the P-body-localized proteins failed to grow in the Arabidopsis thaliana phyllosphere. They conducted detailed imaging analyses, focusing particularly on the co-localization of P-bodies and mRNAs (DAS1) related to the methanol metabolism pathway under various environmental conditions. The study newly revealed that these mRNAs form dot-like structures that occasionally co-localize with a P-body marker. Furthermore, the authors showed that the number of P-body-labeled dots increases under stress conditions, such as H₂O₂ treatment, and that mRNA dots are more frequently localized to P-body-like structures. Based on these detailed observations, the authors hypothesize that P-bodies function to protect mRNAs from degradation, particularly under stress conditions.

Strengths:

I think the authors' attempt to elucidate the potential roles of P-bodies in yeast under stress conditions is novel, and the imaging data are overall very nice.

Weaknesses:

I believe the authors could make additional efforts to more clearly demonstrate that P-bodies are indeed required for yeast proliferation in the phyllosphere, as described below, since this represents the most novel aspect of the study.

Reviewer #3 (Public review):

Summary:

The authors use fluorescent microscopy and fluorescent markers to investigate the requirement of P-bodies during growth on methanol, a common substrate available on plant leaves, by using a yeast edc3 mutant defective in P-body formation. Growth on methanol upregulates the transcription of methanol metabolic genes, which accumulate in granular structures, as observed by microscopy. Co-localization of P-bodies and granules was quantified and described as dynamically enhanced during oxidative stress. Ultimately, the authors suggest a model where methanol induces the accumulation of methanol-induced mRNAs in cytosolic granules, which dynamically interact with P-bodies, especially during oxidative stress, to protect the mRNAs from degradation. However, this model is not strongly supported by the provided data, as the quantification of the co-localization between different markers (of organelles and between P-body and granules) is not well presented or described in the text.

Considering that there is only a small EDC3-dependent overlap between P-bodies and mimRNA granules, the claim that P-bodies regulate mimRNAs is not fully justified. Rather, EDC3 could also be involved in mimRNA granule formation, independent of P-bodies.

Strengths:

(1) The authors could show convincingly that P-bodies (using a P-body-deficient edc3-KO strain) are important for colonizing the plant phyllosphere and for the regulation of methanol-induced mRNAs (mimRNA).

(2) The visualization of mimRNA granules and P-bodies using fluorescent markers is interesting and was validated by alternative methods, such as FISH staining.

(3) The dynamic formation of mimRNA granules and P-bodies was demonstrated during growth on leaves and in artificial medium during oxidative stress. The mimRNA granules showed a similar dynamic as the abundances of several mimRNAs and their corresponding proteins.

(4) A role of EDC3 in the formation of mimRNA granules was demonstrated. However, the link between P-bodies and mimRNA granules was not clearly shown.

Weaknesses:

(1) The study largely relies on fluorescent microscopy and co-localization measurements. However, the subcellular resolution is not very high; it is unclear how dot-like structures were measured and, importantly, how co-localization was quantified.

(2) The text does not clarify to what degree P-bodies and mimRNA granules are different structures. Based on the images, the size of P-bodies and granules seems to be vastly different, making it unclear whether these structures are fused or separate, even if their markers are reported to overlap.

(3) The evidence that mimRNA granules contain ribosome-free and ribosome-associated RNA is only based on inhibitors and microscopy, without providing further evidence measuring granule content by isolation and sequencing approaches.

(4) Similarly, the co-localization with other organelle markers is not supported by quantitative data.

Reviewer #1 (Public review):

Summary

The manuscript by Ma et al. provides robust and novel evidence that the noctuid moth Spodoptera frugiperda (Fall Armyworm) possesses a complex compass mechanism for seasonal migration that integrates visual horizon cues with Earth's magnetic field (likely its horizontal component). This is an important and timely study: apart from the Bogong moth, no other nocturnal Lepidoptera has yet been shown to rely on such a dual-compass system. The research therefore expands our understanding of magnetic orientation in insects with both theoretical (evolution and sensory biology) and applied (agricultural pest management, a new model of magnetoreception) significance.

The study uses state-of-the-art methods and presents convincing behavioural evidence for a multimodal compass. It also establishes the Fall Armyworm as a tractable new insect model for exploring the sensory mechanisms of magnetoreception, given the experimental challenges of working with migratory birds. Overall, the experiments are well-designed, the analyses are appropriate, and the conclusions are generally well supported by the data.

Strengths

(1) Novelty and significance: First strong demonstration of a magnetic-visual compass in a globally relevant migratory moth species, extending previous findings from the Bogong moth and opening new research avenues in comparative magnetoreception.

(2) Methodological robustness: Use of validated and sophisticated behavioural paradigms and magnetic manipulations consistent with best practices in the field. The use of 5-minute bins to study the dynamic nature of the magnetic compass which is anchored to a visual cue but updated with a latency of several minutes, is an important finding and a new methodological aspect in insect orientation studies.

(3) Clarity of experimental logic: The cue-conflict and visual cue manipulations are conceptually sound and capable of addressing clear mechanistic questions.

(4) Ecological and applied relevance: Results have implications for understanding migration in an invasive agricultural pest with an expanding global range.

(5) Potential model system: Provides a new, experimentally accessible species for dissecting the sensory and neural bases of magnetic orientation.

Weaknesses

While the study is strong overall, several recommendations should be addressed to improve clarity, contextualisation, and reproducibility:

(1) Structure and presentation of results

Requires reordering the visual-cue experiments to move from simpler (no cues) to more complex (cue-conflict) conditions, improving narrative logic and accessibility for non-specialists.

(2) Ecological interpretation

(a) The authors should discuss how their highly simplified, static cue setup translates to natural migratory conditions where landmarks are dynamic, transient or absent.

(b) Further consideration is required regarding how the compass might function when landmarks shift position, are obscured, or are replaced by celestial cues. Also, more consolidated (one section) and concrete suggestions for future experiments are needed, with transient, multiple, or more naturalistic visual cues to address this.

(3) Methodological details and reproducibility

(a) It would be better to move critical information (e.g., electromagnetic noise measurements) from the supplementary material into the main Methods.

(b) Specifying luminance levels and spectral composition at the moth's eye is required for all visual treatments.

(c) Details are needed on the sex ratio/reproductive status of tested moths, and a map of the experimental site and migratory routes (spring vs. fall) should be included.

(d) Expanding on activity-level analyses is required, replacing "fatigue" with "reduced flight activity," and clarifying if such analyses were performed.

(4) Figures and data presentation

(a) The font sizes on circular plots should be increased; compass labels (magnetic North), sample sizes, and p-values should be included.

(b) More clarity is required on what "no visual cue" conditions entail, and schematics or photos should be provided.

(c) The figure legends should be adjusted for readability and consistency (e.g., replace "magnetic South" with magnetic North, and for box plots better to use asterisks for significance, report confidence intervals).

(5) Conceptual framing and discussion

(a) Generalisations across species should be toned down, given the small number of systems tested by overlapping author groups.

(b) It requires highlighting that, unlike some vertebrates, moths require both magnetic and visual cues for orientation.

(c) It should be emphasised that this study addresses direction finding rather than full navigation.

(d) Future Directions should be integrated and consolidated into one coherent subsection proposing realistic next steps (e.g., more complex visual environments, temporal adaptation to cue-field relationships).

(e) The limitations should be better discussed, due to the artificiality of the visual cue earlier in the Discussion.

(6) Technical and open-science points

• Appropriate circular statistics should be used instead of t-tests for angular data shown in the supplementary material.

• Details should be provided on light intensities, power supplies, and improvements to the apparatus.

• The derivation of individual r-values should be clarified.

• Share R code openly (e.g., GitHub).

• Some highly relevant - yet missing - recent and relevant citations should be added, and some less relevant ones removed.

Reviewer #2 (Public review):

Summary:

This work provided experimental evidence on how geomagnetic and visual cues are integrated, and visual cues are indispensable for magnetic orientation in the nocturnal fall armyworm.

Strengths:

Although it has been demonstrated previously that the Australian Bogon moth could integrate global stellar cues with the geomagnetic field for long-distance navigation, the study presented in this manuscript is still fundamentally important to the field of magnetoreception and sensory biology. It clearly shows that the integration of geomagnetic and visual cues may represent a conserved navigational mechanism broadly employed across migratory insects. I find the research very important, and the results are presented very well.

Weaknesses:

The authors developed an indoor experimental system to study the influence of magnetic fields and visual cues on insect orientation, which is certainly a valuable approach for this field. However, the ecological relevance of the visual cue may be limited or unclear based on the current version. The visual cues were provided "by a black isosceles triangle (10 cm high, 10 cm 513 base) made from black wallpaper and fixed to the horizon at the bottom of the arena". It is difficult to conceive how such a stimulus (intended to represent a landmark like a mountain) could provide directional information for LONG-DISTANCE navigation in nocturnal fall armyworms, particularly given that these insects would have no prior memory of this specific landmark. It might be a good idea to make a more detailed explanation of this question.

Reviewer #1 (Public review):

Summary:

Zhou and colleagues introduce a series of generalized Gaussian process models for genotype-phenotype mapping. The goal was to develop models that were more powerful than standard linear models, while retaining explanatory power as opposed to neural network approaches. The novelty stems from choices of prior distributions (and I suppose fitted posteriors) that model epistasis based on some form of site/allele-specific modifier effect and genotype distance. The authors then apply their models to three empirical datasets, the GB1 antibody-binding dataset, the human 5' splice set dataset, and a yeast meiotic cross dataset, and find substantially improved variance explained while retaining strong explanatory power when compared to linear models.

Strengths:

The main strength of the manuscript lies in the development of the modeling approaches, as well as the evidence from the empirical dataset that the variance explained is improved.

Weaknesses:

The main weakness of the paper is that none of the models were tested on an in silico dataset where the ground truth is known. Therefore, it is unclear if their model actually retains any explanatory power.

Impact:

Genotype-phenotype mapping is a central point of genetics. However, the function is complex and unknown. Simple linear models can uncover some functional link between genes and their effects, but do so through severe oversimplification of the system. On the other hand, neural networks can, in principle, model the function perfectly, but it does so without easy interpretation. Gaussian regression is another approach that improves on linear regression, allowing better fitting of the data while allowing interpretation of the underlying alleles and their effects. This approach, now computable with state-of-the-art algorithms, will advance the field of genotype-to-phenotype associations.

Reviewer #2 (Public review):

This paper builds on prior work by some of the same authors on how to model fitness landscapes in the presence of epistasis. They have previously shown how simply writing general expansions of fitness in terms of one-body plus two-body plus three-body, etc., terms often fails to generalize to good predictions. They have also previously introduced a Gaussian process regression approach regarding how much epistasis there should be of each order.

This paper contains several main advances:

(1) They implement a more efficient form of the Gaussian process model fitting that uses GPUs and related algorithmic advances to enable better fitting of these models to datasets for larger sequences.

(2) They provide a software package implementing the above.

(3) They generalize the models to allow the extent of epistasis associated with changes in sequence to depend on specific sites, alleles, and mutations.

(4) They show modest improvements in prediction and substantial improvements in interpretability with the more generalized models above.

Overall, while this paper is quite technical, my assessment is that it represents a substantial conceptual and algorithmic advance for the above reasons, and I would recommend only modest revisions. The paper seems well-written and clear, given the inherent complexity of this topic.

Reviewer #3 (Public review):

Summary:

The authors propose three types of Gaussian process kernels that extend and generalize standard kernels used for sequence-function prediction tasks, giving rise to the connectedness, Jenga, and general product models. The associated hyperparameters are interpretable and represent epistatic effects of varying complexity. The proposed models significantly outperform the simpler baselines, including the additive model, pairwise interaction model, and Gaussian process with a geometric kernel, in terms of R^2.

Strengths:

(1) The demonstrated performance boost and improved scaling with increasing training data are compelling.

(2) The hyperparameter selection step using the marginal likelihood, as implemented by the authors, seems to yield a reasonable hyperparameter combination that lends itself to biologically plausible interpretations.

(3) The proposed kernels generalize existing kernels in domain-interpretable ways, and can correspond to cases that would not be "physical" in the original models (e.g., $\mu_p>1$ in the original connectedness model that allows modeling of anticorrelated phenotypes).

Weaknesses:

(1) While enabling uncertainty quantification is a key advantage of Gaussian processes, the authors do not present metrics specific to the predicted uncertainties; all metrics seem to concern the mean predictions only. It would be helpful to evaluate coverage metrics and maybe include an application of the uncertainties, such as in active learning or Bayesian optimization.

(2) The more complex models, like the general product model, place a heavier burden on the hyperparameter selection step. Explicitly discussing the optimization routine used here would be helpful to potential users of the method and code.

Reviewer #1 (Public review):

Summary:

This paper presents an ambitious and technically impressive attempt to map how well humans can discriminate between colours across the entire isoluminant plane. The authors introduce a novel Wishart Process Psychophysical Model (WPPM) - a Bayesian method that estimates how visual noise varies across colour space. Using an adaptive sampling procedure, they then obtain a dense set of discrimination thresholds from relatively few trials, producing a smooth, continuous map of perceptual sensitivity. They validate their procedure by comparing actual and predicted thresholds at an independent set of sample points. The work is a valuable contribution to computational psychophysics and offers a promising framework for modelling other perceptual stimulus fields more generally.

Strengths:

The approach is elegant and well-described (I learned a lot!), and the data are of high quality. The writing throughout is clear, and the figures are clean (elegant in fact) and do a good job of explaining how the analysis was performed. The whole paper is tremendously thorough, and the technical appendices and attention to detail are impressive (for example, a huge amount of data about calibration, variability of the stim system over time, etc). This should be a touchstone for other papers that use calibrated colour stimuli.

Weaknesses:

Overall, the paper works as a general validation of the WPPM approach. Importantly, the authors validate the model for the particular stimuli that they use by testing model predictions against novel sample locations that were not part of the fitting procedure (Figure 2). The agreement is pretty good, and there is no overall bias (perhaps local bias?), but they do note a statistically-significant deviation in the shape of the threshold ellipses. The data also deviate significantly from historical measurements, and I think the paper would be considerably stronger with additional analyses to test the generality of its conclusions and to make clearer how they connect with classical colour vision research. In particular, three points could use some extra work:

(1) Smoothness prior.<br /> The WPPM assumes that perceptual noise changes smoothly across colour space, but the degree of smoothness (the eta parameter) must affect the results. I did not see an analysis of its effects - it seems to be fixed at 0.5 (line 650). The authors claim that because the confidence intervals of the MOCS and the model thresholds overlap (line 223), the smoothing is not a problem, but this might just be because the thresholds are noisy. A systematic analysis varying this parameter (or at least testing a few other values), and reporting both predictive accuracy and anisotropy magnitude, would clarify whether the model's smoothness assumption is permitting or suppressing genuine structure in the data. Is the gamma parameter also similarly important? In particular, does changing the underlying smoothness constraint alter the systematic deviation between the model and the MOCS thresholds? The authors have thought about this (of course! - line 224), but also note a discrepancy (line 238). I also wonder if it would be possible to do some analysis on the posterior, which might also show if there are some regions of color space where this matters more than others? The reason for doing this is, in part, motivated by the third point below - it's not clear how well the fits here agree with historical data.

(2) Comparison with simpler models. It would help to see whether the full WPPM is genuinely required. Clearly, the data (both here and from historical papers) require some sort of anisotropy in the fitting - the sensitivities decrease as the stimuli move away from the adaptation point. But it's >not< clear how much the fits benefit from the full parameterisation used here. Perhaps fits for a small hierarchy of simpler models - starting with isotropic Gaussian noise (as a sort of 'null baseline') and progressing to a few low-dimensional variants - would reveal how much predictive power is gained by adding spatially varying anisotropy. This would demonstrate that the model's complexity is justified by the data.

(3) Quantitative comparison to historical data. The paper currently compares its results to MacAdam, Krauskopf & Karl, and Danilova & Mollon only by visual inspection. It is hard to extract and scale actual data from historical papers, but from the quality of the plotting here, it looks like the authors have achieved this, and so quantitative comparisons are possible. The MacAdam data comparisons are pretty interesting - in particular, the orientations of the long axes of the threshold ellipses do not really seem to line up between the two datasets - and I thought that the orientation of those ellipses was a critical feature of the MacAdam data. Quantitative comparisons (perhaps overall correlations, which should be immune to scaling issues, axis-ratio, orientation, or RMS differences) would give concrete measures of the quality of the model. I know the authors spend a lot of time comparing to the CIE data, and this is great.... But re-expressing the fitted thresholds in CIE or DKL coordinates, and comparing them directly with classical datasets, would make the paper's claims of "agreement" much more convincing.

Overall, this is a creative and technically sophisticated paper that will be of broad interest to vision scientists. It is probably already a definitive methods paper showing how we can sample sensitivity accurately across colour space (and other visual stimulus spaces). But I think that until the comparison with historical datasets is made clear (and, for example, how the optimal smoothness parameters are estimated), it has slightly less to tell us about human colour vision. This might actually be fine - perhaps we just need the methods?

Related to this, I'd also note that the authors chose a very non-standard stimulus to perform these measurements with (a rendered 3D 'Greebley' blob). This does have the advantage of some sort of ecological validity. But it has the significant >disadvantage< that it is unlike all the other (much simpler) stimuli that have been used in the past - and this is likely to be one of the reasons why the current (fitted) data do not seem to sit in very good agreement with historical measurements.

Reviewer #2 (Public review):

Summary:

Hong et al. present a new method that uses a Wishart process to dramatically increase the efficiency of measuring visual sensitivity as a function of stimulus parameters for stimuli that vary in a multidimensional space. Importantly, they have validated their model against their own hold-out data and against 3 published datasets, as well as against colour spaces aimed at 'perceptual uniformity' by equating JNDs. Their model achieves high predictive success and could be usefully applied in colour vision science and psychophysics more generally, and to tackle analogous problems in neuroscience featuring smooth variation over coordinate spaces.

Strengths:

(1) This research makes a substantial contribution by providing a new method to very significantly increase the efficiency with which inferences about visual sensitivity can be drawn, so much so that it will open up new research avenues that were previously not feasible. Secondly, the methods are well thought out and unusually robust. The authors made a lot of effort to validate their model, but also to put their results in the context of existing results on colour discrimination, transforming their results to present them in the same colour spaces as used by previous authors to allow direct comparisons. Hold-out validation is a great way to test the model, and this has been done for an unusually large number of observers (by the standards of colour discrimination research). Thirdly, they make their code and materials freely available with the intention of supporting progress and innovation. These tools are likely to be widely used in vision science, and could of course be used to address analogous problems for other sensory modalities and beyond.

Weaknesses:

It would be nice to better understand what constraints the choice of basis functions puts on the space of possible solutions. More generally, could there be particular features of colour discrimination (e.g., rapid changes near the white point) that the model captures less well? The substantial individual differences evident in Figure S20 (comparison with Krauskopf and Gegenfurtner, 1992) are interesting in this context. Some observers show radial biases for the discrimination ellipses away from the white point, some show biases along the negative diagonal (with major axes oriented parallel to the blue-yellow axis), and others show a mixture of the two biases. Are these genuine individual differences, or could the model be performing less accurately in this desaturated region of colour space?

Reviewer #3 (Public review):

Summary:

This study presents a powerful and rigorous approach for characterizing stimulus discriminability throughout a sensory manifold, and is applied to the specific context of predicting color discrimination thresholds across the chromatic plane.

Strengths:

Color discrimination has played a fundamental role in studies of human color vision and for color applications, but as the authors note, it remains poorly characterized. The study leverages the assumption that thresholds should vary smoothly and systematically within the space, and validates this with their own tests and comparisons with previous studies.

Weaknesses:

The paper assumes that threshold variations are due to changes in the level of intrinsic noise at different stimulus levels. However, it's not clear to me why they could not also be explained by nonlinearities in the responses, with fixed noise. Indeed, most accounts of contrast coding (which the study is at least in part measuring because the presentation kept the adapt point close to the gray background chromaticity, and thus measured increment thresholds), assume a nonlinear contrast response function, which can at least as easily explain why the thresholds were higher for colors farther from the gray point. It would be very helpful if a section could be added that explains why noise differences rather than signal differences are assumed and how these could be distinguished. If they cannot, then it would be better to allow for both and refer to the variation in terms of S/N rather than N alone.

Related to this point, the authors note that the thresholds should depend on a number of additional factors, including the spatial and temporal properties and the state of adaptation. However, many of these again seem to be more likely to affect the signal than the noise.

An advantage of the approach is that it makes no assumptions about the underlying mechanisms. However, the choice to sample only within the equiluminant plane is itself a mechanistic assumption, and these could potentially be leveraged for deciding how to sample to improve the characterization and efficiency. For example, given what we know about early color coding, would it be more (or less) efficient to select samples based on a DKL space, etc?

Reviewer #1 (Public review):

In this paper, the authors wished to determine human visuomotor mismatch responses in EEG in a VR setting. Participants were required to walk around a virtual corridor, where a mismatch was created by halting the display for 0.5s. This occurred every 10-15 seconds. They observe an occipital mismatch signal at 180 ms. They determine the specificity of this signal to visuomotor mismatch by subsequently playing back the same recording passively. They also show qualitatively that the mismatch response is larger than one generated in a standard auditory oddball paradigm. They conclude that humans therefore exhibit visuomotor mismatch responses like mice, and that this may provide an especially powerful paradigm for studying prediction error more generally.

Asking about the role of visuomotor prediction in sensory processing is of fundamental importance to understanding perception and action control, but I wasn't entirely sure what to conclude from the present paradigm or findings. Visuomotor prediction did not appear to have been functionally isolated. I hope the comments below are helpful.

(1) First, isolating visuomotor prediction by contrasting against a condition where the same video stream is played back subsequently does not seem to isolate visuomotor prediction. This condition always comes second, and therefore, predictability (rather than specifically visuomotor predictability) differs. Participants can learn to expect these screen freezes every 10-15 s, even precisely where they are in the session, and this will reduce the prediction error across time. Therefore, the smaller response in the passive condition may be partly explained by such learning. It's impossible to fully remove this confound, because the authors currently play back the visual specifics from the visuomotor condition, but given that the visuomotor correspondences are otherwise pretty stable, they could have an additional control condition where someone else's visual trace is played back instead of their own, and order counterbalanced. Learning that the freezes occur every 10-15 s, or even precisely where they occur, therefore, could not explain condition differences. At a minimum, it would be nice to see the traces for the first and second half of each session to see the extent to which the mismatch response gets smaller. This won't control for learning about the specific separations of the freezes, but it's a step up from the current information.

(2) Second, the authors admirably modified their visual-only condition to remove nausea from 6 df of movement (3D position, pitch, yaw, and roll). However, despite the fact it's far from ideal to have nauseous participants, it would appear from the figures that these modifications may have changed the responses (despite some pairwise lack of significance with small N). Specifically, the trace in S3 (6DOF) and 2E look similar - i.e., comparing the visuomotor condition to the visual condition that matches. Mismatch at 4/5 microvolts in both. Do these significantly differ from each other?

(3) It generally seems that if the authors wish to suggest that this paradigm can be used to study prediction error responses, they need to have controlled for the actions performed and the visual events. This logic is outlined in Press, Thomas, and Yon (2023), Neurosci Biobehav Rev, and Press, Kok, and Yon (2020) Trends Cogn Sci ('learning to perceive and perceiving to learn'). For example, always requiring Ps to walk and always concurrently playing similar visual events, but modifying the extent to which the visual events can be anticipated based on action. Otherwise, it seems more accurately described as a paradigm to study the influence of action on perception, which will be generated by a number of intertwined underlying mechanisms.

More minor points:

(1) I was also wondering whether the authors may consider the findings in frontal electrodes more closely. Within the statistical tests of the frontal electrodes against 0, as displayed in Figure 3c, the insignificance of the effect of Fp2 seems attributable to the small included sample size of just 13 participants for this electrode, as listed in Table S1, in combination with a single outlier skewing the result. The small sample size stands out especially in comparison to the sample size at occipital electrodes, which is double and therefore enjoys far more statistical power. It looks like the selected time window is not perfectly aligned for determining a frontal effect, and also the distribution in 3B looks like responses are absent in more central electrodes but present in occipital and frontal ones. I realise the focus of analysis is on visual processing, but there are likely to be researchers who find the frontal effect just as interesting.

(2) It is claimed throughout the manuscript that the 'strongest predictor (of sensory input) - by consistency of coupling - is self-generated movement'. This claim is going to be hard to validate, and I wonder whether it might be received better by the community to be framed as an especially strong predictor rather than necessarily the strongest. If I hear an ambulance siren, this is an especially strong predictor of subsequent visual events. If I see a traffic light turn red, then yellow, I can be pretty certain what will happen next. Etc.

(3) The checkerboard inversion response at 48 ms is incredibly rapid. Can the authors comment more on what may drive this exceptionally fast response? It was my understanding that responses in this time window can only be isolated with human EEG by presenting spatially polarized events (cf. c1, e.g., Alilovic, Timmermans, Reteig, van Gaal, Slagter, 2019, Cerebral Cortex)

Reviewer #2 (Public review):

Summary:

This study investigates whether visuomotor mismatch responses can be detected in humans. By adapting paradigms from rodent studies, the authors report EEG evidence of mismatch responses during visuomotor conditions and compare them to visual-only stimulation and mismatch responses in other modalities.

Strengths:

(1) The authors use a creative experimental design to elicit visuomotor mismatch responses in humans.

(2) The study provides an initial dataset and analytical framework that could support future research on human visuomotor prediction errors.

Weaknesses:

(1) Methodological issues (e.g., volume conduction, channel selection, lack of control for eye movements) make it difficult to confidently attribute the observed mismatch responses to activity in visual cortical regions.

(2) A very large portion of the data was excluded due to motion artefacts, raising concerns about statistical power and representativeness. The criteria for trial inclusion and the number of accepted trials per participant appear arbitrary and not justified with reference to EEG reliability standards.

(3) The comparison across sensory modalities (e.g., auditory vs. visual mismatch responses) is conceptually interesting, but due to the choice of analyzing auditory mismatch responses over occipital channels, it has limited interpretability.

The authors successfully demonstrate that visuomotor mismatch paradigms can, in principle, be applied in human EEG. However, due to the issues outlined above, the current findings are relatively preliminary. If validated with improved methodology, this approach could significantly advance our understanding of predictive processing in the human visual system and provide a translational bridge between rodent and human work.

Reviewer #3 (Public review):

Summary:

Solyga, Zelechowski, and Keller present a concise report of an innovative study demonstrating clear visuomotor mismatch responses in ambulating humans, using a mobile EEG setup and virtual reality. Human subjects walked around a virtual corridor while EEGs were recorded. Occasionally, motion and visual flow were uncoupled, and this evoked a mismatch response that was strongest in occipitally placed electrodes and had a considerable signal-to-noise ratio. It was robust across participants and could not be explained by the visual stimulus alone.

Strengths:

This is an important extension of their prior work in mice, and represents an elegant translation of those previous findings to humans, where future work can inform theories of e.g., psychiatric diseases that are believed to involve disordered predictive processing. For the most part, the authors are appropriately circumspect in their interpretations and discussions of the implications. I found the discussion of the polarity differences they found in light of separate positive and negative prediction errors, intriguing.

Weaknesses:

The primary weaknesses rest in how the results are sold and interpreted.

Most notably, the interpretation of the results of the comparison of visuomotor mismatches to the passive auditory oddball induced mismatch responses is inappropriate, as suboptimal electrode choices, unclear matching of trial numbers, and other factors. To clarify, regarding the auditory oddball portion in Figure 5, the data quality is a concern for the auditory ERPs, and the choice of Occipital electrodes is a likely culprit. Typically, auditory evoked responses are maximal at Cz or FCz, although these contacts don't seem to be available with this setup. In general, caution is warranted in comparing ERP peaks between two different sensory modalities - especially if attention is directed elsewhere (to a silent movie) during one recording and not during the other. The authors discuss this as a purely "qualitative" comparison in the text, which is appreciated, and do acknowledge the limitations within the results section, but the figure title and, importantly, the abstract set a different tone. At least, for comparisons between auditory mismatch and visuomotor mismatch, trial numbers need to be equated, as ERP magnitude can be augmented by noise (which reduces with increased numbers of trials in the average). And more generally, the size of the mismatch event at the scalp does not scale one-to-one with the size at the level of the neural tissue. One can imagine a number of variables that impact scalp level magnitudes, which are orthogonal to actual cortex-level activation - the size, spread, and polarity variance of the activated source (which all would diminish amplitude at the scalp due to polyphasic summation/cancelation). The variance of phase to a stimulus across trials (cross trial phase locking) vs magnitude of underlying power - the former, in theory, relates to bottom-up activity and the latter can reflect feedback (which has more variability in time across trials; the distance of the scalp electrode from the activated tissue (which, for the auditory system, would be larger (FCz to superior temporal gyrus) than for the visual system (O1 to V1/2)). None of this precludes the inclusion of the auditory mismatch, which is a strength of the study, but interpretations about this supporting a supremacy of sensory-motor mismatch - regardless of validity - are not warranted. I would recommend changing the way this is presented in the abstract.

Otherwise, the data are of adequate quality to derive most of their conclusions.

The authors claim that the mismatch responses emanate from within the occipital cortex, but I would require denser scalp coverage or a demonstration of consistent impedances across electrodes and across subjects to make conclusions about the underlying cortical sources (especially given the latencies of their peaks). In EEG, the distribution of voltage on the scalp is, of course, related to but not directly reflective of the distribution of the underlying sources. The authors are mostly careful in their discussion of this, but I would strongly recommend changing the work choice of "in occipital cortex" to "over occipital cortex" or even "posteriorly distributed". Even with very dense electrode coverage and co-registration to MRIs for the generation of forward models that constrain solutions, source localization of EEG signals is very challenging and not a simple problem. Given the convoluted and interior nature of human V1, the ability to reliably detect early evoked responses (which show the mismatch in mouse models) at the scalp in ERP peaks is challenging - especially if one is collapsing ERPs across subjects. And - given the latency of the mismatch responses, I'd imagine that many distributed cortical regions contribute to the responses seen at the scalp.

I think that Figure 3C, but as a difference of visual mismatch vs halting flow alone (in the open loop) might be additionally informative, as it clarifies exactly where the pure "mismatch" or prediction error is represented.

As a suggestion, the authors are encouraged to analyse time-frequency power and phase locking for these mismatch responses, as is common in much of the literature (see Roach et al 2008, Schizophrenia Bulletin). This is not to say that doing so will yield insights into oscillations per se, but converting the data to the time-frequency domain provides another perspective that has some advantages. It fosters translations to rodent models, as ERP peaks do not map well between species, but e.g., delta-theta power does (see Lee et al 2018, Neuropsychopharmacology; Javitt et al 2018, Schizophrenia research; Gallimore et al 2023, Cereb Ctx). Further, ERP peaks can be influenced by the actual neuroanatomy of an individual (especially for quantifying V1 responses). Time frequency analyses may aid in interpreting the "early negative deflection with a peak latency of 48 ms " finding as well.

Finally, the sentence in the abstract that this paradigm " can trigger strong prediction error responses and consequently requires shorter recording 20 times would simplify experiments in a clinical setting" is a nice setup to the paper, but the very fact that one third of recordings had to be removed due to movement artifact, and that hairstyle modulates the recording SnR, is reason that this paradigm, using the reported equipment, may have limited clinical utility in its current form. Further, auditory oddball paradigms are of great clinical utility because they do not require explicit attention and can be recorded very quickly with no behavioral involvement of a hospitalized patient. This should be discussed, although it does not detract from the overall scientific importance of the study. The authors should reconsider putting this statement in the abstract.

Reviewer #1 (Public review):

Summary:

Goicoechea et al. conducted a timely and thorough meta-analysis on the potential for indirect hippocampal targeted transcranial magnetic stimulation (TMS) to improve episodic memory. The authors included additional factors of interest in their meta-analysis, which can be used to inform the next generation of studies using this intervention. Their analysis revealed critical factors for consideration: TMS should be applied pre-encoding, individualized spatial targeting improves efficacy, and improvement of recollection was stronger than recognition.

Strengths:

As mentioned previously, the meta-analysis is timely and summarizes an emerging set of studies (over the past decade since Wang et al., Science 2014). Those outside of the field may not be aware of the robustness of improvements in episodic memory from hippocampal targeted TMS. The authors were quite thorough in including additional factors that are important for the interpretation of these findings. These factors also address the differences in approach across studies. The evidence that individualized spatial targeting improves TMS efficacy is consistent with recent advances in TMS for major depressive disorder. The specificity of the cognitive improvements to recollection of episodic memory and not for other cognitive domains is consistent with hippocampal targeting. The authors also plan to post the complete dataset on an open-source repository, which enables additional analysis by other researchers.

Weaknesses:

The write-up is succinct and emphasizes the scientific decisions that underlie key differences in the various experimental designs. While the manuscript is written for a scientific audience, the authors are likely aware that findings like this will be of broad appeal to the field of neurology, where treatments for memory loss are desperately needed. For this reason, the authors could consider including a statement regarding an interpretation of this meta-analysis from a clinical standpoint. Statements such as 'safe and effective' imply a clinical indication, and yet the manuscript does not engage with clinical trials terminology such as blinding, parallel arm versus crossover design, and trial phase. While the authors might prefer not to engage with this terminology, it can be confusing when studies delivering intervention-like five days of consecutive TMS (e.g., Wang et al., 2014) are clustered with studies that delivered online rhythmic TMS, which tests target engagement (e.g., Hermiller et al., 2020). While the 'sessions' variable somewhat addresses the basic-science versus intervention-like approach, adding an explicit statement regarding this in the discussion might help the reader navigate the broad scope of approaches that are utilized in the meta-analysis.

Reviewer #2 (Public review):

Summary:

In 2014, Wang et al. showed that noninvasive stimulation of a parietal site, connected functionally to the hippocampus, increased resting state connectivity throughout a canonical network associated with episodic memory. It also produced a memory boost, which correlated with the connectivity increase across subjects. Their discovery that an imaging biomarker could be used to target a network (rather than a single cortical site) in individual subjects and provide a scaling measure of target modulation should have revolutionized the noninvasive neuromodulation field. This meta-analysis by members of the same group covers memory effects from noninvasive stimulation of various nodes of the "hippocampal" network.

Strengths:

This is a very timely summary and meta-analysis of this very promising application of TMS. To the limited extent of my expertise in meta-analysis, the methodology seems rigorous, and the central finding, that high-frequency stimulation of nodes in the hippocampal network reproducibly improves event recall, is amply supported. This should provide impetus for larger clinical trials and further quantification of the optimal dose, duration of effect, etc.

Weaknesses:

My critical comments are mainly on the framing and argument:

(1) While the introduction centers on the role of the hippocampus in episodic memory and posits hippocampal neuromodulation by TMS as causative, the true mechanism may be more complex. Clean hippocampal lesions in primates cause focal loss of spatial and place memory, and I am aware of no specific evidence that the hippocampus does more than this in humans. Moreover, there is evidence that lateral parietal TMS also reaches neighboring temporal lobe regions, which contribute to episodic memory. The hippocampus may, therefore, be a reliable deep seed for connectivity-based targeting of the episodic memory network, but might not be the true or only functional target.

(2) The meta-analysis combines studies with confirmation of targeting and target-network engagement from fMRI and studies without independent evidence of having stimulated the putative target (e.g., Koch et al). That seems like a more important methodological distinction than merely the use of any individual targeting method. In my experience, atlas-based estimates are at least as accurate as eyeballing cortical areas in individuals. Hence, entering individual functional targeting as a factor might reveal an effect on efficacy.

(3) The funnel plot and Egger's regression for episodic memory outcomes suggested possible bias, and the average sample size of 23 is small, contributing to the likelihood of false positive results. It would be informative, therefore, to know how many or which studies had formal power estimates and what the predicted effect sizes were.

(4) In the Discussion, the authors might provide a comparison between the effect size for memory improvement found here with those reported for other brain-targeted interventions and behavioral strategies. It may also be worthwhile pointing out that HITS/memory is one of the very few, or perhaps the only, neuromodulatory effects on cognition that has been extensively reproduced and survived rigorous meta-analysis.

(5) The section of the Discussion on specificity compares HITS to transcranial electrical stimulation without specifying an anatomical target or intended outcome. A better contrast might be the enormous variety of cognitive and emotional effects claimed for TMS of the dorsolateral prefrontal cortex.

(6) With reference to why other nodes in the episodic memory network have not been tested, current flow modeling shows TMS of the medial prefrontal cortex is unlikely to be achievable without stronger stimulation of the convexity under the coil, in addition to being uncomfortable. The lateral temporal lobe has been stimulated without undue discomfort.

(7) Finally, a critical question hanging over the clinical applicability of HITS and other neuromodulation techniques is how well they will work on a damaged substrate. Functional and/or anatomical imaging might answer this question and help screen for likely responders. The authors' opinion on this would be informative.

Reviewer #3 (Public review):

Summary:

The manuscript by Goicoechea et al. assesses the influence of hippocampal-network targeted TMS to parietal cortex on episodic memory using a meta-analytic approach. This is an important contribution to the literature, as the number of studies using this approach to modulate memory/hippocampal function has clearly increased since the initial publication by Wang et al. 2014. This manuscript makes an important contribution to the literature. In general, the analysis is straightforward and the conclusions are well-supported by the results; I have mostly minor comments/concerns.

Strengths:

(1) A meta-analysis across published work is used to evaluate the influence of hippocampal-network-targeted TMS in parietal cortex on episodic memory. By pooling results across studies, the meta-analytic effects demonstrate an influence of TMS on memory across the diversity of many details in study design (specific tasks, stimuli, TMS protocols, study populations).

(2) Selectivity with regard to episodic memory vs. non-episodic memory tasks is evaluated directly in the meta-analysis.

(3) The investigation into supplemental factors as predictors of TMS's influence on memory was tested. This is helpful given the diversity of study designs in the literature. This analysis helps to shed light on which study designs, e.g., TMS protocols, etc., are most effective in memory modulation.

Weaknesses:

(1) My only significant concern is how studies are categorized in the 'Timing' factor (when stimulation is applied). Currently, protocols in which TMS is administered across days are categorized as 'pre-encoding' in the Timing factor. This has the potential to be misleading and may lead to inaccurate conclusions. When TMS is administered across multiple days, followed by memory encoding and retrieval (often on a subsequent day), it is not possible to attribute the influence of TMS to a specific memory phase (i.e., encoding or retrieval) per se. Thus, labeling multi-day TMS studies as 'pre-encoding' may be misleading to readers, as it may imply that the influence of TMS is due to modulation of encoding mechanisms per se, which cannot be concluded. For example, multi-day TMS protocols could be labeled as 'pre-retrieval' and be similarly accurate. This approach also pools results from TMS protocols with temporal specificity (i.e., those applied immediately during encoding and not on board during memory testing) and without temporal specificity (i.e., the case of multi-day TMS) regarding TMS timing. Given the variety of paradigms employed in the literature, and to maximize the utility/accuracy of this analysis, one suggestion is to modify the categories within the Timing factor, e.g., using labels like 'Temporally-Specific' and 'Temporally Non-specific'. The 'Temporally-Specific' category could be subdivided based on the specific memory process affected: 'encoding', 'retrieval', or 'consolidation' (if possible). I think this would improve the accuracy of the approach and help to reach more meaningful conclusions, given the variety of protocols employed in the literature.

(2) As the scope of the meta-analysis is limited to TMS applied to parietal or superior occipital cortex, it is important to highlight this in the Introduction/Abstract. The 'HITS' terminology suggests a general approach that would not necessarily be restricted to parietal/nearby cortical sites.

Minor:

(1) To reduce the number of study factors tested, data reduction was performed via Lasso regression to remove factors that were not unique predictors of the influence of TMS on memory. This approach is reasonable; however, one limitation is that factors strongly correlated with others (and predict less unique variance) will be dropped. This may result in a misrepresentation, i.e., if readers interpret factors left out of this analysis as not being strongly related to the influence of TMS on memory. I do see and appreciate the paragraph in the Discussion which appropriately addresses this issue. However, it may be worth also considering an alternative analysis approach, if the authors have not already done so, which explicitly captures the correlation structure in the data (i.e., shown in Figure S2) using a tool like PCA or an appropriate factor analysis. Then, this shared covariance amongst factors can be tested as predictors of the influence of TMS - e.g., by testing whether component scores for dominant PCs are indeed predictive of the influence of TMS. This complementary approach would capture rather than obfuscate the extent to which different factors are correlated and assess their joint (rather than independent) influence on memory, potentially resulting in more descriptive conclusions. For example, TMS intensity and protocol may jointly influence memory.

(2) Given the specific focus on TMS applied to parietal cortex to modulate hippocampal and related network function, it would be fruitful if the authors could consider adding discussion/speculation regarding whether this approach may be effectively broadened using other stimulation methods (e.g., tACS, tDCS), how it may compare to other non-invasive brain stimulation methods with depth penetration to target hippocampal function directly (transcranial temporal interference, or transcranial focused ultrasound), and/or how or whether other stimulation sites may or may not be effective.

(3) Studies were only included in the meta-analysis if they contained objective episodic memory tests. How were studies handled that included both objective and subjective memory, or other non-episodic memory measures? For example, Yazar et al. 2014 showed no influence of TMS on objective recall, but an impairment in subjective confidence. I assume confidence was not included in the meta-analysis. Similarly, Webler et al. 2024 report results from both the mnemonic similarity task (presumably included) and a fear conditioning paradigm (presumably excluded). Please clarify in the methods how these distinctions were handled.

(4) The analysis comparing memory to non-memory measures is important, showing the specificity of stimulation. Did the authors consider further categorizing the non-memory tasks into distinct domains (i.e., language, working memory, etc.)? If possible, this could provide a finer detail regarding the selectivity of influences on memory vs. other aspects of cognition. It is likely that other aspects of cognition dependent on hippocampal function may be modulated as well, i.e., tasks with high relational/associative processing demands.

(5) In the analysis of the Intensity factor, how were studies using Active (rather than resting) MT categorized? Only resting MT is mentioned in Table S1. This is important as the original theta-burst TMS protocol from Huang et al. 2005 determines intensity based on Active Motor Threshold.

(6) Is there a reason why the study by Koen et al. 2018 (Cognitive Neuroscience) was not included? TMS was performed during encoding to the left AG, and objective memory was assessed, so it would seemingly meet the inclusion criterion.

(7) It would be helpful to briefly differentiate the current meta-analysis from that performed by Yeh & Rose (How can transcranial magnetic stimulation be used to modulate episodic memory?: A systematic review and meta-analysis, 2019, Frontiers in Psychology) (other than being more current).

(8) For transparency and to facilitate further understanding of the literature and potential data re-use, it would be great if the authors consider sharing a supplementary table or file that describes how individual studies/memory measures were categorized under the factors listed in Table S1.

Reviewer #1 (Public review):

Summary:

The authors show that the lower frequency (~5Hz) stimulation of the intermittent theta-burst stimulation (iTBS) via repetitive transcranial magnetic stimulation (rTMS) serves as a more effective stimulation paradigm than the high-frequency protocols (HF-rTMS, ~10Hz) with enhancing plasticity effects via long-term potentiation (LTP) and depression (LTD) mechanisms. They show that the 5 Hz patterned pulse structure of the iTBS is an exact subharmonic of the 10 Hz high-frequency rTMS, creating a connection between the two paradigms and acting upon the same underlying synchrony mechanism of the dominant alpha-rhythm of the corticothalamic circuit.

First, the authors create a corticothalamic neural population model consisting of 4 populations: cortical excitatory pyramidal and inhibitory interneuron, and thalamic excitatory relay and inhibitory reticular populations. Second, the authors include a calcium-dependent plasticity model, in which calcium-related NMDAR-dependent synaptic changes are implemented using a BCM metaplasticity rule. The rTMS-induced fluctuations in intracellular calcium concentrations determine the synaptic plasticity effects.

Strengths:

The model (corticothalamic neural population with calcium-dependent plasticity, with TBS input for rTMS) is thoroughly built and analyzed.

The conclusions seem sound and justified. The authors justifiably link stimulation parameters (especially the alpha subharmonics iTBS frequency) with fluctuations in calcium concentration and their effects on LTP and LTD in relevant parts of the corticothalamic circuit populations leading to a dampening of corticothalamic loop gains and enhancement of intrathalamic gains with an overall circuit-wide feedforward inhibition (= inhibitory activity is enhanced via excitatory inputs onto inhibitory neurons) and a resulting suppression of the activity power. In other words: alpha-resonant iTBS protocols achieve broadband power suppression via selective modulation of corticothalamic FFI.

(1) The model is well-described, with the model equations in the main text and the parameters in well-formatted tables.

(2) The relationship between iTBS timing and the phase of rhythms is well explained conceptually.

(3) Metaplasticity and feedforward inhibition regulation as a driver for the efficacy of iTBS are well explored in the paper.

(4) Efficacy of TBS, being based on mimicry of endogenous theta patterns, seems well supported by this simulation.

(5) Recovery between periods of calcium influx as an explanation for why intermittency produces LTP effects where continuous stimulation fails is a good justification for calcium-based metaplasticity, as well as for the role of specific pulse rate.

(6) Circuit resonance conclusion is interesting as a modulating factor; the paper supports this hypothesis well.

(7) The analysis of corticothalamic dampening and intrathalamic enhancement in the 3D XYZ loop gain space is a strong aspect of the paper.

Weaknesses:

(1) Overall, the paper is difficult to follow narratively - the motivation (formulated as a specific research question) for each section can be a bit unclear. The paper could benefit from a minor rewrite at the start of each section to justify each section's reasoning. The Discussion is too long and should be shortened and limited to the main points.

(2) While the paper refers to modelling and data in discussion, there is no direct comparison of the simulations in the figures to data or other models, so it's difficult to evaluate directly how well the modelling fits either the existing model space or data from this region. Where exactly the model/plasticity parameters from Table 5 and the NFTsim library come from is not easy to find. The authors should make the link from those parameters to experimental data clearer. For example, which clinical or experimental data are their simulations of the resting-state broadband power suppression based on?

(3) The figures should be modified to make them more understandable and readable.

(4) The claim in the abstract that the paper introduces "a novel paradigm for individualizing iTBS treatments" is too strong and sounds like overselling. The paper is not the first computational modelling of TBS - as acknowledged also by the authors when citing previous mean-field plasiticity modelling articles. Btw. the authors could briefly mention and include also references also to biophysically more detailed multi-scale approaches such as https://doi.org/10.1016/j.brs.2021.09.004 and https://doi.org/10.1101/2024.07.03.601851 and https://doi.org/10.1016/j.brs.2018.03.010

(5) The modelling assumes the same CaDP model/mechanism for all excitatory synapses/afferents. How well is this supported by experimental evidence? Have all excitatory synaptic connections in the cortico-thalamic circuit been shown to express CaDP and metaplasticity? If not, these limitations (or predictions of the model) should be mentioned. Why were LTP calcium volumes never induced within thalamic relay-afferent connections se and sr? What about inhibitory synapses in the circuit model? Were they plastic or fixed?

(6) Minor point: Metaplasticity is modelled as an activity-dependent shift in NMDAR conductance, which is supported by some evidence, but there are other metaplasticity mechanisms. Altering NMDA-synapse affects also directly synaptic AMPA/NMDA weight and ratio (which has not been modelled in the paper). Would the model still work using other - more phenomenological implementation of the sliding threshold - e.g. based on shifting calcium-dependent LTP/LTD windows or thresholds (for a phenomenological model of spike/voltage-based STDP-BCM rules, see https://doi.org/10.1007/s10827-006-0002-x and https://doi.org/10.1371/journal.pcbi.1004588) - maybe using a metaplasticity extension of Graupner and Brunel CaDP model. A brief discussion of these issues might be added to the manuscript - but this is just a suggestion.

(7) Short-term plasticity (depression/facilitation) of synapses is neglected in the model. This limitation should be mentioned because adding short-term synaptic dynamics might affect strongly circuite model dynamics.

Reviewer #2 (Public review):

Transcranial magnetic stimulation is used in several medical conditions to alter brain activity, probably by induction of synaptic plasticity. The authors pursue the idea to personalise parameters of the stimulation protocol by adapting the stimulation frequency to an individual's brain rhythm. The authors test this approach in a population model connecting the cortex with deeper brain areas, the thalamocortical loop, which includes calcium-dependent plasticity for the connections within and between brain regions. While the authors relate literature-based experimental findings with their results, their results are so far not supported by experimental work.

The authors successfully highlight in their model that personalization of rTMS stimulation frequency to the brain intrinsic frequency has the potential to improve stimulation impact, and they relate this to specific changes in the network. Their arguments that this resonance improves efficacy are intuitive, and their finding that inhibition and excitation are selectively modulated is a good starting point for analysing the underlying mechanism.

As rTMS is used in clinical contexts, and the idea of aligning intrinsic and stimulation frequency is relatively easy to implement, the paper is conceptually of interest for the rTMS community, despite its weak points on the mechanistic explanation. The authors made the simulation code publicly available, which is a useful resource for further studies on the effects of metaplasticity. The same stimulation parameters have been tested in experiments, and a reanalysis of the experimental results following the idea of this paper could be influential for clinical optimisation of stimulation protocols.

A strength of the paper is that it takes into account also deeper brain areas, and their interaction with the cortex. The paper carefully measures system changes in response to different frequency differences between thalamocortical loop and stimulation. By explicitly modelling changes to connections, the authors do start dissect the mechanism underlying the observed effect. Unfortunately, the dissection of the mechanistic underpinning in the current version of the manuscript does not yet fully exploits the possibility of a computational model. Here are a couple of points related to this critique:

(1) The study reports that connections between thalamus and cortex as well as within the thalamus change, but the model is not used to separate the influence of both.

(2) The paper reports that a resonance between stimulation and brain increases stimulation effectiveness. This conclusion is solely based on the observation of strong reactions in the network to subharmonics of the brain's frequency, and lacks further support such as alternative measures of resonance, or an analysis of the role of the phase difference between stimulation and brain oscillation, which is likely changed by the stimulation. For example, for harmonic oscillators, resonance leads to a 90 degree phase difference between driving force and system response, and for rTMS, phase locking has been shown to be relevant.

(3) The authors claim that over-engagement of plasticity for HF-rTMS makes their intermittent protocol more effective. Yet, the study lacks a direct comparison between stimulation protocols that shows over-engagement of plasticity for the HF-protocol. The study also does not explore which time-scale of the plasticity mechanism rules the optimal stimulation protocol. Moreover, the study reports that only few number of pulses per burst show a good effect. This should depend on how strongly a single pulse changes the calcium volume, but this relation was not explored in the model.

(4) The authors report on the frequency spectrum of the cortical excitatory population, with the argument that the power of this population is most closely related to EEG measurements. A report of the other neuronal populations is missing, which might be informative on what is going on in the network.

Statistics:

(1) The authors do not state whether they test for assumptions of the multiple regression analysis, such as whether errors have equal variance or that residuals are normally distributed.

(2) For the statistical analysis, the authors ignore about half of their model simulations for which the change in the power was negligible. It is not clear to me which statistical analysis is meant; whether the figures show all model simulations, whether regression lines where evaluated ignoring them, and whether the multiple regression analysis used only half of the data points.

Reviewer #3 (Public review):

Summary:

This article presented a novel computer model to address an important question in the field of brain stimulation, using the magnetic stimulation iTBS protocol as an example, how stimulation parameters, frequency in particular, interfere with the intrinsic brain oscillations via plastic mechanisms. Brain oscillation is a critical feature of functional brains and its alteration signals the onset of many neuropsychiatric diseases or certain brain states. The authors suggested with their model that harmonic and subharmonic stimulations close to the individual alpha frequency achieved strong broadband power suppression.

Strengths:

The authors focused on the cortico-thalamic circuitry and managed to generate alpha oscillations in their four-population model. By adding the non-monotonic calcium-based BCM rule, they have also achieved both homeostasis and plasticity in response to magnetic stimulation. This work combined computer simulations and statistical analysis to demonstrate the changes in network architecture and network dynamics triggered by varied magnetic stimulation parameters. By delivering the iTBS protocol to the cortical excitatory population, the key findings are that harmonic and subharmonic stimulations close to the individual alpha frequency (IAF) achieved strong broadband power suppression. This resulted from increased synaptic weights of the corticothalamic feed-forward inhibitory projections, which were mediated by the calcium dynamics perturbed by iTBS magnetic stimulation. This finding endorsed the importance of applying customized stimulation to patients based on their IAFs and suggested the underlying mechanism at the circuitry level.

Weaknesses:

The drawbacks of this work are also obvious. Model validation and biological feasibility justification should be better addressed. The primary outcome of their model is the broadband power suppression and the optimal effects of (sub)harmonic stimulation frequency, but it lacks immediate empirical support in the literature. To the best of my knowledge, many alpha frequency tACS studies reported to increase but not suppress the power of certain brain oscillations. A review by Wang et al., 2024 (Frontiers in System Neuroscience) suggested hybrid changes to different brain oscillations by magnetic stimulation. Developing a model to fully capture such changes might be out of the scope of the present study and challenging in the entire field, but it undermines the quality of the present work if not extensively discussed and justified. Clarity and reproducibility of the work can be improved. Although it is intriguing to see how the calcium-dependent BCM plasticity mediates such changes, the writing of the methods part is not hard to follow. It was also not clear why only two populations were considered in the thalamus, how the entire network was connected, or how the LTP/LTD threshold alters with calcium dynamics. The figures were unfortunately prepared in a nested manner. The crowded layout and the tiny font sizes reduce the clarity. The third point comes to contextualization and comparison to existing models. It will strengthen the work if the authors could have compared their work to other TMS modeling work with plasticity rules, e.g, Anil et al., 2024. Besides, magnetic stimulation is unique in being supra-threshold and having focality compared to other brain stimulation modalities, e.g., tDCS and tACS, but they may share certain basic neural mechanisms if accounting for certain parameters, e.g., frequency. A solid literature review and discussion on this part may help the field better perceive the value and potential limitations of this work.

Reviewer #1 (Public review):

Tamao et al. aimed to quantify the diversity and mutation rate of the influenza (PR8 strain) in order to establish a high-resolution method for studying intra-host viral evolution . To achieve this, the authors combined RNA sequencing with single-molecule unique molecular identifiers (UMIs) to minimize errors introduced during technical processing. They proposed an in vitro infection model with a single viral particle to represent biological genetic diversity, alongside a control model using in vitro transcribed RNA for two viral genes, PB2 and HA.

Through this approach, the authors demonstrated that UMIs reduced technical errors by approximately tenfold. By analyzing four viral populations and comparing them to in vitro transcribed RNA controls, they estimated that ~98.1% of observed mutations originated from viral replication rather than technical artifacts. Their results further showed that most mutations were synonymous and introduced randomly. However, the distribution of mutations suggested selective pressures that favored certain variants. Additionally, comparison with closely related influenza strain (A/Alaska/1935) revealed two positively selected mutations, though these were absent in the strain responsible for the most recent pandemic (CA01).

Overall, the study is well-designed, and the interpretations are strongly supported by the data.

The authors have addressed all the comments from the previous round of reviews. No further concerns.

Reviewer #2 (Public review):

Summary:

This manuscript presents a technically oriented application of UMI-based long-read sequencing to study intra-host diversity in influenza virus populations. The authors aim to minimize sequencing artifacts and improve the detection of rare variants, proposing that this approach may inform predictive models of viral evolution. While the methodology appears robust and successfully reduces sequencing error rates, key experimental and analytical details are missing, and the biological insight is modest. The study includes only four samples, with no independent biological replicates or controls, which limits the generalizability of the findings. Claims related to rare variant detection and evolutionary selection are not fully supported by the data presented.

Strengths:

The study addresses an important technical challenge in viral genomics by implementing a UMI-based long-read sequencing approach to reduce amplification and sequencing errors. The methodological focus is well presented, and the work contributes to improving the resolution of low-frequency variant detection in complex viral populations.

Weaknesses:

The application of UMI-based error correction to viral population sequencing has been established in previous studies (e.g., in HIV), and this manuscript does not introduce a substantial methodological or conceptual advance beyond its use in the context of influenza.

The study lacks independent biological replicates or additional viral systems that would strengthen the generalizability of the conclusions. Potential sources of technical error are not explored or explicitly controlled. Key methodological details are missing, including the number of PCR cycles, the input number of molecules, and UMI family size distributions. These are essential to support the claimed sensitivity of the method.

The assertion that variants at {greater than or equal to}0.1% frequency can be reliably detected is based on total read count rather than the number of unique input molecules. Without information on UMI diversity and family sizes, the detection limit cannot be reliably assessed.

Although genetic variation is described, the functional relevance of observed mutations in HA and NA is not addressed or discussed in the context of known antigenic or evolutionary features of influenza. The manuscript is largely focused on technical performance, with limited exploration of the biological implications or mechanistic insights into influenza virus evolution.

The experimental scale is small, with only four viral populations derived from single particles analyzed. This limited sample size restricts the ability to draw broader conclusions about quasispecies dynamics or evolutionary pressures.

Comments on revisions:

The revised manuscript provides additional methodological detail and clearer presentation, which improves transparency. However, the main limitations persist: the study remains small in scale, lacks independent validation, and relies on theoretical rather than empirical support for its claimed detection sensitivity. As a result, the work represents a modest technical advance rather than a substantive contribution to understanding influenza virus evolution.

Reviewer #2 (Public review):

The authors present a combined experimental and theoretical workflow to study partitioning noise arising during cell division. Such quantifications usually require time-lapse experiments, which are limited in throughput. To bypass these limitations, the authors propose to use flow-cytometry measurements instead and analyse them using a theoretical model of partitioning noise. The problem considered by the authors is relevant and the idea to use statistical models in combination with flow cytometry to boost statistical power is elegant. The authors demonstrate their approach using experimental flow cytometry measurements and validate their results using time-lapse microscopy. The approach focuses on a particular case, where the dynamics of the labelled component depends predominantly on partitioning, while turnover of components is not taken into account. The description of the methods is significantly clearer than in the previous version of the manuscript.

Reviewer #1 (Public review):

Summary:

The aim of this paper is to develop a simple method to quantify fluctuations in the partitioning of cellular elements. In particular, they propose a flow-cytometry based method coupled with a simple mathematical theory as an alternative to conventional imaging-based approaches.

Strengths:

The approach they develop is simple to understand, and its use with flow-cytometry measurements is clearly explained. Understanding how the fluctuations in the cytoplasm partition varies for different kinds of cells is particularly interesting.

Weaknesses:

The theory only considers fluctuations due to cellular division events. Fluctuations in cellular components are largely affected by various intrinsic and extrinsic sources of noise and only under particular conditions does partitioning noise become the dominant source of noise. In the revised version of the manuscript, they argue that in their setup, noise due to production and degradation processes are negligible but noise due to extrinsic sources such as those stemming from cell-cycle length variability may still be important. To investigate the robustness of their modelling approach to such noise, they simulated cells following a sizer-like division strategy, a scenario that maximizes the coupling between fluctuations in cell-division time and partitioning noise. They find that estimates remain within the pre-established experimental error margin.

Comments on previous version:

The authors have addressed all of my comments.

Reviewer #1 (Public review):

Summary:

In this study, participants completed two different tasks. A perceptual choice task in which they compared the sizes of pairs of items and a value-different task in which they identified the higher value option among pairs of items with the two tasks involving the same stimuli. Based on previous fMRI research, the authors sought to determine whether the superior frontal sulcus (SFS) is involved in both perceptual and value-based decisions or just one or the other. Initial fMRI analyses were devised to isolate brain regions that were activated for both types of choices and also regions that were unique to each. Transcranial magnetic stimulation was applied to the SFS in between fMRI sessions and it was found to lead to a significant decrease in accuracy and RT on the perceptual choice task but only a decrease in RT on the value-different task. Hierarchical drift diffusion modelling of the data indicated that the TMS had led to a lowering of decision boundaries in the perceptual task and a lower of non-decision times on the value-based task. Additional analyses show that SFS covaries with model derived estimates of cumulative evidence, that this relationship is weakened by TMS.

Strengths:

The paper has many strengths, including the rigorous multi-pronged approach of causal manipulation, fMRI and computational modelling, which offers a fresh perspective on the neural drivers of decision making. Some additional strengths include the careful paradigm design, which ensured that the two types of tasks were matched for their perceptual content while orthogonalizing trial-to-trial variations in choice difficulty. The paper also lays out a number of specific hypotheses at the outset regarding the behavioural outcomes that are tied to decision model parameters and well justified.

Weaknesses:

In my previous comments (1.3.1 and 1.3.2) I noted that key results could be potentially explained by cTBS leading to faster perceptual decision making in both the perceptual and value-based tasks. The authors responded that if this were the case then we would expect either a reduction in NDT in both tasks or a reduction in decision boundaries in both tasks (whereas they observed a lowering of boundaries in the perceptual task and a shortening of NDT in the value task). I disagree with this statement. First, it is important to note that the perceptual decision that must be completed before the value-based choice process can even be initiated (i.e. the identification of the two stimuli) is no less trivial than that involved in the perceptual choice task (comparison of stimulus size). Given that the perceptual choice must be completed before the value comparison can begin, it would be expected that the model would capture any variations in RT due to the perceptual choice in the NDT parameter and not as the authors suggest in the bound or drift rate parameters since they are designed to account for the strength and final quantity of value evidence specifically. If, in fact, cTBS causes a general lowering of decision boundaries for perceptual decisions (and hence speeding of RTs) then it would be predicted that this would manifest as a short NDT in the value task model, which is what the authors see.

Reviewer #2 (Public review):

Summary:

The authors set out to test whether a TMS-induced reduction in excitability of the left Superior Frontal Sulcus influenced evidence integration in perceptual and value-based decisions. They directly compared behaviour-including fits to a computational decision process model---and fMRI pre and post TMS in one of each type of decision-making task. Their goal was to test domain-specific theories of the prefrontal cortex by examining whether the proposed role of the SFS in evidence integration was selective for perceptual but not value-based evidence.

Strengths:

The paper presents multiple credible sources of evidence for the role of the left SFS in perceptual decision making, finding similar mechanisms to prior literature and a nuanced discussion of where they diverge from prior findings. The value-based and perceptual decision-making tasks were carefully matched in terms of stimulus display and motor response, making their comparison credible.

Weaknesses:

-I was confused about the model specification in terms of the relationship between evidence level and drift rate. While the methods (and e.g. supplementary figure 3) specify a linear relationship between evidence level and drift rate, suggesting, unless I misunderstood, that only a single drift rate parameter (kappa) is fit. However, the drift rate parameter estimates in the supplementary tables (and response to reviewers) do not scale linearly with evidence level.

-The fit quality for the value-based decision task is not as good as that for the PDM, and this would be worth commenting on in the paper.

Reviewer #1 (Public review):

The manuscript by Yin and colleagues addresses a long-standing question in the field of cortical morphogenesis, regarding factors that determine differential cortical folding across species and individuals with cortical malformations. The authors present work based on a computational model of cortical folding evaluated alongside a physical model that makes use of gel swelling to investigate the role of a two-layer model for cortical morphogenesis. The study assesses these models against empirically derived cortical surfaces based on MRI data from ferret, macaque monkey, and human brains.

The manuscript is clearly written and presented, and the experimental work (physical gel modeling as well as numerical simulations) and analyses (subsequent morphometric evaluations) are conducted at the highest methodological standards. It constitutes an exemplary use of interdisciplinary approaches for addressing the question of cortical morphogenesis by bringing together well-tuned computational modeling with physical gel models. In addition, the comparative approaches used in this paper establish a foundation for broad-ranging future lines of work that investigate the impact of perturbations or abnormalities during cortical development.

The cross-species approach taken in this study is a major strength of the work. However, correspondence across the two methodologies did not appear to be equally consistent in predicting brain folding across all three species. The results presented in Figures 4 (and Figures S3 & S4) show broad correspondence in shape index and major sulci landmarks across all three species. Nevertheless, the results presented for the human brain lack the same degree of clear correspondence for the gel model results as observed in the macaque and ferret. While this study clearly establishes a strong foundation for comparative cortical anatomy across species and the impact of perturbations on individual morphogenesis, further work that fine-tunes physical modeling of complex morphologies, such as that of the human cortex, may help to further understand the factors that determine cortical functionalization and pathologies.

Reviewer #2 (Public review):

This manuscript explores the mechanisms underlying cerebral cortical folding using a combination of physical modelling, computational simulations, and geometric morphometrics. The authors extend their prior work on human brain development (Tallinen et al., 2014; 2016) to a comparative framework involving three mammalian species: ferrets (Carnivora), macaques (Old World monkeys), and humans (Hominoidea). By integrating swelling gel experiments with mathematical differential growth models, they simulate sulcification instability and recapitulate key features of brain folding across species. The authors make commendable use of publicly available datasets to construct 3D models of fetal and neonatal brain surfaces: fetal macaque (ref. [26]), newborn ferret (ref. [11]), and fetal human (ref. [22]).

Using a combination of physical models and numerical simulations, the authors compare the resulting folding morphologies to real brain surfaces using morphometric analysis. Their results show qualitative and quantitative concordance with observed cortical folding patterns, supporting the view that differential tangential growth of the cortex relative to the subcortical substrate is sufficient to account for much of the diversity in cortical folding. This is a very important point in our field, and can be used in the teaching of medical students.

Brain folding remains a topic of ongoing debate. While some regard it as a critical specialization linked to higher cognitive function, others consider it an epiphenomenon of expansion and constrained geometry. This divergence was evident in discussions during the Strüngmann Forum on cortical development (Silver et al., 2019). Though folding abnormalities are reliable indicators of disrupted neurodevelopmental processes (e.g., neurogenesis, migration), their relationship to functional architecture remains unclear. Recent evidence suggests that the absolute number of neurons varies significantly with position-sulcus versus gyrus-with potential implications for local processing capacity (e.g., https://doi.org/10.1002/cne.25626). The field is thus in need of comparative, mechanistic studies like the present one.

This paper offers an elegant and timely contribution by combining gel-based morphogenesis, numerical modelling, and morphometric analysis to examine cortical folding across species. The experimental design - constructing two-layer PDMS models from 3D MRI data and immersing them in organic solvents to induce differential swelling - is well-established in prior literature. The authors further complement this with a continuum mechanics model simulating folding as a result of differential growth, as well as a comparative analysis of surface morphologies derived from in vivo, in vitro, and in silico brains.

Conclusion:

This is a well-executed and creative study that integrates diverse methodologies to address a longstanding question in developmental neurobiology. While a few aspects-such as regional folding peculiarities, sensitivity to initial conditions, and available human data-could be further elaborated, they do not detract from the overall quality and novelty of the work. I enthusiastically support this paper and believe that it will be of broad interest to the neuroscience, biomechanics, and developmental biology communities.

[Editor's note: The reviewers were satisfied with the authors' response. The eLife Assessment was slightly updated to reflect the author's response.]

Reviewer #2 (Public review):

Summary:

This study aims to show how structural and functional brain organization develops during childhood and adolescence using two large neuroimaging datasets. It addresses whether core principles of brain organization are stable across development, how they change over time, and how these changes relate to cognition and psychopathology. The study finds that brain organization is established early and remains stable but undergoes gradual refinement, particularly in higher-order networks. Structural-functional coupling is linked to better working memory but shows no clear relationship with psychopathology.

Comments on revisions:

Follow-up: I would like to thank the authors for their thoughtful and comprehensive revisions. The additional analyses addressing developmental differences in structure-function coupling between CALM and NKI are valuable and clearly strengthen the manuscript. I particularly appreciate the inclusion of the neurotypical subgroup within CALM to disentangle neurotypicality from potential site-related effects, as well as the expanded discussion of these findings in the context of individual variability and equifinality.

Regarding my earlier comment on the use of COMBAT, I realize that "exclusion" may have been a poor choice of wording. What I meant was that harmonization procedures like COMBAT can, in some cases, weaken extremes or reduce variability by shrinking values toward the mean, rather than literally excluding participants from the analysis. Nevertheless, I appreciate the authors' careful consideration of this point and their additional analysis examining sample coverage following motion-based exclusions.

Overall, I am satisfied with the revisions, and I believe the manuscript has been substantially improved.

Reviewer #1 (Public Review):

The manuscript by Verma et al. is a simple and concise assessment of the in-cell motility parameters of cytoplasmic dynein. Although numerous studies have focused on understanding the mechanism by which dynein is activated using a complement of in vitro methodologies, an assessment of dynein motility in cells has been lacking. It has been unclear whether dynein exhibits high processivity within the crowded and complicated environment of the cell. For example, does cargo-bound dynein exhibit short, non-processive motility (as has been recently suggested; Tirumala et al., 2022 bioRxiv)? Does cargo-bound dynein move against opposing forces generated by cargo-bound kinesins? Do cargoes exhibit bidirectional switching due to stochastic activation of kinesins and dyneins? The current work addresses these questions quite simply by observing and quantitating the motility of natively tagged dynein in HeLa cells.

Reviewer #2 (Public Review):

Verma et al. provide a short technical report showing that endogenously tagged dynein and dynactin molecules localize to growing microtubule plus-ends and also move processively along microtubules in cells. The data are convincing, and the imaging and movies very nicely demonstrate their claims. I don't have any large technical concerns about the work. It is perhaps not surprising that dynein-dynactin complexes behave this way in cells due to other reports on the topic, but the current data are among some of the nicest direct demonstrations of this phenomenon. It may be somewhat controversial since a separate group has reported that dynein does not move processively in mammalian cells

(https://www.biorxiv.org/content/10.1101/2021.04.05.438428v3).

Reviewer #3 (Public Review):

In this manuscript, Verma et al. set out to visualize cytoplasmic dynein in living cells and describe their behaviour. They first generated heterozygous CRISPR-Cas9 knock-ins of DHC1 and p50 subunit of dynactin and used spinning disk confocal microscopy and TIRF microscopy to visualize these EGFP-tagged molecules. They describe robust localization and movement of DHC and p50 at the plus tips of MTs, which was abrogated using SiR tubulin to visualize the pool of DHC and p50 on the MTs. These DHC and p50 punctae on the MTs showed similar, highly processive movement on MTs. Based on comparison to inducible EGFP-tagged kinesin-1 intensity in Drosophila S2 cells, the authors concluded that the DHC and p50 punctae visualized represented 1 DHC-EGFP dimer+1 untagged DHC dimer and 1 p50-EGFP+3 untagged p50 molecules.

Reviewer #1 (Public review):

This manuscript by Yang et al. presents a potentially novel mechanism by which Plscr1 defends against influenza virus infection. Using a global knockout (KO) and a tissue-specific overexpression mouse model, the authors demonstrate that Plscr1-KO mice exhibit increased susceptibility and inflammation following IAV infection. In contrast, overexpression of Plscr1 in ciliated epithelial cells protects mice from infection. Through transcriptomic analysis in mice and mechanistic studies in cell culture models, the authors reveal that Plscr1 transcriptionally upregulates Ifnlr1 expression and physically interacts with this receptor on the plasma membrane, thereby enhancing IFN-λ-mediated viral clearance.

Overall, it's a well-performed study, however, causality between Plscr1 and Ifnlr1 expression needs to be more firmly established. This is because two recent studies of PLSCR1 KO cells infected with different viruses found no major differences in gene expression levels compared with their WT controls (Xu et al. Nature, 2023; LePen et al. PLoS Biol, 2024). There were also defects in the expression of other cytokines (type I and II IFNs plus TNF-alpha) so a clear explanation of why Ifnlr1 was chosen should also be given.

While Plscr1 has long been recognized as a cell-intrinsic antiviral restriction factor, few studies have explored its broader physiological role. This study thus provides interesting insights into a specific function of Plscr1 in IAV-permissive airway epithelial cells and its contribution to whole body anti-viral immunity.

Comments on revisions:

Most of the requested changes and experiments have been done. One very informative experiment is the expression of Plscr1 in Ifnlr1-KO cells to determine if it still inhibits IAV infection. The authors have indicated that this experiment is currently being pursued by crossing mice to introduce Plscr1 expression into ciliated epithelial cells on an Ifnlr1 KO background. It will show if there are Ifnlr1-independent anti-flu activities that still require Plscr1.

Reviewer #1 (Public review):

Here the authors discuss mechanisms of ligand binding and conformational changes in GlnBP (a small E Coli periplasmic binding protein, which binds and carries L-glutamine to the inner membrane ATP-binding cassette (ABC) transporter). The authors have distinguished records in this area and have published seminal works. They include experimentalists and computational scientists. Accordingly, they provide a comprehensive, high quality, experimental and computational work.

They observe that apo- and holo- GlnBP do not generate detectable exchange between open and (semi-) closed conformations on timescales between 100 ns and 10 ms. Especially, the ligand binding and conformational changes in GlnBP that they observe are highly correlated. Their analysis of the results indicates a dominant induced-fit mechanism, where the ligand binds GlnBP prior to conformational rearrangements. They then suggest that an approach resembling the one they undertook can be applied to other protein systems where the coupling mechanism of conformational changes and ligand binding.

They argue that the intuitive model where ligand binding triggers a functionally relevant conformational change was challenged by structural experiments and MD simulations revealing the existence of unliganded closed or semi-closed states and their dynamic exchange with open unbound conformations, discuss alternative mechanisms that were proposed, their merits and difficulties, concluding that the findings were controversial, which, they suggest is due to insufficient availability of experimental evidence to distinguish them. As to further specific conclusions they draw from their results, they determine that a conformational selection mechanism is incompatible with their results, but induced fit is. They thus propose induced fit as the dominant pathway for GlnBP, further supported by the notion that the open conformation is much more likely to bind substrate than the closed one based on steric arguments.

The paper here, which clearly embodies massive careful and high-quality work, is extensive, making use of a range of experimental approaches, including isothermal titration calorimetry, single-molecule Förster resonance energy transfer, and surface-plasmon resonance spectroscopy. The problem the authors undertake is of fundamental importance.

Reviewer #2 (Public review):

The authors provide convincing data from a whole set of different binding kinetic and thermodynamic experiments to explore whether glutamine binding protein binds glutamine via an induced fit or a conformational selection process.

Weaknesses:

The single-molecule TIRF-smFRET data appear to include spots that may represent more than one molecule, which raises the general issue of how rigorously traces were selected for single photobleaching events.

Reviewer #1 (Public review):

Summary:

This study focuses on the bacterial metabolite TMA, generated from dietary choline. These authors and others have previously generated foundational knowledge about the TMA metabolite TMAO, and its role in metabolic disease. This study extends those findings to test whether TMAO's precursor, TMA, and its receptor TAAR5 are also involved and necessary for some of these metabolic phenotypes. They find that mice lacking the host TMA receptor (Taar5-/-) have altered circadian rhythms in gene expression, metabolic hormones, gut microbiome composition, and olfactory and innate behavior. In parallel, mice lacking bacterial TMA production or host TMA oxidation have altered circadian rhythms.

Strengths:

These authors use state-of-the-art bacterial and murine genetics to dissect the roles of TMA, TMAO, and their receptor in various metabolic outcomes (primarily measuring plasma and tissue cytokine/gene expression). They also follow a unique and unexpected behavioral/olfactory phenotype. Statistics are impeccable.

Reviewer #2 (Public review):

Summary:

In the manuscript by Mahen et al., entitled "Gut Microbe-Derived Trimethylamine Shapes Circadian Rhythms Through the Host Receptor TAAR5," the authors investigate the interplay between a host G protein-coupled receptor (TAAR5), the gut microbiota-derived metabolite trimethylamine (TMA), and the host circadian system. Using a combination of genetically engineered mouse and bacterial models, the study demonstrates a link between microbial signaling and circadian regulation, particularly through effects observed in the olfactory system. Overall, this manuscript presents a novel and valuable contribution to our understanding of host-microbe interactions and circadian biology. The addition of new data following revision adds mechanistic depth to more fully support the authors' conclusions.

Strengths:

(1) The manuscript addresses an important and timely topic in host-microbe communication and circadian biology.

(2) The studies employ multiple complementary models, e.g., Taar5 knockout mice, microbial mutants, which enhances the depth of the investigation.

(3) The integration of behavioral, hormonal, microbial, and transcript-level data provides a multifaceted view of the observed phenotype.

(4) Inclusion of rhythmic analysis of a defined microbial community adds novelty and strength to the overall findings.

(5) The identification of olfactory-linked circadian changes in the context of gut microbes adds a novel perspective to the field.

Weaknesses:

(1) While the authors suggest a causal role for TAAR5 and its ligand in circadian regulation, some of the data remain correlative in this context; however, the authors have appropriately tempered these claims, and mechanistic experiments are proposed to expand upon their compelling findings in future work.

Reviewer #3 (Public review):

Summary:

Deletion of the TMA-sensor TAAR5 results in circadian alterations in the gene expression, particularly in the olfactory bulb; plasma hormones; and neurobehaviors.

Strengths:

Genetic background was rigorously controlled.

Comprehensive characterization.

Impact:

These data add to the growing literature pointing to a role for the TMA/TMAO pathway in olfaction and neurobehavior.

Reviewer #1 (Public review):

Summary:

Overexpression of the mRNA binding protein Ssd1 was shown before to expand the replicative lifespan of yeast cells, whereas ssd1 deletion had the opposite effect. Here, the authors provide initial evidence that overproduced Ssd1 might act via sequestration of mRNAs of the Aft1/2-dependent iron regulon. Ssd1 overexpression restricts activation of the iron regulon and limits accumulation of Fe2+ inside cells, thereby likely lowering oxidative damage. The effects of Ssd1 overexpression and calorie restriction on lifespan are epistatic, suggesting that they might act through the same pathway.

Strengths:

The study is well-designed and involves analysis of single yeast cells during replicative aging. The findings are well displayed and largely support the derived model, which also has implications on lifespan of other organisms including humans.

Weaknesses:

The model is largely supported by the findings, however they remain correlative at the same time. Whether the knockout of ssd1 shortens lifespan by increased intracellular Fe2+ levels is unknown and the shortened lifespan might be caused by different Ssd1 functions. The finding that increased Ssd1 levels form condensates in a cell-cycle dependent is interesting, yet the role of the condensates in lifespan expansion remains untested and unlinked.

Comments on revisions:

In their revised version and response letter the authors have largely addressed my previous concerns. I would have liked to see an experimental response to some of the points of criticism, but I accept that they have been addressed purely in writing. There are some aspects that should be further elaborated by the authors. I agree that determining the mRNAs that co-sequester with Ssd1 foci will be part of an independent study, yet whether Ssd1 foci are relevant for lifespan expansion remains unclear and I would have hoped for some more detailed consideration on this point in the discussion section. Similarly, it should be clearly stated that the impact of Ssd1 overexpression is unlinked from the cellular function of Ssd1 produced at authentic levels and that the short-lived phenotype of a ssd1 knockout is likely not caused by overactivation of the iron regulon (based on the author´s reply). I will appreciate it if the authors include these aspects more clearly in the discussion.

Reviewer #2 (Public review):

This manuscript describes the use of a powerful technique called microfluidics to elucidate the mechanisms explaining how overexpression (OE) of Ssd1 and caloric restriction (CR) in yeast extend replicative lifespan (RLS). Microfluidics measures RLS by trapping cells in chambers mounted to a slide. The chambers hold the mother cell but allow daughters to escape. The slide, with many chambers, is recorded during the entire process, roughly 72 hours, with the video monitored afterwards to count how many daughters each of the trapped mothers produces. The power of the method is what can be done with it. For example, the entire process can be viewed by fluorescence so that GFP and mCherry-tagged proteins can be followed as cells age. The budding yeast is the only model where bona fide replicative aging can be measured, and microfluidics is the only system that allows protein localization and levels to be measured in a single cell while aging. The authors do a wonderful job of showing what this combination of tools can do.

The authors had previously shown that Ssd1, an mRNA-binding protein, extends RLS when overexpressed. This was attributed to Ssd1 sequestering away specific mRNAs under stress, likely leading to reduced ribosomal function. It remained completely unknown how Ssd1 OE extended RLS. The authors observed that overexpressed, but not normally expressed, Ssd1 formed cytoplasmic condensates during mitosis that are resolved by cytokinesis. When the condensates fail to be resolved at the end of mitosis, this signals death.

It has become clear in the literature that iron accumulation increases with age within the cell. The transcriptional programs that activate the iron regulon also become elevated in aging cells. This is thought to be due to impaired mitochondrial function in aging cells, with increased iron accumulation as an attempt at restoring mitochondrial activity. The authors show that Ssd1 OE and CR both reduce the expression of the iron regulon. The data presented indicate that iron accumulation shortens RLS: deletion of iron regulon components extends RLS, and adding iron to WT cells decreases RLS, but not when Ssd1 is overexpressed or when cells are calorically restricted. Interestingly, iron chelation using BPS has no impact on WT RLS, but decreases the elevated RLS in CR cells and cells overexpressing Ssd1. It was not initially clear why iron chelation would inhibit the extended lifespan seen with CR and Ssd1 OE. This was addressed by an experiment where it was shown that the iron regulon is induced (FIT2 induction) when iron is chelated. Thus, the detrimental effects of induction of the iron regulon by BPS and iron accumulation on RLS cannot be tempered by Ssd1 OE and CR once turned on.

Comments on Revised Version:

I am content with the authors' responses to my prior comments.

Reviewer #3 (Public review):

In this paper, the authors investigate how the RNA-binding protein Ssd1 and calorie restriction (CR) influence yeast replicative lifespan, with a particular focus on age-dependent iron uptake and activation of the iron regulon. For this, they use microfluidics-based single-cell imaging to monitor replicative lifespan, protein localization, and intracellular iron levels across aging cells. They show that both Ssd1 overexpression and CR act through a shared pathway to prevent the nuclear translocation of the iron-regulon regulator Aft1 and the subsequent induction of high-affinity iron transporters. As a result, these interventions block the age-related accumulation of intracellular free iron, which otherwise shortens lifespan. Genetic and chemical epistasis experiments further demonstrate that suppression of iron regulon activation is the key mechanism by which Ssd1 and CR promote replicative longevity.

Overall, the paper is technically rigorous, and the main conclusions are supported by a substantial body of experimental data. The microfluidics-based assays in particular provide compelling single-cell evidence for the dynamics of Ssd1 condensates and iron homeostasis.

My main concern, however, is that the central reasoning of the paper-that Ssd1 overexpression and CR prevent the activation of the iron regulon-appears to be contradicted by previous findings, and the authors may actually be misrepresenting these studies, unless I am mistaken. In the manuscript, the authors state on two occasions:

"Intriguingly, transcripts that had altered abundance in CR vs control media and in SSD1 vs ssd1∆ yeast included the FIT1, FIT2, FIT3, and ARN1 genes of the iron regulon (8)"

"Ssd1 and CR both reduce the levels of mRNAs of genes within the iron regulon: FIT1, FIT2, FIT3 and ARN1 (8)"

However, reference (8) by Kaeberlein et al. actually says the opposite:

"Using RNA derived from three independent experiments, a total of 97 genes were observed to undergo a change in expression >1.5-fold in SSD1-V cells relative to ssd1-d cells (supplemental Table 1 at http://www.genetics.org/supplemental/). Of these 97 genes, only 6 underwent similar transcriptional changes in calorically restricted cells (Table 2). This is only slightly greater than the number of genes expected to overlap between the SSD1-V and CR datasets by chance and is in contrast to the highly significant overlap in transcriptional changes observed between CR and HAP4 overexpression (Lin et al. 2002) or between CR and high external osmolarity (Kaeberlein et al. 2002). Intriguingly, of the 6 genes that show similar transcriptional changes in calorically restricted cells and SSD1-V cells, 4 are involved in iron-siderochrome transport: FIT1, FIT2, FIT3, and ARN1 (supplemental Table 1 at http://www.genetics.org/supplemental/)."

Although the phrasing might be ambiguous at first reading, this interpretation is confirmed upon reviewing Matt Kaeberlein's PhD thesis: https://dspace.mit.edu/handle/1721.1/8318

(page 264 and so on)

Moreover, consistent with this, activation of the iron regulon during calorie restriction (or the diauxic shift) has also been observed in two other articles:

https://doi.org/10.1016/S1016-8478(23)13999-9

https://doi.org/10.1074/jbc.M307447200

Taken together, these contradictory data might blur the proposed model and make it unclear how to reconcile the results.

Comments on revisions:

The authors successfully addressed my requests and concerns

Reviewer #1 (Public review):

Summary:

This paper investigates infants' social perception as reflected in looking behavior during face-to-face mother-infant toy play in two groups (5 and 15 months). Using information-theoretic and computer-vision methods, the authors quantify dynamic changes in lower-level (salience) and higher-level (semantic) features in the auditory and visual domains - primarily from mothers - and relate these to infants' real-time attention to toys (and to mothers). Time-lagged correlations suggest dynamic, reciprocal relations between infants' attention and maternal low-level (salience) and high-level (semantic) features at both ages, consistent with an early emergence of interpersonal social contingency based on multi-level information during interaction.

Strengths:

The study uses a naturalistic, multimodal mother-infant free-play paradigm and applies information-theoretic/AI methods to quantify both low- and high-level features of maternal behavior, enabling a fine-grained decomposition of interaction dynamics. The time-lag approach further allows examination of temporal relations between maternal signals and infants' attention.

Weaknesses:

Directionality claims from cross-correlations are sometimes unclear, especially when both positive and negative lags are significant, and the evidence for age effects is not yet convincing. Infant attention was manually coded with only moderate-substantial agreement, and handling of disagreements/uncodable periods should be clarified and acknowledged as a limitation.

Reviewer #2 (Public review):

Summary:

This study examines the dynamic interplay between infant attention and hierarchical maternal behaviors from a social information processing perspective. By employing a comprehensive naturalistic framework, the author quantified interactions across both low-level (sensory) and high-level (semantic) features. With correlation analysis with these features, they found that within social contexts, behaviors such as joint attention - shaped by mutual interaction - exhibit patterns distinct from unilateral responding or mimicry. In contrast to traditional semi-structured behavioral observation and coding, the methods employed in this study were designed to consciously and sensitively capture these dynamic features and relate them temporally. This approach contributes to a more integrated understanding of the developmental principles underlying capacities like joint action and communication.

Strengths:

The manuscript's core strength lies in its innovative, dynamic, and hierarchical framework for investigating early social attention. The findings reveal complex adaptive scaffolding strategies: for instance, when infants focus on objects, mothers reduce low-level sensory input, minimising distractions. Furthermore, the results indicate that, even from early development, maternal behaviors are both driven by and predictive of infant attention, confirming that attention involves complex interactive processes that unfold across multiple levels, from salience to semantics.

From a methodological standpoint, the use of unstructured play situations, combined with multi-channel, high-precision time-series analyses, undoubtedly required substantial effort in both data collection and coding. Compared to the relatively two-dimensional analytical approaches common in prior research, this study's introduction of lower-level and higher-level features to explore the hierarchical organization of processing across development is highly plausible. The psychological processes reflected by these quantified physical features span multiple domains - including emotion, motion, and phonetics - and the high temporal sampling rate ensures fine-grained resolution.

Critically, these features are extracted through a suite of advanced machine learning and computational methods, which automate the extraction of objective metrics from audiovisual data. Consequently, the methodological flow significantly enhances data utilization and offers valuable inspiration for future behavioral coding research aiming for high ecological validity.

Weaknesses:

The conclusion of this paper is generally supported by the data and analysis, but some aspects of data analysis need to be clarified and extended.

(1) A more explicit justification for the selection and theoretical categorization of the eight interaction features may be needed. The paper introduces a distinction between "lower-level" and "higher-level" features but does not clearly articulate the criteria underpinning this classification. While a continuum is acknowledged, the practical division requires a principled rationale. For instance, is the classification based on the temporal scale of the features, the degree of cognitive processing required for their integration, or their proximity to sensory input versus semantic meaning?

(2) The claims regarding age-related differences in Predictions 2 are not fully substantiated by the current analyses. The findings primarily rely on observing that an effect is significant in one age group but not the other (e.g., the association between object naming and attention is significant at 15 months but not at 5 months). However, this pattern alone does not constitute evidence about whether the two age groups differ significantly from each other. The absence of a direct statistical comparison (e.g., an interaction test in a model that includes age as a factor) creates an inferential gap. To robustly support developmental change, formal tests of the Age × Feature interaction on infant attention are required.

(3) Another potential methodological issue concerns the potential confounding effect of parents' use of the infant's name. The analysis of "object naming" does not clarify whether utterances containing object words (e.g., "panda") were distinct from those that also incorporated the infant's name (e.g., "Look, Sarah, the panda!"). Given that a child's own name is a highly salient social cue known to robustly capture infant attention, its co-occurrence with object labels could potentially inflate or confound the measured effect of object naming itself. It would be important to know whether and how frequently infants' names were called, whether this variable was analyzed separately, and if its effect was statistically disentangled from that of pure object labeling.

(4) Interpretation of results requires clarification regarding the extended temporal lags reported, specifically the negative correlation between maternal vocal spectral flux and infant attention at 6.54 to 9.52 seconds (Figure 4C). The authors interpret this as a forward-prediction, suggesting that a decrease in acoustic variability leads to increased infant attention several seconds later. However, a lag of such duration seems unusually long for a direct, contingent infant response to a specific vocal feature. Is there existing empirical evidence from infant research to support such a prolonged response latency? Alternatively, could this signal suggest a slower, cyclical pattern of the interaction rather than a direct causal link?

Reviewer #3 (Public review):

Summary:

This manuscript presents an ambitious integration of multiple artificial intelligence technologies to examine social learning in naturalistic mother-infant interactions. The authors aimed to quantify how information flows between mothers and infants across different communicative modalities and timescales, using speech analysis (Whisper), pose detection (MMPose), facial expression recognition, and semantic modeling (GPT-2) in a unified analytical framework. Their goal was to provide unprecedented quantitative precision in measuring behavioral coordination and information transfer patterns during social learning, moving beyond traditional observational coding approaches to examine cross-modal coordination patterns and semantic contingencies in real-time across multiple temporal scales.

Strengths:

The integration of multiple AI tools into a coherent analytical framework represents a genuine methodological breakthrough that advances our capabilities for studying complex social phenomena. The authors successfully analyzed naturalistic interactions at a scale and level of detail that was not previously possible, examining 33 5-month-old and 34 15-month-old dyads across multiple modalities simultaneously. This sophisticated analytical pipeline, combining speech analysis, semantic modeling, pose detection, and facial expression recognition, provides new capabilities for studying social interactions that extend far beyond what traditional observational coding could achieve.

The specific findings about hierarchical information flow patterns across different timescales are particularly valuable and would not have been possible without this sophisticated analytical approach. The discovery that mothers reduce low-level sensory input when infants focus on objects, while increases in object naming and information rate associate with sustained attention, provides new empirical insights into how social learning unfolds in naturalistic settings. The temporal dynamics analyses reveal interesting patterns of behavioral coordination that extend our understanding of how caregivers adaptively modify their responses to support infant attention across multiple communicative channels simultaneously.

The scale of data collection and the comprehensive multi-modal approach are impressive, opening up new possibilities for understanding social learning processes. The methodological innovations demonstrate how modern computational tools can be systematically integrated to reveal new quantitative aspects of well-established developmental phenomena. The computational features developed for this study represent innovative applications of information theory and computer vision to developmental research.

Weaknesses:

Several major limitations affect the reliability and interpretability of the findings. The sample sizes of 33-34 dyads per age group are relatively modest for the complexity of analyses performed, which include eight different features examined across various time lags with extensive statistical comparisons. The study lacks adequate power analysis to demonstrate whether these sample sizes are sufficient to detect meaningful effect sizes, which is particularly concerning given the multiple comparison burden inherent in this type of multi-modal, multi-timescale analysis.

The statistical framework presents several concerns that limit confidence in the findings. Inter-rater reliability for gaze coding shows substantial but not excellent agreement (κ = 0.628), with only 22% of the data undergoing double coding. Given that gaze coding forms the foundation for all subsequent analyses of joint attention and information flow, this reliability level may systematically influence findings. The multiple comparison correction strategies vary inconsistently across different analyses, with some using FDR correction and others treating lower-level and higher-level features separately. Additionally, object naming analyses employed one-sided tests (p<0.05) while others used two-sided tests (p<0.025) without clear theoretical or methodological justification for these differences.

The validation of AI tools in the specific context of mother-infant interactions is insufficient and represents a critical limitation. The performance characteristics of Whisper with infant-directed speech, the precision of MMPose for detecting facial landmarks in young children, and the accuracy of facial expression recognition tools in infant contexts are not adequately validated for this population. These sophisticated tools may not perform optimally in the specific context of mother-infant interactions, where speech patterns, facial expressions, and body movements may differ substantially from their training data.

The theoretical positioning requires substantial refinement to better acknowledge the extensive existing literature. The authors are working within a well-established theoretical framework that has long recognized social learning as an active, bidirectional process. The joint attention literature, beginning with foundational work by Bruner (1983) and continuing through contemporary theories of social cognition by researchers like Tomasello (1995), has emphasized the communicative and adaptive nature of attentional processes. The scaffolding literature, including seminal work by Wood, Bruner, and Ross (1976), has demonstrated how parents adjust their support based on children's developing competencies. Moreover, there is a substantial body of micro-analytic research that has employed sophisticated quantitative methods to study social interactions, including work by Stern (1985) on microsecond-level interactions and research using time-series methods to examine dyadic coordination patterns.

The cross-correlation analyses have inherent limitations for causal inference that are not adequately acknowledged. The interpretation of temporal correlation patterns in terms of directional influence requires more cautious consideration, as observational data have fundamental constraints for establishing causality. The ecological validity is also questionable due to the laboratory tabletop interaction paradigm and the sample's demographic homogeneity, consisting primarily of white, highly educated, high-income mothers.

Reviewer #1 (Public review):

Summary:

Lumen formation is a fundamental morphogenetic event essential for the function of all tubular organs, notably the vertebrate vascular network, where continuous and patent conduits ensure blood flow and tissue perfusion. The mechanisms by which endothelial cells organize to create and maintain luminal space have historically been categorized into two broad strategies: cell shape changes, which involve alterations in apical-basal polarity and cytoskeletal architecture, and cell rearrangements, wherein intercellular junctions and positional relationships are remodeled to form uninterrupted conduits. The study presented here focuses on the latter process, highlighting a unique morphogenetic module, junction-based lamellipodia (JBL), as the driver for endothelial rearrangements.

Strengths:

The key mechanistic insight from this work is the requirement of the Arp2/3 complex, the classical nucleator of branched actin filament networks, for JBL protrusion. This implicates Arp2/3-mediated actin polymerization in pushing force generation, enabling plasma membrane advancement at junctional sites. The dependence on Arp2/3 positions JBL within the family of lamellipodia-like structures, but the junctional origin and function distinguish them from canonical, leading-edge lamellipodia seen in cell migration.

Weaknesses:

The study primarily presents descriptive observations and includes limited quantitative analyses or genetic modifications. Molecular mechanisms are typically interrogated through the use of pharmacological inhibitors rather than genetic approaches. Furthermore, the precise semantic distinction between JAIL and JBL requires additional clarification, as current evidence suggests their biological relevance may substantially overlap.

Reviewer #2 (Public review):

Summary:

In Maggi et al., the authors investigated the mechanisms that regulate the dynamics of a specialized junctional structure called junction-based lamellipodia (JBL), which they have previously identified during multicellular vascular tube formation in the zebrafish. They identified the Arp2/3 complex to dynamically localize at expanding JBLs and showed that the chemical inhibition of Arp2/3 activity slowed junctional elongation. The authors therefore concluded that actin polymerization at JBLs pushes the distal junction forward to expand the JBL. They further revealed the accumulation of Myl9a/Myl9b (marker for MLC) at the junctional pole, at interjunctional regions, suggesting that contractile activity drives the merging of proximal and distal junctions. Indeed, chemical inhibition of ROCK activity decreased junctional mergence. With these new findings, the authors added new molecular and cellular details into the previously proposed clutch mechanism by proposing that Arp2/3-dependent actin polymerization provides pushing forces while actomyosin contractility drives the merging of proximal and distal junctions, explaining the oscillatory protrusive nature of JBLs.

Strengths:

The authors provide detailed analyses of endothelial cell-cell dynamics through time-lapse imaging of junctional and cytoskeletal components at subcellular resolution. The use of zebrafish as an animal model system is invaluable in identifying novel mechanisms that explain the organizing principles of how blood vessels are formed. The data is well presented, and the manuscript is easy to read.

Weaknesses:

While the data generally support the conclusions reached, some aspects can be strengthened. For the untrained eye, it is unclear where the proximal and distal junctions are in some images, and so it is difficult to follow their dynamics (especially in experiments where Cdh5 is used as the junctional marker). Images would benefit from clear annotation of the two junctions. All perturbation experiments were done using chemical inhibitors; this can be further supported by genetic perturbations.

Reviewer #3 (Public review):

The paper by Maggi et al. builds on earlier work by the team (Paatero et al., 2018) on oriented junction-based lamellipodia (JBL). They validate the role of JBLs in guiding endothelial cell rearrangements and utilise high-resolution time-lapse imaging of novel transgenic strains to visualise the formation of distal junctions and their subsequent fusion with proximal junctions. Through functional analyses of Arp2/3 and actomyosin contractility, the study identifies JBLs as localized mechanical hubs, where protrusive forces drive distal junction formation, and actomyosin contractility brings together the distal and proximal junctions. This forward movement provides a unique directionality which would contribute to proper lumen formation, EC orientation, and vessel stability during these early stages of vessel development.

Time-lapse live imaging of VEC, ZO-1, and actin reveals that VEC and ZO-1 are initially deposited at the distal junction, while actin primarily localizes to the region between the proximal and distal sites. Using a photoconvertible Cdh5-mClav2 transgenic line, the origin of the VEC aggregates was examined. This convincingly shows that VE-cadherin was derived from pools outside the proximal junctions. However, in addition to de novo VEC derived from within the photoconverted cell, could some VEC also be contributed by the neighbouring endothelial cell to which the JBL is connected?

As seen for JAILs in cultured ECs, the study reveals that Arp2/3 is enhanced when JBLs form by live imaging of Arpc1b-Venus in conjunction with ZO-1 and actin. Therefore Arp2/3 likely contributes to the initial formation of the distal junction in the lamellopodium.

Inhibiting Arp2/3 with CK666 prevents JBL formation, and filopodia form instead of lamellopodia. This loss of JBLs leads to impaired EC rearrangements.

Is the effect of CK666 treatment reversible? Since only a short (30 min) treatment is used, the overall effect on the embryo would be minimal, and thus washing out CK666 might lead to JBL formation and normalized rearrangements, which would further support the role of Arp2/3.

From the images in Figure 4d it appears that ZO-1 levels are increased in the ring after CK666 treatment. Has this been investigated, and could this overall stabilization of adhesion proteins further prevent elongation of the ring?

To explore how the distal and proximal junctions merge, imaging of spatiotemporal imaging of Myl9 and VEC is conducted. It indicates that Myl9 is localized at the interjunctional fusion site prior to fusion. This suggests pulling forces are at play to merge the junctions, and indeed Y 27632 treatment reduces or blocks the merging of these junctions.

For this experiment, a truncated version of VEC was use,d which lacks the cytoplasmic domain. Why have the authors chosen to image this line, since lacking the cytoplasmic domain could also impair the efficiency of tension on VEC at both junction sites? This is as described in the discussion (lines 328-332).

Since the time-lapse movies involve high-speed imaging of rather small structures, it is understandable that these are difficult to interpret. Adding labels to indicate certain structures or proteins at essential timepoints in the movies would help the readers understand these.

Reviewer #1 (Public Review):

Summary:

Ravichandran et al investigate the regulatory panels that determine the polarization state of macrophages. They identify regulatory factors involved in M1 and M2 polarization states by using their network analysis pipeline. They demonstrate that a set of three regulatory factors (RFs) i.e., CEBPB, NFE2L2, and BCL3 can change macrophage polarization from the M1 state to the M2 state. They also show that siRNA-mediated knockdown of those 3-RF in THP1-derived M0 cells, in the presence of M1 stimulant increases the expression of M2 markers and showed decreased bactericidal effect. This study provides an elegant computational framework to explore the macrophage heterogeneity upon different external stimuli and adds an interesting approach to understanding the dynamics of macrophage phenotypes after pathogen challenge.

Strengths:

This study identified new regulatory factors involved in M1 to M2 macrophage polarization. The authors used their own network analysis pipeline to analyze the available datasets. The authors showed 13 different clusters of macrophages that encounter different external stimuli, which is interesting and could be translationally relevant as in physiological conditions after pathogen challenge, the body shows dynamic changes in different cytokines/chemokines that could lead to different polarization states of macrophages. The authors validated their primary computational findings with in vitro assays by knocking down the three regulatory factors-NCB.

Reviewer #2 (Public Review):

Summary:

The authors of this manuscript address an important question regarding how macrophages respond to external stimuli to create different functional phenotypes, also known as macrophage polarization. Although this has been studied extensively, the authors argue that the transcription factors that mediate the change in state in response to a specific trigger remain unknown. They create a "master" human gene regulatory network and then analyze existing gene expression data consisting of PBMC-derived macrophage response to 28 stimuli, which they sort into thirteen different states defined by perturbed gene expression networks. They then identify the top transcription factors involved in each response that have the strongest predicted association with the perturbation patterns they identify. Finally, using S. aureus infection as one example of a stimulus that macrophages respond to, they infect THP-1 cells while perturbing regulatory factors that they have identified and show that these factors have a functional effect on the macrophage response.

Strengths:

The computational work done to create a "master" hGRN, response networks for each of the 28 stimuli studied, and the clustering of stimuli into 13 macrophage states is useful. The data generated will be a helpful resource for researchers who want to determine the regulatory factors involved in response to a particular stimulus and could serve as a hypothesis generator for future studies.

The streamlined system used here - macrophages in culture responding to a single stimulus - is useful for removing confounding factors and studying the elements involved in response to each stimulus.

The use of a functional study with S. aureus infection is helpful to provide proof of principle that the authors' computational analysis generates data that is testable and valid for in vitro analysis.

[Reviewing Editor comments on revised version: the authors have made minimal changes and we have made a modest modification to the eLife Assessment, without returning the revised version to the original reviewers.]

Joint Public Review:

From Reviewer 3 previously: Barnett examines a pressing question regarding citing behavior of authors during the peer review process. In particular, the author studies the interaction between reviewers and authors, focusing on the odds of acceptance, and how this may be affected by whether or not the authors cited the reviewers' prior work, whether the reviewer requested such citations be added, and whether the authors complied/how that affected the reviewer decision-making.

Key findings are a) that reviewers were more likely to approve an article if cited in the submission, b) reviewers who requested a citation in an updated version were less likely to approve, and c) reviewers who requested and received a citation were more likely to approve the revised version.

Comment from the Reviewing Editor about the latest version:

This is the third version of this article. Comments made during the peer review of the second version, along with author's responses to these comments, are available below.

Comments made during the peer review of the first version, along with author's responses to these comments, are available with previous versions of the article.

Reviewer #1 (Public review):

Summary:

Crohn's disease is a prevalent inflammatory bowel disease that often results in patient relapse post anti-TNF blockades. This study employs a multifaceted approach utilizing single-cell RNA sequencing, flow cytometry, and histological analyses to elucidate the cellular alterations in pediatric Crohn's disease patients pre and post anti-TNF treatment and comparing them with non-inflamed pediatric controls. Utilizing an innovative clustering approach, , the research distinguishes distinct cellular states that signify the disease's progression and response to treatment. Notably, the study suggests that the anti-TNF treatment pushes pediatric patients towards a cellular state resembling adult patients with persistent relapse. This study's depth offers a nuanced understanding of cell states in CD progression that might forecast the disease trajectory and therapy response.

Robust Data Integration: The authors adeptly integrate diverse data types: scRNA-seq, histological images, flow cytometry, and clinical metadata, providing a holistic view of the disease mechanism and response to treatment.

Novel Clustering Approach: The introduction and utilization of ARBOL, a tiered clustering approach, enhances the granularity and reliability of cell type identification from scRNA-seq data.

Clinical Relevance: By associating scRNA-seq findings with clinical metadata, the study offers potentially significant insights into the trajectory of disease severity and anti-TNF response; might help with the personalized treatment regimens.

Treatment Dynamics: The transition of the pediatric cellular ecosystem towards an adult, more treatment-refractory state upon anti-TNF treatment is a significant finding. It would be beneficial to probe deeper into the temporal dynamics and the mechanisms underlying this transition.

Comparative Analysis with Adult CD: The positioning of on-treatment biopsies between treatment-naïve pediCD and on-treatment adult CD is intriguing. A more in-depth exploration comparing pediatric and adult cellular ecosystems could provide valuable insights into disease evolution.

Areas of improvement:

(1) The legends accompanying the figures are quite concise. It would be beneficial to provide a more detailed description within the legends, incorporating specifics about the experiments conducted and a clearer representation of the data points.

(2) Statistical significance is missing from Fig. 1c WBC count plot, Fig. 2 b-e panels. Please provide even if its not significant. Also, legend should have the details of stat test used.

(3) In the study, the NOA group is characterized by patients who, after thorough clinical evaluations, were deemed to exhibit milder symptoms, negating the need for anti-TNF prescriptions. This mild nature could potentially align the NOA group closer to FIGD-a condition intrinsically defined by its low to non-inflammatory characteristics. Such an alignment sparks curiosity: is there a marked correlation between these two groups? A preliminary observation suggesting such a relationship can be spotted in Figure 6, particularly panels A and B. Given the prevalence of FIGD among the pediatric population, it might be prudent for the authors to delve deeper into this potential overlap, as insights gained from mild-CD cases could provide valuable information for managing FIGD.

(4) Furthermore, Figure 7 employs multi-dimensional immunofluorescence to compare CD, encompassing all its subtypes, with FIGD. If the data permits, subdividing CD into PR, FR, and NOA for this comparison could offer a more nuanced understanding of the disease spectrum. Such a granular perspective is invaluable for clinical assessments. The key question then remains: do the sample categorizations for the immunofluorescence study accommodate this proposed stratification?

(5) The study's most captivating revelation is the proximity of anti-TNF treated pediatric CD (pediCD) biopsies to adult treatment-refractory CD. Such an observation naturally raises the question: How does this alignment compare to a standard adult colon, and what proportion of this similarity is genuinely disease-specific versus reflective of an adult state? To what degree does the similarity highlight disease-specific traits?

Delving deeper, it will be of interest to see whether anti-TNF treatment is nudging the transcriptional state of the cells towards a more mature adult stage or veering them into a treatment-resistant trajectory. If anti-TNF therapy is indeed steering cells toward a more adult-like state, it might signify a natural maturation process; however, if it's directing them toward a treatment-refractory state, the long-term therapeutic strategies for pediatric patients might need reconsideration.

Comments on revisions:

I have no further comments. I am satisfied with the revisions.

Reviewer #2 (Public review):

Summary:

Through this study the authors combine a number of innovative technologies including scRNAseq to provide insight into Crohn's disease. Importantly, samples from pediatric patients are included. The authors develop a principled and unbiased tiered clustering approach, termed ARBOL. Through high-resolution scRNAseq analysis the authors identify differences in cell subsets and states during pediCD relative to FGID. The authors provide histology data demonstrating T cell localisation within the epithelium. Importantly, the authors find anti-TNF treatment pushes the pediatric cellular ecosystem towards an adult state.

Strengths:

This study is well presented. The introduction clearly explains the important knowledge gaps in the field, the importance of this research, the samples that are used and study design.<br /> The results clearly explain the data, without overstating any findings. The data is well presented. The discussion expands on key findings and any limitations to the study are clearly explained.

I think the biological findings from and bioinformatic approach used in, this study, will be of interest to many and significantly add to the field.

Weaknesses:

(1) The ARBOL approach for iterative tiered clustering on a specific disease condition was demonstrated to work very well on the datasets generated in this study where there were no obvious batch effects across patients. What if strong batch effects are present across donors where PCA fails to mitigate such effects? Are there any batch correction tools implemented in ARBOL for such cases?

The authors have addressed this comment during review

(2) The authors mentioned that the clustering tree from the recursive sub-clustering contained too much noise, and they therefore used another approach to build a hierarchical clustering tree for the bottom-level clusters based on unified gene space. But in general, how consistent are these two trees?

The authors have addressed this comment during review

Comments on revisions:

I have no additional comments. The authors addressed my previous comments well.

Reviewer #1 (Public review):

Summary:

In their previous publication (Dong et al. Cell Reports 2024), the authors showed that citalopram treatment resulted in reduced tumor size by binding to the E380 site of GLUT1 and inhibiting the glycolytic metabolism of HCC cells, instead of the classical citalopram receptor. Given that C5aR1 was also identified as the potential receptors of citalopram in the previous report, the authors focused on exploring the potential of immune-dependent anti-tumor effect of citalopram via C5aR1. C5aR1 was found to be expressed on tumor-associated macrophages (TAMs) and citalopram administration showed potential to improve the stability of C5aR1 in vitro. Through macrophage depletion and adoptive transfer approaches in HCC mouse models, the data demonstrated the potential importance of C5aR1-expressing macrophage in the anti-tumor effect of citalopram in vivo. Mechanistically, their in vitro data suggested that citalopram may regulate the phagocytosis potential and polarization of macrophages through C5aR1. Next, they tried to investigate the direct link between citalopram and CD8+T cells by including an additional MASH-associated HCC mouse model. Their data suggest that citalopram may upregulate the glycolytic metabolism of CD8+T cells, probability via GLUT3 but not GLUT1-mediated glucose uptake. Lastly, as the systemic 5-HT level is down-regulated by citalopram, the authors analyzed the association between a low 5-HT and a superior CD8+T cell function against tumor. Although the data is informative, the rationale for working on additional mechanisms and logical link among different parts are not clear. In addition, some of the conclusion is also not fully supported by the current data.

Strengths:

The idea of repurposing clinical-in-used drugs showed great potential for immediate clinical translation. The data here suggested that the anti-depression drug, citalopram displayed immune regulatory role on TAM via a new target C5aR1 in HCC.

Comments on revised version:

The authors have addressed most of my concerns about the paper.

Reviewer #2 (Public review):

Summary:

Dong et al. present a thorough investigation into the potential of repurposing citalopram, an SSRI, for hepatocellular carcinoma (HCC) therapy. The study highlights the dual mechanisms by which citalopram exerts anti-tumor effects: reprogramming tumor-associated macrophages (TAMs) toward an anti-tumor phenotype via C5aR1 modulation and suppressing cancer cell metabolism through GLUT1 inhibition, while enhancing CD8+ T cell activation. The findings emphasize the potential of drug repurposing strategies and position C5aR1 as a promising immunotherapeutic target.

Strengths:

It provides detailed evidence of citalopram's non-canonical action on C5aR1, demonstrating its ability to modulate macrophage behavior and enhance CD8+ T cell cytotoxicity. The use of DARTS assays, in silico docking, and gene signature network analyses offers robust validation of drug-target interactions. Additionally, the dual focus on immune cell reprogramming and metabolic suppression presents a comprehensive strategy for HCC therapy. By highlighting the potential for existing drugs like citalopram to be repurposed, the study also emphasizes the feasibility of translational applications. During revision, the authors experimentally demonstrated that TAM has lower GLUT1, which further strengthens their claim of C5aR1 modulation-dependent TAM improvement for tumor therapy.

Weaknesses:

The authors proposed that CD8+ T cells have an TAM-independent role upon Citalropharm treatment. However, this claim requires further investigation to confirm that the effect is truly "TAM independent".

Reviewer #1 (Public review):

Summary:

The authors examine the neural correlates of face recognition deficits in individuals with Developmental Prosopagnosia (DP; 'face blindness'). Contrary to theories that poor face recognition is driven by reduced spatial integration (via smaller receptive fields), here the authors find that the properties of receptive fields in face-selective brain regions are the same in typical individuals vs. those with DP. The main analysis technique is population Receptive Field (pRF) mapping, with a wide range of measures considered. The authors report that there are no differences in goodness-of-fit (R2), the properties of the pRFs (neither size, location, nor the gain and exponent of the Compressive Spatial Summation model), nor their coverage of the visual field. The relationship of these properties to the visual field (notably the increase in pRF size with eccentricity) is also similar between the groups. Eye movements do not differ between the groups.

Strengths:

Although this is a null result, the large number of null results gives confidence that there are unlikely to be differences between the two groups. Together, this makes a compelling case that DP is not driven by differences in the spatial selectivity of face-selective brain regions, an important finding that directly informs theories of face recognition. The paper is well written and enjoyable to read, the studies have clearly been carefully conducted with clear justification for design decisions, and the analyses are thorough.

Weaknesses:

One potential issue relates to the localisation of face-selective regions in the two groups. As in most studies of the neural basis of face recognition, localisers are used to find the face-selective Regions of Interest (ROIs) - OFA, mFus, and pFus, with comparison to the scene-selective PPA. To do so, faces are contrasted against other objects to find these regions (or scenes vs. others for the PPA). The one consistent difference that does emerge between groups in the paper is in the selectivity of these regions, which are less selective for faces in DP than in typical individuals (e.g., Figure 1B), as one might expect. 6/20 prosopagnosic individuals are also missing mFus, relative to only 2/20 typical individuals. This, to me, raises the question of whether the two groups are being compared fairly. If the localised regions were smaller and/or displaced in the DPs, this might select only a subset of the neural populations typically involved in face recognition. Perhaps the difference between groups lies outside this region. In other words, it could be that the differences in prosopagnosic face recognition lie in the neurons that are not able to be localised by this approach. The authors consider in the discussion whether their DPs may not have been 'true DPs', which is convincing (p. 12). The question here is whether the regions selected are truly the 'prosopagnosic brain areas' or whether there is a kind of survivor bias (i.e., the regions selected are normal, but perhaps the difference lies in the nature/extent of the regions. At present, the only consideration given to explain the differences in prosopagnosia is that there may be 'qualitative' differences between the two (which may be true), but I would give more thought to this.

The discussion considers the differences between the current study and an unpublished preprint (Witthoft et al, 2016), where DPs were found to have smaller pRFs than typical individuals. The discussion presents the argument that the current results are likely more robust, given the use of images within the pRF mapping stimuli here (faces, objects, etc) as opposed to checkerboards in the prior work, and the use of the CSS model here as opposed to a linear Gaussian model previously. This is convincing, but fails to address why there is a lack of difference in the control vs. DP group here. If anything, I would have imagined that the use of faces in mapping stimuli would have promoted differences between the groups (given the apparent difference in selectivity in DPs vs. controls seen here), which adds to the reliability of the present result. Greater consideration of why this should have led to a lack of difference would be ideal. The latter point about pRF models (Gaussian vs. CSS) does seem pertinent, for instance - could the 'qualitative' difference lead to changes in the shape of these pRFs in prosopagnosia that are better characterised by the CSS model, perhaps? Perhaps more straightforwardly, and related to the above, could differences in the localisation of face-selective regions have driven the difference in prior work compared to here?

Finally, the lack of variations in the spatial properties of these brain regions is interesting in light of the theories that spatial integration is a key aspect of effective face recognition. In this context, it is interesting to note the marked drop in R2 values in face-selective regions like mFus relative to earlier cortex. The authors note in some sense that this is related to the larger receptive field size, but is there a broader point here that perhaps the receptive field model (even with Compressive Spatial Summation) is simply a poor fit for the function of these areas? Could it be that these areas are simply not spatial at all? A broader link between the null results presented here and their implications for theories of face recognition would be ideal.

Reviewer #2 (Public review):

Summary:

This is a well-conducted and clearly written manuscript addressing the link between population receptive fields (pRFs) and visual behavior. The authors test whether developmental prosopagnosia (DP) involves atypical pRFs in face-selective regions, a hypothesis suggested by prior work with a small DP sample. Using a larger cohort of DPs and controls, robust pRF mapping with appropriate stimuli and CSS modeling, and careful in-scanner eye tracking, the authors report no group differences in pRF properties across the visual processing hierarchy. These results suggest that reduced spatial integration is unlikely to account for holistic face processing deficits in DP.

Strengths:

The dataset quality, sample size, and methodological rigor are notable strengths.

Weaknesses:

The primary concern is the interpretation of the results.

(1) Relationship between pRFs and spatial integration

While atypical pRF properties could contribute to deficits in spatial integration, impairments in holistic processing in DPs are not necessarily caused by pRF abnormalities. The discussion could be strengthened by considering alternative explanations for reduced spatial integration, such as altered structural or functional connectivity in the face network, which has been reported to underlie DP's difficulties in integrating facial features.

(2) Beyond the null hypothesis testing framework

The title claims "normal spatial integration," yet this conclusion is based on a failure to reject the null hypothesis, which does not justify accepting the alternative hypothesis. To substantiate a claim of "normal," the authors would need to provide analyses quantifying evidence for the absence of effects, e.g., using a Bayesian framework.

(3) Face-specific or broader visual processing

Prior work from the senior author's lab (Jiahui et al., 2018) reported pronounced reductions in scene selectivity and marginal reductions in body selectivity in DPs, suggesting that visual processing deficits in DPs may extend beyond faces. While the manuscript includes PPA as a high-level control region for scene perception, scene selectivity was not directly reported. The authors could also consider individual differences and potential data-quality confounds (tSNR difference between and within groups, several obvious outliers in the figures, etc). For instance, examining whether reduced tSNR in DPs contributed to lower face selectivity in the DP group in this dataset.

(4) Linking pRF properties to behavior

The manuscript aims to examine the relationship between pRF properties and behavior, but currently reports only one aspect of pRF (size) in relation to a single behavioral measure (CFMT), without full statistical reporting:

"We found no significant association between participants' CFMT scores and mean pRF size in OFA, pFUS, or mFUS."

For comprehensive reporting, the authors could examine additional pRF properties (e.g., center, eccentricity, scaling between eccentricity and pRF size, shape of visual field coverage, etc), additional ROIs (early, intermediate, and category-selective areas), and relate them to multiple behavioral measures (e.g., HEVA, PI20, FFT). This would provide a full picture of how pRF characteristics relate to behavioral performance in DP.

Reviewer #1 (Public review):

Summary:

This paper reports model simulations and a human behavioral experiment studying predictive learning in a multidimensional environment. The authors claim that semantic biases help people resolve ambiguity about predictive relationships due to spurious correlations.

Strengths:

(1) The general question addressed by the paper is important.

(2) The paper is clearly written.

(3) Experiments and analyses are rigorously executed.

Weaknesses:

(1) Showing that people can be misled by spurious correlations, and that they can overcome this to some extent by using semantic structure, is not especially surprising to me. Related literature already exists on illusory correlation, illusory causation, superstitious behavior, and inductive biases in causal structure learning. None of this work features in the paper, which is rather narrowly focused on a particular class of predictive representations, which, in fact, may not be particularly relevant for this experiment. I also feel that the paper is rather long and complex for what is ultimately a simple point based on a single experiment.

(2) Putting myself in the shoes of an experimental subject, I struggled to understand the nature of semantic congruency. I don't understand why the builder and terminal robots should have similar features is considered a natural semantic inductive bias. Humans build things all the time that look different from them, and we build machines that construct artifacts that look different from the machines. I think the fact that the manipulation worked attests to the ability of human subjects to pick up on patterns rather than supporting the idea that this reflects an inductive bias they brought to the experiment.

(3) As the authors note, because the experiment uses only a single transition, it's not clear that it can really test the distinctive aspects of the SR/SF framework, which come into play over longer horizons. So I'm not really sure to what extent this paper is fundamentally about SFs, as it's currently advertised.

(4) One issue with the inductive bias as defined in Equation 15 is that I don't think it will converge to the correct SR matrix. Thus, the bias is not just affecting the learning dynamics, but also the asymptotic value (if there even is one; that's not clear either). As an empirical model, this isn't necessarily wrong, but it does mess with the interpretation of the estimator. We're now talking about a different object from the SR.

(5) Some aspects of the empirical and model-based results only provide weak support for the proposed model. The following null effects don't agree with the predictions of the model:

(a) No effect of condition on reward.

(b) No effect of condition on composition spurious predictiveness.

(c) No effect of condition on the fitted bias parameter. The authors present some additional exploratory analyses that they use to support their claims, but this should be considered weaker support than the results of preregistered analyses.

(6) I appreciate that the authors were transparent about which predictions weren't confirmed. I don't think they're necessarily deal-breakers for the paper's claims. However, these caveats don't show up anywhere in the Discussion.

(7) I also worry that the study might have been underpowered to detect some of these effects. The preregistration doesn't describe any pilot data that could be used to estimate effect sizes, and it doesn't present any power analysis to support the chosen sample sizes, which I think are on the small side for this kind of study.

Reviewer #2 (Public review):

Summary:

This work by Prentis and Bakkour examines how predictive memory can become distorted in multidimensional environments and how inductive biases may mitigate these distortions. Using both computational simulations and an original human-robot building task with manipulated semantic congruency, the authors show that spurious observations can amplify noise throughout memory. They hypothesize, and preliminarily support, that humans deploy inductive biases to suppress such spurious information.

Strengths:

(1) The manuscript addresses an interesting and understudied question-specifically, how learning is distorted by spurious observations in high-dimensional settings.

(2) The theoretical modeling and feature-based successor representation analyses are methodologically sound, and simulations illustrate expected memory distortions due to multidimensional transitions.

(3) The behavioral experiment introduces a creative robot-building paradigm and manipulates transitions to test the effect of semantic congruency (more so category part congruency as explained below).

Weaknesses:

(1) The semantic manipulation may be more about category congruence (e.g., body part function) than semantic meaning. The robot-building task seems to hinge on categorical/functional relationships rather than semantic abstraction. Strong evidence for semantic learning would require richer, more genuinely semantic manipulations.

(2) The experimental design remains limited in dimensionality and depth. Simulated higher-dimensional or deeper tasks (or empirical follow-up) would strengthen the interpretation and relevance for real-world memory distortion.

(3) The identification of idiosyncratic biases appears to reflect individual variation in categorical mapping rather than semantic processing. The lack of conjunctive learning may simply reflect variability in assumed builder-target mappings, not a principled semantic effect.

Additional Comments:

(1) It is unclear whether this task primarily probes memory or reinforcement learning, since the graded reward feedback in the current design closely aligns with typical reinforcement learning paradigms.

(2) It may be unsurprising that the feature-based successor model fits best given task structure, so broader model comparisons are encouraged.

(3) Simulation-only work on higher dimensionality (lines 514-515) falls short; an empirical follow-up would greatly enhance the claims.

Reviewer #3 (Public review):

The article's main question is how humans handle spurious transitions between object features when learning a predictive model for decision-making. The authors conjecture that humans use semantic knowledge about plausible causal relations as an inductive bias to distinguish true from spurious links.

The authors simulate a successor feature (SF) model, demonstrating its susceptibility to suboptimal learning in the presence of spurious transitions caused by co-occurring but independent causal factors. This effect worsens with an increasing number of planning steps and higher co-occurrence rates. In a preregistered study (N=100), they show that humans are also affected by spurious transitions, but perform somewhat better when true transitions occur between features within the same semantic category. However, no evidence for the benefits of semantic congruency was found in test trials involving novel configurations, and attempts to model these biases within an SF framework remained inconclusive.

Strengths:

(1) The authors tackle an important question.

(2) Their simulations employ a simple yet powerful SF modeling framework, offering computational insights into the problem.

(3) The empirical study is preregistered, and the authors transparently report both positive and null findings.

(4) The behavioral benefit during learning in the congruent vs incongruent condition is interesting

Weaknesses:

(1) A major issue is that approximately one quarter of participants failed to learn, while another quarter appeared to use conjunctive or configural learning strategies. This raises questions about the appropriateness of the proposed feature-based learning framework for this task. Extensive prior research suggests that learning about multi-attribute objects is unlikely to involve independent feature learners (see, e.g., the classic discussion of configural vs. elemental learning in conditioning: Bush & Mosteller, 1951; Estes, 1950).

(2) A second concern is the lack of explicit acknowledgment and specification of the essential role of the co-occurrence of causal factors. With sufficient training, SF models can develop much stronger representations of reliable vs. spurious transitions, and simple mechanisms like forgetting or decay of weaker transitions would amplify this effect. This should be clarified from the outset, and the occurrence rates used in all tasks and simulations need to be clearly stated.

(3) Another problem is that the modeling approach did not adequately capture participant behavior. While the authors demonstrate that the b parameter influences model behavior in anticipated ways, it remains unclear how a model could account for the observed congruency advantage during learning but not at test.

(4) Finally, the conceptualization of semantic biases is somewhat unclear. As I understand it, participants could rely on knowledge such as "the shape of a building robot's head determines the kind of head it will build," while the type of robot arm would not affect the head shape. However, this assumption seems counterintuitive - isn't it plausible that a versatile arm is needed to build certain types of robot heads?

Reviewer #1 (Public review):

The authors found that high concentrations of a series of monovalent cations, NaCl, KCl, RbCl, and CsCl (although not LiCl), but not equal high osmolarity treatment of cultured cells induced rapid loss of phosphate from pT774 in the activation loop (AL) of the PKN1 Ser/Thr protein kinase, as well the cognate AL phosphoresidue in other related AGC family kinases, including PKCζ, PKCλ, and p70 S6 kinase. Focusing on PKN1, they showed that restoration of the extracellular salt concentration to physiological levels resulted in equally rapid recovery of AL phosphorylation. Using both okadaic acid PP1/PP2A inhibitor, and a selective PP2A inhibitor, PP2A was implicated as the protein phosphatase required for the rapid dephosphorylation of PIN1 pT774 in response to high salt. By making PKN1 T778A knock-in mouse fibroblast cells and re-expressing WT and a kinase-dead mutant PKN1, as well as use of PDK1 KO MEFs, they showed that recovery of T774 phosphorylation did not require PDK1, the protein kinase known to phosphorylate this site in cells, or the kinase activity of PKN1 itself. Surprisingly, they found that dephosphorylation of the PKN1 AL site also occurred when cell lysates were adjusted to high salt, with re-phosphorylation of T774 occurring rapidly when physiological salt level was restored by dilution. Their in vitro lysate experiments also demonstrated that depletion of ATP by apyrase treatment or sequestration of Mg2+ by EDTA did not prevent T744 re-phosphorylation, which would rule out a conventional protein kinase. Various GST-tagged fragments of PKN1, including a 767-780 AL 14-mer peptide,e exhibited the same curious de- and re-phosphorylation effect when mixed with cell lysates and exposed to high KCl followed by dilution. Using 32P γ-ATP and PDK1 to generate 32P-labeled phospho-GST-PKN1 (767-788). They showed the 32P signal was lost from GST-PKN1 (767-788) in lysates exposed to high salt, and restored again upon dilution. Similar results were obtained with unlabeled samples using PhosTag analysis to resolve phosphospecies.

They went on to test three possible models to explain their data:

(1) Model 1. Intramolecular transfer of the pT774 phosphate group, where the pT774 phosphate is reversibly transferred onto another residue in the same PKN1 molecule in response to high and normal salt concentrations. They attempted to rule out this model by mutating possible noncanonical phosphate acceptors in the 776GYGDRTSTFCGTPE788 peptide, making C776, D770A, R771A, and E780A mutant peptides, without observing any effect on the dephosphorylation/re-phosphorylation phenomenon.

(2) Model 2. Re-phosphorylation of T774 involves an unidentified phosphate donor, distinct from ATP or phospho-PKN1. This model was ruled out in several ways, including by demonstrating that added 32P-labeled PKN1 lost its 32P signal in high salt-exposed lysates, with the 32P signal being recovered upon dilution even in the presence of excess unlabeled ATP.

(3) Model 3. Reversible transfer of the pT774 phosphate group onto an intermediary factor (X) in the presence of high salt and re-phosphorylation in cis by phospho-X upon dilution, which is the model they favored. In support of this model, they showed that the pT774 phosphate could not be transferred onto another PKN1 fragment of a different size, nor did GST-PKN1 767-788 pretreated with λ-phosphatase regain phosphate. In the end, however, they were unable to identify the hypothetical factor X, and no 32P-labeled protein was observed in the experiment with 32P-labeled PKN1 upon high salt-induced dephosphorylation.

This is an intriguing and unexpected set of findings that could herald a new protein kinase regulatory mechanism, but ultimately, we are left with an intriguing observation without a clear-cut explanation. The authors have been very methodical in their analysis of this odd phenomenon, and their data and conclusions, for the most part, seem convincing, although some of the blot signals are rather weak. However, despite all their efforts, the identity of the hypothetical factor X, which can transiently accept a phosphate from pT774 in the PKN1 activation loop in response to supraphysiological alkali metal cation concentrations and then donate it back again to T774 in cis, when physiological salt concentrations are restored, remains unclear.

As it stands, there are several unresolved issues that need to be addressed.

(1) The real conundrum, as their data show, is that phospho-X cannot phosphorylate PKN1 in trans, and therefore has to act in cis, meaning that phospho-X must somehow remain associated with the same dephosphorylated PKN1 molecule that the phosphate came from. Because a small molecule would rapidly diffuse away from PKN1, the only reasonable model is that X is a protein and not a small molecule, such as creatine (the authors considered X unlikely to be a small molecule for other reasons). However, if X were a protein, then it should have been labeled and detectable on the gel in the 32P-experiment shown in Figure 6C, but no other 32P-labeled band was observed in lane 5. Even if phospho-X has a labile phosphate linkage that would be lost upon SDS-gel electrophoresis, it is unclear how phospho-X would remain associated with the very short 14-mer PKN1 activation loop peptide, especially under the extremely dilute conditions of a cell lysate.

(2) The evidence that PP2A is required in PKN1 dephosphorylation is reasonable, and in the Discussion, the authors consider various scenarios in which PP2A could be involved in generating the hypothetical phospho-X needed for T774 re-phosphorylation, most of which do not seem very plausible. In the end, it remains unclear how free phosphate released from pT774 in PKN1 by PP2A, which does not employ a phosphoenzyme intermediate, ends up covalently attached to molecule X.

(3) The interpretation of the in vitro data is complicated by the fact that cell lysis results in a massive dilution of both proteins and any small molecules present in the cell (apparently dilution with lysis buffer was at least 10-fold initially, and then a further 2-fold to restore normal salt levels), making it hard to imagine how a large or small molecule would remain tightly associated with a PKN1 molecule, i.e. Model 3 really only works if re-phosphorylation of T774 is a zero order/intramolecular reaction. Moreover, the re-phosphorylation reaction rates would be expected to fall dramatically upon dilution of both the dephosphorylated GST-PKN1 767-788 protein and phospho-X during restoration of normal salt, meaning that the kinetics of T774 re-phosphorylation should be significantly slower in vitro. In this connection, it would be informative if the authors carried out a lysate dilution series to test the extent to which the observed phenomenon is dilution-independent.

(4) Another issue is that most of the results, apart from the 32P-labeling experiment, are dependent on the specificity of the anti-pT774 PKN1 antibodies they used. The fact that the C776A mutant peptide gave a weaker anti-pT774 signal might be because phospho-Ab binding is, in part, dependent on recognition of Cys776. In turn, this suggests the possibility that reversible oxidation of C776 might cause the loss and regain of the pT774 signal at high and low salt concentrations, as a result of the oxidized form of C776 preventing anti-pT774 antibody binding. The Cell Signaling Technology phospho-PRK1 (Thr774)/PRK2 (Thr816) antibody (#2611) that was used here was generated against a synthetic peptide containing pT774, and while the exact antigenic peptide sequence is not given in the CST catalogue, presumably it had 4 or 5 residues on either side of pT774 (GYGDRTSTFCGTPE) (although C776 might have been substituted in the antigenic peptide because of issues with Cys oxidation).

(5) Perhaps the most important deficiency is that the target for the monovalent cation that induces PKN1 activation loop dephosphorylation was not established. Is this somehow a direct effect of cations on PKN1 itself - this seems unlikely, since this effect is observed with a 14-mer PKN1 activation loop peptide - or is this an indirect effect? In terms of possible indirect mechanisms, high salt treatment of cells is known to induce elevated ROS as a result of mitochondrial damage, which could lead to oxidative modification of cysteines, such as C776, in the activation loop and might interfere with anti-pT774 antibody recognition.

In summary, the authors have put a great deal of thought and resources into trying to solve this intriguing puzzle, but despite a lot of effort, have not convincingly elucidated how this dephosphorylation/re-phosphorylation process works. For this, they need to identify phospho-X and define how it remains associated with the original pT774 PKN1 molecule in order to carry out re-phosphorylation.

Reviewer #2 (Public review):

Summary:

This study reports a highly unconventional mechanism by which AGC kinases might undergo reversible activation-loop (T-loop) phosphorylation through an ATP-independent phosphate recycling process that is modulated by alkali metal ions such as Na⁺ and K⁺. The authors propose that these ions trigger phosphate dissociation and subsequent reattachment in the absence of ATP or canonical kinase activity, implying the existence of a novel phosphate-transferring intermediate. If validated, this would represent a radical departure from established models of kinase regulation and signal transduction. I note that this study is personally funded by one of the authors.

Strengths:

The study addresses an important and fundamental question in protein phosphorylation biology. The authors have conducted an impressive number of biochemical experiments spanning cellular and in vitro systems, with multiple orthogonal readouts. The idea of an ATP-independent phosphate recycling mechanism is original and thought-provoking, challenging conventional assumptions and inviting further exploration. The manuscript is well organized and written with considerable technical detail.

Weaknesses:

The central mechanistic claim contradicts extensive existing evidence on AGC kinase regulation derived from decades of biochemical, mechanistic, pharmacological, genetic, and structural studies. The data, while extensive, do not provide sufficiently direct or quantitative evidence to support the existence of ATP-independent phosphate transfer. Alternative explanations, such as low-level residual ATP-dependent re-phosphorylation or assay artifacts, are not fully excluded. They claim that an unidentified factor-x is involved, but do not provide evidence for the existence of this molecule or characterize this. The physiological relevance of the ion concentrations used is unclear, as the conditions far exceed normal intercellular levels. Overall, the findings are not yet convincing enough to support a paradigm shift in our understanding of AGC kinase activation, in my opinion.

Reviewer #3 (Public review):

This is an intriguing paper that reports a potentially novel mechanism of reversible phosphorylation of AGC kinase activation segments by changes in sodium and potassium ion concentrations. The authors show for a variety of AGC kinases that incubating diverse eukaryotic cell types in 450 and 600 mM NaCl results in dephosphorylation of the activation segment. In contrast, phosphorylation of the activation segment for p38 kinases increases. No dephosphorylation of AGC kinases activation segment occurs with sorbitol, thus dephosphorylation is independent of osmotic pressure. This effect is rapidly reversed when cells are returned to normal media and the AGC kinase is re-phosphorylated. This phenomenon is also observed for eukaryotic cell-free extracts, and is induced by other alkali metal ions but not lithium. Importantly, no dephosphorylation is observed in the E. coli cell extract.

The authors also make the following observations:

(1) Dephosphorylation is dependent on PP2A.

(2) Re-phosphorylation is not dependent on PDK1, ATP, and Mg2+.

(3) The K/Na-dependent dephosphorylation/phosphorylation is observed even for relatively short protein segments that incorporate the activation segment.

(4) The phosphorylation observed occurs in cis, i.e., only the activation segment of the protein that is dephosphorylated becomes phosphorylated on reduced KCl. An activation segment from a different length protein is not phosphorylated.

(5) No evidence for auto(de)phosphorylation.

(6) The authors propose three models to explain the dephosphorylation/phosphorylation mechanism. Their experimental data suggest that an acceptor molecule is responsible for accepting the phosphate group and then transferring it back to the activation segment.

Comments on results and experiments:

(1) Are these results an artefact of their assay? The authors mainly use immunoblotting to assess the phosphorylation status of AGC kinase. However, an assay artefact would not show a difference between control and okadaic-acid-treated cells (Figure 3A). Moreover, the authors show dephosphorylation/phosphorylation using radiolabelling (Figure 6C).

(2) Preferably, the authors would have a control to test dephosphorylation/phosphorylation does not occur in the absence of cell extract. The E. coli extract shows that dephosphorylation/phosphorylation is specific to eukaryotic cell extracts.

(3) The authors should show that dephosphorylation/phosphorylation occurs on the same residue of the activation segment (by mass spec).

(4) Since phosphorylation levels are assessed using immunoblots, the levels of dephosphorylation/phosphorylation are not quantified. What proportion of AGC kinase is phosphorylated initially (before Na/K-induced dephosphorylation)?

(5) The experiment to test autophosphorylation (Figure 4, Figure supplement 1B) is not completely convincing because the authors use a cell line with a PKN1 mutant knock-in. Possibly PKN2 or another AGC kinase could phosphorylate the proteins expressed from the transfection vector - although the authors do test with AGC kinase inhibitors.

(6) What are the two bands in Figure 6C (lanes 'Con' and 'diluted)? Only one band disappears with KCl. There is one band in Figure 6 Supplement 2.

In summary, the results presented in this paper are highly unusual. Generally, the manuscript is well written and the figures are clear. The authors have performed numerous experiments to understand this process. These appear robust, and most of their data lend credence to their model in Figure 6Aiii. The idea that a phosphate group can be transferred by an enzyme onto/between molecule(s) is not unprecedented, i.e., phosphoglycerate mutase catalyses 3-phosphoglycerate isomerisation through a phosphorylenzyme intermediate. It will be important to identify this transfer enzyme. One observation that does not fit easily with their model is the role of PP2A. Since protein dephosphorylation by PP2A does not involve a phosphorylenzyme intermediate, if the initial dephosphorylation reaction is catalysed by PP2A, it is very difficult to envision how the free phosphate is then used to phosphorylate the activation segment.

Reviewer #1 (Public review):

Summary:

The authors set out to understand how animals respond to visible light in an animal without eyes. To do so, they used the C. elegans model, which lacks eyes, but nonetheless exhibits robust responses to visible light at several wavelengths. Here, the authors report a promoter that is activated by visible light and independent of known pathways of light responses.

Strengths:

The authors convincingly demonstrate that visible light activates the expression of the cyp-14A5 promoter-driven gene expression in a variety of contexts and report the finding that this pathway is activated via the ZIP-2 transcriptionally regulated signaling pathway.

Weaknesses:

Because the ZIP-2 pathway has been reported to be activated predominantly by changes in the bacterial food source of C. elegans -- or exposure of animals to pathogens -- it remains unclear if visible light activates a pathway in C. elegans (animals) or if visible light potentially is sensed by the bacteria on the plate, which also lack eyes. Specifically, it is possible that the plates are seeded with excess E. coli, that E. coli is altered by light in some way, and in this context, alters its behavior in such a way that activates a known bacterially responsive pathway in the animals. This weakness would not affect the ability to use this novel discovery as a tool, which would still be useful to the field, but it does leave some questions about the applicability to the original question of how animals sense light in the absence of eyes.

Reviewer #2 (Public review):

Summary:

Ji, Ma, and colleagues report the discovery of a mechanism in C. elegans that mediates transcriptional responses to low-intensity light stimuli. They find that light-induced transcription requires a pair of bZIP transcription factors and induces expression of a cytochrome P450 effector. This unexpected light-sensing mechanism is required for physiologically relevant gene expression that controls behavioral plasticity. The authors further show that this mechanism can be co-opted to create light-inducible transgenes.

Strengths:

The authors rigorously demonstrate that ambient light stimuli regulate gene expression via a mechanism that requires the bZIP factors ZIP-2 and CEBP-2. Transcriptional responses to light stimuli are measured using transgenes and using measurements of endogenous transcripts. The study shows proper genetic controls for these effects. The study shows that this light-response does not require known photoreceptors, is tuned to specific wavelengths, and is highly unlikely to be an artifact of temperature-sensing. The study further shows that the function of ZIP-2 and CEBP-2 in light-sensing can be distinguished from their previously reported role in mediating transcriptional responses to pathogenic bacteria. The study includes experiments that demonstrate that regulatory motifs from a known light-response gene can be used to confer light-regulated gene expression, demonstrating sufficiency and suggesting an application of these discoveries in engineering inducible transgenes. Finally, the study shows that ambient light and the transcription factors that transduce it into gene expression changes are required to stabilize a learned olfactory behavior, suggesting a physiological function for this mechanism.

Weaknesses:

The study implies but does not show that the effects of ambient light on stabilizing a learned olfactory behavior are through the described pathway. To show this clearly, the authors should determine whether ambient light has any effect on mutants lacking CYP-14A5, ZIP-2, or CEBP-2. Other minor edits to the text and figures are suggested.

Reviewer #3 (Public review):

Ji et al. report a novel and interesting light-induced transcriptional response pathway in the eyeless roundworm Caenorhabditis elegans that involves a cytochrome P450 family protein (CYP-14A5) and functions independently from previously established photosensory mechanisms. Although the exact mechanisms underlying photoactivation of this pathway remain unclear, light-dependent induction of CYP-14A5 requires bZIP transcription factors ZIP-2 and CEBP-2 that have been previously implicated in worm responses to pathogens. The authors then suggest that light-induced CYP-14A5 activity in the C. elegans hypoderm can unexpectedly and cell-non-autonomously contribute to retention of an olfactory memory. Finally, the authors demonstrate the potential for this pathway to enable robust light-induced control of gene expression and behavior, albeit with some restrictions. Overall, the evidence supporting the claims of the authors is convincing, and the authors' work suggests numerous interesting lines of future inquiry.

(1) The authors determine that light, but not several other stressors tested (temperature, hypoxia, and food deprivation), can induce transcription of cyp-15A5. The authors use these experiments to suggest the potential specificity of the induction of CYP-14A5 by light. Given the established relationship between light and oxidative stress and the authors' later identification of ZIP-2, testing the effect of an oxidative stressor or pathogen exposure on transcription of cyp-14A5 would further strengthen the validity of this statement and potentially shed some insight into the underlying mechanisms.

(2) The authors suggest that short-wavelength light more robustly increases transcription of cyp-14A5 compared to equally intense longer wavelengths (Figure 2F and 2G). Here, however, the authors report intensities in lux of wavelengths tested. Measurements of and reporting the specific spectra of the incident lights and their corresponding irradiances (ideally, in some form of mW/mm2 - see Ward et al., 2008, Edwards et al., 2008, Bhatla and Horvitz, 2015, De Magalhaes Filho et al., 2018, Ghosh et al., 2021, among others, for examples) is critical for appropriate comparisons across wavelengths and facilitates cross-checking with previous studies of C. elegans light responses. On a related and more minor note, the authors place an ultraviolet shield in front of a visible light LED to test potential effects of ultraviolet light on transcription of cyp-14A5. A measurement of the spectrum of the visible light LED would help confirm if such an experiment was required. Regardless, the principal conclusions the authors made from these experiments will likely remain unchanged.

(3) The authors report an interesting observation that animals exposed to ambient light (~600 lux) exhibit significantly increased memory retention compared to those maintained in darkness (Figure 4). Furthermore, light deprivation within the first 2-4 hours after learning appears to eliminate the effect of light on memory retention. These processes depend on CYP-14A5, loss of which can be rescued by re-expression of cyp-14A5 in mutant animals using a hypoderm-specific- and non-light-inducible- promoter. Taken together, the authors argue convincingly that hypodermal expression of cyp-14A5 can contribute to the retention of the olfactory memory. More broadly, these experiments suggest that cell-non-autonomous signaling can enhance retention of olfactory memory. How retention of the olfactory memory is enhanced by light generally remains unclear. In addition, the authors' experiments in Figure 1B demonstrate - at least by use of the transcriptional reporter - that light-dependent induction of cyp-14A5 transcription at 500 - 1000 lux is minimal and especially so at short duration exposures. Additional experiments, including verification of light-dependent changes in CYP-14A5 levels in the olfactory memory behavioral setup, would help further interpret these otherwise interesting results.

(4) The experiments in Figure 4 nicely validate the usage of the cyp-14A5 promoter as a potential tool for light-dependent induction of gene expression. Despite the limitations of this tool, including those presented by the authors, it could prove useful for the community.

Reviewer #1 (Public review):

Summary:

This paper applies ScaiVision, a convolutional neural network (CNN)-based supervised representation learning method, to single-cell RNA sequencing (scRNA-seq) data from six carcinoma types. The goal is to identify a pan-cancer gene expression signature of brain metastasis (BrM) that is both interpretable and clinically useful. The authors report:

(1) High classification accuracy for distinguishing primary tumours from brain metastases (AUC > 0.9 in training, > 0.8 in validation).

(2) Discovery of a 173-gene BrM signature, with a robust top-20 core.

(3) Evidence that the BrM signature is detectable in tumour-educated platelets (TEPs), enabling a potential non-invasive biomarker.

(4) Mechanistic analyses implicating VEGF-VEGFR1 signaling and ETS1 as central drivers of BrM.

(5) A computational drug repurposing screen highlighting pazopanib as a candidate therapeutic.

Strengths:

(1) Biological scope:

Integration of six tumour types highlights shared mechanisms of brain metastasis, beyond tumour-specific studies.

(2) Interpretability:

Use of integrated gradients on ScaiVision models identifies genes that drive classification, linking predictions to interpretable biology.

(3) Multi-modal validation:

BrM signature validated across scRNA-seq, spatial transcriptomics, pseudotime analyses, and liquid biopsy data.

(4) Translational potential:

Detection in TEPs provides a promising path toward a blood-based biomarker.

(5) Therapeutic angle:

Drug repurposing analysis identifies VEGF-targeting compounds, with pazopanib highlighted.

Weaknesses:

(1) Methodological contribution is limited:

ScaiVision is an existing proprietary framework; the paper does not introduce a new method.

No baseline comparisons (e.g., logistic regression, random forest, scVI, simple MLP) are presented, so the added value of CNNs over simpler models is unclear.

(2) Data constraints:

The dataset size is modest (115 samples, of which 21 are BrM), though thousands of cells per sample.

Training relies on patient-level labels, with subsampling to generate examples - a multi-instance learning setup that could be benchmarked more explicitly.

(3) Validation gaps:

Biomarker detection in platelets is based on retrospective bulk RNA-seq; no prospective patient validation is included.

Mechanistic claims (ETS1, VEGF) are computational inferences without wet-lab validation.

Reviewer #2 (Public review):

Summary:

This important study describes a deep learning framework that analyzes single-cell RNA data to identify tumor-agnostic gene signature associated with brain metastases. The identified signature uncovers key molecular mechanisms like VEGF signaling and highlights its potential therapeutic targets. It also assessed the performance of the gene signature in liquid biopsy and showed that the brain metastases signature yields a robust, metastasis-specific transcriptional signal in circulating platelets, suggesting potential for non-invasive diagnostics.

Strengths:

(1) The approach is multi-cancer, identifying mechanisms shared across diseases beyond tumor-specific constraints.

(2) Robust and explainable deep learning method workflow that utilized scRNA-seq data from various cancer types, demonstrating solid predictive accuracy.

(3) The detection of the BrM signature in tumor-educated platelets (TEPs) indicates a promising avenue for developing liquid biopsy assays, which could significantly enhance early detection capabilities.

Weaknesses:

(1) The paper lacks a thorough comparison with other reported signatures in the literature, which could help contextualize the performance and uniqueness of the authors' findings.

(2) The model training focused solely on epithelial cells, potentially overlooking critical contributions from stromal and immune cell types, which could provide a more comprehensive understanding of the tumor microenvironment.

(3) While the results are promising, there is a need for validation across tumor types not included in the training set to assess the generalizability of the signature.

Achievements:

The authors have made significant progress toward their aims, successfully identifying a transcriptional signature that is associated with brain metastasis across multiple cancer types. The results support their conclusions, showcasing the BrM signature's ability to distinguish between metastatic and primary tumor cells and its potential usability as a non-invasive biomarker.

This study has the potential to make a substantial impact in oncological research and clinical practice, particularly in the management of patients at risk for brain metastasis. The identification of a gene signature applicable across various tumor types could lead to the development of standardized diagnostic tools for early detection. Moreover, the emphasis on non-invasive diagnostic techniques aligns well with the current trends in precision medicine, making the findings highly relevant for the broader medical community.

Reviewer #3 (Public review):

Summary:

The article develops a CNN-based metastasis scoring system to distinguish cell subsets with high brain metastatic potential and validates its performance using patient platelet data. The robustness of this approach is further demonstrated across diverse single-cell and spatial datasets from multiple cancers, supported by transcription factor and gene set analyses, as well as novel drug identification pipelines. Together, these findings provide strong evidence that reinforces the central theme of the study.

Strengths:

Development of a CNN-based scoring system to reveal the potential of brain metastasis that is robust across multiple cancer cell types, validated by multiple datasets. Other approaches, including transcription factor analyses, cell-cell communication analysis, and spatial transcriptomic, etc., were included to strengthen the work.

Weaknesses:

The author could identify/validate more signaling pathways beyond the VEGF pathway since it's well known in metastasis.

Reviewer #4 (Public review):

Summary:

This work provides a gene signature for brain metastases derived from an integrated single-cell dataset of six carcinomas. A key rationale for their approach is the notion that metastases originating from different organs may converge upon a similar set of transcriptional states, representing shared functional and developmental programs. By combining primary tumor and metastatic brain tumor, the authors leverage an interpretable deep-learning approach to identify a multi-cancer single-cell dataset to predict brain metastases from a primary tumor that is more robust and generalizable than a signature derived from an individual cancer type. They employ a variety of single-cell tools to identify a putative mechanism of action for metastatic progression to the brain involving VEGF-related signaling, and find some evidence supporting this hypothesis in spatial data. A drug repurposing analysis is performed to identify a potential therapeutic candidate for VEGF-driven brain metastasis, and they demonstrate an intriguing possibility for using their brain metastasis signature in a blood-based test in the clinic.

Strengths:

An interpretable deep-learning approach allows both for high-accuracy classification of brain metastases from primary tumors and the identification of a gene signature. Much work goes into validating the gene signature in different contexts and different modalities, and presents a cohesive picture of metastasis progression. The analysis highlighting certain cells within the primary tumor that may be more likely to metastasize is interesting, and the demonstration of the difference in mean expression of their signature in bulk RNASeq of tumor-educated platelets (TEPs) has strong implications for the clinic.

Weaknesses:

The authors derive the signature from cancerous epithelial cells, citing a desire to avoid bias from differences in cellular composition; yet much of the downstream analysis is performed across different cancer types and different cell types; differential analysis was then performed between the highest scoring cells vs lowest scoring cells, but there does not appear to be any consideration/adjustment for cell type composition at this stage, which could bias results. Given that the signature was initially identified in epithelial cells, there seems to be a leap to applying the signature to immune and stromal compartments. Perhaps the proof is in the pudding, yet it raises the question of what would have happened if the authors had not restricted the initial step of their signature generation to the epithelial cells.

In addition, although a cohesive story around VEGF is presented, VEGF was merely one of the several signaling pathways upregulated. There were quite a few others (ANGPT, CDH1, CADM, IGF), which are not addressed by the authors. VEGF is, of course, very well studied, and while the authors do distinguish their signature from VEGF in the context of TEP, it leaves open the question of whether one of the other highlighted genes may be equally powerful and more feasible (because there are fewer genes) to get into the clinic.

The cell-cell communication analysis seems somewhat weak, although using a standard set of tools. Most of the analysis was done based on single-cell data, without the spatial context, and the authors highlighted epithelial cells as the senders for the VEGF pathway; yet in the Visium data, the expression of the signature seems highest in non-tumor cells, and the strongest interactions seem to be quite spatially separated (Figure 5C and 5E).

Reviewer #1 (Public review):

Summary:

This paper by Boch and colleagues, entitled Comparative Neuroimaging of the Carnivore Brain: Neocortical Sulcal Anatomy, compares and describes the cortical sulci of eighteen carnivore species, and sets a benchmark for future work on comparative brains.

Based on previous observations, electrophysiological, histological and neuroimaging studies and their own observations, the authors establish a correspondence between the cortical sulci and gyri of these species. The different folding patterns of all brain regions are detailed, put into perspective in relation to their phylogeny as well as their potential involvement in cortical area expansion and behavioral differences.

Strengths:

This article is very useful for comparative brain studies. It was conducted with great rigor and builds on numerous previous studies. The article is well written and very didactic. The different protocols for brain collection, perfusion and scanning are very detailed. The images are self-explanatory and of high quality. The authors explain their choice of nomenclature and labels for sulci and gyri on all species, with many arguments. The opening on ecology and social behavior in the discussion is of great interest and helps to put into perspective the differences in folding found at the level of the different cortexes. In addition, the authors do not forget to put their results into the context of the laws of allometry. They explain, for example, that although the largest brains were the most folded and had the deepest folds in their dataset, they did not necessarily have unique sulci, unlike some of the smaller, smoother brains.

Weaknesses:

Although an effort was made to take inter-individual variability into account, this approach could not be applied within each species, given the large number of wild animals. Sex differences could therefore not be analyzed either. However, this does not detract from the aim, which is to lay the foundations for a correspondence between the brains of carnivores in order to simplify navigation within the brains of these species for future studies. The authors also attempted to add measurements of sulcal length to this qualitative study, but it does not include other comparisons of morphometric data that are standard in sulci studies, such as sulcal depth, sulci wall surface area, or thickness of the cortical ribbon around the sulci.

Reviewer #2 (Public review):

Summary:

In this study, Sharma et al. demonstrated that Ly6G+ granulocytes (Gra cells) serve as the primary reservoirs for intracellular Mtb in infected wild-type mice and that excessive infiltration of these cells is associated with severe bacteremia in genetically susceptible IFNγ-/- mice. Notably, neutralizing IL-17 or inhibiting COX2 reversed the excessive infiltration of Ly6G+Gra cells, mitigated the associated pathology, and improved survival in these susceptible mice. Additionally, Ly6G+Gra cells were identified as a major source of IL-17 in both wild-type and IFNγ-/- mice. Inhibition of RORγt or COX2 further reduced the intracellular bacterial burden in Ly6G+Gra cells and improved lung pathology.

Of particular interest, COX2 inhibition in wild-type mice also enhanced the efficacy of the BCG vaccine by targeting the Ly6G+Gra-resident Mtb population.

Strengths:

The experimental results showing improved BCG-mediated protective immunity through targeting IL-17-producing Ly6G+ cells and COX2 are compelling and will likely generate significant interest in the field. Overall, this study presents important findings, suggesting that the IL-17-COX2 axis could be a critical target for designing innovative vaccination strategies for TB.

Comments on revisions:

This article is of significant interest for the research field. In the revised version of the manuscript the authors have addressed the concerns raised during initial review. I do not have further concerns.

Reviewer #3 (Public review):

Summary:

The authors examine how distinct cellular environments differentially control Mtb following BCG vaccination. The key findings are that IL17 producing PMNs harbor a significant Mtb load in both wild type and IFNg-/- mice. Targeting IL17, Cox2, and Rorgt, improved disease in combination but not alone and enhances BCG efficacy over 12 weeks and neutrophils/IL17 are associated with treatment failure in humans. The authors suggest that targeting these pathways, especially in MSMD patients may improve disease outcomes.

Strengths:

The experimental approach is generally sound and consists of low dose aerosol infections with distinct readouts including cell sorting followed by CFU, histopathology and RNA sequencing analysis. By combining genetic approaches and chemical/antibody treatments, the authors can probe these pathways effectively.

Understanding how distinct inflammatory pathways contribute to control or worsen Mtb disease is important and thus, the results will be of great interest to the Mtb field.

Uncovering a neutrophil population that is refractory to BCG-mediated control can help to better define key markers for vaccine efficacy

Weaknesses:

Several of the key findings in mice have previously been shown (albeit with less sophisticated experimentation) and human disease and neutrophils are well described - thus the real new finding is how intracellular Mtb in neutrophils are more refractory to BCG-mediated control and modulating IL17 and inflammation can alter this.

There is a lack of direct evidence that the neutrophils are producing IL-17 or showing that specifically removing IL17 neutrophils has an effect on disease. Thus, many of these data are correlative, or have modest phenotypes. For example if blocking IL17 or alone does not impact disease alone the conclusion that these IL17+ neutrophils limits protection as noted in the title is is not fully supported. The inhibitors used are not cell-type specific.

Reviewer #1 (Public review):

Summary:

The manuscript by Yang et al. investigates the relationship between multi-unit activity in the locus coeruleus, putatively noradrenergic locus coeruleus, hippocampus (HP), sharp-wave ripples (SWR), and spindles using multi-site electrophysiology in freely behaving male rats. The study focuses on SWR during quiet wake and non-REM sleep, and their relation to cortical states (identified using EEG recordings in frontal areas) and LC units.

The manuscript highlights differential modulation of LC units as a function of HP-cortical communication during wake and sleep. They establish that ripples and LC units are inversely correlated to levels of arousal: wake, i.e., higher arousal correlates with higher LC unit activity and lower ripple rates. The authors show that LC neuron activity is strongly inhibited just before SWR is detected during wake. During non-REM sleep, they distinguish "isolated" ripples from SWR coupled to spindles and show that inhibition of LC neuron activity is absent before spindle-coupled ripples but not before isolated ripples, suggesting a mechanism where noradrenaline (NA) tone is modulated by HP-cortical coupling. This result has interesting implications for the roles of noradrenaline in the modulation of sleep-dependent memory consolidation, as ripple-spindle coupling is a mechanism favoring consolidation. The authors further show that NA neuronal activity is downregulated before spindles.

Strengths:

In continuity with previous work from the laboratory, this work expands our understanding of the activity of neuromodulatory systems in relation to vigilance states and brain oscillations, an area of research that is timely and impactful. The manuscript presents strong results suggesting that NA tone varies differentially depending on the coupling of HP SWR with cortical spindles. The authors place their findings back in the context of identified roles of HP ripples and coupling to cortical oscillations for memory formation in a very interesting discussion. The distinction of LC neuron activity between awake, ripple-spindle coupled events and isolated ripples is an exciting result, and its relation to arousal and memory opens fascinating lines of research.

Weaknesses:

I regretted that the paper fell short of trying to push this line of idea a bit further, for example, by contrasting in the same rats the LC unit-HP ripple coupling during exploration of a highly familiar context (as seemingly was the case in their study) versus a novel context, which would increase arousal and trigger memory-related mechanisms. Any kind of manipulation of arousal levels and investigation of the impact on awake vs non-REM sleep LC-HP ripple coordination would considerably strengthen the scope of the study.

The main result shows that LC units are not modulated during non-REM sleep around spindle-coupled ripples (named spRipples, 17.2% of detected ripples); they also show that LC units are modulated around ripple-coupled spindles (ripSpindles, proportion of detected spindles not specified, please add). These results seem in contradiction; this point should be addressed by the authors.

Results are displayed per recording session, with 20 sessions total recorded from 7 rats (2 to 8 sessions per rat), which implies that one of the rats accounts for 40% of the dataset. Authors should provide controls and/or data displayed as average per rat to ensure that results are now skewed by the weight of that single rat in the results.

In its current form, the manuscript presents a lack of methodological detail that needs to be addressed, as it clouds the understanding of the analysis and conclusions. For example, the method to account for the influence of cortical state on LC MUA is unclear, both for the exact methods (shuffling of the ripple or spindle onset times) and how this minimizes the influence of cortical states; this should be better described. If the authors wish to analyze unit modulation as a function of cortical state, could they also identify/sort based on cortical states and then look at unit modulation around ripple onset? For the first part of the paper, was an analysis performed on quiet wake, non-REM sleep, or both?

Reviewer #2 (Public review):

Summary:

In this study, the authors studied the synchrony between ripple events in the Hippocampus, cortical spindles, and Locus Coeruleus spiking. The results in this study, together with the established literature on the relationship of hippocampal ripples with widespread thalamic and cortical waves, guided the authors to propose a role for Locus Coeruleus spiking patterns in memory consolidation. The findings provided here, i.e., correlations between LC spiking activity and Hippocampal ripples, could provide a basis for future studies probing the directional flow or the necessity of these correlations in the memory consolidation process. Hence, the paper provides enough scientific advances to highlight the elusive yet important role of Norepinephrine circuitry in the memory processes.

Strengths:

The authors were able to demonstrate correlations of Locus Coeruleus spikes with hippocampal ripples as well as with cortical spindles. A specific strength of the paper is in the demonstration that the spindles that activate with the ripples are comparatively different in their correlations with Locus Coeruleus than those that do not.

Weaknesses:

The claims regarding the roles of these specific interactions were mostly derived from the literature that these processes individually contribute to the memory process, without any evidence of these specific interactions being necessary for memory processes. There are also issues with the description of methods, validation of shuffling procedures, and unclear presentation and the interpretation of the findings, which are described in the points that follow. I believe addressing these weaknesses might improve and add to the strength of the findings.

Reviewer #3 (Public review):

Summary:

This manuscript examines how locus coeruleus (LC) activity relates to hippocampal ripple events across behavioral states in freely moving rats. Using multi-site electrophysiological recordings, the authors report that LC activity is suppressed prior to ripple events, with the magnitude of suppression depending on the ripple subtype. Suppression is stronger during wakefulness than during NREM sleep and is least pronounced for ripples coupled to spindles.

Strengths:

The study is technically competent and addresses an important question regarding how LC activity interacts with hippocampal and thalamocortical network events across vigilance states.

Weaknesses:

The results are interesting, but entirely observational. Also, the study in its current form would benefit from optimization of figure labeling and presentation, and more detailed result descriptions to make the findings fully interpretable. Also, it would be beneficial if the authors could formulate the narrative and central hypothesis more clearly to ease the line of reasoning across sections.

Comments:

(1) Stronger evidence that recorded units represent noradrenergic LC neurons would reinforce the conclusions. While direct validation may not be possible, showing absolute firing rates (Hz) across quiet wake, active wake, NREM, and REM, and comparing them to published LC values, would help.

(2) The analyses rely almost exclusively on z-scored LC firing and short baselines (~4-6 s), which limits biological interpretation. The authors should include absolute firing rates alongside normalized values for peri-ripple and peri-spindle analyses and extend pre-event windows to at least 20-30 s to assess tonic firing evolution. This would clarify whether differences across ripple subtypes arise from ceiling or floor effects in LC activity; if ripples require LC silence, the relative drop will appear larger during high-firing wake states. This limitation should be discussed and, if possible, results should be shown based on unnormalized firing rates.

(3) Because spindles often occur in clusters, the timing of ripple occurrence within these clusters could influence LC suppression. Indicate whether this structure was considered or discuss how it might affect interpretation (e.g., first vs. subsequent ripples within a spindle cluster).

(4) While the observational approach is appropriate here, causal tests (e.g., optogenetic or chemogenetic manipulation of LC around ripple events and in memory tasks) would considerably strengthen the mechanistic conclusions. At a minimum, a discussion of how such approaches could address current open questions would improve the manuscript.

(5) Please show how "Synchronization Index" (SI) differs quantitatively across behavioral states (wake, NREM, REM) and discuss whether it could serve as a state classifier. This would strengthen interpretations of the correlations between SI, ripple occurrence, and LC activity.

(6) The current use of SI to denote a delta/gamma power ratio is unconventional, as "SI" typically refers to phase-locking metrics. Consider adopting a more standard term, such as delta/gamma power ratio. Similarly, it would be easier to follow if you use common terminology (AUC) to describe the drop in LC-MUA rather than using "MI" and "sub-MI".

(7) The logic in Figure 3 is difficult to follow. The brain state (delta/gamma ratio) appears unchanged relative to surrogate events (3C), while LC activity that is supposedly negatively correlated to delta/gamma changes markedly (3D-E). Could this discrepancy reflect the low temporal resolution (4-s windows) used to calculate delta/gamma when the changes occur on a shorter time scale?

(8) There are apparent inconsistencies between Figures 4B and 4C-D. In B, it seems that the difference between the 10th and 90th percentile is mostly in higher frequencies, but in C and D, the only significant difference is in the delta band.

(9) Because standard sleep scoring is based on EEG and EMG signals, please include an example of sleep scoring alongside the data used for state classification. It would also be relevant to include the delta/gamma power ratio in such an example plot.

(10) Can variability in modulation index (subMI) across ripple subsets reflect differences in recording quality? Please report and compare mean LC firing rates across subsets to confirm this is not a confounding factor.

(11) Figure 6B: If the brown trace represents LC-MUA activity around random time points, why would there be a coinciding negative peak as relative to real sleep spindles? Or is it the subtracted trace?

(12) On page 8, lines 207-209, the authors write "Importantly, neither the LC-MUA rate nor SIs differed during a 2-sec time window preceding either group of spindles". It is unclear which data they refer to, but the statement seems to contradict Figure 6E as well as the following sentence: "Across sessions, MI values exceeded 95% CI in 17/20 datasets for isoSpindles and only 3/20 for ripSpindles". This should be clarified.

(13) The results in Figures 5C and 6F do not align. It seems surprising that ripple-coupled spindles show a considerably higher LC modulation than spindle-coupled ripples, as these events should overlap. Could the discrepancy be due to Z-score normalization as mentioned above? Please include a discussion of this to help the interpretation of the results.

(14) The text implies that 8 recordings came from one rat and two each from six others. This should be confirmed, and it should be explained how the recordings were balanced and analyzed across animals.

Reviewer #1 (Public review):

Summary:

Syed et al. investigate the circuit underpinnings for leg grooming in the fruit fly. They identify two populations of local interneurons in the right front leg neuromere of ventral nerve cord, i.e. 62 13A neurons and 64 13B neurons. Hierarchical clustering analysis identifies each 10 morphological classes for both populations. Connectome analysis reveals their circuit interactions: these GABAergic interneurons provide synaptic inhibition either between the two subpopulations, i.e. 13B onto 13A, or among each other, i.e. 13As onto other 13As, and/or onto leg motoneurons, i.e. 13As and 13Bs onto leg motoneurons. Interestingly, 13A interneurons fall into two categories with one providing inhibition onto a broad group of motoneurons, being called "generalists", while others project to few motoneurons only, being called "specialists". Optogenetic activation and silencing of both subsets strongly effects leg grooming. As well activating or silencing subpopulations, i.e. 3 to 6 elements of the 13A and 13B groups has marked effects on leg grooming, including frequency and joint positions and even interrupting leg grooming. The authors present a computational model with the four circuit motifs found, i.e. feed-forward inhibition, disinhibition, reciprocal inhibition and redundant inhibition. This model can reproduce relevant aspects of the grooming behavior.

Strengths:

The authors succeeded in providing evidence for neural circuits interacting by means of synaptic inhibition to play an important role in the generation of a fast rhythmic insect motor behavior, i.e. grooming. Two populations of local interneurons in the fruit fly VNC comprise four inhibitory circuit motifs of neural action and interaction: feed-forward inhibition, disinhibition, reciprocal inhibition and redundant inhibition. Connectome analysis identifies the similarities and differences between individual members of the two interneuron populations. Modulating the activity of small subsets of these interneuron populations markedly affects generation of the motor behavior thereby exemplifying their important role for generating grooming. The authors carefully discuss strengths and limitations of their approaches and place their findings into the broader context of motor control.

Weaknesses:

Effects of modulating activity in the interneuron populations by means of optogenetics were conducted in the so-called closed-loop condition. This does not allow to differentiate between direct and secondary effects of the experimental modification in neural activity, as feedforward and feedback effects cannot be disentangled. To do so open loop experiments, e.g. in deafferented conditions, would be important. Given that many members of the two populations of interneurons do not show one, but two or more circuit motifs, it remains to be disentangled which role the individual circuit motif plays in the generation of the motor behavior in intact animals.

Comments on revisions:

The careful revision of the manuscript improved the clarity of presentation substantially.

Reviewer #2 (Public review):

Summary:

This manuscript by Syed et al. presents a detailed investigation of inhibitory interneurons, specifically from the 13A and 13B hemilineages, which contribute to the generation of rhythmic leg movements underlying grooming behavior in Drosophila. After performing a detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits, the authors build on this anatomical framework by performing optogenetic perturbation experiments to functionally test predictions derived from the connectome. Finally, they integrate these findings into a computational model that links anatomical connectivity with behavior, offering a systems-level view of how inhibitory circuits may contribute to grooming pattern generation.

Strengths:

(1) Performing an extensive and detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits.

(2) Making sense of the largely uncharacterized 13A/13B nerve cord circuitry by combining connectomics and optogenetics is very impressive and will lay the foundation for future experiments in this field.

(3) Testing the predictions from experiments using a simplified and elegant model.

Weaknesses:

(1) In Figure 4-figure supplement 1, the inclusion of walking assays in dusted flies is problematic, as these flies are already strongly biased toward grooming behavior and rarely walk. To assess how 13A neuron activation influences walking, such experiments should be conducted in undusted flies under baseline locomotor conditions.

(2) Regarding Fig 5: The 70ms on/off stimulation with a slow opsin seems problematic. CsChrimson off kinetics are slow and unlikely to cause actual activity changes in the desired neurons with the temporal precision the authors are suggesting they get. Regardless, it is amazing the authors get the behavior! It would still be important for authors to mention the optogentics caveat, and potentially supplement the data with stimulation at different frequencies, or using faster opsins like ChrimsonR.

Overall, I think the strengths outweigh the weaknesses, and I consider this a timely and comprehensive addition to the field.

Reviewer #3 (Public review):

Summary:

The authors set out to determine how GABAergic inhibitory premotor circuits contribute to the rhythmic alternation of leg flexion and extension during Drosophila grooming. To do this, they first mapped the ~120 13A and 13B hemilineage inhibitory neurons in the prothoracic segment of the VNC and clustered them by morphology and synaptic partners. They then tested the contribution of these cells to flexion and extension using optogenetic activation and inhibition and kinematic analyses of limb joints. Finally, they produced a computational model representing an abstract version of the circuit to determine how the connectivity identified in EM might relate to functional output. The study makes important contributions to the literature.

The authors have identified an interesting question and use a strong set of complementary tools to address it:

They analysed serial‐section TEM data to obtain reconstructions of every 13A and 13B neuron in the prothoracic segment. They manually proofread over 60 13A neurons and 64 13B neurons, then used automated synapse detection to build detailed connectivity maps and cluster neurons into functional motifs.

They used optogenetic tools with a range of genetic driver lines in freely behaving flies to test the contribution of subsets of 13A and 13B neurons.

They used a connectome-constrained computational model to determine how the mapped connectivity relates to the rhythmic output of the behavior.

Reviewer #1 (Public review):

Summary and strengths:

In this manuscript, the authors endeavor to capture the dynamics of emotion-related brain networks. They employ slice-based fMRI combined with ICA on fMRI time series recorded while participants viewed a short movie clip. This approach allowed them to track the time course of four non-noise independent components at an effective 2s temporal resolution at the BOLD level. Notably, the authors report a temporal sequence from input to meaning, followed by response, and finally default mode networks, with significant overlap between stages. The use of ICA offers a data-driven method to identify large-scale networks involved in dynamic emotion processing. Overall, this paradigm and analytical strategy mark an important step forward in shifting affective neuroscience toward investigating temporal dynamics rather than relying solely on static network assessments.

(1) One of the main advantages highlighted is the improved temporal resolution offered by slice-based fMRI. However, the manuscript does not clearly explain how this method achieves a higher effective resolution, especially since the results still show a 2s temporal resolution-comparable to conventional methods. Clarification on this point would help readers understand the true benefit of the approach.

(2) While combining ICA with task fMRI is an innovative approach to study the spatiotemporal dynamics of emotion processing, task fMRI typically relies on modeling the hemodynamic response (e.g., using FIR or IR models) to mitigate noise and collinearity across adjacent trials. The current analysis uses unmodeled BOLD time series, which might risk suffering from these issues.

(3) The study's claims about emotion dynamics are derived from fMRI data, which are inherently affected by the hemodynamic delay. This delay means that the observed time courses may differ substantially from those obtained through electrophysiology or MEG studies. A discussion on how these fMRI-derived dynamics relate to-or complement-is critical for the field to understand the emotion dynamics.

(4) Although using ICA to differentiate emotion elements is a convenient approach to tell a story, it may also be misleading. For instance, the observed delayed onset and peak latency of the 'response network' might imply that emotional responses occur much later than other stages, which contradicts many established emotion theories. Given the involvement of large-scale brain regions in this network, the underlying reasons for this delay could be very complex.

Added after revision: In the response letter, the authors have provided clear responses to these comments and improved the manuscript.

Reviewer #1 (Public review):

Ejdrup, Gether and colleagues present a sophisticated simulation of dopamine (DA) dynamics based on a substantial volume of striatum with many DA release sites. The key observation is that reduced DA uptake rate in ventral striatum (VS) compared to dorsal striatum (DS) can produce an appreciable "tonic" level of DA in VS and not DS. In both areas they find that a large proportion of D2 receptors are occupied at "baseline"; this proportion increases with simulated DA cell phasic bursts but has little sensitivity to simulated DA cell pauses. They also examine, in a separate model, the effects of clustering dopamine transporters (DAT) into nanoclusters and say this may be a way of regulating tonic DA levels in VS. I found this work of interest and I think it will be useful to the community.

The conclusion that even an unrealistically long (1s) and complete pause in DA firing has little effect on DA receptor occupancy is potentially very important. The ability to respond to DA pauses has been thought to be a key reason why D2 receptors (may) have high affinity. This simulation instead finds evidence that DA pauses may be useless, from the perspective of reward prediction error signals.

Reviewer #2 (Public review):

The work presents a model of dopamine release, diffusion and reuptake in a small (100 micrometer^2 maximum) volume of striatum. This extends previous work by this group and others by comparing dopamine dynamics in the dorsal and ventral striatum and by using a model of immediate dopamine-receptor activation inferred from recent dopamine sensor data. From their simulations the authors report three main conclusions: that ventral and dorsal striatum have consistently different distributions of dopamine; that dorsal striatum does not appear to have a clear "tonic" dopamine -- the sustained, relatively uniform concentration of dopamine driven by the constant 4Hz firing of dopamine neurons; and that D1 receptor activation is able to track rapid increases in dopamine concentration changes D2 receptor activation cannot -- and neither receptor-type's activation tracks pauses in pacemaker firing of dopamine neurons.