Reviewer #2 (Public review):

This manuscript reveals the functional connectivity of two different classes of cortical neurons that respond in opposite ways to mismatches between sensory and top-down inputs. These data are very valuable because different theories of information processing in the cortex make different predictions on the patterns of connectivity of these neurons. Therefore, these data strongly constrain possible theories of cortical processing.

General comments:

(1) The methods of statistical testing are insufficiently described. I did not understand the description in lines 1105-1119. The authors should provide sufficient details so the reader can reproduce their analyses. For example, it may be helpful to provide specific details of the testing procedure for one of the comparisons (e.g. the first comparison in Table S1).

(2) The authors should clarify how the problem of multiple comparisons was addressed for comparisons performed in multiple moments of time, where significance is indicated by a black bar (e.g. in Figure 2F).

(3) It would be helpful to add a figure in the Discussion summarising the functional connectivity suggested by all experiments.

(4) Throughout the manuscript, the authors use the term "teaching signals", but I am unclear what they mean by it: after reading the definition in lines 45-46, I thought that they corresponded to values (as they are compared to sensory signals). Later (428-430), the text suggests that they correspond to error neurons. But then lines 605-607 say it is not an error signal. The authors should define teaching signals very precisely or remove this term.

Reviewer #3 (Public review):

Vasilevskaya and Keller set out to experimentally distinguish between two variants of predictive processing: a hierarchical and a non-hierarchical variant. The hierarchical variant assumes a hierarchical organization in which internal representation neurons (believed to be a subset of layer 5 excitatory neurons) serve as a source of a teaching signal for local prediction error neurons as well as for the next higher level of the hierarchy, while simultaneously providing prediction signals to the preceding lower level. In contrast, the non-hierarchical variant posits that these layer 5 internal representation neurons provide local predictions to layer 2/3 prediction error neurons.

The interaction between internal representation neurons and prediction error neurons differs fundamentally between the two variants. In the hierarchical variant, internal representation neurons excite positive prediction error neurons and inhibit negative prediction error neurons, while at the same time being inhibited by positive prediction error neurons and excited by negative prediction error neurons. In the non-hierarchical variant, this pattern of connectivity is reversed.

This work is very exciting, timely, and carefully executed. The authors functionally, and later molecularly, identify layer 2/3 prediction error neurons in V1 and probe their interactions with genetically defined neuron types in cortical layers 5 and 6 using optogenetics. They demonstrate that the functional influence of putative prediction error neurons in layer 2/3 onto layer 5 is incompatible with the hierarchical variant, whereas the influence of layer 5 onto putative prediction error neurons in layer 2/3 is incompatible with the non-hierarchical variant. They then test an alternative hypothesis, in which layer 2/3 responses resemble prediction errors with respect to perturbations of artificial layer 5 activity patterns. To investigate this, they designed an experiment in which optogenetic activation of L5 IT neurons was closed-loop coupled to the mouse's locomotion speed in the absence of visual feedback, allowing them to probe the causal influence of L5 activity on layer 2/3 responses.

Finally, the authors hypothesize that their data are more consistent with a joint embedding predictive architecture (JEPA) and outline experimentally testable predictions arising from this framework.

While the work is overall convincing and significantly advances our understanding of the circuit-level implementation of predictive processing, there are a few weaknesses that should be addressed or discussed:

(1) The authors define putative positive prediction error neurons as the 15% of neurons most responsive to grating onset and putative negative prediction error neurons as the 15% most responsive to visuomotor mismatch. While this selection would be expected to overlap with negative and positive prediction error neurons, the criterion is not sufficiently stringent (independent of the exact percentage chosen). In particular, classification of a neuron as a prediction error neuron should ideally be accompanied by evidence that it does not exhibit a significant increase in activity when the prediction matches the sensory input or teaching signal.

(2) The authors "speculate that the prediction error responses in layer 2/3 may not be computed with respect to sensory input, but with respect to layer 5 activity as a teaching signal." However, it is unclear how this perspective differs from earlier statements in the manuscript. In the Introduction, the authors note that "these signals, typically referred to as sensory signals, we will refer to as teaching signals," and later describe the hierarchical variant as one "in which internal representation neurons act as a source of the teaching signal." Given this framing, it is difficult to identify what is conceptually novel in the updated view. Is the key distinction that layer 2/3 neurons are now proposed to generate predictions in an internal representation space rather than in sensory input space, as briefly suggested in the Discussion? Or are the authors introducing a distinction between an external (sensory) and an internal (cortical) teaching signal? If so, this distinction should be made explicit. Clarifying this point would considerably strengthen the manuscript.

(3) The authors propose that "L2/3 neurons predict L5 activity, hence making predictions in the internal representation space rather than the input space," and further suggest that, since both deep and superficial cortical layers receive thalamic input, the cortex may function like a JEPA. This idea appears closely related to the model introduced by Nejad et al. (2025), which effectively implements a JEPA-like architecture: L5 activity serves as a target against which L2/3 predictions are compared in a self-supervised manner, with both L5 and L2/3 (via L4) receiving thalamic input. It would be helpful for the authors to clarify how their framework differs from that model, and to specify the key conceptual or mechanistic distinctions between the present proposal and the approach described by Nejad et al..

Reviewer #1 (Public review):

Summary:

This manuscript investigates mutations and expression patterns of zinc finger proteins in Kenyan breast cancer patients. Whole-exome sequencing and RNA-seq were performed on 23 breast cancer samples alongside matched normal tissues.

Strengths:

Whole-exome sequencing and RNA-seq were performed on 23 breast cancer samples alongside matched normal tissues in Kenyan breast cancer patients. The authors identified mutations in ZNF217, ZNF703, and ZNF750.

Weaknesses:

(1) Research scope:

The results primarily focus on mutations in ZNF217, ZNF703, and ZNF750, with limited correlation analyses between mutations and gene expression. The rationale for focusing only on these genes is unclear. Given the availability of large breast cancer cohorts such as TCGA and METABRIC, the authors should compare their mutation profiles with these datasets. Beyond European and U.S. cohorts, sequencing data from multiple countries, including a recent Nigerian breast cancer study (doi: 10.1038/s41467-021-27079-w), should also be considered. Since whole-exome sequencing was performed, it is unclear why only four genes were highlighted, and why comparisons to previous literature were not included.

(2) Language and Style Issues

There are many typos and clear errors in the main text (e.g. (ref)).

Additionally, several statements read unnaturally. For example:

"Investigators uncovered 170 mutations ..." should instead be phrased as "We identified 170 mutations ...."

"The research team ..." should be rephrased as "Our team ...."

(3) Methods and Data Analysis Details

The methods section is vague, with general descriptions rather than specific details of data processing and analysis. The authors should provide:

(a) Parameters used for trimming, mapping, and variant calling (rather than referencing another paper such as Tang et al. 2023).

(b) Statistical methods for somatic mutation/SNP detection.

(c) Details of RNA purification and RNA-seq library preparation.

Without these details, the reproducibility of the study is limited.

(4) Data Reporting

This study has the potential to provide a valuable resource for the field. However, data-sharing plans are unclear. The authors should:

a) Deposit sequencing data in a public repository.

b) Provide supplementary tables listing all detected mutations and all differentially expressed genes (DEGs).

c) Clarify whether raw or adjusted p-values were used for DEG analysis.

d) Perform DEG analyses stratified by breast cancer subtypes, since differential expression was observed by HER2 status, and some zinc finger proteins are known to be enriched in luminal subtypes.

(5) Mutation Analysis

Visualizations of mutation distribution across protein domains would greatly strengthen interpretation. Comparing mutation distribution and frequency with published datasets would also contextualize the findings.

Comments on revisions:

The revised manuscript hasn't addressed any of these concerns. Careful proofreading is recommended, even if the authors do not intend to make further modifications to the manuscript.

Reviewer #2 (Public review):

Summary:

This work integrated the mutational landscape and expression profile of ZNF molecules in 23 Kenyan women with breast cancer.

Strengths:

The mutation landscape of ZNF217, ZNF703, and ZNF750 were comprehensively studied and correlate with tumor stage and HER2 status to highlight the clinical significance.

Weaknesses:

The current cohort size is relatively small to reach significant findings, and targeted exploration on ZNF family without emphasizing the reason or clinical significance hinders the overall significance of the entire work.

Reviewer #3 (Public review):

Summary:

This revised study analyzes the somatic mutational profiles and transcriptomic expression of three zinc-finger genes (ZNF217, ZNF703, ZNF750) in 23 Kenyan women with breast cancer, using whole-exome sequencing and RNA-sequencing of paired tumor-normal tissues. A total of 358 somatic mutations were detected, and all three genes were significantly upregulated in tumors compared to normal tissues (ZNF217 showing the most prominent difference). Higher expression was observed in HER2-positive tumors, though mutation burden for each gene did not correlate significantly with HER2 status or cancer stage. The findings provide preliminary evidence for the idenfication of diagnostic/prognostic biomarkers or therapeutic targets in sub-Saharan African populations.

Strengths:

The study's key strengths lie in its focus on an underrepresented Kenyan cohort, addressing a critical gap in sub-Saharan African breast cancer genomic research. It integrates DNA-level mutation analysis with RNA-level expression data, leveraging standardized bioinformatics pipelines (e.g., Mutect2 for variant calling, DESeq2 for differential expression) and rigorous quality control to deliver detailed insights into mutation types, functional impacts, and amino acid changes. Additionally, it explores gene expression patterns across different cancer stages and HER2 status subgroups, generating targeted hypotheses for future validation and enhancing the reliability of its findings.

Weaknesses:

The author has enhanced the descriptive depth of the study by adding details on mutations, expression subgroup analyses, and functional annotations but has not addressed the core weaknesses of small cohort size and lack of functional validation. While the revised version is more comprehensive in cataloging molecular alterations, it remains confined to descriptive analysis, with no substantial improvement in the reliability or generalizability of its conclusions.

Reviewer #1 (Public review):

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

Summary:

Here, the authors have addressed the recruitment and firing patterns of motor units (MUs) from the long and lateral heads of triceps in the mouse. They used their newly developed Myomatrix arrays to record from these muscles during treadmill locomotion at different speeds, and they used template-based spike sorting (Kilosort) to extract units. Between MUs from the two heads, the authors observe differences in their firing rates, recruitment probability, phase of activation within the locomotor cycle and interspike interval patterning. Examining different walking speeds, the authors find increases in both recruitment probability and firing rates as speed increases. The authors also observed differences in the relation between recruitment and the angle of elbow extension between motor units from each head. These differences indicate meaningful variation between motor units within and across motor pools, and may reflect the somewhat distinct joint actions of the two heads of triceps.

Strengths:

The extraction of MU spike timing for many individual units is an exciting new method that has great promise for exposing the fine detail in muscle activation and its control by the motor system. In particular, the methods developed by the authors for this purpose seem to be the only way to reliably resolve single MUs in the mouse, as the methods used previously in humans and in monkeys (e.g. Marshall et al. Nature Neuroscience, 2022) do not seem readily adaptable for use in rodents.

The paper provides a number of interesting observations. There are signs of interesting differences in MU activation profiles for individual muscles here, consistent with those shown by Marshall et al. It is also nice to see fine scale differences in the activation of different muscle heads, which could relate to their partially distinct functions. The mouse offers greater opportunities for understanding the control of these distinct functions, compared to the other organisms in which functional differences between heads have previously been described.

The Discussion is very thorough, providing a very nice recounting of a great deal of relevant previous results.

Reviewer #2 (Public review):

The present study, led by Thomas and collaborators, aims to characterise the firing activity of individual motor units in mice during locomotion. To achieve this, the team implanted small arrays of eight electrodes into two heads of the triceps and performed spike sorting using a custom implementation of Kilosort. Concurrently, they tracked the positions of the shoulder, elbow, and wrist using a single camera and a markerless motion capture algorithm (DeepLabCut). Repeated one-minute recordings were conducted in six mice across five speeds, ranging from 10 to 27.5 cm-1.

From these data, the authors demonstrate that:

- Their recording method and adapted spike-sorting algorithm enable robust decoding of motor unit activity during rapid movements.

- Identified motor units tend to be recruited during a subset of strides, with recruitment probability increasing with speed.

- Motor units within individual heads of the triceps likely receive common synaptic inputs that correlate their activity, whereas motor units from different heads exhibit distinct behaviour.

The authors conclude that these differences arise from the distinct functional roles of the muscles and the task constraints (i.e., speed).

Strengths:

- The novel combination of electrode arrays for recording intramuscular electromyographic signals from a larger muscle volume, paired with an advanced spike-sorting pipeline capable of identifying motor unit populations.

- The robustness of motor unit decoding during fast movements.

Weaknesses:

- The data do not clearly indicate which motor units were sampled from each pool, leaving uncertainty as to whether the sample is biased towards high-threshold motor units or representative of the entire pool.

- The results largely confirm the classic physiological framework of motor unit recruitment and rate coding, offering limited new insights into motor unit physiology.

Comments on previous version:

I would like to thank the authors for their thorough and insightful revisions. I am particularly pleased with the inclusion of the new analyses demonstrating the robustness of motor unit decoding, as well as the improved transparency regarding spike-sorting yield for each muscle and animal. Additionally, the new analyses illustrating that recruitment within muscle heads is consistent with the presence of common synaptic inputs and orderly recruitment significantly strengthen the manuscript.

Reviewer #3 (Public review):

Summary:

Using the approach of Myomatrix recording, the authors report that 1) motor units are recruited differently in the two types of muscles and 2) individual units are probabilistically recruited during the locomotion strides, whereas the population bulk EMG has a more reliable representation of the muscle. Third, the recruitment of units was proportional to walking speed.

Strengths:

The new technique provides a unique dataset, and the data analysis is convincing and well-executed.

Weaknesses:

After the revision, I no longer see any apparent weaknesses in the study.

Reviewer #1 (Public review):

Summary:

The manuscript by Bohra et al. describes the indirect effects of ligand-dependent gene activation on neighboring non-target genes. The authors utilized single-molecule RNA-FISH (targeting both mature and intronic regions), 4C-seq, and enhancer deletions to demonstrate that the non-enhancer-targeted gene TFF3, located in the same TAD as the target gene TFF1, alters its expression when TFF1 expression declines at the end of the estrogen signaling peak. Since the enhancer does not loop with TFF3, the authors conclude that mechanisms other than estrogen receptor or enhancer-driven induction are responsible for TFF3 expression. Moreover, ERα intensity correlations show that both high and low levels of ERα are unfavorable for TFF1 expression. The ERa level correlations are further supported by overexpression of GFP-ERa. The authors conclude that transcriptional machinery used by TFF1 for its acute activation can negatively impact the TFF3 at peak of signaling but once, the condensate dissolves, TFF3 benefits from it for its low expression.

Strengths:

The findings are indeed intriguing. The authors have maintained appropriate experimental controls, and their conclusions are well-supported by the data.

Weaknesses:

There are some major and minor concerns that related to approach, data presentation and discussion. But the authors have greatly improved the manuscript during the revision work.

Comments on latest version:

The authors have done a lot of work for the revision. The manuscript has been greatly improved.

Reviewer #3 (Public review):

Summary:

In this manuscript Bohra et al. measure the effects of estrogen responsive gene expression upon induction on nearby target genes using a TAD containing the genes TFF1 and TFF3 as a model. The authors propose that there is a sort competition for transcriptional machinery between TFF1 (estrogen responsive) and TFF3 (not responsive) such that when TFF1 is activated and machinery is recruited, TFF3 is activated after a time delay. The authors attribute this time delay to transcriptional machinery that was being sequestered at TFF1 becomes available to the proximal TFF3 locus. The authors demonstrate that this activation is not dependent on contact with the TFF1 enhancer through deletion, instead they conclude that it is dependent on a phase-separated condensate which can sequester transcriptional machinery. Although the manuscript reports an interesting observation that there is a dose dependence and time delay on the expression of TFF1 relative to TFF3, there is much room for improvement in the analysis and reporting of the data. Most importantly there is no direct test of condensate formation at the locus in the context of this study: i.e. dissolution upon the enhancer deletion, decay in a temporal manner, and dependence of TFF1 expression on condensate formation. Using 1,6' hexanediol to draw conclusion on this matter is not adequate to draw conclusions on the effect of condensates on a specific genes activity given current knowledge on its non-specificity and multitude of indirect effects. Thus, in my opinion the major claim that this effect of a time delayed expression of TFF3 being dependent on condensates in not supported by the current data.

Strengths:

The depends of TFF1 expression on a single enhancer and the temporal delay in TFF3 is a very interesting finding.

The non-linear dependence of TFF1 and TTF3 expression on ER concentration is very interesting with potentially broader implications.

The combined use of smFISH, enhancer deletion, and 4C to build a coherent model is a good approach.

Weaknesses:

There is no direct observation of a condensate at the TFF1 and TFF3 locus and how this condensate changes over time after E2 treatment, upon enhancer deletion, whether transcriptional machinery is indeed concentrated within it, and other claims on condensate function and formation made in the manuscript. The use of 1,6' HD is not appropriate to test this idea given how broadly it acts.

Comments on latest version:

I don't think the response to Reviewer 2's comment on LLPS condensates on TFF1 are adequate and given this point is essential to the claims of the manuscript they must be addressed. Namely, the data from Saravavanan, 2020 actually suggest that condensate formation at the locus is not very predictive and barely enriched over random spots. The claims in the manuscript on the dependence of the condensate being responsible for sequestering transcriptional machinery are quite strong and the crux of the current model. To continue to make this claim (which I don't think is necessary since there are other possible models) the authors must test if the condensate at his locus (1) shows time dependent behavior, (2) is not present or weakened at the locus in cells that show high TFF3 expression, (3) is indeed enriched for transcriptional machinery when TFF1 peaks. The use of 1,6 hexanediol is not appropriate as pointed out by reviewer 2 and is no longer considered as an appropriate experiment by many as the whole notion of LLPS forming nuclear condensates is now under question. Such condensates can form through a variety of mechanisms as reviewed for example by Mittaj and Pappu (A conceptual framework for understanding phase separation and addressing open questions and challenges, Molecular Cell, 2022). Furthermore, given the distance between TFF1 and TFF3 it is hard to imagine that if a condensate that concentrates machinery in a non-stoichiometric manner was forming how it would not boost expression on both genes and be just specific to one. There must be another mechanism in my opinion.

I would recommend the authors remove this aspect of their manuscript/model and simply report their interesting findings that are actually supported by data: The temporal delay of TFF3 expression, the dependence on ER concentration, and the enhancer dependence.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors present comprehensive experimental observations and a theoretical framework to explain the heterogeneous behaviour of sarcomeres in cardiomyocytes. They show that a stochastic component exists in their contractile activity, which may act as a feedback mechanism regulating physiological function.

Strengths:

Experiments and data analysis are robust and valid. The rigorous statistical analysis and unbiased methods enable the authors to draw well-supported conclusions that go beyond the existing literature. Their outcomes inform about cellular activity at the individual level and the authors explain how the transient dynamics of single sarcomeres are governed by a force-velocity relationship and lead to the complex contractile patterns. The similarity of the results to the study cited in [24] demonstrates the validity of the in vitro setup for answering these questions and the feasibility of such in-vitro systems to extend our knowledge of out-of-equilibrium dynamics in cardiac cells.

Very interesting the suggestion that the interplay between intrinsic fluctuations and the dynamic instability are part of a feedback mechanism for maintaining structural and functional homeostasis.

The addition of the theoretical model and the new text of the manuscript improves the clarity of the study.

Reviewer #2 (Public review):

Summary:

Sarcomeres, the contractile units of skeletal and cardiac muscle, contract in a concerted fashion to power myofibril and thus muscle fiber contraction.

Muscle fiber contraction depends on the stiffness of the elastic substrate of the cell, yet it is not known how this dependence emerges from the collective dynamics of sarcomeres. Here, the authors analyze contraction time series of individual sarcomeres using live imaging of fluorescently labeled cardiomyocytes cultured on elastic substrates of different stiffness. They find that a reduced collective contractility of muscle fibers on unphysiologically stiff substrates is partially explained by a lack of synchronization in the contraction of individual sarcomeres.

This lack of synchronization is at least partially stochastic, consistent with the notion of a tug-of-war between sarcomeres on stiff sarcomeres. A particular irregularity of sarcomere contraction cycles is 'popping', the extension of sarcomers beyond their rest length. The statistics of 'popping' suggest that this is a purely random process.

Strengths:

This study thus marks an important shift of perspective from whole-cell analysis towards an understanding the collective dynamics of coupled, stochastic sarcomeres.

Reviewer #3 (Public review):

The manuscript of Haertter and coworkers studied the variation of the length of a single sarcomere and the response of microfibrils made by sarcomeres of cardiomyocytes on soft gel substrates of varying stiffness.

The measurements at the level of a single sarcomere are an important new result of this manuscript. They are done by combining the labeling of the sarcomeres z line using genetic manipulation and a sophisticated tracking program using machine learning. This single sarcomere analysis shows strong heterogeneities of the sarcomeres that can show fast oscillations not synchronized with the average behavior of the cell and what the authors call popping eveents which are large amplitude oscillations. Another important result is the fact that cardiomyocyte contractility decreases with the substrate stiffness, although the properties of single sarcomeres do not seem to depend on substrate stiffness.

The authors suggest that the cardiomyocyte cell behavior is dominated by sarcomere heterogeneity. They show that the heterogeneity between sarcomere is stochastic and that the contribution of static heterogeneity (such as composition differences between sarcomeres) is small.

Strengths:

All the results are, to my knowledge, new and original. The authors also made a theoretical model where each sarcomere is described by a Langevin equation based on a non-linear coupling between force and velocity of the sarcomeres. This model accounts well for the experimental results including the observation of what the authors call popping events.

Reviewer #1 (Public review):

Actin filaments and their kinetics have been the subject of extensive research, with several models for filament length control already existing in the literature. The work by Rosario et al. focuses instead on bundle length dynamics and how their fluctuations can inform us on the underlying kinetics. Surprisingly, the authors show that irrespective of the details, typical "balance point" models for filament kinetics give the wrong scaling of bundle length variance with mean length compared to experiments. Instead, the authors show that if one considers a bundle made of several individual filaments, length control for the bundle naturally emerges even in the absence of such a mechanism at the individual filament level. Furthermore, the authors show that the fluctuations of the bundle length display the same scaling with respect to the average as experimental measurements from different systems. This work constitutes a simple yet nuanced and powerful theoretical result that challenges our current understanding of actin filament kinetics and helps relate accessible experimental measurements such as actin bundle length fluctuations to their underlying kinetics. Finally, I found the manuscript to be very well written, with a particularly clear structure and development, which made it very accessible.

Comments on revisions:

I maintain my original favorable assessment of this manuscript.

I thank the authors for considering my comments and for their thoughtful replies. It would have been helpful to see some of the comments reflected in the text and discussion. I leave this to the authors.

I appreciate that the authors replaced the figures with higher-resolution versions, but I maintain my assessment that the graphical and aesthetic quality of the figures, especially the size of the legends (which are often tiny and difficult to read), labels, colors, etc., could be improved. Again, I leave this to the authors.

Reviewer #2 (Public review):

The authors present a theoretical study of the length dynamics of bundles of actin filaments. They first show that a "balance point model" in which the bundle is described as an effective polymer. The corresponding assembly and disassembly rates can depend on bundle length. This model generates a steady-state bundle-length distribution with a variance that is proportional to the average bundle length. Numerical simulations confirm this analytic result. The authors then present an analysis of previously published length distributions of actin bundles in various contexts and argue that these distributions have variances that depend quadratically with the average length. They then consider a bundle of N independent filaments that each grow in an unregulated way. Defining the bundle length to be that of the longest filament, the resulting length distribution has a variance that does scale quadratically with the average bundle length.

The manuscript is very well written, and the computations are nicely presented. The work gives fundamental insights into the length distribution of filamentous actin structures. The universal dependence of the variance on the mean length is of particular interest. It will be interesting to see in the future how many universality classes there are, and which features of a growth process determine to which class it belongs.

Comments on revisions:

I thank the authors for their detailed and thorough answers to the points that had been raised. I have no further recommendations.

Reviewer #1 (Public review):

Summary:

The factors that create and maintain diversity in host-associated microbiomes remain poorly understood. A better understanding of these factors will help in the efforts to leverage the adaptive potential of the microbiome to help solve pressing problems in health and agriculture.

Experimental evolution provides a promising path forward as we can track the causes and consequences in the emergence of novel variants, but experimental evolution remains underutilized in host-microbiome interactions. Here, Gracia-Alvira utilizes a long-term experimental evolution study in Drosophila simulans under hot and cold temperature regimes to identify strain-level variation in an important fly bacterium, Lactiplantibacillus plantarum. They identify three strains of L. plantarum, which are most prevalent in their respective three temperature regimes, suggesting that these are locally adapted bacteria. Then, using a combination of genomics, in vitro, and in vivo, Gracia-Alvira et al attempt to understand the factors that led to the differentiation of the hot and cold L. plantarum and their impacts on the fly host.

Strengths:

This is an excellent use of experimental evolution to track the emergence of novelty in the microbiome. The genomic analyses are all solid and appropriate for the data sets. It is especially striking that the comparisons with the other, independent experimental evolution studies in different labs (and across continents between Portugal and South Africa) show a consistent response to temperature. Many have disregarded the microbiome as it is something that is too sensitive to seemingly innocuous variables (particularly in the fly microbiome), such that we cannot find generalities. However, this finding highlights the potential for experimental evolution to uncover these dynamics. The question of how strains emerge and are maintained is timely and is one of the key open questions in host-microbiome evolution currently.

Weaknesses:

(1) The framing in the title and throughout the discussion about "subspecies competition" does not match the data that was collected. The subspecies competition requires actually tracking the competitive outcomes between the hot, cold, and unevolved L. plantarum. In the in vivo work, I can see that mixes of the strains were made, but they did not track whether the cold strain outcompeted the hot strain in vivo under cold conditions, for example. While Figure 4 is suggestive that there is ongoing competition in the hot temperature regime, this is not necessarily shown in the cold, which is dominated by the C clade. It could also be that the bacteria cannot survive in the flies at the different temperatures. The growth curve assays hint that the bacteria can grow, but the plate reader couldn't actually maintain the 18 {degree sign}C temperature (line 455). So all of this evidence is very indirect and insufficient to say that strain competition is driving these patterns.

(2) The in vivo results are interesting in that there appears to be a fitness cost of clade C, but the explanation is underdeveloped. I say under-developed because in Figure 4, the cold L. plantarum remains much higher throughout adaptation to the hot temperature regime than the hot L. plantarum in the cold regime. The hot L. plantarum is low abundance throughout the cold regime. I felt like this observation was not explained, but it seems relevant to understanding the strain dynamics.

I will also note that this is not the first time that L. plantarum or other Lactobacillus have been shown to exert fitness costs to Drosophila. Gould, PNAS, 2018, shows that both Lactobacillus plantarum and Lactobacillus brevis in mono-association have lower fitness (measured through Leslie matrix projections using lifespan and fecundity) than axenic flies. Many studies of wild Drosophila fail to find Lactobacillus, or it is low abundance (e.g., Chandler, PLoS Genetics, 2014; Wang, Environmental Microbiology Reports, 2018; Henry & Ayroles, Molecular Ecology, 2022; Gale, AEM, 2025). This might help provide useful context for the in vivo results.

(3) The data in Figure 4 are compelling to focus on the L. plantarum variants. However, I can see from the methods that the competitive mapping included only other strains of Wolbachia. It is not clear how other members of the microbiome changed in response to the temperature regimes. As I note in point #2, given that Lactobacillus is often rare, it is not clear what the rest of the microbiome looks like over the course of adaptation. Indeed, it seems like Mazzucco & Schlotterer, PRSB, 2021 did a broader analysis of the microbiome and found that Acetobacter is by far the most common bacterium (I think this data is also part of the data shown here?). Expanding on why or why not in this context is important and will improve this study, particularly if the focus is on connecting these evolutionary dynamics to ecological competition to explain the emergence of strain diversity.

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

The laboratory evolutionary experiment can be limited due to its artificial experimental setup. For example, wild flies rely on a more diverse set of food sources and are constantly exposed to new bacterial inoculations, whereas under laboratory conditions, flies live in a more restricted ecosystem. In addition, environmental temperatures differ among different locations, but they also involve seasonal changes within the same region. This manuscript can be strengthened with further discussions that elaborate on these limitations.

Moreover, the extent of host effects involved in these experiments remains ambiguous, because it is unclear whether these Lactiplantibacillus plantarum mostly reside within fly guts or on Drosophila medium. The laboratory evolutionary experiment possibly favored better colonizers on Drosophila medium under either cold or hot temperatures, which subsequently can saturate fly guts. As fully dissociating these variables can be experimentally tedious, the authors may want to comment more on these aspects in the discussion. Or they may want to consider some measurements. For example, measuring the growth rate of these bacteria on Drosophila medium under different temperatures, in addition to the current MRS culture experiments, or measuring the portion of the Lactiplantibacillus on Drosophila medium versus these stably colonizing fly guts.

Reviewer #3 (Public review):

Summary:

The study presents an analysis of 297 pangenomes derived from 20 populations of Drosophila simulans, at 19 time points for fast-reproducing individuals in a hot environment, or at 10 time points for slow-reproducing individuals in a cold environment, over a period of more than 10 years. The authors select a particular microbial component of the pangenomes and study the dynamics of Lactiplantibacillus plantarum strains in two environments. They discover that the revealed operational taxonomic units could be divided into three phylogenetic clades, which have their own genomic and genetic features, different adaptive capabilities that depend on the environment, and have a distinct impact on the fitness of the host.

Strengths:

The authors prove that bacterial microbiome components are sensitive to the environment and could rapidly (years) be fixed in eukaryotic populations. This study establishes a tractable model that potentially enables the study of variability of the physiological influence of distinct strains of an important commensal species, Lactiplantibacillus plantarum, on the Drsosophila host. It is clearly shown that this single species consists of several phylogenetically and functionally diverse strains. The authors did not limit their interest to their own model, but rather they have integrated a comparative approach by analysing phylogenetic relationships among 92 described L.plantarum strains.

Overall, the study is novel and delivers important discoveries of a longitudinal, well-replicated experiment, generating a substantial amount of genomic data. It highlights an important dimension of research that environmental selection operates at the subspecies level.

Weaknesses:

Even though the authors show only one particular example by conducting their longitudinal experiment, they honestly acknowledge failures important for interpretation of the biological significance of the results (gnotobiotic mono-association experiments was done with D.melanogaster, but not D. simulans) and therefore they state limitations of their conclusions (weaker effects in the non-axenic flies are due to the presence of other taxa or to higher-order interactions with other members of the microbiome). These interactions could significantly affect bacterial growth, metabolism, and physiological influence on the host.

The authors exploit the results of their experiment to speculate about a wide range of evolutionary phenomena, like within-species competition, ecological adaptation and evolution of the host, fitness advantage of bacteria to the host, the benefits of parasitism or mutualism, the domestication of the microbiome, etc. At the end, they conclude that their study "highlights that even subspecies diversity plays a key role in adaptation to environmental temperature". However, the potential mechanisms of such adaptation are barely discussed, so that the focus of the study shifts from the temperature-induced changes in microbial population structures toward metabolism-related adaptations of clade representatives that enable them to diversify their carbon and nitrogen sources. The role of the temperature factor remains elusive.

In addition to that, the paper has a clearly minimalistic experimental approach to address functional properties of the revealed L.plantarum strains, so that their own fitness, or their relationship with the Drosophila host, is characterised superficially. Therefore, the authors' discourse can be speculative rather than factual (especially when the authors use the expression "likely" to share their guesses in the "Results" section). Nevertheless, these minor drawbacks do not underscore the novelty of the discovered phenotypes and the importance of their further investigation.

Reviewer #1 (Public review):

Giordano et al. demonstrate that yeast cells expressing separated N- and C-terminal regions of Tfb3 are viable and grow well. Using this creative and powerful tool, the authors effectively uncouple CTD Ser5 phosphorylation at promoters and assess its impact on transcription. This strategy is complementary to previous approaches, such as Kin28 depletion or the use of CDK7 inhibitors. The results are largely consistent with earlier studies, reinforcing the importance of the Tfb3 linkage in mediating CTD Ser5 phosphorylation at promoters and subsequent transcription.

Notably, the authors also observe effects attributable to the Tfb3 linker itself, beyond its role as a simple physical connection between the N- and C-terminal domains. These findings provide functional insight into the Tfb3 linker, which had previously been observed in structural studies but lacked clear functional relevance. Overall, I am very positive about the publication of this manuscript and offer a few minor comments below that may help to further strengthen the study.

Page 4 PIC structures show the linker emerging from the N-terminal domain as a long alpha-helix running along the interface between the two ATPase subunits, followed by a turn and a short stretch of helix just N-terminal to a disordered region that connects to the C-terminal region (see schematic in Fig. 1A).

The linker helix was only observed in the poised PIC (Abril-Garrido et al., 2023), not other fully-engaged PIC structures.

Page 8 Recent structures (reviewed in (Yu et al., 2023)) show that the Kinase Module would block interactions between the Core Module and other NER factors. Therefore, TFIIH either enters into the NER complex as free Core Module, or the Kinase Module must dissociate soon after.

To my knowledge, this is still controversial in the NER field. I note the potential function on the kinase module is likely attributed to the N-terminal region of Tfb3 through its binding to Rad3. Because the yeast strains used in Fig. 6 retain the N-terminal region of Tfb3, the UV sensitivity assay presented here is unlikely to directly address the contribution of the kinase module to NER.

Page 11. Notably, release of the Tfb3 Linker contact also results in the long alpha-helix becoming disordered (Abril-Garrido et al., 2023), which could allow the kinase access to a far larger radius of area. This flexibility could help the kinase reach both proximal and distal repeats within the CTD, which can theoretically extend quite far from the RNApII body.

Although the kinase module was resolved at low resolution in all PIC-Mediator structures, these structural studies consistently reveal the same overall positioning of the kinase module on Mediator, indicating that its localization is constrained rather than variable. This observation suggests that the linker region may help position the kinase module at this specific site, likely through direct interactions with the PIC or Mediator. This idea is further supported by numerous cross-links between the linker region and Mediator (Robinson et al., 2016).

Comments on revisions:

Revised ms clarified all my points, including those I previously misunderstood.

Reviewer #2 (Public review):

Summary:

This work advances our understanding of how TFIIH coordinates DNA melting and CTD phosphorylation during transcription initiation. The finding that untethered kinase activity becomes "unfocused," phosphorylating the CTD at ser5 throughout the coding sequence rather than being promoter-restricted, suggests that the TFIIH Core-Kinase linkage not only targets the kinase to promoters but also constrains its activity in a spatial and temporal manner.

Strengths:

The experiments presented are straightforward and the model for coupling initiation and CTD phosphorylation and for evolution of these linked processes are interesting and novel. The results have important implications for the regulation of initiation and CTD phosphorylation.

Comments on revisions:

The revised version with revisions to figures, text and new data has addressed all of our prior comments.

Reviewer #3 (Public review):

Summary:

Eukaryotic gene transcription requires a large assemblage of protein complexes that govern the molecular events required for RNA Polymerase II to produce mRNAs. One of these complexes, TFIIH, comprises two modules, one of which promotes DNA unwinding at promoters, while the other contains a kinase (Kin28 in yeast) that phosphorylates the repeated motif at the C-terminal domain (CTD) of the largest subunit of Pol II. Kin28 phosphorylation of Ser5 in the YSPTSPS motif of the CTD is normally highly localized at promoter regions, and marks the beginning of a cycle of phosphorylation events and accompanying protein association with the CTD during the transition from initiation to elongation.

The two modules of TFIIH are linked by Tfb3. Tfb3 consists of two globular regions, an N-terminal domain that contacts the Core module of TFIIH and a C-terminal domain that contacts the kinase module, connected by a linker. In this paper, Giordano et al. test the role of Tfb3 as a connector between the two modules of TFIIH in yeast. They show that while no or very slow growth occurs if only the C-terminal or N-terminal region of Tfb3 is present, near normal growth is observed when the two unlinked regions are expressed. Consistent with this result, the separate domains are shown to interact with the two distinct TFIIH modules. ChIP experiments show that the Core module of TFIIH maintains its localization at gene promoters when the Tfb3 domains are separated, while localization of the kinase module, and of Ser5 phosphorylation on the CTD of Pol II, is disrupted. Finally, the authors examine the effect of separating the Tfb3 domains on another function of TFIIH, namely nucleotide excision repair, and find little or no effect when only the N-terminal region of Tfb3 or the two unlinked domains are present.

Strengths:

Experiments involving expression of Tfb3 domains in yeast are well-controlled and the data regarding viability, interaction of the separate Tfb3 domains with TFIIH modules, genome-wide localization of the TFIIH modules and of phosphorylated Ser5 CTDs, and of effects on NER, are convincing. The experiments are consistent with current models of TFIIH structure and function and support a model in which Tfb3 tethers the kinase module of TFIIH close to initiation sites to prevent its promiscuous action on elongating Pol II.

Weaknesses:

The work is limited in scope and does not provide major insights into the mechanism of transcription. The main addition to current models of transcription is that tethering of Kin28 to Tfb3 may limit kinase action from occurring downstream from the initiation site.

The first described experiment, which purports to show that three kinases cannot function in place of Kin28 when tethered (by fusion) to Tfb3 is missing the crucial control of showing that Kin28 can support viability in the same context. This result also does not connect with the rest of the manuscript, although the experiment apparently motivated the subsequent studies reported here.

Finally, the authors present the interesting and reasonable speculation that the TFIIH complex and connecting Tfb3 found in mammals and yeast may have evolved from an earlier state in which the two TFIIH subdomains were present as unconnected, distinct enzymes. It will be interesting to have this idea tested more thoroughly as more molecular evolutionary data becomes available.

Comments on revisions:

For the most part, the authors have satisfactorily addressed my previous critique. In particular, they have added to their discussion of evolutionary implications, and performed an experiment casting doubt on the assertion of a dominant negative effect, and as a consequence removed this claim from the manuscript. I also pointed out that the fusion experiments that lead off the Results section are missing the crucial control of including a Tfb3-Kin28 fusion. The authors have elected not to perform this control experiment, pointing out that even this control would be imperfect in some respects, and agreeing that this experiment is somewhat disconnected from the rest of the paper. The reason for including it, in spite of its somewhat tangential nature, is that it provides something of a rationale for the experiments that follow. I don't so much mind their retaining the experiment, as the absence of this control (and indeed, the results) does not so much impact the later results. However, I think if it is to be included, this shortcoming should be explicitly recognized, especially as a service to younger scientists who could benefit from an exposition that includes a thorough consideration of potential control experiments.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

This manuscript examines whether the theta-beta ratio as derived from EEG data relates to ADHD diagnoses. To do so, it performs a multiverse analysis across a large number of analytical choices, applied to a large EEG dataset, and corroborated in an additional validation set. The results overall show that the TBR is not a reliable indicator of ADHD diagnosis. In discussing the patterns of results across analytical choices, the authors also demonstrate some key points about what appears to be driving the ratio measures, noting that significant results appear to be driven by choices regarding aperiodic-correction and the use of individualized alpha frequencies, suggesting TBR measures can be affected by these features rather than reflecting theta and/or beta activity.

Strengths:

This manuscript addresses a clearly posed and important question in the literature, addressing a longstanding discussion on the relationship between TBR and ADHD, and uses a large dataset and an expansive analysis approach to provide a definitive answer. The strengths of the approach allow for a clear answer, providing a notable contribution to the field.

Weaknesses:

I find no notable weaknesses in the current manuscript nor any major issues that I think challenge the key findings of this manuscript.

Reviewer #3 (Public review):

Summary:

In this manuscript, Strzelczyk, Vetsch, and Langer tackle an incredibly important question in clinical neuroscience: the use of the theta/beta ratio as a biomarker of attention deficit hyperactivity disorder (ADHD). The theta/beta ratio is argued to be so reliable as an ADHD biomarker that, in the United States, the Food and Drug Administration has approved its use as a biomarker for ADHD diagnosis. However, there is mounting evidence that the theta/beta ratio is likely not really measuring the relative power between two oscillations - the theta rhythm and the beta rhythm - but rather reflects differences in a singular, non-oscillatory aperiodic process. In this very convincing study, Strzelczyk and colleagues take a "multiverse" analysis approach to show that aperiodic activity differences between healthy controls and people with ADHD are driving the apparent theta/beta ratio differences. While in a vacuum, where a measure is a measure and if it's related to a diagnosis it's still useful no matter what, this distinction might not seem important, from a neuroscientific perspective this is a critical distinction, because the ratio between two oscillations has fundamentally very different underlying physiological mechanisms than aperiodic differences, and this framing has a major impact on guiding research on the diagnosis and treatment of ADHD.

Strengths:

While smaller studies and analyses have already hinted at similar results as shown here, the current study's multiverse analysis approach is comprehensive, convincing, and very well done. The large sample size of 1,499 participants is very impressive, as is the use of an independent validation sample of 381 participants.

Overall, the technical and statistical aspects are very well done: the multiverse approach, the validation set, the resampling methods, and even the shiny apps. The authors should be applauded for being so thorough and making their data and analyses publicly accessible.

Weaknesses:

To be clear, I see no breaking weaknesses in the theoretical foundations, methods, statistical analyses, or interpretations. All of my recommendations below are for the sake of clarity, which I believe is especially important because this is such an important paper that many people should read.

Comments:

(1) Some figures are mislabeled. For example, Supplementary Figure 1 says (C) are scalp topographies, but those are (A), while (C) shows power spectra, but it's unclear what (C) is. I assume it's only the aperiodic part of the spectrum (oscillations removed)? But it would be better to plot on a log-log scale if so.

In fact, I recommend showing all spectra on a log-log scale.

(2) Supplementary Figure 6 is also mislabeled, saying (A) shows age (it does not) and so on.

(3) In Supplementary Figure 7, is (B) the aperiodic-removed spectrum? The authors are very inconsistent with what they're showing in these spectral plots, and not actually explaining what they're showing: raw spectra, semi-logged or not, aperiodic-removed or oscillations-removed, etc.

(4) For the HBN data, it is said that, "electrode impedances were kept below 40 kΩ, lower than EGI's standard recommendation of 50 (Net Station Acquisition Technical Manual)." For the validation data: "... electrode impedances were maintained below 5 kΩ." These are big impedance threshold differences. Of course, these recommendations differ by recording system, the use of active electrodes, and so on. But such differences can certainly influence signal-to-noise. The fact that the results are so consistent between them is a strength that perhaps should be explicitly called out.

(5) The authors cite a lot of foundational / related work here, such as Finley et al, but they should also cite several other highly relevant ones:

- Saad et al., "Is the Theta/Beta EEG Marker for ADHD Inherently Flawed?", J Atten Disord, 2015

- Donoghue, Dominguez, Voytek, "Electrophysiological frequency band ratio measures conflate periodic and aperiodic neural activity", eNeuro, 2020

- Karalunas et al., "Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development", Develop Psychobiol, 2022

- Donoghue, "A systematic review of aperiodic neural activity in clinical investigations", Eur J Neurosci 2025

Reviewer #1 (Public review):

Summary:

The authors set out to conduct a behavioral comparison of macaque and human vision across a wide range of visual properties. Such a comparison is critical for evaluating the use of macaques as a model system for understanding human vision and the underlying neural mechanisms. This goal represents a unique endeavour since prior studies have typically focused on only highly specific tasks. While the authors found consistent coarse representational structure for objects, evidence for Weber's Law and amodal completion, there was divergence for mirror image confusion and the use of global or local image properties.

Strengths:

There are three major strengths of the study. First, the authors employed a behavioral paradigm (oddball search) that allowed them to test multiple perceptual phenomena without having to train the macaques on the specific type of stimuli tested. Second, humans and macaques could be tested in an identical manner. Third, the authors tested a range of different visual properties and phenomena, allowing a broader comparison between species.

There are also some weaknesses to the study (described below), but that doesn't change the fact that the authors have demonstrated and validated a novel approach for systematic and comprehensive comparisons of vision across species.

Weaknesses:

The weaknesses of the study arise in part because of the breadth of the work. In cases where there are divergences between the two species, it would be helpful to know what might account for such divergence, to have more depth. Is it really a species difference, or could there be a different account? For example, does the difference in mirror image confusion arise because the stimuli were objects that would have been highly familiar to the humans but not the macaques? Further, the authors often used small sets of stimuli (e.g. 8 objects only in the test of object similarity; a small set of highly specific occlusion stimuli), and how well the findings will generalize beyond those stimuli is unclear.

The authors discuss the implications of training macaques to perform specific tasks on specific stimuli in comparing across species. While I agree that extensive training in monkeys could change perception, it is important to also consider that humans have been extensively trained through the types of visual tasks we conduct throughout our lives, so I'm not sure it is universally true that the best comparison is between humans and untrained monkeys. But this just consideration just highlights the general problem of comparing across species.

Reviewer #2 (Public review):

Summary:

The macaque monkey is often considered as the animal model of choice to study the neural correlates of visual perception, due to the close similarities to humans in terms of anatomy, physiology and behaviour (Van Essen and Dierker, 2007; DiCarlo et al., 2012; Roelfsema and Treue, 2014; Picaud et al., 2019; Van Essen et al., 2019; Hesse and Tsao, 2020). Quite some studies have been performed to compare the behaviour of macaque monkeys and humans on visual perception tasks. However, it remains difficult to compare the results of these studies as the methods that are used differ significantly between these studies. Furthermore, behavioural studies of macaque monkeys often involve extensive training as the tasks were relatively hard, making it difficult to compare the results with humans, who generally require very little training. The authors present a set of experiments to compare visual perception between macaque monkeys and humans, using the exact same behavioral task that is easy to learn and therefore requires very little training. As expected, they overall find that the two species behave similarly. However, they find a number of interesting exceptions.

Strengths:

A major strength of the current study is the relatively large number of tasks that were tested in the same subjects. This is made possible by using the oddball visual search task, which macaque monkeys can learn very quickly. This means that few trials are sufficient to obtain a significant difference between conditions, minimizing learning effects. Although this type of task has been used in previous studies (Sablé-Meyer et al., 2021), the current manuscript makes better use of the advantages and explains them more explicitly.

In addition, the study finds a number of interesting differences between macaque monkeys and humans. In particular, while humans can dissociate horizontally mirrored images better than vertically mirrored images, monkeys show no difference between these two conditions (Experiment 4). Also, while humans dissociate images better based on the global shape of a stimulus, monkeys dissociate images better based on local elements of a stimulus (Experiments 5 and 6). Although these findings are largely a replication of previous results, they have not yet been studied together with other tasks within the same individual subjects, and the low number of trials avoids any learning effects.

Weaknesses:

A weakness of the study is that while the objects that were used can be considered to be familiar to humans, they are not familiar to macaque monkeys.

In Experiment 4, humans can be expected to have 3D representations of familiar objects such as a Roman helmet or an office chair. Humans can therefore be expected to have view-invariant representations of these objects, predominantly for rotations around the vertical axis of the object (as movements are most common in the horizontal plane). This can explain why only humans confuse objects more often when mirrored vertically than when mirrored horizontally.

Similarly, in Experiment 5, humans can be expected to be familiar with abstract geometric shapes such as squares and circles, while monkeys likely are not. This could explain why monkeys find it hard to recognize these geometric shapes in the global shape of the stimuli, even when thin grey lines are drawn to connect the local elements that constitute the global shape (Experiment 6). Instead, the combination of local shapes can be expected to form a texture that might be more easily recognized by the monkeys.

More generally, as proposed by Fagot et al, it might well be that monkeys tend to conceive stimuli as a combination of low-level visual features, instead of as references to objects in the outside world, as humans have learned to do (Fagot et al., 2010). This line of critique would be relevant to take into account.

Another weakness could be that only three monkeys are tested, while 24 human subjects are tested. According to some theoretical work, a finding in 3 animals is not sufficient to make a claim about an animal species (Fries and Maris, 2022). However, it seems that the results are largely consistent between the different monkeys. Moreover, the results generally agree with the results from previous literature.

The conclusions by the authors are therefore largely supported by the results. Some results could be strengthened by additional experiments, or at least a more extensive discussion of the potential weaknesses.<br /> The potential impact of the paper is significant, as a start of a comprehensive comparison of visual perception between humans and macaque monkeys, which can inspire other labs to contribute to. This comparison can also be extended to other animal species (e.g. crows and rodents), as well as to different types of artificial neural networks (Leibo et al., 2018).

Reviewer #3 (Public review):

Summary:

In this study, Cherian and colleagues compare visual perception between humans and monkeys using a common oddball visual search task across a battery of perceptual phenomena. By keeping the task constant and varying only the stimulus sets, the authors aim to isolate perceptual similarities and differences between species. Across six experiments, they report that monkeys and humans share similarities in coarse object representations, Weber's law, and amodal completion, but differ in mirror confusion and global/local processing.

Strengths:

A major strength of the study is the unified experimental framework. The authors designed the experiments such that the task procedures are identical across conditions and species, differing only in the images shown. This is a significant methodological advantage, as it minimizes task-related confounds that often complicate cross-species and cross-experiment comparisons. As a result, observed similarities and differences can be more directly attributed to perceptual processes rather than differences in training or task demands. This allows for a more comprehensive evaluation of visual perception than is typical in the literature, where individual studies often focus on a single effect with specialized training. The data are carefully collected, and the analyses are systematic and appropriate for the questions posed.

Weaknesses:

Despite its strengths, the study is largely descriptive and provides limited mechanistic or theoretical explanation for the observed similarities and differences. While the authors document several convergences and divergences between humans and monkeys, there is relatively little discussion of why these patterns arise or how they relate to existing theories of visual processing. As a result, it is difficult to assess the broader implications or generalizability of the findings beyond the specific task and stimuli used.

Relatedly, the rationale for selecting the particular set of perceptual phenomena is not fully developed. Some tasks appear motivated by prior work comparing humans and deep neural networks, but it is unclear whether this set constitutes a representative or theoretically grounded sampling of visual perception. Without a clearer justification, it is difficult to interpret the absence or presence of specific effects (e.g., mirror confusion or global advantage) as reflecting fundamental species similarities/differences.

Reviewer #1 (Public review):

Summary:

This paper describes a complex series of studies that measure and explain object recognition in mice. The authors demonstrate that mice are capable of solving an object recognition task, that object identity is decodable in different regions of cortex, and the decodability, to some extent, can be captured by extant theory on object manifolds in deep neural networks. The authors further add some correlational analysis of the time courses of object discriminability to bolster their claim of an object processing hierarchy in the mouse cortex.

The behavioral and neural data described in this paper are likely to be of interest to the general neuroscience community. That said, I have some issues with the analyses, modeling, and image dataset that I'll detail below.

Strengths:

(1) The behavioral work is incredibly cool. Getting mice to solve this task is a real achievement and opens up new avenues for mice as a model for complex visual tasks.

(2) Similarly, the neural recordings are astounding in their scale.

(3) This could be the most complete demonstration of a primate-analogous object processing network in the mouse.

Weaknesses:

No new weaknesses were noted by this reviewer.

Reviewer #2 (Public review):

Summary:

The paper argues that mice are capable of some view-invariant object recognition and that some of their visual areas (especially LM, LI, and AL) carry linearly-decodable signals that could, in principle, help in this process. Further, it argues that the population code in those areas makes linear decodability easier in two ways (fewer dimensions and a smaller radius).

Strengths:

It is very useful to see the performance of the mice in this difficult task, and to compare it to the performance of neurons in the mouse visual system. It is also useful to see analyses of the neural code that seek to understand how the code in some visual areas may be particularly suited to decoding object identity.

Weaknesses:

Though the paper has improved from the previous submission, there are still some open questions, especially about whether some lower-level properties of the neurons (such as receptive field location) might explain the differences between visual areas. This and other concerns are outlined below.

(1) Do the signals from the visual areas outperform or underperform the mice? It is hard to tell, because for mice we get numbers in percent correct (Figure 1e, based on 2 alternatives), whereas for visual areas we get numbers in bits (Figure 2c, where it is not clear whether there are 2 or 4 alternatives). This makes it very hard to compare the two. The authors should provide a statement or figure where readers can compare the two. Also, if the behavioral data are obtained with 2AFC, why not run the analyses as 2AFC too?

(2) Differences in discriminability across objects (Figure 1f). Are these differences also seen for the model based on the difference of Gaussians? (The authors should add those predictions to the plot.) If so, this could further point to possible low-level explanations. It is already quite interesting that the difference of Gaussians model predicts ~58% accuracy, which is not far from the ~65% accuracy of the mice.

(3) Similarly, in a later figure about decoding visual cortical activity, the authors should show a similar breakdown by object. Are certain objects more decodable than others?

(4) Number of neurons. It is wonderful to see so many neurons (489182, i.e., an average of ~15k per mouse). But might the same neurons have been recorded multiple times? Has a tool like ROICat or similar been run to exclude this? If not, that is ok, but the authors should add a sentence in Results to indicate that these are not unique neurons (some neurons may be duplicates or triplicates).

(5) Retinotopy: "within the same ∼20o area of visual space". This is a useful analysis, but which 20 deg area was considered? Was it the one in front of the mice? This would be surprising, because some of the regions do not cover that area (Zhuang et al, eLife 2017). Was a different area chosen? What are its coordinates in azimuth and elevation? And how does it compare to the region where the stimulus was shown during imaging? The Methods do not explain where the stimulus was placed (only that it was in front of the left eye). This information should be added. Also, the screen covered ~120 deg of visual space (63 cm monitor placed 15 cm away), so the emphasis on a 20 deg area is not clear. The authors should provide a figure showing coverage of the screen by each visual area and the position of the stimuli presented during imaging.

(6) If during imaging the stimuli were presented slightly above the horizontal meridian, then a possible explanation for the superiority of LM, AL, and LI is that their receptive fields tend to be in the upper visual field, whereas the rest of the higher visual areas tend to have receptive fields in the lower visual field (Zhuang et al, eLife 2017).

(7) Dimensionality: "number of directions in which this variability is spread". Unless I missed the explanation, the Methods don't provide any information on how the dimensionality is computed. Is it done with cross-validation? If not, noise can be interpreted as having high dimension. There are methods to estimate dimensionality with cross-validation, thus excluding the contribution of noise (e.g., Stringer et al 2019). The authors should confirm that this was done with cross-validation and provide information in the Methods.

(8) Temporal dynamics: "evidence for temporal integration during a trial". Are there really dynamics in the visual responses that last on the scale of seconds? This would be remarkable. Image recognition is usually thought to be done in 100 ms. The long scales presented here are more likely associated with behavioral responses or state responses, or similar. Might there be different brain state correlates in the different cases? For instance, pupil dilation might be different.

(9) Methods: "to ensure animals were in an attentive state (eyes clear and open)". This sounds peculiar. Did the mice ever close their eyes? If so, that's a discovery. Mice keep their eyes open at all times, even when they are sleeping. So, using eye closure for online detection of "inattentive states" does not seem to make sense. (Also, and this is a minor point: why stop a scan when the animal is "inattentive"? Wouldn't one want to acquire the associated data for comparison? Is the point to save disk space?). This whole set of statements is a bit concerning.

Reviewer #1 (Public review):

Summary:

Liao et al. performed a large-scale integrative analysis to explore the function of two cancer genes (BRCA1 and BRCA2) in lung cancer, which is one of the cancers with an extremely high mortality rate. The detailed genetic analysis demonstrated new roles of BRCA1/2 in causing the tumor microenvironment in lung cancer. In particular, the discovery of different mechanisms of BRCA1 and BRCA2 provides an essential foundation for developing drugs that target BRCA1 or BRCA2 in lung cancer therapy.

Strengths:

(1) This study leveraged large-scale genomic and transcriptomic datasets to investigate the prognostic implications of BRCA1/2 mutations in LUAD patients (~2,000 samples). The datasets range from genomics to single-cell RNA-seq to scTCR-seq.

(2) In particular, the scTCR-seq offers a powerful approach for understanding T cell diversity, clonal expansion, and antigen-specific immune responses. Leveraging these data, this study found that BRCA1 mutations were associated with CD8+ Trm expansion, whereas BRCA2 mutations were linked to tumor CD4+ Trm expansion and peripheral T/NK cell cytotoxicity.

(3) This study also performed a comprehensive analysis of genomic variation, gene expression, and clinical data from the TCGA program, which provides an independent validation of the findings from LUAD patients newly collected in this study.

(4) This study provides an exemplary integration analysis using both computational biology and wet bench experiments. The experimental testing in the A549 cell line further supports the robustness of the computational analysis.

(5) The findings of this study offer a comprehensive view of the molecular mechanisms underlying BRCA1 and BRCA2 mutations in LUAD. BRCA1 and BRCA2 are two well-known cancer-related genes in multiple cancers. However, their role in shaping the tumor microenvironment, particularly in lung cancer, is largely unknown.

(6) By focusing on PD-L1-negative LUAD patients, this study demonstrated the molecular mechanisms underlying resistance to immune therapy. These new insights highlight new opportunities for personalized therapeutic strategies to BRCA-driven tumors. For example, they found histone deacetylase (HDAC) inhibitors consistently downregulated 4-R genes in A549 cells.

(7) The deposition of raw single-cell sequencing (including scRNA-seq and scTCR-seq) data will provide an essential data resource for further discovery in this field.

Comments on revisions:

The author has revised accordingly. I have no further comments.

Reviewer #2 (Public review):

Summary:

This study investigates the impact of BRCA1/2 mutations on immunotherapy in lung adenocarcinoma using multi-omics approaches. The work highlights distinct roles of BRCA1 and BRCA2 mutations in shaping immune-related processes, and is logically structured with clearly presented analyses. However, the conclusions rely primarily on descriptive computational analyses and would benefit from additional immunological validation.

Strengths:

By integrating public datasets with in-house data, this study examines the impact of BRCA1/2 mutations on immunotherapy in lung adenocarcinoma from multiple perspectives using multi-omics approaches. The analyses are diverse in scope, with a clear overall logic and a well-organized structure.

Weaknesses:

The study is largely descriptive and would benefit from additional immunological experiments or validation using in vivo models. The fact that the BRCA1 and BRCA2 samples were each derived from a single patient also limits the robustness of the conclusions.

Comments on revisions:

The authors have addressed my concerns satisfactorily

Reviewer #1 (Public review):

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

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.

Strengths:

Authors have undertaken a thorough investigation and used several methods to investigate the role of Candida albicans in Colorectal cancer progression.

Reviewer #1 (Public review):

In this revision the authors address some of the points, but they also make some technical errors. My overall view of the manuscript hasn't changed since the original evaluation.

Previously I noted that SC sparsity presents an issue when comparing to full FC matrices. They authors misinterpreted the Honey et al paper. They resampled ALL entries of the SC matrix (including zeros) from a Gaussian distribution. In effect, this assigns zeros small (but uniform) weights. In Honey et al, the authors resampled only existing edge weights from a gaussian distribution (the rationale at the time was that there might be pushback against the extremely heavy-tailed edge weight distribution). In other words, the zeros are still zeros following this resampling procedure.

That said, I agree that the log transform is likely useful or necessary given edge weight distributions.

In short, I still think that the approach is interesting and meritorious, I just don't think the execution is correct.

Reviewer #3 (Public review):

Summary:

In this manuscript, Okuno et al. re-analyze whole-brain imaging data collected in another paper (Brezovec et al., 2024) in the context of the two currently available Drosophila connectome datasets: the partial "FlyEM" (hemibrain) dataset (Scheffer et al., 2020) and the whole-brain "FlyWire" dataset (Dorkenwald et al., 2024). They apply existing fMRI signal processing algorithms to the fly imaging data and compute function-structure correlations across a variety of post-processing parameters (noise reduction methods, ROI size), demonstrating an inverse relationship between ROI size and FC-SC correlation. The authors go on to look at structural connectivity amongst more polarized or less polarized neurons, and suggest that stronger FC-SC correlations are driven by more polarized neurons.

Strengths:

(1) The result that larger mesoscale ROIs have higher correlation with structural data is interesting. This has been previously discussed in Drosophila in Turner et al., 2021, but here it is quantified more extensively.

(2) The quantification of neuron polarization (PPSSI) as applied to these structural data is a promising approach for quantifying differences in spatial synapse distribution. The revision now uses morphological cable length for some analyses rather than straight-line distance, which improves the realism and interpretability of these results.

Weaknesses:

One should not score noise/nuisance removal methods solely by their impact on FC-SC correlation values, because we do not know a priori that direct structural connections correspond with strong functional correlations. In fact, work in C. elegans, where we have access to both a connectome and neuron-resolution functional data, suggests that this relationship is weak (Yemini et al., 2021; Randi et al., 2023). Similarly, I don't think it's appropriate to tune the confidence scores on the EM datasets using FC-SC correlations as an output metric. While it is likely that some FC-SC relationship does exist at large scales, it does not in my view justify use of this metric for evaluating noise removal methods, since such methods may inadvertently remove real neural correlates. This concern remains unaddressed in the revision.

Any discussion of FC-SC comparisons should include an analysis of excitatory/inhibitory neurotransmitters, which are available in the fly connectome dataset. The authors examine the ratios of input and output neurotransmitters in different defined regions. However, I think it would be more useful to integrate the neurotransmitter information more fully into the assessment of SC, for instance: examining the signed weight (excitatory - inhibitory), or by examining the excitatory and inhibitory networks separately.

Comparisons between fly and human MRI data are also premature here. Firstly, the fly connectomes, which are derived from neuron-scale EM reconstructions, are a qualitatively different kind of data from human connectomes, which are derived from DSI imaging of large-scale tracts. Likewise, calcium data and fMRI data are very different functional data acquisition methods-the fact that similar processing steps can be used on time-series data does not make them surprisingly similar, and does not in my view constitute evidence of "similar design concepts."

The comparison of FlyEM/FlyWire connectomes concludes that differences are more likely a result of data processing than of inter-individual variability. If this is the case, the title should not claim that the manuscript covers individual variability.<br /> The analysis of the wedge-AVLP neuron strikes me as highly speculative, given that the alignment precision between the connectome and the functional data is around 5 microns (Brezovec* et al, PNAS 2024).

Reviewer #1 (Public review):

In this manuscript, the authors aim to determine the ligand on Plasmodium falciparum infected erythrocytes for the NK cell integrin, LFA-1, following up on previous evidence that LFA-1 is important for immune cell-mediated recognition of iRBCs.

They start by incubating LFA-1 with iRBCs and show by flow analysis that a substantial population of these iRBCs binds to the LFA-1 (Fig 1C). They do conduct the control with uninfected RBCs, but put this in the supplementary material. As this is a critical control, I think that it should be moved to Figure 1C as it is essential to allow interpretation of the iRBC data. The authors also do not state which strain of P. falciparum they used (line 144). This is critical information, as different strains have different variant surface antigens and should be included. With these changes, this data seems convincing.

They next incubated LFA-1 with the iRBCs, cross-linked and conducted a pulldown, identifying GP130 as a binding partner. Using cross-linkers is a dangerous strategy as it risks non-specific cross-linking. Did they try without cross-linking and find an interaction?

They raised antibodies to PfGBP and showed IFA, which reveals that these antibodies stain iRBCs (Figure 2Ciii). This experiment lacks a critical control of uninfected RBCs, which needs to be included to show that the staining is specific. Without this, it is not possible to conclude that there is iRBC-specific staining with PfGBP.

They then conduct a pulldown using LFA-Fc, which does show GP130 only in the presence of the LFA-Fc, but not when empty beads are used. This is convincing. BLI measurements are also used to study this interaction (Figure 2Ci). The BLI data is presented in such a way that any association phase is obscured by the y-axis, which makes it impossible to know whether there is binding here. I think that the data needs to be shown with some baseline before the addition of the ligand so that association can be seen. The data is also a bit messy with a downward drift and the curves showing different shapes, for example, with the 1.0uM curve seeming to have a different association rate. As this is the only data which shows a direct interaction between LFA1 and GBP, as pulldowns are done with lysates, which might mean bridging components. I think that it is important to repeat the BLI, or use additional biophysical methods to assess binding, to obtain more convincing data.

The authors next do some modelling of the putative complex. This is done by homology modelling and docking, which is not the most up-to-date method and is overinterpreted. Personally, I would remove this data as I did not find it convincing and it is not important for the story. If the authors wish to include it, then I think that they should validate the modelling by mutagenesis to show that the residues which the models indicate might bind are involved in the interaction.

They next made GP130 and tested the binding of this to THP-1 cells, which are often used as a model for macrophages. They observe greater binding of PfGBP-Fc to these cells when compared with hIgG and show that LFA-1 siRNA reduces this binding. I was a little confused about how the flow plots related to the graph in the bottom right corner of Figure 3Bii. In the flow plots, hIgG control shows 12.8% of cells in the gated region, while the unstained cells has 5.63%, but the MFI data shows a decrease in binding for hIgG vs unstained cells. How is this consistent? Also the siRNA reduces the number of cells in gated region from 66.6% to 25.9%, which is still substantially more that 5.63% in the unstained control. This also doesn't seem quite consistent with the MFI data. Could the authors explain this? Also perhaps an additional experiment would be to add soluble LFA-1 into this assay as an additional control to determine whether this blocks PfGBP binding to the THP-1 cells? It could. Be that there are additional mechanisms of binding which indicate why the siRNA has a partial effect. The same is true for the NK cell experiments in Figure 3Ci in which the siRNA has a partial effect. The authors also test binding to HEK, HepG2 and 'stem' cells and claim 'only background levels of binding', but in each case, there is more binding to these cells by PfGBP-Fc than by hIgG, albeit less than in THP-1 and NK cells. Why have the authors decided that these increases are not significant? All in all, these experiments do indicate a role for the GBP-LFA1 interaction in the binding of immune cells to iRBCs, but perhaps not as absolutely as is suggested.

The authors next produce CHO cells with PfGBP on the surface. These cells bind to LFA-1 specifically. When these cells were incubated with primary NK cells, they did see increases in activation markers, which were reduced by addition of antiCD11a, suggesting these to be specific. They also conduct the same experiment with anti-GBP with iRBCs but this is in a different figure. It would be easier for the reader if Figure 5B were in the same figure as Figure 4B as it is related data using the same method. I found this data convincing, showing that the LFA1:GBP interaction does contribute to immune cell recognition and activation.

The authors next conduct an experiment in which they assess parasite growth in the presence of NK cells and in the presence of anti-GBP. They use Heochst staining as a measure of parasite growth and claim that NK cells reduce the number of parasites, but that anti-GBP abolishes this effect (Figure 5A). I found this experiment very unconvincing as there are small effects and no demonstration of significance. More commonly used approaches to study parasite growth are lactate dehydrogenase GIA assays or calcein-AM labelling. I did not find this experiment convincing and would either remove or supplement with additional data using a more robust assay, with repeats and tests of statistical significance.

In summary, the authors present a set of data which comes together to indicate an interaction between LFA1 and PfGBP on the Plasmodium infected erythrocyte surface. Pulldown studies show convincingly that these two proteins co-precipitate and BLI data suggest that this is direct. Also convincing is that NK cell activation can be reduced using antibodies against either LFA1 or PfGBP, indicating that this interaction does play a role in immune cell recognition of iRBCs.

Comments on revised version:

The authors made some minor changes in response to my review, but did not present any substantial new data to demonstrate a direct interaction between PfGBP and LFA1 or to convincingly show differences in NK cell-mediated killing.

Reviewer #2 (Public review):

Summary:

The authors used an LFA-1 αI-Fc fusion protein to pull down potential ligands and LC-MS/MS, leading to selection of PfGBP-130 as a potential membrane protein on the surface of infected cells. PfGBP-130 antibodies were raised and used to support the surface localization. This putative ligand interacted strongly with LFA-1 (Kd = 15 nM). A presumed PfGBP-130 ectodomain interacts with monocytes and NK cells but not cells that lack LFA-1. PfGBP-130 antibodies also interfered with NK cell-mediated infected cell killing; the effect, although statistically significant, is modest. The authors propose that NK cells recognize infected cells via LFA-1 interaction with PfGBP-130 exposed on the host cell and that this interaction is critical to initiation of NK cell activation and killing of infected cells.

Comments on revised version:

The authors submit a minimally revised manuscript that does not address any of my comments, as itemized here:

(1) This reviewer suggested immunoblotting with hypotonic lysis and alkaline extraction as a simple test of whether PfGBP-130 is a membrane protein as the authors propose despite PEXEL cleavage that removes a signal peptide they originally proposed to be a TM domain. Instead of performing this simple immunoblot, the authors state that it is unnecessary because their LC-MS/MS of membrane-associated proteins recovered PfGBP-130, it must be a membrane protein. Unfortunately, this is insufficient because the high sensitivity of LC-MS/MS leads to detection of many soluble proteins. (For example, it is almost certain that their LC-MS/MS recovered hemoglobin, which is soluble and not a surface-exposed protein on infected cells.)

(2) I also suggested a simple immunoblot using a few different immature-stage cultures to detect the full-length and pre-proteins of PfGBP-130 because their immunoblot detected only a 95 kDa band whereas the PEXEL-processed protein is expected to migrate at 85 kDa. The authors state this is unnecessary because their LC-MS/MS of LFA-1 pulldowns enriched for PfGBP-130 and that a single band was detected in immunoblots. This is insufficient because pulldowns often enrich for more than one protein (e.g. some proteins adsorb onto the immunoprecipitation beads or precipitate with beads in certain buffers); immunoblotting often fails to detect some proteins depending on stringency of blocking and wash buffers. They state that the processed form at 85 kDa "may not be well resolved under our current conditions" as a reason not to perform the simple experiment. This reviewer's original statement that P. falciparum antigens frequently cross-react with nominally specific antibodies (with two examples provided in my original review) remains an important concern that would undermine the authors' main conclusion.

(3) As PfGBP-130 is not essential, a knockout was suggested to more directly test their model given the above concerns. The authors state this cannot be done and that their "multiple orthogonal approaches" suggest it is unnecessary. This reviewer considers this an essential experiment to support a provocative, fundamentally new finding, such as the identification of the NK cell activation ligand.

(4) This reviewer suggested that the authors add some speculation about why PfGBP-130 is retained in parasites if triggers NK cell-mediated killing and is nonessential. Rather than adding relevant hypotheses to the Discussion, the authors appear to dismiss this suggestion by stating that PfEMP1, STEVOR, and RIFIN are retained despite being nonessential. The problem with this response is that each of these other antigens has a clearly defined role on the surface of infected erythrocytes that benefits the parasite. It is not clear that the authors have considered possible advantages the parasite may gain from exposing PfGBP-130 on the red cell surface.

Reviewer #3 (Public review):

Summary:

Malhotra and colleagues present evidence that the integrin LFA-1 on NK cells is a ligand for the Plasmodium falciparum protein GBP130 on the infected erythrocyte surface and that this interaction plays a role in the clearance of infected erythrocytes by NK cells.

The authors first select a subdomain contained within the CD11a subunit of LFA-1 as a probe to discover possible binding proteins on the infected erythrocyte surface. Parasite-infected erythrocytes stained positively with this probe; the level of staining increased as the parasites progressed through the life cycle. Using the LFA-1-based probe in cross-linking pull-down experiments, GBP130 was identified by mass spectrometry as a co-purifying parasite protein. The N-terminal portion of GBP130 was recombinantly expressed and shown to interact with LFA-1 alpha-I by biolayer interferometry experiments. The full-length extracellular domain of GBP130 was then recombinantly expressed and used to stain primary human NK cells and THP-1 cells. Knocking down LFA-1 by siRNA reduced staining by GBP130. To assess the contribution of GBP130 to the activation of NK cells, CHO cells exogenously expressing GBP130 were incubated with primary NK cells. Transfecting CHO cells with GBP130 led to increased activation of co-incubated NK cells compared to mock-transfected and compared to GBP130 transfected cells, with the inclusion of anti-CD11a to block NK cell adhesion. Finally, CHO cells expressing GBP130 led to increased activation of NK cells compared to mock-transfected CHO cells.

Overall, although the authors present data from NK cell killing assays that include appropriate controls, the data suggesting a direct interaction between PfGBP-130 and LFA-1 does not include the same necessary controls, for example, the use of blocking antibodies. Most critically, the biolayer interferometry experiments use a recombinant fragment of PfGBP-130, which does not include the residues predicted to be important for mediating specific interaction with LFA1. The biolayer interferometry data instead suggest non-specific interactions between PfGBP-130 and LFA1, as binding does not reach saturation.

Comments on revised version:

The authors have addressed all minor concerns, however the major point regarding the biophysical data supporting direct interaction between PfGB130 and LFA-1, in my opinion, has not been satisfactorily addressed. Biophysical data supporting the interaction was generated using a fragment of PfGB130, which does not include residues that the authors predict by structural modelling to be important for the interaction. The authors argue that PfGB130 is a repeat containing protein and may have multiple binding sites for LFA-1. If this is the best mechanistic hypothesis given the current data, the authors need to explain this in the results section.

Overall though, I agree with Reviewer#1 that the structural modelling results are not convincing and given that the modelling data do not straightforwardly agree with the experiment, the clarity of the manuscript would benefit from their omission.

Reviewer #1 (Public review):

In this study, the authors investigate responses to methionine in the olfactory system of the Xenopus tadpole. They show that the LFP response is local to the glomerular layer, arises ipsilaterally, and is blocked by pharmacological blockade of AMPA and NMDA receptors, with little modulation during blockade of GABA-A receptors. They then show that this response is translently enlarged following transection of the contralateral olfactory nerve, but not the optic lobe nerve. Measurement of ROS- a marker of inflammation- was not affected by contralateral nerve transection, and LFP expansion was not affected by pharmacological blockade of ROS production. Imaging biased towards presynaptic terminals suggests that the enlargement of the LFP has a presynaptic component. A D2 antagonist increases the LFP size and variability in intact tadpoles, while a GABA-B antagonist does not. Finally, the authors provide anatomical and physiological evidence that the contralateral dopamine signal may arise from the lateral pallium. Overall, I found the array of techniques and approaches applied in this study to be creatively and effectively employed.

Reviewer #1 (Public review):

Summary:

The authors convincingly demonstrate that when PKMzeta is genetically deleted from the hippocampus, the related atypical PKC, PKClambda is upregulated and compensates both neurophysiologically and behaviorally for the missing PKMzeta. Specifically, the upregulatiion of PKClambda supports late-phase hippocampal long-term potentiation (L-LTP) and long-term spatial memory in the PKMzeta knockout mice.

Strengths:

The study uses up-to-date transgenic techniques to alter the expression of the two atypical PKCs. The synaptic and behavioral experiments are well-controlled and appear to have been carefully executed.

Weaknesses:

None

Reviewer #2 (Public review):

Summary:

The authors significantly advance understanding of the role of unconventional PKC's, PKCM𝛇 and PKC𝜄/𝝀 in maintenance of late-phase LTP. Their results help to clarify the interplay between "structural" and "biochemical/enzymatic" mechanisms of LTP and learning in the hippocampus.

Strengths:

A strength is the use of state-of-the-art conditional knock-outs of PKCM𝛇 and PKC𝜄/𝝀 to confirm that PKC𝜄/𝝀 compensates for KO of PKCM𝛇 in the hippocampus to maintain long-term potentiation even when PKCM𝛇 is conditionally knocked out in the adult. The authors use both electrophysiological and behavioral methods to assess the effects of genetic manipulations on late-phase LTP and long-term memory. The authors present an informative discussion of the possible molecular mechanisms that may enable compensation by PKC𝜄/𝝀 for KO of PKCM𝛇 in the hippocampus. They correctly emphasize that the notions of "structural" and "enzymatic" mechanisms for maintenance of LTP are not mutually exclusive. With this publication, the experimental case for a role of PKCM𝛇 in maintenance of late-phase LTP is now quite strong.

Weaknesses:

There are no significant weaknesses.

Reviewer #1 (Public review):

The manuscript by Tang et al. characterizes the expression dynamics and functional roles of aldehyde dehydrogenase 1 activity in uterine physiology. Using a combination of in vivo lineage tracing and cell ablation coupled with organoid culture, the authors propose that Aldh1a1 lineage-marked cells contribute to uterine gland development and epithelial regeneration. The descriptive data will be of interest to reproductive biologists and clinicians and will build on established hypotheses in the field. The manuscript is well written and scientifically sound; however, several experimental limitations and interpretation caveats should be addressed.

The methods surrounding the passage number and duration of culture following sorting prior to transcriptomic profiling should be clarified in the figure legends. Related to this, the representative images in Figures 1D and 1E do not appear consistent with the quantification presented in Figures 1F-H and should be reconciled.

The conclusion that ALDH1A1+ cells are enriched in populations with stem cell characteristics relies primarily on transcriptomic analysis. Protein-level co-localization should be performed to strengthen this claim.

The overlap of 19 genes between the data set here and AXIN2 HI data is presented as evidence of shared stemness identity, but no statistical assessment of this overlap is provided. A hypergeometric test should be performed to determine whether this overlap is greater than expected by chance.

The impact of tamoxifen injection on Aldh1a1 expression should be characterized in the neonatal uterus, as tamoxifen itself has known estrogenic activity that could confound interpretation of the lineage tracing results at early postnatal timepoints. Related to this, while low-dose tamoxifen is shown to label individual cells within 24 hours of injection, the translation dynamics of the label following Cre-mediated recombination can require up to 72 hours. The presence of only a few labeled clones at PND8 but multiple separate clones per cross-section at later timepoints warrants discussion and may reflect labeling kinetics rather than clonal expansion.

It would strengthen the in vivo ablation data to validate the degree of cell death following diphtheria toxin treatment directly. It is possible that a general decrease in cell number rather than specific loss of a stem cell population is responsible for the observed reduction in gland number and FOXA2 expression (Tongtong et al 2017).

The lineage tracing data in the postpartum endometrium demonstrate that Aldh1a1-marked cells are present during regeneration, but it remains unclear whether these cells are preferentially activated or expanded in response to tissue injury. Coupling these studies with diphtheria toxin-mediated ablation during active regeneration would more directly test the proposed regenerative role of this population.

The contribution of stromal Aldh1a1 lineage-positive cells is underexplored in the discussion, given the lineage tracing data showing stromal labeling across multiple timepoints and its potential relevance to mesenchymal-to-epithelial transition.

Finally, the word 'control' may overstate the functional evidence presented. 'Contribute' may be more accurate given the partial and context-dependent nature of the phenotypes observed.

Reviewer #2 (Public review):

Tang et al. investigated the contribution of Aldh1a1+ cells, as putative stem/progenitor cells, to endometrial development, maintenance during the estrous cycle, and postpartum repair in mouse models. They employed in vitro organoid formation and in vivo lineage tracing models coupled with RNA-seq to test the stem-ness of Aldh1a1+ cells. They found that mouse endometrial cells with high ALDH activity (using the ALDEFLUOR assay) formed more and larger organoids and were enriched for stem/progenitor cell gene signatures. Similar results were shown using endometrial cells from a human patient sample. Epithelial ALDH1A1 expression was shown to be hormonally regulated, becoming more restricted to the glands, a putative epithelial stem cell niche, under estrogen stimulation. Using lineage-tracing initiated postnatally/prepubertally, Aldh1a1+ epithelial cells were shown to expand, contributing to both the luminal and glandular epithelium into adulthood, whereas adult initiation of labeling showed expansion of stromal Aldh1a1+ cells but not epithelial. Postnatal ablation of single-labeled Aldh1a1+ epithelial cells resulted in impaired gland development. Lastly, Aldh1a1-lineage traced cells (adult labeled) were present during postpartum endometrial repair as were epithelial/mesenchymal transitional cells.

This study addresses an important area of research in the field of endometrial stem/progenitor cell biology. The authors are commended for their use of multiple complementary methods, including lineage tracing, DTR-mediated cell ablation, organoid assays, and RNA-seq in mouse and human models to assess the stem-like nature of Aldh1a1+ cells. The data support the stem/progenitor phenotype of Aldh1a1+ epithelial cells during endometrial development; however, there are noted discrepancies between organoid formation assays and lineage tracing experiments regarding the stemness of Aldh1a1+ epithelial cells in adults. Specifically, organoids were generated from adult cells and demonstrated in vitro stem cell activity; however, in vivo lineage-tracing of adult cells either during the estrous cycle or postpartum repair does not show expansion of Aldh1a1+ cells, suggesting they do not have stem/progenitor activity. Additionally, the stem-ness of epithelial vs stromal Aldh1a1+ cells is confounded in the study because epithelial cells were not purified for organoid experiments, epithelial cells were not exclusively lineage-traced as stromal cells were also labeled, and mesenchymal-epithelial transition was suggested to occur during postpartum repair. The following specific comments are presented to detail these concerns:

(1) The statement in the brief summary, "...critical for lifelong endometrial regeneration," is not supported by the data provided.

(2) AlDH1A1 is not restricted to the endometrial epithelium, and epithelial cells were not purified by flow cytometry for experiments in Figure 1. Figure 2 clearly shows the presence of mesenchymal cells, even using the described method for enriching for epithelial cells. Therefore, contaminating mesenchymal cells with high ALDH activity may confound the experimental results in Figure 1, either through promoting epithelial cell growth or through MET. The authors should provide clear evidence of epithelial purity in organoid experiments or that mesenchymal cells are not contained in the ALDHhi population. These comments also apply to the human organoid experiments in Figure 7.

(3) Lines 186-187: Susd2 was increased in EpSC clusters, yet this is a mesenchymal stem/progenitor marker in humans. The authors should discuss the implications of this.

(4) In Figure 5, RFP+ epithelial cells should be quantified as in previous figures to substantiate the statement in lines 279-280, "At PPD5, the proportion of RFP+ epithelial cells had expanded relative to PPD1 and PPD3 (Figure 5E-E')." Especially because in the low mag images (C-E), RFP+ epithelial cells appear to be most abundant at PPD1 and decrease at PPD3 and PPD5, suggesting that they may not be involved in endometrial regeneration/repair (contradicting the interpretation in line 285). Further, if there is in fact a decrease over postpartum repair, then regeneration should be removed from the title of the manuscript. RFP+ stromal cells should also be quantified.

(5) For Figure 7F, it should be clearly stated in the main text that the results are from one patient sample and the data presented are experimental replicates, so as not to be confused with biological replicates (the same for Supplementary Figure S4). Were B and G in Figure 7 also from one patient?

(6) Lines 425-427: "Ovariectomized mice treated with 90-day E2 pellets, on the other hand, showed a complete restriction of ALDH1A1 to the glandular crypts." In Figure 2 S' ALDH1A1+ cells are visible in the LE (the staining is lighter than in the GE but looks real), contradicting this statement.

(7) Lines 466-467: "In cycling mice, we found sporadic cells that expressed both stromal and epithelial markers in the ALDHA1+ cells." These data are not presented.

(8) These data support the role of Aldh1a1+ cells in endometrial epithelial development, but conclusions about their role in repair/regeneration should be tempered as the data are much weaker here.

Reviewer #3 (Public review):

Summary:

Tan et al demonstrated the importance of ALDH-high cells in the epithelial development in the mouse endometrium, and these cells displayed properties of stem cells.

Strengths:

The findings are solid, supported and validated through a combination of technical methods. I appreciated this combined use of mouse and human endometrial cells to strengthen the findings. Genomic results from a single-cell sequencing dataset were informative as they depicted the different stages of the estrus cycle during the regeneration process. Verification with immunostainings with various markers made it convincing for readers to visualize the cell's location, progression, and status at different timepoints. Utilizing human endometrial cells further demonstrated that the phenomenon observed in mice can be translated to humans.

This work will greatly advance the understanding of endometrial regeneration for reproductive biologists.

Weaknesses:

No major weaknesses were identified by this reviewer.

Reviewer #1 (Public review):

Summary:

The authors investigate how methicillin-resistant (MRSA) and sensitive (MSSA) Staphylococcus aureus adapt to a new host (C. elegans) in the presence or absence of a low dose of the antibiotic oxacillin. Using an "Evolve and Resequence" design with 48 independently evolving populations, they track changes in virulence, antibiotic resistance, and other fitness-related traits over 12 passages. Their key finding is that selection from both the host and the antibiotic together, rather than either pressure alone, synergistically results in the evolution of the most virulent pathogens. Genomically, they find that this adaptation repeatedly involves parallel mutations in a small number of key regulatory genes, most notably codY, agr, and saeRS.

Strengths:

The main advantage of the research lies in its strong and thoroughly replicated experimental framework, enabling significant conclusions to be drawn based on the concept of parallel evolution. The study successfully integrates various phenotypic assays (virulence, growth, hemolysis, biofilm formation) with whole-genome sequencing, offering an extensive perspective on the adaptive landscape. The identification of certain regulatory genes as common targets of selection across distinct lineages is an important result that indicates a level of predictability in how pathogens adapt. Furthermore, the detailed mapping of specific parallel mutations provides a highly useful genomic resource for the microbiology community.

Revisions and Re-Appraisal:

In the initial version of the manuscript, a primary limitation was the use of causal language to link specific mutations to phenotypes, despite the evidence from the evolution experiment being correlational. In this revised version, the authors have excellently addressed this limitation. They have meticulously revised the text to accurately reflect these relationships as strong, statistically significant genetic associations rather than confirmed facts. Furthermore, they explicitly acknowledge that future ancestral reconstruction experiments will be required to confirm direct causality. The authors have also appropriately clarified the visual interpretations of their data (such as the PCA clustering) and refined their discussion of mutation rates. With these revisions, the claims made are fully supported by the data presented.

Impact and Context:

The authors successfully achieve their aims, demonstrating that the combined effects of host and antibiotic pressures collaboratively propel the evolution of heightened virulence. While the nematode model does not perfectly mimic human or mammalian infection, the evolutionary principles uncovered here are highly relevant to both evolutionary biology and infectious disease management. The evidence presented is compelling, and the strong correlational hypotheses generated by this study offer a robust and significant basis for upcoming mechanistic research into pathogen adaptation.

Comments on revisions:

I commend the authors for their thorough, thoughtful, and highly constructive revision. You have successfully addressed all of my major and minor comments. The addition of Table S2 and the careful revisions to the causal language have significantly strengthened the manuscript and clarified the data interpretation. I have no further recommendations. Great work!

Reviewer #2 (Public review):

Summary:

The manuscript describes the results of an evolution experiment where Staphylococcus aureus was experimentally evolved via sequential exposure to an antibiotic followed by passaging through C. elegans hosts. Because infecting C. elegans via ingestion results in lysis of gut cells and an immune response upon infection, the S. aureus were exposed separately across generations to antibiotic stress and host immune stress. Interestingly, the dual selection pressure of antibiotic exposure and adaptation to a nematode host resulted in increased virulence of S. aureus towards C. elegans.

Strengths:

The data presented provide strong evidence that in S. aureus traits involved in adaptation to a novel host and those involved in antibiotic resistance evolution are not traded-off. On the contrary, they seem to be correlated, with strains adapted to antibiotics having higher virulence towards the novel host. As increased virulence is also associated with higher rates of haemolysis, these virulence increases are likely to reflect virulence levels in vertebrate hosts.

Weaknesses:

Right now, the results are presented in the context of human infections being treated with antibiotics, which, in my opinion, is inappropriate. This is because

(1) exposure to the host and antibiotics was sequential, not simultaneous, and thus does not reflect the treatment of infection, and

(2) because the site of infection is different in C. elegans and human hosts.

Nevertheless, the results are of interest; I just think the interpretation and framing should be adjusted.

Comments on revisions:

Following the revision, I now think the weakness I initially described has been addressed well by the authors.

Reviewer #3 (Public review):

Summary:

Su et al. sought to understand how the opportunistic pathogen Staphylococcus aureus responds to multiple selection pressures during infection. Specifically, the authors were interested in how the host environment and antibiotic exposure impact the evolution of both virulence and antibiotic resistance in S. aureus. To accomplish this, the authors performed an evolution experiment where S. aureus were fed to Caenorhabditis elegans as a model system to study the host environment and then either subjected to the antibiotic oxacillin or not. Additionally, the authors investigated the difference in evolution between an antibiotic-resistant stain MRSA and an isogenic susceptible strain MSSA. They found that MRSA strains evolved in both antibiotic and host conditions became more virulent and that strains evolved outside these conditions lost virulence. Looking at the strains evolved in just antibiotic conditions, the authors found that S. aureus maintained its ability to lyse blood cells. Mutations in codY, gdpP and pbpA were found to be associated with increased virulence. Additionally, these mutations identified in these experiments were found in S. aureus strains isolated from human infections.

Strengths:

The data are well-presented, thorough, and are an important addition to the understanding of how certain pathogens might adapt to different selective pressures in complex environments.

Comments on revisions:

For the most part, my comments have been addressed. It seems that the authors have not addressed my comments about quantifying population sizes in order to understand mutation supply, particularly in light of which experimental phase exhibits the strongest selection and possible increases in mutation rates. While I think this information would be very useful if they had collected it during the experiment, I don't think it is important enough to require additional experiments. I am therefore satisfied with the current state of the manuscript.

Reviewer #1 (Public review):

Summary:

This paper examines whether action potentials (APs) reliably propagate to the distal axon in neocortical parvalbumin-expressing interneurons (PV-Ins) during prolonged high-frequency activity, as occurring during epileptiform activity. The authors use dual soma and axon-attached patch-clamp recordings from mouse and human PV-INs and show that axon AP amplitude declines when the firing frequency exceeds ~200 Hz and fails during seizure-like bursts. Finally, they show that elevation of external K+ to 10 mM also reduces AP amplitude. Taken together, these data strongly suggest that the reduction in transmitter release observed during intense PV-INs activity or during seizure-like events is mainly mediated by the reduction in the presynaptic AP amplitude in PV-INs.

Strengths:

This paper is very interesting, well-written and technically impressive. It provides new and important results. The paper will have a great impact in the field of both axon physiology and epilepsy.

Weaknesses:

I did not find any significant weakness in the methods, data analysis and results.

Reviewer #2 (Public review):

Summary:

The authors demonstrate a frequency-dependent progressive failure of action potential propagation through the axonal arbors in fast-spiking interneurons

Strengths:

The experimental protocols are technically challenging, but the data is of very high quality, and the presentation and writing are very clear.

I congratulate the authors on submitting a really excellent study demonstrating an activity-dependent alteration in the efficacy of axonal propagation of action potentials in fast-spiking interneurons. It is a well-designed project involving technically challenging experiments, and yet the data is of very high quality, the results are compelling, and the presentation is clear.

Weaknesses:

I have some minor suggestions and comments, including those below, but I hope and expect that these could be performed quickly and without difficulty.

Two of the most interesting figures were consigned to the supplementary information, and I would recommend that they are "upgraded" to be in the main document. The two figures are Figure 1 - Figure Supplement 2, showing the inverse correlation of the AP size with recording distance and branch; and Figure 6 - Figure Supplement 1, showing the postsynaptic effect. My rationale for saying this is that I feel that both add useful biological information to the narrative.

I was glad to see that "realistic" firing patterns were used, because I recall an old modelling paper from Mainen and Sejnowski (https://pubmed.ncbi.nlm.nih.gov/7770778/) that is highly relevant to this paper and should be referenced. However, I would like to suggest one further bit of analysis of the data presented in Figure 4, because I think it will support the main story. In Figure 4, the ostensible conclusion is that there is relative preservation of spike amplitude for this natural firing pattern, but that is almost certainly because the average firing rate is substantially below the level where spike amplitude suppression was seen in Figures 2 & 3. Instead, I recommend analysing for each consecutive spike pair, the ratio of the heights of the two spikes with respect to the interspike interval. Viz<br /> t2 - t1 versus spike 2 amplitude / spike 1 amplitude

The data may be a little noisy, but given the very large number of spike pairs, I would expect to see the suppression effect to be fully evident, and that can feed directly into the model.<br /> I think the author's intuition that dissipation of ionic gradients is a key factor is correct, so I was pleased that Na+ was not ignored in the discussion (the results section only talked about K+).

Perhaps the fact that Na gradients may also be depleted could be mentioned in the results section, too. In the discussion, perhaps the authors could mention two other details: that this "fatigue" may reflect ATP depletion, and progressive failure of the Na-K-ATPase in the axons. That could be examined in a follow-up study (I certainly am not suggesting a raft of experiments for this study), but it could be mentioned in the discussion. And second, that the ionic depletion may be greater within the confines of the cell-attached pipette tip, which is why the branching pattern/distance data (F1FS2), the Ca imaging data and the post-synaptic effects (F6FS1) are such important additional supporting data, because together they indicate that the effect is along the whole axon.

Regarding the rise in [K+]o, it would be worth mentioning the fact that this will be greatly exacerbated by the postsynaptic effects of high-frequency PV activity, because the consequent Cl loading of the postsynaptic cell is subsequently cleared by coupling to K+ extrusion. A good reference for this is http://www.ncbi.nlm.nih.gov/pubmed/20211979; a recent review (https://pubmed.ncbi.nlm.nih.gov/39637123/), which argues that this may even be the dominant source of raised [K+]o in the immediate preictal period, larger even than that exiting cells through the Hodgkin-Huxley mechanism.

The referencing needs some attention. In some instances, the citations either do not really illustrate preceding statements or are simply the wrong citation.

Reviewer #3 (Public review):

Summary:

This is an interesting paper which asks a compelling and translationally relevant question: since the firing rate of GABAergic PV+ interneurons (which powerfully control pyramidal cell excitability) increases prior to and during seizures, why doesn't this increase in inhibition do more to prevent seizure propagation? The authors hypothesize that increased PV+ spiking might lead to spike propagation failures in the axon.

To test this hypothesis, the authors conduct paired electrophysiological recordings from PV+ neurons in acute barrel cortex slices of mice and from a handful of human neurosurgical samples. They use patch clamp recordings to measure the membrane potential of PV+ neurons at the soma, while simultaneously measuring spike propagation with a recording electrode in the axon of the same neuron.

After a variety of elegant experiments and modeling, the authors conclude that extracellular K+ accumulation around the axon during high-frequency firing might be causing propagation failures.

Strengths:

Overall, the paper is nicely written, the experiments are technically challenging, and the figures are, for the most par,t well laid out. The topic will be of broad interest for the neuroscience field, given the relevance of PV+ interneurons to cortical circuit function, plasticity across development, and disease.

Weaknesses:

In addition to the strengths here, I feel the need to highlight a few weaknesses which, if rectified, could improve the work.

(1) The key hypothesis in this paper is that extended periods of somatic spiking lead to progressive decreases in the axonal AP amplitude, which eventually lead to failures, potentially (but not necessarily) at branchpoints. Two comments here.

It would be helpful for the authors to show us examples of the axonal spike waveforms at a faster time base (along with the somatic recording) so that we can really understand what's happening to the spike in the axon.

Their data are also compatible with failures of spike initiation at the AIS. Could the authors show us the first derivative of somatic voltage for successes and failures, and maybe show us some phase plots of Vm vs dV/dt for the failures, successes, and attenuated spikes? Effectively, what I'm asking is whether the changes they see in the distal axon are downstream of the initiation zone. It's very possible that extended spiking is simply depolarizing the AIS and inactivating Na+ channels there. In which case, the authors should be able to pull this out in a phase plot.

(2) There's no baseline period for their calcium fluorescence signals, which is necessary to compare their "signal" magnitude to frame-by-frame variance of dG/R. Could the authors correct this issue in Figure 6B?

(3) Some of their stats are a bit unorthodox. Why are they doing two separate Wilcoxon tests in 6D and 6E? Why not throw all that into a one-way ANOVA model followed by appropriate post hoc tests?

(4) Why don't the authors observe washout of their effect after high K+ application? This concerns me that their high K+ application is having secondary and long-lasting effects on PV excitability, which mimic (but are not necessarily identical) to their hypothesized mechanism of axonal failures.

Reviewer #1 (Public review):

This study investigates how astrocyte metabolic state influences astrocyte-synapse interactions and the organization of the dopaminergic circuit in the Drosophila CNS. Using a creative split-GFP-based contact reporter ("PEAPOD"), combined with genetic perturbations of glycolytic enzymes, synaptic labeling, EM, transsynaptic tracing, single-cell transcriptomics, and behavioral assays, the authors propose that disruption of astrocyte glycolysis enhances astrocyte-dopamine neuron contacts, promotes synaptogenesis, and biases dopaminergic-motor circuit connectivity through a mechanism involving altered Neuroligin 2 trafficking.

The work is conceptually ambitious and technically broad. The development and application of a contact reporter for astrocyte-neuronal interfaces is potentially valuable to the field, and the convergence of multiple glycolytic perturbations on similar phenotypes is a notable strength. However, several central conclusions currently extend beyond the direct evidence presented. Clarification and calibration of these claims would substantially strengthen the manuscript.

Major Points:

(1) Astrocyte glycolytic impairment is inferred rather than directly demonstrated

The central premise of the manuscript is that reduced astrocyte glycolysis drives the observed phenotypes. While multiple glycolytic enzymes (e.g., pfk, eno, pyk) are genetically perturbed and produce similar increases in PEAPOD signal, the manuscript does not directly demonstrate altered glycolytic flux or metabolic state in astrocytes under these conditions. Reduced enzyme levels or genetic mutation do not necessarily establish functional metabolic deficiency, particularly given potential compensatory mechanisms.

Because glycolytic impairment is foundational to the proposed mechanism, the conclusions should either be supported by direct metabolic readouts in astrocytes or framed more cautiously as perturbations of glycolytic enzymes rather than confirmed metabolic deficiency.

(2) Interpretation of the PEAPOD signal requires clearer calibration

The PEAPOD system is an innovative tool to detect membrane proximity between astrocytes and dopamine neurons. However, the manuscript frequently interprets increased PEAPOD intensity and volume as increased "ensheathment" or increased synaptic contact. A split-GFP-based reporter measures membrane apposition within a defined spatial range but does not directly quantify structural ensheathment, synapse number, or functional synaptic engagement.

Although the authors show an association of the PEAPOD signal with presynaptic markers in some regions, the distinction between increased membrane contact, altered membrane organization, and true changes in perisynaptic coverage should be more explicitly discussed. Several conclusions would benefit from clearer wording that distinguishes contact proximity from ultrastructural or functional synapse remodeling.

(3) Evidence for biased dopaminergic-motor circuit wiring is indirect

The manuscript proposes that disruption of astrocyte glycolysis biases dopaminergic-motor connectivity. This conclusion relies heavily on trans-Tango labeling intensity and downstream cell-type composition analysis via FACS and single-cell RNA sequencing.

Transsynaptic labeling approaches can be influenced by expression levels, reporter trafficking, labeling efficiency, and differential recovery during dissociation and FACS. Changes in labeled cell abundance or reporter intensity do not necessarily equate to altered synaptic wiring. Given that this conclusion represents a major conceptual advance of the study, the manuscript should either provide additional orthogonal support or temper the claim to reflect that altered labeling efficiency or synaptic engagement, rather than definitive rewiring, has been demonstrated.

(4) Mechanistic claims regarding Neuroligin 2 trafficking are suggestive but not definitive

The authors propose that astrocyte glycolytic disruption alters Neuroligin 2 (Nlg2) trafficking, leading to ER retention and downstream synaptogenic effects. The observation of Nlg2-positive intracellular bodies colocalizing with ER markers is intriguing. However, quantitative analysis, additional compartment markers, and/or biochemical support would be necessary to firmly establish altered ER exit or glycosylation status.

At present, the mechanistic model is plausible but should be presented more explicitly as a working model supported by suggestive evidence rather than a fully established trafficking defect.

(5) Behavioral phenotypes are not yet causally linked to dopaminergic circuit changes

The locomotor phenotypes observed upon astrocyte glycolytic perturbation are clear. However, the manuscript attributes these changes to altered dopaminergic-motor connectivity. A direct causal linkage between astrocyte metabolic state, dopaminergic circuit remodeling, and behavior is not conclusively demonstrated. The discussion should either clarify the inferential nature of this link or provide additional evidence supporting dopamine-specific dependence.

Minor Points:

(1) Statistical analyses across multi-group comparisons should be more clearly justified, particularly where multiple pairwise tests are performed. A clarification of the multiple-comparison correction and the exact comparison strategy would improve rigor.

(2) The temporal interpretation of activity-dependent remodeling experiments would benefit from a clearer explanation of what timescale is being tested.

(3) Developmental compensation versus the acute effects of glycolytic perturbation are not fully distinguished and should be discussed.

(4) The orthology and functional equivalence of Drosophila Nlg2 should be described with greater precision to avoid potential confusion.

Reviewer #2 (Public review):

Summary:

This manuscript presents a significant advance in our understanding of how metabolic states in astrocytes directly influence the structural assembly and functional output of neural circuits. By focusing on the Drosophila larval dopaminergic system, the authors uncover an interesting mechanism: astrocyte glycolysis acts as a negative regulator of PEAPODs, ultimately altering locomotor behavior. Metabolic fluctuations (e.g., due to diet, development, or disease) could fundamentally reshape neural connectivity, with broad implications for neurodevelopmental and metabolic disorders.

Strengths:

The manuscript offers a compelling narrative linking astrocyte metabolism to DA-MN circuit wiring and behavior. For the field, this study serves as an important prompt to investigate how metabolic states might dynamically tune neural connectivity during development and in disease.

Weaknesses:

The definitive acceptance of the proposed linear mechanism depends on future validation through genetic interaction tests and rescue experiments.

Reviewer #3 (Public review):

Summary:

The authors are trying to demonstrate how astrocytes influence the connections within neural circuits that control behavior.

Strengths:

The data presented in the manuscript are thorough and well-executed, using advanced Drosophila approaches (Ca2+ imaging, GRASP, clonal analysis, trans-Tango) in new ways (PEAPODS) and with new tools (pyk mutants, anti-pyk Ab, LexAop2-pykRNAi). Use of two RNAi lines for each of three glycolytic enzymes is strong evidence that perturbation of glycolysis is responsible, though it does not rule out that inappropriate build-up of intermediates, or shunting to alternative pathways, may play a role here. Subsequent focus on Pyk alone is understandable.

Weaknesses:

As strong as the data is, it does not always support some of the stated claims, and this should be addressed in any revision. In addition, there seems to be an oversimplification of the possible effects of Pyk RNAi, and some missing pieces that could fill in gaps and align the proposed mechanism with observed phenotypes.

Where the data does not support stated claims:

(1) The authors claim larvae executed more reorientation actions during locomotion "as a result" of attenuated astrocyte-to-DAergic neuron signaling through neuroligin 2 (astrocyte) and neurexin 1 (DA Neuron). They correlated these, but did not make a direct connection.

(2) There is a claim that "at the circuit level, behavioral alterations were found to arise from increased DAergic neuronal synaptogenesis and DAergic-motor connection" (sic). However, the work does not build a causal relationship between behavior and synaptogenesis or connectivity. At present, the manuscript does not directly address whether increased DA-motor neuron synapses are sufficient to explain the increased orientation reactions observed.

(3) It is asserted that (line 182, and elsewhere) "astrocyte glycolysis deficiency increased PEAPODs and DAergic neuron synaptogenesis". While astrocyte Pyk KD increased PEAPODS (Figure 2), and it also increased endogenous Brp-GFP in DA neurons (via STaR, Figure 3F), the added Brp-GFP was not localized to synapses under these conditions (pyk KD), to unequivocally demonstrate that the increased PEAPODS are at the sites of DAergic synapses. Also seen in 6I-J.

(4) It may be premature to refer to this strictly as synaptogenesis, as alternative explanations (e.g., stabilization or impaired pruning) could also account for the observations.

(5) The use of trans-Tango is an elegant way to support the idea that extra DAergic synapses are formed onto motor neurons, with potential impact on motor circuits. But again, the claim (line 215, and elsewhere) that this "Biased DAergic-motor wiring" is what "alters motor output", would benefit from additional evidence.

(6) Oversimplification of the possible effects of Pyk RNAi: Because Pyk knockdown is likely to alter glycolytic flux rather than abolish glycolysis entirely, it may be clearer to describe the manipulation as 'Pyk loss' rather than 'glycolysis-deficient' in most contexts.

(7) Filling gaps to align the proposed mechanism with observed phenotypes:

a) Figure 6K-M - the ER retention of Nlg2 should also be tested using Pyk-RNAi, in addition to the pyk mutants shown. This would confirm the astrocyte-specific nature of this effect and close the loop to align the phenotypes.

b) From the mechanism proposed (ER retention of Nlg, presumably leading to loss of Nlg function in astrocytes), one might expect that the effects of loss of Nlg2 from astrocytes could phenocopy the behavioral deficits seen in pyk KD (from astrocytes). Ackerman et al (2021) knocked down Nlg2 from astrocytes and examined motor behavior with FIMTrack. They saw increased accumulated distance but did not see the effects seen upon pyk KD in this manuscript (increased pausing, sweeping). The authors could perform this experiment themselves or alternatively should address this inconsistency in the discussion.

Reviewer #1 (Public review):

Summary:

In this manuscript, Kong et al. conduct a systematic analysis of the multi-cancer risk locus at 2q33. The authors start with a careful analysis of co-localization between the melanoma risk SNPs and several other cancers and conclude that a subset of credible causal SNPs is shared among different cancers, including breast cancer. Next, they define a starting list of 27 SNPs as potential credible causal SNPs and analysis of TADs (topologically associating domains) to zoom in on CASP8 and FLA CC1 as potential target genes. They then systematically rule out coding and splicing variants in the set and focus on a smaller set of three SNPs constituting a melanocyte enhancer element. Using a combination of mass spectrometry, reporter assays, and electrophoretic mobility shift assays, the authors define a role for transcription factors IRF2 and E4F1 in the regulatory network driving risk at the locus.

This work represents a high-quality tour de force, using multiple tools, to zoom in on a gene expression regulatory network associated with risk for multiple cancers. It provides a detailed framework for analyses of other multi-cancer risk loci. Limitations of the work, which is rather a current limitation of the field, is the lack of a model to study how the identified network of regulatory elements, transcription factors, and target genes mechanistically drive risk at the organismal level. Advances such as those described in this manuscript contribute significantly to our knowledge of how common risk variants drive risk.

Reviewer #2 (Public review):

Summary:

Kong et al. investigate a well-validated risk locus at chromosome band 2q33.1 adjacent to CASP8, a ubiquitously expressed and central initiator caspase in the extrinsic apoptotic pathway. Importantly, this region is a multi-cancer risk locus harboring multiple highly correlated risk alleles that are confounded by linkage. In addition to protein coding and splicing variants, further evaluation of eQTL and TWAS results for the locus suggests a cis-regulatory effect is present for CASP8 and nearby FLACC1. The authors prioritize variants using orthogonal statistical fine-mapping approaches and triage top candidates for functional assays. Luciferase reporter assays demonstrated convincing allele-specific regulatory activity of rs3769823 variant as well as suggestive evidence for rs3769821 and rs59308963. These three variants lie in close proximity within a melanocyte regulatory element marked by overlapping promoter and enhancer chromatin state signals. The authors employ a haplotype reporter assay, which shows that the combination of risk alleles in the forward direction has additive effects compared to the protective haplotype. These effects are also cell type specific among melanocytes, melanoma, and breast cancer cell states. Utilizing electron mobility shift assays, the authors convincingly show augmented nuclear protein binding of the rs3769823-A risk allele, and mass spectrometry of allele-specific rs3769823 binding proteins revealed specific activity of E4F1 and IRF2, whose motif score is strengthened by the risk allele. Correlation of these transcription factors' expression with CASP8 expression suggested repressive effects of E4F1 and activating effects of IRF2, which were confirmed in siRNA assays across multiple cell types. These data provide important evidence towards the molecular mechanisms governing disease susceptibility at the 2q33.1 risk locus and nominate s3769823 as a causal variant through cis-regulatory activity by E4F1 and IRF2.

Strengths:

Major strengths of the work include the authors' employment of orthogonal fine-mapping approaches and functional assays in multiple cell types. These help to fortify a novel molecular mechanism of rs3769823 and also work together to propose a complicated multi-variant and cell-type-specific effect at this locus, which is worth future investigation.

Weaknesses:

The rs3769823 variant is a protein-coding variant for CASP8. While the authors conclude that this is likely neutral to CASP8 function, their evidence is suggestive at best and does not close the door on a protein-coding function for this variant.

Similarly, another variant, rs10804111, is associated with alternative splicing of CASP8. The authors do well to include the potent rs10804111 sQTL effect on CASP8 and further confirm it by a minigene assay. However, its exclusion from the fine-mapping results may be due to a potent bias towards active chromatin marks. Therefore, rs10804111 still requires further investigation.<br /> Some attention is given to FLACC1, whose promoter may be in contact with multiple variants. However, little is known about FLACC1 function, and the authors don't provide meaningful supporting data to illustrate whether FLACC1 is relevant in the context of melanocyte, melanoma, or other cancer types that share this risk locus (breast, prostate). Showing the absolute expression levels in the eQTL analysis would be helpful towards this.

Phenotypic assays interrogating the rs3769823-E4F1-IRF2 relevance to melanocyte biology and melanoma pathogenesis are not included.

Finally, the segmented figure organization negatively impacts the readability of the paper.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the major comments raised in the previous round of review. Public Reviews below refer to the version submitted to Review Commons.]

Summary:

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

Major comments:

(1) Lack of direct in vitro binding measurements:

The authors do not provide measurements of sybody affinities, binding/ unbinding kinetics, stoichiometries with respect to Smc-ScpAB. Additionally, do the sybodies preferentially interact with Smc in ATP/ DNA-bound state? And do the sybodies affect the interaction of ScpAB with SMC?

It is understandable that such measurements for 14 sybodies is challenging, and not essential for this study. Nonetheless, it is informative to have biochemical characterization of sybody interaction with the Smc-ScpAB complex for at least 1-2 candidate sybodies described here.

(2) Many modes of sybody binding to Smc are plausible

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

(3) Sybody expression in vivo

Have the authors estimated sybody expression in vivo? Are they all expressed to similar levels?

(4) Sybodies should phenocopy ATP hydrolysis mutant of Smc

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

Significance:

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

Reviewer #2 (Public review):

Summary:

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

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

Significance:

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

Reviewer #3 (Public review):

Summary:

Gosselin et al. use the sybody technology to study effects of in vivo inhibition of the Bacillus subtilis SMC complex. Smc proteins are central DNA binding elements of several complexes that are vital for chromosome dynamics in almost all organisms. Sybodies are selected from three different libraries of the single domain antibodies, using the "transition state" mutant Smc. They identify 14 such mutant sybodies that are lethal when expressed in vivo, because they prevent proper function of Smc. The authors present evidence suggesting that all obtained sybodies bind to a coiled-coil region close to the Smc "neck", and thereby interfere with the Smc activity cycle, as evidenced by defective ATPase activity when Smc is bound to DNA.

The study is well done and presented and shows that the strategy is very potent in finding a means to quickly turn off a protein's function in vivo, much quicker than depleting the protein.

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

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

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

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

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

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

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

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

Significance:

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

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

Reviewer #1 (Public review):

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

Original 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):

Original 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):

Original 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:

In their paper, Shimizu and Baron describe the signaling potential of cancer gain-of-function Notch alleles using the Drosophila Notch transfected in S2 cells. These cells do not express Notch or the ligand Dl or Dx, which are all transfected. With this simple cellular system, the authors have previously shown that it is possible to measure Notch signaling levels by using a reporter for the 3 main types of signaling outputs, basal signaling, ligand-induced signaling and ligand-independent signaling regulated by deltex. The authors proceed to test 22 cancer mutations for the above-mentioned 3 outputs. The mutation is considered a cluster in the negative regulatory region (NRR) that is composed of 3 LNR repeats wrapping around the HD domain. This arrangement shields the S2 cleavage site that starts the activation reaction.

The main findings are:

(1) Figure 1: the cell system can recapture ectopic activation of 3 existing Drosophila alleles validated in vivo.

(2) Figure 2: Some of the HD mutants do show ectopic activation that is not induced by Dl or Dx, arguing that these mutations fully expose the S2 site. Some of the HD mutants do not show ectopic activation in this system, a fact that is suggested to be related to retention in the secretory pathway.

(3) Figure 3: Some of the LNR mutants do show ectopic activation that is induced by Dl or Dx, arguing that these might partially expose the S2 site.

(4) Figure 4-6: 3 sites of the LNR3 on the surface that are involved in receptor heterodimerization, if mutated to A, are found to cause ectopic activation that is induced by Dl or Dx. This is not due to changes in their dimerization ability, and these mutants are found to be expressed at a higher level than WT, possibly due to decreased levels of protein degradation.

Strengths and Weaknesses:

The paper is very clearly written, and the experiments are robust, complete, and controlled. It is somewhat limited in scope, considering that Figure 1 and 5 could be supplementary data (setup of the system and negative data). However, the comparative approach and the controlled and well-known system allow the extraction of meaningful information in a field that has struggled to find specific anticancer approaches. In this sense, the authors contribute limited but highly valuable information.

Comments on revised version:

I reviewed the changes and response to criticism, and it seems to me that all has been reasonably addressed.

Reviewer #3 (Public review):

Summary:

This manuscript by Shimizu et al., systematically analyzes cancer-associated mutations in the Negative Regulatory Region (NRR) of Drosophila Notch to reveal diverse regulatory mechanisms with implications for cancer modelling and therapy development. The study introduces cancer-associated mutations equivalent to human NOTCH1 mutations, covering a broad spectrum across the LNR and HD domains. By linking mutant-specific mechanistic diversity to differential signaling properties, the work directly informs targeted approaches for modulating Notch activity in cancer cells. These are an exciting set of observations from S2 cells, which should be taken up further for further assessment in any physiological implications.

Strengths:

This manuscript by Shimizu et al., systematically analyzes cancer-associated mutations in the Negative Regulatory Region (NRR) of Drosophila Notch to reveal diverse regulatory mechanisms with implications for cancer modelling and therapy development. The study introduces cancer-associated mutations equivalent to human NOTCH1 mutations, covering a broad spectrum across the LNR and HD domains. The authors use rigorous phenotypic assays to classify their functional outcomes. By leveraging the S2 cell-based assay platform, the work identifies mechanistic differences between mutations that disrupt the LNR-HD interface, core HD, and LNR surface domains, enhancing understanding of Notch regulation. The discovery that certain HD and LNR-HD interface mutations (e.g., R1626Q and E1705P) in Drosophila mirror the constitutive activation and synergy with PEST deletion seen in mammalian T-ALL is nice and provides a platform for future cancer modelling. Surface-exposed LNR-C mutations were shown to increase Notch protein stability and decrease turnover, suggesting a previously unappreciated regulatory layer distinct from canonical cleavage-exposure mechanisms. By linking mutant-specific mechanistic diversity to differential signaling properties, the work directly informs targeted approaches for modulating Notch activity in cancer cells.

Weaknesses:

This is an exciting set of observations, however the work is entirely cell line based, and is the primary weakness. I list my main specific concerns herewith:

(1) The analysis is confined to Drosophila S2 cells, which may not fully recapitulate tissue or organism-level regulatory complexity observed in vivo.

(2) And perhaps for this reason too, some Drosophila HD domain mutants accumulate in the secretory pathway and do not phenocopy human T-ALL mutations. Possibly due to limitations on physiological inputs that S2 cells cannot account for or species-specific differences such as the absence of S1 cleavage. Thus, the findings may not translate directly to understanding Notch 1 function in mammalian cancer models.

(3) Also, while the manuscript highlights mechanistic variety, the functional significance of these mutations for hematopoietic malignancies or developmental contexts in live animals remains untested. Thus even though the changes are evident in Notch signaling, any impact on blood cells or hematopoiesis leading to aberrant malignancies remains to be seen.

(4) Which hematopoietic cell type, progenitor or differentiating cells, would be most sensitive to this kind of altered Notch signaling also remains unclear.

Reviewer #1 (Public review):

Summary:

The authors have studied how a virus (EMCV) uses its RNA (Type 2 IRES) to hijack the host's protein-making machinery. They use cryo-EM to extract structural information about the recruitment of viral Type 2 IRES to ribosomal pre-IC. The authors propose a novel interaction mechanism in which the EMCV Type 2 IRES mimics 28S rRNA and interacts with ribosomal proteins and initiator tRNA (tRNAi).

Strengths:

(1) Getting structural insights about the Type 2 IRES-based initiation is novel.

(2) The study allows a good comparison of other IRES-based initiation systems.

(3) The manuscript is well-written and clearly explains the background, methods, and results.

Comments on revised version:

I have gone through the revised manuscript by Das and Hussain along with the rebuttal comments. While the poor resolution of the ribosomal complex limits detailed analysis of the molecular interactions, addition of the luciferase reporter assay in the supplementary has enriched the paper.

Reviewer #2 (Public review):

Summary:

The field of protein translation has long sought the structure of a Type 2 Internal Ribosome Entry Site (IRES). In this work, Das and Hussain pair cryo-EM with algorithmic RNA structure prediction to present a structure of the Type 2 IRES found in Encephalomyocarditis virus (EMCV). Using medium to low resolution cryo-EM maps, they resolve the overall shape of a critical domain of this Type 2 IRES. They use algorithmic RNA prediction to model this domain onto their maps and attempt to explain previous results using this model.

Strengths:

(1) This study reveals a previously unknown/unseen binding modality used by IRESes: a direct interaction of the IRES with the initiator tRNA.

(2) Use of an IRES-associated factor to assemble and pull down an IRES bound to the small subunit of the ribosome from cellular extracts is innovative.

(3) Algorithmic modeling of RNA structure to complement medium to low resolution cryo-EM maps, as employed here, can be implemented for other RNA structures.

Comments on revised version:

Thanks to the authors for providing thorough responses to the reviewer questions and comments. I appreciate their attempts of improving overall resolution of the complex via various processing strategies that the reviewers suggested.

The authors interpretations of their cryo-EM data match those reported by Bhattacharjee et al. 2025 (EMCV-IRES 48S) and can be contextualized in the light of Velazquez et al. 2025 (poliovirus IRES-48S).

The authors' contextualization of their results with previously published studies (Discussion section lines 355-402) is satisfactory to me but can be improved.

Reviewer #3 (Public review):

Summary:

Type II IRES, such as those from encephalomyocarditis virus (EMCV) and foot-and-mouth disease virus (FMDV), mediate cap-independent translation initiation by using the full complement of eukaryotic initiation factors (eIFs), except the cap-binding protein eIF4E. The molecular details of how IRES type II interacts with the ribosome and initiation factors to promote recruitment have remained unclear. Das and Hussain used cryo-electron microscopy to determine the structure of a translation initiation complex assembled on the EMCV IRES. The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Strengths:

The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Comments on revised version:

The revised manuscript does not improve the resolution; however, the authors provide a detailed and well-reasoned rationale that directly addresses the concerns I raised about their structural interpretation. In addition, two independent preprints have been released since the initial submission. In one case, the authors report a higher-resolution, and importantly, all three studies present consistent assignments and interpretations. Together, these observations strengthen confidence in the authors' conclusions. I therefore do not have major concerns regarding the publication of this revised manuscript.

Reviewer #1 (Public review):

Summary:

The authors dissected the ears with some surrounding tissue from 600 embryos at 4 developmental time points of wild-type larvae, as well as from an lmx1bb mutant, performed scRNA-seq analyses, and subclustered the ear/neuromast clusters. They identified cluster markers and performed PAGA pseudotime analyses to build developmental timelines of lineages. They validated some of the cluster markers with HCRs. Many of the clusters are not annotated in detail, but the data sets are still valuable for the community.

Strengths:

Using scRNA-Seq, the authors identified cluster markers for tissues of the developing zebrafish ear and validated some of them with HCRs. The data they compiled and submitted to public databases is a valuable resource for the community.

Weaknesses:

Many of the clusters have not been annotated or rely on published data. For the ones for which no HCRs or UMAPs are shown, it is therefore difficult to estimate which of the markers are indeed the most cell type/state-specific ones.

Major comments:

(1) It would be very useful if the cluster numbers in the Excel files also had the associated cell type annotations as a second column (at least for the ones that are known). E.g., in Supplemental Table 2, the text states which clusters represent which neuromast and ear cell type, but these are not mentioned in the Excel table.

(2) Many of the clusters have not been annotated or rely on published data. For the ones for which no HCRs or UMAPs are shown, it is therefore difficult to estimate which of the markers are indeed the most cell-type/state-specific ones.

(3) Uploading the data to gEAR (https://umgear.org/dataset_explorer.html), a web-based, publicly available ear database, would further increase the usefulness of this study to the broader community.

Method:

The authors should provide the details about how many cells were sequenced for each ear developmental stage, how many cells were present per cluster (page 8), and how many cells were present in each subcluster of ear and lateral line clusters (page 10).

Reviewer #2 (Public review):

Summary:

Munjal and colleagues present a single-cell RNAseq atlas of otic tissue at 4 developmental stages, generate coarse-grained PAGA graphs to describe the development of various otic cell types, rigorously validate their scRNAseq annotations using fluorescent in situ hybridization, and identify changes in epcam expression in lmx1bb mutants that potentially cause the dramatic defects in otic vesicle formation in these mutants.

Strengths:

The data set is very nice, and the annotations are extremely rigorous and more in-depth than other datasets that include these tissues, since these investigators have enriched significantly for this tissue of interest. Their use of PAGA to identify potential developmental relationships within the data is rigorous. I also would like to specifically point out how incredibly gorgeous the microscopy of the lmx1bb phenotype is in Figure 7. Wow.

Weaknesses:

A missed opportunity is that the authors describe creating an additional scRNAseq dataset from lmx1bb mutants, but do not show any comparative scRNAseq analyses that would identify broader sets of differentially expressed genes. It seems almost as if a key element of the study was removed at the last minute, and as a result, the discussion of changes in epcam expression in lmx1bb mutants in Figure 7 seems somewhat tacked onto the end of the study and not motivated by the analyses presented in the manuscript.

Overall, I do not think this study requires any major revisions to be appropriate and useful to the community. This study would be potentially stronger with a more formal analysis of what gene expression changes occurred in otic tissue in lmx1bb mutants, but it is also useful without this. I did have a couple of minor suggestions for the presentation of some aspects that would have made it easier for me as a reader.

Reviewer #3 (Public review):

Summary:

The authors use single-cell transcriptomic analysis to identify distinct cell types in the zebrafish inner ear. They identify markers of hair cells and supporting cells associated with sensory patches, cells that generate the semicircular canals, endolymphatic duct and sac, and periotic mesenchymal cells.

Strengths:

The computational analysis is thorough, and the findings are clearly described. In situ hybridization provides corroboration of cell identities in many cases. This resource atlas will be of particular interest for studies of inner ear morphogenesis. Indeed, the identification of a smooth muscle marker in the endolymphatic sac suggests future analysis of the degree to which this structure undergoes contraction. Identification of cell signaling components in BMP, Wnt, FGF, and other signaling pathways will also provide a resource for understanding signals coordinating ear development.

Weaknesses:

The manuscript is incomplete. Important details that would allow replicable analysis are not provided, with notebooks not available on the referenced GitHub site, and additional files are missing.

The authors make a detailed description of hair cells and supporting cells that are consistent with previous findings (Figures 2 and 3). By contrast, the analysis of distinct cell types that have not been previously well characterized in zebrafish is somewhat incomplete. Markers are described for cells forming the semicircular canals, including ccn1l1 (Figure 4). The authors report an intriguing pattern of its expression before overt bud formation; however, they provide no detailed expression analysis to support this assertion.

The authors also identify new markers for subsets of periotic mesenchyme (Figure 6). These include epyc and otos, which mark distinct populations within the mammalian inner ear - cochlea supporting cells, spiral limbus, and ligament, respectively. Identification of the equivalent of the spiral ligament would be of particular interest. However, the expression analysis is not of sufficient resolution to identify which cell types these represent in the zebrafish inner ear.

Differences in gene expression are reported for lmx1bb mutants. However, none of the single-cell data for mutants is provided, and the table (S8) of differential gene expression is missing. Significantly more detail would be needed to interpret these findings.

Reviewer #1 (Public review):

Summary:

In this work, the authors revisit a well-defined experimental system for studying temporal gene expression mechanisms in TNF-alpha-stimulated macrophages, bringing new tools to the process. Using a hybrid-capture approach, they are able to obtain deeper RNA sequencing of target genes, which allows them to identify potential differences in splicing kinetics of individual introns. Further implementing transcriptional blocks to measure intron half-lives, and predictive machine learning models to identify potential contributing cis-acting RNA elements, they define a group of 'bottleneck' introns whose delayed splicing is a rate-limiting step in mRNA maturation.

Strengths:

(1) The hybrid-capture approach enables deeper RNA sequencing of target transcripts.

(2) The neural network application to identify motifs outside of splice sites could be related to intron removal kinetics.

(3) The paper uses splicing reporters with modulation of 5' splice sites to test the effect on reporter gene expression in the context of 'bottleneck' introns.

Weaknesses:

(1) While evidence is provided that these introns are distinct from previously published splicing kinetics studies, 'bottleneck' introns are not adequately placed in context for assessment of how they are similar or different.

(2) Splicing reporters are a good approach, but the complexities of post-transcriptional gene expression regulation are not adequately addressed

(3) Deep learning models are a potentially powerful tool for identifying novel regulatory sequences; however, their use here is underdeveloped.

Reviewer #2 (Public review):

Summary:

The authors analyzed the temporal dynamics of gene expression patterns within the inflammatory response transcriptome following TNF stimulation, and proposed that the splicing rate of certain introns is a key mechanism of regulating mature mRNA expression rate.

Strengths:

The measurement strategy is generally well-designed to understand the core question of splicing rate and gene expression. The following computation analysis, as well as the mutation or repair studies, further supported the claims. The writing and presentation of the results are also generally clear and easy to follow. I think this manuscript will be of interest to a wide audience.

Weaknesses: 

I do have some questions regarding some of the results and conclusions, and I think either more analysis or more explanation and discussion can make the claims more solid. Please see below for details:<br /> <br /> (1) On the hybrid capture method and the RNA coverage results: The strategy of enriching for the last exon before sequencing does have significance in linking pre-mRNA and mature mRNA. If I understand correctly, this enriches for pre-mRNA molecules that are about to finish the full-length elongation of RNA polymerase. However, is this strategy biased towards measuring the splicing rate variation on introns closer to the 3-prime end? For example, if a gene takes 5 minutes for the RNA polymerase to elongate through the full length of the gene, for intron #1 that's very close to the 5' end, you can't tell if it takes 20s to be spliced out or 4 minutes, as both will show as fully spliced out in the sequencing library. In other words, for introns near the 5' end, a consistent "CoSI=1" pattern in the data doesn't necessarily suggest a true consistent fast splicing of that intron. Do you observe any general pattern of the measured "slowliness" in relation to the 5'-3' location of the introns? If so, should the 5' introns be specially considered or even excluded from certain analyses that use all introns?<br /> <br /> (2) Following on my last point, it may benefit the readers if the author can provide a more detailed comparison of possible sequencing library construction choices. For example, is it feasible to also enrich for other exons for the sequencing library, etc?<br /> <br /> (3) Figure 1C: Are there biological replicates, and should there be error bars and statistics on the plot? Similarly, in places like Figure 2, Supplemental Figure 4C, Supplemental Figure 6, etc., is there any statistical analysis that can be done to show if the claimed differences are statistically significant?<br /> <br /> (4) The logic behind measuring the half-lives of introns seems a little unclear to me.  From the time-dependent RNA coverage plots in Figure 2, it seems that, if we assume a constant transcription elongation rate, then the splicing rate of a specific intron can vary across time after TNF stimulation, as represented by the temporal change of CoSI values, or the heights of the coverage plot relative to neighboring exons. This means the splicing rate or half-life of an intron is not necessarily constant but may be time-dependent, at least in the case of TNF stimulation. Shouldn't the half-life measurements be designed in a way to measure the half-life at multiple time points after TNF stimulation? And maybe the measured half-lives of some introns will show as time-dependent?<br /> <br /> (5) In Supplemental Figure 6, the interpretation is a little confusing to me: If delayed splicing is causing delayed expression of the corresponding gene, shouldn't the non-immediate gene groups (early/intermediate/Late) have low CoSI beginning from the early time points (e.g. 4 minutes)? Why does the slowdown of splicing seem to peak at a later time point? Does it mean immediately after TNF stimulation, there's a different mechanism in delaying the expression of the non-immediate gene groups? Maybe it's better to have more explanation or use a different visualization to show what non-immediate gene groups are experiencing at very early time points.<br /> <br /> (6) On the fine-tuning of the deep sequence model: it's a little unclear whether the input and output are time-dependent. It's stated that expression at multiple time points is used for training, but it's unclear whether the model outputs time-dependent expression patterns and whether the time information is used as input.

Reviewer #3 (Public review):

Summary:

The manuscript by Dearborn et al investigates the kinetics of intron splicing in inflammation-associated transcripts after TNF-stimulation of macrophages, using targeted sequencing of chromatin-associated RNA to obtain high coverage across a focused set of induced genes. The authors' main conclusion is that splicing kinetics are heterogeneous across these transcripts, and that delayed introns (which they term "bottleneck introns") are associated with weak donor sequences. Using a deep learning approach, they have also identified additional sequence features that might contribute to intron splicing kinetics.

Overall, I think the findings in the manuscript are very intriguing and will be of interest to readers working on RNA biology. The changes the authors have made to the manuscript in response to some very valid comments from reviewers have strengthened the manuscript. While the existing data might not be sufficient to directly address some of the broader mechanistic claims made by the authors, I think the findings are nonetheless very interesting and should contribute towards a better understanding of the post-transcriptional regulation of gene expression.

Strengths:

A strength of the manuscript is the experimental design. The targeted capture approach is innovative and well-suited to the goal of measuring intron-specific splicing behaviour across time. The inclusion of experimental validation in minigene assays of some of the computational predictions also strengthens the claims made by the authors.

The authors have made a constructive effort to address some of the concerns raised in a previous round of review. The revised manuscript reads as a balanced text.

Weaknesses:

The study still does not fully resolve the downstream consequences of delayed splicing. In particular, it remains unclear whether the bottleneck introns lead primarily to delayed production of mature transcripts, reduced productive transcript output, or some combination of the two.

On a related point, the minigene reporter assays measure a steady-state level of the transcript and don't provide insights into the kinetics directly.

Lastly, given that the detailed analyses were performed on a selected subset of (inflammation-induced) transcripts, a broader evolutionary interpretation needs to be restrained given the current data.

Reviewer #1 (Public review):

Summary:

In this manuscript, Matsuda and collaborators present a model of how tracheal tubulogenesis is controlled in Drosophila embryos. Some of the results backing the model are new, but others are based on information already published by the authors. However, the results in this manuscript present different molecular markers not published before, which agree with previous conclusions. The manuscript also analyses the requirement of the dpp and EGFR signalling pathways for trachealess (trh) maintenance, one of the main tracheal transcription factors.

Strengths:

The two most interesting novel points of the manuscript are:

(1) Its contribution to the analysis of how the dpp and EGFR pathways contribute to the maintenance of trh expression.

(2) The experimental evidence showing that mechanical invagination is not a requirement for trh maintenance in the tracheal cells, an intriguing hypothesis previously suggested by (Kondo Hayashi 2019 eLife 8:e45145) that can now be discarded by the data presented in this work.

Weaknesses:

Because of the mixture of new and already published data, this manuscript can be considered as a review/experimental paper.

Already known data:<br /> - The results showing that hh and vvl drive tracheal invaginaton independently of trh are reported in Figure 5 of (Matsuda et al. 2015 eLife 4:e09646).<br /> - The results showing dpp requirement for trh maintenance are partially reported in Figure 6 of (Matsuda 2015 eLife 4:e09646).

Reviewer #2 (Public review):

Summary:

Matsuda et al. investigate the regulatory mechanisms controlling gene expression and morphogenesis in the Drosophila embryonic trachea. Building on previous findings that tracheal invagination can occur independently of trh, they identify extrinsic hh and intrinsic vvl as key regulators that cooperatively promote this process. The study also integrates major signaling pathways (Dpp/BMP and EGFR) in defining tracheal cell identity and demonstrates that Ras activation can upregulate trh. Overall, the work supports a model in which multiple transcription factors and signaling inputs coordinate airway progenitor specification.

Strengths:

This study uses genetic analysis of various mutants to dissect regulatory relationships underlying tracheal development. While the uncoupling of tracheal invagination from trh function has been previously recognized, this work advances the field by identifying hh and vvl as key regulators of invagination independent of trh. The study also integrates multiple signaling pathways, such as Dpp/BMP and EGFR, into a coherent framework for tracheal cell specification. In addition, the demonstration that Ras activation can upregulate trh provides a clear mechanistic link between RTK signaling and transcriptional regulation. Overall, the work offers important and broadly relevant insights into how gene expression and morphogenesis are coordinated during development.

Weaknesses:

Data presentation and clarity of interpretation could be improved. Many images primarily show lateral views of whole embryos, which can make it difficult to fully assess some phenotypes; higher-magnification or sectional views would enhance clarity. There are also some minor inconsistencies in the description of invagination phenotypes, particularly regarding whether all trh+ cells remain in a 2D plane versus indications of partial invagination in hh vvl double mutants blocking apoptosis, which would benefit from further clarification. Finally, some statements in the abstract, especially regarding the role of grn, are not directly supported by data in this study and could be better aligned with the scope of the presented results.

Reviewer #1 (Public review):

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

Summary:

Zacharia and colleagues investigate the role of the C-terminus of IFT172 (IFT172c), a component of the IFT-B subcomplex. IFT172 is required for proper ciliary trafficking and mutations in its C-terminus are associated with skeletal ciliopathies. The authors begin by performing a pull-down to identify binding partners of His-tagged CrIFT172968-C in Chlamydomonas reinhardtii flagella. Interactions with three candidates (IFT140, IFT144, and a UBX-domain containing protein) are validated by AlphaFold Multimer with the IFT140 and IFT144 predictions in agreement with published cryo-ET structures of anterograde and retrograde IFT trains. They present a crystal structure of IFT172c and find that a part of the C-terminal domain of IFT172 resembles the fold of a non-canonical U-box domain. As U-box domains typically function to bind ubiquitin-loaded E2 enzymes, this discovery stimulates the authors to investigate the ubiquitin-binding and ubiquitination properties of IFT172c. Using in vitro ubiquitination assays with truncated IFT172c constructs, the authors demonstrate partial ubiquitination of IFT172c in the presence of the E2 enzyme UBCH5A. The authors also show a direct interaction of IFT172c with ubiquitin chains in vitro. Finally, the authors demonstrate that deletion of the U-box-like subdomain of IFT172 impairs ciliogenesis and TGFbeta signaling in RPE1 cells.

However, some of the conclusions of this paper are only partially supported by the data, and presented analyses are potentially governed by in vitro artifacts. In particular, the data supporting autoubiquitination and ubiquitin-binding are inconclusive. Without further evidence supporting a ubiquitin-binding role for the C-terminus, the title is potentially misleading.

Strengths:

(1) The pull-down with IFT172 C-terminus from C. reinhardtii cilia lysates is well performed and provides valuable insights into its potential roles.

(2) The crystal structure of the IFT172 C-terminus is of high quality.

(3) The presented AlphaFold-multimer predictions of IFT172c:IFT140 and IFT172c:IFT144 are convincing and agree with experimental cryo-ET data.

Reviewer #2 (Public review):

Summary:

Cilia are antenna-like extensions projecting from the surface of most vertebrate cells. Protein transport along the ciliary axoneme is enabled by motor protein complexes with multimeric so-called IFT-A and IFT-B complexes attached. While the components of these IFT complexes have been known for a while, precise interactions between different complex members, especially how IFT-A and IFT-B subcomplexes interact, are still not entirely clear. Likewise, the precise underlying molecular mechanism in human ciliopathies resulting from IFT dysfunction has remained elusive.

Here, the authors investigated the structure and putative function of the to-date poorly characterised C-terminus of IFT-B complex member IFT172 using alpha-fold predictions, crystallography and biochemical analyses including proteomics analyses followed by mass spectrometry, pull-down assays, and TGFbeta signalling analyses using chlamydomonas flagellae and RPE cells. The authors hereby provide novel insights into the crystal structure of IFT172 and identify novel interaction sites between IFT172 and the IFT-A complex members IFT140/IFT144. They suggest a U-box-like domain within the IFT172 C-terminus could play a role in IFT172 auto-ubiquitination as well as for TGFbeta signalling regulation.

As a number of disease-causing IFT72 sequence variants resulting in mammalian ciliopathy phenotypes in IFT172 have been previously identified in the IFT172 C-terminus, the authors also investigate the effects of such variants on auto-ubiquitination. This revealed no mutational effect on mono-ubiquitination which the authors suggest could be independent of the U-box-like domain but reduced overall IFT172 ubiquitination.

Strengths:

The manuscript is clear and well written and experimental data is of high quality. The findings provide novel insights into IFT172 function, IFT complex-A and B interactions, and they offer novel potential mechanisms that could contribute to the phenotypes associated with IFT172 C-terminal ciliopathy variants.

Reviewer #3 (Public review):

Summary:

Zacharia et al report on the molecular function of the C-terminal domain of the intraflagellar transport IFT-B complex component IFT172 by structure determination and biochemical in vitro and cell culture-based assays. The authors identify an IFT-A binding site that mediates a mutually exclusive interaction to two different IFT-A subunits, IFT144 and IFT140, consistent with interactions suggested in anterograde and retrograde IFT trains by previous cryo-electron tomography studies. Additionally, the authors identify a U-box-like domain that binds ubiquitin and conveys ubiquitin conjugation activity in the presence of the UbcH5a E2 enzyme in vitro. RPE1 cell lines that lack the U-box domain show a reduction in ciliation rate with shorter cilia, and heterozygous cells manifest TGF-beta signaling defects, suggesting an involvement of the U-box domain in cilium-dependent signaling.

Strengths:

(1) The structural analyses of the C-terminal domain of IFT172 combine crystallography with structure prediction using state-of-the-art algorithms, which gives high confidence in the presented protein structures. The structure-based predictions of protein interactions are validated by further biochemical experiments to assess the specific binding of the IFT172 C-terminal domains with other proteins.

(2) The finding that the IFT172 C-terminus interactions with the IFT-A components IFT140 and IFT144 appear mutually exclusive confirm a suggested role in mediating the binding of IFT-B to IFT-A in anterograde and retrograde IFT trains, which is of very high scientific value.

(3) The suggested molecular mechanism of IFT train coordination explains previous findings in Chlamydomonas IFT172 mutants, in particular an IFT172 mutant that appeared defective in retrograde IFT, as well as mutations identified in ciliopathy patients.

(4) The identification of other IFT172 interactors by unbiased mass spectrometry-based proteomics is very exciting. Analysis of stoichiometries between IFT components suggests that these interactors could be part of IFT trains, either as cargos or additional components that may fulfill interesting functions in cilia and flagella.

(5) The authors unexpectedly identify a U-box-like fold in the IFT172 C-terminus and thoroughly dissect it by sequence and mutational analyses to reveal unexpected ubiquitin binding and potential intrinsic ubiquitination activity.

(6) The overall data quality is very high. The use of IFT172 proteins from different organisms suggests a conserved function.

Overall, the authors achieved to characterize an understudied protein domain of the ciliary intraflagellar transport machinery and gained important molecular insights into its role in primary cilia biology, beyond IFT. By identifying an unexpected functional protein domain and novel interaction partners the work makes an important contribution to further our understanding of how ciliary processes might be regulated by ubiquitination on a molecular level. Based on this work it will be important for future studies in the cilia community to consider direct ubiquitin binding by IFT complexes.

Conceptually, the study highlights that protein transport complexes can exhibit additional intrinsic structural features for potential auto-regulatory processes. Moreover, the study adds to the functional diversity of small U-box and ubiquitin-binding domains, which will be of interest to a broader cell biology and structural biology audience.

Reviewer #1 (Public review):

Summary:

This manuscript asks how the uterine lumen is remodeled across the peri-implantation window and whether this remodeling is functionally linked to embryo attachment and subsequent pregnancy establishment. The authors combine whole-organ three-dimensional imaging of optically cleared mouse uteri with single-cell and spatial transcriptomic profiling, conditional deletion of p38α at the uterine-wide versus epithelial-restricted level, and rescue experiments using progesterone and leukemia inhibitory factor. Based on these datasets, the authors propose that the luminal epithelium undergoes a previously underappreciated phase of organ-scale architectural reorganization before attachment, and that a p38α-dependent stress-responsive program coordinates epithelial remodeling together with epithelial-stromal communication required for implantation competence.

Strengths:

By moving beyond local attachment-site morphology to a horn-level representation of luminal topology, the work provides anatomical context that is difficult to reconstruct from conventional section-based approaches and should be broadly useful to the implantation community. The integration of organ-scale morphology with single-cell and spatial transcriptomic datasets, together with genetic perturbation and rescue experiments, adds breadth and increases the potential long-term utility of the dataset for investigators interested in uterine receptivity and embryo-uterine interactions.

Weaknesses:

(1) The whole-uterus analysis of luminal folds and creases requires stronger methodological support. Given the mechanical compliance of the uterine lumen, it is difficult to evaluate from the current description whether dissection, fixation, clearing, and/or mounting could influence the observed luminal topography. This issue is particularly important because several key conclusions depend on the spatial distribution of folds across the uterine horn. A fuller account of tissue handling and reconstruction, together with validation that the preparation preserves native morphology, would substantially strengthen confidence in the organ-scale conclusions.

(2) Several of the central morphological claims are supported primarily only by representative reconstructions. This includes the proposed flattening/creasing dynamics, alternating stretched and shrunken regions, persistence of abnormal folding in the mutant uterus, and the extent of structural rescue following progesterone supplementation. The authors could extract objective measures from the reconstructed luminal surface and provide more statistical analysis to demonstrate the reproducibility of the results.

(3) The manuscript appears to over-reach in concluding that luminal remodeling zones embryos before attachment from day 4 to 5. As presented, the data support a correlation between luminal architecture and embryo position, but do not discriminate between (i) luminal remodeling directing embryo positioning, (ii) embryos locally shaping the lumen, or (iii) parallel regulation of both. The evidence is based on observations of the uterus and the inside blastocysts at certain time points around implantation. Without the time-lapse analysis within the uterus, the dynamic interactions between embryos and the uterus couldn't be determined.

(4) The key conclusion of the manuscript is that uterine p38α regulates luminal epithelial remodeling required for embryo attachment, as shown in the title. Against this background, the finding that epithelial-restricted loss of p38α does not overtly impair fertility is notable, as it suggests that the major function of p38α may not be epithelial cell-autonomous but instead may arise through other uterine compartments that secondarily influence the epithelium. At present, however, this conclusion remains insufficiently supported: the epithelial-specific model is not characterized in sufficient depth during the peri-implantation period, and the transcriptomic evidence for altered epithelial-stromal communication does not by itself explain the phenotypic difference between uterine-wide and epithelial-specific deletion. If stromal p38α is proposed as the critical upstream regulator, more direct testing, such as stromal-specific deletion, would be needed.

Reviewer #2 (Public review):

Summary:

In this study, the authors aimed to characterize the architectural reorganization of the uterine luminal epithelium during the implantation period. Using 3D histological reconstruction, single-cell RNA sequencing, and spatial transcriptomics, the authors characterize luminal remodeling during the peri-implantation period and employ a mouse model to explore the role of p38α in regulating luminal flattening.

Strengths:

This study clearly described the changes in luminal architecture during implantation. Moreover, they also used integration of multiple advanced techniques, including 3D tissue reconstruction, single-cell transcriptomics, and spatial transcriptomics, which together provide a detailed description of the molecular characteristics of the uterine architecture during implantation.

Weaknesses:

The authors used PR-Cre to generate uterine p38α knockout mice. This Cre driver deletes p38α not only in epithelial cells but also in stromal compartments. Therefore, it remains unclear whether the observed phenotype arises from epithelial cells, stromal cells, or a combination of both. Previous studies have shown that p38α regulates epithelial polarity, cytoskeletal organization, and E-cadherin localization. However, the current study does not examine changes in cell adhesion or epithelial junction integrity. Previous studies have reported that uterine fluid absorption during implantation is closely associated with luminal closure and remodeling. It would be important to determine whether epithelial transport-related genes are altered in the mutant uterus. Could dysregulated fluid homeostasis contribute to the implantation defects observed in the p38α-deficient mice?

Reviewer #1 (Public review):

Summary:

The authors provide high-resolution cryoEM structures to map and functionally characterize human antibodies against SARS-CoV-2 elicited by a standard mRNA vaccine. Here, they report high-resolution structural information on seven previously documented neutralizing antibodies from this response, which were produced from early plasmablasts and which engage diverse targets on the viral spike glycoprotein. This structural information is then integrated with functional assays to define how antibody epitope specificity, geometry, and conformational dynamics may shape neutralization outcomes.

Strengths:

A core strength of the study is a technically-well executed analysis of multiple 'ectopically balanced' mAbs elicited by early B cell plasmablast responses. These antibodies engage different neutralizing targets on the S-trimer of SARS-CoV-2, including the RBD and NTD domains. This has resolved a core distinction in terms of how nAbs engaging these features (and subfeatures, e.g., more conserved hydrophobic pocket within NTD) neutralize the virus.

Weaknesses:

A general weakness is that these antibody classes have been structurally characterized already (albeit individually), and much of this work has been done in the context of understanding susceptibility to escape mutations (delta, omicron, and subvariants therein; class I-IV antibody crossreactivity on Wuhan SARS-CoV-2 to present). It is exceptionally fine technical work presenting the antibodies in a collection like this, but perhaps the new predictive power of this analysis is somewhat overstated.

The early plasmablast angle seems like it could be better fleshed out. Many of the known SARS-CoV-2 nAbs are from the plasmablast pool, but how does this predict the antibody profile at latter stages, as per the stated goal and claim of the current study? Does the paratope pattern of plasmablast antibodies then change within the immune sera at later time points? New or existing cryoEMPEM data could shed light on this.

Reviewer #2 (Public review):

Summary:

This manuscript provides important insights into the interaction between early vaccine-elicited antibodies and SARS‑CoV‑2 evolution. The work will be of broad interest to researchers in structural virology, immunology, and vaccine development. However, several conclusions-particularly those involving neutralization breadth and spike destabilization-require additional functional and biophysical validation.

Strengths:

The manuscript provides an unusually comprehensive structural dataset, resolving all neutralizing antibodies in complex with the SARS‑CoV‑2 spike and enabling direct mechanistic comparison across epitope classes. Its integration of cryo‑EM structures with variant binding, sequence analysis, and fusion‑inhibition assays offers a coherent, multidimensional explanation for antibody breadth and escape. Notably, the identification of a conserved NTD hydrophobic pocket targeted by broad-reactive antibodies represents a conceptually important advance with clear implications for future vaccine design.

Weaknesses:

The study lacks variant-specific neutralization assays, limiting the ability to directly correlate binding breadth with functional viral inhibition. It also omits kinetic affinity measurements, leaving important mechanistic questions, such as why certain antibodies retain breadth, only partially resolved. Additionally, reliance on HEK293T-based spike display raises concerns about glycosylation-related artifacts, especially for NTD loop-dependent antibodies.

Reviewer #3 (Public review):

Summary:

In this manuscript by Jaiswal et al., the authors used structural biology combined with cellular assays to determine the molecular basis underlying the neutralizing ability of the SARS-CoV-2 antibodies. The authors compared the binding mode of the neutralizing antibodies that have two distinct binding interfaces and identified key sites that determine their vulnerability to virus evolution. Interestingly, the author also demonstrated that the trimer-disrupting antibody has the broadest activity as the variations at the trimer interface are limited in evolution.

Strengths:

This manuscript reported a large collection of structures and covered a broad range of binding modes and mechanisms of action. Many of the cryo-EM structures are of good quality. The authors' hypothesis regarding the molecular determinants of evolution vulnerability is solid.

Weaknesses:

However, in my opinion, several points listed below need to be addressed.

(1) At the beginning of the results section, the authors started by determining the structures of Fab PVI.V3-9 and Fab PVI.V6-4 in complex with the ancestral SARS-CoV-2 spike. However, the readers could benefit from a brief introduction of the Fabs PVI.V3-9 and PVI.V6-4. The same applies to the anti-NTD Fabs.

(2) In Figure 1A and E, the spike protein is shown with two different views. It is best to show the same view for comparison.

(3) Throughout the manuscript, the map quality of some Fabs (e.g., V6-11, V6-7, V6-2) is suboptimal. Does the map support the claims on the residues that form the interface? The authors should provide a figure showing the cryo-EM density for all side-chain residues involved at the interface.

(4) Line 152, the terminology "NTD top binders" could be ambiguous, as it could mean those Fabs have the strongest binding affinity. Maybe the authors can change the "top" to "tip".

(5) The authors described the interface between the spike protein and the Fabs in great detail. However, it would be nice if the authors could summarize the common binding strategy for each group of antibodies that utilize the same binding surface.

(6) Line 275, the authors should define what strain of Omicron is in Figure 4. The authors should also explain that the strains in Figure 4A are ordered by evolutionary age.

(7) Lines 286-287, isn't this conclusion already made from the cell-based flow cytometry binding assay? This sentence could be deleted.

(8) In both Figures S10 and S11, the readers could benefit from an additional row highlighting the residues interacting with ACE2.

(9) Lines 298-301, based on Figure S11, no contact is made between the N2 loop and the Fab. The authors may elaborate on why the mutations observed in the N2 loop indirectly influenced Fab recognition.

(10) Lines 321-323, even though this is a well-established assay, it is probably better to clearly explain that one pool of cells expresses the spike and the other pool of cells expresses ACE2.

Reviewer #1 (Public review):

Summary:

The authors set out to evaluate how accurately direct sequencing of RNA can identify and quantify several chemical modifications on RNA molecules, focusing primarily on m6A. A central goal of the work is to compare this approach with an independent chemical-based method (glyoxal and nitrite-mediated deamination of unmethylated adenosines (GLORI), using the same RNA samples, in order to assess reproducibility, false-positive signals, and sensitivity across a range of detection strategies. The authors further aim to demonstrate the biological utility of this approach by applying it to two human cell types, primary human fibroblasts and HD10.6 neurons. While the manuscript also reports detection of additional RNA modifications (pseudouridine and m5C, the depth of analysis and strength of controls are greatest for m6A, which forms the primary focus of the study

Strengths:

A strength of this work is the direct comparison of two distinct measurement approaches performed on the same RNA input material; this has not been done in other recently published benchmarking studies evaluating the utility of the recent direct RNA sequencing for calling m6A. The authors systematically test multiple analysis models and show that, when appropriate filtering is applied, detection of modified sites is reproducible across software versions. The use of synthetic RNA standards and METTL3 inhibitors as negative controls helps to reinforce the overall results.

The data show good agreement between the two methods at higher m6A modification levels, supporting the conclusion that direct RNA sequencing can reliably detect high-confidence modification sites. The authors also demonstrate that this approach can, in principle, provide information at the level of individual RNA variants (although only one example was provided), which is difficult to achieve with short-read methods. The methodology described here is likely to be useful to others seeking to apply similar approaches to identify and quantify m6A. The study also explores the detection of other RNA modifications, which highlights the broader potential of the approach, although these analyses are necessarily more exploratory given the more limited controls and data available.

Weaknesses:

Despite these strengths, several issues limit the interpretation of the results and should be clarified for readers.

First, the authors appropriately address false-positive signals by estimating expected false-positive rates and by quantitatively comparing sequence motif enrichment before and after filtering. These analyses provide important support for the use of stoichiometry-based thresholds and demonstrate that filtering substantially improves specificity. However, even after filtering, a subset of detected sites remains outside the expected sequence context. It therefore remains unclear to what extent these non-canonical sites reflect genuine biology versus residual technical artifacts.

Second, claims regarding the ability of direct RNA sequencing to resolve modification patterns across different RNA variants are supported by very limited evidence. The conclusion that this approach provides superior isoform-level quantification relative to short-read methods is based largely on a single gene example. While this case is interesting, it does not establish how widespread or general this advantage is. A broader analysis indicating how many genes show isoform-specific modification patterns detectable by this method, and how often these are missed by the comparison approach, would be necessary to support a general claim.

Third, the biological interpretation of cell type-specific differences in modification levels remains underdeveloped. Although differences in modification stoichiometry are reported between fibroblasts and neuron-derived cells, the functional consequences of these differences are not addressed. It is unclear whether changes in modification levels are associated with differences in RNA abundance, stability, or translation. As a result, statements suggesting that these modifications fine-tune core cellular pathways are speculative and should either be supported with additional analyses or framed more cautiously.

Related to this point, differences in gene expression between the two cell types are a potential confounding factor. The pathway enrichment patterns presented appear biased toward particular functional categories, but without clear control for differential gene expression, it is difficult to determine whether the observed enrichment reflects cell type-specific regulation of RNA modification or simply differences in which genes are expressed. Clarifying how background gene sets were defined for these analyses would help readers interpret the results.

The manuscript also suggests broader differences in overall modification levels between cell types, but this is not validated using an independent global assay. An orthogonal measurement of total modification levels on polyadenylated RNA (for example, dot blot) would help place site-specific stoichiometry differences in a clearer biological context.

Finally, the effects of the METTL3 inhibitor on these cell types are not fully characterized. While changes in m6A modification patterns are reported following treatment, the manuscript does not address whether the treatment affects cell growth or viability.

Appraisal of conclusions and impact:

Overall, the study provides an informative technical assessment of direct RNA sequencing for modification detection and establishes clear conditions under which the method performs well. The evidence strongly supports conclusions related to technical benchmarking, reproducibility, and the importance of filtering and controls, particularly for m6A. In contrast, conclusions regarding isoform-specific regulation and cell type-specific biological roles of RNA modification are less well supported by the data currently presented, and would benefit from either additional analysis or more restrained interpretation.

The work is likely to have a meaningful impact as a practical reference for researchers using direct RNA sequencing, particularly by clarifying sources of false positives and the value of appropriate controls. With clearer limits placed on biological interpretation or more data presented in support of the biological interpretation, the study would serve as a valuable reference for users seeking to apply these technologies reliably.

Reviewer #2 (Public review):

Summary:

In this study, the authors aim to establish a calibrated framework for detecting RNA modifications using long-read sequencing and apply it to compare modification patterns between fibroblasts and neuron-like cells. The work combines long-read sequencing, in vitro transcribed controls, methyltransferase inhibition, and comparison to an orthogonal sequencing-based method in an attempt to derive filtering strategies that reduce false positive modification calls. The authors further apply this framework to explore differences in modification levels between the two cell types.

The resulting dataset may be of interest to researchers working on RNA modification detection using long-read sequencing technologies. Independent datasets across additional cellular systems can be useful for benchmarking computational methods and evaluating the behavior of modification detection models. However, the conceptual advance of the analytical framework presented here remains somewhat unclear, as many aspects of the analysis closely resemble strategies that have already been described in recent benchmarking studies.

Strengths:

A clear strength of the study is the generation of a relatively large long-read sequencing dataset together with several useful experimental controls, including in vitro transcribed RNA and pharmacological inhibition of the methyltransferase enzyme responsible for installing this modification. These controls are helpful for illustrating the challenges associated with distinguishing high-confidence modification sites from background signals. The inclusion of two different human cellular systems also provides an additional dataset that may be useful for benchmarking and cross-validation in the field. The study addresses a practically relevant question for the community, namely, how to reduce false positive calls in long-read sequencing-based RNA modification analyses.

Weaknesses:

The main weakness of the manuscript is its limited methodological novelty. Much of the analytical framework presented here closely follows benchmarking strategies that have already been described in recent studies of RNA modification detection using long-read sequencing. Several previous studies have evaluated modification-aware basecalling approaches, discussed the need for stringent filtering strategies, and compared long-read sequencing-based predictions with orthogonal mapping approaches. The manuscript would therefore benefit from a deeper engagement with the recent benchmarking literature and a clearer explanation of what conceptual or methodological advance the present study provides beyond these earlier analyses.

A second concern relates to the filtering strategy that forms the core of the proposed workflow. The manuscript applies several thresholds, including modification probability, stoichiometry, and read coverage cutoffs, but it is not clearly explained how these thresholds were determined. It remains unclear whether these cutoffs were derived from statistical calibration, empirical optimization using the presented dataset, or adopted from previous studies. Because the downstream conclusions depend strongly on these filtering choices, a clearer methodological justification would strengthen the work and help readers assess the robustness of the proposed framework.

The interpretation of the comparison between the two modification detection approaches also appears somewhat overstated. Differences between the methods are frequently interpreted as evidence that one approach produces large numbers of false positive calls, but the analyses presented do not fully exclude alternative explanations such as differences in sensitivity, sequencing depth, or methodological biases. A more cautious interpretation of these discrepancies would therefore be appropriate.

Some discussion points also appear speculative. In particular, certain interpretations propose mechanistic explanations without presenting analyses that would allow these possibilities to be distinguished. Such interpretations would benefit from either additional supporting analyses or more cautious phrasing.

From a methodological perspective, the statistical robustness of the thresholds used throughout the analysis could also be discussed in more detail. Given the relatively modest read coverage cutoff applied in the study, low stoichiometry estimates may be strongly influenced by sampling noise, and fixed stoichiometry thresholds may therefore not correspond to a consistent level of confidence across sites. In addition, the manuscript relies heavily on fixed modification probability cutoffs to define high-confidence calls, but it does not discuss whether these scores are statistically calibrated or how they relate to expected error rates. Neural network outputs are often not well-calibrated probabilities, and interpreting these values as direct confidence estimates can therefore be problematic. Finally, modification detection models trained on known modification sites may capture sequence-context patterns present in the training data, meaning that motif enrichment or positional distributions along transcripts may partly reflect model biases rather than purely biological signals. A brief discussion of these limitations would help readers better interpret the robustness of the proposed filtering strategy and the downstream biological conclusions.

Overall, while the dataset may be of interest to the community, the extent to which the study advances current methodological understanding beyond recent benchmarking efforts remains limited.

Minor comments:

The discussion of the "DRACH" versus "all-context" outputs would benefit from greater technical precision. The statement that the number of sites within DRACH motifs identified by the all-context approach was nearly identical to the number reported by the DRACH model may suggest that these outputs derive from fundamentally different predictive models. As I understand it, the underlying neural network is the same, whereas the distinction lies primarily in the classification context. Clarifying this explicitly in the manuscript would improve interpretability and avoid potential confusion for readers.

The manuscript compares results obtained with different basecalling and modification settings but refers primarily to Dorado software versions. This may be misleading, as software version and model version are not necessarily equivalent. Different basecalling or modification models can be used with the same software release, and newer software versions may still use older models. For clarity and reproducibility, the authors should report the exact basecalling and modification model names used in the analyses rather than referring only to the Dorado software version.

Reviewer #3 (Public review):

In this study, the authors aim to establish a calibrated framework for identifying RNA chemical marks from direct RNA sequencing data using a modification-aware basecalling workflow, with a particular focus on N6-methyladenosine. By combining native RNA sequencing with an unmodified control transcriptome, enzyme inhibition, comparison across multiple software versions, and orthogonal validation using an independent mapping approach, the authors seek to define a best-practice pipeline for reducing false-positive calls and improving confidence in quantitative interpretation across cell types.

A major strength of the work is the rigor of the benchmarking strategy. In particular, the inclusion of an unmodified control transcriptome is both important and useful, and the study provides compelling evidence that this control remains necessary for robust interpretation, despite being omitted in many current workflows. The comparison across software versions and the matched analysis with an independent sequencing-based approach also substantially strengthen the evidence presented. The work therefore makes a valuable contribution to the community by offering a more stringent analytical framework that will likely be broadly useful to groups applying native RNA sequencing to study RNA chemical marks.

The evidence supporting the main conclusions is solid overall. The authors convincingly show that stringent filtering substantially reduces false-positive calls and improves agreement with orthogonal approaches, particularly at highly modified sites. The observation that many sites are conserved across cell types, while showing differences in relative modification levels, is also supported by the presented analyses.

At the same time, several conceptual issues limit the strength of some downstream interpretations. Most importantly, the manuscript repeatedly refers to the reported values as "stoichiometry," whereas the underlying software output is more appropriately interpreted as a statistical estimate of the proportion of aligned reads classified as modified. This distinction is important because the conclusions regarding cell-type differences rely on quantitative comparisons of these values. In addition, the current calling framework depends on successful canonical base assignment before modification calling, which raises an important limitation: sites with the strongest signal deviations may be underrepresented if they are more likely to be miscalled during basecalling. This issue may be especially relevant for RNA marks that induce stronger mismatch signatures than N6-methyladenosine and should be more explicitly discussed.

Overall, the authors largely achieve their primary aim of establishing a more rigorous and broadly applicable analytical framework for direct RNA sequencing-based modification detection. The work is likely to have a meaningful impact on the field, particularly by reinforcing the importance of appropriate negative controls and benchmarking standards. With clearer framing of the quantitative outputs and explicit discussion of current software limitations, this study will serve as a highly useful resource for the community.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

Comments on revisions:

The authors have addressed my questions in their revision. I have no other questions. Thank you for the opportunity to read and evaluate this interesting study and also for all the work carried out in response to the comments.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

(3) Appropriate use of quantification and statistics

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Comments on second revision:

The authors have adequately addressed several points. However, there is still no information in the material and methods for how they measured and quantified the HCR-FISH probe signal. They have the same size region that they use for each genotype, but they do not control for the number of nuclei in each square. I would also be helpful if they provided a different image for the gbb probe stained in the mutant background. It is the only panel that does not have other germaria in very close proximity. I am still not fully convinced of the HCR data, esp for gbb.

Reviewer #2 (Public review):

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

Comments on revision:

If the exact number of germaria was not recorded (as described), an approximate number can be provided in the Materials and Methods; for example, stating that more than 10 germaria were analyzed per biological replicate.

Reviewer #3 (Public review):

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

This manuscript makes a significant contribution to the field by exploring the dichotomy between chemical synaptic and gap junctional contributions to extracellular potentials. While the study is comprehensive in its computational approach, adding experimental validation, network-level simulations, and expanded discussion on implications would elevate its impact further.

Strengths:

Novelty and Scope:

The manuscript provides a detailed investigation into the contrasting extracellular field potential (EFP) signatures arising from chemical synapses and gap junctions, an underexplored area in neuroscience.<br /> It highlights the critical role of active dendritic processes in shaping EFPs, pushing forward our understanding of how electrical and chemical synapses contribute differently to extracellular signals.

Methodological Rigor:

The use of morphologically and biophysically realistic computational models for CA1 pyramidal neurons ensures that the findings are grounded in physiological relevance.<br /> Systematic analysis of various factors, including the presence of sodium, leak, and HCN channels, offers a clear dissection of how transmembrane currents shape EFPs.

Biological Relevance:

The findings emphasize the importance of incorporating gap junctional inputs in analyses of extracellular signals, which have traditionally focused on chemical synapses.<br /> The observed polarity differences and spectral characteristics provide novel insights into how neural computations may differ based on the mode of synaptic input.

Clarity and Depth:

The manuscript is well-structured, with logical progression from synchronous input analyses to asynchronous and rhythmic inputs, ensuring comprehensive coverage of the topic.

Comments on revised version:

The authors have addressed all my concerns in the revised version of the manuscript.

Reviewer #2 (Public review):

Summary:

This computational work examines whether the inputs that neurons receive through electrical synapses (gap junctions) have different signatures in the extracellular local field potential (LFP) compared to inputs via chemical synapses. The authors present the results of a series of model simulations where either electric or chemical synapses targeting a single hippocampal pyramidal neuron are activated in various spatio-temporal patterns, and the resulting LFP in the vicinity of the cell is calculated and analyzed. The authors find several notable qualitative differences between the LFP patterns evoked by gap junctions vs. chemical synapses. For some of these findings, the authors demonstrate convincingly that the observed differences are explained by the electric vs. chemical nature of the input, and these results likely generalize to other cell types. However, in other cases, it remains plausible (or even likely) that the differences are caused, at least partly, by other factors (such as different intracellular voltage responses due to differences in the amplitudes and time courses of the input currents). Furthermore, it was not immediately clear to me how the results could be applied to analyze more realistic situations where neurons receive partially synchronized excitatory and inhibitory inputs via chemical and electric synapses.

Strengths:

The main strength of the paper is that it draws attention to the fact that inputs to a neuron via gap junctions are expected to give rise to a different extracellular electric field compared to inputs via chemical synapses, even if the intracellular effects of the two types of input are similar. This is because, unlike chemical synaptic inputs, inputs via gap junctions are not directly associated with transmembrane currents. This is a general result that holds independent of many details such as the cell types or neurotransmitters involved.

Another strength of the article is that the authors attempt to provide intuitive, non-technical explanations of most of their findings, which should make the paper readable also for non-expert audiences (including experimentalists).

Weaknesses:

The most problematic aspect of the paper relates to the methodology for comparing the effects of electric vs. chemical synaptic inputs on the LFP. The authors seem to suggest that the primary cause of all the differences seen in the various simulation experiments is the different nature of the input, and particularly the difference between the transmembrane current evoked by chemical synapses and the gap junctional current that does not involve the extracellular space. However, this is clearly an oversimplification: since no real attempt is made to quantitatively match the two conditions that are compared (e.g., regarding the strength and temporal profile of the inputs), the differences seen can be due to factors other than the electric vs. chemical nature of synapses. In fact, if inputs were identical in all parameters other than the transmembrane vs. directly injected nature of the current, the intracellular voltage responses and, consequently, the currents through voltage-gated and leak currents would also be the same, and the LFPs would differ exactly by the contribution of the transmembrane current evoked by the chemical synapse. This is evidently not the case for any of the simulated comparisons presented, and the differences in the membrane potential response are rather striking in several cases (e.g., in the case of random inputs, there is only one action potential with gap junctions, but multiple action potentials with chemical synapses). Consequently, it remains unclear which observed differences are fundamental in the sense that they are directly related to the electric vs. chemical nature of the input, and which differences can be attributed to other factors such as differences in the strength and pattern of the inputs (and the resulting difference in the neuronal electric response).

Some of the explanations offered for the effects of cellular manipulations on the LFP appear to be incomplete. More specifically, the authors observed that blocking leak channels significantly changed the shape of the LFP response to synchronous synaptic inputs - but only when electric inputs were used, and when sodium channels were intact. The authors seemed to attribute this phenomenon to a direct effect of leak currents on the extracellular potential - however, this appears unlikely both because it does not explain why blocking the leak conductance had no effect in the other cases, and because the leak current is several orders of magnitude smaller than the spike-generating currents that make the largest contributions to the LFP. An indirect effect mediated by interactions of the leak current with some voltage-gated currents appears to be the most likely explanation, but identifying the exact mechanism would require further simulation experiments and/or a detailed analysis of intracellular currents and the membrane potential in time and space.

In every simulation experiment in this study, inputs through electric synapses are modeled as intracellular current injections of pre-determined amplitude and time course based on the sampled dendritic voltage of potential synaptic partners. This is a major simplification that may have a significant impact on the results. First, the current through gap junctions depends on the voltage difference between the two connected cellular compartments and is thus sensitive to the membrane potential of the cell that is treated as the neuron "receiving" the input in this study (although, strictly speaking, there is no pre- or postsynaptic neuron in interactions mediated by gap junctions). This dependence on the membrane potential of the target neuron is completely missing here. A related second point is that gap junctions also change the apparent membrane resistance of the neurons they connect, effectively acting as additional shunting (or leak) conductance in the relevant compartments. This effect is completely missed by treating gap junctions as pure current sources.

One prominent claim of the article that is emphasized even in the abstract is that HCN channels mediate an outward current in certain cases. Although this statement is technically correct, there are two reasons why I do not consider this a major finding of the paper. First, as the authors acknowledge, this is a trivial consequence of the relatively slow kinetics of HCN channels: when at least some of the channels are open, any input that is sufficiently fast and strong to take the membrane potential across the reversal potential of the channel will lead to the reversal of the polarity of the current. This effect is quite generic and well-known, and is by no means specific to gap junctional inputs or even HCN channels. Second, and perhaps more importantly, the functional consequence of this reversed current through HCN channels is likely to be negligible. As clearly shown in Supplementary Figure S4, the HCN current becomes outward only for an extremely short time period during the action potential, which is also a period when several other currents are also active and likely dominant due to their much higher conductances. I also note that several of these relevant facts remain hidden in Figure 3, both because of its focus on peak values, and because of the radically different units on the vertical axes of the current plots.

Finally, I missed an appropriate validation of the neuronal model used, and also the characterization of the effects of the in silico manipulations used on the basic behavior of the model. As far as I understand, the model in its current form has not been used in other studies, although it is closely related to models used in earlier modeling work from the same laboratory. If this is the case, it would be important to demonstrate convincingly through (preferably quantitative) comparisons with experimental data using different protocols that the model captures the physiological behavior of at least the relevant compartments (in this case, the dendrites and the soma) of hippocampal pyramidal neurons sufficiently well that the results of the modeling study are relevant to the real biological system. In addition, the correct interpretation of various manipulations of the model would be strongly facilitated by investigating and discussing how the physiological properties of the model neuron are affected by these alterations.

Comments on revised version:

The authors made mainly cosmetic changes in the manuscript (primarily by adding more discussion), and most of these do not affect my earlier assessment. I have updated my Public Review in a few places to reflect those few changes that substantially address my previous concerns.

Reviewer #1 (Public review):

Summary:

The authors Hall et al. establish a purification method for snake venom metalloproteinases (SVMPs). By generating a generic approach to purify this divergent class of recombinant proteins, they enhance the field's accessibility to larger quantity SVMPs with confirmed activity and, for some, characterized kinetics. In some cases, the recombinant protein displayed comparable substrate specificity and substrate recognition compared to the native enzyme, providing convincing evidence of the authors' successful recombinant expression strategy. Beyond describing their route towards protein purification, they further provide evidence for self-activation upon Zn2+ incubation. They further provide initial insights on how to design high throughput screening (HTS) methods for drug discovery and outline future perspectives for the in-depth characterization of these enzyme classes to enable the development of novel biomedical applications.

Strengths:

The study is well presented and structured in a compelling way and the universal applicability of the approach is nicely presented.<br /> The purification strategy results in highly pure protein products, well characterized by size exclusion chromatography, SDS page as well as confirmed by mass spectrometry analysis. Further, a significant portion of the manuscript focuses on enzyme activity, thereby validating function. Particularly convincing is the comparability between recombinant vs. native enzymes; this is successfully exemplified by insulin B digestion. By testing the fluorogenic substrate, the authors provide evidence that their production method of recombinant protein can open up possibilities in HTS. Since their purification method can be applied to three structurally variable SVMP classes, this demonstrates the robust nature of the approach.

Weakness

The product obtained from the purification protocol appears to be a heterogenous mixture of self-activated and intact protein species. The protocol would benefit from improved control over the self-activation process. The authors explain well why they cannot deplete Zn2+ in cell culture or increase the pH to prevent autoactivation during the current purification steps. However, this leads me to the suggestion, if the His tag could be exchanged to a different tag that is less pH sensitive and not dependent on divalent ions (Strep-Tactin XT?) to allow for removal of divalent ions and low pH during purification steps. Another suggestion would be if they could replace the endogenous protease cleavage site in their expression construct design to a TEV protease recognition site, for example, to have more control over activation of the recombinant proteins.

The graphic to explain the universal applicability of the approach, Figure S1, has some mistakes, like duplication of text, an arrow without a meaning and should be revised.

Overall, the authors successfully purified active SVMP proteins of all three structurally diverse classes in high quality and provided convincing evidence throughout the manuscript to support their claims. The described method will be of use for a broader community working with self-activating and cytotoxic proteases.

Comment on the revised version:

I find that the clarity and overall structure of the manuscript have improved. However, the weakness I previously highlighted has neither been addressed experimentally nor convincingly explained. Therefore, the assessment stayed unchanged from my side.

Reviewer #2 (Public review):

Summary:

The aim of the study by Hall et al. was to establish a generic method for production of Snake Venom Metalloproteases (SVMPs). These have been difficult to purify in the mg quantities required for mechanistic biochemical and structural studies.

Strengths:

The authors have successfully applied the MultiBac system and describe with a high level of details, the downstream purification methods applied to purify the SVMP PI, PII and PIII. The paper carefully presents the non-successful approaches taken (such as expression of mature proteins, the use of protease inhibitors, prodomain segments and co-expression of disulfide-isomerases) before establishing the construct and expression conditions required. The authors finally convincingly describe various activity assays to demonstrate the activity of the purified enzymes in a variety of established SVMP assays.

Weaknesses:

Some experiments are difficult to perform with relevant controls (i.e. native SVMP from the venome), but authors have explained this and provided the best possible assessment.

Overall, the data presented demonstrates a very credible path for production of active SVMP for further downstream characterization. The generality of the approach to all SVMP from different snakes remains to be demonstrated by the community, but if generally applicable, the method will enable numerous studies with the aim of either utilizing SVMPS as therapeutic agents or to enable generation of specific anti-venom reagents such as antibodies or small molecule inhibitors.

Comment on the revised version:

I think the manuscript has benefited from the review and the revised version provides more clarity, is more concise and reads significantly better with the preliminary data/experiments moved to the supplements. My overall assessment of the manuscript remains unchanged.

Reviewer #1 (Public review):

Summary:

This study identifies HSD17B7 as a cholesterol biosynthesis gene enriched in sensory hair cells, with demonstrated importance for auditory behavior and potential involvement in mechanotransduction. Using zebrafish knockdown and rescue experiments, the authors show that loss of hsd17b7 reduces cholesterol levels and impairs hearing behavior. They also report a heterozygous nonsense variant in a patient with hearing loss. The gene mutation has a complex and somewhat inconsistent phenotype, appearing to mislocalize, reduce mRNA and protein levels, and alter cholesterol distribution, supporting HSD17B7 as a potential deafness gene.

The study presents an interesting deafness candidate with a complex mechanism, and highlights an underexplored role for cholesterol (and lipids) in hair cell function.

The authors were very responsive to the initial reviews, and the manuscript is now significantly stronger.

Strengths:

- HSD17B7 is a new candidate deafness gene with plausible biological relevance.

- Cross-species RNAseq convincingly shows hair-cell enrichment.

- Lipid metabolism, particularly cholesterol homeostasis, is an emerging area of interest in auditory function.

- The connection between cholesterol levels and MET is potentially impactful and, if substantiated, would represent a significant advance.

- The localization of HSD17B7 is reasonably convincing, despite the lack of a KO control: In HEI-OC1 cells, HSD17B7 localizes to the ER, as expected. In mouse hair cells, the staining pattern is cytosolic. The developmental increase between P1 and P4, and the higher expression in OHCs aligns nicely with RNAseq data.

Weaknesses:

- The pathogenic mechanism of the E182STOP variant is unclear: The mutant protein presumably does not affect WT protein localization, arguing against a dominant-negative effect. Yet, overexpression of HSD17B7-E182* alone causes toxicity in zebrafish and it binds and mislocalizes cholesterol in HEI-OC1 cells, suggesting some gain-of-function or toxic effect. In addition, the mRNA of the variant has low expression level, suggesting nonsense-mediated decay. The mechanistic conclusions of the study are therefore not as clear cut as one would had hoped, but it might just be a reflection of real biological complexity.

- The link to human deafness is based on a single heterozygous patient with no syndromic features. Given that nearly all known cholesterol metabolism disorders are syndromic, this raises concerns about causality or specificity. HSD17B7 is therefore, at this point, a candidate deafness gene, and not a fully established "novel deafness gene". This is acknowledged by the authors.

- This study does not directly investigate how reduced cholesterol levels affect MET. However, this is not a significant limitation given the study's scope, and it is reasonable that such detailed functional analyses are left to specialists in hair cell physiology.

Reviewer #2 (Public review):

A summary of what the authors were trying to achieve.

The authors aim to determine whether the gene Hsb17b7 is essential for hair cell function and, if so, to elucidate the underlying mechanism, specifically the HSB17B7 metabolic role in cholesterol biogenesis. They use animal, tissue, or data from zebrafish, mouse, and human patients.

Strengths:

(1) This is the first study of Hsb17b7 in the zebrafish (a previous report identified this gene as a hair cell marker in the mouse utricle).

(2) The authors demonstrate that Hsb17b7 is expressed in hair cells of zebrafish and the mouse cochlea.

(3) In zebrafish larvae, a likely KO of the Hsb17b7 gene causes a mild phenotype in an acoustic/vibrational assay, which also involves a motor response.

(4) In zebrafish larvae, a likely KO of the Hsb17b7 gene causes a mild reduction in lateral line neuromast hair cell number and a mild decrease in the overall mechanotransduction activity of hair cells, assayed with a fluorescent dye entering the mechanotransduction channels.

(5) When HSB17B7 is overexpressed in a cell line, it goes to the ER, and an increase in Cholesterol cytoplasmic puncta is detected. Instead, when a truncated version of HSB17B7 is overexpressed, HSB17B7 forms aggregates that co-localize with cholesterol.

(6) It seems that the level of cholesterol in crista and neuromast hair cells decreases when Hsb17b7 is defective

Comments on the revised version:

Overall, the paper has been improved, but it still needs to be moderated regarding the observed effects and their qualification. I suggest expressing each effect as % {plus minus} SD and indicating it in the main text to inform the reader.

- The title " HSD17B7 is required for the function of sensory hair cells by regulating cholesterol Synthesis" should be moderated: "affects" instead of "required" would be better.

- In the abstract "conserved and essential role for HSD17B7-mediated cholesterol biosynthesis", the term essential seems overstated and premature

- In the discussion: "Collectively, these results suggest that the heterozygous c.544G>T (p.E182*) variant contributes to auditory dysfunction through potential pathogenic mechanisms: haploinsufficiency caused by reduced"...; "could contribute" would be safer.

- In the discussion: "In summary, our study identifies HSD17B7 as a critical regulator of cholesterol synthesis in sensory hair cells and as an essential factor in normal MET and sound-evoked sensory responses. "This part is still an overstatement. The effect in zebrafish is not directly shown to affect hearing, and startle reflex impairment is mild. It is not essential.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors describe the use of BindCraft computational protein design to create a series of binders to the chemokine CCL25. This chemokine normally mediates CCR9-dependent trafficking of immune cells to the gut, making it a potential target for the treatment of inflammatory bowel disease and related conditions. Importantly, CCL25 also binds a scavenging receptor, ACKR4. The computational protein design approach used does not involve defining particular epitopes to be targeted, allowing a free search for any potential interaction surface.

Among four designs tested, three were predicted to interact at a similar site on the chemokine, while a fourth clone, VUP25111, was predicted to bind to a different site. All four designs showed binding to CCL25, with similar high-nM KD values in all cases. The first three clones showed evidence of direct competition with the receptor for CCL25 binding, while VUP25111 showed incomplete inhibition of binding. In functional assays, all clones acted as antagonists except for VUP25111, which inhibited arrestin recruitment by CCR9, but did not affect G protein activation by CCR9 or arrestin recruitment by ACKR4 (which signals exclusively through arrestin and not G protein).

Strengths:

The work is completed to a high technical standard, and the functional diversity of the clones is intriguing. It is exciting to see pathway-selective modulation of signaling, and this basic paradigm is likely to generalize to other chemokine/receptor systems. The exceptional complexity of chemokine signaling makes this an excellent area to explore the development of modulators that can restrict signaling to a specific subset of receptors.

Weaknesses:

No major weaknesses were noted by this reviewer.

Reviewer #2 (Public review):

This study from de Boer, Lamme, Verdwaald and Schafer describes the de novo AI-guided design of miniproteins that target the chemokine CCL25, with the aim to modulate the activation and signalling of the chemokine receptors CCR9 and ACKR4. The study focuses on characterising four miniproteins that all bind CCL25 with good affinity. Three designs appear to prevent CCL25 binding to both CCR9 and ACKR4, with increasing concentrations of miniproteins resulting in decreased arrestin (both receptors) and mini G protein recruitment (CCR9), less inhibition of forskolin-stimulated cAMP (CCR9), and decreased GRK3 recruitment and receptor internalisation (CCR9). One miniprotein, VUP25111, changes the properties of CCL25 rather than preventing ligand/receptor interactions, resulting in greater selectivity for CCR9 over ACKR4 and a G protein-biased signalling profile (maintenance of mini G protein recruitment, GRK3 recruitment, inhibition of cAMP and receptor internalisation, but loss of arrestin recruitment). VUP25111 also maintained chemotactic migration in MOLT-4 T lymphoblast cells, whereas this response was lost in the presence of the other three miniproteins.

Overall, this is a very interesting application of AI-designed de novo miniproteins to modulate GPCR responses by directly binding the ligand rather than the receptor. This is a conceptually very intriguing approach that could, in principle, be extended to other GPCR systems beyond the chemokine family. The authors deploy an impressive array of assays spanning multiple signalling endpoints, providing a thorough picture of how each miniprotein influences receptor activation and downstream signalling. The presentation of concentration-response relationships for CCL25 alone and in the presence of each miniprotein is particularly informative, and the figures are very well constructed throughout. The inclusion of clear cartoons illustrating the basis of each assay is a nice touch that will help readers from outside the immediate field follow the logic of each experiment.

There are two main conclusions that are not currently as well-supported by the evidence as they might be, and that would benefit from some qualification. The first concerns the selectivity of the miniproteins for CCL25. Testing the impact of the miniproteins on CXCL12 activation of CXCR4 is an important and welcome experiment, but it addresses selectivity against only one other chemokine system, and the current claim of specificity is therefore stronger than the data allow. Additionally, at the highest concentration tested (10 µM), the more potent miniproteins (VUP25101, VUP25107) appear to show some inhibition of arrestin recruitment to CXCR4 - perhaps unsurprising given the degree of structural conservation among chemokines. The statements regarding selectivity and the lack of effect on the CXCL12/CXCR4 system would benefit from revision to more accurately reflect these observations.

The second concern relates to the interpretation of the preserved GRK3 recruitment, but the complete loss of arrestin recruitment observed with VUP25111. In the GRK3 recruitment experiments, 20 nM CCL25 was used, representing an EC40 concentration in this assay. VUP25111 causes a concentration-dependent reduction in CCL25-induced GRK3 recruitment, down to approximately 15% of the maximal response. It is worth considering whether this degree of reduction in GRK3 recruitment could itself be sufficient to disrupt patterns of receptor phosphorylation and thereby prevent observable arrestin recruitment. Both interpretations are complicated by the fact that the GRK3 recruitment and arrestin recruitment assays likely differ in their sensitivity and dynamic windows, making direct quantitative comparisons between them difficult. In the absence of direct measurements of CCR9 phosphorylation in the presence of VUP25111, the alternative interpretation remains open and would benefit from acknowledgement. Given recent work from the same group demonstrating that receptor internalisation is only partially dependent on arrestins (Lamme et al., 2025, J Biol Chem), further discussion of the relationship between GRK and arrestin recruitment and CCR9 internalisation would be of value to the broader GPCR audience this work is likely to attract.

Finally, some additional justification for the use of 20 nM CCL25 across all assays would strengthen the study, as this concentration represents different points on the concentration-response curve depending on the assay and receptor in question. It ranges from an EC40 for CCR9 GRK3 recruitment and internalisation, to an EC50 for CCR9 arrestin and mini-Gi recruitment, an EC80 for CCR9 cAMP inhibition, and an EMax for ACKR4 arrestin recruitment. This has potential consequences for the interpretation and cross-assay comparison of miniprotein potency, and the authors are encouraged to acknowledge and discuss this in the context of their conclusions.

Reviewer #3 (Public review):

Summary:

The authors employed the BindCraft platform to develop binders targeting the chemokine CCL25, a selective activator of the chemokine receptor CCR9. They successfully generated two classes of binders: one that inhibits all CCL25-mediated CCR9 activation, and another that permits CCR9 G protein signaling while simultaneously preventing arrestin recruitment. These tools will enable the dissection of arrestin involvement in regulating cell migration.

My comments, in the order of reading:

(1) Title: I strongly recommend removing the term "biasing" from the title. In this context, it does not convey a specific mechanistic concept. The term "biased signaling" has been used for a very broad range of mechanistically distinct pharmacological phenomena, and without a precise definition, it adds more confusion than clarity. I therefore suggest refraining from using it in the title.

(2) Abstract, line 34: The term "bias" should be replaced. As currently used, it appears to suggest a dichotomy between G protein signaling and arrestin recruitment. However, arrestin recruitment is a consequence of G protein signaling, and it is not conceptually sound to compare a signaling event mediated by one protein family with a protein-protein interaction involving another protein family. A meaningful comparison requires experimental paradigms that differ by a single variable; in this case, there are two - distinct protein families and fundamentally different types of readouts (signaling versus protein-protein interaction).

(3) Abstract, line 34: The term "balanced agonist" should be removed. Any chosen reference ligand is, by definition, the "balanced" agonist for that analysis, regardless of its actual signaling profile. Consequently, the expression "balanced agonist" adds no mechanistic information beyond "the agonist used as reference in a particular bias calculation" and is potentially misleading, as it implies that this ligand possesses a uniquely unbiased, system‑independent signaling profile, which is not the case.

(4) Abstract, line 36: I also recommend removing the term "bias" at this point. The concept of bias typically arises from experiments that quantitatively compare more than one variable. As currently written, the phrasing suggests a dichotomy between G protein- and arrestin-mediated signaling, yet the study does not assess arrestin signaling, only arrestin recruitment. Under these conditions, the use of "bias" is not appropriate. The data are clear and compelling on their own without the need for this potentially misleading terminology.

(5) Introduction: This is interesting to read and generally well written, though certain statements would benefit from improved semantic precision. For example, in lines 110-111, the phrase "G protein-biased complex" should be reconsidered, as it relies on the notion of G protein- versus arrestin-mediated signaling. Arrestins themselves do not signal; what is measured here is their recruitment. Comparing G protein signaling with arrestin recruitment is therefore conceptually unsound, since arrestin engagement is a downstream consequence of G protein activation. Comparisons become meaningful only when designed to differentiate between G protein-dependent and G protein-independent arrestin recruitment, which is not the case in this study.

(6) Results, 122,123: The authors should consider being more precise; possibly, the truncated CCL25 is somewhat less potent on CCR9. The authors should make a statistical test and then decide whether to rephrase or not for enhanced precision.

(7) Figure S5: This figure is currently confusing and needs clarification. The authors state in the main text that CXCR4 is stimulated with CXCL12, yet the figure legend refers to CCL25; this discrepancy should be corrected to ensure consistency. In addition, inhibition of CXCR4 by the miniprotein binders should be analyzed and presented with normalization to CXCR4 responses, not to CCL25-stimulated CCR9. To avoid misinterpretation, inhibition by the miniproteins should be quantified separately for CCR9 and CXCR4, each normalized to its own receptor-specific and functionally equivalent stimulation condition, rather than to the "other" receptor.

(8) Results, lines 211-213: The authors should be more semantically precise. They state that no binder has any effect on arrestin recruitment to CXCR4. If I see the data, this is not really true, as 25101 and 25107 inhibit arrestin recruitment by about 50 % or more at the highest applied concentrations; only 111 and 112 are completely inactive. As already commented, normalization should be done to arrestin recruitment of CXCR4 and not CCR9.

Reviewer #1 (Public review):

This manuscript makes a significant contribution to the field by exploring the dichotomy between chemical synaptic and gap junctional contributions to extracellular potentials. While the study is comprehensive in its computational approach, adding experimental validation, network-level simulations, and expanded discussion on implications would elevate its impact further.

Strengths:

Novelty and Scope:<br /> The manuscript provides a detailed investigation into the contrasting extracellular field potential (EFP) signatures arising from chemical synapses and gap junctions, an underexplored area in neuroscience.<br /> It highlights the critical role of active dendritic processes in shaping EFPs, pushing forward our understanding of how electrical and chemical synapses contribute differently to extracellular signals.

Methodological Rigor:<br /> The use of morphologically and biophysically realistic computational models for CA1 pyramidal neurons ensures that the findings are grounded in physiological relevance.<br /> Systematic analysis of various factors, including the presence of sodium, leak, and HCN channels, offers a clear dissection of how transmembrane currents shape EFPs.

Biological Relevance:<br /> The findings emphasize the importance of incorporating gap junctional inputs in analyses of extracellular signals, which have traditionally focused on chemical synapses.<br /> The observed polarity differences and spectral characteristics provide novel insights into how neural computations may differ based on the mode of synaptic input.

Clarity and Depth:<br /> The manuscript is well-structured, with a logical progression from synchronous input analyses to asynchronous and rhythmic inputs, ensuring comprehensive coverage of the topic.

Weaknesses and Areas for Improvement:

Generality and Validation:<br /> The study focuses exclusively on CA1 pyramidal neurons. Expanding the analysis to other cell types, such as interneurons or glial cells, would enhance the generalizability of the findings.<br /> Experimental validation of the computational predictions is entirely absent. Empirical data correlating the modeled EFPs with actual recordings would strengthen the claims.

Role of Active Dendritic Currents:<br /> The paper emphasizes active dendritic currents, particularly the role of HCN channels in generating outward currents under certain conditions. However, further discussion of how this mechanism integrates into broader network dynamics is warranted.

Analysis of Plasticity:<br /> While the manuscript mentions plasticity in the discussion, there are no simulations that account for activity-dependent changes in synaptic or gap junctional properties. Including such analyses could significantly enhance the relevance of the findings.

Frequency-Dependent Effects:<br /> The study demonstrates that gap junctional inputs suppress high-frequency EFP power due to membrane filtering. However, it could delve deeper into the implications of this for different brain rhythms, such as gamma or ripple oscillations.

Visualization:<br /> Figures are dense and could benefit from more intuitive labeling and focused presentations. For example, isolating key differences between chemical and gap junctional inputs in distinct panels would improve clarity.

Contextual Relevance:<br /> The manuscript touches on how these findings relate to known physiological roles of gap junctions (e.g., in gamma rhythms) but does not explore this in depth. Stronger integration of the results into known neural network dynamics would enhance its impact.

Suggestions for Improvement:

Broader Application:<br /> Simulate EFPs in multi-neuron networks to assess how the findings extend to network-level interactions, particularly in regions with mixed synaptic connectivity.

Experimental Correlation:<br /> Collaborate with experimental groups to validate the computational predictions using in vivo or in vitro recordings.

Mechanistic Insights:<br /> Provide a more detailed mechanistic explanation of how specific ionic currents (e.g., HCN, sodium, leak) interact during gap junctional vs. chemical synaptic inputs.

Implications for Neural Coding:<br /> Discuss how the observed differences in EFP signatures might influence neural coding, especially in circuits with heavy gap junctional connectivity.

Reviewer #2 (Public review):

Summary:

This computational work examines whether the inputs that neurons receive through electrical synapses (gap junctions) have different signatures in the extracellular local field potential (LFP) compared to inputs via chemical synapses. The authors present the results of a series of model simulations where either electric or chemical synapses targeting a single hippocampal pyramidal neuron are activated in various spatio-temporal patterns, and the resulting LFP in the vicinity of the cell is calculated and analyzed. The authors find several notable qualitative differences between the LFP patterns evoked by gap junctions vs. chemical synapses. For some of these findings, the authors demonstrate convincingly that the observed differences are explained by the electric vs. chemical nature of the input, and these results likely generalize to other cell types. However, in other cases, it remains plausible (or even likely) that the differences are caused, at least partly, by other factors (such as different intracellular voltage responses due to, e.g., the unequal strengths of the inputs). Furthermore, it was not immediately clear to me how the results could be applied to analyze more realistic situations where neurons receive partially synchronized excitatory and inhibitory inputs via chemical and electric synapses.

Strengths:

The main strength of the paper is that it draws attention to the fact that inputs to a neuron via gap junctions are expected to give rise to a different extracellular electric field compared to inputs via chemical synapses, even if the intracellular effects of the two types of input are similar. This is because, unlike chemical synaptic inputs, inputs via gap junctions are not directly associated with transmembrane currents. This is a general result that holds independent of many details such as the cell types or neurotransmitters involved.

Another strength of the article is that the authors attempt to provide intuitive, non-technical explanations of most of their findings, which should make the paper readable also for non-expert audiences (including experimentalists).

Weaknesses:

The most problematic aspect of the paper relates to the methodology for comparing the effects of electric vs. chemical synaptic inputs on the LFP. The authors seem to suggest that the primary cause of all the differences seen in the various simulation experiments is the different nature of the input, and particularly the difference between the transmembrane current evoked by chemical synapses and the gap junctional current that does not involve the extracellular space. However, this is clearly an oversimplification: since no real attempt is made to quantitatively match the two conditions that are compared (e.g., regarding the strength and temporal profile of the inputs), the differences seen can be due to factors other than the electric vs. chemical nature of synapses. In fact, if inputs were identical in all parameters other than the transmembrane vs. directly injected nature of the current, the intracellular voltage responses and, consequently, the currents through voltage-gated and leak currents would also be the same, and the LFPs would differ exactly by the contribution of the transmembrane current evoked by the chemical synapse. This is evidently not the case for any of the simulated comparisons presented, and the differences in the membrane potential response are rather striking in several cases (e.g., in the case of random inputs, there is only one action potential with gap junctions, but multiple action potentials with chemical synapses). Consequently, it remains unclear which observed differences are fundamental in the sense that they are directly related to the electric vs. chemical nature of the input, and which differences can be attributed to other factors such as differences in the strength and pattern of the inputs (and the resulting difference in the neuronal electric response).

Some of the explanations offered for the effects of cellular manipulations on the LFP appear to be incomplete. More specifically, the authors observed that blocking leak channels significantly changed the shape of the LFP response to synchronous synaptic inputs - but only when electric inputs were used, and when sodium channels were intact. The authors seemed to attribute this phenomenon to a direct effect of leak currents on the extracellular potential - however, this appears unlikely both because it does not explain why blocking the leak conductance had no effect in the other cases, and because the leak current is several orders of magnitude smaller than the spike-generating currents that make the largest contributions to the LFP. An indirect effect mediated by interactions of the leak current with some voltage-gated currents appears to be the most likely explanation, but identifying the exact mechanism would require further simulation experiments and/or a detailed analysis of intracellular currents and the membrane potential in time and space.

In every simulation experiment in this study, inputs through electric synapses are modeled as intracellular current injections of pre-determined amplitude and time course based on the sampled dendritic voltage of potential synaptic partners. This is a major simplification that may have a significant impact on the results. First, the current through gap junctions depends on the voltage difference between the two connected cellular compartments and is thus sensitive to the membrane potential of the cell that is treated as the neuron "receiving" the input in this study (although, strictly speaking, there is no pre- or postsynaptic neuron in interactions mediated by gap junctions). This dependence on the membrane potential of the target neuron is completely missing here. A related second point is that gap junctions also change the apparent membrane resistance of the neurons they connect, effectively acting as additional shunting (or leak) conductance in the relevant compartments. This effect is completely missed by treating gap junctions as pure current sources.

One prominent claim of the article that is emphasized even in the abstract is that HCN channels mediate an outward current in certain cases. Although this statement is technically correct, there are two reasons why I do not consider this a major finding of the paper. First, as the authors acknowledge, this is a trivial consequence of the relatively slow kinetics of HCN channels: when at least some of the channels are open, any input that is sufficiently fast and strong to take the membrane potential across the reversal potential of the channel will lead to the reversal of the polarity of the current. This effect is quite generic and well-known and is by no means specific to gap junctional inputs or even HCN channels. Second, and perhaps more importantly, the functional consequence of this reversed current through HCN channels is likely to be negligible. As clearly shown in Supplementary Figure S3, the HCN current becomes outward only for an extremely short time period during the action potential, which is also a period when several other currents are also active and likely dominant due to their much higher conductances. I also note that several of these relevant facts remain hidden in Figure 3, both because of its focus on peak values, and because of the radically different units on the vertical axes of the current plots.

Finally, I missed an appropriate validation of the neuronal model used, and also the characterization of the effects of the in silico manipulations used on the basic behavior of the model. As far as I understand, the model in its current form has not been used in other studies. If this is the case, it would be important to demonstrate convincingly through (preferably quantitative) comparisons with experimental data using different protocols that the model captures the physiological behavior of at least the relevant compartments (in this case, the dendrites and the soma) of hippocampal pyramidal neurons sufficiently well that the results of the modeling study are relevant to the real biological system. In addition, the correct interpretation of various manipulations of the model would be strongly facilitated by investigating and discussing how the physiological properties of the model neuron are affected by these alterations.

Reviewer #1 (Public review):

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

Summary:

This study resolves a cryo-EM structure of the GPCR, GPR30, in the presence of bicarbonate, which the author's lab recently identified as the physiological ligand. Understanding the ligand and the mechanism of activation is of fundamental importance to the field of receptor signaling. This solid study provides important insight into the overall structure and suggests a possible bicarbonate binding site.

Strengths:

The overall structure, and proposed mechanism of G-protein coupling are solid. Based on the structure, the authors identify a binding pocket that might accommodate bicarbonate. Although assignment of the binding pocket is speculative, extensive mutagenesis of residues in this pocket identifies several that are important to G-protein signaling. The structure shows some conformational differences with a previous structure of this protein determined in the absence of bicarbonate (PMC11217264). To my knowledge, bicarbonate is the only physiological ligand that has been identified for GPR30, making this study an important contribution to the field. However, the current study provides novel and important circumstantial evidence for the bicarbonate binding site based on mutagenesis and functional assays.

Weaknesses:

Bicarbonate is a challenging ligand for structural and biochemical studies, and because of experimental limitations, this study does not elucidate the exact binding site. Higher resolution structures would be required for structural identification of bicarbonate. The functional assay monitors activation of GPR30, and thus reports on not only bicarbonate binding, but also the integrity of the allosteric network that transduces the binding signal across the membrane. However, biochemical binding assays are challenging because the binding constant is weak, in the mM range.

The authors appropriately acknowledge the limitations of these experimental approaches, and they build a solid circumstantial case for the bicarbonate binding pocket based on extensive mutagenesis and functional analysis. However, the study does fall short of establishing the bicarbonate binding site.

Reviewer #2 (Public review):

Summary:

In this manuscript, "Cryo-EM structure of the bicarbonate receptor GPR30," the authors aimed to enrich our understanding of the role of GPR30 in pH homeostasis by combining structural analysis with a receptor function assay. This work is a natural development and extension of their previous work on Nature Communications (PMID: 38413581). In the current body of work, they solved the cryo-EM structure of the human GPR30-G-protein (mini-Gsqi) complex in the presence of bicarbonate ions at 3.15 Å resolution. From the atomic model built based on this map, they observed the overall canonical architecture of class A GPCR and also identified 3 extracellular pockets created by ECLs (Pockets A-C). Based on the polarity, location, size, and charge of each pocket, the authors hypothesized that pocket A is a good candidate for the bicarbonate binding site. To identify the bicarbonate binding site, the authors performed an exhaustive mutant analysis of the hydrophilic residues in Pocket A and analyzed receptor reactivity via calcium assay. In addition, the human GPR30-G-protein complex model also enabled the authors to elucidate the G-protein coupling mechanism of this special class A GPCR, which plays a crucial role in pH homeostasis.

Strengths:

As a continuation of their recent Nature Communications publication, the authors used cryo-EM coupled with mutagenesis and functional studies to elucidate bicarbonate-GPR30 interaction. This work provided atomic-resolution structural observations for the receptor in complex with G-protein, allowing us to explore its mechanism of action, and will further facilitate drug development targeting GPR30. There were 3 extracellular pockets created by ECLs (Pockets A-C). The authors were able to filter out 2 of them and hypothesized that pocket A was a good candidate for the bicarbonate binding site based on the polarity, location, and charge of each pocket. From there, the authors identified the key residues on GPR30 for its interaction with the substrate, bicarbonate. Together with their previous work, they mapped out amino acids that are critical for receptor reactivity.

Weaknesses:

When we see a reduction of a GPCR-mediated downstream signaling, several factors could potentially contribute to this observation: 1) a reduced total expression of this receptor due to the mutation (transcription and translation issue); 2) a reduced surface expression of this receptor due to the mutation (trafficking issue); and 3) a dysfunctional receptor that doesn't signal due to the mutation.

Altogether, the wide range of surface expression across the different cell lines, combined with the different receptor function readouts, makes the cell functional data only partially support their structural observations.

Reviewer #3 (Public review):

Summary

GPR30 responds to bicarbonate and plays a role in regulating cellular pH and ion homeostasis. However, the molecular basis of bicarbonate recognition by GPR30 remains unresolved. This study reports the cryo-EM structure of GPR30 bound to a chimeric mini-Gq in the presence of bicarbonate, revealing mechanistic insights into its G-protein coupling. Nonetheless, the study does not identify the bicarbonate-binding site within GPR30.

Strengths

The work provides strong structural evidence clarifying how GPR30 engages and couples with Gq.

Weaknesses

Several GPR30 mutants exhibited diminished responses to bicarbonate, but their expression levels were also reduced. As a result, the mechanism by which GPR30 recognizes bicarbonate remains uncertain.

Reviewer #1 (Public review):

Summary:

This study identifies NK2R as an intestinal GPCR that tunes enterocyte lipid uptake, lipid droplet storage, and chylomicron output, with loss or antagonism enhancing post‑prandial triglyceridemia and epithelial lipid stores, and agonism reducing adiposity and improving glycemia in DIO mice. Through bulk RNA‑seq, deconvolution, DSS colitis, and 16S profiling, the authors link Tacr2 deletion to coordinated induction of epithelial lipid‑metabolic programs, dampened immune gene expression, sex‑specific remodeling of secretory lineages, and male‑biased protection from experimental colitis despite dysbiotic microbiota. This is an overall important and thorough paper on an emerging obesity drug target, but it should temper some interpretations, and the following points would be needed to strengthen the claims in the manuscript.

Strengths:


The study uses an impressive combination of genetic loss‑of‑function, pharmacological agonism/antagonism, transcriptomics, and in vivo physiology to establish NK2R as a bidirectional regulator of epithelial lipid handling. The integration of RNA‑seq, epithelial cell‑type deconvolution, DSS colitis, and microbiome profiling provides a rich, systems‑level view of how Tacr2 deletion reshapes epithelial metabolism, lineage allocation, and inflammatory responsiveness in a sex‑specific manner. The gain- and loss‑of‑function data particularly support a model in which NK2R acts as an epithelial metabolic rheostat that restrains lipid absorption and chylomicron export, with downstream consequences for barrier fitness and immune tone.

Weaknesses:

Major points

While the data convincingly establish NK2R's role in epithelial lipid handling, the manuscript arguably overstates a primary "pro‑inflammatory" function for NK2R, given that Tacr2‑/‑ mice show enhanced enterocyte lipid uptake and storage, higher post‑prandial triglycerides, and a dysbiotic microbiota yet reduced mucosal immune gene expression and, in males, protection from DSS colitis. It remains equally plausible that the apparent "protection" reflects a mucosa that is less reactive to unfavorable microbiota rather than genuinely protected, and that NK2R's main function is metabolic, with immune changes emerging secondarily. Such a model would actually help reconcile the long-standing question as to why NK2R antagonism has not translated into clear benefit in clinical trials for GI inflammation over the past several decades.

Without temporal resolution, it is equally plausible that antagonists primarily perturb epithelial lipid homeostasis rather than directly and beneficially modulating immune tone. To discriminate between these possibilities and strengthen the potential direct inflammatory claims, the authors should:

(1) generate epithelial‑specific, immune‑cell-specific, and nociceptor‑specific Tacr2 deletions in the DSS model

(2) test gut‑restricted NK2R agonism versus antagonism under controlled dietary fat conditions for effects on LD load, barrier integrity, and colitis severity

(3) perform ex vivo tachykinin/NK2R stimulation of isolated epithelial versus immune compartments with functional readouts

(4) assess whether microbiota transfer from Tacr2‑/‑ versus WT donors into germ‑free or antibiotic‑treated recipients can recapitulate protection or susceptibility independently of epithelial NK2R status.

Minor points

Additional clarifications on Tac1 and tachykinin receptor expression in male/female colitis models, and validation of the NK2R antibody in KO tissue (or in situ hybridization), would also be needed to strengthen key mechanistic and localization claims.

Reviewer #2 (Public review):

Summary:

This manuscript- "NK2R signaling governs intestinal lipid mobilization and mucosal inflammation" by Perez et al investigates the role of the neurokinin-2 receptor (NK2R) as a regulatory node connecting intestinal lipid metabolism, mucosal immunity, and the gut microbiome. The authors utilized a ubiquitous deleted Tacr2 mouse model alongside targeted pharmacological treatments to demonstrate that NK2R limits luminal lipid uptake and chylomicron secretion. Additionally, the study uncovers that Tacr2 deficiency promotes male-biased protection against DSS-induced colitis and drives distinct diet- and genotype-dependent shifts in the fecal microbiota.

Strengths:

(1) The authors successfully utilized both a genetic whole-body knockout model (Tacr2-/-) and targeted pharmacological agents, such as the antagonist GR159897 and the agonist EB1002. This dual approach effectively corroborates the core phenotypic findings.

(2) The study provides a compelling case for targeting the tachykinin-NK2R axis therapeutically. The remarks that NK2R agonists could be leveraged to treat obesity, while antagonists might be used for inflammatory bowel disease, will be an exciting clinical outcome if further validated.

(3) The integration of RNAseq for epithelial lineage analysis, combined with in vivo gut permeability assays, lipid tolerance assays, and 16S microbiome sequencing, provides a robust and highly detailed physiological picture.

Weaknesses:

This manuscript has some notable limitations. While the transcriptomic data show an upregulation of the enterocyte lipid droplet program in Tacr2-/- mice, the manuscript lacks biochemical experiments to conclude the downstream signaling mechanism driving such changes. The reliance on a global whole-body knockout model confounds the ability to definitively conclude that the observed metabolic and inflammatory phenotypes are linked to the intestinal epithelium. The authors discuss a male-biased protection against DSS-induced colitis, but they rely on speculation regarding sex hormones rather than providing experimental data to explain this dimorphism.

Reviewer #1 (Public review):

Summary:

The authors have attempted to establish a role for XAP5, a transcriptional regulator they have previously identified for flagellar biogenesis in Chlamydomonas and mice, in primary cilia differentiation.

Strengths:

Genetic and biochemical analysis using a cultured mouse cell line, NIH3T3.

Weaknesses:

(1) The authors have ignored established data that, like in C. elegans and Drosophila, there is in vivo genetic evidence that primary cilia formation is regulated by the RFX transcriptional module (for example, PMID 19887680, PMID 29510665).

(2) The analysis with one mammalian cell line, NIH3T3, while done quite rigorously, is not sufficient. Also, the effect on cilia differentiation is very modest - a shortening of cilia length on XAP5, NONO and SOX5 knockout - which can happen for a variety of reasons, especially in culture conditions. In my view, this relatively mild phenotype does not establish that the XAP5/NONO and SOX5 axis is an important regulator of primary cilia differentiation.

(3) The lack of any data that validates the findings in the model vertebrate is a major weakness of this paper. Validation using clean genetics (whole body knockouts or tissue-specific conditional knockouts) is absolutely essential for these data to be acceptable.

Reviewer #2 (Public review):

Summary:

This study investigates how evolutionarily conserved transcription factors are repurposed to regulate the functional diversification of cilia. Building on previous work identifying Xap5 as a regulator of motile ciliogenesis during spermatogenesis, the authors now propose a broader role for Xap5 as a master regulator of primary ciliogenesis. Through extensive mechanistic analyses, they identify an Xap5-NONO-SOX transcriptional axis and suggest that this module contributes to ciliary diversity and may be implicated in ciliopathies.

Overall, the work addresses an important and timely question regarding the transcriptional control of primary ciliogenesis. However, additional evidence is required to fully support the proposed conceptual framework linking evolutionary conservation to functional specialization.

Strengths:

(1) Addresses a timely and fundamental question in cilia biology.

(2) Extends Xap5 function beyond motile ciliogenesis.

(3) Identifies a novel regulatory axis (Xap5-NONO-SOX).

(4) Combines multiple well-designed mechanistic approaches.

(5) Proposes an interesting conceptual framework linking evolution and ciliogenesis.

Weaknesses:

(1) Specificity for primary ciliogenesis not demonstrated.

(2) No data on motile ciliogenesis in somatic MCCs.

(3) Conclusions drawn from NIH/3T3 cells (murine stromal cells).

(4) GC-rich motif identified but underexplored.

(5) Link to ciliopathies is speculative.

Reviewer #1 (Public review):

Summary:

Heller et al use a murine model of AIRE deficiency, a disease that leads to systemic autoimmune disease, to demonstrate differential effects of selective JAK inhibitors. This group and others have previously demonstrated the efficacy of the JAK1/2 inhibitor ruxolitinib in patients with AIRE deficiency. Here, they focus on the ability of ruxolitinib versus drugs inhibiting either JAK1, JAK2, or JAK3 to alter organ pathology and accumulation of interferon-gamma producing immune cells in the lungs, which are important mediators of inflammation in patients with this disease. The current study provides evidence that selective JAK2 or JAK1 both reduce disease in this mouse model. There is potentially a more beneficial effect of selective JAK2 inhibition, although these differences are minor, and it is uncertain whether this is clinically relevant for patients. They demonstrate that inhibition of JAK3 alone in the mouse was clearly not beneficial for disease. Overall, this study provides evidence for consideration of more selective JAK inhibition in patients with AIRE deficiency.

Strengths:

(1) Robust model for investigating AIRE deficiency.

(2) They combine cellular studies (immune cell production of IFN-g) and robust organ pathology scoring to evaluate the effects of the drugs tested here.

(3) Data clearly demonstrates that JAK3 inhibition, at least as used here, may increase IFN-g production and does not reduce organ pathology.

Weaknesses:

(1) There is no direct comparison of the effects of JAK2 vs. JAK1 inhibition to support that JAK2 inhibition is clearly superior.

(2) They were not able to perform pharmacokinetic studies or measure the efficacy of JAK inhibition in their model, and it is uncertain how the doses of drug used here will translate to the treatment of patients.

(3) It is uncertain whether this study, performed in a murine model, will correspond to tissue/cell specificity of JAK inhibition in patients.

Reviewer #2 (Public review):

Summary:

This work from Heller et al. examines the differential responses of treatment with selective JAK inhibitors in Aire knockout mice, which develop several autoimmune diseases. The authors had previously shown efficacious responses in both mice and humans with a broader JAK-I, Ruxolitinib, that had Aire-deficiency. Because of the side effect profile, it may be better to determine if selective JAK-I therapy could continue to work with less of the side effects of Ruxolitinib. Here, they develop a protocol of treating mice for four weeks with JAK1,2, and 3 inhibitors and then examining tissues for infiltration of T cells and gamma-interferon-producing T cells. They also perform analyses of infiltration of the tissues versus intravascular localization of T cells. They find that JAK2 inhibition provided the most robust results for decreasing infiltrates and gamma interferon-producing T cells. All JAK-I's resulted in decreased T cell infiltration of tissues, and somewhat paradoxically, the JAK3 inhibitor caused an increased accumulation of gamma-interferon-producing T cells in tissues.

Strengths:

This is a nice set of studies that makes some inroads on a more refined approach to treating autoimmunity in the Aire knockout model. The work here will be important for developing the next clinical trial for patients with APS1 and represents an advance for efforts in that space.

Weaknesses:

The increase in gamma-interferon-producing cells in tissues with JAK3 inhibition is interesting, but essentially remains unanswered in any way. There is a minimal assessment of the broad STAT pathways that the selective JAK-i's could be hitting, and perhaps that could be assessed more systematically. Finally, there is no pharmacokinetic data, which makes comparisons between the treatments a bit limited.

大多数人认为同行评审的核心价值在于主观判断和批判性思维，但作者主张将客观检查自动化，让人类评审员专注于更高级的判断。这一观点挑战了同行评审在学术质量控制中的传统角色。

Reviewer #1 (Public review):

Summary:

This study examines the role of the long non-coding RNA Dreg1 in regulating Gata3 expression and ILC2 development. Using Dreg1 deficient mice, the authors show a selective loss of ILC2s but not T or NK cells, suggesting a lineage-specific requirement for Dreg1. By integrating public chromatin and TF-binding datasets, they propose a Tcf1-Dreg1-Gata3 regulatory axis. The topic is relevant for understanding epigenetic regulation of ILC differentiation.

Strengths:

(1) Clear in vivo evidence for a lineage-specific role of Dreg1.

(2) Comprehensive integration of genomic datasets.

(3) Cross-species comparison linking mouse and human regulatory regions.

Weaknesses:

(1) Mechanistic conclusions remain correlative, relying on public data.

(2) Lack of direct chromatin or transcriptional validation of Tcf1-mediated regulation.

(3) Human enhancer function is not experimentally confirmed.

(4) Insufficient methodological detail and limited mechanistic discussion.

Comments on revisions:

The authors have provided clear evidence that Dreg1 is necessary for ILC2 development, but their refusal to perform any mechanistic experiment remains a significant weakness. While their appeal to the 3Rs and the use of public datasets is noted, re-analyzing external data from heterogeneous sources cannot substitute for direct, internal validation of the Tcf1-Dreg1-Gata3 axis in their specific knockout model. This is particularly problematic because ILC2 progenitors, though rare, can be isolated from bone marrow, especially since assays like CUT&Tag and others are specifically designed for low cell numbers. By relying on public T-cell CRISPR screens to justify human ILC2 functions, the authors are substituting cross-cell-type correlation for definitive functional proof. Consequently, the manuscript currently describes a discovery of necessity without providing a verified molecular mechanism, which should be more explicitly reflected in the title and conclusions.

Reviewer #2 (Public review):

The authors investigate the role of the long non-coding RNA Dreg1 for the development, differentiation or maintenance of group 2 ILC (ILC2). Dreg1 is encoded close to the Gata3 locus, a transcription factor implicated in the differentiation of T cells and ILC, and in particular of type 2 immune cells (i.e., Th2 cells and ILC2). The center of the paper is the generation of a Dreg1-deficient mouse. The role of Dreg1 in ILC2 was documented by mixed bone marrow experiments. While Dreg1-/- mice did not show any profound ab T or gd T cell, ILC1, ILC3 and NK cell phenotypes, ILC2 frequencies were reduced in various organs tested (small intestine, lung, visceral adipose tissue). In the bone marrow, immature ILC2 or ILC2 progenitors were reduced whereas a common ILC progenitor was overrepresented suggesting a differentiation block. Using ATAC-seq, the authors find the promoter of Dreg1 is open in early lymphoid progenitors and the acquisition of chromatin accessibility downstream correlates with increased Dreg1 expression in ILC2 progenitors. Examining publicly available Tcf1 CUT&Run data, they find that Tcf1 was specifically bound to the accessible sites of the Dreg1 locus in early innate lymphoid progenitors. Finally, the syntenic region in the human genome contains two non-coding RNA genes with an expression pattern resembling mouse Dreg1.

The topic of the manuscript is interesting. The article is focused on the first description of the Dreg1 knockout mouse and the specific effect of Dreg1 deficiency on ILC2 development.

(1) The data of how Dreg1 contributes to the differentiation and or maintenance of ILC2 is not addressed at a very definitive level. Does Dreg1 affect Gata3 expression, mRNA stability or turnover in ILC2? Previous work of the authors indicated that knock-down of Dreg1 does not affect Gata3 expression (PMID: 32970351). The current data (Figure 2H) showed small differences in Gata3 expression in CHILP which were, however, not statistically significant. No differences were found in ILCP and ILC2P.

(2) How Dreg1 exactly affects ILC2 differentiation remains unclear.

Reviewer #1 (Public review):

In this manuscript, the authors combine single-nucleus RNA sequencing with spatial transcriptomics to generate a spatiotemporal atlas of mouse placental development and explore the role of glycogen trophoblast cells in fetal viability. The study integrates several computational approaches, including trajectory analysis, regulatory network inference, and spatial mapping, together with histology and glycogen measurements. Based on these analyses, the authors propose that glycogen trophoblast cells provide metabolic support that is important for maintaining placental function and fetal survival.

One of the main strengths of the study is the quality and scope of the dataset. The integration of snRNA-seq with Stereo-seq spatial transcriptomics provides a detailed view of placental organization across regions and developmental stages. This type of combined spatial and transcriptional analysis is still relatively rare in placental biology and represents an important contribution to the field. The atlas itself will likely be a valuable resource for future studies.

Another strength is the effort to connect transcriptional findings with tissue-level validation. The glycogen staining and biochemical measurements support the interpretation that glycogen trophoblast cells contribute to placental metabolic function. The spatial analyses identifying macrophage accumulation in the labyrinth region of mutant placentas are also interesting and illustrate how spatial approaches can reveal microenvironmental changes that are difficult to detect otherwise.

The main limitation of the study is that the conclusion that glycogen cells are essential mediators of metabolic support for fetal viability remains partly indirect. The transcriptomic and spatial data strongly suggest a role for these cells, but it is still difficult to determine whether glycogen cell dysfunction is the primary cause of fetal lethality or a consequence of broader placental abnormalities. Clarifying this point would strengthen the central message of the paper.

Similarly, the macrophage accumulation observed in the labyrinth appears consistent with a response to tissue stress or injury, but its relationship to glycogen cell function is not fully explained. A clearer discussion of whether this represents a primary mechanism or a secondary effect would improve the interpretation.

Overall, this is a strong dataset and a useful spatial atlas of placental development. The study provides convincing descriptive insight into glycogen trophoblast biology, and with some clarification of the mechanistic conclusions, the manuscript will be even stronger.

Reviewer #2 (Public review):

This manuscript constructs a spatiotemporal transcriptomic atlas (STAMP) of the mouse placenta from E9.5-E18.5 by integrating Stereo-seq and snRNA-seq, and identifies two glycogen trophoblast cell (GC) subtypes (GC-1 and GC-2), a spatial transition from the junctional zone (JZ) to the decidua, and metabolic defects in Ano6-null placentas including GC persistence, glycogen accumulation, reduced glycogenolysis metabolites, and partial rescue by maternal glucose supplementation. The breadth of the dataset and the integration of atlas construction with PAS/TEM/LC-MS analyses are impressive, and the study has the potential to provide a valuable resource for the placental biology community.

However, in its current form, the central claim that "GC-mediated metabolic support is essential/indispensable for fetal viability" is not sufficiently disentangled from the complex phenotype of a global Ano6 knockout model. In addition, the stage-level biological replication in the atlas and the claim of "single-cell resolution" require more careful presentation. Therefore, while the study is interesting and potentially impactful, substantial revisions are required, particularly to recalibrate the strength of the conclusions and causal interpretations.

Major comments

(1) The most significant concern is that the manuscript overinterprets the phenotype observed in a global Ano6 knockout as direct evidence that GC glycogen metabolism is essential for fetal viability. The authors themselves report multiple severe placental abnormalities in the knockout, including reduced placental size and weight, structural defects in the labyrinth, impaired vascularization, and accumulation of abnormal regions. Previous studies cited in the manuscript also indicate that Ano6 deficiency leads to defects in syncytiotrophoblast formation, impaired maternofetal exchange, and perinatal lethality.

In this context, the current data support an association between GC metabolic defects and fetal lethality, but do not establish that GC glycogen metabolism is the primary causal driver. The conclusion should therefore be moderated (e.g., "contributes to" rather than "is essential for"), unless additional placenta-specific or GC-specific functional validation is provided.

(2) Maternal glucose supplementation is an interesting functional experiment, but in its current form, it provides supportive rather than definitive mechanistic evidence. While survival improves (from ~3% to ~10%), the rescue remains partial. Moreover, the readouts are largely limited to metabolite restoration (glucose, G1P, G6P) in the placenta and fetal liver.

To support a stronger causal claim, the authors should assess whether glucose supplementation also rescues: placental morphology (especially labyrinth structure), GC number and PAS staining, ultrastructural glycogen features (TEM), fetal growth and developmental outcomes.

(3) The atlas is constructed from nine placentas across developmental stages, suggesting limited biological replication per stage. It remains unclear how robust the observed temporal trends are to litter effects, sex differences, or sectioning variability.

Furthermore, the "single-cell resolution" is not directly measured but inferred via image segmentation and reference-based mapping (e.g., TACCO). This should be more explicitly stated, as it represents computational inference rather than direct single-cell measurement.

The authors should:<br /> - clearly report biological replicates per stage (including litter and sex),<br /> - demonstrate reproducibility of key patterns across independent samples,<br /> - refine the wording to reflect segmentation- and reference-based single-cell inference.

(4) The proposed developmental trajectory (JZ progenitor → GC precursor → GC-1 → GC-2) and the claim of GC migration from JZ to decidua are based on spatial distribution and computational trajectory analyses (Monocle, CytoTRACE).

While this is a compelling model, it remains inferential. The language throughout the manuscript should be softened (e.g., "consistent with spatial transition" rather than "migration"). Ideally, additional experimental validation, such as stage-resolved RNAscope/immunostaining quantification or lineage tracing, would strengthen this claim.

(5) The manuscript concludes that ANO6 deficiency leads to impaired glycogen utilization, based primarily on the observation that differentiation markers and glycogenolytic enzyme transcripts are unchanged.

However, this demonstrates what is not altered rather than what is mechanistically responsible for the defect. A more direct mechanistic link is needed, such as changes in enzyme activity, altered intracellular localization, effects on ion homeostasis or membrane biology.

(6) The statistical framework requires clarification. Several analyses use n = 4-8 placentas or "independent experiments," but it is unclear whether these represent independent litters or multiple samples from the same dam.

Given the risk of pseudoreplication in placental studies, the authors should define whether n refers to placentas or litters, report the number of dams per genotype, and ensure appropriate statistical treatment (e.g., litter-based analysis or mixed-effects models).

Reviewer #1 (Public review):

Summary:

The authors present a new autofocusing method, LUNA (Locking Under Nanoscale Accuracy), designed to overcome severe focus drift, a major challenge in long-term time-lapse microscopy. Using this method, they address a fundamental question in bacterial cold shock response: whether cells halt growth and division following an abrupt temperature downshift. Through single-cell analysis, the authors uncover a multi-phase adaptation process with distinct growth deceleration dynamics, and show that bacterial cells adapt to cold shock in a largely uniform manner across the population. Overall, this work provides new insights into the bacterial cold shock response at the single-cell level, extending beyond what can be inferred from population-level measurements.

Strengths:

(1) The LUNA method shows improved performance compared to existing autofocusing systems, achieving nanoscale precision over a large focusing range. Its focusing speed is sufficient for the experiments presented, with potential for further improvement through faster motors and optimized control algorithms, suggesting broad applicability. Theoretical simulations and experimental validation together provide strong support for the method's robustness.

(2) Using LUNA, the authors address a long-standing question in bacterial physiology: whether cells arrest growth and division during the acclimation phase following cold shock. Single-cell analyses across the full course of cold adaptation reveal features that are obscured in bulk-culture studies. Cells continue to grow and divide at reduced rates while maintaining cell size regulation, and exhibit a three-phase adaptation program with distinct growth dynamics. This response appears uniform across the population, with no evidence for bet-hedging. Overall, the experiments are well designed, and the analyses are solid and support the authors' conclusions.

(3) The authors further propose a model describing how population-level optical density (OD) depends on cell dry mass density, volume, and concentration. Following cold shock, cells grow more slowly and exhibit smaller sizes, explaining the apparently unchanged OD. This model provides a valuable conceptual framework for interpreting OD-based growth measurements, a widely used method in microbiology, and will be of broad interest to the field.

Weaknesses:

No major weaknesses identified.

Comments on revisions:

The authors have thoroughly addressed all of my questions. I thank them for their clear clarifications and thoughtful revisions, and I greatly appreciate their efforts in improving the manuscript.

Reviewer #2 (Public review):

Summary:

This study presents LUNA, an autofocus method that compensates for focus drift during rapid temperature changes. Using this approach, the authors show that E. coli cells continue to grow and divide during cold shock, revealing a coordinated, multi-phase adaptation process that could not be deduced from traditional population measurements. They propose a scattering-theory-based model that reconciles the paradox between growth differences of the bacteria at the single-cell level vs population level.

Strengths:

(1) The LUNA approach is pretty creative, turning coma aberration from what is normally a nuisance into an exploit. LUNA enabled long-term single-cell imaging during rapid temperature downshifts.

(2) The authors show that the long-assumed growth arrest during cold shock from population-level measurements is misleading. At the single-cell level, bacteria do not stop growing or dividing but undergo a continuous, three-phase adaptation process. Importantly, this behavior is highly synchronized across the population and not based on bet-hedging.

(3) Finally, the authors propose a model to resolve a long-standing paradox between single-cell vs population behavior: if cells keep growing, why does optical density (OD) of the culture stop increasing? Using light-scattering theory, they show that OD depends not only on cell number but also on cell volume, which decreases after cold shock. As a result, OD can remain flat, or even decrease, despite continued biomass accumulation. This demonstrates that OD is not a reliable proxy for growth under non-steady conditions.

Weaknesses:

(1) While the authors theoretically explain the advantages of LUNA over existing autofocus methods, it is unclear whether practical head-to-head comparisons have been performed, apart from the comparison to Nikon PFS shown in Video S1. As written, the manuscript gives the impression that only LUNA can solve this problem, but such a claim would require more systematic and rigorous benchmarking against alternative approaches.

(2) No mutants/inhibitors used to test and challenge the proposed model.

(3) Cells display a high degree of synchronization, but they are grown in confined microfluidic channels under highly uniform conditions. It is unclear to what extent this synchrony reflects intrinsic biology versus effects imposed by the microfluidic environment.

(4) To further test and generalize the model, it would be informative to also examine bacterial responses at intermediate temperatures rather than focusing primarily on a single cold-shock condition.

Comments on revisions:

The authors have addressed my comments in their response, but have chosen not to incorporate most of them into the manuscript. Readers may refer to the peer review section for further details.

Reviewer #1 (Public review):

Summary:

The manuscript, titled Hippocampal Single-Cell RNA Atlas of Chronic Methamphetamine Abuse-Induced Cognitive Decline in Mice, focuses on single-cell RNA sequencing (scRNA-seq) analysis following chronic methamphetamine (METH) treatment in mice. The authors propose two hypotheses: (1) METH induces neuroinflammation involving T and NKT cells, and (2) METH alters neuronal stem cell differentiation.

Strengths:

The authors provide a substantial dataset with numerous replicates, offering valuable resources to the research community.

Weaknesses:

Concerns remain regarding the interpretation of the data and the appropriateness of the statistical analyses.

Although the authors provided detailed responses to the reviewer's concerns, I am still concerned that several key issues have not yet been fully addressed in the revised manuscript.

First, in Figure 5, the authors state that neural stem cells (NSCs) preferentially differentiate into astrocytes rather than neuroblasts following METH treatment. However, based on the presented trajectories, it is difficult to visually confirm differences in the relative proportions of astrocyte versus neuroblast differentiation between the control and METH-treated conditions. The current figures do not provide a quantitative or clearly interpretable comparison of lineage allocation that would support this conclusion.

Moreover, in Figures 5C and 5F, the inferred pseudotime trajectories differ both the starting cell populations and the intermediate and terminal cell identities. As a result, the trajectories are not directly comparable between the control and METH conditions. Under these circumstances, it is inappropriate to interpret gene expression changes as occurring along equivalent differentiation paths, and the current analysis does not convincingly support the stated conclusions regarding altered NSC differentiation.

If the authors intend to claim differential gene expression associated with altered differentiation trajectories, the analysis should at minimum present the expression of the same set of genes (e.g., Bsg, Ccl4, Fos, Sox11, Flt1, Hspb1, Igfbp7, and Tmsb10) plotted along a matched trajectory (for example, NSC-to-astrocyte or NSC-to-neuroblast lineages) in both control and METH-treated samples, so that readers can directly compare expression dynamics across conditions.

In addition, several statements throughout the manuscript describing changes in cell-type proportions are not supported by corresponding statistical analyses. For example, in Figure 2C (around line 430), the authors report changes in cell proportions of ~0.1% or 2-3%. Without appropriate statistical testing, it is unclear whether such marginal differences are biologically meaningful or reproducible. The authors should either provide statistical testing (e.g., sample-level proportion analysis with p-values or confidence intervals) or revise the text to describe these findings as descriptive rather than significant changes.

Finally, the reported decrease in astrocyte proportion following METH exposure (from 6.6% to 5.5%), together with the lack of reported changes in neuroblast proportions, appears inconsistent with the trajectory-based conclusion that NSCs preferentially differentiate into astrocytes in METH-treated mice. This apparent discrepancy should be clarified or the conclusions appropriately tempered.

Reviewer #1 (Public review):

Summary:

This study combined high-field fMRI with computational modelling (including a Bayesian population receptive field [pRF] model and functional gradient analysis) in humans to demonstrate that the architecture of the corpus callosum (CC) and its interhemispheric connections is organized into parallel ipsilateral and contralateral streams, rather than functioning as a mixed integration of inputs from both hemispheres. The human findings were validated through preclinical experiments in mice using viral axonal tracing, which revealed a non-overlapping laminar arrangement of axons carrying left and right visual field information.

These results suggest that the CC operates as a set of parallel, segregated pathways, with each stream independently conveying information from one side of the visual field. This organization preserves the spatial origin of visual signals within the white matter. Although the overall concept of interhemispheric parallel pathways is not entirely unexpected, this refined understanding of callosal organization provides important scientific and clinical insights in relation to pathway-specific perturbations and in neurological disorders.

Strengths:

The manuscript is well written, the methodology is sound, and the analyses are carefully conducted. I particularly appreciate the effort to integrate functional and structural approaches and to validate the human neuroimaging findings with more sensitive preclinical techniques, such as viral tracing.

Weaknesses:

Several points require clarification to allow a more complete interpretation of the results. In addition, some further analyses are necessary to fully substantiate the claims made in the manuscript. These are detailed below

Comment 1:

BOLD signals in white matter remain a matter of debate, although this is not the central focus of the present study. Nevertheless, it is important to establish whether the underlying data have sufficient tSNR to support robust pRF estimation in white matter. In Figure 1, the EV appears relatively robust; however, it seems that only the best-fitting examples are shown. In contrast, the group-average EV reported in Figure 2, and the individual maps in the Supplementary Information indicate very low EV values, typically below 5%. In conventional fMRI analyses, thresholds of approximately 15-20% EV are often applied to exclude poor fits that may bias pRF parameter estimates. It appears that no such threshold was applied here. Interestingly, in Figure S6, the average EV for dual pRF models appears to be approximately 17%. Do dual and triple pRF models systematically produce higher EV compared to single pRF models? Additionally, Figure 2 suggests the presence of baseline activation that is captured by the model. Could this be related to a delayed or altered hemodynamic response function (HRF) in white matter? Clarification would be helpful. To better assess the robustness of the reported findings, the authors should provide quantitative measures of tSNR within the white matter tracts where the pRF model was fitted. Furthermore, a plot showing the average BOLD signal during visual stimulation versus baseline in those tracts would greatly strengthen confidence in the signal quality.

Although the reported linear relationship between pRF size and eccentricity, as well as the test-retest reliability analyses, suggest the presence of consistent receptive field estimates, these analyses are based on distributions and may lack the sensitivity required to differentiate single, dual, and triple pRF models. Moreover, the pRF estimates within the FMA appear noisy, particularly at the individual level (Figure S4), making it difficult to clearly dissociate information originating from the left and right hemifields.

Comment 2.1:

The Bayesian modelling approach is interesting and robust. However, as I understand it, the authors must specify a priori the number of pRFs to be estimated. This introduces a strong assumption about the expected underlying receptive field structure. An alternative Bayesian approach, such as micro-probing (Carvalho et al., 2020), does not require prior assumptions regarding the number or shape of pRFs. Instead, it estimates receptive field profiles in a more data-driven manner and provides a direct visualization of the pRF structure. Implementing such an approach, or at least comparing it with the current modelling strategy, could yield more reliable and potentially less biased estimates of multiple pRFs, particularly in white matter where signal quality is limited.

Comment 2.2:

Some clarifications regarding the pRF model are needed: in the Methods section, the authors mention the use of a Difference-of-Gaussians (DoG) model. However, it appears from the Results that the analyses were performed using a single-Gaussian model. Additionally, in Section 5.6, the authors state that six different pRF models were tested. Which specific models were included in this comparison? A clear description of each model, along with justification for the final model selection criteria, would help better understand the study

Comment 3:

Throughout the manuscript, the authors repeatedly refer to laminar-specific findings. However, the reported functional resolution of 1.6 mm isotropic is insufficient to reliably resolve cortical layers. Given this limitation, the laminar interpretations appear overstated. For example, in the Discussion section titled "Integrating White Matter with Laminar-Resolved Function", the authors state: "The combination of anatomically segregated white matter pathways with functionally specific cortical laminae presents a powerful synergy for human brain circuit research." Given the spatial resolution of the functional data, how are laminar-specific functional claims justified?

Similarly, the authors suggest that: "It becomes possible to assess not just if the CC is damaged, but precisely which directional pathways are compromised-either the pathways projecting from the lesioned hemisphere, or those projecting to the other, or both." It is unclear to me how the current methodology uniquely enables this level of directional specificity, and whether this was not already feasible using existing structural and diffusion-based approaches. The authors should clarify what is genuinely novel in this study.

Comment 4:

In the Discussion, the authors state: "These findings fundamentally reframe our understanding of interhemispheric communication, moving beyond static connectivity to reveal a dynamic, directionally specific highway where spatial location encodes the origin of information. This framework provides a novel blueprint for decoding directional information flow in the living human brain." Based on the analyses presented, it is unclear how the findings of this study demonstrate dynamic connectivity or true directional specificity. The reported results appear to characterize spatial organization and segregation of callosal pathways, but they do not measure the directionality of information flow, temporal dynamics, or causal directionality between hemispheres. To substantiate claims regarding dynamic or directional communication, additional analyses, such as connective field model (Haak et al.2013), effective connectivity modelling, time-resolved approaches, or perturbation-based methods (neuromodulation) would be required. As currently presented, the findings seem to support structural and functional segregation rather than dynamic or directionally resolved interhemispheric information transfer. The authors should either provide stronger evidence for these claims or moderate them.

Comment 5:

I agree with the authors that pooling of information across hemispheres represents a plausible explanation for the presence of dual pRFs. As discussed in the manuscript, such an effect would be expected to predominantly affect pRFs located near the vertical meridian. However, Figures S6C and S6D do not appear to demonstrate that bilateral pRFs are preferentially located along the vertical meridian.

Reviewer #2 (Public review):

Summary:

The manuscript proposes a "parallel wires" architecture for the visual corpus callosum, suggesting that contralateral and ipsilateral visual streams remain spatially segregated into distinct anatomical channels. The authors use a cross-species approach, combining Bayesian population receptive field (pRF) modeling in humans with dual-color viral tracing in mice. The analysis of the publicly available human fMRI dataset indicates a 92% probability of single-hemifield representation, arguing for functional segregation. The mouse mesoscale tracing data support the idea of anatomical parallel wires by displaying dorso-ventral segregation of callosal axons post-midline crossing.

Strengths:

The primary strength of this study is its cross-species integration. Observing that functional segregation in humans is mirrored by specific anatomical pathways in the mouse provides a convincing, multimodal argument for the "parallel wires" hypothesis. The data is generally well-presented, and the Bayesian modeling of the human data is a robust methodological choice.

Weaknesses:

There are weaknesses in the description, presentation, and methodological details of the mouse tracing data. First, the authors must provide detailed information regarding spectral unmixing, intensity normalization, and threshold-sensitivity analyses. These factors are critical as they directly influence the Dice and Jaccard overlap estimates that underpin the study's primary conclusions. Second, it is unclear which cortical layers have been virally labelled as there is no quantification of the spatial extent of the injection site, and there is ambiguity regarding the dorso-ventral stereotaxic coordinates.

Reviewer #3 (Public review):

Summary:

This manuscript describes a study into the functional organization of the forceps major (FMA). The authors present a Bayesian population receptive field (pRF) analysis of group-averaged HCP fMRI retinotopic mapping data, focusing on voxels within the FMA. This is unconventional because pRF modelling is usually limited to gray matter voxels, where synaptic activity underlying neural computation is the highest. Nevertheless, some previous work suggests that meaningful fMRI signals can also be gleaned from white matter voxels, where the signals are thought to reflect metabolic activity from action potentials that travel along axons. However, these signals are generally much noisier, and possible confounding effects due to partial voluming, draining veins, and different hemodynamics must be carefully ruled out. Based on the Bayesian pRF analysis, the authors claim evidence of segregated contralateral and ipsilateral representations of the visual field in the FMA. Anatomical tract tracing based on HCP diffusion MRI data from seeds identified using the pRF analysis further suggests that these representations are underpinned by separate fiber bundles, which also appear to be consistent with the results of viral tracing in mice. The results of this study could mean an important step forward in understanding transcallosal signaling.

Strengths:

The study treads uncharted territory, leveraging multiple data modalities across species and advanced analytical approaches.

Weaknesses:

The study does not address potential confounds related to BOLD imaging in white matter structures. If the fMRI results can be explained based on neighboring grey matter responses, the evidence that remains is limited to an apparent anatomical segregation of white matter bundles that appear to be present in both mice and humans.

Further details are also missing regarding the Bayesian pRF approach, including the priors used for the pRF model. These are important as they will dominate the estimates when the data are very noisy, and the authors have adopted unconventional, more complex pRF models compared with earlier work employing Bayesian pRF analyses.

It appears that the authors have not applied any statistical thresholding to ensure that only good-quality model fits are entered into subsequent analyses (i.e., the reported probabilities pertain to model comparisons, not goodness of fit). From Figure 2, it appears that the majority of the FMA voxels, barring those adjacent to visual gray matter, do not exhibit more than a few percentage points of explained variance (EV). In fact, a common threshold is >15% EV, but it looks like none of the FMA voxels exceed this threshold.

Reviewer #1 (Public review):

Summary:

The manuscript entitled "Autonomic reflex plasticity associates with time-dependent SUDEP susceptibility in a murine model with hyperactive stress circuits" by Dr. Saunders and colleagues combined a traditional mouse model of SUDEP, ventral intrahippocampal kainite (vIHKA) injection, with a genetic model of chronic hyperactivity of central corticotropin-releasing hormone (CRH) neurons (Kcc2/Crh) that further increases the risk of SUDEP in the weeks following seizure.

Strengths:

Their results show during spontaneous seizures Kcc2/Crh mice had more pronounced reflex-like ictal bradycardias compared to WT controls that notably occurred prior (~10 sec) to seizure termination and had greater autonomic disturbances compared to WT controls, including a pronounced serotonin-mediated Bezold Jarisch reflex. These results show chronic hyperactivity of central corticotropin-releasing hormone (CRH) neurons (Kcc2/Crh) increased autonomic disturbances and risk of SUDEP in a kainic acid model of epilepsy.

Weaknesses:

This study could be improved with a more thorough assessment of heart rate, blood pressure and breathing during and following the seizures, and in particular the fatal event. It is unclear if the bradycardias were spontaneous or a result of preceding central or obstructive apneas, oxygen desaturations, hypercapnia, arrhythmias, or other possible triggers.

Considerable prior work in the literature suggests SUDEP could be mediated, in some patients, by a burst of parasympathetic activity to the heart. Were the heart rate changes in these animals during seizures inhibited or blocked by atropine or atenolol?<br /> The injection of the 5HT agonist phenylbiguanide into the right jugular is not a selective approach for activating the Bezold Jarisch Reflex (BJR), which is caused by increased activity of intracardiac sensory neurons (generally activated with ischemia or a combination of low preload with high contractility). The results should be interpreted more cautiously, as a response to systemic administration of phenylbiguanide only.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors set out to evaluate the role of hypothalamic pituitary axis hyperactivity on cardiac and autonomic changes during epileptogenesis and following seizures in a mouse model of temporal lobe epilepsy. Epilepsy is very common. It can frequently result in death from sudden unexpected death in epilepsy, or SUDEP. SUDEP is thought to be at least in part due to seizure-related cardiac and autonomic instability. Increased stress states are well known to be comorbid with epilepsy. This comorbidity is thought to increase the risk of SUDEP. Here, the authors hypothesized that a mouse model of heightened stress in which there is hyperactivity of the CRH neurons in the hypothalamus would demonstrate exaggerated cardiac and autonomic effects of seizures and epilepsy.

Strengths:

For the chronic stress model, they employed the Kcc2/Crh mice that have a genetic deletion of the potassium chloride cotransporter in CRH neurons. They treated these mice and their wild-type littermates with intra-hippocampal kainic acid or saline, as epileptic and sham-treated animals, respectively. The assessed cardiac activity, blood pressure, baroreflex, and the Bezold-Jerisch reflex during epileptogenesis. This, in general, is an interesting study. They make some interesting and potentially important observations regarding heart rate and blood pressure in seizures and epilepsy.

Weaknesses:

Some of the conclusions may be a bit overstated as is and would benefit from more discussion and perhaps additional data.

Reviewer #1 (Public review):

In this study, Szinte et al. measured the spatial selectivity of fMRI BOLD responses while subjects viewed dynamic noise stimuli vignetted by a moving bar aperture. Subjects viewed these moving bar stimuli as they fixated at one of three screen locations. This design enabled the authors to test whether fMRI responses are better explained by a model in which stimulus location is encoded relative to the retina or relative to the screen (in other words, 'retintopic' vs. 'spatiotopic' encoding). In retinotopic encoding, the pRFs should move with the eyes. In spatiotopic encoding, the pRFs should be locked to particular screen locations, regardless of eye position. The results are unambiguous: the retinotopic model wins.

A number of prior human fMRI studies have addressed this issue, and there is an overwhelming consensus in the field that spatial encoding throughout human visual cortex (and high-level cortex) is retinotopic (during fixation). All of the results shown in the present manuscript are consistent with these earlier observations. Szinte et al. also find that the degree of retinotopic selectivity is not affected by the task or locus of spatial attention. This too has been observed in multiple prior studies.

So, while this manuscript is primarily confirmatory, the study does nonetheless provide valuable measurements at 7T with a higher signal-to-noise ratio and high spatial resolution than previous studies. The authors also apply an innovative Bayesian decoding analysis (which is beautifully documented on their webpage, with a step-by-step tutorial and ample examples). So, a major strength of this paper is the methods; this study does set a high standard and is an ideal example for a rigorous, replicable analysis pipeline and cutting-edge statistical inference.

The results focus on the spatial profile of pRFs with different eye positions. However, the main idea behind eye-position gain fields is that the amplitude of the visual responses changes with eye position. I could not find any analysis testing response amplitude as a function of eye position. In the Discussion, the authors assert: "We did not find an influence of gaze position at the level of individual voxels nor at the level of visual areas." The authors speculate that this might be because gain fields have a salt-and-pepper organization in the cortex that cancel out when pooled across a voxel. While the salt-and-pepper explanation seems like perfectly fine speculation, here they are discussing a result that isn't shown in the Results!

Several prior human fMRI studies have reported eye position gain fields in humans, suggesting that the salt-and-pepper explanation is not correct. Rather, it is likely the case that the authors did not test a sufficiently wide range of eye positions to detect a gain modulation. For example, a study from Merriam et al. (J. Neurosci, 2013), which is mysteriously not cited here, measured both the spatial selectivity of visual receptive fields AND the response amplitude at 8 different eye positions that were spaced by as much as 24 degrees of visual angle (including both vertical and horizontal changes in eye position). Under these conditions, Merriam et al. did find reliable modulation in response amplitude with changes in eye position, even though the spatial selectivity of the responses did not change. Importantly, Merriam et al. found that visual response selectivity was consistent with a retinotopic reference frame (not a spatiotopic reference frame) and that this selectivity was invariant to the attention task. Consideration of these issues suggests that the experimental design used in the current experiment may have precluded the detection of eye position gain fields. The current manuscript would be much improved by a careful consideration of this prior literature, which is so closely related to what the authors report here.

Reviewer #2 (Public review):

Summary:

This manuscript describes a study using fMRI voxel-wise receptive field modeling and Bayesian decoding to assess the reference frame (spatiotopic vs retinotopic) of visual information. Participants viewed sequences of visual stimuli that moved across different screen locations. Across different conditions, participants either fixated at the screen center and viewed stimuli drifting across the full screen (full-screen condition), or fixated at a central, left, or right fixation position while stimuli drifted across a 4-deg aperture centered on that fixation (gaze-center, gaze-left, gaze-right conditions). Within each of those conditions, participants either attended to visual changes around fixation (attend-fix) or in the stimulus bar (attend-bar). First, standard population receptive field mapping was conducted on the full-screen conditions to obtain fiducial maps for each subject. Then, a variety of different analyses were performed, testing retinotopic vs spatiotopic predictions for the gaze-left and gaze-right conditions. Across the extensive set of analyses performed, and across all ROIs tested, the results always best matched the retinotopic predictions. This was the case for both attend-fix and attend-bar conditions. The authors conclude that visual representations operate in a retinotopic reference frame throughout the visual hierarchy, necessitating a "re-orienting" of the search for visual stability mechanisms.

Strengths:

The analyses are sophisticated and thorough, and the results are convincingly in favor of retinotopic representations. The attention manipulation is carefully done. And the finding that the most informative/reliable voxels are the most retinotopic is an important novel contribution.

Weaknesses:

(1) The theoretical advance of this work is unclear, because the finding that visual representations operate in a retinotopic reference frame throughout the visual hierarchy, and regardless of the deployment of spatial attention, has already been demonstrated with fMRI pattern analysis almost 15 years ago (Golomb & Kanwisher, 2012). To be clear, the techniques used in this current study are considerably more modern and sophisticated, and the attention manipulation is much better, but the finding is the same. More importantly, it is never really explained why, from a theoretical perspective, the results might have been expected to differ. Referring to this as an open question feels like a copout. The manuscript needs to engage more with the prior findings and explain the motivation for the current study. Was there something about the prior findings that caused them to doubt the retinotopic conclusion? Did they think that the 7T resolution or alternative decoding approaches might uncover something different? Was this intended as a replication test with more sophisticated techniques?

(2) I think there are definitely some new and useful things this study has to offer, but the overall theoretical contribution needs to be better clarified and contextualized within the prior literature. I would strongly recommend revisiting things like the title (not a novel contribution of this study) and the implication that the current findings "reframe" or "reorient" the search for visual stability mechanisms away from static spatiotopic maps (the field has arguably been "reoriented" in that way for some time now, and this study is certainly not the first to suggest a reframing along these lines). The discussion section, in particular, has little to no acknowledgement that these findings and ideas have been shown before.

(3) The analyses always pit retinotopic vs spatiotopic predictions. But what if both types co-existed, just with retinotopic more predominant? I think this general idea needs some discussion, if not additional analyses. Would the analyses be sensitive enough to pick up sparse spatiotopic coding if present?

Additional questions/critiques/suggestions:

(4) For the out-of-sample predictions analysis (Figure 2):

a) The spatiotopic predictions are much worse for earlier visual regions, but don't seem so different from gaze-center or retinotopic in later areas. How much might this be driven by the fact that pRF size increases along the hierarchy, and for large pRF sizes, the retinotopic and spatiotopic predictions might not be very differentiable? Is there a way to quantify this or include a control model that is neither retinotopic nor spatiotopic?

b) It looks like in some of the regions, the retinotopic (and maybe even spatiotopic) R2 change compared to the gaze center is reliably positive. Why would this be? Is there a reason the fit should be better for the gaze right or gaze left conditions compared to the gaze center?

(5) For the fitting retinotopic and spatiotopic pRF models (Figure 3) and other voxel-specific analyses:

a) For many of the statistics, results are averaged across voxels. This makes sense. But it also seems to me that taking a simple average might obscure some of the potential advantages of this voxel-wise approach. For example, what if there are sparse spatiotopic effects that are washed out by the averaging? Perhaps some way of looking at the statistical distribution of voxels' RFIs could be worth considering?

b) Are there some spatiotopic areas in the searchlight maps? It looks like there may be some blue clusters, but these cortical map figures are really hard to resolve.

(6) For the RFI as a function of model overlap and explained variance (Figure 4):

a) I like this analysis; I find it convincing and novel. Could it be further quantified by correlating on a voxelwise basis the reliability (e.g., explained variance) vs RFI?

b) I'm intrigued by the seemingly reliable blueish (spatiotopic) cells at the bottom of the V1-V3 grids. These seem to suggest that for the voxels with less spatial relevance (overlap), there might be something spatiotopic, even for relatively informative voxels (high explained variance)?

c) On a related note, is the "spatial relevance" measure the same as, or correlated with, eccentricity? It sounds like voxels with high spatial relevance (overlap with the central 4deg aperture) are the more foveal voxels. Intuitively, foveal voxels might be expected to be more retinotopic, right? In addition to clarifying this measure, it'd be nice to see a similar plot with eccentricity on the y-axis.

(7) For the Bayesian decoding (Figure 5):

a) A benefit of the Bayesian decoding (e.g., over the earlier studies using non-Bayesian decoding of retinotopic vs spatiotopic) is the uncertainty estimates. I think these analyses are interesting and should be in the main text figures, not a supplement.

b) Instead of line plots showing the decoded (best) position using the posterior distribution STD as the error shading, could you show the actual posterior distribution as heat maps (like the cartoon in B)? Is it possible there could be a second peak (or clear absence of one) at the spatiotopic prediction location?

(8) Also note that Golomb & Kanwisher also calculated the RFI measure for similar ROIs for both of their attention conditions. It may be worth comparing.

(9) Methods:

a) Is it true that 2 of the authors were actually naïve as to the purpose of the study? Regardless, given the small number of subjects and high ratio of authors as subjects, it might be nice to confirm that the results are not driven by the author-participants.

b) I think 44ms TR is a typo?

c) Why was the order of the bar movement directions always the same? Wouldn't this make the stimuli very predictable for the subjects, which could be potentially problematic?

d) I'm also curious why the gaze conditions were all presented in separate runs, as opposed to different blocks within a run.

e) The eccentricity maps for the fiducial maps (Figure 1G) seem a bit strange to me. Shouldn't the foveal representation be centered at the occipital pole, not the lateral surface?

Reviewer #1 (Public review):

Summary:

This manuscript investigates whether newborns can use speaker identity to separate verbal memories, aiming to shed light on the earliest mechanisms of language learning and memory formation. The authors employ a well-designed experimental paradigm using functional near-infrared spectroscopy (fNIRS) to measure neural responses in newborns exposed to familiar and novel words, with careful counterbalancing and acoustic controls. Their main finding is that newborns show differential neural activation to novel versus familiar words, particularly when speaker identity changes, suggesting that even at birth, infants can use indexical cues to support memory.

Strengths:

Major strengths of the work include its innovative approach to a longstanding question in developmental science, the use of appropriate and state-of-the-art neuroimaging methods for this age group, and a thoughtful experimental design that attempts to control for order and acoustic confounds. The study addresses a significant gap in our understanding of how infants process and remember speech, and the data are presented transparently, with clear reporting of both significant and non-significant results.

A previous concern was that the recognition effect appeared restricted to a subgroup of participants. The authors clarify that the bilateral STG and left IFG effects were present in both groups - it was only the right IFG modulation that was group-dependent. This is an important distinction and is now clearer in the revised manuscript. The timing of the effect emerging in a specific testing window also appears less arbitrary given the authors' explanation that prior work guided the analytical approach, and that task difficulty was expected to determine whether recognition would appear in earlier or later test blocks.

The sample size question is handled honestly. A power analysis based on a related ANOVA study produced an implausibly small estimate of N=5-7, which the authors rightly set aside. Aligning with fNIRS neonate studies - where mean sample sizes around N=24 are standard - is defensible, and the within-subject design with mixed-model analysis does improve sensitivity relative to simpler approaches. This is now explained in the manuscript.

The episodic memory framing has been scaled back appropriately. The revised discussion is clear that the study demonstrates what-who binding - an early component of episodic-like processing - rather than mature episodic memory in the Tulvingian sense. This is a more honest characterization of what the paradigm can show, and it opens a reasonable developmental question about how the remaining components (where, when) come online over the first months and years of life.

Weaknesses

The weaknesses are largely interpretive rather than fatal to the core findings. The absence of a same-speaker interference control within the current paradigm means the causal role of speaker change cannot be established entirely from internal evidence alone - the inference relies partly on comparison with Benavides-Varela et al. (2011), which used a somewhat different design. This is a reasonable approach given the ethical and practical constraints of testing newborns, and the authors are transparent about it, but readers should keep in mind that the conclusion about speaker change as the critical variable is supported by converging evidence across studies rather than a direct within-study manipulation.

Overall, the study contributes new and meaningful data on an underexplored aspect of early speech processing: the role of the speaker as a contextual dimension in word memory. The findings, taken together with the prior literature, tell a coherent story and have real implications for theories of early language acquisition and the developmental origins of episodic-like memory. The paradigm is sound and the results are worth pursuing in larger and more controlled follow-up studies.

Reviewer #2 (Public review):

Summary

Previous studies by some of the same authors of the actual manuscript showed that healthy human newborns memorize recently learned nonsense words. They exposed neonates to a familiarization period (several minutes) when multiple repetitions of a bisyllabic word were presented, uttered by the same speaker. Then they exposed neonates to an "interference period" when newborns listened to music or the same speaker uttering a different pseudoword. Finally, neonates were exposed to a test period when infants hear the familiarized word again. Interestingly, when the interference was music, the recognition of the word remained. The word recognition of the word was measured by using the NIRS technique, which estimates the regional brain oxygenation at the scalp level. Specifically, the brain response to the word in the test was reduced, unveiling a familiarity effect, while an increase in regional brain oxygenation corresponds to the detection of a "new word" due to a novelty effect. In previous studies, music does not erase the memory traces for a word (familiarity effect), while a different word uttered by the same speaker does.

The current study aims at exploring whether and how word memory is interfered with by other speech properties, specifically the changes in the speaker, while young children can distinguish speakers by processing the speech. The author's main hypothesis anticipates that new speaker recognition would produce less interference in the familiarized word because somehow neonates "separate" the processing of both words (familiarized uttered by one speaker, and interfering word, uttered by a different speaker), memorizing both words as different auditory events.

From my point of view, this hypothesis is interesting since the results would contribute to estimate the role of the speaker in word learning and speech processing early in life.

Major strengths:

(1) New data from neonates. Exploring neonates' cognitive abilities is a big challenge, and we need more data to enrich the knowledge of the early steps of language acquisition.

(2) The study contributes new data showing the role of speaker (recognition) on word learning (word memory), a quite unexplored factor. The idea that neonates include speakers in speech processing is not new, but its role in word memory has not been evaluated before. The possible interpretation is that neonates integrate the process of the linguistic and communicative aspects of speech at this early age.

(3) The study proposes a quite novel analytic approach. The new mixed models allow exploring the brain response considering an unbalanced design. More than the loss of data, which is frequent in infants' studies, the familiarization, interference and learning processes may take place at different moments of the experiment (e.g. related to changes in behavioural states along the experiment) or expressed in different regions (e.g. related to individual variations in optodes' locations and brain anatomy).

Main weaknesses:

I did not find major weaknesses. However, I would like to have more discussion or explanation in the following points.

(1) It would be fine to report the contribution of each infant to the analysis, i.e. how many good blocks, 1 to 5 in sequence 1 and 2, were provided by each infant.

(2) Why did the factor "blocknumber" range from 0 to 4? The authors should explain what block zero means and why not 1 to 5.

(3) I may suggest intending to integrate the changes in brain activity across the 3 phases. That is, whether changes in familiarization relate to changes in the test and interference phases. For instance, in Figure 2, the brain response distinguishes between same and novel words that occurred over IFG and STG in both hemispheres. However, in the right STG there was no initial increase in the brain response, and the response for the same was higher than the one for novels in the 5th block.

(4) Similarly, it is quite amazing that the brain did not increase the activity with respect to the familiarization during the interference phase, mainly over the left hemisphere, even if both the word and speaker changed. Although the discussion considers these findings, an integrated discussion of the detection of novel words and the detection of a novel speaker over time may benefit from a greater integration of the results.

Appraisal

The authors achieved their aims, because the design and analytic approaches showed significant differences. The conclusions are based on these results. Specifically, the hypothesis that neonates would memorize words after interference, when interfered speech is pronounced by a different speaker was supported by the data, in block 2 and 5 and discussed the potential mechanisms underlying these findings, such as separate processing for different speakers, likely related to the recognition of speaker identity.

I think the discussion is well structured, although I may suggest integrating the changes into the three phases of the study. Maybe comparing with other regions, not related to speech processing.

Evaluating neonates is a challenge. Because physiology is constantly changing. For instance, in 9 minutes newborns may transit from different behavioral states and experience different physiological needs.

This study offers the opportunity to inspire looking for commonalities and individual differences when investigating early memory capacities of newborns.

Comments on revisions:

The authors provided satisfactory answers to my concerns.

I recognize that, because of technical and ethical reasons, the studies with neonates are particularly challenging, however, with a well-balanced design as the one the authors applied, even with small samples the data constitute valuable sources to advance in the field.

Neonate brain works in a particularly state of intense metabolic, functional and structural changes, which we are far to understand. Current data contribute to fill this gap in knowledge.

Reviewer #1 (Public review):

Summary

The manuscript by K.H. Lee et al. presents Spyglass, a new open-source framework for building reproducible pipelines in systems neuroscience. The framework integrates the NWB (Neurodata Without Borders) data standard with the DataJoint relational database system to organize and manage analysis workflows. It enables the construction of complete pipelines, from raw data acquisition to final figures. The authors demonstrate their capabilities through examples, including spike sorting, LFP filtering, and sharp-wave ripple (SWR) detection. Additionally, the framework supports interactive visualizations via integration with Figurl, a platform for sharing neuroscience figures online.

Strengths:

Reproducibility in data analysis remains a significant challenge within the neuroscience community, posing a barrier to scientific progress. While many journals now require authors to share their data and code upon publication, this alone does not ensure that the code will execute properly or reproduce the original results. Recognizing this gap, the authors aim to address the community's need for a robust tool to build reproducible pipelines in systems neuroscience.

Comments on revisions:

In this revised version, the authors have addressed the majority of the concerns raised in the initial review. The manuscript is clearer, the documentation and explanations have been strengthened, and several important practical issues-particularly regarding usability, terminology, and deployment-have been meaningfully improved. While the framework continues to position itself both as a flexible analysis environment and as a mechanism for freezing and preserving reproducible pipelines, the authors have clarified their rationale for maintaining this dual role. I have no additional comments at this stage.

Reviewer #2 (Public review):

Summary:

Lee et al. introduce Spyglass, an open-source Python framework designed to tackle the reproducibility crisis in systems neuroscience by integrating the Neurodata Without Borders (NWB) standard with DataJoint relational databases. The framework aims to standardize data ingestion, preprocessing, analysis pipelines, and data sharing for complex electrophysiological and behavioral experiments.

Strengths:

(1) Handling of Complex Workflows: The architectural design is pragmatic and robust. Features such as the "cyclic iteration" motif for spike-sorting curation and the "merge" motif for consolidating multiple data streams effectively handle the iterative nature of data processing without incurring database bloat.

(2) Ecosystem Integration: The revised manuscript clarifies that Spyglass acts as a community hub, explicitly detailing its integration with established tools like SpikeInterface, DeepLabCut, GhostiPy, MoSeq, and Pynapple.

(3) Pipeline Clarity & Practical Demonstration: The addition of Supplementary Figure 1, in conjunction with Figure 5, successfully maps out the complex, multi-step decoding workflow for both the UCSF and NYU datasets. Together, these figures tell a complete and compelling story of how this pipeline can be used in practice, providing much-needed visual clarity on how raw data moves through the database to generate final results.

Appraisal:

The authors have successfully achieved their aims. Spyglass is a highly functional system capable of handling the heavy lifting of data management. The revisions have significantly improved transparency regarding the tool's limitations and its onboarding process, making it a highly attractive blueprint for labs aiming to adhere to FAIR principles.

Reviewer #1 (Public review):

Summary:

The authors wanted to determine whether the set-19 gene, one of 38 SET-domain containing genes in C elegans, has a clear function in vivo with respect to lysine methylation. The question is not only whether it can modify this histone tail residue, but also what the impact of a loss of this locus is on the inheritance of repressive chromatin states.

Strengths:

The authors clearly achieved their goal, and it is convincingly shown that SET_19 is indeed a somatic cell histone methyltransferase with a striking specificity for H3K23. There is both recombinant protein work, quantitative mapping in vivo, of histone marks and transcriptional changes, and the authors rule out some other hypotheses that have been in the literature. Overall, this provides a compelling argument that SET-19 is indeed the major somatic cell HMT for this residue. Interestingly, the phenotypes are rather minimal, consistent with redundancy in the physiological roles of histone methylation, and redundancy as well in HMT function. For the most part, the data are not over-interpreted. The genetic alleles used, assuming they are confirmed, were revealing and well-documented.

Weaknesses:

The major weaknesses are easily fixed. The major weaknesses mainly reflect a slight overstatement of certain data (claiming insignificance, when it is not clear how that was determined) and claiming a bit too much about SET-32, which was independently claimed to be an H3K23 HMT. Clearly, the two SET domain enzymes are not redundant, nor is the claim that SET-32 has no role in H3K23 methylation completely convincing. Especially in germline or embryonic conditions. Finally, the imaging is not of very high quality, nor are the images fully quantitated. These points can be easily remedied.

Reviewer #2 (Public review):

Summary:

This manuscript identifies SET-19 as a somatic H3K23 methyltransferase in C. elegans, building on previous genetic evidence for a role of set-19 in H3K23me3 regulation. The authors combine quantitative mass spectrometry, western blotting, in vitro methyltransferase assays, ChIP-seq, and RNA-seq to show that loss of set-19 causes a strong reduction of H3K23me3, particularly in somatic tissues, and is associated with derepression of a subset of genes enriched for H3K23me3. They further conclude that SET-19 is dispensable for canonical feeding RNAi and for transgenerational or intergenerational inheritance of RNAi, distinguishing its function from other heterochromatin-associated methyltransferases such as SET-25, SET-32, and the H3K27 HMTs. Overall, the work adds an important piece to the H3K23 methylation pathway and tissue-specific chromatin regulation in C. elegans.

Strengths:

Very strong genetic and biochemical evidence for SET-19 as the major H3K23me3 HMT.

The mass spectrometry and western blot data convincingly demonstrate a strong reduction of H3K23me3 in two independent set-19 alleles and rescue by GFP::SET-19, which is a major strength (Figure 1, including Figure 1f).

The in vitro methyltransferase assays (Figure 2) showing robust H3K23me1/2/3 activity for SET-19 SET+CC and only modest H3K23me activity for SET-32, together with the SAM titration experiment in Figure 2C, are very informative and nicely support the conclusion that SET-19 is a high-activity H3K23 methyltransferase compared to SET-32.

The ChIP-seq analysis is central to the conclusions that H3K23me3 is enriched on chromosome arms, co-localizes with H3K9me3/H3K27me3, and is strongly reduced in set-19 mutants.

Weaknesses:

(1) The global reduction of H3K23me3 in Figure 3b,c and Figure S4c is convincing, but the correlation analysis between H3K23me3 loss and mRNA changes in Figure 3g could be strengthened. Currently, the analysis appears to focus on broad categories; it would be helpful to provide:

Representative genome browser tracks (e.g., exemplary gene coverage plots) for several genes that show clear H3K23me3 peaks in wild type, reduction in set-19, and concomitant upregulation of mRNA levels, and for a few genes that retain H3K23me3 and do not change expression. This would make the link between chromatin changes and transcriptional output more concrete.

(2) In Figure S4C, the authors note a pronounced reduction of H3K23me3 mainly on chromosome arms, but in the current data, it appears that the impact might be arm-specific (i.e., stronger reduction in one arm than the other in a chromosome), with a notable pattern at the X chromosome tip where H3K23me3 seems increased. This is potentially interesting and should be briefly commented on in the Results or Discussion, for example, whether this reflects compensatory activity of another HMT, changes in chromatin organization, or could be a technical artifact.

(3) Figure 3d suggests that some actively expressed genes can also display relatively high H3K23me3 levels, which complicates a simple model of H3K23me3 as exclusively repressive. If feasible, a limited additional analysis stratifying genes by both H3K23me3 and H3K9me3/H3K27me3 status might clarify whether these highly expressed, H3K23me3‑marked genes differ in other chromatin features.

(4) The authors argue that SET-19 primarily affects H3K23me3 and not other canonical repressive marks, based largely on mass spectrometry. It would significantly strengthen the mechanistic conclusions if the authors could assess H3K9me3 and H3K27me3 profiles in set-19 mutants, ideally by ChIP-seq or at least by focused ChIP-qPCR at a subset of loci that lose H3K23me3 and are derepressed at the RNA level. This would address whether H3K23me3 loss occurs independently of changes in other heterochromatin marks, or whether there is crosstalk.

Reviewer #2 (Public review):

Summary:

This study aims to examine the effects of the subcellular localization of the mammalian clock protein PER2 and its dedicated binding partners CRY1 and the kinase CK1. Using a combination of transient transfection and a Dox-inducible expression system, they show that CRY1 promotes nuclear retention of PER2, and that phosphorylation of PER2 by CK1 promotes cytoplasmic localization and release of CRY1. Changes in complex assembly and subcellular localization could impact the transcriptional repressive function of the CK1-PER2-CRY1 complex in the molecular clock.

Strengths:

The study establishes a system of transient transfection and Dox-inducible expression that allows for strict temporal control of the presence of fluorescently-tagged clock proteins. This is essential to conduct time-lapse microscopy studies that determine changes in the apparent subcellular localization and stability of associated clock proteins. With the potential caveats of overexpression set aside, the authors make use of good controls and supplement cell-based work with in vitro experiments where possible. The discovery that phosphorylation of PER2 by CK1 in the nucleus leads to cytoplasmic localization of PER2 and PER2-CRY1 complexes is a new finding. Moreover, the apparent dissociation of CRY1 from PER2 after CK1 phosphorylation provides a potentially new mechanism by which the repressive activity of this complex could be regulated.

Weaknesses:

Overexpression of circadian clock components, normally expressed at low levels, could disrupt the stoichiometry of native interactions, Although the authors provide a reasonable rationale for the Dox-inducible approach and use appropriate controls throughout the experiments, there is still concern that overexpression of the components of this transcriptional repressive complex far exceed the concentration of the transcription factor they regulate, and this has not been taken into consideration here. In addition, the interesting discovery that CK1 phosphorylation of PER2 leads to dissociation of CRY1 has not identified the phosphorylation site(s) responsible for this, so the mechanism by which this occurs is still unknown. Still, this study provides some interesting hypotheses regarding CK1 regulation of PER2 and CRY1 that could drive future work in the field.

Comments on latest version:

This manuscript has already undergone two rounds of review at a reputable journal, and we have been provided with the previous reviewers' comments and the authors' responses. I am satisfied with the responses and changes to the manuscript made in these previous rounds of review and don't have any further experiments to suggest that wouldn't represent significant additional work.

Reviewer #2 (Public review):

In this study, the authors investigate how increasing cognitive demand shapes activity patterns in the dorsal dentate gyrus (DG). Using a touchscreen-based TUNL task combined with TRAP/c-Fos tagging, birth-dating of adult-born granule cells (abDGCs), and chemogenetic inhibition, they show that higher task demand increases mature granule cell (mGC) recruitment and enhances suprapyramidal (SB) versus infrapyramidal (IB) blade bias. Functionally, mGC inhibition reduces overall activity and impairs performance without disrupting blade bias, whereas inhibition of {less than or equal to}7-week-old abDGCs increases mGC activity, abolishes blade bias, and impairs discrimination under high-demand conditions. These findings suggest that effective pattern separation depends not only on overall DG activity levels but also on the spatial organization of recruited ensembles.

The integration of touchscreen TUNL with temporally controlled activity tagging and birth-dated cohorts is technically strong. Quantification of SB-IB bias and radial/apical distributions adds anatomical precision beyond bulk activity measures. The comparison between mGC and abDGC inhibition is conceptually compelling and supports dissociable functional roles. Overall, the data convincingly demonstrate that increasing cognitive demand amplifies blade-biased DG recruitment and that mGCs and abDGCs differentially contribute to both behavioral performance and network organization.

However, how abDGCs are integrated into the mGC network under high cognitive demand remains unresolved. Additional experiments are needed to clarify how abDGCs shape spatial recruitment patterns and whether they directly inhibit or indirectly regulate mGC activity to maintain high performance.

Furthermore, the authors frame "high cognitive demand" as a multidimensional construct encompassing broad behavioral challenge. It would strengthen the work to delineate how local abDGC-mGC circuit interactions regulate specific task components in real time. This will require higher temporal resolution approaches, as TRAP and c-Fos labeling integrate activity over prolonged windows and primarily reflect sustained engagement rather than moment-to-moment computations.<br /> The central conclusion that dentate function depends on coordinated spatial recruitment rather than total activity magnitude is supported by the data, although mechanistic interpretations are tempered given methodological limitations.<br /> Overall, this work advances models of adult neurogenesis by emphasizing a critical-period modulatory role of abDGCs in organizing DG network activity during high-demand discrimination. The combined behavioral and circuit-level framework is likely to be influential in the field.

Comments on revisions:

None remaining.

Reviewer #1 (Public review):

The manuscript presents a compelling new in vitro system based on isogenic co-cultures of human iPSC-derived hepatocytes and macrophages, enabling the modelling of hepatic immune responses with unprecedented physiological relevance. The authors show that co-culture leads to enhanced maturation of hepatocytes and tissue-resident macrophage identity, which cannot be achieved through conditioned media alone. Using this system, they functionally validate immune-driven hepatotoxic responses to a panel of drugs and compare the system's predictive power to that of monocyte-derived macrophages. The results underscore the necessity of macrophage-hepatocyte crosstalk for accurate modelling of liver inflammation and drug toxicity in vitro. The manuscript is clearly written and addresses a key limitation in liver organoid systems: the lack of immune complexity and tissue-specific macrophage imprinting.

Strengths:

• Novelty and Relevance: The study presents a highly innovative co-culture system based on isogenic human iPSCs, addressing an unmet need in modelling immune-mediated hepatotoxicity.

• Mechanistic Insight: The reciprocal reprogramming between iHeps and iMacs, including induction of KC-specific pathways and hepatocyte maturation markers, is convincingly demonstrated.

• Functional Readouts: The application of the model to detect IL-6 responses to hepatotoxic compounds enhances its translational relevance.

Weaknesses:

The co-culture model with monocyte-derived macrophages is not fully characterised, making comparisons less informative.

Reviewer #3 (Public review):

Summary:

In this study, the authors establish a human in vitro liver model by co-culturing induced hepatocyte-like cells (iHEPs) with induced macrophages (iMACs). Through flow cytometry-based sorting of cell populations at days 3 and 7 of co-culture, followed by bulk RNA sequencing, they demonstrate that bidirectional interactions between these two cell types drive functional maturation. Specifically, the presence of iMACs accelerates the hepatic maturation program of iHEPs, while contact-dependent cues from iHEPs enhance the acquisition of Kupffer cell identity in iMACs, indicating that direct cell-cell interactions are critical for establishing tissue-resident macrophage characteristics.

Functionally, the authors show that iMAC-derived Kupffer-like cells respond to pathological stimuli by producing interleukin-6 (IL-6), a hallmark cytokine of hepatic immune activation. When exposed to a panel of clinically relevant hepatotoxic drugs, the co-culture system exhibited concentration-dependent modulation of IL-6 secretion consistent with reported drug-induced liver injury (DILI) phenotypes. Notably, this response was absent when hepatocytes were co-cultured with monocyte-derived macrophages from peripheral blood, underscoring the liver-specific phenotype and functional relevance of the iMAC-derived Kupffer-like cells. Collectively, the study proposes this co-culture platform as a more physiologically relevant model for interrogating macrophage-hepatocyte crosstalk and assessing immune-mediated hepatotoxicity in vitro.

Strengths:

A major strength of this study lies in its systematic dissection of cell-cell interactions within the co-culture system. By isolating each cell type following co-culture and performing comprehensive transcriptomic analyses, the authors provide direct evidence of bidirectional crosstalk between iMACs and iHEPs. The comparison with single-culture controls is particularly valuable, as it clearly demonstrates how co-culture enhances functional maturation and lineage-specific gene expression in both cell types. This approach allows for a more mechanistic understanding of how hepatocyte-macrophage interactions contribute to the acquisition of tissue-specific phenotypes

Weaknesses:

(1) Overreliance on bulk RNA-seq data:

The primary evidence supporting cell maturation is derived from bulk RNA sequencing, which has inherent limitations in resolving heterogeneous cellular states and functional maturation. The conclusions regarding hepatocyte maturation are based largely on increased expression of a subset of CYP genes and decreased AFP levels - markers that, while suggestive, are insufficient on their own to substantiate functional maturation. Additional phenotypic or functional assays (e.g., metabolic activity, protein-level validation) would significantly strengthen these claims.

(2) Insufficient characterization of input cell populations:

The manuscript lacks adequate validation of the cellular identities prior to co-culture. Although the authors reference previously published protocols for generating iHEPs and iMACs, it remains unclear whether the cells used in this study faithfully retain expected lineage characteristics. For example, hepatocyte preparations should be characterized by flow cytometry for ALB and AFP expression, while iMACs should be assessed for canonical macrophage markers such as CD45, CD11b, and CD14 before co-culture. Without these baseline data, it is difficult to interpret the magnitude or significance of any co-culture-induced changes.

(3) Quantitative assessment of IL-6 production is insufficient:

The analysis of drug-induced IL-6 responses is based primarily on relative changes compared to control conditions. However, percentage changes alone are inadequate to capture the biological relevance of these responses. Absolute cytokine production levels - particularly in response to LPS stimulation - should be reported and directly compared to PBMC-derived macrophages to determine whether iMAC-derived Kupffer-like cells exhibit enhanced cytokine output. Moreover, the Methods section should clearly describe how ELISA results were normalized or corrected to account for potential differences in cell number, viability, or culture conditions.

(4) Unclear mechanistic interpretation of IL-6 modulation:

The observed changes in IL-6 production upon drug treatment cannot be interpreted solely as evidence of Kupffer cell-specific functionality. For instance, IL-6 suppression by NSAIDs such as diclofenac is well known to result from altered prostaglandin synthesis due to COX inhibition, while leflunomide's effects are linked to metabolite-induced modulation of immune cell proliferation and broader cytokine networks. These mechanisms are distinct from Kupffer cell identity and may not directly reflect liver-specific macrophage function. Consequently, changes in IL-6 secretion alone - particularly without additional mechanistic evidence or analysis of other cytokines - are insufficient to conclude that co-culture with hepatocytes drives the acquisition of bona fide Kupffer cell maturity.

Reviewers comments to revised manuscript.

The authors successfully established an isogenic, iPSC-derived human liver co-culture model to investigate the role of hepatocyte-macrophage interactions in driving Kupffer cell (KC) identity and hepatocyte maturation. By utilizing a single genetic background, the authors effectively minimized the experimental variability often encountered in non-isogenic systems. A significant highlight of this work is the demonstration that direct co-culture-as opposed to conditioned media alone-is a primary driver for critical KC identity markers such as ID1 and ID3. Furthermore, the model's ability to recapitulate complex clinical IL-6 responses to known hepatotoxicants where standard models have failed underscores its potential utility for early-stage DILI screening. However, there are significant methodological concerns regarding the data analysis. While the study compares four or five distinct experimental groups (e.g., Day 0, Day 7, Day 3 co-culture, and Day 7 co-culture), the authors utilized Student's t-tests for these comparisons. This approach does not account for the multiple comparisons problem and increases the risk of Type I errors. Additionally, while IL-6 secretion is used as a primary functional readout, the individual mechanisms behind these drug responses were not explored experimentally. Finally, Pearson correlation analysis indicates that the iMacs remain poorly correlated with actual in vivo human embryonic liver macrophages, suggesting that the "imprinting" of true KC identity remains incomplete.

Reviewer #1 (Public review):

The authors previously reported that Heliconius, one genus of the Heliconiini butterflies, evolved to be efficient foragers to feed pollen of specific plants and have massively expanded mushroom bodies. Using the same image dataset, the authors segmented the central complex and associated brain regions and found that the volume of the central complex relative to the rest of brain are largely conserved across the Heliconiini butterflies. By performing immunostaining to label specific subset of neurons, the authors found several potential sites of evolutional divergence in the central complex neural circuits, including the numbers of GABAergic ellipsoid body ring neurons and the innervation patterns of Allatostatin A expressing neurons in the noduli. These neuroanatomical data will be helpful to guide the future studies to understand the evolution of the neural circuits for vector-based navigations.

Strength

The authors used sufficiently large scale of dataset from 307 individuals of 41 specifies of Heliconiini butterflies to solidify the quantitative conclusions, and present new microscopy data for fine neuroanatomical comparison of the central complex.

Weakness

(1) Although the figures display a concise summary of anatomical findings, it would be difficult for non-experts to learn from this manuscript to identify the same neuronal processes in the raw confocal stacks. It would be helpful to have instructive movies to show step by step guide for identifications of neurons of interests, segmentations and 3D visualizations (rotation) for several examples including ER neurons (to supplement texts in line 347-353) and Allatostatin A neurons.

(2) Related to (1), it was difficult for me to access if the data in Fig 7 support the author's conclusions that ER neuron number increased in Heliconius Melpomene. By my understanding, the resolution of this dataset isn't high enough to trace individual axons and therefore authors do not rule out that the portion of "ER ring neurons" in Heliconius may not innervate the ER, as stated in Line 635 "Importantly, we also found that some ER neurons bypass the ellipsoid body and give rise to dense branches within distinct layers in the fan-shaped body (ER-FB)". If they don't innervate the ellipsoid body, why are they named as "ER neurons"?

(3) Discussions around the line 577-584 requires the assumption that each ellipsoid body (EB) ring neuron typically arborise in a single microglomerulus to form largely one-to-one connection with TuBu neurons within the bulb (BU), and therefore the number of BU microglomeruli should provide an estimation of the number of ER neurons. Explain this key assumption or provide an alternative explanation.

(4) The details of antibody information are missing in the Key resource table. Instead of citing papers, list the catalogue numbers and identifier for commercially available antibodies, and describe the antigen and if they are monoclonal or polyclonal. Are antigens conserved across species?

(5) I did not understand why authors assume that foraging to feed on pollens is more difficult cognitive task than foraging to feed on nectars. Would it be possible that they are equality demanding tasks but pollen feeding allows Heliconius to pass more proteins and nucleic acids to their offsprings and therefore they can develop larger mushroom bodies?

Comments on revisions:

The authors fully addressed my concerns and significantly improved the accessibility of the manuscript.

Reviewer #2 (Public review):

Summary

In this study, Farnsworth et al. ask whether the previously established expansion of mushroom bodies in the pollen foraging Heliconius genus of Heliconiini butterflies co-evolved with adaptations in the central complex. Heliconius trap line foraging strategies to acquire pollen as a novel resource require advanced spatial memory mediated by larger mushroom bodies but the authors show that related navigation circuits in the central complex are highly conserved across the Heliconiini tribe, with a few interesting exceptions. Using general immunohistochemical stains and 3D reconstruction, the authors compared volumes of central complex regions and unlike the mushroom bodies, there was no evidence of expansion associated with pollen feeding. However, a second dataset of neuromodulator and neuropeptide antibody labeling reveal more subtle differences between pollen and non-pollen foragers and highlight sub-circuits that may mediate species-specific differences in behavior. Specifically, the authors found an expansion of GABAergic ER neurons projecting to the fan shaped body in Heliconius which may enhance their ability to path-integrate. They also found differences in Allatostatin A immunoreactivity, particularly increased expression in the noduli associated with pollen feeding. These differences warrant closer examination in future studies to determine their functional implication on navigation and foraging behaviors.

Strengths

The authors leveraged a large morphological data set from the Heliconiini to achieve excellent phylogenetic coverage across the tribe with 41 species represented. Their high quality histology resolves anatomical details to the level of specific, identifiable tracts and cell body clusters. They revealed differences at a circuit level, which would not be obvious from a volumetric comparison. The discussion of these adaptations in the context of central complex models is useful for generating new hypotheses for future studies on the function of ER-FB neurons and the role of Allatostatin A modulation in navigation.<br /> The conclusions drawn in this paper are measured and supported by rigorous statistics and evidence from micrographs.

Weaknesses

The majority of results in this study do not reveal adaptations in the central complex associated with pollen foraging. However, reporting conserved traits is useful and illustrates where developmental or functional constraints may be acting. The authors have now revised the introduction to set up two alternate hypotheses..

In the main text, the authors describe differences in GABAergic ER neurons between H. melpomene and an outgroup species, with additional images from other species in Figure S4. Quantification of ER cells in these other species would strengthen the claim that these are increased in Heliconius and not just the focal species, but this may hopefully be pursued in future studies.

Comments on revisions:

I am satisfied with the authors' revisions.

Reviewer #1 (Public review):

The author presents a new method for microRNA target prediction based on (1) a publicly available pretrained Sentence-BERT language model that the author fine-tunes using MeSH information and (2) downstream classification analysis for microRNA target prediction. In particular, the author's approach, named "miRTarDS", attempts to solve the microRNA target prediction problem by utilizing disease information (i.e., semantic similarity scores) from their language model. The author then compares the prediction performance with other sequence- and disease-based methods and attempts to show that miRTarDS is superior or at least comparable to existing methods. The author's general approach to this microRNA target prediction problem seems promising, but fails to demonstrate concrete computational evidence that miRTarDS outperforms other existing methods. The author's claim that disease information-based language models are sufficient is unfounded. The manuscript requires substantial rewriting and reorganization for readers with a strong background in biomedical research.

A major issue related to the author's claim of computational advance of miRTarDS: The author does not introduce existing biomedical-specific language models, and does not compare them against miRTarDS's fine-tuned model. The performance of miRTarDS is largely dependent on the semantic embedding of disease terms. The author shows in Figure 5 that MeSH-based fine-tuning leads to a substantial improvement in MeSH-based correlation compared to the publicly available pretrained SBERT model "multi-qa-MiniLM-L6-cos-v1" without sacrificing a large amount of BIOSSES-based correlation. However, the author does not compare the performance of MeSH- and BIOSSES-based correlation with existing language models such as ChatGPT, BioBERT, PubMedBERT, and more. Also, the substantial improvement in MeSH-based correlation is a mere indication that the MeSH-based fine-tuning strategy was reasonable and not that it's superior to the publicly available pretrained SBERT model "multi-qa-MiniLM-L6-cos-v1".

Another major issue is in the author's claim that disease-information from miRTarDS's language model is "sufficient" for accurate microRNA target prediction. Available microRNA targets with experimental evidence are largely biased for those with disease implications that have been reported in the biomedical literature. It's possible that their language model is biased by existing literature that has also been used to build microRNA target databases. Therefore, it is important that the author provides strong evidence that excludes the possibility of data leakage circularity. Similar concerns are prevalent across the manuscript, and so I highly recommend that the author reassess the evaluation frameworks and account for inflated performance, biased conclusions, and self-confirming results.

Last but not least, the manuscript requires a deeper and careful description and computational encoding of microRNA biology. I'd advise the author to include an expert in microRNA biology to improve the quality of this manuscript. For example, the author uses the pre-miRNA notation and replaces the mature miRNA notation to maintain computational encoding consistency across databases. However, the mature microRNA notation "the '-3p' or '-5p' is critical as the 3p and 5p mature microRNAs have different seed sequences and thus different mRNA targets. The 3p mature microRNA would most likely not target an mRNA targeted by the 5p mature microRNA.

Reviewer #2 (Public review):

Summary:

This study introduces a novel knowledge-driven approach, miRTarDS, which enables microRNA-Target Interaction (MTI) prediction by leveraging the disease association degree between a miRNA and its target gene. The core hypothesis is that this single feature is sufficient to distinguish experimentally validated functional MTIs from computationally predicted MTIs in a binary classification setting. To quantify the disease association, the authors fine-tuned a Sentence-BERT (SBERT) model to generate embeddings of disease descriptions and compute their semantic similarity. Using only this disease association feature, miRTarDS achieved an F1 score of 0.88 on the test set.

Strengths:

The primary strength is the innovative use of the disease association degree as an independent feature for MTI classification. In addition, this study successfully adapts and fine-tunes the Sentence-BERT (SBERT) model to quantify the semantic similarity between biomedical texts (disease descriptions). This approach establishes a critical pathway for integrating powerful language models and the vast growth in clinical/disease data into biochemical discovery, like MTI prediction.

Weaknesses:

The main weakness lies in its definition of the ground-truth dataset, which serves as a foundation for methodological evaluation. The study defines the Negative Set as computationally predicted MTIs that lack experimental evidence. However, the absence of experimental validation does not equate to non-functionality. Similarly, the miRAW sets are classified by whether the target and miRNA could form a stable duplex structure according to RNA structure prediction. This definition is biologically irrelevant, as duplex stability does not fully encapsulate the complex in vivo binding of miRNAs within the AGO protein complex.

Reviewer #1 (Public review):

Summary:

This study examines how traumatic brain injury (TBI) alters hippocampal network dynamics and single-unit activity in awake, behaving rats. Using laminar recordings, the authors report reductions in theta power, theta-gamma phase-amplitude coupling, and spike-field entrainment, alongside impairments in spatial memory performance.

Strengths of the study include the use of high-density laminar electrodes to localize activity across hippocampal layers and the integration of electrophysiological and behavioral measures. Analyses that consider behavioral state and account for broadband power changes improve confidence in the interpretation of oscillatory effects. Additional controls suggest that the observed differences are unlikely to be explained by gross motor or motivational deficits. The reported relationships between theta amplitude, phase-amplitude coupling, and spike entrainment provide useful insight into how network coordination is disrupted following injury.

There are a few minor weaknesses. The analyses of single-unit activity across environments are relatively limited, and more comprehensive approaches to characterizing spatial coding would strengthen conclusions about how TBI impacts hippocampal representations. The behavioral assessment relies primarily on a single task, which constrains the interpretation of the cognitive deficits. In addition, the relatively small number of animals is a limitation, although this is partially mitigated by the number of recorded units and the consistency of effects across measures.

Overall, this work provides a careful characterization of hippocampal circuit dysfunction following TBI and contributes to understanding how disruptions in oscillatory coordination and spike timing may relate to cognitive impairment.

Comments on revisions:

The authors have adequately addressed all of my concerns.

Reviewer #3 (Public review):

Summary:

In this study, authors studied the effects of traumatic brain injury created by LFPI procedure on the CA1 at network level. The major findings in this study seem to be that the TBI reduces theta and gamma powers in CA1, reduces phase amplitude coupling in between theta and gamma bands as well as disrupts the gamma entrainment of interneurons. I think the authors have made some important discoveries that could help advance the understanding of TBI effects at physiological level, however, more investigations into deciphering the relationship of the behavioral and brain states to the observed effects would help clarify the interpretations for the readers.

Strengths:

The authors in this study were able to combine behavioral verification of the TBI model with the laminar electrophysiological recordings of CA1 region to bring forward network level anomalies such as the temporal coordination of network level oscillations as well as in the firing of the interneurons. Indeed, it seems that the findings may serve future studies to functionally better understand and/or refine the therapies for the TBI.

Weaknesses:

Discoveries made in the paper and their broad interpretations can be helped with further characterization and comparison among the brain and behavioral states both during immobility and movement. The impact of brain injury in several parts of the brain can alter brain wide LFP and/or behavior. The altered behavior and/or LFP patterns might then lead to reduced spiking and unreliable LFP oscillations in the hippocampus. Hence, claims made in abstract such as "These results reveal deficits in information encoding and retrieval schemes essential to cognition that likely underlie TBI-associated learning and memory impairments, and elucidate potential targets for future neuromodulation therapies" does not have enough evidence in testing whether the disruptions were information encoding and retrieval related or due to sensory-motor and/or behavioral deficits that could also occur during TBI.

Movement velocity is already known to be correlated to the entrainment of spikes with the theta rhythm and also in some cases with the gamma oscillations. So, it is of importance to disentangle the differences in behavioral variables and the observed effects. As an example, the author's claims of disrupted temporal coding (as shown in the graphical abstract) might have suffered from these confounds. The observed results of reduced entrainment might on one hand be due to the decreased LFP power (induced by injury in different brain areas) resulting in altered behavior and/or the unreliable oscillations of the LFP bands such as theta and gamma, rather than memory encoding and retrieval related disruption of spikes synchrony to the rhythms, while on the other hand they may simply be due to reduced excitability in the neurons particularly in the behavioral and brain state in which the effects were observed, rather than disrupted temporal code. Hence, further investigations into dissociating these factors could help readers mechanistically understand the interesting results observed by the authors.

Comments on revisions:

The authors have substantially improved the manuscript in response to the previous reviews. In particular, the revisions addressing the issue of behavioral deficits that could be caused due to the TBI, which were surprisingly not present (if anything minimal) in the injured rats, have strengthened the study and improved the support for the main conclusions. Overall, the manuscript is now clearer and more rigorous. Authors have also addressed all the minor points raised in the study. As a result, the study is now solid, with the major findings broadly supported by the data.

Reviewer #1 (Public review):

Summary:

This study utilizes fNIRS to investigate the effects of undernutrition on functional connectivity patterns in infants from a rural population in Gambia. fNIRS resting-state data recording spanned ages 5 to 24 months, while growth measures were collected from birth to 24 months. Additionally, executive functioning tasks were administered at 3 or 5 years of age. The results show an increase in left and right frontal-middle and right frontal-posterior connections with age and, contrary to previous findings in high-income countries, a decrease in frontal interhemispheric connectivity. Restricted growth during the first months of life was associated with stronger frontal interhemispheric connectivity and weaker right frontal-posterior connectivity at 24 months of age. Additionally, the study describes some connectivity patterns, including stronger frontal interhemispheric connectivity, which is associated with better cognitive flexibility at preschool age.

Strengths:

- The study analyses longitudinal data from a large cohort (n = 204) of infants living in a rural area of Gambia. This already represents a large sample for most infant studies, and it is impressive, considering it was collected outside the lab in a population that is underrepresented in the literature. The research question regarding the effect of early nutritional deficiency on brain development is highly relevant and may highlight the importance of early interventions. The study may also encourage further research on different underrepresented infant populations (i.e., infants not residing in Western high-income countries) or in settings where fMRI is not feasible.

- The preprocessing and analysis steps are carefully described, which is very welcome in the fNIRS field, where well-defined standards for preprocessing and analysis are still lacking.

Weaknesses:

- The study provides a solid description of the functional connectivity changes in the first two years of life at the group level and investigates how restricted growth influences connectivity patterns at 24 months. However, it does not explore the links between adverse situations and developmental trajectories for functional connectivity. Given the longitudinal nature of the dataset, future work should expand the analysis using more sophisticated tools to link undernutrition to specific developmental trajectories in functional connectivity, and eventually incorporate additional data to increase statistical power.

- Connectivity was assessed in 6 big ROIs to reduce variability due to head size and optode placement. Nevertheless, this also implies a significant reduction in spatial resolution. Individual digitalisation and co-registration of the optodes to a head model, followed by image reconstruction, could provide better spatial resolution. This is not a weakness specific to this study but rather a limitation common to most fNIRS studies, which typically analyse data at the channel level since digitalisation and co-registration can be challenging, especially in complex setups like this. The authors made an important effort to identify subjects with major optode displacement; however, future work might use tools to digitally record the positions of optodes and head markers.

Reviewer #2 (Public review):

Strengths:

The article addresses a topic of significant importance, focusing on early life growth faltering in low-income countries-a key marker of undernutrition-and its impact on brain functional connectivity (FC) and cognitive development. The study's strengths include the laborious data collection process, as well as the rigorous data preprocessing methods employed to ensure high data quality. The use of cutting-edge preprocessing techniques further enhances the reliability and validity of the findings, making this a valuable contribution to the field of developmental neuroscience and global health.

Weaknesses:

The study lacks specificity in identifying which specific brain networks are affected by growth faltering, as the current exploratory analyses mainly provide an overall conclusion that infant brain network development is impacted without pinpointing the precise neural mechanisms or networks involved.

Reviewer #3 (Public review):

Summary

This study aimed to investigate whether the development of functional connectivity (FC) is modulated by early physical growth, and whether these might impact cognitive development in childhood. This question was investigated by studying a large group of infants (N=204) assessed in Gambia with fNIRS at 5 visits between 5 and 24 months of age. Given the complexity of data acquisition at these ages and following data processing, data could be analyzed for 53 to 97 infants per age group. FC was analyzed considering 6 ensembles of brain regions and thus 21 types of connections. Results suggested that: i) compared to previously studied groups, this group of Gambian infants have different FC trajectory, in particular with a change in frontal inter-hemispheric FC with age from positive to null values; ii) early physical growth, measured through weight-for-length z-scores from birth onwards, is associated with FC at 24 months. Some relationships were further observed between FC during the first two years and cognitive flexibility, in different ways between 4- and 5-year-old preschoolers, but results did not survive corrections for multiple comparisons.

Strengths

The question investigated in this article is important for understanding the role of early growth and undernutrition on brain and behavioral development in infants and children. The longitudinal approach considered is highly relevant to investigate neurodevelopmental trajectories. Furthermore, this study targets a little studied population from a low-/middle-income country, which was made possible by the use of fNIRS outside the lab environment. The collected dataset is thus impressive and it opens up a wide range of analytical possibilities.

Weaknesses

Data analyses were constrained by the limited number of children with longitudinal data on NIRS functional connectivity. Applying advanced statistical modeling approaches such as structural equation modelling would provide further insights on neurodevelopmental trajectories and relationships with early growth and later cognitive development.

Reviewer #1 (Public review):

Summary:

Hoverflies are renowned for their striking sexual dimorphism in eye morphology and early visual system physiology, as well as in sexually dimorphic behaviors. Surprisingly, male and female flight behaviors in response to optic flow exhibit only subtle differences. Nicholas et al. investigate the sensorimotor transformation of sexually dimorphic visual information into flight steering commands via descending neurons. Using a combination of intracellular and extracellular recordings, neuroanatomical analysis, and behavioral assays, the authors convincingly demonstrate that descending neurons-particularly at high optic flow velocities-exhibit pronounced sexual dimorphisms, while wing steering responses remain largely monomorphic. The study highlights a very interesting discrepancy between neuronal and behavioral response properties.

More specifically, the authors focused on two types of descending neurons that receive inputs from well-characterized wide-field sensitive tangential cells: OFS DN1 and OFS DN2. Their likely counterparts in Drosophila connect to neck, wing and haltere neuropils. The authors characterized the visual response properties of these two neuronal classes in both male and female hoverflies and identified several interesting differences. They then presented the same set of stimuli, tracked wing beat amplitude and analyzed the sum and the difference of right and left wing beat amplitude as a readout of lift or thrust, and yaw turning, respectively. Behavioral responses showed little to no sexual dimorphism, despite the observed neuronal differences.

Strengths:

I find the question very interesting and the results both convincing and intriguing. A fundamental goal in neuroscience is to link neuronal responses and behavior. The current study highlights that the transformations - even at the level of descending neurons to motoneurons - is complex and less straightforward than one might expect.

Weaknesses:

The authors investigated two types of descending neurons, but it was not clear to me how many other descending neurons are thought to be involved in wing steering responses to wide-field motion. I would suggest providing a more in-depth overview of what is known in hoverflies and Drosophila, since the conclusions drawn from the study would be different if these two types were the only descending neurons involved, as opposed to representing a subset of the neurons conveying visual information to the wing neuropil.

Both neuronal classes have counterparts in Drosophila that also innervate neck motor regions. The authors filled hoverfly DNs in intracellular recordings to characterize their arborization in the ventral nerve cord. In my opinion, these anatomical data could be further exploited and discussed a bit more: is the innervation in hoverflies also consistent with connecting to the neck and haltere motor regions? Are there any obvious differences and similarities to the Drosophila neurons mentioned by the authors? If the arborization also supports a role in neck movements, the authors could discuss whether they would expect any sexual dimorphism in head movements.

Revision comment:

I thank the authors for their detailed replies to my questions and the additional clarifications and analysis included in the paper. All my concerns have been addressed.

Reviewer #2 (Public review):

Summary:

Many fly species exhibit male-specific visual behaviors during courtship while little is known about the circuit underlying the dimorphic visuomotor transformations. Nicholas et al focus on two types of visual descending neurons (DNs) in hoverflies, a species in which only males exhibit high-speed pursuit of conspecifics. They combined electrophysiology and behavior analysis to identify these DNs and characterize their response to a variety of visual stimuli in both male and female flies. The results show that the neurons in both sexes have similar receptive fields but exhibit speed-dependent dimorphic responses to different optic flow stimuli.

Strengths:

Hoverflies, though not a common model system, show very interesting dimorphic behaviors and provide a unique and valuable entry point to explore the brain organization behind sexual dimorphism. The findings here are not only interesting on their own right but will also likely inspire those working in other systems, particularly Drosophila.

The authors employed rigorous morphology, electrophysiology, and behavior methods to deliver comprehensive characterization of the neurons in question. The precision of the measurements allowed for identifying a subtle and nuanced neuronal dimorphism and set a standard for future work in this area.

Weaknesses:

I'd like to thank the authors for the revised manuscript, especially the new analyses and figures. Most of my earlier concerns have been satisfactorily addressed by now. Interested readers are kindly referred to the authors' responses for the discussion of the limitations of this work.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The editors have determined that the authors adequately addressed the prior reviewer comments.]

Summary:

The author's goal was to arrest PsV capsids on the extracellular matrix using cytochalasin D. The cohort was then released and interaction with the cell surface, specifically with CD151 was assessed.

Note on previous revisions:

The authors did an excellent job in their revision to include data from the effect of proteolytic priming on their observed virion transfer to the cell body. All other minor issues were addressed adequately.

The work could be especially critical to understanding the process of in vivo infection.

Reviewer #2 (Public review):

Review of the previous version:

The study design involves infecting HaCaT cells (immortalised keratinocytes mimicking basal cells of a target tissue) and observing virus localization with and without actin polymerization inhibition by cytochalasin D (cytoD) to analyze virion transfer from the ECM to the cell via filopodial structures, using cellular proteins as markers.

In the context of the model system, the authors stress in the revised version the importance of using HaCaT cells as a relevant 'polarized' cell model for infection. The term 'polarized' is used in the cell biological literature for epithelial cells to describe a strict apical vs. basolateral demarcation of the plasma membrane with an established diffusion barrier of the tight junction. However, HaCat cells do not form tight junctions. In squamous epithelia, such barriers are only found in granular layers of the epithelium. The published work cited in support of their claims either does not refer to polarity or only in the context of other cells such as CaCo-2 cells.

Overall, the matter of polarity would be important, if indeed the virus could only access cell-associated HSPGs as primary binding receptor, or the elusive secondary receptor via the ECM in the used model system (HaCaT cells), if they would locate exclusively basolaterally. This is at least not the case for binding, as observed in several previous publications (just two examples: Becker et al, 2018, Smith et al., 2008). With only a rather weak attempt at experimental verification of their model system with regards to polarity of binding, the authors then go on to base their conclusions on this unverified assumption.

This is one example of several in the manuscript, where claims for foundational premises, observations, and/or conclusions remain undocumented or not supported by experimental data.

Another such example is the assumption of transfer of the virus from ECM to the tetraspanin CD151. Here, the conclusions are based on the poorly documented inability of the virus to bind to the cell body, which is in stark contrast to several previous publications, and raises questions. Thus, association with CD151 likely occurs both from ECM derived virus AND virus that binds to cells, so that any conclusions on the mode of association is possible only in live cell data (which is not provided). Overall, their proposed model thus remains largely unsubstantiated with regards to receptor switching.

There are a number of important additional issues with the manuscript:

First, none of the inhibitors have been tested in their system for efficacy and specificity, but rely on published work in other cell types. This considerably weakens the confidence on the conclusion drawn by the authors.

Second, the authors aim to study transfer from ECM to the cell body and effects thereof. However, there are still substantial amounts of viruses that bind to the cell body compared to ECM-bound viruses in close vicinity to the cells. This is in part obscured by the small subcellular regions of interest that are imaged by STED microscopy, or by the use of plasma membrane sheets. This remains an issue despite the added Supple. Fig. 1, where also only sub cellular regions are being displayed. As a consequence the obtained data from time point experiments is skewed, and remains for the most part unconvincing, largely because the origin of virions in time and space cannot be taken into account. This is particularly important when interpreting the association with HS, the tetraspanin CD151, and integral alpha 6, as the low degree of association could be originating from cell bound and ECM-transferred virions alike.

Third, the use of fixed images in a time course series also does not allow to understand the issue of a potential contribution of cell membrane retraction upon cytoD treatment due to destabilisation of cortical actin. Or, of cell spreading upon cytoD washout. The microscopic analysis uses an extension of a plasma membrane stain as marker for ECM bound virions, this may introduce a bias and skew the analysis.

Fourth, while the use of randomisation during image analysis is highly recommended to establish significance (flipping), it should be done using only ROIs that have a similar density of objects for which correlations are being established. For instance, if one flips an image with half of the image showing the cell body, and half of the image ECM, it is clear that association with cell membrane structures will only be significant in the original. But given the high density of objects on the plasma membrane, I am not convinced that doing the same by flipping only the plasma membrane will not also obtain similar numbers than the original.

Reviewer #2 (Public review):

Summary:

The authors used single-nuclei sequencing of benign fallopian tubes and ovarian cancer to delineate the plausible cell of origin of high-grade serous ovarian cancer.

Strengths:

These substantial data provide the field with significant research resources to examine additional features in normal fallopian tubes and ovarian cancers. The highly detailed bioinformatic analysis, rooted in a strong biological framework, is convincing. The methodology was appropriate and used validated methodology based on biological relevance (region selection and transcriptomics analysis).

The authors propose a convincing model of epithelial progenitor cells and their localisation in high-grade serous ovarian cancers. These findings are important and useful.

Weaknesses:

Overall, the weaknesses are clearly stated in the discussion. The study provides a novel framework for future study, and proposes a model which will require validation.

Within the ovarian cancer field, the endometrioid and clear cell histotypes are thought to arise from ciliated or secretory cells. Typically these are thought to be from the cervix or uterus. This concept was not mentioned in the work.

Further, in the ovarian cancer field, stemness is judged by some classic assays - aldehyde assays looking at ALDH1A1 and spheroid-producing ability. These were not mentioned - could these be useful in a population of fallopian tube epithelial cells, or would other assays/markers be more appropriate?

The choice of ES2 and OVCAR was not sufficiently justified, as ES2 is widely regarded as a clear cell ovarian cancer cell line in many research circles. Additionally, I did not see confirmation of gene knockdown by Western blot or qPCR.

PGR loss through copy number variant was surprising, as this was a marker. So would the marker be lost through one of these mechanisms randomly or specifically?

Reviewer #1 (Public review):

Summary:

Using comprehensive profiling of normal and cancerous tissue via bulk and single-cell RNA sequencing, the authors identified that high-grade serous ovarian cancer is likely to originate from the epithelial progenitor cells from the distal fimbrial region of the fallopian tube, where it has been previously shown to be most prone to ovulatory stress and other microenvironmental influences. The authors also included a CNV analysis to identify hotspots in HGSOCs.

The findings are preliminary, but the resource on its own has great potential and can be used for developing methods for early detection, stratification and treatment.

The main limitation of this study is that the lineage is purely inferred from bioinformatics analysis. More validation work is required, perhaps using cell models / other model organisms.

Strengths and weaknesses:

The authors investigated the origin of high-grade serous ovarian cancer, which is one of the deadliest. They performed comparative analysis using both bulk and single-nucleus RNA sequencing between cancerous and normal tissues (fallopian tube and ovaries) and identified a population of epithelial progenitor cells from the distal fimbrial region that are exposed to ovulatory stress, as the most plausible cells of origin. The extensive profiling of the molecular signatures can also be used for early detection and stratification for treating the disease.

Previous studies have shown that HGSOCs likely originated from the epithelial lining of the fallopian tubes (PMID 32349388). The bulk RNAseq data is confusing in that neither the overall correlation of the transcriptome nor the sample clustering (Figure 1) supports the idea that the HGSOCs are close to the fallopian tube. The authors could perform a more comprehensive marker gene-based analysis to demonstrate their relationship.

The authors also performed a comprehensive analysis of single-cell datasets on both normal and cancerous tissue in humans. From there, they performed a combination of RNA velocity, PAGA and pseudotime, etc, to try and delineate the relationship amongst related cell populations. It would be helpful if the authors could clarify why they applied this particular suite of tools (explaining the differences between tools and bioinformatic approaches) to assist the broader readership who may not be familiar with this type of single-cell bioinformatic analysis.

It also seems to me that the authors did not account for patient effect when they performed the data integration (this point is discussed in the text). This may explain at least partially why the clusters are segregated by patient samples. Another explanation is that it could be due to uneven sampling, as only very few cells (1000s) were captured from each of the tumour samples, and this is clear when a dramatic difference can be seen in their cellular composition.

The trajectory analysis of normal and cancer single-cell data should also include other cells to prevent confirmation bias, as these analyses would only consider relationships amongst the cells available in the model.

As the authors indicated in the limitations, the cell lineage in the studies is largely inferred from the bioinformatics analysis. Experimental lineage tracing via other experimental models (organoids/animal models) would be required.

Despite these limitations, this study will serve as an important resource for the scientific community. I would also suggest that the authors should share this resource via additional portals in addition to the GEO data deposit (e.g. the HCA, or single-cell portals such as at the Broad Institute or CellXGene Discover).

Reviewer #1 (Public review):

This work demonstrates that MORC2 undergoes phase separation (PS) in cells to form nuclear condensates, and the authors demonstrate convincingly the interactions responsible for this phase separation. Specifically, the authors make good use of crystallography and NMR to identify multiple protein:protein interactions and use EMSA to confirm protein:DNA interactions. These interactions work together to promote in vitro and in cell phase separation and boosted ATPase activity by the catalytic domain of MORC2.

Moreover, the authors show solid evidence supporting their important claim that MORC2 PS is important for MORC2-mediated gene regulation. Exploring causal links between PS and function is an important need in the phase separation field, particularly as regards the role of condensates in gene regulation, and is a non-trivial matter. It is crucial and challenging to properly explore the alternative possibility that soluble complexes, existing in the same conditions as phase-separated condensates, are the functional species. The authors have attempted to address this concern by manipulating the physical nature of the MORC2 condensates using a killswitch (KS) peptide (MORC2 +KS), finding that reducing condensates dynamics results in a cellular phenotype very similar to that of the phase separation-deficient MORC2 condensates. While not fully ruling out the alternative, soluble-complex hypothesis, this experiment suggests that function is indeed localized inside the MORC2 condensates, and that perturbing the condensate can be functionally equivalent to removing condensate formation.

The authors also look at several disease related mutants of MORC2. While most of these do not seem to have an obvious connection to the phase separation data, it is quite interesting that one mutant, E236G, displays similar intra-condensate dynamics compared to MORC2 +KS, strengthening the claim that MORC2 phase separation is important for function and suggesting that the observations in this paper may indeed have some disease relevance.

Strengths

Static light scattering and crystallography are nicely used to demonstrate the dimerization of MORC2FL and to discover the structure of the CC3 domain dimer, presumably responsible for the dimerization of MORC2FL (Figure 1).

Extensive use of deletion mutants in multiple cell lines is used to identify regions of MORC2 that are important for forming condensates in the nucleus: the IBD, IDR, and CC3 domains are found to both be essential for condensate formation, while the CW domain plays an unknown role in condensate morphology (Figure 3). The authors use NMR to further identify that the IBD domain seems to interact with the first third of the centrally located IDR, termed IDRa, but not with the latter two thirds of the IDR domain (Figure 4). This leads them to propose that phase separation is the product of IDB:IDRa interaction, CC3 dimerization, and an unknown but important role for the CW domain.

Based on the observation that removal of the NLS resulted in diffuse cytoplasmic localization, they hypothesized that DNA may play an important role in MORC2 PS. EMSA was used to demonstrate interaction between DNA and several MORC2 domains: CC1, CC2, IDR, and TCD-CC3-IBD. Further in vitro microscopy with purified MORC2 showed that DNA addition significantly reduces MORC2 saturation concentration (Figure 5).

These assays convincingly demonstrate that MORC2 phase separates in cells and identifies the protein domains and interactions responsible for this phenomenon.

Weaknesses

The connection between condensates and function, while improved from the original manuscript, still has some weak points.

The central experiment demonstrating that MORC2 condensates mediate function takes the form of RNA-Seq in MORC2 KO HeLa cells (Figure 6), rescued with WT, condensate-deficient mutants, and a KS peptide mutant that reduces dynamics by increasing homotypic protein interactions. The observation that rescuing with MORC2 +KS is ineffective, in a manner similar to rescue with condensate-deficient MORC2 mutants, suggests that unperturbed condensates are important for function. An alternative possibility, however, is that condensates are non-functional bystanders, and that the increased homotypic interactions present in MORC2 +KS result in stronger MORC2 +KS recruitment to condensates, reducing the pool of functional, dilute phase MORC2 +KS and squashing function via sequestration. Similar ideas have been explored by others for transcription factors (e.g. Chong et al, Mol Cell, 2022). This possibility is neither discussed nor ruled out. The absence of microscopy data showing similar localization of MORC2 and MORC2 +KS (particularly the amount of diffuse MORC2 outside condensates) amplifies this concern.

The RNA-Seq data presented in Figure 6h also has some concerning qualities. Inter-replicate variability is higher than ideal, particularly for MORC2 deltaCC3. This may be a product of the transient transfection system used for these experiments, which inherently results in stochasticity. Specific sets of genes regulated by MORC2 are consistent with the main conclusion (Figure 6i, individual genes in 6h, showing that all mutants are more similar to one another than to WT MORC2), but global transcription shifts seem quite different between MORC2 condensate-deficient mutants and MORC2 +KS (Figure 6h heatmap), suggesting much more than simple condensate disruption is taking place. Together, this weakens the conclusion that MORC2 condensates are the functional form of MORC2.

Reviewer #2 (Public review):

Summary:

The study by Zhang et al. focuses on how condensation of a chromatin-associated protein MORC2 regulates gene expression. Their study shows that MORC2 forms dynamic nuclear condensates in cells. In vitro, MORC2 phase separation is driven by dimerization and multivalent interactions involving the C-terminal domain but interplay with other parts of MORC2 too. A key finding is that the intrinsically disordered region (IDR) of MORC2 exhibits strong DNA binding. They report that DNA binding enhances MORC2's phase separation and its ATPase activity, offering new insights into how MORC2 contributes to chromatin organization and gene regulation. Authors correlate MORC2's condensate forming ability and material properties with its gene silencing function using a few variants. Moreover, they investigate the effect of disease-linked mutations in the N-terminal domain of MORC2 on its ability to form cellular condensates, ATPase activity and DNA-binding. Their work implies that proper material properties of MORC2 condensates may be important to their biological function.

Strengths:

The authors determined a 3.1 Å resolution crystal structure of the dimeric coiled-coil 3 (CC3) domain of MORC2, revealing a hydrophobic interface that stabilizes dimer formation. They present extensive evidence that MORC2 phase separates across multiple contexts, including in vitro, in cellulo, and in vivo. Through systematic cellular screening, they identified the C-terminal domain of MORC2 as a key driver of condensate formation. Biophysical and biochemical analyses further show that the IDR within the C-terminal domain interacts with the C-terminal end region (IBD) and also exhibit strong DNA-binding capacity (using 601 DNA), both of which promote MORC2 phase separation. Together, this study emphasizes that interactions mediated by multiple domains-CC3, IDR, and IBD- drives MORC2 phase separation. Additionally, the work uses a unique kill-switch peptide fused to the MORC2 sequence to disrupt its material properties -- this permits the authors to examine the link between material properties and transcription function. The study is overall strengthened by (1) the combination of variants tested both in vitro and in cellulo, and (2) the systematic examination of domain contributions that highlight the multivalent interactions at play mediating MORC2 condensation.

Weaknesses:

The employed MORC2 variants have enabled the beginning of an investigation linking condensation and biological function, but more work will be needed to really dissect the contribution of condensation to DNA-binding, ATPase activity, and gene silencing. A systematic investigation of differential material properties on MORC2 condensates will be needed to assess the link to biological function, especially as the authors' work is reminiscent of how the liquidity of Caulobacter crescentus PopZ condensates tunes bacterial fitness.

Reviewer #3 (Public review):

Summary:

The manuscript by Zhang et al. demonstrates that MORC2 undergoes liquid-liquid phase separation (LLPS) to form nuclear condensates critical for transcriptional repression. Using a combination of in vitro LLPS assays, cellular studies, NMR spectroscopy, and crystallography, the authors show that a dimeric scaffold formed by CC3 drives phase separation, while multivalent interactions between an intrinsically disordered region (IDR) and a newly defined IDR-binding domain (IBD) further promote condensate formation. Notably, LLPS enhances MORC2 ATPase activity in a DNA-dependent manner and contributes to transcriptional regulation, establishing a functional link between phase separation, DNA binding, and transcriptional control.

Strengths:

The manuscript is well organized and logically structured. It provides valuable mechanistic insights into MORC2 function, and the majority of the conclusions are well supported by the data presented.

Reviewer #1 (Public review):

Summary:

This study investigates how ingestive behaviors are reflected in muscle activity and how these behaviors relate to neural dynamics in the brain. By combining muscle recordings with computational analysis, the authors identify patterns of mouth movements and show that these change over time and align with changes in brain activity. The work suggests that ingestion is not defined by a single action but by coordinated changes across multiple behaviors.

Strengths:

(1) Addresses an important and underexplored question about how ingestive behavior is organized.

(2) Combines behavioral, physiological, and computational approaches creatively.

(3) Provides a novel framework for quantifying complex ingestive movements.

(4) Demonstrates a clear temporal relationship between behavior and brain activity.

Weaknesses

(1) Behavioral labels rely on video-based scoring, which may not fully capture subtle or hidden movements.

(2) The relationship between brain activity and behavior is correlational, but sometimes interpreted more strongly.

(3) The manuscript could be clearer and more accessible to readers outside the field.

Reviewer #2 (Public review):

Summary:

In this study, Baas-Thomas et al. aim to study the neural mechanisms underlying ingestive versus rejection responses to taste stimuli by developing an EMG-based approach to identify ingestion-related orofacial movements. Whereas prior work has focused primarily on detecting rejection-related gapes, the authors introduce a machine-learning classifier that uses waveform features extracted from anterior digastric (AD) EMG signals to detect mouth- and tongue-movement (MTM) events associated with ingestion. Clustering analyses further suggest that ingestive behavior consists of multiple MTM subtypes whose relative frequencies vary across trial time and taste conditions. Finally, simultaneous recordings indicate that shifts in MTM expression follow transitions in gustatory cortex (GC) population dynamics into palatability-related firing states, supporting a role for cortical ensemble activity in coordinating ingestive motor responses.

Strengths:

Overall, the scientific question addressed in this study is well motivated. A mechanistic understanding of ingestive decision-making requires a precise characterization of the motor patterns that implement ingestion, and these behaviors have remained insufficiently resolved in prior work. The authors take a reasonable and technically innovative approach by leveraging AD EMG recordings to classify distinct orofacial movement patterns. The extracted waveform features appear effective in separating gapes from ingestion-related mouth-tongue movements, and clustering analyses further suggest the presence of distinguishable MTM subtypes that show meaningful temporal structure and neural correlates. Taken together, the work provides a potentially useful framework for linking gustatory cortical dynamics to the motor expression of taste-guided decisions.

A particularly valuable aspect of this work is the attempt to move beyond a binary characterization of ingestive behavior and instead identify multiple subtypes of ingestion-related movements. This finer behavioral resolution has the potential to provide a more realistic account of how complex consummatory actions are organized. More broadly, the effort to relate structured behavioral motifs to population-level neural dynamics is conceptually interesting and could prove useful for future studies seeking to connect circuit dynamics with the motor implementation of motivated behaviors.

Weaknesses:

(1) I have several concerns regarding the methodological comparisons used to establish the superiority of the proposed XGBoost classifier. In particular, the comparison between the XGBoost classifier and previously used QDA approaches (Figure 3) may not be entirely well-matched. The QDA framework was originally designed primarily to detect gape events and does not explicitly assign labels to MTM movements. As a result, the apparent advantage of XGBoost in identifying MTMs may partly reflect differences in task formulation rather than intrinsic differences in classification performance. From visual inspection, gape detection performance appears broadly comparable across methods.

A more informative benchmark would involve comparing XGBoost to an extended pipeline in which QDA-based gape detection is combined with a secondary movement-detection stage, distinguishing MTMs from periods of no movement. Such a comparison would better isolate the contribution of classifier architecture per se. Without this control analysis, the strength of the claim that XGBoost provides superior performance for behavioral decoding remains somewhat uncertain.

(2) The presentation of the neural ensemble analyses is considerably less comprehensive and intuitive than that of the behavioral analyses. The manuscript would benefit from more direct visualization of inferred neural state transitions. For example, plotting predicted neural states in a manner analogous to the behavioral states illustrated in Figure 6B would improve interpretability and help readers understand how neural dynamics relate temporally to behavioral changes.

In addition, the interpretation that GC ensemble dynamics drive behavioral state transitions may require further clarification. If GC activity plays a causal role in initiating behavioral changes, one might expect a consistent brain-to-behavior lag across changepoints. However, Figure 6 appears to show such lag primarily at the second transition but not at the first. This raises questions about how uniformly the proposed causal interpretation applies across state boundaries, and additional analysis or discussion is needed.

(3) The neural ensemble analyses primarily focus on constructing higher-level behavioral state variables rather than directly testing how individual movement subtypes relate to neural activity. The behavioral interpretation of the inferred state structure, therefore, remains somewhat unclear. While this approach is consistent with previous work from the authors and with broader state-transition frameworks of gustatory processing, it is not immediately obvious that this is the most informative level of analysis for the present dataset.

In particular, it would strengthen the manuscript to examine whether GC neurons or ensembles also encode lower-level motor structure, such as the occurrence of gapes or specific MTM subtypes. Demonstrating selective or mixed encoding across hierarchical levels (movement motifs versus abstract behavioral states) would help clarify the functional interpretation of the reported neural dynamics. At present, the manuscript largely assumes that GC activity reflects higher-order behavioral states without directly testing alternative representational possibilities.

(4) Because direct behavioral ground truth for intra-oral ingestive movements is difficult to obtain, MTM subtypes are inferred primarily through clustering of EMG waveform features. Although the authors demonstrate statistical separability and cross-session stability of these clusters, it remains unclear whether they correspond to discrete motor programs or instead reflect a structured partitioning of a continuous behavioral space shaped by feature selection and preprocessing choices. Perhaps some additional robustness analyses or convergent validation (e.g., alternative clustering methods, feature perturbation tests, or stronger neural and behavioral dissociations) would help clarify the biological significance of the inferred subtype structure.

Reviewer #3 (Public review):

Summary:

This study examines how ingestive-related orofacial movements relate to ensemble dynamics in gustatory cortex (GC) during taste processing. Previous work has shown that GC activity evolves through a sequence of population states following taste delivery, culminating in a transition to palatability-related firing that precedes rejection-related orofacial movements (e.g., gaping). Importantly, perturbing GC activity around the time of this transition alters the timing of gaping, suggesting that these ensemble dynamics play a functional role in linking taste evaluation to behavioral responses. The present study asks whether similar neural dynamics are also associated with ingestive-related orofacial movements that occur during the consumption of palatable stimuli.

To address this question, the authors develop a machine-learning classifier to identify distinct orofacial movements from anterior digastric EMG recordings. Using a set of labeled EMG waveforms obtained from video-scored trials, a gradient-boosted (XGBoost) classifier is trained to detect gapes, mouth/tongue movements (MTMs), and periods of no movement. Applying this classifier to a larger EMG dataset reveals that ingestive-related MTMs cluster into three distinct movement subtypes whose frequencies change systematically within trials.

The authors then relate these behavioral dynamics to previously described GC ensemble transitions identified using changepoint modeling. They report that changes in MTM subtype frequencies tend to occur shortly after the transition to palatability-related activity in GC. These results suggest that GC population dynamics are temporally associated not only with rejection-related behaviors but also with ingestive motor patterns that occur as animals prepare to consume palatable tastants.

Strengths:

The study introduces an innovative framework for extracting intricate orofacial movement information from EMG recordings. The machine-learning classifier provides a scalable method for identifying specific orofacial movements and performs better than previously published algorithms designed for gape detection. This approach allows the authors to examine movement microstructure at a temporal resolution that cannot be achieved with video scoring in freely moving animals.

A second strength is the integration of orofacial movement analysis with neural population dynamics. By relating EMG-derived movement subtypes to ensemble state transitions in GC, the study builds on a substantial body of work examining the temporal evolution of taste responses in cortex. The finding that ingestive-related movement dynamics occur shortly after the emergence of palatability-related firing provides an interesting extension of previous observations linking GC state transitions to rejection behavior.

The manuscript is also commendable in its commitment to data accessibility. By providing clear information about how the datasets can be accessed and making training data for the classifier publicly available, the authors make it possible for other researchers to examine the analytical pipeline and apply similar approaches to their own datasets. This transparency provides a useful framework for extending and building upon the methods presented here.

Weaknesses:

Some aspects of the EMG-based movement classification pipeline warrant careful interpretation. The training dataset used for classifier development is relatively small and is derived from a subset of trials in which mouth movements were clearly visible in video recordings. While the classifier performs well on this labeled dataset, it is not entirely clear how representative these labeled examples are of the full range of EMG signals present in the larger dataset.

The interpretation of the three identified MTM subtypes also remains somewhat tentative. The study convincingly demonstrates that distinct waveform-defined clusters exist in the EMG data, but the functional significance of these clusters as ingestive "behaviors" is less clear. As acknowledged by the authors, the specific roles of these movement patterns in the ingestion process remain speculative.

Finally, several conclusions in the Discussion rely on relatively strong mechanistic language when describing the relationship between GC dynamics and ingestive behavior. The data clearly demonstrate a temporal association between GC state transitions and changes in the frequencies of the different MTM subtypes. However, the results primarily support the interpretation that similar cortical dynamics are associated with ingestive and rejection-related behaviors rather than definitively establishing that these behaviors "are governed by the same underlying neural mechanisms".

Reviewer #1 (Public review):

Summary:

The authors have used a macaque (two animals only) to follow the migration of 'seeded' TDP43 protein in neuronal pathways - thus mimicking the spread of ALS in the human CNS. Previous experiments in rodents failed to demonstrate this, posing interesting and important biological differences, possibly related to the UMN-LMN system in higher order apes and humans.

Strengths:

An important step forward.

Weaknesses:

No weaknesses were identified by this reviewer. Only 2 animals were used, but that is appropriate given the sensate status of the macaque. In the opinion of this reviewer, the results are entirely convincing.

Reviewer #2 (Public review):

Summary:

There are astonishingly few papers trying to reproduce the process of initiation and spreading that Braaks studies have suggested and postulated. The authors should be applauded for pioneering such a difficult experiment. They overexpressed the TDP-43 protein in the motor neuron pool of the brachioradialis muscle and showed that by this technique, motor neurons in this pool died, and the muscle got denervated. They had evidence of a spreading process from the spinal cord to the cortex, demonstrated by showing widespread deposits of phosphorylated TDP-43 bilaterally in the cervical cord and the motor cortex. By their experiment, they created a dying-backwards model, not a model of corticofugal spread, like that shown by Braak. No muscle weakness was observed, not even in the brachioradialis.

Strengths:

The strength of this innovative study is the fact that this spreading experiment uses the phylogenetically young connectome of primates (macaques). They also made the thought-provoking observation of spreading from the cord to the motor cortex, not the corticofugal spread model observed by Heiko Braak. This is thought-provoking because this enables the observer to compare their model with the findings in humans.

Weaknesses:

The following aspects are not a weakness but need to be better explained for the interested reader - and potentially improved in future studies for which the authors laid the foundation:

(1) Why do the authors use the brachioradialis motor neuron pool to overexpress TDP-43? More is known about other muscles and how they are embedded in the motor connectome of primates. Why not the biceps brachii or the hand extensors or - even better - the small muscles of the hand? These are known to be strongly monosynaptically connected with the motor cortex. The authors should explain this. I am unclear if there was a specific reason which I did not see or understand. In my view, the brachioradialis is not the best representative of the primate connectome, for example, to examine this model and compare it with the corticofugal spread.

(2) In the Braaks experiment, only (seemingly soluble) non-phoshorylated TDP-43 "crossed" synapses. Phosphorylated TDP-43 did not do this. The authors of this study saw phosphorylated TDP43 in motor neurons and the cortex. Is there any potential explanation for how it crosses synapses? If it really does, there is an obvious difference to the human situation which needs to be emphasized and explained (in the future).

(3) There were significant deposits of phosphorylated TDP-43 in oligodendrocytes in humans. Whilst I understand that one experiment cannot solve every question - I am curious about whether the authors saw anything in oligodendrocytes?

(4) Which was the pattern of damage? Of course, this pattern is not likely to have a monosynaptic pattern - like in humans........but was there a pattern? Did it have a physiologically meaningful basis? Was there any relation to the corticofugal monosynaptic pattern? What are the differences? The authors speak of "multiple waves". Does this mean that if this were a corticofugal model, for example, oculomotor neurons would also degenerate?

Reviewer #3 (Public review):

Summary:

In this paper by Jones and colleagues, a non-human primate model is described in which wild-type TDP-43 is expressed in the cervical spinal cord. This gave rise to loss of motor neurons in the ventral horn at that level in the cervical spinal cord. MRI of the muscles allowed to see increased intensity in the mostly affected brachioradialis muscle, suggesting this muscle becomes denervated. At the neuropathological level, TDP-43 and pTDP-43 staining in the cytoplasm is increased, not only at the specific level of the cervical spinal cord, but also at a distance.

Strengths:

A clear strength is the state-of-the art focal expression of the TDP-43 transgene at a focal site in the cervical spinal cord. This is achieved by combining a general expression of a flipped loxP flanked TDP-43 vector using AAV9 intrathecal administration, followed by an intramuscular AAV2 hSyn CRE-TdTomato vector in the brachioradialis muscle in order to induce focal recombination and expression of TDP-43 in motor neurons innervating this muscle on one side.

Another strength is the non-human primate background, which is much closer to the human situation.

Weaknesses:

Given the complexity and cost of the model, the n is very low.

The design of the experiments and the results shown about the toxicity induced by this focal TDP-43 expression do not allow us to conclude that it is a good model for ALS for several reasons. It is not clear that the TDP-43 overexpression results in spreading weakness or in spreading motor neuron loss. The neuropathological changes described suggest that there is a kind of stress response, which extends to regions away from the site of primary damage, but more is needed to provide convincing evidence that there is spreading of disease pathology reminiscent of human ALS.

Reviewer #4 (Public review):

Summary:

In this manuscript, the authors present data describing the development of a model of ALS in rhesus macaques. They use a viral intersectional model to overexpress TDP-43 in a population of motor neurons and then study the spread of the pathology about 7 months later. They demonstrate that both the cervical spinal cord and motor cortex (new and old M1) are full of TDP-43, suggesting that the pathology spreads from the single motor pool to presumably related neurons.

Strengths:

This is a super-important study in two main ways:

(1) This could be the birth of a really important model, one that is really needed for making progress in understanding ALS and the development of therapeutics. There are shortfalls with all the rodent models. Models dependent on cell cultures are superb for understanding cell-autonomous processes, but miss out on connectivity, particularly the long-range connectivity. Organoids may ultimately prove to be beneficial, but they would need cortex, spinal cord, and muscle, and translatability from them is not assured. So a NHP model is needed, and this may be it. Furthermore, the Methods are meticulously described and will undoubtedly facilitate reproducibility.

(2) The concept of the spread of pathology has been proposed for some time, I think, based initially on the detailed clinical observations of Ravits and colleagues. The authors have looked at this directly and provide supporting evidence for this interesting hypothesis. They show spread locally and contralaterally in the spinal cord (although a figure would be nice) and to the motor cortex.

Taking only these 2 points into account is more than sufficient for me to be enthusiastic about this work.

Weaknesses:

I'd like to make a couple of points that if addressed, could, in my view, help the authors strengthen this work.

(1) We don't know how many MNs were transduced by the rAAV. There was no tdTom expression, for whatever reason. The authors show an image of a control experiment with a single MN transduced, but there should be a red motor pool, at least in the control experiments. The impression that I get is that very few were transduced, and, in my mind, this makes the findings even more interesting - maybe you don't need many "starter" MNs.

(2) Continuing on this point, this leads the authors to conclude that all BR MNs have died. They support this by the reduced MN count (see point 3). Firstly, do we know how many BR MNs there are in the rhesus macaque, and does the reduction seen correspond to this number? Secondly, and more importantly, the muscle looks normal on MRI at 28 weeks - it does not look like a denervated muscle. The authors state that it has maybe been reinnervated, but by what, if all the BR MNs are dead? This does not seem like a plausible explanation to me. Muscle histology, NMJs, and fibre typing would have been useful to understand what's going on with the MNs. (And electrophysiology would have been wonderful, but beyond the scope of this study.)

(3) Some MN biologists, like me, fuss a lot about how to count MNs, which is almost as difficult as counting the number of angels on the head of a pin. Every method has its problems. Focusing on the two methods here: (a) ChAT immunohistochemistry is pretty good in healthy states, but we don't know what happens to ChAT expression in different diseases, particularly when you have a new model. If its expression is decreased, then it is not a good marker for MNs; (b) Identifying MNs based on the size and morphology of neurons in the ventral horn is also insufficient. For example, ~30% of neurons in a typical pool are small gamma MNs, and a significant proportion (depending on the muscle) of the remainder will be small alpha MNs. So what one is counting is, at best, the large alpha MNs, not all the MNs in a pool. And in ALS, it's these largest MNs that are affected at the earliest stages. The small ones might be fine. So results will be skewed. (Hence, it would be interesting to see if the muscle had a higher proportion of Type I fibres after being reinnervated by S-type MNs.)

(4) Statistics. These are complex experiments looking at the spread of a disease. The experimental unit is therefore the monkey, n=2. In each monkey, multiple sections are analysed, which are key technical replicates and often summative. For example, do we care about the average cell number in Figures 4D, E, 5 I, J or 6G, H, or rather the total cell number? Do the error bars mean anything? To be clear, I am by no means minimising the importance of the overall convincing findings. But I do not think this statistical analysis is particularly meaningful.