Reviewer #1 (Public review):

Summary:

This is a useful study by Angla et al that investigates the basis of observations made from previous studies where loss of Onecut (OC) transcription factors lead to changes in spinal interneuron populations that do not themselves normally express OC. The authors hypothesize that OC expression in spinal motor neurons has non-cell-autonomous effects on pre-motor interneuron (V1, V2a/b/c) population size and distribution. By knocking out OC in the motor neuron lineage (i.e., downstream of Olig2, a motor neuron progenitor marker gene), they indeed show that motor neuron-specific loss of OC expression decreases V2c interneuron number and alters the spatial distribution of V1, V2a, V2b, V2c populations. Using bulk RNA-sequencing of WT and OC conditional knockout (cKO) motor neurons, the authors identify that neurotrophic factor Ntf3 as another factor that is responsible for the non-cell-autonomous effects on the interneuron population; upon knock out of Ntf3 downstream of Olig2 they show that this leads to increased interneuron numbers and alters their spatial distribution, ultimately leading to dysregulation of spinal motor circuits and motor activity.

Strengths:

The authors use sophisticated genetic tools to precisely remove OC and Ntf3 expression in a lineage-specific manner and comprehensively assess the downstream effects across brachial, thoracic, lumbar levels of the spinal cord, as well as at two developmental timepoints, E12.5 and E14.5.

Weaknesses:

The evidence supporting their claims is incomplete. In figure 2 and 4, the control distributions (despite reporting the same populations in the same regions) look different, suggesting large sample-to-sample variance in distribution. The authors explain that this is due to several technical limitations in their study.

Furthermore, the way the Ntf3 results are framed can be misleading; it reads as though one of the main claims of this study is that OC acts through Ntf3.

Reviewer #2 (Public review):

The study by Angla et al. proposes a model in which NT-3 produced by motor neurons regulates interneuron numbers and distribution in a non-cell autonomous manner. The authors demonstrate that ablation of motor neurons (MNs) and global and conditional deletion of OC transcription factors lead to changes in interneuron distribution. They identify that NT3 is upregulated after MN-specific OC deletion in RNA-seq experiments and show that olig2-cre mediated NT3 deletion leads to increased ventral interneuron numbers, altered distribution and defects in locomotor behavior. The authors conclude that MN-derived NT3, under OC control, regulates interneuron development. While this is an intriguing hypothesis, the study does not consolidate the results from the different experimental models in a cohesive model to provide any mechanistic insights into how NT3 may accomplish this function.

(1) The authors utilize different experimental models (OC transcription factor global and conditional knockouts, DTA ablation and NT3 deletion) to show that MNs may influence interneuron numbers and positioning, but there doesn't seem to be a direct link into how each manipulation relates to the others in an informative way. For example, the relationship between OC and NT3 deletion data is not entirely clear, even though NT3 is downstream of OC transcription factors. Both deletions lead to changes in interneuron distribution but there doesn't seem to be any correlation between the two that relates to relative changes in NT3 levels. Although it is understandable that transcription factor manipulations may have additional effects, it is hard to derive any mechanistic insights into how exactly MNs influence interneuron differentiation or migration from the data presented here. Similarly, while the rationale for following up on NT3 seems to be the chick electroporation experiments, only a very minor decrease in Chx10 interneurons is shown in those experiments. Shouldn't NT3 overexpression lead to substantial decreases in IN numbers according to the authors' model?

(2) The study primarily quantifies interneuron numbers and distribution at different levels of the spinal cord and under different genetic manipulations. In different figures, the values and distributions shown for controls vary quite a lot. For example, in figure 2B vs figure 4B, the number of FoxP2+ V1 neurons at brachial levels is ~350 vs 125. Similarly, the control distributions in 2I and 4I are quite different. While this can be partially explained by technical issues, it remains challenging to determine whether the conclusions regarding the impact of each genetic manipulation on interneuron numbers and distribution are valid.

(3) It is not clear that the behavioral phenotypes seen in the olig2-cre mediated deletion of NT3 can be attributed to specific changes in interneuron development, connectivity or function. While the behavioral phenotype is in theory consistent with defects in motor circuits, there is a big gap between the embryonic changes shown here and behavior, with no in between circuit level changes in locomotor circuits shown. Thus, the behavioral experiments are not very informative as to how exactly components of motor circuits might be altered with the NT3 deletion.

Reviewer #3 (Public review):

This manuscript aims to investigate cell extrinsic mechanisms that regulate differentiation and distribution of interneuron types in the spinal cord. The authors demonstrate that the loss of motor neurons leads to changes in number and distribution of different interneuron types, specifically V0v, V1, and V2b (but not V2a). The authors then hypothesize that this phenotype may be controlled by the action of Onecut (OC) transcription factors in motor neurons. Conditional knockout of OC1 + OC2 in motor neurons using Olig2-Cre, however, does not lead to significant changes in numbers of V1, V2a, V2b interneurons, although there is a change in their spatial distribution. While the authors do not check V0v neurons in OC mutants, they do check V2c, which show a reduction in number and change in distribution. Why the same neurons are not checked across experiments is unclear. The authors then analyze existing RNA-seq data to identify factors that could be mediating effects of the OC factors in motor neurons. They identify Ntf3 as a candidate and confirm that it is upregulated in OC mutants. Conditional loss of function of Ntf3 (Olig2-Cre) leads to increases in V1, V2a, and V2b (but not V2c) interneurons and changes in distribution of all four interneuron types. However, the Ntf3 phenotypes do not necessarily suggest that it works in the same pathway as OC to control interneuron development. Finally, the authors demonstrate that these Ntf3 conditional mutants have major defects in motor function.

The main conclusions of the manuscript are that i) motor neurons non-cell autonomously control interneuron development, ii) that OC activity in Olig2 lineage cells non-cell autonomously controls aspects of interneuron development, and iii) that Ntf3 in motor neurons non-cell autonomously controls aspects of interneuron development. The mechanistic link between OC and Ntf3 activity is not clear. And the interneuron phenotype seen when motor neurons are depleted it not phenocopied by either loss of OC or Ntf3 from motor neurons. While the authors addressed some of my initial comments, the impact of this work on the field remains difficult to assess for the following reasons:

(1) The manuscript relies heavily on quantifying numbers and spatial distribution of interneuron populations. However, these do not seem to be consistent in control animals across experiments, making it difficult to interpret any changes observed in genetic manipulations. Specifically, in Fig. 2 and 4, the same markers are being used to quantify V1, V2a, V2b, and V2c interneurons in controls vs. OC (Fig. 2) or Ntf3 (Fig. 4) conditional knockouts, but the numbers of neurons and their distribution in control animals are variable between these two figures. For example, there seem to be a mean of >300 V1 neurons in E12.5 brachial sections of Fig. 2 controls, but this number is <150 in Fig. 4 controls. The cell distribution scoring is similarly variable between these controls without any explanation. The same is true for E14.5 controls used in Fig. S2 vs. Fig. S6.<br /> The authors explain in response to the 1st review that technical variability including different antibodies, different researchers performing experiments, and different mouse backgrounds may contribute to this variation, but do not include this explanation clearly in the manuscript where it would be useful for readers. It would have also helped, for example, if authors had labeled datapoints on bar plots based on the animal that they came from to understand inter-animal variability within experiments. Overall, the biological significance of the results remain difficult to interpret due to high technical variability.

(2) In the initial review, it was noted that there is no data to support the claim that OC1/2 mutants have altered interneuron development because of higher Ntf3 expression. In response to this, the authors note that the manuscript does not claim that OC exerts its non-cell autonomous effects through Ntf3 and add a new paragraph in the discussion which now addresses the fact that it is difficult to compare the phenotypes observed the different genetic manipulations presented in the work. While this paragraph addresses the incomplete mechanistic link between OC and Ntf3, it also decreases the mechanistic impact of the work.

(3) In Figure 3E-3G, the authors show that some V1, V2a, V2b interneurons express the Ntf3 receptor TrkC. It is not stated (apologies if I missed it) what age this expression is, and from the images, it looks like in addition to medially located newborn neurons being negative, laterally located Gata3 interneurons are also broadly negative for TrkC, and many Foxd3 and Chx10 interneurons are also negative. Separate images of each channel and/or scoring would help clarify this. If many relevant interneurons are indeed TrkC negative, how this affects the conclusions should be addressed in the manuscript.

(4) Behavioral phenotypes are not necessarily caused by Ntf3's role in premotor interneuron development.

Reviewer #1 (Public Review):

In this study, the authors set out to investigate spatial RNA processing events, specifically alternative splicing and 3' UTR usage, in mouse brain and kidney tissues using ReadZS and SpliZ methodologies on spatial transcriptomics data. The research contributes to understanding tissue-specific gene expression regulation from a spatial perspective. The study introduces a novel approach for analyzing spatial transcriptomics data, allowing for the identification of RNA processing and regulation patterns directly from 10X Visium data. The authors present convincing evidence supporting the identification of novel RNA processing patterns using their methodology, which holds significant implications for researchers in the field of spatial transcriptomics and the study of alternative splicing and 3' UTR usage.

The conclusions of the study are mostly well-supported by the data; however, certain aspects could be improved to strengthen the findings.

(1) The conclusions of this study would be strengthened by conducting a more extensive tissue sample analysis and including biological replicates. Additionally, appropriate batch effect corrections should be applied when dealing with biological replicates.

(2) The 3' UTR usage and alternative splicing should be compared among clearly labeled clusters for a more comprehensive analysis.

(3) The authors should clarify their rationale for choosing ReadZS and SpliZ approaches and provide comparisons with other methods to demonstrate the advantages and potential limitations of their chosen methodologies.

Reviewer #2 (Public Review):

The authors applied existing ReadZS and the SpliZ methods, previously developed to analyze RNA process in scRNA-seq data, to Visium data to study spatial splicing and RNA processing events in tissues. The authors showed several example genes in mouse brain and kidney, whose processing are spatially regulated. The paper touches an important question in RNA biology about how RNA processing is regulated spatially. Both experimental and computational challenges remain to address it. The low discovery rates in Visium may have significantly limited the discovery. Despite some potentially interesting findings, the claims remain to be validated by orthogonal experimental approaches.

Reviewer #1 (Public review):

Sensory hair cells of the inner ear convert mechanical sound vibrations into electrical signals through mechano-electrical transduction (MET). While the protein components of the MET machinery have been studied extensively, much less is known about how the surrounding membrane lipid environment contributes to hair cell function. The recent discovery that TMC1 and TMC2 also function as lipid scramblases has brought renewed attention to the importance of membrane lipid asymmetry and the mechanisms that maintain it in sensory hair cells.

In this study, the authors identify the P4-ATPase ATP8B1 and its partner TMEM30B as key regulators of membrane lipid asymmetry in outer hair cells. Using complementary genetic models, HA-tagged knock-in mice, localization analyses, and functional experiments, they show that ATP8B1-TMEM30B is enriched in stereocilia and the apical membrane of outer hair cells and is required to maintain phosphatidylserine asymmetry, support hair cell survival, and preserve normal hearing. The parallels between the ATP8B1/TMEM30B loss-of-function phenotypes and TMC1 deafness-associated mutants with constitutive scrambling support a model in which ATP8B1-TMEM30B flippase activity maintains membrane lipid asymmetry and homeostasis, whereas constitutive TMC1-mediated phospholipid scrambling disrupts this balance and contributes to membrane instability.

The authors have addressed the points raised during the initial review thoroughly. The revised manuscript includes clearer methodological details, additional physiological characterization, improved presentation and quantification of several datasets, and a more balanced interpretation of the localization and mechanistic findings. These changes improve both the clarity and rigor of the study while leaving its main conclusions unchanged.

As with any study that opens a new area of investigation, important mechanistic questions remain. In particular, it will be interesting to determine how disruption of membrane lipid asymmetry ultimately impairs MET function and triggers hair cell degeneration, how flippase and scramblase activities are coordinated in vivo, and how these pathways are integrated with the broader molecular machinery underlying mechanotransduction. These questions highlight the exciting directions that this study opens for the field.

Overall, this work provides evidence that ATP8B1-TMEM30B is a critical regulator of stereocilia membrane lipid asymmetry and represents an important contribution to our understanding of membrane homeostasis in auditory hair cells. I have no further major concerns and support publication.

Comments on second revision:

I appreciate the authors' efforts to address the issue and perform the additional experiment, and I agree that removing these data is reasonable and does not affect the main conclusions of the manuscript.

Reviewer #2 (Public review):

Summary:

Prior work identified TMEM30B (knockout mice) as well as ATP8B1 (human genetics and mouse model), ATP8A2 (knockout mice), and ATP811A (human genetics) as relevant for hearing. The authors also reasoned that given the recent discovery of TMC1 and TMC2's dual function as mechanotransduction channels of the inner ear and as lipid scramblases, a counterpart flippase should be in the sensory hair-cell stereocilia bundle where mechanotransduction happens. They use CRISPR/CAS to modify the endogenous mouse genes and add an HA tag at the N-terminus of the ATP8B1, ATP8A1, ATP8A2, and ATP11A proteins. Their experiments with these mice unambiguously localized ATP8B1 at the base of outer hair cell stereocilia bundles. Knockout of ATP8B1 results in loss of outer hair cells, deficient auditory function (ABR), and degeneration of outer hair cell stereocilia bundles. Similarly, hair cells from genetically modified mice with endogenous HA-tagged TMEM30B proteins show localization of this protein to outer hair cell stereocilia bundles. TMEM30B knock out mice phenocopy the ATP8B1 knock out model. Interestingly, the authors show that annexing V staining precedes hair cell loss in ATP8B1 and TMEM30B knockout mice and that proper localization of these proteins is lost in mice that lack CIB2, a protein essential for hair cell mechanotransduction.

Strengths:

(1) Use of knock-in HA-tagged proteins to unambiguously localize ATP8B1 and TMEM30B

(2) Systematic characterization of auditory function (ABR), hair cell loss, and hair-cell stereocilia bundle morphology.

(3) Advances our understanding of the role played by lipid homeostasis in auditory function.

(4) Reports on mouse models that will be helpful to further understand the mechanistic role played by ATP8B1 and TMEM30B in normal hearing and hereditary deafness.

Comments on second revision:

It is interesting that in the images selected as representative of the new experiments the effect seems to be opposite to what was reported before - more ATP8B1-HA in the bundle with transduction blockage. However, this does not seem to be supported by the quantification, and further exploration at different ages, blocker concentrations, effective transduction blockage, etc. might be needed to really have a solid result. Thus, I agree that leaving out these results, and the previous sections linking mechanotransduction to localization of ATP8B1, is the best option.

Reviewer #1 (Public review):

Summary:

The authors present a novel approach to subcellular spatial proteomics by combining laser microdissection with expansion microscopy and LC-MS/MS analysis (SPEx). They implement two different workflows for LMD and LC-MS/MS quantification:

(1)The standard approach in which an area of interest is cut out by LMD, subjected to proteomics analysis and compared to the rest of the cell without the dissected ROI.

(2) The subtraction approach in which ROIs are removed and the remaining cellular material is compared to samples containing both the surrounding material and the ROI.

The authors assess the technique by applying it to subcellular targets of various size, volume and protein compositions such as the nucleus, nucleoli and Golgi. They demonstrate that SPEx can identify proteins enriched or reduced in ROIs.

Strengths:

The general, relatively easy and inexpensive nature of this approach, as well as its potential applicability to many cell types and subcellular areas of interest provides an exciting alternative to subcellular fractionation, native immunoprecipitation or genetically encoded proximity labeling constructs. Moreover, by visually selecting ROIs for subsequent analysis, subcellular context or organelle morphology can be taken into account as discussed by the authors in the discussion section.

Weaknesses:

The authors addressed our concerns and technical questions in revision.

A main limitation of the current method is that it relies on isolation of structures of interest in 2D. The authors discuss some alternative approaches that might allow future access to more 3D target isolation.

There remain several unexplored minor extensions of the technical development of the method, including comparing the proteomes of organelles that are visually distinct, and applying different anchoring procedures to try to increase protein retention.

Reviewer #2 (Public review):

Summary:

This study introduces SPEx, an approach that combines physical expansion, imaging-guided microdissection, and mass spectrometry for compartment-resolved proteomic analysis. Using the nucleus, nucleolus, and Golgi apparatus as examples, the authors demonstrate that SPEx can identify proteins enriched or depleted in defined subcellular regions.

Strengths:

A major strength of the method is its accessibility and flexibility. It combines established technologies without requiring genetic manipulation or specialized proximity-labeling strategies, while direct imaging-based selection allows regions of interest to be defined according to their spatial and morphological context. The complementary comparative strategies used in the study provide convincing support for compartment-specific protein enrichment and depletion.

An important limitation is the lack of depth resolution during microdissection, which can introduce contributions from material above or below the selected region, particularly for small structures. Appropriate comparative controls are therefore important for interpreting SPEx measurements. The current study is also primarily a proof of principle in a single cultured cell type, and its ability to capture changes across biological states or perturbations remains to be established.

Overall, the study provides convincing evidence that SPEx is a useful and flexible addition to the spatial proteomics toolbox, with particular potential for applications requiring direct image-based selection of subcellular regions.

Reviewer #3 (Public review):

Franziscus et al. describe an elegant approach for spatially specific proteome analysis. To achieve this, they expand fixed cells and subsequently use a laser to micro-dissect a region of interest, which is then analyzed by MS.

They demonstrate the effectiveness of their approach by analyzing nucleus, nucleolus and the Golgi and benchmark their hits against previous dataset for these organelles.

The manuscript is very well written and nicely guides the reader through the applied methods. The presented data is convincing and I do not see the need for additional experimental verification of the protocol. The only minor concern is the novelty of the method and the presentation. A combination of expansion, laser microdissection and proteomics has been applied in the past (PMID: 36450705, PMID: 39477916). In the manuscript one of these studies is cited, though it does not become clear that this approach is already described. However, Franziscus et al. describe the approach better and makes it more accessible to the reader, especially since the other studies described this methodology in combination with tissue expansion and not in combination with single cell expansion as it is done here. I would ask the authors to be clearer in the introduction with what others have already done and what is there contribution here. In general, I am convinced that the community will benefit from the presented protocol to analyze organelle proteomics in detail.

Comments on revised version.

I had only minor issues with the manuscript, which have now been addressed.

Reviewer #1 (Public review):

Summary:

The study identifies and characterizes a set of amino acid states that make the protein robust to other mutations, to the point of being able to compensate mutations that render wildtype proteins entirely non-functional. The study uses a previously published dataset and uses it to find and study such super-compensators. It then analyzes the biophysics and fitness landscape structure of what may be behind the compensation, identifying stability as an important parameter that, nevertheless, is not sufficient to explain all of the compensatory effect. These findings have important implications for our understanding of protein evolution, with these super-compensators possibly acting in a role of "permissive mutations" and open up evolutionary trajectories that may be closed without them. Perhaps the identification of such super-compensator substitutions can be incorporated into various protein design approaches.

Strengths:

The paper presents a compelling case with a rigorous analysis of the expected error rates of observation. While not unique, the current state-of-the-art in the field typically does include experimental error rate estimation like this work. The paper also does a good job in exploring the issue, including looking at plausible biophysical basis of super-compensators.

Comments on revised version.

The authors have addressed all of my concerns in the revision.

Reviewer #2 (Public review):

Summary:

This manuscript presents an interesting and conceptually valuable analysis of compensatory evolution using a large combinatorial deep-mutational-scanning dataset for yeast His3p.

Strengths:

I particularly like the identification of "super compensatory" substitutions that improve fitness across diverse genetic backgrounds and apparently reduce the sensitivity of the local fitness landscape to subsequent mutations. The work connects epistasis, protein stability, mutational robustness, and evolvability in a clear and potentially broadly relevant manner.

The authors provide several complementary lines of evidence in support of this central conclusion. In particular, the new experimental validation of S189A is an important strength because it directly demonstrates that a predicted super compensator can buffer the effects of diverse deleterious substitutions, while analyses of additional DMS datasets from other proteins and assay systems suggest that the phenomenon is not restricted to the original His3p landscape.

Comments on revised version.

The authors have satisfactorily addressed all of my previous concerns.

Reviewer #3 (Public review):

Summary:

The manuscript by Jiang and co-authors presents an analysis of experimental measurements (about 400k variants) from a deep mutational scan of HIS3 enzyme. The authors assess the ability of a genotype to be "rescued" and show that this depends on mutation sites (in particular their solvent accessibility) and mutations effects (should be mild on folding stability or binding affinity). They further identify a set of super-compensatory mutations and their results suggest that these mutations flatten the fitness landscape.

Strengths:

This finding is interesting and likely of interest to a broad community. The analysis seems sound.

Comments on revised version.

The authors have satisfactorily addressed my concern. Additional robustness experiments and case studies from ProteinGym are valuable.

Reviewer #1 (Public review):

Summary:

The authors provide a compelling story linking NAD dependent HSD3b6 gene expression in the eyelid to a viscous cycle involving decreased steroidogenesis and AR signaling, pro-inflammatory cytokine release, inflammation, CD38 activation and further NAD+ decline that induces meibomian gland atrophy leading to dry eye disease. The work follows on studies published by the authors in Nature Aging and uses a previously reported HSD3b6 knockout mouse to try and show meibomian gland atrophy, inflammation and CD38+ cell recruitment. Overall the data presented does not appear to support the authors conclusions, and the described interactions seem to be overstated, in that if HSD3b6 was critical to meibomian gland function, one might expect complete loss of the meibomian gland in knockout mice, which is not the case.

Comments on revised version:

It should be noted that while the authors indicate the CD38 is significantly up-regulated in the HSD3b6 KO mouse, the increase was not sufficient to show a significant adjusted P-value after revision of the manuscript. Bulk RNA sequencing also shows no significant change in meibum lipid gene expression for aged mice that are treated with 78c, an inhibitor of CD38, that the authors indicate increases NAD levels leading to increased meibomian gland size compared to vehicle treated mice. Unfortunately, there was no increase in meibum lipid gene expression with 78c or with 78c+NMN treatment as identified by adjusted P-value.

Reviewer #2 (Public review):

Summary:

In the manuscript entitled, "NAD boosting mediated by CD38 inhibition drives reversal of a pathological vicious cycle of intracrine activity and inflammation in eyelid meibomian gland dysfunction", the authors demonstrate strong correlations between a pro-inflammatory state, the activity of an intracrine hormone (3 beta-hydroxysteroid dehydrogenase, 3B-HSD) and the NAD co-factor. Specifically, in a 3B-HSD knockout mouse, there was an upregulation in pro-inflammatory cytokines and increased CD38+ cells (CD38 is an enzyme that depletes NAD, a necessary cofactor for 3B-HSD activity). Conversely, induction of inflammation in the eyelids resulted in reductions in 3B-HSD activity. Supplementation with 5 alpha-dihydrotestosterone (DHT) or the NAD precursor NMN, and inhibition of CD38 activity (78c), corrected the pathologies observed in both the 3B-HSD knockout mouse and the pro-inflammatory model (LPS injection into eyelids).

Strengths:

The experiments were performed with good rigor, assessing the impact of inflammation and 3B-HSD activity using multiple model systems. The endpoints represented a combination of transcriptional changes, protein quantification, enzymatic activity, and immunofluorescent microscopy. The authors use human tissue from both younger and older individuals to justify their hypotheses that increased CD38 + cells and reduced 3B-HSD quantity exist in older individuals. The data provide the foundation for assessing more global changes to the tear film and ocular surface.

Weaknesses:

The main weaknesses of the study include:

(1) An absence of information on meibomian gland health, tear film and ocular surface.

(2) Too few human subjects to validate the hypotheses.

Overall, this study demonstrates an important relationship that exists between intracrine signaling, inflammation and cofactor signaling, and represents a novel approach in therapeutic design for patients with meibomian gland dysfunction.

Comments on the revised version:

The title of the manuscript is a bit deceiving, particularly because it mentions "meibomian gland dysfunction". The authors would need to demonstrate abnormalities in lipid synthesis (quantity or quality) and ocular surface changes associated with loss of this intracrine signaling. The data presented are strong for demonstrating the role of NAD synthesis and utilization in meibomian gland homeostasis, but do not explain the role in ophthalmic diseases, such as MGD. This could possibly be applicable to forms of blepharitis or conjunctivitis as well given their findings of CD38+ cells present in the eyelids of the KO mice. Therefore, the authors should remove the claims of meibomian gland dysfunction in the title and comments in the abstract. Instead, they could use meibomian gland homeostasis for both sections.

Reviewer #1 (Public review):

This is a very elegant and experimentally convincing study. Using systematic screening of actin tail formation in two bacterial strains and employing a panel of CRISPR-CAS ko cell lines, the authors identify the dynamin-related GTPases GVIN as antibacterial agent, which forms an oligomeric coat around an intracellular Burkholderia strain. The bacterial O-antigen LPS layer is required for the formation of the GVIN coat, which disturbs the polar localization of the bacterial actin-polymerizing BimA protein.

The experiments appear of high quality, figures are well prepared, and clean and statistically significant results are provided. I have no criticism on the presented approaches.

The identification of a novel GBP1-independent pathway targeting intracellular bacteria is not only of fundamental importance for the immunity field, but also of high interest to researchers in other areas, for example, evolutionary or structural biologists.

Comments on revised version.

This is a beautiful study.

Reviewer #2 (Public review):

Public summary

Summary:

The authors wanted to investigate how cells defend against intracellular pathogens, such as Shigella and Burkholderia species, that coopt the host actin machinery to spread from cell-to-cell. Previous work has identified IFNg-inducible GTPase of the Guanylate Binding Protein (GBP) family in cytosolic defence against Gram-negative bacteria. By forming a coat around Shigella, human GBP1 suppresses actin-based motility by displacing IcsA, which is the actin-polymerising virulence factor present at bacterial poles. In addition, GBP1 recruits the cytosolic LPS-sensor, caspase-4, to the bacterial surface, which results in the removal of bacterial replicative niches via pyroptotic cell death. Here they followed up their finding that GBP1 can reduce actin-based motility of Shigella in HeLa cells and surprisingly fails to do so during Burkholderia infection. In contrast, in T24 bladder epithelial cells, GBP1 is competent in blocking Burkholderia actin-tails. They therefore wanted to identify the GBP1-independent factor that blocks actin-based motility in IFNg-treated cells that is absent in HeLa cells.

Major strengths and weaknesses of the methods and results:

The authors report a second IFNg-dependent pathway involving the protein product of the gene GVIN1, which was previously thought to be a pseudogene. GVIN1 (GTPase, very large interferon inducible 1) is thus the first human member of this family of ~250 kDa putative GTPases to be demonstrated to be functional and have potential antimicrobial roles. The discovery that GVIN1 is indeed functional, forms coats on Burkholderia in an LPS O-antigen-dependent manner, and limits actin-dependent motility are the main strengths of this paper. The authors use CRISPR/Cas9-based knockouts in HeLa and T24 cells, and complement them, to demonstrate GBP1 and GVIN1 are both required to inhibit actin-based motility.

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

The authors achieved their main goals through well-planned experiments and unbiased screens. They succeeded in finding the factor that blocks actin-based motility independently of GBP1. This is driven by GVIN1, which coats bacteria and limiting actin-tail formation by reducing the expression of BimA through currently unknown mechanisms. Further, they found that an O-antigen mutant can escape coating by GVIN1, indicating the requirement for these polysaccharides in GVIN1-dependent bacterial sensing. However, the authors have not investigated whether GVIN1, which has two GTPase-domains, does indeed have GTPase activity and whether GVIN1 and GBP1 together completely block cell-to-cell spread by Burkholderia and thereby restrict bacterial numbers over the infection time course. They also do not show whether GBP1 and GVIN1 target the same bacterial cell or different populations of bacteria.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

This work uncovers the antimicrobial actions of a member of yet another family of IFNg-induced GTPases, which potentially acts against other intracellular pathogens. GVIN1 appears to operate independently and in parallel to GBP1, pointing to the breadth and complexity of the IFNg-inducible GTPase families.

Comments on revised version.

They have addressed my comments.

Reviewer #3 (Public review):

Summary:

Here, Guo et al. (2025) propose that the IFN-induced GTPase GVIN1 forms a coat on cytosolic Burkholderia thailandensis, blocking actin tail formation through a mechanism analogous to GBP1-mediated restriction of Shigella motility.

Their study was prompted by the intriguing observation that IFNγ priming and GBP1 coat formation fail to inhibit B. thailandensis actin-based motility in HeLa cells, yet IFNγ restricts the motility of Burkholderia in T24 cells. Further investigation revealed that IFNγ restricts B. thailandensis motility in T24 cells via both GBP1-dependent and -independent mechanisms, suggesting that HeLa cells lack a critical GBP1 co-factor required to inhibit actin tail formation.

To identify the GBP1-independent mechanism, the authors performed an siRNA screen of interferon-stimulated genes (ISGs) and identified GVIN1, a large IFN-induced GTPase, as essential for restricting B. thailandensis motility. To identify the GBP1-independent mechanism, perform a knock-down screen for ISGs and find that the loss of GVIN, a very large IFN-induced GTPase, results in higher actin tail positive B. thailandensis in T24 cells. They further demonstrate that GVIN forms coats on the surface of B. thailandensis which prevent the polar localization of BimA and thus actin tail formation. In summary, the data reveal two independent IFNγ-induced pathways that restrict bacterial motility: one GBP1-dependent and the other GVIN1-dependent, each relying on distinct host co-factors.

Global assessment:

This is a well-executed study that convincingly demonstrates how GVIN1 restricts actin-based motility of B. thailandensis through the assembly of coatomers. The results are clearly described, the manuscript is easy to follow, and the data are overall compelling and well presented. I have only a few suggestions on how the manuscript could be further improved.

Comments on revised version.

The authors have answered all my points.

Reviewer #1 (Public Review):

[Editor's note: this version has been assessed by the Reviewing Editor without further input from the original reviewers, given the substantial revisions completed since the original submission: these have improved the clarity and transparency of the results in a relatively complex study. These changes largely address those concerns raised in review that do not relate to the underlying experimental design and the control of the distinct task conditions.]

Summary:

In this manuscript, the authors investigate the impact of rare and extreme events on rodents' decision-making under risk, in gain and loss contexts. They describe the behavior of 20 rats performing a four-armed bandit task, where probabilistic gains (sugar pellets) and losses (time-out punishments) can - in some arms - incorporate extremely large - but rare - outcomes. They report that most rats are sensitive to rare and extreme outcomes despite their infrequent occurrence, and that this sensitivity is primarily driven by extreme loss events which they try to avoid, rather than extreme gains that they seek to obtain.

They finally propose a modification of standard reinforcement-learning, which features a specific sensitivity to rare and extreme outcomes and can account for the observed behavior.

Strengths:

The manuscript really taps into a surprisingly neglected but very relevant aspect of decision-making: the effect of rare and extreme events (REE). The authors have developed an experimental setup that seemingly allows investigation of this aspect, which is not trivial given the idiosyncratic properties of rare and extreme events.

The parameters of the experimental setup seem also to be well thought off: basically, in the absence of REE, some options are objectively better than others (because, in expectation, they overall deliver more food, or minimize time-out punishments), but this ordering reverses if REE are taken into account. This allows for a clean test of the integration of REE in the rodent's decision-making model.

The data is presented and analyzed in a very descriptive but exhaustive and transparent way, down to the description of individual rodent's behavior.

Reviewer #2 (Public review):

Summary:

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

Strengths.

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

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

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

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

Reviewer #1 (Public review):

The authors did very little to address our concerns outside of written clarifications. While they altered wording to describe Gsb from "pioneering" to "chromatin priming" our stance remains that there is no evidence shown to confirm that Gsb plays a role in chromatin remodelling. (IE while ectopic expression of Gsb results in a different chromatin conformation, there is no evidence that the ectopic expression of Gsb alters an already established chromatin state.) Additionally, the phenotypic experiments show loss of NB5-6 reporter and neuronal markers upon loss of Gsb which confirmed the developmental requirement for Gsb within the endogenous NB5-6 domain, but another striking feature that was described was that loss of Gsb also results in loss of NB3-5 identity. The abrupt change to a new NB lineage made this portion of the manuscript feel a bit disjoined and while this is described as an additional reciprocal phenotype, there are still no sequencing data or other evidence as to how this loss has occurred.

We disagree that pioneer or chromatin-priming activity should be inferred strictly from single timepoint binding profile. We also disagree that any "remodeling" was shown. To sufficiently show remodeling, one must express a factor at/after a time where the chromatin conformation has been established to be already open/closed and then show the expression of that factor induces conformation changes to the opposite conformation. Otherwise, it is hard to infer whether these chromatin changes are due to the presence of the factor blocking the normal conformational changes that should occur.

The authors mention they walked back the terminology from "pioneering" to "chromatin priming" and make it clear that Gsb does not act alone to establish the chromatin landscape. We are not sure that this is proper terminology either.

We mention that the phenotypic data do not align with the sequencing data. The authors added text to clarify the rationale behind each experiment - some being to induce outside of the endogenous domain with clean genetic access while others to obtain information within the endogenous spatial domain.

Reviewer #2 (Public review):

Summary:

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

Comments on revised version.

The authors have made extensive revisions and addressed the concerns raised in my previous review. Overall, I find the revised manuscript substantially improved, both experimentally and conceptually. In particular, the additional locus-level and genome-wide analyses, the motif analysis of En occupancy, the attempted endogenous Gsb depletion, and the revised interpretations and distributed STF-code model have strengthened the manuscript considerably.

(1) Adding Gsb occupancy profiles at specific gene loci such as lbe, ap, and eya, along with chromatin accessibility, Hb binding, and expression data at ems, enhances the link between TaDa results and lineage phenotypes. Although these data do not prove direct regulation of each locus, they strongly support a correlation between Gsb binding, chromatin state, and neural stem cell identity. Therefore, this aspect is mostly addressed, as long as the manuscript does not present these correlations as conclusive evidence of direct regulation.

(2) We appreciate the additional genome-wide analysis distinguishing genomic loci that gain versus lose accessibility following Gsb misexpression, as well as the comparison with two independent Gsb occupancy datasets and the corresponding Hb occupancy analysis. These data strongly support the conclusion that Gsb has chromatin-remodelling capacity and that accessibility loss occurs at loci associated with Gsb occupancy.<br /> We also appreciate the authors attempt to examine endogenous Gsb function using early CRISPR depletion and their explanation of why this approach could not subsequently be coupled to lineage-specific TaDa. The inability to directly assess chromatin consequences of Gsb loss in native NB5-6 remains a limitation, but the authors have now acknowledged this appropriately. We therefore consider this comment substantially addressed.<br /> We would, however, encourage the authors to distinguish clearly between demonstrated chromatin-remodelling capacity and the stronger, physiologically unresolved claim that Gsb establishes chromatin priming in NB5-6.

(3) The response to the question about "En not being a pioneer factor" is particularly satisfactory. The new motif analysis directly tackles the concern that the lack of En occupancy at NB5-6-specific sites might just be due to the absence of En binding motifs. The similar presence of En motifs at NB5-6- and NB7-4-specific sites strengthens the conclusion that differing occupancy is unlikely caused by motif absence. Additionally, comparing the accessibility dependence of En and Gsb occupancy helps argue against the idea that TaDa cannot detect binding at less accessible loci. Importantly, the authors have removed the categorical claim that "En is not a pioneer factor" and now limit their conclusion to the specific developmental and cellular context studied. This response adequately addresses the comment.

(4) The revised distributed STF-code model is clearer and provides a useful framework for explaining the complementary roles of Gsb, En, and other spatial factors. I would encourage the authors to clarify that the proposed stepwise model is a conceptual framework rather than an experimentally established temporal sequence. In particular, the data do not directly demonstrate that Gsb-dependent chromatin priming is causally followed by Hb recruitment at the same loci. Similarly, the division of labour among STF components should not be interpreted as mutually exclusive, since individual STFs could contribute to both chromatin remodelling and TTF-dependent enhancer regulation. The schematic and accompanying text should therefore distinguish the demonstrated differences in Gsb and En chromatin engagement from the broader proposed model.

(5) The authors correctly recognise that the factors determining the NB7-4 accessibility landscape are still unknown. Pinpointing Msh and other motif-enriched candidates clarifies this issue and offers valuable leads for future research.

Overall, the authors have responded constructively to the major concerns raised in the review. The revised manuscript presents a substantially more coherent model in which STF-code components have distinct but complementary roles in shaping the chromatin landscape and enabling lineage-specific Hb engagement.

Reviewer #1 (Public review):

Summary:

In this manuscript the authors study the distribution of the KMT2A methytransferase across the human ribosomal gene loci (rDNA) and the effects of its depletion on rDNA chromatin modification using a KMT2A conditional cell line. They used ChIP mapping data and claim to show that KMT2A depletion does not affect H3K4me3 levels but does reduce acetylation of H3K9 and reduces PCAF (KAT2B) binding. They further claim that KMT2A depletion causes stalling of RNA Polymerase I at the upstream "Spacer" promoter duplication on the rDNA.

Weaknesses:

I found the manuscript very confusing. In particular, I found it short on short on experimental detail and I found the data in general to be unconvincing. It is my opinion that the authors overinterpret their data, leading to unrealistic and unsupported claims. The manuscript also avoided discussing relevant recent studies on the regulation of the rDNA and of rDNA chromatin, most citations being to data 15 to 20 or more years old. This is most apparent when discussing the factors such as UBTF and SL1 that determine preinitiation complex assembly and the status of active versus inactive rDNA chromatin. Both have seen significant advances in recent years, and these should be thoroughly discussed.

Specific comments:

The authors do not describe the generation of the KMT2A conditional HEK293T cells or their treatment. Since these cells form the basis for subsequent experiments, they must be explained fully. I found that the conditional cell line had already been used in a previous unrelated publication "Chinchole et al. 2022". This manuscript explained that the KMT2A knockout is induced via Dox controlled Cas9 in a HEK cell clone constitutively expressing guide RNAs to KMT2A. The KMT2A knockout requires a 7 day treatment with Dox, leaving wide open the question of direct versus indirect effects of KMT2A loss, and perhaps explaining some of the discrepancies with published data.

In mapping the interactions of KMT2A across the rDNA by ChIP they use 18 PCR amplicons, some of which the authors inaccurately state as targeting specific rDNA elements. For example, they state Primers (that is, Amplicons) #1-3 target the spacer and core 47S promoters. In fact, amplicon #2 overlaps neither of these promoters and lies centrally within the 1kbp of enhancer repeats between them. Exactly why emphasis is put on KMT2A chipping at these two promoters is anyhow questionable since KMT2A levels (Figure 1) appear equally high at multiple IGS amplicons.

Discrepancies with published data are particularly apparent in their mapping of H3K4me2, H3K4me3 and H3K9ac, all directly relevant to their conclusions. These modifications have been shown in several publications to map to a single major site a few hundred bp upstream of the spacer promoter in multiple human and mouse cell types. Since the authors fail to monitor H3K4 methylation and H3K9 acetylation at this dominant site they also fail to provide significant evidence that KMT2A in fact affects H3 methylation or H3 acetylation on the rDNA.

The KMT2A deletion drastically reduces PolI and UBF (UBTF) engagement across the rDNA, and consistent with this, SL1 and RRN3 engagement is reduced as are 47S levels. Thus, whatever the 7-day KMT2A deletion is doing to the cells it is certainly inducing a very significant shut-down of active rDNA. How such an extreme experimental model can tell anything of KMT2A functions is unclear.

In the last results section, the authors further claim "Loss of KMT2A results in a stalled RNA Pol I complex at the spacer promoter" (Figure 4 and S4). To support this the authors try to map spacer promoter transcripts but mistakenly claim that the amplicon #1 can detect these transcripts. In fact, this amplicon overlaps the Spacer Promoter initiation site and so could not detect transcripts coming from this promoter. Published data, not cited, have demonstrated that PolI stalling at the spacer promoter occurs as a normal phenomenon on the active rDNA. Its loss is associated with suppression, not the opposite as inferred in this manuscript.

Reviewer #2 (Public review):

Summary:

This important manuscript presents solid evidence that the major contribution of KMT2A to ribosomal gene transcription is through promoting histone acetylation rather than H3K4 methylation. It is suggested this occurs in part through interactions of KMT2A with PCAF.

Strengths:

This manuscript advances an important concept that chromatin modifying enzymes can also function to promote transcription through interactions with other transcriptional regulators independently of their catalytic functions. The data are overall supportive of this conclusion for KDM2A.

Weaknesses:

(1) Care needs to be taken regarding interpreting the lack of effect of depleting KMTs or HATs on histone acetylation or methylation levels. There are several overlapping HATS or KMTs that generate these modifications. The most that can be said from these negative results is that the depleted HAT or KMT is not the sole enzyme generating these modifications at these loci.

(2) The biochemistry supporting an interaction between KDM2A and PCAF is under-developed and not strong enough to support this conclusion at this point.

(3) It is concluded that acetylation causes increased rDNA transcription. However, acetylation may be a consequence of transcription instead, especially over transcribed regions.

(4) Some conclusions, for example implicating PCAF, are based on overexpression experiments that are very artificial and difficult to draw conclusions from.

(5) Several figures are generated using publicly available data sets that were reanalyze using updated reference genomes, sometimes from different cell lines/types. This seems to be a useful exercise but the methods should include information regarding the robustness of these data sets.

Reviewer #3 (Public review):

Summary:

This manuscript examines a key mechanism by which KMT2A regulates RNA polymerase I transcription at ribosomal DNA. The authors suggest that KMT2A facilitates the recruitment of PCAF to rDNA. This process enhances histone acetylation and supports assembly of the RNA polymerase I transcription machinery. The study addresses an intriguing question by proposing a catalytic-independent role for KMT2A in rDNA transcription, potentially linking the KMT2A-dependent pathway to histone acetylation. However, several aspects of the proposed mechanism require further experimental validation. Notably, while the current evidence demonstrates associations among KMT2A, PCAF occupancy, histone acetylation, and rRNA transcription, it does not fully support the proposed objectives.

Strengths:

This could be the first report of a lysine methyltransferase regulating an acetyltransferase in Pol I-mediated gene expression.

Weaknesses:

(1) The causal relationship between KMT2A and the recruitment of PCAF needs to be demonstrated. Simply showing reduced PCAF occupancy after depleting KMT2A does not, on its own, prove direct recruitment. The authors should investigate whether KMT2A and PCAF physically interact, and whether this interaction depends on DNA or RNA.

(2) The main prediction of the model is that restoring PCAF activity should reverse the molecular effects caused by the depletion of KMT2A. The authors should test whether PCAF restoration can mitigate the consequences of KMT2A loss. They should also assess PCAF occupancy, H3K9 acetylation levels, Pol I occupancy, and nascent rRNA synthesis.

(3) The catalytic-independent function of KMT2A should be directly investigated. The absence of a significant change in H3K4me3 levels following KMT2A depletion does not confirm that KMT2A's catalytic activity is not essential. Rescue experiments comparing wild-type KMT2A with a catalytically inactive variant would provide a much stronger assessment of the proposed non-catalytic mechanism.

(4) The manuscript highlights H3K9ac; however, various acetyltransferases and histone acetylation marks are linked to active rDNA. The authors should investigate whether KMT2A specifically regulates H3K9ac or leads to broader changes in histone acetylation at rDNA.

(5) Due to the highly repetitive nature of rDNA, the manuscript must include detailed information about the reference assembly, mapping strategy, management of multimapping reads, normalisation processes, and biological replicates. The authors should demonstrate that any observed changes in occupancy are not due to technical differences in rDNA mapping or variations in copy number.

(6) KMT2A regulates a wide range of transcriptional programs, and its depletion can affect proliferation, cell-cycle distribution, or nucleolar function. The authors should include appropriate controls to demonstrate that the rDNA phenotype is not a result of changes in cellular proliferation or a generalised response to transcriptional or nucleolar stress.

(7) The CSB and B-WICH complex have previously been linked to the recruitment of PCAF and the acetylation of H3K9 at active rDNA. The authors should investigate whether KMT2A operates independently of these factors or if KMT2A functions within an established pathway for remodeling rDNA chromatin.

(8) The distinction between active and silent rDNA should be taken into account. The authors need to determine whether KMT2A/PCAF occupancy is preferentially associated with active rDNA copies. Sequential ChIP analysis or the use of established markers for active, poised, and silent rDNA would help clarify the chromatin context in which the proposed mechanism operates.

Reviewer #2 (Public review):

The authors examine the relative contributions of SNP and transcriptomic information to predicting trait variation and phenotypic plasticity in switchgrass. Using a diversity panel of 426 genotypes evaluated in Texas and Michigan, USA., this study demonstrates that transcriptomic predictors could outperform genomic markers in predicting environmental differences for traits like biomass yield and tiller count, highlighting the trait-dependent advantage of transcriptomic information over SNPs. However, combining SNPs and transcriptomic predictors generally yielded limited improvement in prediction accuracy.

A principal limitation of this study concerns the experimental design and the robustness of transcriptomic predictors. With only one transcriptomic sample per genotype in each environment, within-genotype variation in transcript abundance cannot be estimated, making it difficult to distinguish genotype-specific expression responses from individual-plant variation. Unlike SNP genotypic information, which provides a stable representation of a genotype's inherited genetic variation underlying phenotypic differences, transcript abundance is highly sensitive to environmental conditions, developmental stages, and physiological state at the time of sampling. As a result, the stability of the transcriptomic predictors and their predictive performance across independent environments, years, or sampling conditions remain untested. Furthermore, the stability of feature selection across different training populations has not been established, raising additional questions about the reproducibility of the identified candidate genes. Together, these limitations constrain their generalizability and the biological interpretation of transcriptomic associations with phenotypic plasticity.

Overall, this study provides valuable comparative evidence regarding the predictive performance of genomic and transcriptomic information. Its principal contribution lies in demonstrating that the relative predictive advantages of these information sources are trait-dependent and their integration does not necessarily improve prediction accuracy. While the predictive findings are supported, the biological interpretations extend beyond the evidence presented, as the identified transcriptomic predictors and their interactions do not establish causal genetic effects, epistasis, or direct regulatory relationships. Therefore, the proposed genetic mechanisms and environment-specific interactions should be regarded as hypotheses requiring independent biological validation, rather than established mechanistic findings.

Reviewer #1 (Public review):

Summary:

The authors ask whether Samp1 contributes to the reorganization of the genome and the regulation of gene expression during muscle-cell differentiation. Using C2C12 cells, they find that Samp1 depletion impairs differentiation and is accompanied by altered transcriptional responses and changes in peripheral chromatin and chromosome 8 distribution. These findings support Samp1's involvement in the differentiation program, although the causal relationship between the spatial and transcriptional phenotypes remains unresolved.

Strengths:

The study addresses a biologically significant question: how proteins at the nuclear envelope contribute to the coordinated changes in genome organization and gene expression that accompany cell differentiation. Its main contribution is to extend the established requirement for Samp1 in myogenesis to measurable spatial and transcriptional phenotypes, providing a more specific basis for investigating its function.

The complementary imaging approaches examine chromatin organization at different scales. Fluorescence ratiometric imaging assesses the relative distribution of chromatin reporters across the nucleus, whereas chromosome 8 imaging provides a chromosome-specific measurement. Agreement between these approaches strengthens the observation that Samp1-depleted cells undergo an altered spatial response to differentiation conditions, while leaving the underlying mechanism open.

The four-time-point transcriptional analysis adds temporal information that an endpoint comparison would miss. It allows readers to examine baseline differences and subsequent responses to differentiation conditions, and provides a potentially useful resource for identifying genes and pathways associated with the impaired differentiation program.

The work also builds on earlier validation with an independent depletion construct and rescue of differentiation by RNAi-resistant Samp1. This prior evidence strengthens the biological foundation of the present study. Together, the results identify Samp1 as a useful target for further investigation of the relationship between nuclear organization and muscle development.

Weaknesses:

The study leaves open how the differentiation, spatial, and transcriptional changes are connected. By day three, control cells have formed myotubes, whereas Samp1-depleted cells largely remain myoblasts. This is a consequence of the depletion and does not invalidate the comparison. However, it means that altered chromosome positioning could contribute to the differentiation defect or arise because differentiation has been disrupted. The authors acknowledge that the mechanism remains unresolved; the findings should therefore be interpreted as evidence that these processes depend on Samp1, without establishing that changes in genome positioning drive the transcriptional response.

The earlier rescue of differentiation strengthens the evidence for Samp1's role in myogenesis. The new spatial and transcriptional findings, however, have not been tested by rescue. This leaves some uncertainty about whether these particular effects are specific to Samp1 depletion in the stable cell line used here.

The connection between impaired gene repression and association with the nuclear lamina remains indirect. The published Lamin B1 DamID data describe normal differentiation and do not measure what happens to lamina association after Samp1 depletion. They support the proposed connection but cannot establish it.

The imaging measurements also need a cautious interpretation. The peripheral chromatin index describes relative fluorescence enrichment, rather than directly measuring heterochromatin abundance. The biological meaning of changes such as an index of approximately 1.1 versus 1.3 is therefore not immediately clear. Similarly, the chromosome 8 analysis measures the distribution of chromosome-paint signal within five optical sections rather than the entire chromosome territory. The results support spatial differences, but their magnitude may depend on nuclear geometry, the sampled sections, and image-processing choices whose influence is not fully assessed.

Some conclusions about the transcriptional response go beyond what the statistical comparisons directly test. A significant change during differentiation in control cells and a nonsignificant change in depleted cells do not, by themselves, establish that the responses differ. The comparison between groups at day three provides evidence of expression differences, but does not determine whether the changes from their respective starting points differ.

Reviewer #2 (Public review):

Summary:

Kašnik et al. set out test whether the previously reported requirement for the inner nuclear protein Samp1in myogenic differentiation and its also previously reported role in chromatin organization at the nuclear periphery are functionally linked. The authors confirm using a previously characterized shRNA system in cultured C2C12 cells that Samp1 loss leads to myogenic differentiation defects. Loss of Samp1 results in gene expression changes, including of genes associated with myogenesis and also interfered with the formation of peripheral heterochromatin and peripheralization of chromosome 8 which has previously been reported to be associated with myogenic differentiation. Finally, many of the downregulated genes by Samp1 loss were found in chromatin domains bound by lamin B.

Strengths:

The strength of this study is that is represents a rather straightforward analysis of the loss of Samp1 in myogenic differentiation in vitro. The RNAseq data is informative and is in line with the observed differentiation defects. In addition, the experiments are clearly designed and cleanly performed.

Weaknesses:

The major weakness of the study is that major conclusions are strongly overinterpreted and the degree of advance compared to prior studies is limited.

The study does not - as claimed by the authors - functionally link Samp1 to myogenic differentiation via its role in genome organization. As outlined by the authors there is already considerable evidence for a role of Samp1 in myogenic differentiation. There is also prior data to show that Samp1 loss prevents peripheralization of chromosome 8. The data reported in this study add further data to a possible functional link between these two events, but similar to the currently available data, the evidence remain circumstantial and they fall short of the authors' claim - and stated goal - of directly demonstrating a functional role of Samp1 in myogenesis via genome organization. The presented data does not rule out that the two effects represent unrelated functions of Samp1. The authors have not shown as claimed that Samp1 exerts its effect on myogenesis via genome reorganization.

The most relevant piece of data to point to such a link is the DamID data in Figure 3 showing that many downregulated Samp1-sensitive genes reside in lamin B associated chromatin. However, in this experiment, critical controls are missing to demonstrate that this effect represents an enrichment of Samp1-sensitive genes (see Recommendations).

While the RNAseq data offers the opportunity to speculate on pathways involved in myogenesis, none of the proposed pathways are experimentally tested to validate their functional roles. They remain hypothetical but are presented as demonstrated facts.

Finally, many of the findings are confirmatory in nature, such as the finding that Samp1 inhibits myogenic differentiation, that differentiation is accompanied by chromatin peripheralization or affects MAPK signaling.

Reviewer #3 (Public review):

The study by Kašnik et al investigates the role of Samp1, an inner nuclear membrane protein, in muscle cell differentiation. Samp1 was already known to be required for myogenic differentiation and to influence chromatin organization at the nuclear periphery, but whether these two functions were connected was unclear. The authors set out to address this by asking whether the differentiation defect caused by Samp1 depletion is accompanied by failures in genome reorganization at the nuclear periphery.

Using C2C12 mouse myoblasts with stable Samp1 knockdown, they combine RNA sequencing, immunofluorescence, FRIC analysis, and chromosome 8 DNA-FISH to track both the transcriptional and spatial consequences of Samp1 loss during differentiation. The goal is to show that Samp1 is needed not just for muscle gene activation but also for the peripheral repositioning of chromatin that normally accompanies differentiation, and that these two processes are linked.

The biggest weakness of this study is that it does not explain how Samp1 actually controls chromatin organization at the nuclear periphery. Is it acting through emerin, the LINC complex, or lamin A/C? This is never addressed. At minimum, the authors should test whether a Samp1 mutant that cannot bind emerin or SUN2 can still rescue the chromatin and differentiation defects.

Related to this, only chromosome 8 is examined for repositioning defects. This chromosome was deliberately chosen because it is known to move during myogenesis, so it is not surprising that it fails to reposition upon Samp1 depletion. A chromosome that does not normally reposition should be included to show the effect is specific, and genes that are expected to remain away from the periphery should also be examined to test whether Samp1 depletion causes indiscriminate rather than selective chromatin reorganization defects.

More broadly, the study is largely descriptive. The main finding is that knocking down Samp1 disrupts gene expression and chromatin repositioning, and that these two things correlate. But which comes first? Does the chromatin move and then genes get silenced, or the other way around? This question is central to the paper's argument and is never tested. Without some attempt to address this, the study reads more as an observation than a mechanistic advance.

Reviewer #1 (Public review):

The manuscript combines MspA nanopore measurements with molecular simulations to investigate p53-TAD, its cancer-associated N29K/N30D variant, and its interactions with MDM2 and EGCG. The experiments demonstrate a promising approach for distinguishing sequence- and ligand-dependent single-molecule signals. The manuscript is clearly written, and the use of multiple simulation trajectories is a strength. However, the structural and thermodynamic interpretations extend beyond what the current simulations establish. I recommend addressing the following points.

(1) The assignment of low-current states to compact conformations near the constriction, and intermediate-current states to more expanded conformations higher in the vestibule, is plausible but not directly validated (Figures 1-2; main text, pp. 4-9). Current blockage can depend on protein position, orientation, charge distribution, and pore contacts as well as compactness. The simulations do not calculate ionic conduction, and the four structural clusters in Figure 2f do not have an established correspondence to the six experimental current states. Could the authors provide a validated conductance calculation for representative configurations, or explicitly present these structural assignments as hypotheses? In particular, an EGCG-associated current level overlapping S4 does not establish that the ligand stabilizes the same structural ensemble sampled by apo p53-TAD.

(2) Figure 2c-d presents occupancy-derived profiles and regional differences using free-energy terminology. However, the simulations describe finite-duration, constant-force capture trajectories initiated outside the pore, rather than demonstrated equilibrium sampling. Could the authors establish stationarity, evaluate dependence on the analysis window, and explain how the applied bias and normalization are treated? Otherwise, these results should be described as driven occupancy profiles, without interpreting their differences as equilibrium stabilization energies. Similarly, Figure 5 should be clearly identified as a schematic reconstruction rather than a measured molecular free-energy landscape.<br /> The apparent EGCG affinity of 0.43 {plus minus} 0.49 μM also requires cautious interpretation. This value comes from event-frequency measurements at +75 mV and is poorly constrained. Event frequency depends on capture efficiency as well as solution binding populations. At +200 mV, manual voltage reversal censors spontaneous pore-escape times for long-lived complexes; moreover, pore escape and molecular dissociation are distinct processes. Please clarify which kinetic and thermodynamic quantities the assay can establish. The proposed confinement-mediated reduction in the entropic cost of EGCG binding is reasonable as a hypothesis, but is not demonstrated by these measurements.

(3) The SMD protocol distributes a constant force over acidic residues, restrains the pore, and confines p53-TAD within a cylindrical potential (Figure S12; Supplementary Methods, lines 174-198). These approximations are acknowledged, but their influence on the WT-mutant comparison remains unresolved. The double substitution approximately preserves net charge while changing its distribution, and the force prescription treats acidic and basic residues differently. Do the positional and conformational differences persist across pulling strengths, restraint widths, and alternative force assignments? Please also confirm that the simulated pore contains the mutations present in the experimental M2-MspA-NNN construct.<br /> The selected p53-TAD and MDM2 fragments omit full-length p53 regulation and interdomain coupling. This is a construct limitation, rather than a necessary consequence of the narrow constriction, because capture occurs within the wider vestibule. The existing discussion of wider pores and tethering could be extended to clarify this distinction. Phosphorylation-dependent TAD dynamics would be an interesting future application, although the present states cannot be assigned to modification-sensitive conformers without additional evidence.

(4) The mutant exhibits increased intramolecular contacts, but the pore-resident radius-of-gyration differences are only approximately 0.2-0.3 Å, with small standardized effects (Figure 2e; Table S1). Several other descriptors have confidence intervals spanning zero, including the bulk Rg difference. Consequently, the simulations support modest sequence-dependent changes more strongly than a pronounced shift in global compactness. Figure S15 also identifies altered protein-pore contacts, which could contribute directly to positional occupancy and current differences. Could the authors provide convergence analyses, trajectory-level uncertainty, and sensitivity to starting ensembles, and distinguish intrinsic compaction from altered pore association?

(5) The replacement of MDM2-complex events by EGCG-associated events is consistent with inhibition, but differential capture could also change the observed event populations (Figures 3-4). The manuscript acknowledges this possibility on p. 12. Could the authors clarify its implications for the inhibition claim and provide an orthogonal measurement or appropriate controls to distinguish reduced complex abundance from preferential detection? Charge, steric geometry, and pore interactions should be considered without assuming that compaction necessarily explains enrichment.

No new simulations directly investigate EGCG binding or MDM2 competition. Ensemble-based ligand modeling could strengthen the interpretation, but docking poses or short trajectories alone would not establish preferential binding to S4, binding affinity, or conformational selection versus induced fit. The discussion should place the proposed mechanism alongside prior EGCG-p53 studies, including the computational predictions involving Trp23-Lys24 and Pro47-Thr55 reported by Bahena Culhuac and Bello, 2024. If additional modeling is beyond the scope of this work, the claims concerning S4 stabilization, compaction, and MDM2 displacement should be qualified accordingly.

Overall, the experimental platform is promising. The principal revision needed is to distinguish the observed nanopore signal changes from their proposed molecular explanations. Targeted robustness tests and appropriately qualified structural and thermodynamic claims would substantially strengthen the manuscript.

Reviewer #2 (Public review):

Summary:

This manuscript presents an MspA nanopore-based approach for investigating the conformational dynamics and interactions of the intrinsically disordered transactivation domain of p53. The authors resolve multiple current states associated with trapped p53-TAD, identify differences between the wild-type protein and the N29K/N30D variant, and extend the approach to examine MDM2 binding and the effects of EGCG. The combination of single-molecule recordings, hidden Markov modeling, and molecular dynamics simulations provides a useful framework for studying disordered proteins under nanopore confinement.

Strengths:

The experimental observations are compelling, and the manuscript makes a valuable methodological contribution.

Weaknesses:

The analysis and interpretation could be strengthened.

Reviewer #3 (Public review):

Summary:

DeCoeur et al. explored the conformational dynamics of the intrinsically disordered protein (IDP) p53-TAD and a cancer-associated variant by examining its conformation-dependent block of conductance through a nanopore. Using this approach, they identified a preferred conformational ensemble stabilized by the anti-cancer compound EGCG. They supplemented their experimental observations with steered molecular dynamics simulations of the IDP's conformational ensembles. Much of the work appears to recapitulate prior observations for this IDP, and as such, this work reads more like a method development paper. Although the use of nanopores to study IDPs as done here is not completely novel, these sorts of applications remain relatively few, and thus this work is still useful. However, there are several issues with the data and its interpretation that suggest only incomplete evidence for the conclusions. Namely, what appear to be very low statistics for the observed long-lived pore-blockage events on which much of the experimental conclusions seem to be based, and also conclusions from simulations that do not appear to align with the data. Until these issues are addressed, I find some of the conclusions to be only weakly supported by the data.

Strengths:

The use of nanopore current blockage to interrogate the dynamics of IDPs has the potential to be an ideal approach to investigate both mechanisms of variant pathology and drug efficacy.

Weaknesses:

The apparently small dataset may be limiting, and in several cases the authors appear to also over-interpret their results based on small effects.

Reviewer #1 (Public review):

Summary:

Dila Rizalar, Villaseñor and colleagues report the membrane trafficking steps undergone by CD98hc, a major component of the LAT1 amino acid transporter. They found that CD98hc entered cells through Fast Endophilin-Mediated Endocytosis (FEME) and quickly recycled back to the plasma membrane (PM) together with beta1-Integrin to mediate a stable supply of amino acids and support metabolic homeostasis.

Strengths:

The work was planned, executed and analysed to a high standard. A variety of cell models, techniques and assays were performed and combined with mathematical modeling. Appropriate analyses and statistical tests were used to support their conclusions.

Overall, the evidence provided is convincing.

Weaknesses:

(1) The proposed endosomal recycling pathway(s) (positive for Rab9, Rab11 and Rab14) is/are not believed to support recycling fast enough (c. 10-20 min) to explain the very short timeframe (c. 1 min) reported by the authors. This apparent discrepancy should be clarified by the authors, ideally with small additional work.

Notably, the fast Rab4-dependent recycling of TfR directly from Rab5 sorting endosomes has more consistent kinetics (c. 2-4 min, see the classic papers from the Mellman, Maxfield and Zerial labs) and should be investigated or at least discussed.

(2) LAT1 (and thus possibly CD98hc) were previously reported to enter cells through Clathrin-mediated Endocytosis (CME) upon potent PKC activation (PMA) or entry into cell quiescence (Barthelemy and André, Sci Rep 2019 and Kahlhofer J et al. EMBO J 2025). Interestingly, instead of being recycled back to the PM, LAT1 was then routed to lysosomes for degradation.<br /> Does LAT1 co-traffic with CD98hc along the FEME - fast endosomal recycling loop identified by the authors?

If so, the authors could discuss the physiological context in which the whole transporter shifts between the 2 routes (FEME-recycling in steady-state maintenance and CME-Lysosome upon acute signaling? There is a precedent in EGFR for such variation in endocytic fates upon signal strength).

If not, the authors could discuss why the 2 chains would traffic differently (in such a scenario: CD98hc through the FEME-recycling arm and LAT1 through CME-Lysosome).

(3) The study would benefit from mechanistic insights about the molecular details linking CD98hc to FEME.

Specifically, identifying motif(s) and/or adaptor(s) binding to the main FEME adaptors (Endophilin A1, A2 or Bin1) would be a valuable improvement of the work and would provide a strategy to design a point mutant version of CD98hc that would be unlinked to FEME.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors demonstrated that the amino acid transporter CD98hc (SLC3A2) undergoes fast endophilin-mediated endocytosis (FEME), followed by rapid β1-integrin-dependent recycling back to the plasma membrane. The latter is essential to maintain CD98hc membrane levels and counteract bursts of endocytosis in both endothelial and osteosarcoma cells.

Strengths:

The authors used pharmacological inhibitors, siRNA-mediated downregulation and CRISPR approaches to identify the regulators of CD98hc trafficking in different cell systems, strongly supporting their conclusions. In addition, they complemented their experimental results with computational models, which further validate their conclusions

Weaknesses:

The mechanistic details of how the interaction between CD98hc and b1 integrin determines CD98hc recycling have not been explored in the current version of the manuscript. Moreover, despite the title mentioning metabolic homeostasis, there is limited insight into how changes in CD98hc recycling affect intracellular amino acid content and cell metabolism, and how this impacts cell growth.

Reviewer #3 (Public review):

This paper from the Villasenor lab describes the results of experiments aimed at determining the role of the interaction of β1 integrin with CD98hc on the endo- exocytic trafficking of CD98hc, and its ability to function as an amino acid transporter. The paper demonstrates the following novel and interesting observations:

(1) CD98hc is internalised via fast endophilin-mediated endocytosis (FEME).

(2) A mutant of CD98hc that has been reported to oppose its interaction with β1 integrin (CD98hc ISM) alters CD98hc endo- exocytic trafficking to favour a situation in which CD98hc adopts a more endosomal distribution (than does wild-type CD98hc) following stimulation of FEME with a β-adrenergic agonist.

(3) Driving CD98hc endocytosis with a β-adrenergic agonist reduces uptake of branched-chain amino acids (BCAAs); an effect which is more pronounced following expression of the β1 integrin binding-deficient CD98hc ISM mutant.

General comments:

The findings of this paper are, at their core, very interesting and important and most of the work is well-executed. I consider that the 3 conclusions that I have listed above are solid and interesting (although some additional controls, etc would help consolidate these findings (see below)). However, the authors have vastly overplayed their hand in attempting to use mathematical modelling approaches and (in my opinion) invoking unnecessary working hypotheses concerning endocytic noise for which they have little evidence. I feel that the paper needs to be restructured to simplify the route between the core findings and the interpretation, and to move the mathematical modelling and 'endocytic noise' analyses and hypotheses to the discussion.

Specific points (in order of appearance):

(1) I am puzzled by how the graphs in Figures 1E&F have been derived from primary data and am sceptical of their validity. The curves don't look like anything that has been derived from experimental data. I gather that the curves in Figures 1E&F were generated by the equations described in the 'modelling CD98hc internalisation kinetics..'methods section, but I really cannot see how. Specifically, how can measurements of steady-state plasma membrane (PM) and endosomal pools of CD98 possibly allow the derivation of the APM and ATE operands in the equations in the 'modelling CD98hc internalisation kinetics..' methods section? Moreover, I don't see how this could be achieved in a way that could demonstrate a statistically significant difference in the trafficking of TfnR and CD98hc. The authors cite their previous 2017 Cell Reports paper to support this figure. However, the data in this paper are clearly experimental data (therefore understandable and clear), and the analysis in Vallasenor et al. (2017) does not resemble what has been performed to generate Figures 1E&F. Can the authors please properly clarify and justify this figure?

(2) Figures 1G&H. In the assay described in Figures 1G&H, it doesn't look like the CD98hc mAb is internalised in the control. And with the siRNAs that are purported to oppose CD98 internalisation (ARF6 and endophilin), it looks like CD98 has disappeared completely, rather than accumulating at the PM as one would expect if internalisation were blocked.

(3) In Figure 1H, the authors state that knockdown of endophilin A1 and A2 reduced CD98hc endocytosis, but it doesn't really look like this to me and there are neither quants nor stats on this.

(4) In Figure S1D, the conclusion that knockdown of dynamin or endophilin A1 and A2 decreases intracellular CD98hc-positive vesicles is not apparent to me, and these data need quantification and statistical analysis.

(5) In Figure 1J, the conclusion that addition of dyngo4A reduces internalisation of anti-CD98hc is not statistically supported. Moreover, although β-adrenergic agonists are clearly boosting internalisation of CD98hc in U2OS cells (Figure S1J), this, despite the asterisks (stats), does not appear to be the case in ICE-BECs (Fig. 1J); indeed, the increase is marginal, and the inter-experimental variation is too great to support a statistically significant increase in internalised CD98hc in ICE-BECs.

(6) The authors need to display representative images from experiments leading to the data in Figure 2B.

(7) The data in Figure 2 do not 'show that CD98hc is sorted to recycling endosomes after FEME internalisation' in an integrin-dependent manner. For this, one would need to demonstrate localisation of the CD98hc-β1 integrin complex to Rab11 (or Rab14)-positive compartments and these data are missing.

(8) I cannot find any details of the coordinates or construction of the CD98hc ISM mutant in this paper. I am assuming that this has been informed by the Ingber JCS paper from 2020, but the authors need to describe the construction and the provenance of their mutant. Also, importantly, the authors need to validate that this mutation opposes co-immunoprecipitation of β1 integrin and CD98hc. Or to use some other approach, such as FRET, to show this). OK, some of this has been performed by Ingber's group, but as it this is so important to the authors' conclusions, they need to confirm this. Also, it would be nice if the authors were to use their IP/proteomic approach to compare the interactomes of WT CD98hc and CD98 ISM.

(9) I think that some of the callouts to Figures 3 A-F are not correct in the manuscript. For instance, at the top of the page of the results (no page numbers), the callout to the sentence '...expressing either mGL-CD98hc WT or ISM (Figure 3A-D)' is likely wrong, and this is confusing.

(10) The data in Figures 3A,B,D&E do show that the CD98hc ISM mutant moves from the PM to endosomes more than does WT CD98hc following β-adrenergic stimulation. This is fine. However, I don't agree that the conclusions summarised in Figure 3C&F can be justified from these data. In fact, I do not believe it is necessary or justified to attempt to extrapolate these quite basic immunofluorescence data into an involved kinetic model. It would so improve the paper if these mathematical/kinetic extrapolations were moved to the discussion and presented as discursive points rather than hard-and-fast conclusions.

(11) The data in Figure 4A do not appear to show anything further than those in Figures 3 3A,B,D&E. Indeed, in light of Figure 3 A,B,D&E one would expect the vesicular CD98hc to be increased by the ISM mutation, but I'm not convinced that the amplitude of these so-called oscillations is necessarily different between WT and ISM mutant of CD98hc. All the differences between WT and ISM in Figure 4A&B appear proportionally larger than they are because of the truncated (non-zero baseline) axes in these graphs; a linear axis such as the y-axes in Fig. 4A&B should begin at zero, surely?

(12) In Figure 4D, it doesn't look at all as if the PM localisation of CD98hc and intracellular levels of BCAAs are similar between WT and ISM mutant of CD98hc - as the authors state in their results text.

(13) Why do the authors need to invoke what appear to me to be rather far-fetched and poorly supported arguments around 'endocytic noise' when the data in Figures 3A,B,D&E seem to be sufficiently consistent with those in Figure 4E to support the assertion that altered trafficking of CD98hc influences BCAA uptake?. Surely, it would be sufficient to argue that a mutant transporter that assumes a state which is distributed more endosomally than at the PM would be less efficient in uptake of BCAAs. So why the noisy arguments? It just seems to me to undermine what is potentially quite a nice and interesting paper.

Reviewer #4 (Public review):

Summary:

Rizalar and colleagues present a study where they identified fast endophilin-mediated endocytosis (FEME) as the primary driver of CD98hc internalization in brain endothelial cells. They have characterized trafficking, intracellular localization, and amino acid transport under physiological conditions or metabolic stress using both experimental and mathematical modelling approaches.

Strengths:

The author presents an interesting study. The authors provide experimental and mathematical modeling to dissect integrin-dependent trafficking of CD98hc. They give an interesting role of integrin β1 different from adhesion.

Weaknesses:

(1) Overall, it is difficult to follow which cells were used for each experiment and the reasons. The authors should comment on the choice of osteosarcoma cells for the study, as the paper mainly discusses the choice of brain endothelial cells. Some key experiments in U2OS must be repeated in brain endothelial cells; targets identified in the siRNA screening should be tested in brain endothelial cells to increase the strength of conclusions.

(2) Figure 1B is not clear in this disposition; it seems the same markers were used twice. Additionally, to improve clarity in understanding and reading, I suggest adding the cell line used in the figures.

(3) In Figure S1A, it is difficult to conclude to the presence or absence of colocalization, zoom on ROI should be added as well as a quantification of the colocalization to conclude on the experiment.

(4) Some context on intracellular sorting tubules can be added in the manuscript to improve clarity.

(5) Figure S1C needs to be quantified to see if the mGL does not affect the expression of CD98hc; images could be added to support the absence of impact on cellular phenotype.

(6) It could be worth measuring the localization of the surface pool of CD98hc to increase the strength of the conclusion, maybe by immunofluorescence colocalization between a cell membrane marker and CD98hc, or by pHluorine marker to differentiate cell surface from endosomes.

(7) In Figure 1D, it seems that CD98hc have a different pattern in the upper panel compared to the lower one; it seems that vesicles are larger close to the membrane compared to deeper in the cytoplasm.

(8) Some crucial controls are missing, mainly in siRNA experiments. For all siRNA experiments, efficiency and silencing must be shown by Western blot and quantified (Figure 1G, S1D-E).

(9) In Figure S1D, the authors said "knockdown of ARF6 led to the accumulation of enlarged intracellular CD98hc positive structure". Size and number must be quantified to validate this assumption; I recommend adding an arrow to show structures for clarity. Same comment for the knockdown of dynamin and endophilin, the decrease of intracellular CD98hc positive structure need to be quantified.

(10) In Figure S1G, only one or two cells are positive for GFP; the efficacy seems poor; in this sense, it's difficult to conclude whether they have an impact on internalization.

(11) In Figure 2A, LAMP1 is significantly upregulated in the interactome; how do you interpret this, given that you show in Figure 1B that there is no colocalization.

(12) Figure 2B: CD98hc colocalization with RAB subunits needs to be shown by microscopy in brain endothelial cells.

(13) Overall, it is not clear if it's the integrin that is mutated for the binding domain or if it is the CD98hc binding domain that is mutated. If the integrin was not mutated, this type of experiment needs to be done to confirm the role of integrin. Additionally, immunoprecipitation needs to be done to show the absence or reduction of binding between CD98hc ISM and integrin.

(14) Figure 3 was mainly done on U2OS cells; the integrin mutant needs to be done in at least one brain endothelial cell line. Do you observe the same results between the mutant integrin and KO integrin? The impact of b1 integrin on CD98hc could be tested using integrin b1 blocking antibodies.

(15) The article focuses primarily on β1 integrin; could other integrins be involved? Given that integrins are often overexpressed in cancers, could this increase promote CD98hc internalization and trafficking? Adding this type of experiment could reinforce the conclusion and importance of integrin.

(16) The proliferation of cells re-expressing CD98hc ISM under starvation or not shows a decrease in relative proliferation after 144 h. Could the authors comment on this? Is cell death happening?

(17) The relevance of the results to the cancer field should be discussed in the discussion section, as osteosarcoma cells were used in the majority of experiments.

(18) Integrins are mainly involved in adhesion; do the authors observe the same results between integrin and CD98hc if cells are cultivated on matrices like fibronectin?

Please note, I have mainly reviewed the experimental part of the paper as I am not a specialist in mathematical modelling.

Reviewer #3 (Public review):

This manuscript focuses on the presence/origin of directional memory during epithelial cell migration. It starts by analyzing single cells and then moves to more complex systems (confluent layers and scratch assays). The paper first demonstrates that the migration in all of these systems is well-described by persistent random walks which likely emerge from fractional Brownian motion. This is an important demonstration, as it implies orientation memory in the systems. Then the paper proceeds to attempt to discern the origin of this memory and claims to establish key roles for adherens junctions and vinculin dimerization. While for the most part the manuscript is well-written, there are some signification overinterpretations in experimental results. The largest issue is demonstrating the role for vinculin dimerization, which is not a well-studied phenomena inside living cells, as all data is reliant on a single point mutation (Y1065E).

(1) Likely the most significant issue with the manuscript is the interpretation of the vinculin Y1065E variant and the assumption that the only defect the mutations causes is a lack of dimerization. Vinculin dimerization is mediated by a conformational change in the vinculin tail domain induced by F-actin binding (Thompson, FEBS Letters, 2013). Dimerization of the vinculin tail domain has been clearly demonstrated in in vitro systems involving purified proteins, as the authors point out in the manuscript. However, the dimerization of full-length vinculin has not been well characteristic in living cells. There are several reasons to suspect dimerization is potentially not prevalent in cells. For instance, in the presence of other actin binding proteins, there may not be sufficient available binding sites available on neighboring actin filaments to facilitate dimerization. Additionally, pY1065 vinculin and vinculin Y1065E have been associated with increased vinculin activation (Huang, JBC, 2014), so other effects seem possible. While the Y1065E variant clearly has an effect on the tension sensor readout and vinculin dynamics, further experimental evidence is needed to show that these effects are due to lack of dimerization in living cells. Perhaps most importantly, the vinculin tension sensors used in this study have been used extensively in knock-out systems and intermolecular FRET has not been observed (PMID: 20613844, 33335089, 30523675). To justify the definitive claims made in the manuscript, the authors likely need to develop, or employ, an assay for detecting vinculin dimerization in living cells. The authors could choose between intermolecular FRET, proximity labeling assays (i.e. antibodies with DNA for signal amplification), bimolecular fluorescent complementation (i.e. split GFP) based approaches, or some other approach. It should be noted that working with full length vinculin, not just Vt, and designing assay that can incorporate vinculin Y1065 variants (Y1065E and potentially Y1065A/F) would strengthen results. Also, the authors should be aware that the observation of strong dimerization may invalidate the use of FRET-based tension sensors in this system or at least necessitate intermolecular FRET control experiments.

(2) A major conclusion in the manuscript is that in response to overexpression of a specific vinculin constructs, focal adhesions behave the same in single cells, confluent cells, and collectively migrating cells for all the mutants but Y1065E. However, outside of the FRET measurements, there is not much evidence to support this claim. The authors should perform a greater comparison of the focal adhesions between the systems used in the manuscript (single cell, confluent cells, collectively migrating cells). Key measurements would include focal adhesion number per cell, focal adhesion size, focal adhesion orientation, vinculin dynamics (e.g. FRAP), focal adhesion stability, and some indicators of focal adhesion composition. For the last aspect, focusing on focal adhesion components that also have roles in adherens junctions, such as VASP, seems appropriate. Without such characterization, it is an overinterpretation to assume that focal adhesions are the same in each system and, therefore, effects are due to vinculin behavior in the adherens junctions.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors aim to assess how the functional organisation of the hippocampus is related to the geometry and neurobiological differences of the hippocampus. In particular, the authors focus on the first three eigenvectors of hippocampal-cortical functional connectivity, based on non-linear dimensionality reduction on resting-state functional MRI data. Furthermore, the work aims to describe changes in these functional axes and their relation to other factors throughout youth and evaluate whether they are predictive of individual variations in cognition.

Strengths:

A major strength of this study is the attempt to replicate key findings across multiple developmental cohorts.

Weaknesses:

The major weaknesses of the manuscript center on gaps in technical transparency and several conceptual inaccuracies. The machine learning methodology used for cognitive prediction is scarce, leaving little means to evaluate whether the behavioral results suffer from data leakage or overfitting. The introduction sets up an oversimplified historical premise regarding the field's understanding and appreciation of hippocampal connectivity, and contains several incorrect references that throw doubt on the argumentation. Additionally, T1w/T2w signal intensity is incorrectly used as synonymous with myelin, despite gold-standard histological validation showing a non-significant correlation between T1w/T2w and myelin staining (Sandrone et al., 2013).

Appraisal of Aims and Conclusions:

The authors partially achieve their aims by illustrating certain age-related changes in hippocampal function; however, the correlative study design is not equipped to examine how these changes are "shaped" by geometry, myelination, or gene expression (especially the latter two). Furthermore, conclusions were often overstated based on small effect sizes.

Context and Field Impact:

This work adds to a growing body of literature focused on gradient-based representations of hippocampal topology. By applying these methods across a wide developmental age bracket, it provides a useful reference point for how the hippocampus and wider cortex interact during maturation. To improve utility to the neuroimaging and cognitive neuroscience communities, the nesting of subfields within the eigenvector topology should be addressed, too.

Reviewer #1 (Public review):

Summary:

The authors studied the development of hippocampal connectivity gradients based on open datasets and performed correlation analyses with other MRI features as well as gene expression information from other datasets. Although the main findings are correlational and cross-sectional, the analyses are overall sophisticated and replicated in several datasets.

Strengths:

The hippocampus is a key region in understanding large-scale brain organization and cognition, and the authors applied advanced and suitable analytics to study its development. The paper is overall well-organized and well-written, and the findings are relevant for studying large-scale brain development.

Weaknesses:

While sophisticated, several of the analyses appear mainly correlational, cross-sectional, and rely on cross-dataset contextualization, which should also be stated as a limitation of the current work.

Reviewer #2 (Public review):

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

Summary:

The authors set out to investigate how well the onset of a self-initiated movement could be predicted at different times prior to action onset. To do so, they collected EEG and MEG data across 15 human participants who watched natural landscape images on a screen. These participants performed active self-initiated movements, or observed passive actions, to have a new image appear. By comparing the neural activity prior to active and time-matched passive actions, the authors found that even though a build-up of neural activity is visible close to 1s prior to action, action onset could only be reliably predicted around 100ms prior to action. These results confirm what was already suggested in previous literature: the commitment to action is only clear from the late stages in the visible neural ramp-up to action onset.

Strengths:

(1) The paper presents a well thought out methodology to assess the predictive value of neural activity prior to a self-initiated movement and passively observed action, while keeping all other experimental factors identical. This methodology can be applied outside the specific scope of this paper as well, in efforts to assess the correspondence of a neural signature with an observed behavior.

(2) The results are a strong confirmation of what was suggested less clearly in previous research (Trevena & Miller, 2010, Consciousness & Cognition; Schmidt et al., 2016, Neuroscience & Biobehavioral Reviews; Travers et al., 2020, NeuroImage).

Comments on revised version.

I thank the authors for addressing my concerns in the Introduction and Discussion of their paper. I don't require any further edits at this point.

Reviewer #1 (Public review):

Niu and colleagues tackle several questions concerning the assembly of the heterotetrameric kinesin-1 complex, comprising kinesin heavy chains (KHCs) and kinesin light chains (KLCs), its interaction with adaptor proteins (the mitochondrial adaptor TRAK2), and the implications of these interactions for kinesin autoregulation.

Broadly, the study comprises four parts; strengths and weaknesses are described for each in turn.

First, the authors examine the coiled-coil interface that enables tight binding between KHC and KLC. Binding-site mapping supports previous observations that KHC and KLC interact through the CC3 region of KHC and the amino-terminal coiled-coils of KLC. These regions are modelled using AlphaFold3, and the resulting models are used to inform mutagenesis experiments that identify critical residues within the hydrophobic core of the interface. Although the binding site has previously been reasonably well defined, both biochemically and using computational models, this work extends those findings to individual residues at the interface and provides supporting biophysical analysis. This provides useful experimental validation of the computational models and important tools for the field to enable the selective uncoupling of KHC and KLC.

Second, the authors examine how the mitochondrial adaptor TRAK2 interacts with KHC. Previous studies have defined the critical regions of KHC CC4 and TRAK2 reasonably well, and the authors' biochemical mapping broadly supports these findings. They extend this through biophysical analysis and computational modelling that proposes a 2:2 stoichiometry and an interesting assembly in which CC4 separates to enable formation of a four-helix bundle with TRAK2. This AlphaFold model is supported by biophysical, biochemical, and cell-imaging analyses, including the identification of point mutations that disrupt the interaction. These findings are important because the molecular basis of KHC CC4 interactions with diverse cargo adaptors has remained somewhat opaque, and this study begins to place these interactions on a firmer molecular footing. Some of the principles proposed here may also extend to other KHC-binding adaptors.

Third, and of particular interest, the authors examine the interplay between KLC and TRAK binding to KHC. This is important because previous work has generally favoured the view that mitochondrial transport is independent of KLCs, although some studies disagree. Whether KLCs are excluded from TRAK-containing complexes and vice versa and, if so, how this occurs has remained unclear. The authors provide evidence suggesting that the extended coiled-coil region of KLC, outside the direct high-affinity KHC-binding interface, modulates CC4 to prevent TRAK binding, suggesting that KHC-TRAK and KHC-KLC complexes may be mutually exclusive. Precisely how this mechanism translates into transport regulation within the cell remains unclear, but the effort to establish a structural hypothesis for understanding this interplay is valuable.

Fourth, the authors provide evidence for a novel, weak interaction between CC1 and CC4 that they propose may contribute to kinesin-1 autoinhibition and its regulation by KLC and TRAK-an 'autoinhibitory latch'. I found this part of the work more preliminary because, although the biophysical and biochemical analyses provide some support for such an interaction, unlike other aspects of the study, it is less well supported by computational modelling or mutational analysis. Moreover, there is no direct evidence for how this interaction might function in the context of full-length kinesin-1 autoinhibition, for example through assays of motor activity or conformational state, and the proposed model is not particularly clear with respect to full-length KLC (including its TPR domains) or regulation by MAP7.

Taken together, the study makes a valuable contribution to our understanding of several facets of kinesin-1 assembly, cargo recognition, and regulation. Key strengths lie in the complementary biochemical, biophysical, computational, and cell-imaging approaches used to support most of the key findings, together with the definition of several very useful point mutants that should inform future work.

There are, however, some general weaknesses surrounding unnecessary or overstated claims of novelty and the connection to prior work, and some factual errors. These can be straightforwardly addressed through better integration of the existing literature into the narrative, without detracting from the authors' contributions. Some conclusions appear to extend beyond the available data, and these should be tempered accordingly. Computational models should also be presented more thoroughly and systematically, together with confidence metrics and multiple ranked outputs. The Methods should more clearly describe the constructs and sequences used so that the work can be straightforwardly reproduced, alongside information on replication and additional quantification. Some clarification around measured stoichiometries and the authors' interpretation for some of the short coiled-coil fragments will also be important.

Reviewer #2 (Public review):

Summary:

Niu et al. develop a biochemical pipeline using systematic co-immunoprecipitation from cell lysate coupled with in vitro binding studies to test hypotheses generated based on structure prediction of how the kinesin heavy chain is autoinhibited through intramolecular interactions, as well as interactions with the kinesin light chain. Additionally, the authors provide insight into kinesin heavy chain binding to the cargo adaptor TRAK. Structural understanding of kinesin regulation has been elusive due to apparent flexibility of the motor. The advent of protein structure prediction, validated through cross-linking mass spectrometry (XL-MS), has provided an inroad to understanding kinesin regulation (Tan et al., Weijman et al.). Most recently, Ashaduzzaman et al. have used cross-linking and local refinement to obtain a reconstruction of the autoinhibited KHC:KLC tetramer. The biochemical analysis presented here extends previous studies and has enabled the authors to define the hierarchy of competing KHC interactions with KLC and the TRAK2 adaptor.

Strengths:

While other studies relied predominantly on XL-MS to validate autoinhibitory contacts in the KHC:KLC autoinhibited tetramer, the present study used in-cell interactions to define minimal interacting units, and measured the effects of prediction-guided mutagenesis on their interaction in vitro. This complementary approach stratifies the multitude of predicted autoinhibitory interactions and offers a functional test for reported structures and structure predictions.

Weaknesses:

The work is not appropriately placed in the context of previous literature. The KHC:KLC assembly interface (Figures 1 and 2) was previously predicted by Weijman et al., supported by XL-MS in Tan et al., and subsequently visualized by cryo-EM in Ashaduzzaman et al. Contacts between the regions designated in this study as KHC CC1 and CC4 (Figure 6) were also reported by Tan et al. and Ashaduzzaman et al. Similarly, a structural model of TRAK2:KHC (Figures 3-4) was previously reported and tested by co-immunoprecipitation and mutagenesis by Gladkova et al. Tan et al. additionally mapped binding of the related adaptor TRAK1 to KIF5B using XL-MS. These results should be discussed throughout to help the reader understand whether biochemical observations are consistent with prior work, or whether they hint at previously unknown regulatory mechanisms.

To test the KHC:KLC and KHC:TRAK2 predicted interfaces, mutations of hydrophobic residues were introduced in KHC (Figures 2 and 4), and TRAK2 (Figure 4). However, both KHC and TRAK2 exist as homodimers in solution. Are these mutations specific to destabilizing the heterotetramers, or do they also affect the KHC and TRAK2 homodimers? Such an effect could provide an alternative explanation for reduced binding and impaired mitochondrial recruitment of KIF5C by TRAK2 mutants, such as L217Q/L220Q. Retained mitochondrial localization of TRAK2 mediated by the C-terminus is not a good proxy for functionality of the N-terminal motor-binding region. Mutants should be assessed for stability and dimerization propensity, and their effects fully described.

Several structure-prediction models are introduced without pLDDT and PAE confidence plots. These are necessary to assess structure prediction confidence. Additionally, residue number labels would help orient the reader as to the boundaries of the protein fragments modelled.

Reviewer #3 (Public review):

The authors report an interaction between KHC and KLC1 through the coiled coil 3 of KHC, and then characterise the binding of TRAK2 to KHC through coiled coil 4 of KHC. The reporting of this second interaction is novel. The authors make a number of mutants which change the affinity of TRAK2 for KHC, but it is not clear if it ultimately prevents binding. They then demonstrate through competitive binding that TRAK2 binds more tightly to CC4 than CC1 and therefore can trigger release of Kinesin-1 auto-inhibition. The authors, however, do not show directly that the kinesin is no longer inhibited. This could be tested using negative stain EM, SAXS or motility assays in the future. The experiments are performed with immunoprecipitation pulldowns coupled with western blotting in cells, and ITC with recombinantly purified proteins.

Strengths:

The data showing the binding of KLC1 to KHC and the TRAK2 binding to KHC are interesting.

The ITC data looks very good from the profiles. It seems, however, that the experiments were done once rather than replicated (at least twice, ideally three times to accommodate variation). The size-exclusion chromatography experiments showing competition between different adaptors to KHC are nicely done, and the SEC-MALS is informative in terms of determining the stoichiometry and size of the assembled complexes. The work is performed with recombinant proteins and therefore removes any ambiguity about the interactions in cells.

Weaknesses:

The experiments lack appropriate controls, with some experiments incomplete, and the statements claimed are not supported by the data. There are some problems with figure and data presentation and the number of replicates/lack of quantification. The validation of AlphaFold models and predictions is missing. The cell biology experiments are preliminary.

There is a concern with the cell biology experiments. All experiments are done with transfections in the presence of the endogenous proteins. It is not clear if the proteins are overexpressed and they appear cytoplasmic. Beyond this, it is not possible to claim co-localization of the transfected proteins.

The manuscript would benefit from general editing to improve understanding. The names used to describe the methods are incorrect. This has implications for the solidity of the statements and conclusions of the paper.

Reviewer #1 (Public review):

Summary:

Nakatani and De Schutter conduct an in-depth computational investigation of astrocyte electrophysiology. Utilizing a conductance-based NEURON model with realistic astrocyte morphology, the study replicates both in vitro and in vivo experimental protocols to characterize local and global depolarizations of the astrocyte membrane. The findings indicate that bath-applied (global) potassium stimuli induce membrane potential changes that follow the potassium equilibrium potential throughout the cell, whereas localized potassium stimulation disrupts this behavior. In the latter case, the local response is governed by the ratio of local conductances. The authors further analyze the observed electrical compartmentalization to identify which molecular components directly affected by synaptic stimuli - such as potassium clearance, glutamate transport, and GABAA and NMDA receptors - could plausibly generate this effect under physiological conditions. The study concludes that perisynaptic astrocyte processes (PAPs) can respond to specific synaptic inputs in an electrically isolated manner, exerting minimal influence on the soma.

The central prediction - that astrocyte processes can be electrically compartmentalized from the soma under local synaptic stimulation, while bath stimulation produces a globally uniform response - is both interesting and surely significant for understanding and measuring astrocyte electrophysiology. However, the manuscript's incomplete model description and disorganized Results section hinder assessment of how well the conclusions are supported. Addressing these issues could render the model a valuable resource for the community and a useful framework for guiding and interpreting voltage-imaging experiments in astrocytes.

Strengths:

(1) The study addresses a timely, long-standing question and may pioneer a multiscale approach to quantifying astrocyte electrophysiology beyond the traditional view of an electrically passive, isopotential glial syncytium. The findings are consistent with recent voltage-imaging evidence of localized depolarization in astrocyte processes.

(2) A key strength of the study is the use of a state-of-the-art, morphologically realistic compartmental model that captures both somatic and peripheral dynamics. This approach addresses spatial and temporal resolutions that are challenging to achieve experimentally.

(3) Simulating both in vitro-like (bath perfusion) and in vivo-like (local, synapse-driven) stimulation protocols within a unified framework may explain discrepancies observed between experimental results obtained under these differing conditions.

(4) The model predictions are benchmarked against experimental voltage-imaging data, and the systematic analysis of individual molecular contributors-including potassium channels, glutamate transporters, and synaptic receptors-yields testable predictions.

(5) The topic is relevant to a broad and multidisciplinary audience, including researchers in glial biology, synaptic physiology, and computational neuroscience.

Weaknesses:

(1) Terminology and clarity

The manuscript relies heavily on jargon and abbreviations, which impedes readability. The central concept of a "Nernstian" depolarization or response, introduced in the title, is unconventional and not explicitly defined. It appears that "Nernstian" refers to a membrane potential that follows the potassium Nernst potential (EK) of the astrocyte. This terminology is ambiguous, as the analysis is restricted to K+ and does not ensure that the astrocyte response excludes contributions from other ion Nernst potentials.

Furthermore, "Nernstian" reflects a modeling choice rather than an alternative such as the Goldman-Hodgkin-Katz formalism. Additional terms in the abstract, including "a variety of biochemicals," "astrocyte peripheries," "isolated peripherals," and "empirical" model, are also ambiguous. Because these terms are central to the main claim, they may confuse readers.

(2) Framing of the research question

The introduction is brief and does not clearly articulate the research question or the manner in which the study addresses it. The motivation, which should draw on cell anatomy (including the gap-junction-coupled syncytium), molecular composition, and stimulation protocol, is underdeveloped, and the knowledge gap is not explicitly identified.

(3) Incomplete model description

Model design, parameters, and stimulation protocols are often not described or justified, and references are missing (e.g., for K2P, TREK1/TWIK1, and the Na+/K+ pumps). In particular:

a) it is not explained how the three ionic species (Na+, K+, Cl−) needed to account for synaptic currents interact and are modeled in the extracellular and intracellular spaces, nor how they contribute to the astrocyte membrane potential;<br /> b) the diffusion formulation (Equation 9) neglects any tangential component of diffusion, an approximation that is left unjustified;<br /> c) gap junctions are modeled as ohmic currents driven by the potential difference between astrocytes, but the network arrangement, location, and distribution of the coupled astrocytes and of the gap junctions in the simulations are not specified;<br /> d) the analytical results (Equations 46-50) are not obviously derived from Equation 16, and the essential derivation steps are missing;<br /> e) it is unclear whether and how the voltage indicator ArcLight is modeled when comparing simulated traces with experimental fluorescence data.

Overall, the simulation setup lacks clarity, which limits reproducibility and impedes evaluation of the study's conclusions.

(4) Organization and rationale of the results

The Results are difficult to interpret owing to unclear organization and an ambiguous rationale. The derivation and application of the scale parameter ΓK (Equation 1) are unclear; in particular, its dependence on axial conductance appears inconsistent with the assumption that intracellular diffusion does not influence PAP responses (cf. van Egeraat et al., Biophys. J. 1993).

(5) Unsupported or missing results

Several statements are not supported by the data presented: some claims could not be matched to any evidence (e.g., L104-106), results described in the text could not be located in the figures (L159-164), comparisons are reported as "data not shown" (L140-143), and the source of the "realistic ECS sizes" (L191-194) is unclear. The authors do not demonstrate the validity of the extracellular K+ perturbation used to mimic action potentials.

(6) Presentation of figures

The figures are often crowded and stylistically inconsistent. Most importantly, in Figures 3, 4, and 6, the rendering of the noisy experimental data obscures the simulation results they are meant to benchmark, making it difficult for readers to assess agreement between model and experiment.

Reviewer #2 (Public review):

Summary:

Nakatani et al. developed a conductance-based, morphologically realistic computational model of an astrocyte soma and its perisynaptic processes (PAPs) to examine how extracellular potassium shapes the astrocytic membrane potential across spatial scales. The model builds on previously published morphological and electrophysiological data, as well as experimental measurements of transporter and ion-channel expression levels. It incorporates the astrocytic potassium channels Kir4.1, the Na⁺/K⁺-ATPase, the K2P leak channels TREK-1 and TWIK-1, and gap-junctional coupling to neighboring processes via connexin-43. Glutamate transporters (GLT-1), NMDA receptors, and GABA_A receptors are also included to examine neurotransmitter-mediated depolarization.

The authors propose that a compartmental model that explicitly distinguishes the soma from the PAPs, provides important insights into realistic potassium uptake and the electrophysiological properties of these distinct compartments.

Strengths:

(1) Clearing extracellular K⁺ is one of the most fundamental functions of astrocytes. Failures of K⁺ buffering are implicated in epilepsy, spreading depolarization, and several neurodegenerative conditions. Yet how K⁺ handling is divided between perisynaptic processes and the rest of the cell, and how this shapes local membrane potential, remains poorly understood. The study gives a mechanistic account. The soma behaves as a low-resistance K⁺ "sink" with little voltage changes for local K⁺ elevations around the PAPs, while producing much smaller, than expected by simply by Nernst equation. The local membrane potential deviates from E_K because current escapes to the rest of the cell. This framing separates physiological, synapse-scale K⁺ handling from global, pathological K⁺ load.

(2) The model described here fills an important gap in computational modelling of astrocytes. While computational models are now central to neuroscience, astrocytes and their interactions with neurons remain relatively underrepresented in mainstream computational modeling. Existing models mostly treat either whole-cell K⁺ buffering or the isolated perisynaptic compartment. This is novel in that it connects the two within one framework and provides a tool to address how astrocytes integrate local and global signals.

(3) Including realistic morphology shows that spatial heterogeneity matters. A key conceptual strength is that the model does not treat the astrocyte as a single, well-mixed, isopotential compartment. Using an EM-informed morphology with perisynaptic astrocytic processes, it shows that stimulus location and spatial extent shape the membrane response. Fine processes, constricted segments, and local conductance ratios generate responses that cannot be predicted from whole-cell properties alone. These questions are difficult to address experimentally because fine processes are below the resolution of conventional electrophysiology, and somatic recordings are limited by space-clamping. An in silico approach is therefore especially valuable, as it allows controlled manipulation of individual variables, such as stimulus location, channel density, and extracellular volume, that cannot be varied independently in tissue.

(4) It is built using standard, open tools, and therefore the model can be extended with ease by future studies. The model is implemented in NEURON, the most widely used simulator for biophysically detailed cells, with mechanisms written in NMODL and driven through Python. It also uses a previously published astrocyte morphology. Parameters are listed, and the authors state that the model files are in a repository. Together, these make the model accessible and reproducible if the repository link is also provided.

(5) The model is anchored to experimental data. Kir4.1 and K2P leak conductance were fitted to experimental current-voltage data (Figure S2), and other channel parameters were constrained by the literature. Notably, the authors avoided channel formulations with built-in mechanisms that fix the resting potential, which are common in earlier astrocyte models. The resting potential and K⁺ responses therefore emerge from the conductance rather than being imposed. The model also reproduces established whole-cell behavior, including Nernstian responses to bath K⁺ application, before being used for new predictions.

(6) The study tests several candidate mechanisms for peripheral depolarization within one framework and offers a way to reconcile conflicting results: K⁺ through Kir4.1, GLT-1, Na⁺/K⁺-ATPase, K⁺/glutamate spillover, and astrocytic GABA_A and NMDA receptors, all of which in principle can produce responses of the observed depolarization under various conditions. The model therefore offers a biophysical explanation for discrepancies in the astrocyte literature.

(7) The work makes several testable predictions:<br /> a) the local [K⁺]o required for a given PAP depolarization;<br /> b) local depolarization depends on the spatial extent of activation;<br /> c) somatic recordings would not detect PAP depolarizations.

Weaknesses:

(1) The Nernst equation itself does not appear to break down; local E_K should still follow it. Rather, the membrane potential deviates from E_K because low-resistance electrical coupling allows current to spread to the rest of the cell. I suggest rewording this statement for accuracy.

(2) The Methods mention an uploaded repository, but no link is provided. The authors should include a public link and deposit the model in ModelDB or a comparable repository.

(3) The deployed morphology is hippocampal, not cortical. But the paper's conclusions are for cortical somatosensory astrocytes based on morphological similarity. That deserves additional explanation, especially since PAP geometry is central to the conclusions.

(4) Astrocytic NMDA receptors best fit the observed depolarizations. However, because the experiments in Armbruster et al. were performed in the presence of APV, an NMDA receptor antagonist, the authors suggest that the responses may involve APV-insensitive NMDA receptor subunits. This explanation would be stronger if supported by evidence that these subunits are expressed in cortical astrocyte PAPs.

(5) For activation of GABA_A receptors on astrocyte PAPs, the source of GABA is unclear, since the stimulated ascending axons are glutamatergic. The model shows these receptors could explain the data, not how they might be activated. This deserves an explanation.

(6) I also recommend adding a discussion paragraph on the functional implications of heterogeneous astrocytic control of potassium homeostasis. Do the results suggest that astrocytes efficiently buffer K⁺ under normal conditions but fail during widespread K⁺ accumulation?

Reviewer #3 (Public review):

Summary:

This study uses computational modeling to examine how local synaptic activity influences astrocyte membrane potential and, in particular, whether the classical Nernstian relationship between extracellular potassium concentration and astrocyte membrane potential is maintained throughout the astrocyte. The authors focus on the possibility that local ionic changes associated with synaptic activity produce membrane potential responses in fine astrocytic processes that differ from those predicted by a global Nernstian relationship. The study addresses an important and longstanding question in astrocyte physiology and provides a potentially useful computational framework for examining how cellular morphology, membrane conductances, and local synaptic inputs shape astrocyte membrane potential. The central conclusion is that Nernstian behavior can break down during local synaptic input, particularly in fine astrocytic processes. The modeling framework could therefore be useful for understanding how the spatial organization of astrocytes influences their responses to local neuronal activity. However, the strength and generality of this conclusion depend on several biological assumptions that are not sufficiently considered in the current model or discussion.

Strengths:

This computational modeling addresses a longstanding question in astrocyte physiology dating back to the 1960s: whether astrocyte membrane potential (VM) follows the Nernstian prediction in response to changes in extracellular K+ concentration ([K+]e). Significant recent progress in astrocyte studies has provided a renewed opportunity to revisit this question. In this regard, the effort to develop a conductance-based NEURON model that incorporates understanding of astrocyte structure and physiology is highly valuable. The model has also shown the potential to simulate how changes in astrocyte membrane Na+, Ca2+, and Cl+ permeabilities, resulting from synaptic inputs, could alter the relative K+ permeability, and therefore the Nernstian behavior of astrocyte VM at different subregions of astrocytes. These questions are important to astrocyte physiology and remain technically challenging to experimentally evaluate. However, several concerns about the omission of recent studies on astrocyte structure and function from the model and the physiological interpretation of the Nernstian behavior of astrocytes need to be addressed before the conclusions can be adequately supported.

Weaknesses:

(1) The premise of the modeling:<br /> The cellular context should be clearly stated. The model is intended to simulate rodent grey matter cortical protoplasmic astrocytes, but not white matter fibrous astrocytes or other specialized astrocyte populations, which differ morphologically and, to some extent, functionally.

(2) The architecture of astrocytes:<br /> This model can be refined by incorporating recent advances in our understanding of astrocyte morphology (PMID: 35283239, PMID: 31550514). For example, (a) the branching pattern of astrocytic processes, particularly the terminal processes (or leaflets), which is a key element of the model; (b) the location of synapses within an astrocytic domain. Specifically, synapses can make contact with virtually every structural component of an astrocyte, from the soma to different levels of processes, including endfeet contacting blood vessels. This anatomical evidence is in sharp contrast to the prevailing perisynaptic astrocytic process (PAP) concept adopted in the modeling; and (c) the existence of a large quantity of reflective astrocytic processes that loop back and form gap junctional coupling at their stemming points that wrap around bundles of axons or large dendrites, forming a scaffold-like architecture (also see PMID:32312988 in your reference list). This may create regions of strong local astrocyte VM response to activity-dependent elevations of [K+]e, which could be an important feature to consider in the modeling.

(3) Astrocyte membrane potential is not an autonomous feature of individual astrocytes:<br /> If the goal is to simulate astrocyte VM, astrocyte syncytial coupling cannot be ignored. Specifically, the very low astrocyte-to-astrocyte pathway resistance (4.2 MΩ) provides strong electrical coupling that equalizes the VM of astrocytes within a syncytium to a uniform level, a property termed astrocyte syncytial isopotentiality (PMID: 26435164; PMID: 30277762). Experimental studies have demonstrated that coupling among 7-9 astrocytes is sufficient to maintain this isopotential state. Thus, the VM of an individual astrocyte is not determined solely by its own K+ conductance and transmembrane K+ gradient, but rather the result of the electrotonic spatiotemporal summation of voltage across the syncytium. A realistic model that seeks to predict astrocyte VM should incorporate syncytial coupling as a fundamental component.

(4) The historical debate about the Nernstian behavior of astrocyte VM in response to [K+]e:<br /> The authors should acknowledge the longstanding debate over the Nernstian behavior of glial VM in response to changes in [K+]e. This relationship was first described in the seminal neuroglia studies of Kuffler and colleagues in 1966, using optic nerve neuroglia, which are likely equivalent to fibrous astrocytes in rodent white matter. They reported a slope of ~58 mV per 10-fold change in [K+]e (PMID: 5966434). This interpretation was subsequently challenged by a series of in vivo studies of neuroglia VM in cortical grey matter, including Pape and Katzman (1972; PMID: 5016004), Ransom and Goldring (1973; PMID: 4805015), and Futamachi and Pedley (1976; PMID: 1276917). Importantly, the Futamachi study provided early in vivo evidence consistent with syncytial isopotentiality, although the existence of the astrocyte syncytium could not be demonstrated experimentally until the later work of Ransom and colleagues in 1981 (PMID: 7248773). This historical literature should be introduced in the Introduction to establish the significance of the present modeling work.

(5) Astrocyte syncytial isopotentiality:<br /> The authors should consider that syncytial isopotentiality is a system-wide feature of both grey and white matter astrocytes. At minimum, this means that somatic astrocyte VM recordings are not consistent with a simple Nernstian prediction of VM in response to changes in [K+]e. Therefore, a model of astrocyte VM should account for syncytial coupling rather than treating VM as a property determined by individual astrocytes.

(6) Distinguish K+ Nernstian behavior from multi-ion permeability and transporter-mediated flux:<br /> Conceptually, the high and near-exclusive K+ permeability of the resting astrocyte VM provides the basis for applying the Nernst equation to describe VM. In the present, Na+ and Ca2+ permeabilities are also introduced without a clear distinction between the conditions under which a Nernst equation remains applicable and those in which it does not. The authors should clearly define Nernstian behavior as referring specifically to K+ permeability. When the Na+ and Ca2+ permeabilities, resulting from the activation of AMPA/NMDA receptors, are involved in astrocyte across-membrane ionic activities, the VM follows the GHK prediction. Additionally, the model includes Na+-dependent transporters, which do not represent channel-mediated permeability. The authors should therefore distinguish channel-mediated ionic permeability from transport-mediated ionic flux when describing and modeling changes in astrocyte VM.

Reviewer #1 (Public review):

Summary

In "Mouse models with human antibody repertoires for inducing multiple lineages of HIV-1 broadly neutralizing antibodies", Tian et al. report the production of 6 humanized mouse models to support HIV-1 vaccine design studies aiming to induce different classes of broadly neutralizing antibodies.

These mice were designed to express immunoglobulin genes of human bNAbs targeting four different conserved epitopes of the HIV-1 Envelope while allowing the development of CDRH3 and CDRL3 diversity during gene rearrangements.

Strengths:

(1) The manuscript is clearly written, and findings are clearly presented with self-explanatory figures. The Introduction and Discussion sections provide very valuable context to understand the relevance of these mouse models and are accessible to a general reader. It is appreciated that the data are presented with transparency, highlighting both strengths and weaknesses of the mouse models.

(2) The characterization of the mouse models is overall thorough and rigorous. Immunization data to confirm functionality are not presented; however, it is understandable that this information will be presented in the context of subsequent studies. The authors indicate that these mice have been distributed to different teams.

(3) These mice will be very valuable tools for the HIV-1 vaccine community to evaluate vaccine candidates in a "closer-to-human" setting that allows a certain level of polyclonality while using human immunoglobulin genes.

Weaknesses:

(1) The authors investigate the use of the knocked-in human immunoglobulin genes in heavy chains and light chains and provide the frequency of productive rearrangements using these genes. However, there is no information about the frequency of the right heavy-light chain pairs. This information would be important to calculate the real frequencies of potential bNAb precursors in these mouse models.

(2) The authors use HTGTS-rep-seq to investigate immunoglobulin gene rearrangements at the genomic DNA level. This method is very useful for analyzing gene recombination because it provides information about both productive and non-productive rearrangements. However, this method does not provide information about BCR expression, and it is not optimal for investigating repertoire composition. BCR expression would provide a more rigorous characterization of the mice and confirm functionality.

(3) The goal of this study was to produce models that could provide a diverse bNAb-like repertoire. Specific quantification of the diversity of the repertoire is not provided. The study does not provide information about the sequences that do not use the intended genes.

Overall, the availability of these humanized mouse models will be very valuable for the HIV-1 vaccine community and will provide valuable pre-clinical models to advance the design and testing of vaccine candidates.

Reviewer #2 (Public review):

Tian & colleagues generate and characterize six novel humanized mouse models engineered to express precursor human antibody repertoires for inducing multiple lineages of broadly neutralizing antibodies (bnAbs) against HIV-1. Building on prior "VRC01-class" proof-of-concept work, this manuscript advances the field by demonstrating the value and suitability of these sophisticated models for testing the ability of vaccine candidates to induce bnAbs. By utilizing flow cytometry and HGTS sequencing techniques, the authors demonstrate that the targeted bnAb minigenes successfully undergo physiological V(D)J recombination and junctional diversification. Despite phenotypic heterogeneity in B-cell development, they show that these models reproducibly produce diverse repertoires enriched for functional humanized B-cell receptors expressing knock-in-derived bnAb precursor rearrangements, a subset of which yield the long, correctly charged CDRH3 loops required to target conserved but glycan-shielded bnAb epitopes. This achievement successfully overcomes a major vaccine testing bottleneck by providing a pool of patient-deduced precursor configurations in sufficient amounts to reliably study how subsequent somatic mutation events are induced by candidate vaccine protocols (with the potential exception of the PG9 model). Crucially, the comparative utilization of Kymab data serves as an excellent control to demonstrate the extreme rarity of bnAb rearrangements in fully locus-humanized mice, beautifully illustrating the design limitations of humanized models without adjusting for the much smaller repertoires of mice. Finally, the discussion is nuanced and well-written, including articulating the models' free availability and highlighting why these small-animal systems are complementary-and sometimes essential-in relation to rhesus macaque systems.

The paper's conclusions are robustly data-supported, with the only slight weakness being that the Kymab data were extrapolated rather than derived from actual head-to-head analyses. The comments below predominantly aim to provide further context and clarity-mainly in the abstract, results, and discussion sections-to this already excellent, highly detailed study. Refining the text clarity and phenotypic observations will further polish this impressive manuscript.

(1) Abstract Clarity:<br /> Concluding sentences should incorporate the discussion's explanation: finding natively low-frequency precursors in standard humanized locus mice is statistically prohibitive due to their smaller absolute B-cell compartment. These models bypass this restriction by maximizing relevant segment expression to expand the functional CDR3 search space.

(2) Results (Line 183, BG18 expansion):<br /> The intriguing expanded B1 cell population warrants more conservative commentary regarding genuine autoreactivity versus other possibilities, such as hyperactive baseline signaling via autophosphorylation (as shown by Hassan Jumaa for certain HC/LC clonotypes). While beyond this manuscript's scope, these mechanisms are differentiable via calcium flux assays with general anti-BCR signaling reagents.

(3) Results (p15/Line 324, PCT64/CH235 Vκ Allelic Architecture):<br /> The text states that lacking a recombination cassette caused hVκ3-20 to account for only a fraction of rearrangements. While Figure 1B's general engineering feature (tandem reverse hJκ-RSS 23 and 12 RSS hJκ) implies human Vκ should predominate everywhere, Figure 1C suggests hVκ3-20's low representation in CH235 and PCT64 models stems from being introduced differently-specifically via pre-rearranged minigenes rather than unaltered RSS orientations in an unrearranged cassette. The authors should clarify this point in the text and/or Figure 1.

(4) Results (p14, Clever Engineering of λLC rearranging mice):<br /> Replacing mCκ with hCλ3 is a clever genetic trick and an important proof of concept for stably accommodating a human lambda rearranging light chain cassette within the mouse kappa locus. This excellent methodological highlight deserves prominent recognition as a template for other model developers. I suggest giving this further play in the discussion.

(5) Discussion (Model Comparisons & Baseline Resolution):<br /> The authors aptly compare these humanized mice to macaques; in addition to their superior scalability, they point out that macaques don't recapitulate human-specific bnAb-relevant configurations in certain instances (like lacking the VH1-2 minigene for VRC01-class responses, or utilizing germline D segments that favor V2 apex responses, potentially overestimating human responses). To further enhance nuance, referencing other prior review KI model literature on this topic (some published by these authors) would be beneficial. For context, KI-derived adoptive transfer models offer more potential diversity (thus, 'higher bar' off-target response testing) and frequency control but are restricted to short priming studies. Conversely, the rearranging models in this study support full vaccine regimens and generate multiple variant bnAb precursors to compare potential in vivo response plasticity.

(6) Discussion (Baseline phenotypes):<br /> One limitation of the cellular flow data, albeit beyond the manuscript's current scope, is that the baseline phenotypes predominantly reflect variations of a fixed pair of V(D)J rearrangements, rather than bona fide patient-derived UCA bnAb precursors with correct charges/lengths. To help resolve pre-antigenic functional signaling boundaries and thus potentially aid immunogen design, the discussion could note how future work could aim to use techniques like single-cell transcriptomics to map expressed selection signatures of bnAb precursor paired V(D)J's to rule out anergy or survival/death gene signatures.

(7) Discussion (PG9 Model & HCDR3 Length Constraints):<br /> While the PG9 model's failure to produce bona fide precursors is unsurprising given its long HCDR3, the authors should provide a bit more detail as to whether a subset of PG9 variants with slightly shorter HCDR3s are permissive for bnAb production (analogous to DH511/10E8). Providing the specific HCDR3 sizes generated by this model in relation to recently discovered monkey antibodies-which are shorter/different but function like PG9 bnAbs (e.g., Guenega et al Nature 2026) - would offer excellent context. Such related lineage and/or lineage precursor variants could serve as structural alternatives, and highlighting how these length constraints impact selection provides vital boundary conditions for the immunogen design field when planning germline-targeting regimens.

Reviewer #1 (Public review):

Summary:

This study explores the role of Dynamin 2 (Dnm2)-mediated clathrin-mediated endocytosis (CME) in the fate determination of neural progenitor cells in the embryonic cerebral cortex, with particular attention to its asymmetric regulation of ligand and receptor functions in the Notch signaling pathway. Using conditional knockout mice, in vitro co-culture systems, in utero electroporation, and luciferase reporter assays, the authors propose that Dnm2 is required for neural stem cell maintenance and that CME plays a dual role in Notch signaling.

Strengths:

The authors systematically demonstrate the critical role of Dnm2 in embryonic cerebral cortical development. They show that Dnm2 is the predominant dynamin isoform expressed in the embryonic central nervous system, and that its conditional knockout disrupts ventricular zone (VZ) architecture, causes premature depletion of radial glia (RG), and leads to precocious neurogenesis, accompanied by periventricular hemorrhage and perinatal death.

Weaknesses:

I believe that some conclusions of this study should still be treated with caution. Specific comments follow:

(1) scRNA-Seq analysis of the Dnm2-cKO cortex at E11.5 and E13.5 is needed, or alternatively, additional LGE-MGE scRNA-Seq should be performed, to examine whether the key gene expression changes are conserved across the MGE-CGE-LGE and cortex.

(2) Regarding Figure 5 (Loss of Dynamin 2 leads to abnormal accumulation of Dll1 and altered NICD distribution in the E11.5 cortex), I do not understand why Dll1 accumulation occurs primarily in the medial cortex.

(3) A detailed schematic model is needed to illustrate the function and mechanism of action of Dnm2.

(4) Dnm2-cKO exhibits extensive cell death and vascular abnormalities. The authors should explain the possible specific causes.

(5) Clinical relevance: Are there specific forebrain phenotypes associated with Dnm2 mutations in humans? What are the possible reasons? This study appears to have limited relevance to Dnm2-related human disease.

Reviewer #2 (Public review):

Summary:

Breuig et al. studied the effect of Dnm2 conditional knockout in the developing neocortex. The authors generated CNS-specific Dnm2 conditional knockout (cKO) mice using Nestin-Cre, Emx1-Cre, and Nex1-Cre drivers. Confirming existing findings of previous research, they reported that Dnm2 was the predominant dynamin isoform in the embryonic forebrain by immunoblot, as well as by analysis of published single-cell and spatial transcriptomic atlases. The authors went on to demonstrate that deletion of Dnm2 in progenitors causes disorganization of the ventricular zone, depletion of Sox2+ progenitors, an excess of Tbr2+ intermediate progenitor cells and Tuj1+ immature neurons, as well as periventricular hemorrhage and perinatal lethality. On the contrary, deletion of Dnm2 in postmitotic neurons produces no detectable phenotype. In the mutant cortex, Dnm2 conditional knockout led to accumulation of Dll1, reduced cleavage of NICD, and reduced Hes1/Hes5 expression. To separate ligand- from receptor-side effects, they utilized a neural stem cell co-culture assay. They tested dynamin function in the in vitro assay using a variety of molecules and probes, including a dominant-negative Dnm2 (K44A), an shRNA, and the tamoxifen-inducible deletion in reprogrammed Dnm2-floxed neural stem cells. They further performed postnatal electroporation with Hes5-EGFP and Dll1-mOrange2 reporters and an in vivo dual-luciferase assay to test the effect of receptor side effects. In summary, they showed that inhibiting or downregulating Dnm2 increases Notch signaling levels, while the involvement of Dll1 remains unclear depending on the experimental condition.

Strengths:

The topic of the manuscript, which is the molecular mechanisms and the signal transduction of neural stem cell maintenance, is of fundamental importance to the field of neurodevelopment. The conditional knockout strategy is compelling.

Weaknesses:

The central claims of the current manuscript are not sufficiently supported by experimental results. The rigor of the experiments conducted also needs to be significantly improved.

(1) Quantification and statistical analyses of the core in vivo phenotypes are essential to establish the central claim of progenitor depletion and premature neurogenesis. Only representative images of Sox2, Ki67, Tbr2, Ngn2, Tuj1, and cleaved Caspase-3 are shown (Figures 2, 3, 4, S2, S3), as is the EM observation of increased clathrin-coated vesicles (Figure 3G-H).

(2) The authors report that "the brain of mutant animals appears indistinguishable from control littermates". However, based on the representative images of Figure 2, at 11.5 the disorganization of the neocortex and the disruption of the radial columns have already manifested. The authors should carefully describe the aspects of the brain that they deemed indistinguishable between control and mutant animals, and by what criteria.

(3) The transition from Dnm2 conditional knockout (cKO) to Notch signaling appears somewhat abrupt. The authors should provide additional experimental evidence or literature precedents to logically bridge this gap.

(4) The claim that dynamin inhibition in receptor-expressing cells increases Notch signaling is not supported by the comparisons shown. In Figure 7D, the significant receptor-side comparison is K44A-receiver + Dll1 versus K44A-receiver without ligand, which tests whether ligand-driven activation survives dynamin inhibition, not whether it is enhanced. The in vivo results are marked p = 0.07 (Fig. 8F), p = 0.07 (Fig. 8L), and n.s. (Fig. 8Q), yet the text states the increase was significant, and the Figure 8 title asserts it. This is the paper's most novel claim and the basis of the ligand/receptor dichotomy in the title and Abstract. As stated, it is not supported at conventional significance.

(5) Since the prominent phenotype of Dnm2 cKO was a reduction in radial glial cells, the subsequent reduction of signaling molecules (Hes1, Hes5, etc.) may be due to the reduced progenitor pool, rather than cell-autonomous reduction of Notch signaling. Quantitative results, such as the ones in Figure 6, should take changes in cellular composition into consideration and normalize to the reduced proportion of apical progenitors.

(6) The authors stated that Notch ligands "accumulate at the cell surface," but all immunostaining appears to be on permeabilized tissue, and no internalization assay is reported. The proposed mechanism is a block in ligand endocytosis. Accumulated Dll1 in permeabilized sections is equally consistent with intracellular retention in stalled endocytic intermediates, which the EM in Figure 3G-H might actually favor.

(7) Inconsistency in the cortical regions examined (Anterior vs posterior, medial vs lateral, etc.). Are the observations and experiments conducted in the same region of the cortex? If so, which region and why?

(8) Statistical tests should be described in sufficient detail in the methods section. The number of samples or replications needs to be reported for all experiments. The "Imaging and Quantification" section also did not provide enough information (the authors only stated that "Cell counts were performed on matched cortical regions across genotypes and developmental stages"). Figure legends report significance (Figs. 6B, 6C, 7D, 7F, 8F, 8L, 8Q, S6) without specifying what was compared or how in the corresponding main text or methods.

(9) The main text attributes the cNICD (Val1744) result to Figure 5A-B, but the Figure 5 legend describes the red channel as "NICD (red; total Notch intracellular domain)," which is the antibody that the main text explicitly distinguishes. Figs. 4 and 5 appear to show overlapping Dll1/NICD data.

Reviewer #1 (Public review):

Summary and Strengths:

In this manuscript, the authors continue their efforts to characterize metabolism-related phenotypes and molecular changes in Down syndrome mouse models, with a focus on Ts66Yah in the present study. Because the phenotypes described are relevant to metabolic abnormalities observed in individuals with Down syndrome, these findings are important. In addition, Ts66Yah is a relatively new model generated by removing the triplicated region of Mmu17 from Ts65Dn, which is not syntenic to Hsa21. Thus, Ts66Yah retains the triplicated region of Mmu16 that is syntenic to a region of Hsa21, carried on an extra chromosome.

Weaknesses:

(1) Figure 3 and Figure 10: The authors should offer a possible explanation for why the GTT and ITT differences between Ts66Yah and wild-type males observed under chow conditions are no longer evident under HFD conditions. The statement in the Abstract that "both male and female Ts66Yah mice share a common phenotype of pronounced glucose intolerance and insulin resistance" appears stronger than the data support. Under chow conditions, impaired insulin sensitivity by ITT is observed in males but not females. Moreover, although the Abstract states that glucose intolerance and insulin resistance are further exacerbated by HFD, the GTT and ITT abnormalities under HFD appear to be predominantly observed in females, whereas the genotype differences are no longer evident in males. The Abstract should therefore be revised to more accurately reflect these sex- and diet-dependent effects.

(2) Figure 7B: In the female liver PCA plot, PC1 is labeled as explaining 13.2% of the variance, whereas PC2 explains 21.6%. Since principal components are ordered by decreasing explained variance, PC1 should explain greater variance than PC2. The authors should verify whether the axis labels or percentages are correct.

(3) Figure 14 legend: A cutoff of log2FC > 0 was used, whereas Figure 8 uses a log2FC cutoff of 0.5. The authors should explain the rationale for using different thresholds in the two analyses.

Reviewer #2 (Public review):

Summary:

The authors of Saqib et al "Deep phenotyping and multi-omics analyses reveal systems-wide metabolic dysregulation in a refined trisomy mouse model of Down syndrome" were testing the hypothesis that metabolic dysregulation in Ts66Yah Down syndrome model, as compared to euploid mice, would replicate metabolic dysregulation in individuals with DS. The authors then compared the results from Ts66Yah mice in the present study mostly with results from Ts65Dn and Dp1Yey mice, where they had reported metabolic differences in previous studies.

Strengths:

Strengths of the methods and results include multiple phenotypes examining potential metabolic differences, analysis of male and female mice separately with appropriate numbers of mice (power), and the ability to compare the results to other DS mouse models, which is very useful in selecting appropriate DS mouse models to test metabolic and other related phenotypes.

The authors have shown that Ts66Yah mice differ from euploid mice in many of their metabolic parameters. Male and female Ts66Yah mice have differing phenotypes in specific metabolic parameters, but the overall result of glucose intolerance, insulin resistance, altered inflammatory cytokines, and dysregulated serum and liver metabolomes is shared between male and female mice.

Weaknesses:

Weaknesses include how the data are presented. There are some questions about methodology that leave some doubt in the analyses. The comparisons of Ts66Yah to Ts65Dn and Dp1Yey mice are less convincing, mostly due to oversimplifying the genetic constructs of DS mouse models.

Reviewer #3 (Public review):

The study by Saqib M. et al. aimed to develop a refined murine model for the study of Down syndrome. The authors developed a Ts66Yah murine line by removing triplicated genes using CRISPR technology from the previously developed Ts65Dn. In this study, the authors provide a deep metabolic profiling of the murine model, studying highly metabolic tissues (liver, skeletal muscle and adipose depots) in both sexes.

The major strength of the study is the large variety of metabolic assessments in males and females, identifying key dimorphic features in the model. However, this could also be considered a point of weakness since this is not a hypothesis-driven study, but rather a descriptive/exploratory study.

Due to the thorough description of the model and the metabolic profile, this study will be very useful for people working on research in Down syndrome. However, there is a lack of information related to cognition, behaviour, and other key features relevant to Down syndrome that would need further study.

Joint Public Review:

Kitanovski and colleagues compare TCRβ repertoires from paired primary tumors and sentinel lymph nodes from 24 treatment-naive melanoma patients. The paired design and comprehensive repertoire analyses are important strengths, and the study provides solid evidence for differences between these compartments, including reduced diversity and greater clonal dominance in primary tumors.

The principal limitation is that antigen specificity is inferred from bulk TCRβ sequencing rather than directly demonstrated. Database annotation and sequence similarity are useful for generating hypotheses, but neither an annotated β-chain nor membership in a sequence-similar community establishes antigen specificity. In particular, the absence of a database match should not be interpreted as evidence of neoantigen recognition. Claims concerning neoantigen specificity should therefore be moderated or supported by additional evidence.

The ClustIRR analysis is potentially useful for identifying relationships among predominantly private TCR repertoires, but its added value and robustness require further evaluation. Benchmarking against conventional repertoire-overlap or alternative TCR-similarity approaches, testing sensitivity to key clustering parameters, and determining whether the results are driven by highly abundant communities would substantially strengthen the analysis. HLA genotype and the non-independence of pairwise repertoire comparisons should also be considered when interpreting inter-individual repertoire similarity.

Overall, the study provides a valuable dataset and convincing evidence for spatial differences in melanoma TCR repertoire architecture, while the biological interpretation of individual TCRs and communities in terms of defined antigen or neoantigen recognition remains less well supported.

Reviewer #1 (Public review):

Damphousse, Calvin and Redish recorded from dorsal hippocampus in rats running past a robotic predator to reach food. They re-analyzed the cohort from Calvin et al. (2025, PLoS Biol) and added a new cohort. They compared three behaviors: retreats right after an attack, mid-track aborts (MTAs) where the rat turns back without being attacked, and mid-track continues (MTCs) where the rat pauses and then carries on. The authors report that retreats and MTAs, though both end at the nest, differed in speed and in decoded position. During retreats, decoding stayed near the attack zone. During MTAs, it was weighted more toward the nest and the far end of the track. Pauses that ended in an abort showed more decoding of the attack zone than pauses that ended in continuing, and a similar difference was seen on the way out, before the pause.

The main contribution is conceptual. Separating fear-like escape from anxiety-like withdrawal within the same task is a real step beyond showing that the hippocampus represents threat. The task places the robot, the attack threshold and the reward at different points on the track, which makes the decoding easier to interpret. Figs. 5-7 use the same analysis layout, so the comparisons are easy to follow. The main patterns are seen in both cohorts, even though the cohorts differ in several procedural details.

The revision tightens the abstract, clarifies how epochs were defined and normalized, and acknowledges that the results are correlational. Overall, the data support the main conclusion that hippocampal representations differ between reactive and anticipatory responses to threat. The paper should interest people working on hippocampus, defensive behavior and approach-avoidance conflict, and the task offers a good way to study how threat and reward are weighed against each other.

Reviewer #2 (Public review):

Summary:

This manuscript extends previous work from Calvin et al. and examines hippocampal representations during approach-avoidance conflict in a robotic predator foraging task. The paradigm itself is very interesting and addresses an important but relatively understudied question in the navigation and foraging literature: how the brain balances risk versus reward during goal-directed behavior. While hippocampal representations of positively valenced goals and future intentions have been extensively studied, much less is known about how these representations evolve during risk-reward tradeoffs involving threat.

The authors use a relatively simple and interpretable decoding approach together with thoughtful behavioral comparisons to ask whether future behavioral outcome can be read out from hippocampal activity before behavior diverges. The most compelling comparison is between mid-track aborts (MTAs) and mid-track continues (MTCs), where the animals initially exhibit very similar pause behavior but ultimately either abort or continue the trajectory. The authors show that decoded location during these pauses differs prior to the overt manifestation of the behavioral decision, suggesting that hippocampal representations may reflect evolving internal evaluation processes during approach-avoidance conflict.

Strengths:

A major strength of the work is the behavioral paradigm itself. This type of risk-reward conflict task is relatively uncommon in the hippocampal navigation literature and provides a rich framework for examining defensive decision-making during naturalistic foraging behavior.

The decoding analyses are also relatively simple and easy to interpret. Rather than relying on highly complex modeling approaches, the authors use straightforward comparisons of decoded spatial representations across behavioral conditions, making the results accessible and conceptually clear.

Another strength is the use of behavioral controls to isolate comparisons between related behaviors. In particular, the comparison between MTAs and MTCs is compelling because the animals exhibit similar pause states before the behavioral outcomes diverge. This provides a useful framework for asking whether hippocampal activity reflects future behavioral outcome before the decision is overtly expressed.

Overall, the study asks an interesting question using a novel paradigm and provides evidence that hippocampal representations during approach-avoidance conflict may reflect future behavioral trajectory.

Weaknesses:

The main weakness is that many of the reported effects are relatively subtle and are not sufficiently controlled for differences in speed, trajectory structure, and other behavioral variables across conditions. While the subtraction plots (green versus purple decoding differences) appear visually striking, the actual effect sizes are fairly small, making it difficult to assess how robust or behaviorally meaningful these differences are.

Relatedly, many of the most interesting questions in this task concern how behavior unfolds dynamically within a trial, yet much of the analysis averages across events and trajectories. As a result, potentially important aspects of the behavior may be obscured.

In particular, the manuscript would benefit from richer characterization of the animals' actual movement trajectories and spatial strategies. Because the analyses rely heavily on linearized position, it is difficult to determine whether animals behave differently in two-dimensional space across conditions. For example, during continued approaches, do animals preferentially hug the wall opposite the robot? Do different behavioral conditions show distinct lateral occupancy or trajectory structure? These types of analyses would make the behavioral interpretation substantially more compelling.

Reviewer #3 (Public review):

Summary:

The study reanalyzes data from a previously published cohort together with an additional cohort to investigate hippocampal activity during approach-avoidance conflict. Unlike many prior studies that isolate reward- or threat-based learning, this task requires animals to evaluate reward and threat concurrently. The central finding is that hippocampal representations differ between hesitant behaviors that lead to approach versus avoidance outcomes, with representations of the attack zone more likely during pauses preceding abort decisions. This is an important extension of prior work on hippocampal activity and deliberation, suggesting that the hippocampal content may help shape the eventual outcome.

Strengths:

All behavioral findings are replicated independently across cohorts, making the behavioral results highly convincing. The design is robust, and the task is especially valuable for studying approach-avoidance conflict. The behavioral paradigm is complex and rare, and neuronal recordings in such a paradigm are of great value.

The major strength of the study is the comparison of neural activity during hesitant behavior leading to different outcomes; namely, pauses followed by the animal aborting the approach (mid-track aborts), and pauses followed by the animal committing to the approach (mid-track continues). Hippocampal activity differed between the two pauses: the attack zone was more likely to be represented during mid-track aborts. The same effect was observed on the journey before the pause: even before the animal hesitates, hippocampal activity before a pause that led to a mid-track abort was more likely to represent the attack zone than hippocampal activity before pauses that led to continued approach. This analysis suggests that hippocampal content before and during deliberative behavior is predictive of the animal's decision.

Weaknesses:

The interpretation of the retreat-related decoding results is less clear. The study compares two sets of retreating behavior: on the one hand, retreat after being attacked, and on the other hand, retreat after hesitation in the absence of an attack (a mid-track abort). Hippocampal activity represents the attack zone more after the animal is attacked. However, these two retreating behaviors originate from different spatial locations: retreats always start past the "attack threshold", while mid-track aborts always start before this threshold. Given that hippocampal decoding is strongly location-dependent, this difference in position makes the neural decoding results difficult to interpret. The increased representation may be due to differences in physical location, rather than distinct processing of immediate threat and an anticipatory return state.

Comments on revised version.

The revised manuscript is indeed more clear, but the study is largely unchanged.

Reviewer #1 (Public review):

Summary:

Charonitakis and co-authors characterize dishabituation in adult flies, where they use olfactory habituation to octanol, then dishabituate the flies with disruptions of electric shock or yeast odors. They systematically investigate the neurotransmitters and neural circuits involved in dishabituation and figure out a lot about how this process works in the brain, as an independent circuit. I like the paper and I like the very structured approach to figuring out the problem.

Strengths:

The introduction nicely sets the stage for the work presented, bringing in knowledge from other organisms and motivating the study.

The results section lays out a logical set of experiments, using a common set of behavioral assays in many flies exposed to thermogenetic or optogenetic manipulation. The paper systematically figures out the necessity and/or sufficiency of specific brain regions and neurotransmitters, culminating in a new understanding of how the important process of dishabituation works.

I like the bar graph representation for the data throughout, with the helpful icons - if a paper figures are going to be 90% bar graphs it helps when they are super clear like this. And I like how the all the parts build up to the conclusion in the last figure, nicely summarizing the thorough characterization of dishabituation.

Weaknesses:

There are no major concerns, and I appreciate the addition of a new figure that details the habituation / induced dishabituation protocol, along with additional discussion of dishabituation, and framing the topic for a more general audience.

Reviewer #2 (Public review):

This is an interesting study in Drosophila comparing potentially differential requirements for subsets of Kenyon Cells (KCs) and Dopaminergic neurons (DANS) in olfactory dishabituation driven by either a novel odor ("homosensory") or footshock ("heterosensory). The authors measure olfactory aversion to Octanol (OCT) in a T-maze, induce olfactory habituation with 4-minunute prior exposure to OCT, and use either brief yeast odor (YO) or footshock (FS) to achieve dishabituation. The major observation that YO-mediate dishabituation is mediated by reward activated DANs (PAM cluster), while FS mediated dishabituation is mediated by punishment-activated PPL-DANs is generally solid and convincing. Also convincing are experiments showing the involvement of KCs in the pathway for YO and FS induced dishabituation, and the argument that KCs drive DAN activation that causes dishabituation, though not experimentally shown, is more than reasonable. The work is significant because, as the authors take pains to point out, circuits and pathways for dishabituation have been very lightly studied and clear identification of dopaminergic neuron subsets in dishabituation achieved to different means, represents unique and interesting progress.

Comments on revised version.

I am appreciative of the authors' effort to address the various concerns and queries of the reviewers. The revised work, which more clearly positions the work, better specifies the conclusions reached and their relevance to the broader field, is a very interesting and useful addition to the field.

Reviewer #1 (Public review):

Summary:

This manuscript describes how defective food grinding due to loss of TGF-beta signalling leads to fat loss via specific neuropeptides and their cognate receptors in C. elegans. They define transcriptional mechanisms from the ADL sensory neuron to neuropeptide relays from neurons to the gut, wherein enzymatic effectors mediate fat metabolism. The genetic evidence presented to support this neuropeptide circuitry is based largely on transcriptional changes detected in bulk mRNA, cell-type-specific rescue, and lipid measurements using Nile Red staining in permeabilized/fixed animals and with lipidomics. The findings are of interest to researchers in neuropeptide regulation of physiology.

Strengths:

(1) The multifaceted analysis of dbl-1 and sma-6 provides good evidence that the primary defect leading to metabolic phenotypes is due to pharyngeal function.

(2) The transcriptional survey of neuropeptide expression under fasting and in TGF-beta pathway mutants will be a useful resource for many labs studying neuropeptides in food sensing.

(3) Outlining the process from dietary restriction to fat metabolism in the gut provides a big-picture view that establishes physiological relevance.

(4) Identifying new functions for specific neuropeptides and their receptors in fat metabolism.

Weaknesses:

Evidence that links NLP-10-NPR-35 signalling to FLP-18-NPR-4 is weak, and primarily rests on overlaps in npr-35 and flp-18 expression, as well as rescue of flp-18 under the npr-35 promoter. It remains possible that FLP-18 acts from those neurons, but is not activated by NPR-35 signalling, since the mechanisms downstream of NPR-35 are unclear in this context. Strengthening this link would require showing a change in FLP-18 activity; for example, showing npr-35-dependent changes in flp-18 transcription in npr-35-expressing neurons.

The expression patterns of the neuropeptides and receptors that guide the sites of cell-specific rescue experiments are from well-fed animals. However, it remains a possibility that starvation alters neuropeptides and their receptors are expressed, which would produce a different circuit.

Different food levels produce different physiological and transcriptional responses. The manuscript largely assumes that starvation is equivalent to food reduction when they are physiologically distinct states.

Reviewer #2 (Public review):

Summary:

This manuscript investigates how animals adapt their metabolism to conditions of reduced nutrient availability. Using C. elegans, the authors identify a neuropeptide signaling network that promotes intestinal lipid mobilization when nutrient acquisition is compromised. The study proposes that nutrient stress activates signaling involving NLP-10, NPR-35, FLP-18, and NPR-4, ultimately leading to increased utilization of stored triglycerides.

A major strength of the work is the combination of multiple experimental approaches, including genetic analyses, neuron-specific rescue experiments, starvation paradigms, and lipid measurements. Together, these experiments support the conclusion that the identified neuropeptide pathway contributes to the regulation of energy stores during nutrient stress. The observation that similar signaling components function in both the mutant and starvation contexts strengthens the physiological relevance of the findings.

Strengths:

The revised manuscript is substantially improved and presents a coherent model linking sensory and interneuronal signaling to peripheral metabolic responses. The data convincingly establish functional roles for the identified neuropeptide components in controlling fat storage and mobilization.

Readers should keep in mind that the exact relationships among pathway components are not completely resolved. While the genetic evidence supports placement of NLP-10, NPR-35, FLP-18, and NPR-4 within a common regulatory framework, the data are somewhat less definitive regarding the precise order of signaling events and whether all interactions are direct. The authors appropriately acknowledge some of this complexity, and a clearer distinction between experimentally demonstrated versus inferred connections would further strengthen the presentation.

Overall, this is a carefully executed and biologically interesting study that advances our understanding of neuroendocrine control of metabolism during nutrient limitation.

Weaknesses:

No weaknesses were noted by this reviewer in the revision. My main comment is that whilst I appreciate the authors' efforts to refine the signaling model in response to earlier comments, the pathway architecture remains somewhat complex, particularly regarding the relationships among NLP-10, NPR-35, FLP-18, and NPR-4. I encourage the authors to make one final pass through the text and model figure to clearly distinguish interactions that are directly supported by experimental evidence from those that remain inferred based on genetic relationships. This would help readers accurately interpret the strength of evidence supporting each step of the proposed pathway.

Reviewer #3 (Public review):

Summary:

In this manuscript, Liu et al. used C. elegans animals lacking functional BMP as a model to dissect the neuropeptide signaling pathways that lead to BMP-associated fat loss. First, they established the BMP mutants as a model of dyspepsia that results in chronic dietary restriction. They then used this model to identify neuropeptide genes that are upregulated and required for BMP-associated fat loss. Next, they sought to identify the receptors for these neuropeptides and to place the genes in a genetic and cellular pathway. They also showed that the same neuropeptides and receptors contribute to fat loss in diet-restricted C. elegans. They conclude that they have identified a neuropeptide signaling cascade in which impaired pharyngeal function activates sensory neurons, which then signal through interneurons to enhance triglyceride hydrolysis in the intestine.

Strengths:

In this manuscript, Liu et al. characterized the pharyngeal muscles and the intestinal bacterial context of animals lacking functional BMP. They showed a correlation between BMP-associated fat loss and pharyngeal impairments that could lead to insufficient food grinding. This finding suggests that C. elegans lacking functional BMP could serve as a model for dyspepsia and chronic dietary restriction. Moreover, the authors analyzed previously published gene expression data to identify neuropeptides that are overexpressed in animals lacking BMP. They then used mutants for these neuropeptides and their receptors to determine which of these genes contribute to BMP-associated fat loss. Finally, they showed that the same neuropeptides and receptors also contribute to fat loss in diet-restricted C. elegans.

Weaknesses:

(1) The authors use double mutants between BMP pathway mutants and neuropeptide or neuropeptide receptor mutants to argue that the reduced fat levels of BMP mutants are suppressed. However, with the exception of unc-31, the fat content of the corresponding single neuropeptide or neuropeptide receptor mutant is not shown. Without these, the double-mutant phenotype cannot be interpreted as suppression, since it could reflect an independent effect on fat storage. To show that the effects are related to BMP signaling, the authors should compare wild-type animals to neuropeptide mutants in a sma-6(+) background. Similarly, the fat content of ADL-ablated animals is not shown in an otherwise wild-type background.

(2) In several places, the authors use cell-specific expression of neuropeptides to conclude that a given neuropeptide acts in a particular cell type (e.g., Figure 4B,C; Figure 5C,D). However, because neuropeptides are secreted and can act at a distance, expression in a non-native cell type may rescue the phenotype. These experiments therefore show sufficiency but do not establish the endogenous site of action. Cell-specific knockout or knockdown, or expression from control promoters in unrelated cell types, would help address this.

(3) The authors report that expressing npr-35 from two promoters with non-overlapping expression each fully restores the low-fat phenotype of sma-6; npr-35 mutants. This is surprising for a receptor and is difficult to interpret.

(4) The authors identify neuropeptide genes co-expressed with npr-35 and follow up on flp-18. The rationale for restricting the search to co-expressed neuropeptides may bias hypothesis generation. Similarly, the authors show that flp-1 and flp-4, in addition to flp-18, contribute to BMP-associated fat loss, but only flp-18 is examined further because of its overlapping co-expression with npr-35, again creating a bias in the investigation of the role of neuropeptide signaling in BMP-associated fat loss.

(5) It is difficult to interpret the experiment in Figure 5E, where the authors conclude that npr-4 is required in flp-18-overexpressing animals. Without a demonstrated fat-loss phenotype from overexpressing flp-18, a requirement for npr-4 in those animals cannot be meaningfully assessed.

Overall, the authors convincingly identify neuropeptides and receptors that are overexpressed and that contribute to fat loss. However, several points make it difficult to fully support the proposed model and the linear pathway from the pharynx to the intestine through ADL sensory neurons and interneurons. These include the absence of phenotypic analysis of the single mutants, the challenges of interpreting cell-specific rescue experiments when studying neuropeptide function, the fact that additional neuropeptides causing BMP-associated fat loss are identified but not studied further, and results that do not necessarily point to a linear pathway (e.g., the triple mutant sma-6; nlp-10; npr-35 has a stronger phenotype than the double mutants with sma-6, the fat loss caused by overexpression of nlp-10 in a sma-6 background is not suppressed by npr-35, and the flp-18 overexpression experiment is difficult to interpret). The reviewer therefore suggests that the linearity of this pathway, and the sites of action of the neuropeptides and receptors, should be presented more cautiously or supported by additional data.

Reviewer #1 (Public review):

Summary:

The manuscript describes results from a series of experiments designed to test different facets of the gating model and potential alternative explanations. In most respects, the results are squarely inconsistent with the gating model. Some of the authors' attempts to test the model force the authors to define the model in greater detail than even the model's proponents typically do. That is a revealing exercise in itself, which is well worth bringing to the field's attention. The manuscript is well written for the most part, and figures illustrate results clearly.

Strengths:

The gating model states that pauses in the activity of a select subset of SNr neurons disinhibit (i.e., "open the gate" to) a specific desired action, whereas a larger population of SNr neurons increase their firing so as to suppress other potentially conflicting actions. Selection of different actions is performed by the generation of pauses and increases in activity in different subsets of SNr neurons. Contrary to that prediction, the authors report that SNr neurons that show a reduction in firing for one action tend to show reductions in activity for other actions as well and, similarly, increase-type neurons tend to show increases for all of the actions examined. The authors tested the generality of these observations across a series of experiments that compared activity between markedly different actions (moving forelimb vs. tongue), similar actions (different directions of forelimb movement) and several forms of self-directed and spontaneous movement. Moreover, the authors examined the activity of anatomically identified subsets of SNr neurons that innervate forelimb and orofacial sub-regions of the superior colliculus. Combined, the series of experiments described here provide overwhelming evidence against any straightforward "simple" form of the gating model of basal ganglia function.

Weaknesses:

The most important limitations of the manuscript actually arise from additional ambiguities of the gating model - ambiguities that may be worth mentioning in the manuscript. For example, how granular is the action selection process supposed to be? Perhaps the SNr circuits examined in the present study are selecting "actions" defined at a level that the authors did not examine. This is not a major limitation, however, given the concerted effort invested in testing different levels of granularity and contexts. This relates to another ambiguity of the model in that basal ganglia output does not affect actions directly, but only indirectly via its influences on downstream multi-synaptic neural circuits.

Another ambiguity of the model is how to interpret the activity of neurons that don't respond. In other words, are comparisons of decrease- and increase-type responses the appropriate comparisons to make to test the model? The present study compares decrease- vs. increase-type responses, but perhaps no-response and increase-type neurons should be lumped together. After all, the simplest version of the action selection hypothesis (cf. Hikosaka) considers the high baseline tonic activity of SNr neurons to be action suppressive by itself, without an additional task-related increase. A comprehensive action selection model should include an explanation of the relative roles of non-response and increase-type responses. The current manuscript would benefit from a discussion of this ambiguity and a rationale for the decisions the authors made to make the model testable.

A third ambiguity of the model is how to interpret graded changes in SNr firing rate. It comes as no surprise that the authors observed slow ramp-like changes in SNr firing rate prior to movement initiation in the absence of an immediate sensory trigger. The authors interpret these ramp-like changes as being equivalent (from the perspective of the gating model) to the abrupt changes in firing rate observed around the time of initiation for movements that are sensory-triggered. But the simple gating model presents basal ganglia output as having only three states: no-change, pause, and increase. Implied in the model is that these changes in firing rate influence not only what action is performed but also when it is performed. It is not clear that slow ramping responses qualify as "open the gate" disinhibitory temporally-abrupt signals. The manuscript could provide more explanation concerning the position the authors take to make ramping responses fit into the gating model.

Reviewer #2 (Public review):

The manuscript by Hou et al. tests the hypothesis that SNr neurons' activity is inhibited during specific movements, allowing the selection of specific movements. They do so by performing electrophysiological recordings in SNr, in head-fixed mice trained to perform self-paced reaching or self-paced licking. They first show that SNr neurons present ramping activity hundreds of milliseconds before reaching, with the vast majority of cells presenting positive increases in activity in these rampings. Then they show that the activity of these cells is related to the velocity (and ramping) of movements, independently of the timing to initiate. They verify these findings in spontaneous reaching. To then test the activity of the same cells in self-paced reaching vs. licking, they found that the vast majority of cells present congruent firing between conditions (not opposite firing as expected). Further, they ran controls asking whether dips in activity are related to specific movements, checked if the specific cells that are correlated with lick cycles respond differently in self-paced reaching vs. licking, asked whether there could be differences in activity in reaching to different targets, to finally "opto-tag" SNr- orofacial vs SNr-arm cells and asked whether these differentiate their spiking in reaching vs. licking, concluding that a very minority of SNr neurons distinguished between movements while the vast majority presented congruent modulations among movements.

Strengths:

The study is a tour de force, testing the general assumption of action selection being reflected in SNr spiking.

Weakness:

Even though the authors set up an amazing set of experiments to test the mentioned hypothesis, their final opto-tagging experiment needs to be screened to better support their conclusion.

Reviewer #3 (Public review):

Summary:

The authors aimed to answer the question of whether the basal ganglia, specifically neurons in the substantia nigra pars reticulata (SNr), directly select actions. This was done by recording the activity of SNr neurons, as well as neurons in the superior colliculus (a major recipient of SNr projections), during cued and self-directed actions, specifically recording the same SNr units during clearly distinct actions.

Strengths:

As the major output nucleus of the rodent basal ganglia, the SNr is a key location to determine if single-cell activity within the basal ganglia is specific to the selection or suppression of specific actions. This choice also provides a natural transition to the activity/roles of downstream motor nuclei that receive basal ganglia output. Multiple definitions of "action" were addressed throughout this manuscript, which is essential for an ambiguous concept like "action selection." They convincingly demonstrate that a "simple action selection hypothesis" in which there is a 1:1 correspondence between SNr single-unit activity and specific actions is incompatible with their data. The manuscript was clearly written and well-organized.

Weaknesses:

While the authors systematically address multiple definitions of an "action," all of the actions in this work are variations of either licking or reaching. The similar SNr activity during different types of reaches has several potential interpretations, which the authors enumerate and present arguments for why their interpretation (SNr does not implement action selection) is the most plausible. However, the study does not quite fully eliminate these possibilities. Finally, while the authors essentially eliminate the possibility of a 1:1 correspondence between SNr units and specific actions, it remains possible that SNr selects actions through a more complex population coding scheme. Indeed, they demonstrate that they can distinguish lick and reach actions by examining population SNr activity.

Appraisal of whether authors achieved their aims/evidence supports conclusions:

The authors achieved their aims of determining whether individual SNr neurons select for specific actions. This includes multiple definitions of "actions," with some actions differentiated by muscle group and body part (licking vs reaching), target, or timing. The evidence provided largely supports the authors' claims, though the low number of subjects in the causal optogenetic experiments makes interpretation of those results more difficult.

Discussion of likely impact of the work in the field:

This work will provide a foundation for future research into more nuanced dissections of basal ganglia circuitry, specifically between the SNr and its downstream target nuclei.

Reviewer #1 (Public review):

Summary:

Movements, including reflexes, can undergo changes during development. This is the case for the angular vestibulo-ocular reflex in many species, including the Xenopus laevis frog. This study aimed to study the underlying mechanisms by which this reflex matures during development of larval Xenopus laevis using electrophysiology and electron microscopy. Their electrophysiological recordings of the motor axons of the abducens motor nerve during evoked angular vestibulo-ocular reflex reveal that a subpopulation of abducens motoneurons, those that are silent rather than spontaneously active, increase their firing rate and modulation during the angular vestibulo-ocular reflex during development. Furthermore, electron microscopy imaging of the developing abducens motor nerve shows increased myelination. Light microscopy reveals increased innervation of the lateral rectus muscle

Strengths:

Recordings during the evoked angular vestibulo-ocular reflex enable assessment of activity of motoneurons during the movement. Recordings done at specific developmental stages reveal convincing changes in modulation of firing of silent motor units that are distinguished by their high-amplitude spikes in nerve recordings. Microscopy, both electron and light, provide strong evidence for increased myelination of the abducens motor nerve and innervation of the lateral rectus muscle.

Weaknesses:

The authors did not provide data showing why the silent motor units became more responsive with development. Is it related to maturation of the sensory inputs to these neurons or changes in intrinsic properties?

Reviewer #2 (Public review):

Summary:

The manuscript by Pain et al. explores the functional maturation of key oculomotor populations responsible for gaze stabilization. The work is motivated by our poor understanding of this process in establishing appropriate reflexes during development. The authors focus on two types of abducens motoneurons implicated in the maturation of angular vestibulo-ocular reflexes (VOR) in amphibian tadpoles, namely spontaneous motor units that provide baseline control and silent motor units recruited only during head motion. This is a particularly good choice given the well-defined and evolutionarily conserved functions of these populations and the ease with which they can be tracked during changes in angular VOR as tadpoles develop.

The authors first use semi-intact larval preparations and both step and sinusoidal horizontal stimuli to characterize developmental improvements in angular VOR, which include faster, more robust compensation of head rotation and higher frequency tuning. Next, to explore the central versus peripheral origins of maturational changes, the authors use galvanic vestibular stimulation of the vestibular nerve (which should be independent of canal size) and BrdU-pulse chase to track neurogenesis, which strongly link improvements in VOR to changes in existing circuits, rather than the addition of new ones. To provide a more comprehensive assessment of what these functional changes may entail, they then perform recordings of changes in motor neuron discharge patterns during development specific to horizontal motion. By combining with sophisticated spike sorting analysis, these data reveals improvements in head velocity encoding by silent subtypes, while head positioning by spontaneous subtypes is relatively consistent during development. Finally, electron microscopy analysis of the abducens motor nerve is used to demonstrate that improvements in encoding are accompanied by broad changes in peripheral myelination and innervation.

Together, the data argue that silent abducens motor units show the largest changes in recruitment in response to sinusoidal head rotations, which is accompanied by increases in myelination and signal fidelity. Ultimately, the work suggests that improvements in oculomotor reflexes during development are accomplished by non-uniform changes within defined oculomotor populations, which is novel and interesting.

Strengths:

Overall, the data are very convincing, the analysis is thorough, the findings will be of interest to those in the VOR field, and also set the stage for future investigations aimed at uncovering the mechanistic underpinnings of this cool observation. I only have a few comments to consider for improvement.

Weaknesses:

Major.

(1) A bit more clarity is needed.

For non-specialists, it would be helpful in the introduction (e.g., Line 52) to spell out what the functional differences are to begin between spontaneous and silent subtypes. The contribution to head positioning versus head velocity is raised later on, but bringing it up earlier would be helpful to understand the functional implications of differences up front.

(2) Figure call outs.

These should be double-checked. I found one that did not seem appropriate on line 332. In addition, the reference to Figure 3A and the spike rate are not illustrated.

(3) Spelling and grammar.

It would be helpful to proofread more carefully for typographical errors, particularly in terms of using singular versus plural and the appropriate articles. Most are minor (those that I found listed below), but in one case the lack of clarity was particularly problematic. On Line 518: 'Altogether, past studies and our present work suggest the existence'. Consider revising this sentence; it reads now that motoneurons mature 'conjointly'; however, this does not seem to capture the main point here, which is that they are not maturing at the same time. Do the authors mean staggered or conjointly with the behaviours they control?

(4) Interpretation.

In the discussion, it would be good to be clearer that myelination will not explain the changes in firing pattern, but could explain the behavioural changes. Ideally, this would be linked to the different types, but I appreciate it is not easy to decipher that from EM.

Reviewer #1 (Public review):

The authors investigate a previously described behavior of sexually immature female Drosophila melanogaster, wherein immature females flick their wings in response to male courtship. They record, analyze, and characterize the auditory signals generated by these wing-flicks and show that these sounds reduce male courtship song and copulation attempts. The study combines several techniques including behavioral observations, acoustic analysis, developmental experiments, sensory manipulations, and neuronal manipulations to investigate the stimulus dependence and neural basis of this behavior. Amidst all the literature on sexual communication, this study uniquely addresses an interesting question on communication during sexual maturation, providing valuable insights on why and how sexual maturation could be signaled.

A major strength of the study is its behavioral and acoustic measurements with manipulations of sensory input and identified neurons. Comparative analysis of immature wing-flick, male-male aggression, and male courtship song was equally interesting. The developmental analysis also provides a clear temporal relationship between female maturation, wing flicking, and mating receptivity. Together, these experiments provide compelling evidence that immature female wing flicking is a reproducible behavior in response to male courtship, and that it significantly reduces male copulation attempts. Identifying a defined population of neurons that coordinate this response behavior further supports this study.

However, it must be pointed out that some of the broader conclusions extend beyond what is directly demonstrated. In particular, while the evidence supports the interpretation that wing flicking functions as a rejection or deterrence signal from sexually immature females, the designation of this behavior as a signal of "asexuality" and the proposed evolutionary relationship between female wing flicking and male agonistic wing flicking are less firmly established. Furthermore, I think the similarity in acoustic structure and the involvement of related neuronal populations is not sufficient to conclude a) that the two behaviors share an evolutionary origin or b) that one behavior evolved through modification of the other. Similarly, proposed explanations concerning energetic costs, predation risk, and adaptive benefits should be presented as hypotheses rather than conclusions unless directly tested. These interesting points can be included in a subsection "Ideas and Speculation", to clearly distinguish directly demonstrated effects and evolutionary or mechanistic interpretations.

In conclusion, the study contributes to our understanding on communication during sexual maturation.

Reviewer #2 (Public review):

Summary:

This study characterizes wing flicking in sexually immature female Drosophila as a transient behavior that produces patterned acoustic signals and suppresses male courtship. The authors show that wing flicking is restricted to a short developmental window, is influenced by multiple components of male courtship and juvenile hormone-dependent maturation, and depends on Dsx+ pC1 neurons. They further show that wing flicking shortens male song bouts and reduces copulation attempts, and propose that it functions as a rejection signal associated with sexual immaturity. Overall, this is an interesting and valuable study that provides new insight into a little-characterized stage of female sexual maturation. The core characterizations are compelling, and some additional clarification and refinement of the mechanistic interpretations would further strengthen the manuscript.

Strengths:

The work expands our understanding of female sexual behavior and highlights sexual maturation as an active transition between distinct behavioral states rather than simply the acquisition of receptivity. The comparison with the post-mating unreceptive state is especially informative. It also brings richness to the understanding by combining behavioral, developmental, and neural analyses and interpretation. The data showing how male behaviors are influenced by female wing flicks are particularly compelling and represent a critical aspect of behavioral characterization that is often overlooked.

Weaknesses:

Event-level analysis of behaviors such as singing, chasing, tapping, licking, or attempted copulation would help clarify the sensory and social context of female wing flicks. The neural conclusion also requires some caution. The split-GAL4 reagent used to assign the phenotype to pC1a labels two pC1 neurons per hemisphere in this study, whereas pC1a is generally described as a single bilateral pair. Given the functional heterogeneity of pC1 subtypes, the current data do not fully exclude a contribution from another pC1 subtype. While the comparison with male agonistic wing signals is informative and interesting, the rejective wing flicks of immature females may be more directly comparable to the previously reported wing flicks and song produced by young and mature males in response to male courtship, which are not mentioned or explored in this study.

Reviewer #3 (Public review):

Summary:

Female receptivity to male courtship is regulated by many factors, including development and internal status. In mature, mated females, male courtship triggers ovipositor extrusion, which prevents intromission. In immature flies, however, rejection occurs through another behavior: immature wing flicking. How this behavior is engaged and regulated is unclear. Here, Bernet & Philipsborn advanced in this matter by defining behavioral and neuronal keys related to this phenotype. The authors show that immature wing flicking depends on pheromones and acoustic cues from the males to be triggered. Additionally, the authors identify the pC1a cluster as key for this behavior.

Strengths:

(1) In contrast to the regulation of ovipositor extrusion, immature wing flicker, while observed, has not been fully characterized. This is a significant step forward in our understanding of the ethology around female receptivity.

(2) The behavioral approaches that the authors used to determine the nature and correlation of immature wing flicker with male courtship are very convincing.

Weaknesses:

While the manuscript has clear strengths, particularly in describing immature wing flicking behavior, several analyses make some conclusions difficult to assess. This is especially troublesome since some of these assertions are the main points the authors highlight as significant findings. In particular:

(1) The authors describe immature female wing flicking as resembling that produced by mature males during agonistic encounters. While I see some similarities, this assessment is subjective. In Figure 1D, the authors present comparative metrics without statistics; thus, it is difficult to assess the validity of the results. Implications, however, can be direct: do females produce acoustic waves to mimic males and deter mating partners (or vice versa)?

(2) From Figure 3F, it is difficult to conclude that the arborization pattern does not differ between mature and immature flies (here, referred to as a 5h vs 10d difference). This may be an artifact of the representative images, but the arborization at 10d appears less dense, particularly around the PI area. If this difference is consistent, this could also reflect neuronal maturation contributing to this phenotype, which is particularly relevant to what the authors discuss in lines 309-339. Differences in synaptic partners might potentially explain the contrasting action of pC1 neurons in immature vs mature females (PMCID: PMC11386958).

(3) The experiments presented do not exclude the possibility that other pC1 neurons, in addition to pC1a neurons, affect the behavior. As written (line 363), the statement indicating that pC1a is the only one affecting immature female flicking is then incorrect.

Reviewer #1 (Public review):

Proctor et al. developed the COB (chloroplast outer-envelope beta-barrel) database, a resource for identifying, classifying and structurally analysing chloroplast outer envelope beta-barrel proteins, built by combining sequence search, machine-learning classification, structural prediction and similarity-based database expansion. The scale and accessibility of the resource is a genuine strength, and it will be useful to researchers working on chloroplast protein import, outer envelope membrane biology and membrane protein evolution. However, several of the manuscript's central claims are not adequately supported by the data presented.

First, the classifier's negative training set consists entirely of non-transmembrane beta-barrel proteins, so its reported accuracy does not demonstrate reliable discrimination from other protein classes (like alpha-helical membrane proteins). The similarity-based expansion that substantially grew the database demonstrates only internal consistency among related sequences, not independent validation, and errors in the seed set will propagate through it. By design, this approach also misses divergent beta-barrels, as the author's own recovery of OEP40 and TOC159 outside the classifier illustrates. This is important because the authors identify the evasion of divergent barrels from these types of tools as a gap in the field. For these reasons, the database should not be described as "comprehensive".

Second, the structural conclusions and the biological implications made substantially outpace the evidence. The reported prevalence of "open" barrel conformations is, for now, a feature of the AlphaFold predictions, not a demonstrated biological phenomenon, and the authors themselves note these may be artefactual. Similarly, the hybrid-barrel analysis is built almost entirely around TOC159-family proteins, which are heavily overrepresented in the Arabidopsis input set. The manuscript does not test whether this apparent "universal" interaction is anything more than a compositional artefact of that enrichment, and most of the novel interactions have only moderate confidence scores. As framed, both the title and multiple statements in the text assert a general topology of "open and hybridized" for this group of proteins that only experimental data can establish.

Third, the evolutionary comparisons between Streptophyta and Chlorophyta, and between chloroplast and bacterial beta-barrels, are presented as robust differences but are not supported by any formal statistical treatment of proteome size, taxonomic sampling or phylogenetic non-independence.

Overall, the database and classifier are a valuable contribution for further exploration and experimental validation of chloroplast outer envelope membrane beta-barrel proteins, but the manuscript's broader structural and evolutionary claims need to be substantially tempered, and the title changed, to match what the data actually show.

Reviewer #2 (Public review):

Summary:

The manuscript presents COB, a computational database for the systematic identification and classification of chloroplast-associated beta-barrel proteins across Viridiplantae. The authors combine evolutionary contact-map features with machine-learning classification, sequence-based searches and structural predictions to generate a large collection of candidate beta-barrel proteins. In addition to established OEP families, they identify two previously uncharacterized groups, BUL16 and BUL22, analyze the evolutionary distribution and structural diversity of beta-barrel proteins, and propose a number of novel hybrid beta-barrel assemblies involving members of the TOC159 family.

Strengths:

A major strength of the study is the establishment of a dedicated and comprehensive resource for chloroplast beta-barrel proteins. These proteins have historically been difficult to identify and predict reliably, and the broad taxonomic coverage of COB should therefore be highly useful for the chloroplast and plant membrane biology communities.

The manuscript also goes beyond database construction by exploring structural and evolutionary properties of the identified proteins. In particular, the analyses of beta-strand number, open versus closed barrel conformations, taxonomic distribution, and the identification of BUL16 and BUL22 generate several interesting hypotheses that may stimulate future experimental work.

The authors are generally appropriately cautious in acknowledging limitations of the structural predictions, particularly with respect to open and hybrid barrel conformations.

Weaknesses:

The principal limitation of the study is that it is entirely computational. This is not necessarily problematic for a database/resource paper, but several of the most interesting biological conclusions remain speculative and are not independently validated.

This is particularly relevant for the proposed hybrid beta-barrel assemblies. While the known TOC75-TOC159 interaction provides a biologically established example, the predicted interactions of TOC159-family proteins with OEP24, OEP80 and TGD4 are more difficult to reconcile with the known functions of these proteins and currently lack experimental support.

A second conceptual issue concerns the newly identified BUL16 and BUL22 groups. The authors explicitly state that these proteins cannot confidently be assigned to the chloroplast outer envelope, yet they are subsequently analyzed together with established OEP families. This makes it difficult to distinguish between evidence for a beta-barrel fold and evidence for chloroplast outer-envelope localization.

Reviewer #1 (Public review):

Maeda et al. propose an algorithm for meta reinforcement learning (Meta RL) that they term "hybrid deep reinforcement learning" (HDRL), which combines two distinct learning mechanisms: one that occurs through continual synaptic plasticity (via backpropagation-through-time), and the other that occurs through recurrent dynamics (i.e., in-context learning). The standard approach to Meta-RL, which I will refer to here as "RL2", is one that involves learning only through recurrent dynamics; this model has found recent application to understanding meta reinforcement learning in the brain. One downside of RL2 as a model of meta-learning in the brain is that, in learning only through recurrent dynamics, RL2 essentially throws the baby out with the bathwater by assuming synaptic plasticity does not have a role in moment-by-moment learning. This study therefore makes a valuable contribution in showing how both synaptic plasticity and recurrent dynamics can be combined in a single model. Importantly, this model does not require an explicit mechanism for mediating between these two learning mechanisms; rather, the authors show that the task (and meta-task) itself determines which mechanisms the model comes to rely on. They then show that their HDRL model can better explain differences in behavioral strategies across conditions of a perceptual decision-making task, and use in silico perturbations to establish that these differences in strategies are due to the distinct contributions of their model's two learning mechanisms. Finally, the authors provide evidence that the neural population activity of mice recording during each task has a consistent structure as the activity of artificial neurons in their HDRL model, but not to the standard RL2 model, which always relies on learning through recurrent activity. This study offers an important extension of the standard method for Meta RL, to one that is both more biologically plausible (i.e., because it seems unreasonable for plasticity to only occur periodically as it does in RL2) and more computationally interesting (i.e., as it explains how these two distinct learning strategies may be deployed automatically based on task demands).

The paper's conclusions seem fairly well supported by their analyses; however, there were a few details that need to be clarified.

(1) There are many architectural differences between the H-DRL vs RL2 model, which precludes a true comparison between the two models solely on the addition of Weight RL. The implementation of H-DRL is relatively stripped down compared to RL2, in terms of using Elman rather than LSTM, standard RL vs A2C, only positive activations allowed, and Q-learning instead of policy gradient. Because of these implementation details (none of which seem essential to the 'real' difference between these models), it is difficult to know exactly how these models differ. E.g., if the differences in behavior are in part due to the differences in architecture between HDRL and RL2 rather than to the differences in learning mechanisms.

(2) The conclusion that mouse behavior is more consistent with HDRL than with RL2 seems likely but is not sufficiently supported by the analyses. One thing I wondered was whether all model instances were truly converged; RL2 will of course take much longer than HDRL to learn since it is only updated per session. The authors' claim that mouse behavior is more like HDRL than RL2 seems to be based on the BIC comparison in Figure 2f, but insufficient detail is given to understand what this analysis is. Figure 2e, by comparison, seems to present all three models as similar (though this comparison is difficult to make given the different y-axes).

(3) The interpretation of the two-step task feels incomplete, and potentially in conflict with the interpretation of HDRL in the rest of the paper. When trained on the two-step task, HDRL is presented as reproducing a mixture of model-based and model-free behavior. What does the RL2 model do in this context? Later, on the perceptual decision-making task, HDRL seems to be presented as being model-based-like (and using recurrent activity) in one context and model-free-like (and using plasticity) in the other. Is it the case that in the two-step task, HDRL is simply doing both? As is, it's unclear to me how I should interpret the analyses related to the two-step task.

Reviewer #2 (Public review):

Strengths

The manuscript claims that standard methods for deep recurrent RL ("meta-RL"; Wang et al., 2017) provide a poor match to behavior data from humans and animals in many situations, including the two-step task and a mouse behavior dataset previously published by these authors. This negative story is solid and convincing.

The manuscript refines this model by introducing an online variant of deep recurrent RL. This is a good idea for many reasons. One compelling one, which the authors could choose to emphasize more, is that neurobiology does not respect a strict separation between "online" periods during which weights do not change and "offline" periods in which they do. Both weights and activities are changing all the time. Another is that the behavior literature provides abundant reason to believe in short-timescale innate reward-seeking mechanisms, which offline batch-based RL does not provide.

The manuscript shows that this online deep recurrent RL can provide a good match to their mouse behavior data. This involves a 2AFC perceptual decision-making task with strong temporal dependencies between the stimuli. In one condition, the stimulus on a particular trial is very likely to be identical to the one on the previous trial and only rarely switches; in another, the stimulus is very likely to differ, and only rarely repeats. Mice find the first condition much easier to learn, perhaps because of an innate win-stay/lose-switch bias which is helpful in the first but harmful in the second.

It also provides evidence that online deep recurrent RL can provide a good match to neural data from the OFC in this task. In the "repeat" condition, their model learns the task largely in weights, which is possible because the weight learning rule is hard-coded to be win-stay/lose-switch. In the "alternate" condition, the model must learn it in activities. Broadly consistent with this, mice in the "alternate" condition, but not the "repeat" condition, show strong task-based representations in the OFC.

Taken together, these results are a meaningful advance in applying the "NeuroAI" research program to the case of trial-by-trial reward learning.

Weaknesses

(1) The key innovation of this manuscript is replacing batch-based recurrent deep RL (which they call "meta RL"), in which weights are updated once per batch via backpropagation through time, with a version of online recurrent deep RL in which weights are updated after every trial.

It is difficult for readers to understand how the version of online deep RL being used works, how it compares to other possibilities, and why it was chosen. The manuscript describes it as truncated backprop through time (TBPTT, equation 9). It is not clear from the equation what the truncation window is. If it is only a single trial, then it is unclear how a network trained in this way manages to do the representation learning necessary to do anything model-based at all.

Various other techniques for real-time recurrent learning exist, many under the heading of "biologically plausible" deep learning. These include techniques involving synaptic eligibility traces (e.g., Murray et al., 2019; Bellec et al., 2020) and possibly others.

The manuscript calls this "Hybrid Deep RL". This name is confusing to me and may be confusing to readers. If this method is a "hybrid" between deep RL and some other thing, it's not clear to me what that other thing might be. It seems rather to be a variant of deep RL. I'd suggest a name that's clearer about that and more specific, perhaps "Online" or "Real-time" Deep RL, or similar. The manuscript also describes it in places as a "two-module" system, which is confusing to readers because it has one module: a single RNN with a single learning rule.

The manuscript would benefit greatly from a more careful discussion of these issues and a positioning of the model within the literature. What is lost when giving up BPTT? (long-timescale representation learning, which is often crucial for RL). What is gained? (learning rules that are local in time and space, and therefore more plausible as mechanistic models of biological learning). Where can we expect this method to fail? (tasks with temporal dependencies that are meaningfully longer than the TBPTT window). What alternatives exist? (eligibility trace methods, etc)

(2) The manuscript claims that their model "reproduced mixed strategies of humans in a two-step task". This portion of the paper is brief and unconvincing.

The key diagnostic plot, the trial-history regression in figure 1e, shows behavior that looks almost entirely model-free. There is little, if any, separation between the lines for "common" and "uncommon" within either of "rewarded" or "unrewarded". The bar plot does show an apparent difference, but these one-trial-back analyses are subject to many confounds.

Human studies involving this task typically involve each participant performing only a single session lasting ~100s of trials. When participants are run for multiple sessions, behavioral strategies change, becoming substantially more "model-based" (Economides et al., 2015). If I understand correctly, the model here is trained for 100 sessions of 100 trials, and the data we are shown comes only from the last 25. A fairer comparison would be to show only the first, or first few, of these sessions.

I imagine that H-DRL is unlikely to show much of a model-based pattern within the first session, as it will require averaging over many trials to learn to sculpt activity dynamics appropriately. By the same token, I am skeptical that H-DRL is plausible as a mechanistic account of the human two-step task - instead, I think it is much more likely that explicit working memory mechanisms are involved (e.g., Otto et al., 2013). H-DRL may be a better model of the two-step task as implemented in animals (Miller et al, 2017; Miranda et al., 2020; Akam et al., 2021).

The manuscript claims that human subjects on the two-step task typically use a mixture of both model-based and model-free strategies, and that neurobiology supports a division between the two. Both of these points are controversial, at least: da Silva and Hare (2020) present a compelling argument that humans are primarily model-free, and neural recordings dating back to the very first two-step task paper (Daw et al., 2011) show overlapping signals.

I do think it might be possible to address this with more careful writing and further simulations. But I do not think this is necessary. The rhetorical role of the two-step task in this manuscript seems really just to be illustrating that H-DRL can exhibit different online learning strategies in activities and in weights. This point is made far more convincingly by the analysis of the mouse perceptual decision-making task. I'd recommend simply dropping the two-step task, and its associated claims, from the manuscript.

Reviewer #3 (Public review):

Summary:

This paper proposes and tests a modified reinforcement learning model that can express different decision-making strategies which spontaneously emerge when performing a task and match real animal behavior. A recurrent neural network (RNN) is trained in the style of prior work on meta reinforcement learning (e.g. Wang et al. 2016, 2018), where an initial algorithm is used to train the network and a second algorithm emerges later from the recurrent dynamics of the network. These are termed weight-RL and recurrent-RL, respectively. By making several adjustments to the meta-RL training procedure, the authors find that their version (called hybrid deep reinforcement learning, or H-DRL) shows a better match to real animal behavior and orbitofrontal cortical activity dynamics. Overall, this paper is an improvement on the prior meta-RL model, but it is unclear what neuroscientists should take away from this.

Strengths:

(1) The authors introduce a model, H-DRL, whose main strength is that it can better approximate animal behavior and neural activity on specific tasks compared to its predecessor, meta-RL. This could potentially be important for interpreting the algorithms underlying animal learning.

(2) The changes that have been made from meta-RL are not overly complex.

(3) The model is assessed using two types of decision-making tasks that are similar to many of the tasks used in rodent behavioral and systems neuroscience.

(4) In addition to behavioral data, RNN unit activity is compared directly with single unit orbitofrontal cortical activity. This makes the model appealing for neuroscience applications.

Weaknesses:

(1) In the introduction, the authors mention that other extensions of meta-RL have been tried. How does H-DRL compare with these other modifications?

(2) The authors try to convey the impact of their model in the introduction by stating that, unlike meta-RL, "it embodies a fundamentally different theoretical claim in the context of neuroscience". It is not clear what the fundamentally different theoretical claim is. While meta-RL implements weight updates over longer timescales, H-DRL implements weight updates after each trial. Is this the fundamentally different theoretical claim? If so, that does not seem like a novel concept for behavioral and systems neuroscience.

(3) It is not clear what inspired the specific changes to the meta-RL training procedure. Were they decisions based on incremental trial and error to intentionally reproduce the empirical behavioral and neural results, or were they based on prior logic?

(4) Regarding the inconsistency between OFC neurons and H-DRL in previous outcome encoding (Figure 5c), it would be helpful to speculate on why this happens because it seems to be the only place where H-DRL fails.

(5) Figure 5e seems tangential to the overall message of the paper. The paper is about H-DRL as a superior approach to modelling real behavior and neural activity compared to meta-RL. But this panel, which is the last panel in the paper, is not directly relevant because there is no comparison with the model.

Reviewer #1 (Public review):

Summary:

"A comparative single-cell transcriptomic atlas for diverse populations of vertebrate hair cells" by Basu et al. aims to define pan-hair cell enriched (HCE) genes across hair cell subtypes from four vertebrate species through integration of published scRNA-seq, SMART-seq, and single-nucleus RNA-seq datasets. Pan-HCE genes are weighted using AUC values, where AUC is used to identify genes enriched in hair cells relative to supporting cells. The resulting dataset is made publicly available, allowing researchers to compare gene expression across species. The dataset integration and creation of a public resource are clear strengths for the inner ear community.

Strengths:

This manuscript presents a valuable cross-species resource and a substantial integration effort.

Weaknesses:

Weaknesses addressed by the authors in the text include the lack of human cochlear scRNA-seq comparisons to mouse/chicken cochlear hair cells. While the rationale that the datasets were not available at the time of investigation is acceptable, the use of human cochlear datasets would be the ideal experimental comparison to mouse and chicken cochlear hair cells.

Reviewer #2 (Public review):

In this manuscript, the authors investigate transcriptional diversity and similarities between mechanosensory hair cells across different species to provide an atlas of hair-cell-enriched genes.

Their analysis encompasses publicly available and a few newly generated single-cell RNA-seq data from human, mouse, chick and zebrafish, in total comprising 43 populations of hair cells from different organs (cochlea, vestibular sense organs, lateral line) and different stages. They first compare the transcriptome of each hair cell population with that of supporting cells from the same data to find hair-cell-enriched genes. To do this, they use a score (AUC) that reflects how well a gene separates both cell populations; this approach identifies about 5000 genes enriched in at least one of 29 mechanosensory populations. Grouping these genes by their enrichment pattern gives 22 clusters of co-regulated genes, many species- or organ-specific and a few shared by most hair cells.

Compared with previously published lists of hair cell genes, they define 884 "pan-HCE" genes enriched in most hair cells across species. This set is enriched for stereocilia and deafness genes. In situ hybridization is presented to support enrichment for 7 of 10 tested genes.

Adding 14 immature populations splits the list into early-and-sustained, transient and late genes. The authors then use these to assess the maturity of hair cells from cochlear organoids, regenerating chicken basilar papilla, reprogrammed fibroblasts, and developing zebrafish, mouse and human tissue. All data are released through a Shiny app and the gEAR portal.

Overall, this is a useful resource for the community, presented in an exploitable format. It is timely; the inclusion of many datasets is very valuable, and the comparison of HCs vs SCs from each organ is a good approach. I have some concerns regarding the clarity in describing the analysis, whether some findings could reflect differences between datasets, and how cell 'maturity' is defined.

Major comments:

(1) The data were generated by different labs, different methods and sequencing platforms. Hierarchical clustering of the data splits cells mainly by species. It is not clear to me that the authors have ruled out that this indeed reflects biology or could be due to technical differences. This needs to be discussed. Is it possible to show that the results hold for data generated with the same platform?

(2) The calculation of enrichment scores needs clarification. In the methods, it says that AUC values below 0.5 were set to 0.5 and then supporting cell-enriched genes were set to 1 − AUC. How do supporting cell-enriched genes like ID2 end up with values like 0.11 (Figure 2C)? Please describe the calculation step by step; how were skipped genes because of low expression handled?

(3) It is not clear how duplicated genes were handled when assigning orthologues; the approach allows one-to-many. How were they combined into a single score? The results say that each human gene was linked separately to each ortholog?

(4) The reference gene list for known hair cell genes seems to overlap with genes reported to be enriched; this seems to be a circular argument. Among the hand-curated list are stereocilia-associated genes, which are then reported to be enriched. Is it possible to test enrichment using gene sets that were not used to choose the threshold?

(5) The authors need to define how they classify cells as mature, maturing, and immature - there are no definitions at all. Provide these and appropriate references. Mouse P7 cells are classified as mature, which they are not. Later data depend on this classification, so it needs to be justified properly.

(6) The authors assess maturity of hair cells in organoids; it seems that this depends on the % of genes in each set (mature, maturing, immature) that pass the cutoff. Has this been compared to a random or control set?

(7) Verification of hair cell enrichment by HCR/RNAscope: This verification is not very convincing for most genes tested. 30% of the genes tested fail, while genes described to be HC-specific are also expressed in surrounding cells. E.g. Figure 5C: both Apba1 and Pcsk5 seem to be expressed in hair cells as well as the surrounding cells; Figure 5D: both genes are not expressed in all hair cells. Figure 5 supplement 2B: the images of the organ of Corti are too low resolution and magnification to identify hair cells reliably. It is not possible to see if these genes are indeed expressed in hair cells. If the authors claim HC enrichment, they need to provide quantification of expression levels e.g. HCs vs SCs and show negative controls.

(8) A new single-cell dataset from human embryos has just become available; van der Valk et al., 1026, Nat. Neuroscience. This dataset fills an important gap in their current analysis, and it would be very good to integrate it.

(9) The figure legends are often a bit short and do not explain the figures well; please review and expand.

Reviewer #3 (Public review):

Summary:

The authors generated a cross-species single-cell transcriptomic atlas of inner ear and lateral line organs across four species (human, mouse, chicken, zebrafish) and 11 developmental stages. By comparing mechanosensitive hair cells to surrounding supporting cells, they identified hair cell-enriched (HCE) genes, conserved transcriptional programs, and stratified developmental markers. They used these core and temporally stratified gene sets to evaluate hair cell identity and maturation state in models of avian regeneration, mouse cochlear organoids, and transcription factor-reprogrammed fibroblasts. A publicly available web-based resource (HRP Hair Cell Gene Explorer and gEAR portal) was created to facilitate field-wide data exploration.

Strengths:

(1) Comprehensive Multi-Species Benchmark:

Cross-species analysis spanning 43 hair cell populations and 11 developmental stages establishes a rigorous reference dataset for benchmarking hair cell identity across development, cell biology, and regenerative models.

(2) Biological Relevance:

The pan-HCE gene set captures 25% of all known human deafness genes, confirming its strong predictive value for hair cell-specific function and disease mechanisms.

(3) Methodological Rigor & Robust Thresholding:

The use of area under the receiver operating characteristic curve (AUC) provides a platform-independent metric. Testing four calculation strategies (combining 29 mechanosensitive vs. 23 mature populations with unweighted [ES1] vs. species-balanced [ES2] weighting) and taking their intersection ensures robust gene selection without species bias.

(4) Temporal Stratification & Application:

Categorizing pan-HCE genes into early, sustained (ES-HCE), late (L-HCE), and transiently enriched (T-HCE) sets enables tracking of maturation state across different animal and cellular models, proving effective in evaluating regenerating avian hair cells, mouse cochlear organoids (Lgr5^+ lineage), and Gfi1/Atoh1/Pou4f3/Six1-reprogrammed fibroblasts.

(5) Experimental Validation:

Key pan-HCE genes were independently validated across species using spatial transcriptomics, RNAscope, and Hybridization Chain Reaction (HCR).

(6) Community Utility:

Broad accessibility via an interactive ShinyApp and gEAR portal integration facilitates customized field-wide analysis.

Weaknesses:

Weaknesses were considered minor.

(1) Interspecies Homology Resolution:

Machine learning-based clustering (MetaNeighbor) showed that factors such as age, species, and sensory modality often override subtype identity, failing to resolve fine-grained cross-species homologies for specific subtypes (e.g., distinguishing mouse inner vs. outer hair cells relative to chicken tall vs. short hair cells).

(2) Species Imbalance & Mapping Artifacts:

Despite weighting schemes (ES2), teleost genome duplications (1-to-many ortholog mapping in zebrafish) complicate cross-species signal normalization. This remains an overarching issue in many fields when performing cross-species analysis.

(3) Different mechanisms of regeneration:

Converted fibroblasts induced only a percentage of pan-HCE genes (28.7%) and very few transient genes (9.3%). This could reflect incomplete programming towards a fully mature native hair cell state, as the authors suggest. It is also possible that direct programming via transcription factor expression or Lgr5+ cell conversion could employ mechanisms distinct from those used in normal development or in regenerative species.

Reviewer #1 (Public review):

Summary:

This study develops a large-language-model implementation of the complementary learning system (CLS) framework for continual episodic learning. The authors employ a pretrained open-weights model to serve as a neocortex-like semantic knowledge store and introduce a novel hippocampus-like memory system in which each episode is assigned its own sparse rank-2 masked LoRA adapter, eliminating interference at encoding. A separate semantic embedding model provides a retrieval key for each episode, and winner-take-all routing selects the adapter whose stored embedding best matches an incoming cue. The reported storage efficiency of approximately 2-3 trainable parameters per token suggests that this one-adapter-per-episode scheme could scale to large numbers of memories, although its total storage requirements also include the embeddings used for content-addressable retrieval.

A key finding is that successful encoding of an episode does not guarantee flexible access to its contents. Although a correctly selected adapter can reconstruct its stored episode, the model performs poorly when asked to answer questions about that episode directly. The iRAG procedure addresses this by inserting the reconstruction into the base model's context. Consolidation through replay of the original passages enables reconstruction without adapters, but direct question answering remains substantially less successful, even with richer training objectives. Overall, the paper provides a technically impressive implementation of episodic memory in LLMs. However, several aspects of its learning and consolidation procedures significantly limit its correspondence to CLS, as discussed below.

Strengths:

A major strength of this work is the concrete parametric implementation of a CLS-like division of labor within a modern transformer network. The episodic memory system incorporates key properties of the biological hippocampus, including pattern-separated encoding and content-addressable retrieval. The interaction between the selected adapter and the LLM provides an appealing functional analogue of hippocampal-cortical reinstatement, in which activation of a sparse episodic trace biases distributed cortical processing so as to reconstruct the mnemonic content of an episode.

A second strength is that the model cleanly separates interference during episodic storage from interference during retrieval. By assigning memories to non-overlapping parameter traces, individual episodes remain faithfully stored even at large memory loads. Performance declines for more strongly overlapping memories can therefore be attributed largely to competition during retrieval. This distinction shows that successful pattern separation does not by itself guarantee reliable access to stored memories.

A final strength is the careful optimization and evaluation of the episodic memory system. The authors introduce a masked LoRA that substantially reduces parameter cost relative to standard LoRA approaches, systematically identify the most parameter-efficient placement of these adapters, and show that storage cost scales approximately linearly with episode length, with similar results across two base models. They also test the framework on two datasets with quite different memory structures: largely independent, topically diverse Wikipedia passages and more strongly correlated autobiographical episodes with recurring people, places, and themes.

Weaknesses:

(1) The model implements the two learning regimes motivated by CLS: pattern-separated episodic learning and slower learning in shared weights. However, it does not implement the central proposed interaction between them, whereby hippocampal traces reinstate experiences that serve as training events for neocortical consolidation during offline periods (e.g., Káli & Dayan 2004, Nat. Neuro.; Singh et al. 2022, PNAS; Sun et al. 2023, Nat. Neuro). Instead, consolidation here relies on replaying the retained ground-truth passages and therefore does not depend on the episodic memory system at all. The authors could either demonstrate consolidation driven by replay from the episodic adapters or more explicitly discuss this simplification and its implications for the CLS comparison.

(2) The authors characterize the episodic system as implementing "fast learning" or "rapid storage". However, encoding involves repeated optimization on the same passage, with up to 1,000 epochs per attempt and potentially multiple retraining attempts. This raises questions about its correspondence to the rapid, single-shot learning that characterizes the hippocampal component of CLS and is supported by evidence in the trisynaptic pathway (e.g., Nakazawa et al. 2003, Neuron; Nakashiba et al. 2008, Science). The authors should (a) report the distribution of optimization steps, retraining attempts, and mask sizes required for successful encoding on the two datasets and (b) clarify this distinction when relating the model to biological hippocampal learning. They could also examine whether larger adapters reduce the optimization required, clarifying the trade-off between storage efficiency and encoding speed.

(3) Relatedly, it is unclear whether there is meaningful variability in the number of optimization steps required across episodes and whether characteristics of the passages explain this variability. For example, do passages containing less predictable content require more training and/or larger adapters? One way to examine this would be to consider the frozen base model's pre-learning perplexity for each passage and test whether this predicts optimization steps to criterion or final parameter count, controlling for passage length. Such an analysis could help clarify what determines the difficulty and resource cost of forming a new episodic memory.

(4) The consolidation results raise concerns about the model's capacity for stable lifelong learning, as incorporating new memories comes at a cost to existing knowledge. The presented consolidation for 1000 memories produces substantial degradation of the base model's existing capabilities, particularly under the conditions that most improve direct access to consolidated knowledge. In a lifelong setting, subsequent consolidation would presumably continue to cause interference not only for pre-existing semantic knowledge but also for memories incorporated during earlier consolidation cycles. The alternation between NREM and REM sleep has been proposed as one biologically motivated solution to this problem, with NREM supporting integration of recent hippocampal memories into cortex and REM helping to reactivate and protect pre-existing cortical knowledge (Norman et al. 2005, Neural Networks; Singh et al. 2022, PNAS). The authors already note that a lifelong system would need to interleave encoding and consolidation and determine which memories to replay; it would also be valuable to discuss how previously consolidated knowledge and broader base-model capabilities could be protected across repeated cycles of consolidation.

(5) It appears that the consolidation conditions reported in Fig. 9 might differ substantially in training exposure. As described in Supplementary Note 5, "passage-only" uses one example per episode, "paraphrase augmentation" uses ten, and the "knowledge instruction" and "masked reconstruction" conditions add many further training examples. Because training is specified in epochs, could the authors clarify whether these conditions are matched for optimizer steps or training tokens? If not, it would be helpful to determine how much of the improvement in direct QA and accompanying loss of base-model capabilities reflects the specific objectives versus greater training exposure. A step- or token-matched control would help disentangle these possibilities.

(6) The reported storage efficiency of approximately 2-3 parameters per token appears to refer only to the masked LoRA adapters, despite each episode also requiring a stored 1,024-dimensional embedding for routing. The authors appropriately note that this index is part of the memory system's storage cost and is in fact substantially larger than the adapter store. It would therefore be useful to incorporate the routing embeddings into the reported per-token storage estimate, or otherwise report the total storage cost per episode/token alongside the adapter-only figure.

Reviewer #2 (Public review):

Summary:

The paper asks whether the classic complementary learning system (CLS) can be instantiated in an LLM.

The authors first show that continual fine-tuning and three regularization remedies (L2, EWC, on-policy distillation) fail to encode new episodes without memory interference. They then propose to use gated LoRA instead: one dedicated low-rank adapter per episode for encoding, with winner-take-all selection among adapters based on embedding similarity for retrieval, and finally replays stored episodes in batches to fine-tune the base weights for consolidation.

The evaluation covers storage capacity, end-to-end recall at up to 1,000 episodes, question answering (via a two-stage "internal RAG" and via direct parametric access), and consolidation, on two datasets: Wikipedia paragraphs and an autobiographical dataset, and two LLMs: Llama-8B and Qwen-8B.

Strengths:

(1) The attempt to instantiate a CLS in an LLM is very interesting, timely, and potentially useful for AI.

(2) The use of LoRA as the episodic encoding mechanism is efficient: they showed that LLMs can store 1,000 episodes with a small amount of parameters (< 0.01% relative to the base model).

(3) The evaluation is careful. The negative results on regularization are informative. An LLM judge is validated against other metrics, with very informative failure examples (e.g., the "Man on the Run" case, where teacher-forced accuracy is 0.91 but every fact is fabricated).

Weaknesses:

I think the main thing is that the two systems, at this point, do not richly interact:

(1) The episodic component is not meaningfully involved in consolidation. In this model, consolidation is batch re-training with replay, not transfer. The consolidated model is trained from the raw passages - the authors state that it "relearns the same episodes into the shared base weights by batch replay of the original passages, rather than transferring parameters from the episodic adapters."

(2) The influence of semantic memory on episodic encoding is not deeply investigated. For example, empirically, schema-consistent information is acquired rapidly (Tse et al., 2007). It would be informative to understand if this model shows a similar effect. Overall, showing that this method works for a pre-trained LLM is a strength, but it would be very exciting to know how that impacts encoding.

(3) This might be out of scope, but I still wonder, eventually, how this kind of system would decide when to use episodic vs semantic memory. This is relevant to the author's points that the low-rank adaptor can be freed after consolidation, which is true, but to do that autonomously, the system needs to know whether a memory is fully consolidated. Overall, I think this would be an interesting future direction.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors implement a complementary learning system within a pretrained LLM. Each new episode is stored in its own small adapter, a routing step selects the right adapter at recall, and a slower consolidation stage transfers the stored episodes into the base weights. The main contributions are a demonstration that this fast store retains a thousand episodes without the catastrophic forgetting seen under vanilla sequential fine-tuning, a retrieval scheme in which a stored episode is regenerated into the context window so the model can answer questions about it, and a consolidation procedure that transfers the episodes into shared weights while leaving the model's general capabilities largely intact. The authors also show that neither the adapters nor the consolidated weights support direct question answering on their own, which separates storing a memory from being able to access it.

Strengths:

The paper is technically sound and, to my knowledge, one of the first efforts to make the connection between complementary learning systems and the LLM plus adapter configuration explicit and then build it end to end. It is very clearly written and honest about negative results. The authors have built a system in which the fast and slow systems and the transfer between them are all explicit and measurable, on top of semantically rich pretrained representations, which is something memory research has underutilized in the LLM era.

Weaknesses:

My main comment is that the paper should be positioned more explicitly. The framing should be clearer if the paper is more of an ML contribution or a neuroscience contribution (it is both, but there should be an emphasis). Routed adapters/loras are already popular, and many studies have addressed continual learning in the ML literature (gated LoRA and related methods), and the authors should be upfront about this in the introduction rather than only in the discussion to help readers understand the framing earlier. Previous studies on adapters do not diminish the paper, because in my opinion its value is the neuroscience framing, and that is where I think the paper has not explored sufficiently. The classic phenomena that CLS was proposed to explain are cited but not directly tested, which I think is a missed opportunity, since most of them can be tested with materials already in the paper. Below are my main suggestions:

The original 95 CLS paper proposes a slow cortical system because integrated knowledge supports inferences/generalizations that the episodic memory system cannot, and people need time and often sleep to form such generalizations. The paper's adapter loads one episode at a time, but the consolidated model might be able to extract the core rules or schemas of the episodic memories. If consolidation can support inference/generalizations that retrieval cannot, that is the strongest possible argument for the two-system design.

Additionally, in the original CLS proposal, replay is interleaved so that new memories are transferred into cortex alongside old knowledge. Here, consolidation trains on the new episodes only, and the general capability of the LLM degrades accordingly. The effect of rehearsal or pseudo-rehearsal, and the interference with existing knowledge close to the consolidated content, are not measured.

In human memory, repeated visits to the same scenario are a core aspect of episodic memory: you visit your office many times a day with slight changes in context, and episodic memory has to keep track of these visits while knowing which differences should be memorized. In the paper's design, a repeated episode would create a second adapter with a near-identical embedding, and a slightly paraphrased episode would become a new memory. Human memory studies show memory strengthens or updates the existing trace when revisitation happens, as in reconsolidation. The current design also rules out any overlap between adapters, giving a hippocampal module that does pattern separation but not the "pattern completion" half.

Two well-established findings in the consolidation literature are worth examining: consolidated memories keep the gist and lose specific details (Winocur and Moscovitch), and new information consolidates much faster when it fits something already known (Tse et al. 2007's schema study in rodents). The paper's judge scores each character episode on separate details, and the supplement contains an example in which a question about one episode makes the consolidated model reconstruct a different episode from years earlier with the same theme, which resembles a classic "false memory". The current setup in the paper could examine these false memory structures in a much more nuanced way.

I believe experiments tied to the memory, psychology, and cognitive science literature along these lines would greatly strengthen the study, and could make this paper a "template" for how the field can study effects that have been observed in humans but have lacked a concrete and powerful enough modern modeling system.

Reviewer #1 (Public review):

Summary:

This study describes targeting Poltheta Helicase with a new drug RP-3467. The properties of the drug and a clinical response in a gBRCA1 Ovarian Cancer Patient with a mutation in TP53BP1 are described.

Strengths:

This is an important and interesting set of observations.

Weaknesses:

(1) The response of the patient with a TP53BP1 mutant tumor is of interest but requires more clarification before solid conclusions can be drawn. The presence of the TP53BP1 mutation after but not before olaparib treatment is suggestive of selection, but in the absence of demonstration of a biallelic loss, this is conjecture. It could be a co-selected passenger mutation. The authors should report the TP53BP1 variant allele fraction, LOH, and orthogonal validation should be performed by targeted deep sequencing or ddPCR. This would establish the prevalence before and after selection and enhance the interpretation of the functional effects of the mutation observed.

(2) To further strengthen the proposed mechanism, the authors should consider knocking in the mutation (or a similar LOF mutation) into an appropriate cell line and establish resistance to olaparib and sensitivity to the combination, as well as each drug individually. This will address the latter question of whether it is the poltheta inhibitor or the combo that is responsible for the clinical response.

(3) In the animal experiments, "synergy" is overstating the situation. Without an interaction analysis, this is an enhanced combination activity. Also, several of the animal experiments have a small N. It would be better to show the growth of tumors in individual mice.

(4) The authors propose that Poltheta inhibition could prevent the emergence of reversion mutations. This is not addressed in the paper, and if they want to suggest this, they should provide some evidence.

Reviewer #2 (Public review):

Summary:

The authors set out to assess the in vitro potency of a new POLQ helicase inhibitor, assess its in vitro BRCA1/2 synthetic lethal properties, and confirm how it performs in vivo in PDX and cell line xenografts. The authors also show an anecdotal patient response with a PARP inhibitor + POLQ inhibitor combination.

Strengths:

This is a novel POLQi of the helicase activity. The anecdotal clinical response in a heavily pre-treated patient is interesting.

Weaknesses:

Most of these issues could be addressed by changes to the main text, i.e. by the authors describing limitations/caveats to interpretation, etc.

(1) The clinical anecdote is interesting but underanalyzed. It's difficult, though, to know one way or the other whether the PARPi+ POLQi treatment effect (only partial response) has anything to do with the 53bp1 mutation. There is a causative sign that the 53bp1 mutation could be the cause of PARPi resistance (the increase in 53bp1 mutation allele frequency in PARPi-naïve vs PARPi-resistant tumors), but no similar sign to show that the 53bp1 mutant tumor clone is targeted by POLQi treatment. For example, does 53bp1 mutation allele frequency fall in ascites or ctDNA during the course of treatment, or is the 53bp1 mutation lost in a tumor taken at the end of treatment?

(2) Although the relevant work is cited, the authors should be more explicit in the introduction and state that there is already evidence from 2 papers that Combined loss of BRCA1 and 53bp1 causes enhanced sensitivity to small-molecule POLQ helicase or polymerase inhibitors.

(3) Figure 4. Not exacerbating existing PARPi myelosuppression is great, but the authors have to acknowledge that the myelosuppression is still present when POLQ+ PARPI is used. Single-agent POLQi doesn't give myelotoxicity, but it also does not give strong anti-tumor efficacy. i.e. it would be fair for the authors to acknowledge this.

(4) The increase in reticulocytes when mice are treated with POLQi could be a sign that there is an effect on myeloid lineages, but the animals are responding by increased production of this young myeloid lineage. Alternatively, this reticulocyte increase could drive or be a sign of pathology (polycythaemia vera, etc.). It would be good to see a commentary on this in the Discussion.

(5) Re Figure 4. A therapeutic advance would be to see that, compared to other PARPi combinations such as platinum + PARPi or temozolomide + PARPi, a POLQ + PARPi combination doesn't give the same myelosuppression that these combinations do (by analogy, combined platinum + PARPi gives curative responses in mice but also wipes out the adaptive immune system). The authors need to add data showing the platinum+ PARPi positive control, or at the very least add a limitations of the study section to the manuscript to explain this fairly.

(6) Figure 2 might give the impression that the new POLQi gives a similar, if not greater, BRCA2 synthetic lethal effect than a PARPi (e.g. 2d-f). It would help readers to see the full saruparib and olaparib dose-response curves, not (as per 2 A, B, C) just the single point estimates.

(7) Why was DOTC42510 not profiled for PARPi?

(8) Figure 2f shows error bars on an n=2 dataset - one cannot estimate variance from only an n=2 dataset range.

(9) Is there a statistical analysis on Figure 2G?

(10) Figure 3. Olaparib 25 mg/kg QD is well below the MTD in mice. This makes the olaparib response in HCT116 BRCA2 k/o xenografts look modest when compared to the combinations.

Reviewer #3 (Public review):

Summary:

The authors seek to validate the effectiveness of an inhibitor of the Pol theta helicase-like domain ATPase activity in its ability to ablate MMEJ activity in cell line models. They then confirm the inhibitor is synthetic lethal in a variety of BRCA1/2 deficient cancer models (and one PALB2 model). Finally, they report apparent effectiveness in treatment of a single patient that failed therapy with PARPi due to 53BP1 deficiency, a hypothesized special use-case for targeting pol theta.

Strengths:

It is a clear bench-to-bedside translation of mechanistic-oriented basic science to a clinical therapy. The experiments are largely rigorously performed. The approaches employed are impressively diverse, from in vitro to cell-line model, to preclinical model, to clinical application. The interpretations made are well supported by the available data.

Weaknesses:

The addition of a few missing details would improve the usefulness to the reader. Additionally, the rationale behind some approaches could be clarified.

Reviewer #1 (Public review):

This manuscript by Zhou, Williams, Millner, and colleagues reports findings from a large-scale, multicenter study of Klebsiella pneumoniae (Kp) isolates. These isolates were sequenced using short-read sequencing and extensively characterized using a variety of field-appropriate tools, such as Kleborate, AMRFinderPlus, and PlasmidFinder. Then, the authors measured the growth of these Kp isolates across many (>200) environmental conditions they pre-determined as important for making human-centric fitness conclusions. These genotype and phenotype data were integrated, and the authors deployed an association-based strategy to identify genetic determinants of the observed fitness effects. Finally, the authors built models to predict fitness outcomes using the associated genetic determinants. From these data, the authors conclude that fitness is "an emergent property" of a given Kp isolate's genetic background and environmental constraints, with a nod toward the future development of biomarkers to predict fitness outcomes using genotype alone.

The primary strength of this study is the integration of two large datasets, one genotypic and the other phenotypic. This approach is novel in its scale (genotypic, geographic, etc.), which adds to the strength of the overall conclusions. These conclusions, for the most part, are consistent with the data. As such, this study can act as an example to other studies tying genotype to phenotype, especially in the context of careful genetic analysis and rational phenotypic design. A secondary strength is that the findings are supported by other, smaller-scale studies such that there is a growing consensus as to the general fitness determinants employed by Kp that are potentially driving its increased global importance as a bacterial pathogen.

The primary weakness of this study is that some confounding factors are not taken into account or are deemphasized. The primary confounding factor is that the associations between genetic signatures of fitness are not fully considered. This is especially important given that an important driver of Kp fitness is its accessory genome, which is heavily influenced by mobile genetic elements and plasmids. The use of short-read sequencing leads to an inherent inability to tie fitness effects to such genetic elements, which may lead to masking or overinterpretation of particular genotype-phenotype relationships. For example, nearly all of the gene-level determinants of condition-specific fitness and pleiotropic genes are known plasmid-associated genes; however, it is unclear if these specific genes are driving fitness or if they are merely signatures of other associated genetic elements. This broadly weakens the conclusions and translatability, and in the absence of long-read sequencing to resolve genetic architecture, it is an unresolvable weakness. A second is the over-interpretation of the variables used to assess bacterial growth, which is the primary fitness metric. Colony opacity can be influenced by Kp capsule production, which is a highly variable trait that is associated with environmental conditions. Finally, it is unclear whether clonal isolates are excluded to avoid overinterpretation of fitness outcomes.

Reviewer #2 (Public review):

Summary:

The authors mapped context-dependent fitness of more than 1,400 clinical K. pneumoniae isolates across 214 antibiotic, chemical, environmental, metal, envelope and nutrient conditions, integrating fitness measurements with whole-genome sequencing to build a genotype-phenotype-fitness framework and a machine learning pipeline for predicting bacterial fitness across environments.

Strengths:

The scale of the dataset is remarkable both in terms of isolates and conditions tested. The central findings that fitness is strongly condition-dependent and that resistance-associated features help under selection are well supported. Resource accessibility (ColonyExplorer portal, code, trained models) is excellent, allowing future integration of different genetic backgrounds and environmental contexts.

Weaknesses:

(1) Normalisation uses a plate-level random effect and plate-median scaling, whereas within-plate (position) effects and their correction are not described, although typically required in such high-density arrays.

(2) Hypermucoid or heavily encapsulated isolates may form smaller but denser colonies. The area-opacity correlation (Figure S3A) does not exclude this, so the virulence-associated costs in selected conditions (Figures 2F-G, S5A) could be partly measurement-driven.

(3) Plasmid effects are attributed causally from observational data. Fitness effects are inferred from short-read replicon calls (Figure 3) and may be confounded by lineage and co-occurring resistance determinants.

(4) The study does not apply linear mixed model GWAS on the continuous fitness phenotype, relying instead on median dichotomization with a non-mixed model that is vulnerable to population structure (especially in a highly clonal species) and could exacerbate apparent separation, inflating association counts.

(5) GWAS-derived features appear to have been selected using all isolates rather than within each training fold. Test-set labels thus influenced which features entered the models, biasing correlation, coverage and interval estimates upward. Although mitigating overfitting, nested hyperparameter tuning and lineage-stratified tests do not remove this bias. Because of this, I do not find the claim that genomic variants predict fitness beyond population structure adequately supported yet.

With the points above addressed, I believe this work would be highly valuable for prioritising genes and variants for functional validation and for generally studying fitness trade-offs in hospital pathogens.

Reviewer #2 (Public review):

Summary:

This is an interesting study that explores how human RPE could be used as source for new retinal neurons. This is a welcome addition to the field of retinal regeneration which is currently focused almost exclusively on the regenerative capacity of Muller glia cells. The line of inquiry is firmly rooted in findings from amphibian and embryonic chick model systems and advances a fetal human retina RPE based screening system as a rich resource for insights into human RPE biology, including as a potential stem cell source.

The authors investigate the potential of fetal human RPE cells to be reprogrammed into retinal neurons using overexpression of pro-neural factors. While this is a critical knowledge gap in the field of retinal regeneration with significant promise for developing regenerative therapies, several methodological concerns impact the interpretation of results. Firstly, while the authors sought to evaluate factors that enhance RPE reprogramming when co-expressed with ASCL1, nearly all co-expression constructs tested failed to achieve appreciable expression of ASCL1, leaving a central hypothesis of this study largely untested (Major concern 1). Second, although the authors were able to detect a cluster of photoreceptor-like cells in their screen, they were unable to identify which reprogramming construct generated this cluster (Major concern 2). Finally, an essential control that definitively demonstrates the value of combinatorial transcription factor reprogramming is missing (Major concern 3). In summary, the authors establish a valuable new paradigm for culturing and reprogramming fetal human RPE, and even more importantly, demonstrate successful reprogramming to neural fates. However, the discussion and interpretation of results needs to be modified significantly to make it clear that (i) the outcome of many co-expression paradigms remains effectively unknown/untested due to failed over-expression of ASCL1, and that (ii) the reprogramming construct giving rise to photoreceptor-like cells could not be conclusively identified from their initial screen.

Strengths:

(1) Powerful new screening system advanced for exploring the regenerative potential of human feta RPE cells.

(2) Co-expression vector system for testing additive effects of proneural transcription factors

Weaknesses:

Major concerns:

(1) The authors executed a screen for combinations of factors that can enhance ASCL1-mediated reprogramming of RPE into retinal neurons. However, the expression level of ASCL1 was remarkably low in virtually all co-expression paradigms (see Fig. 3C). Notably, the reprogramming combination with the highest potency (ASCL1 + NEUROD1) was also the one exhibiting the highest level of ASCL1 expression. The "failed" reprogramming of most of the co-expression constructs (ASCL1+LMO1, ASCL1+EZH2, and ASCL1+RAX2) is potentially a false negative resulting from low transgenic expression of ASCL1.

(2) The authors interpretation is that the overexpression of NeuroD1 and Ascl1 generated a new cluster that expressed markers of photoreceptors such as RXRG and RCVRN (see Figure 3D). However, there does not actually appear to be any overlap between the ASCL1+NEUROD1 cluster (orange dots, left panel) and the cells expressing markers of photoreceptors (yellow/green/purple?/black? dots, right panel; yellow being ASCL1-EZH2, green being ASCL1, purple being ASCL1-and black being control - though color coding here is admittedly somewhat confusing). Thus, the photoreceptor-like cluster of interest actually seems to correspond to gray cells that were unmapped / exposed to an unknown programming cocktail. So, it remains completely unknown which reprogramming construct generated this cluster.

(3) To conclusively establish the additive role of NEUROD1 in reprogramming, it would be prudent to compare ASCL1 + FA directly to ASCL1+NeuroD1+FA. This control was not included but is needed for a more complete interpretation of results.

Comments on revised version.

We thank the authors for their thoughtful responses to our concerns and for modifying a figure to improve clarity. However, we were disappointed to see that the responses to our major concerns were not incorporated into the revised manuscript, as we believe they provide context that is essential to proper interpretation of the data. Minimal revisions of the current manuscript would address our first two major concerns. This could be achieved by adding two additional sentences, for example incorporating the sentences below which are excerpted from the authors response to our concerns:

The following excerpt from the authors response would address major concern 1 if incorporated into the relevant section in the main text of the manuscript:

"Insufficient ASCL1 expression in the combinations might have led to lack of reprogramming, representing a false-negative result."

And this excerpt would address major concern 2:

"The farthest subcluster with highest RCVRN/RXRG expression was not assigned ASCL1-NEUROD1 identity."

Reviewer #2 (Public review):

Summary:

The authors investigate chemical strategies to disrupt the interaction between the SARS-CoV-2 accessory protein Orf9b and the host mitochondrial receptor Tom70, an interaction implicated in suppression of type-I interferon responses. They employ two discovery approaches: a crystallographic fragment screen against the Orf9b homodimer and a high-throughput fluorescence polarization screen for compounds that compete with Orf9b binding to Tom70. The study identifies fragment-binding hotspots on Orf9b, develops lipidated analogs that stabilize the Orf9b homodimer, and discovers Tom70-binding compounds with low micromolar activity that inhibit Orf9b binding in vitro.

Strengths:

Impressive amount of work using a variety of complementary approaches and methods to validate binding (FP, SPR, and computational modelling and SAR). The combination of crystallographic fragment screening on Orfb9 and HTS on Tom70 provides two independent routes for perturbing the Orf9b-Tom70 interaction. The structural work seems to be of very high-quality. The fragment campaign is extensive, yielding a substantial number of fragment-bound structures and identifying biologically meaningful binding hotspots on Orf9b.

Finally, the screen results in reporting useful chemical starting points. Although potency remains modest, the study provides tractable scaffolds and a clear framework for future optimization.

Comment on revised version.

I don't have any further comments. The authors have addressed my suggestion by adding a statement for future work in the discussion.

Reviewer #1 (Public review):

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

Summary:

The main goal of this manuscript is to develop a mathematical model of the regulation of cortical dynamics by Cdk1 activity to explain why, in some embryos (e.g., Xenopus), surface contraction waves are believed to move in the same direction as Cdk1, while in other embryos (e.g., starfish) they are believed to move in the opposite direction.

Strengths:

(1) The paper addresses a very important question.

(2) The mathematical model is sensible and suggests that the different relationship between Cdk1 and surface contraction waves might arise from the different behavior of the mitotic entry wave and the mitotic exit wave.

(3) The authors propose a mechanism by which the wave observed at mitotic exit might not passively follow the trigger wave observed at mitotic entry'

(4) The proposed mechanism is a potential explanation of the observed differences.

(5) The proposed mechanism is centered on different dynamics between the nucleus and the cytoplasm, highlighting the potential importance of the nucleus (and nuclear size) in organizing cortical dynamics.

Weaknesses:

(1) The proposed mechanism works if the activity in the nucleus is much higher than the high activity (high state of the bistable system) of the cytoplasm. So, as the wave propagates across the cytoplasm, the activity around the nucleus remains higher, which potentially causes a delay in the onset of Cyclin B-Cdk1 degradation in the region around the nucleus compared to the surrounding cytoplasm. This effect happens over a typical length scale, and if such a length scale is comparable to embryo size, this becomes the predominant mechanism. However, such a mechanism should exist near the nucleus independently of embryo size. So, it seems that for embryos where the wave back and wave front should travel together, nuclear activity must be adjusted not to be much higher than cytoplasmic activity. Is there any experimental evidence that the overall activity of Cdk1 is higher in the nucleus than in the cytoplasm?

(2) The authors give the existence of trigger waves as a fact. While the predominant view is that such waves exist in the first cycle of the Xenopus embryos (however, this is from measurement of the cortical contractions, so a bit circular for this paper), it is unclear if waves exist in the starfish embryo, so the potential explanation that the starfish embryo simply has different Cdk1 dynamics cannot be ruled out.

Reviewer #2 (Public review):

Summary:

Large oocytes show prominent waves of cortical contractions. Previous works combining experiments and computational modeling have shown that the waves are driven by gradients of CDK1 kinase activity that trigger excitable Rho activity patterns on the cortex. This present work combines two previously published mathematical models for CDK1 activation and Rho activation, respectively. They show that the models combined can explain diverse shapes of cortical contractions observed in different species and at various stages of development. This shows how the same molecular machinery can generate diverse patterns dependent on the size of the system and the size and position of the cell nucleus.

Strengths:

(1) Carefully done modeling work providing a simple and elegant explanation for a complex cellular behavior.

(2) Very nicely illustrated, simulations can be directly compared to previous experimental observations.

(3) Explains observations made in different model systems, providing a unifying model.

Overall, I find this work important, as it shows that combining models of the CDK1 gradient and Rho activation modules can explain the surface contraction waves observed in oocytes. Strikingly, it elegantly explains the differences seen between different experimental systems. While previously these were considered a 'controversy', modeling shows that the differences are simply a consequence of the difference in the size of the oocytes. In addition, the model makes several intriguing predictions that can be tested in future experiments.

Reviewer #3 (Public review):

Summary:

Using realistic mathematical models, Cebrián-Lacasa et al. address the relationship between waves of activation of Cyclin B-Cdk1 that propagate through the cytoplasm of large (~1 mm) oocytes and fertilized eggs and surface contraction waves (SCWs) driven by Rho GTPase activity in the cell cortex. They present numerical simulations of the underlying reaction-diffusion equations that account in broad strokes for both the expected behavior of 'fronts' of Cdk1 activation (that propagate at constant velocity from the nucleus-the source of Cdk1 activity-to the cell cortex) and the unusual behavior of 'backs' of Cdk1 inactivation (that may propagate either away from or towards the nucleus, or exhibit simultaneous inactivation throughout the cytoplasm). They also model Rho GTPase activity in the cortex as an excitable system that propagates SCWs (target patterns, spiral waves, and more complicated patterns). When Cdk1 is activated in the cortex, it phosphorylates and inhibits the RhoGEF, Ect1, which suppresses SCWs by reducing Rho GTPase activity. As the wave-back of Cdk1 inactivation moves across the cortex, Rho GTPase activity recovers abruptly, and SCWs reappear as 'phase waves' whose speed and directionality are determined by the underlying cytoplasmic Cdk1 signal.

Strengths:

As a theoretical examination of an interesting and puzzling aspect of early embryonic development, this study shares the same strengths and weaknesses as all mathematical and computational approaches to molecular cell biology. The mathematical models are precise formulations of the underlying assumptions of the authors (which are quite reasonable in this reviewer's opinion), and the analysis and computational results are dependable consequences of the molecular mechanisms the authors have in mind. The model is expertly analyzed, and the results are both reliable and intriguing. The results are discussed in light of experimental evidence. Because the authors' methods and results suggest novel-and sometimes counterintuitive-avenues for experimental research, this paper is likely to have a significant impact on the field of Rho GTPase signaling in oocytes and early embryos, and perhaps in other cells as well.

Weaknesses:

Like all mathematical models, the underlying assumptions can be critiqued as neglecting this -or-that 'crucial' effect (e.g., mechanical coupling via cortical tension or cytoplasmic flow, as the authors acknowledge), and the highly technical methods of analysis and simulation can be unfamiliar and off-putting to experimental cell biologists. For those who take the time to understand this paper, it may change the way they think about the coupling of cell cycle control (Cdk1 activation and inactivation) and cell surface contraction waves.

Reviewer #1 (Public review):

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

The study provides a robust bioinformatic characterization of the evolution of pT181. My main criticism of the work is the lack of experimental validation for the hypotheses proposed by the authors.

Previous comments on the study:

(1) One potential reason for the decline in pT181 copy number over time may be a high cost associated with the multicopy state. In this sense, it would be interesting if the authors could use (or construct) isogenic strains differing only in the state of the plasmid (multicopy/integrated). With this system, the authors could measure the fitness of the strains in the presence and absence of tetracycline, and they could be able to understand the benefit associated with the plasmid transition. The authors discuss these ideas, but it would be nice to test them.

(2) It would be interesting to know the transfer frequencies of the multicopy mobilizable pT181 plasmid, compared to the transfer frequency of the plasmid integrated into the SSCmec element (which can be co-transferred, integrated in conjugative plasmids, or by transduction).

(3) One important limitation of the study that should be mentioned is that inferring pT181 PCN from whole-genome data can be problematic. For example, some DNA extraction methods may underestimate the copy number of small plasmids because the small, circular plasmids are preferentially depleted during the process (see, for example, https://www.nature.com/articles/srep28063).

Comment from the Reviewing Editor:

In the revised version the authors have addressed comment 3 from Reviewer #1 through additional analysis, finding that the decline in copy number over time remains significant after controlling for instrument used, type of DNA extraction technique, and sequencing centre. The suggestion in Comment 1 from Reviewer #1 has not been acted upon as the authors state that this additional experimental work "goes beyond the scope of the paper" and is being explored for a future study. Similarly, the suggestion in Comment 2 from Reviewer #1 has not been acted upon as the authors state that "inferring these rates from natural sequence data is not feasible in this case given the low heterogeneity of the plasmid sequence. A laboratory-based experimental study could not address the real transfers we observe over the course of decades, as in vitro S. aureus transfer rates are often not good proxies for in vivo".

Reviewer #2 (Public review):

Summary:

The authors performed bioinformatic analyses to trace the genomic history of the clinically relevant pT181 plasmid. Specifically, they:

(1) tracked the presence of pT181 across different S. aureus strain backgrounds through time. It was first found in one, later multiple strains, though this may reflect changes in sampling over time.

(2) estimated the mutation rate of the chromosome and plasmid.

(3) estimated the plasmid copy number of pT181, and found that it decreased over time. The latter was supported by two sets of statistical analyses, first showing that the number of single-copy isolates increased over time, and second, that the multicopy isolates demonstrated a lower PCN over time.

(4) reported the different integration sites at which pT181 integrated into the genome.

As a caveat, they mentioned that identical plasmid sequences have variable plasmid copy numbers across different genomes in their dataset.

Strengths:

This is a very solid, well-considered bioinformatic study on publicly available data. I greatly appreciate the thoughtful approach the authors have taken to their subject matter, neither over- nor underselling their results. It is a strength that the authors focussed on a single plasmid in a single bacterial species, as it allowed them to take into account unique knowledge about the biology of this system and really dive deep into the evolution of this specific plasmid. It makes for a compelling case study. At the same time, I think the introduction and discussion can be strengthened to demonstrate what lessons might be drawn from this case study for other plasmids.

Weaknesses:

The finding that the pT181 copy number declined over time is the most interesting claim of the paper to me, and not something that I have seen done before. While the authors have looked at some confounders in this analysis, I think this could be strengthened further in a revision.

For the flow of the storyline, I also think the estimation of mutation rates (starting L181) and integration into the chromosome (starting L255) could be moved to the supplement or a later position in the main text.

Clearly, the use of publicly available data prevents the authors from controlling the growth and sequencing conditions of the isolates. It is striking that they observe a clear signal in spite of this, but I would have loved to see more discussion of the metadata that came with the publicly available sequences and even more use of that metadata to control for confounding.

Comment from the Reviewing Editor:

Reviewer #2's comment about the weakness due to "confounders in this analysis" has been addressed in the revision through additional analysis using metadata to control for confounding.

Reviewer #1 (Public review):

Summary:

This manuscript reports exceptionally preserved, millimeter-scale segmented bilaterian microfossils from the Fortunian Kuanchuanpu Formation in South China. The authors propose that these non-biomineralized animals were preserved through microbial pseudomorphs rather than direct phosphatization, filling a critical taphonomic and evolutionary gap in early animal segmentation.

Strengths:

This paper filled the critical taphonomic and evolutionary gap in early animal segmentation.

Weaknesses:

Extremely similar work has already been published by Xian et al., 2026, PNAS, with specimens from absolutely the same formation, and they also proposed that these animals are segmented. So in this context, this work's significance is eclipsed.

Reviewer #2 (Public review):

Summary:

The manuscript by Yang and authors presents an interesting set of new microfossils from the early Cambrian of South China. These are interpreted as segmented bilaterians, and so may be the oldest such examples in the fossil record, with implications for our understanding of early animal evolution. Further, the preservation of these fossils as microbial pseudomorphs is a unique pathway to fossilize such important organisms.

Strengths:

The fossils are impressive and arguments for their affinities as segmented bilaterians are convincing. Discussion of somewhat novel preservation is detailed and helpful to understand this taphonomic mode.

Weaknesses:

It needs to be clear what the authors' definition of a "segment" is and how that matches (or not) other interpretations. Arguments by Hannibal and Patel (2013, EvoDevo) convincingly define all segmented organisms based on polarity within repeated units of bilaterally symmetrical animals, but others may disagree. The manuscript would benefit from discussion of this in the introduction, laying out a hypothesis for what we might expect to find as evidence of the earliest segmented animals and then describing these new fossils within that context.

As it currently reads, the definition of a segment is not presented until the discussion and is somewhat ambiguous, seemingly referring to any organism with bilateral symmetry and repeated units. However, modern (e.g. tardigrades) non-segmented animals exhibit these features but are not considered truly segmented. This is critical as various Precambrian fossils (Arborea (Dunn et al., 2019, Palaeontology); Cloudina (Schiffbauer et al., 2020, Nature Communications) Dickinsonia (Gold et al., 2015, Evolution and Development); Parvancorina (Darroch et al., 2017, Biology Letters); Spriggina (Evans et al., 2026, Scientific Reports); Yilingia (Chen et al., 2019, Nature) etc.), have been argued to possess bilateral symmetry ad have bodies composed of repeated units. These older examples should be acknowledged and discussed in detail. Stating that "the identity of these [Precambrian] organisms is controversial" (line 51) is not justification for the main claims of this paper, and it should be clear what distinguishes the new fossils from previously proposed evidence of earlier segmented bilaterians. I believe there is a case to be made in support of the authors' claims, but this context is vital to make that argument convincingly. It is also worth discussing evidence of segmented bilaterians in the early Cambrian trace fossil record, such as Treptichnus pedum (Kesidis et al., 2019, Proceedings B) and Rusophycus (Daley et al., 2018 PNAS), that may predate or be roughly coeval with these fossil remains.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors employed Cryo-ET to visualize the structural organization of the tripartite attachment complex or the TAC, which is a unique structure connecting the flagellar basal body to the mitochondria in Trypanosoma brucei, a protozoan parasite causing human sleeping sickness in sub-Saharan Africa. The TAC is essential for segregation of the mitochondrial DNA, or kDNA, via the connection to the segregating basal bodies during the cell cycle. Although molecular characterization of the TAC and its components has yielded insights into the function of the TAC, the structural organization of the TAC, especially the largest protein p197 within the TAC, remains poorly defined. Using intact zoid cells, the authors provided, for the first time, a high-resolution image of the cytoplasmic part of the TAC in its native context, resolved the basal body, the mitochondrial membranes, the exclusion zone filament connecting the BB to the outer mitochondrial membrane, and quantified the geometry of the region surrounding the TAC. These observations led to a proposed role of p197 as a length-variable connector to reconcile stable kDNA anchoring with the mechanical demands of BB movement. Overall, the work advances our understanding of the structure and function of p197 within the TAC in trypanosomes.

Strengths:

A major strength of the manuscript is the application of Cryo-ET in imaging the structure of the cytoplasmic part of the TAC, with a focus on the largest protein p197, which provided novel insights into its molecular function. Trypanosome cells are too thick for Cryo-ET, and the authors adopted intact zoid cells for Cryo-ET, allowing the resolution of the TAC structure in native conditions.

Weaknesses:

While this is the first attempt to image the TAC by Cryo-ET, which provided new insights into the potential role of p197 in mediating the connection between the BB and the mitochondria, there is a lack of mechanistic understanding of how p197's length is regulated and how the varying length of p197 coordinates with basal body movement. Also, it is unclear why the cell needs to regulate the length of p197, given that the main function of p197 is to connect the BB and the mitochondria for the segregation of kDNA via the BB.

The proposed length variation for p197 is an interesting idea, but it is unclear what exactly the authors meant by saying that p197 is length variable. Do they mean that the central repeats in p197 can fold/extend to variable lengths, or do they mean that p197 with variable lengths contains different numbers of the central repeats? This should be clarified.

The current work can be strengthened by performing some molecular and/or biochemical experiments to assess the function of p197 to complement the Cryo-ET-based structural characterization reported in the manuscript. Expression of some smaller-sized p197 proteins with varying numbers of central repeats in p197 RNAi cells will provide evidence for the proposed function of p197 length variation.

Reviewer #2 (Public review):

While the aim of this study is interesting and the methods are advanced, the novel insight and conclusions rather feel limited at present, particularly considering the literature already available on the functional role of p197. There are some notable methodological issues in quantifying the EZF that need to be addressed.

The claims accompanying p197 and its role in EZF tethering to the BB and pBB to the OMM, as well as its effect on mitochondrial membranes, as displayed in Figure 3, should be augmented via additional comparative measurements with tdzoid cell tomograms, given that tomograms were generated for tdzoid cell lines, but are only utilised in a limited manner for Figures S4 and S5. This represents an underutilised opportunity to use this technique to contrast BB, pBB and mitochondrial membrane separation in a comprehensive manner beyond what previous studies could achieve.

The authors need to justify specifically why different methodologies were employed for minimum filament length measurements between NP-40 wild-type cells and non-detergent-treated Zoid cells, preventing these datasets from being broadly comparable.

The reliance on mutant cell lines (primarily zoids) for conclusions needs to be acknowledged to a greater extent, such as when comparing results with previous works (Line 334) and naturally within the limitations mentioned in the discussion.

The integration of the discussion with relevant literature concerning Cryo-EM and trypanosomatid cytoskeletal studies feels somewhat limited; for example, no mention or comparison with the formative Höög et al. 2012 study for Cryo-EM within trypanosome flagella and basal bodies.

Reviewer #3 (Public review):

Summary:

The authors use the advanced technique of cryo-electron tomography to understand the connections between the basal body and the mitochondrion in trypanosomes. They show that this connection is mediated by flexible and extendable filaments.

Strengths:

The authors use an array of advanced cryo-electron microscopy techniques that provide solid evidence that p197 forms the filaments between the basal body and the mitochondrion and demonstrate that these filaments are flexible and able to span a wide range of distances. This provides an important insight into how the trypanosome basal body, which has a dynamic localisation through the cell cycle, can manage its critical connection to the mitochondrion. The filaments provide the necessary flexibility to maintain the basal body - mitochondrion connections as the basal body is repositioned.

Weaknesses:

The authors were unable to directly connect the filaments to the structural models for their protein component, p197 and how the filament flexibility relates to the changes in the repeat region of the protein. In part, this is due to the flexibility of these filaments and demonstrates the limitation of cryo-EM to resolve such structures, but this in itself is an important outcome, as it will inform the community's use of this technology.

Reviewer #1 (Public review):

Summary:

Ribeiro et al. investigate the molecular mechanisms affecting the efficiency of synthetic cell-penetrating peptide (CPP) uptake by cultured mammalian cells. CPPs are frequently coupled to cargoes that can change cell function and are highly useful to investigators studying cell biology. Moreover, CPPs are potentially amenable to delivery of cargoes that can affect cells in clinically useful ways, with several under investigation as potential anti-cancer treatments. A challenge to the therapeutic use of CPPs is their inefficient delivery to their intended molecular targets, with poor cellular uptake being a major barrier.

The present study presents new insights into the cellular and plasma membrane properties that can affect CPP uptake. In the studied cell line, the authors find that cell cycle-dependent changes in the plasma membrane potential correlate with uptake of a CPP conjugated to a fluorescent dye. These changes correlate with mRNA abundance of a voltage-gated potassium channel, KCNH1. RNA silencing of KCNH1 expression or pharmacological blockade of KCNH1 currents impairs CPP uptake, supporting the proposed model.

Strengths:

(1) Identified KCNH1 expression and function as a specific modulator of CPP uptake in U2OS cells and LAN1 cells.

(2) Data show a clear correlation between the plasma membrane potential, cell cycle stage, and efficiency of intracellular CPP delivery in these cells.

Weaknesses:

(1) Some of the claims are more generalized than warranted by the presented evidence. Is KCNH1-dependent modulation of cell cycle-dependent changes in the plasma membrane potential and ensuing effect on CPP uptake a niche property of this channel in some cell lines? Or a more broadly generalized function? I think this can be addressed merely through more careful or caveated language in the manuscript. However, an additional experiment that would be potentially informative and complementary to the chronic high-potassium depolarization experiments would be to examine the impact of heterologous expression of a hyperpolarizing potassium channel (for example, Kir2.1).

(2) Please provide more details on how endosomes were identified and measured.

Reviewer #2 (Public review):

Summary:

The article by Ribeiro et al. provides further mechanistic understanding of the abilities of cell-penetrating peptides (CPPs) to cross directly through the membrane. The authors performed a transcriptomic evaluation to conclude that CPPs enter via direct translocation through cells undergoing cell division, particularly in G2/M phase cells. They reasoned that this is due to the expression of KCNH1, a potassium channel, which is hypothesized to fluctuate throughout the cell cycle and peak at G2/M phase. This expression, in turn, hyperpolarizes the cells, enabling the direct translocation of CPPs.

The uptake mechanism of CPPs has long been debated. The authors and other groups have previously mentioned the importance of cell membrane potential and the involvement of specific ion channels in its regulation. The current work further adds a layer of understanding by connecting the occurrence of direct translocation to a specific cell cycle phase, which may be of great relevance when considering the use of CPPs for cells that are constantly dividing, such as cancer cells.

Strengths:

The work took advantage of transcriptomics and the unique use of the PIP-FUCCI system to determine the different uptake of CPPs in different cell cycle stages. All the data are in agreement with each other, especially when observing the membrane potential change through the cell cycle stages. The experiments are well executed and are all complementary to the authors' hypothesis.

Weaknesses:

The article could be further strengthened through:

(1) Implementation of additional confirmatory experiments

(2) Positioning against other recently reported channel proteins

(3) Confirming whether CPPs with different physicochemical properties are also affected by the fluctuation of KCNH1 expression

(4) Determining whether the concept of cell cycle-dependent membrane potential change is applicable not just in model cells, but also in cells that undergo rapid cell cycles, and

(5) Relating CPP direct translocation to known biological phenomena that are physiologically similar.

Reviewer #1 (Public review):

Summary:

The authors have carried out a study of lipid membrane permeabilities on different nucleoside/tide isomers. The study primarily relied on PAMPA as the experimental technique for assessing permeabilities, although interestingly, the authors found that there was a correlation between permeability and retention time on a C18 column. The authors specifically looked at the permeabilities of furano and pyrano alpha/beta nucleosides/tides, since a major question is why biochemistry uses only the beta furano isomers, when three other isomers based on anomeric configuration and ring size are also possible. The authors found that for all A, U, G or C nucleoside isomers, the beta-furanoside is consistently the most lipid-permeable. These findings were also supported by computational (DFT and MD) studies. The authors conclude that such a property could plausibly have been a chemical selection mechanism that favoured these isomers when they first started interacting with protocell membranes.

Strengths:

There are two major strengths to this study: (1) it offers an alternative/additional explanation backed by experimental and computational evidence as to how the canonical ribonucleotides (namely, the beta-furanoside isomers) were selected by interactions with protocell membranes; (2) it makes the important observation that retention time on a C18 column is correlated with permeabilities determined by PAMPA, the "industry standard", suggesting that LC could also be a convenient way to assess relative permeabilities of analytes of interest. With this report, there are many promising follow-up studies that can be carried out in the context of synthetic lipid protocells in order to further support the hypothesis that selective permeability played a major role in early chemical/Darwinian evolution.

Weaknesses:

The biggest weakness is that there needs to be more of a critical discussion of chemical vs Darwinian selection. There are two basic hypotheses for how the canonical ribonucleotides came to be: they were the products of prebiotic synthesis and chemical selection pressures, or they emerged as a product of natural selection after Darwinian evolution already became established. While the experimental evidence in terms of ribonucleotide prebiotic synthesis is discussed well, the work of Eschenmoser in particular, which supports the alternative hypothesis that RNA is the product of Darwinian evolution, should be discussed in more detail on page 4.

The authors should also acknowledge somewhere in the discussion/conclusion that there is a certain amount of bias that the field has towards extant biology, and this bias could be influencing how certain results are interpreted as beneficial for ribonucleotide selection, when the same result might be used to argue against it. For example, the fact that the 2',3'-cyclic phosphates are the most permeable among the phosphate regioisomers might be used to argue these were the preferred substrates for oligomerization in a protocell, given it had a catalyst which could do it, like a short peptide. While the differences in permeabilities are quite solid, what it means in terms of selection could be applied to multiple, specific scenarios, each with its own interpretations and implications.

Reviewer #2 (Public review):

Summary:

The authors ask whether membrane transport could have contributed to the emergence of the molecular configuration characteristic of extant RNA. They examine the permeability of α- and β-furanosides and of different nucleotide phosphate isomers using fatty-acid-based PAMPA systems, supported by partitioning experiments and molecular simulations.<br /> The central finding is that closely related molecular species show different transport behaviour: β-furanosides are preferentially transported, while 5′-phosphorylated nucleotides are less permeable than 2′- and 3′-phosphates. These observations are potentially interesting because membrane transport could influence the composition of an evolving compartment.

Strengths:

(1) The study addresses an interesting and relatively underexplored role for membranes in prebiotic chemical evolution.

(2) The comparison of closely related molecular species provides a potentially useful demonstration that membrane transport can discriminate between different nucleotide configurations. The experimental and computational approaches complement each other.

(3) The analysis of 5′-, 2′-, 3′- and cyclic phosphate species is particularly interesting and could provide a useful framework for thinking about how membrane transport interacts with nucleotide chemistry.

Weaknesses:

(1) The principal concern is that the manuscript moves from differential permeability to evolutionary selection rather too quickly. The experiments demonstrate differences in transport, but do not establish that these differences would produce sufficient intracellular enrichment, persistence or functional consequences to drive selection of the canonical RNA configuration.

(2) The relevance of the PAMPA system to primitive vesicles is also not fully established. Membrane permeability depends strongly on membrane composition and environmental conditions, and the relationship between the membrane compositions used in the principal experiments and plausible primitive vesicles needs to be clearer.

(3) Similarly, the pH-gradient experiments show an effect of pH on transport, but a gradient between two macroscopic PAMPA reservoirs does not demonstrate that primitive fatty-acid vesicles could maintain a comparable gradient.

(4) Finally, some of the key results are presented as changes in relative composition rather than direct measurements of molecular flux. The distinction between compositional enrichment, partitioning and permeability should therefore be clearer. The molecular simulations provide useful support but, given the small calculated free-energy differences and acknowledged limitations in the nucleotide simulations, do not independently establish the mechanism.

Overall, the work contains potentially valuable observations, but I think the conclusions should be framed more cautiously. The most compelling interpretation is that membrane transport could have been one component of a multifactorial chemical-selection process, alongside prebiotic synthesis, stability and nucleotide chemistry, rather than that membrane permeability itself explains the emergence of canonical RNA.

Reviewer #1 (Public review):

Summary:

The authors studied the development of hippocampal connectivity gradients based on open datasets and performed correlation analyses with other MRI features as well as gene expression information from other datasets.

Strengths:

The hippocampus is a key region in understanding large scale brain organization and cognition, and the authors applied advanced and suitable analytics to study its development. The paper is overall well organized and well written, and the findings are relevant for studying large-scale brain development.

Weaknesses:

While sophisticated, several of the analyses appear mainly correlational, cross-sectional, and rely on cross-dataset contextualization, which should also be stated as a limitation of the current work.

Comments on revised version.

I thank the authors for their revisions which have improved the paper.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors aim to assess how the functional organisation of the hippocampus is related to the geometry and neurobiological differences of the hippocampus. In particular, the authors focus on the first three eigenvectors of hippocampal-cortical functional connectivity, based on non-linear dimensionality reduction on resting-state functional MRI data. Furthermore, the work aims to describe changes in these functional axes, and their relation to other factors, throughout youth and evaluate whether they are predictive of individual variations in cognition.

Strengths:

A major strength of this study is the attempt to replicate key findings across multiple developmental cohorts.

Weaknesses:

T1w/T2w signal intensity is incorrectly used as synonymous with myelin.

Appraisal of Aims and Conclusions:

The authors partially achieve their aims by illustrating certain age-related changes in hippocampal function; however, the correlative study design is not equipped to examine how these changes are "shaped" by geometry, myelination or gene expression (especially the latter two). Furthermore, conclusions were often overstated based on small effect sizes.

Context and Field Impact:

This work adds to a growing body of literature focused on gradient-based representations of hippocampal topology. By applying these methods across a wide developmental age bracket, it provides a useful reference point for how the hippocampus and wider cortex interact during maturation. To improve utility to the neuroimaging and cognitive neuroscience communities, the nesting of subfields within the eigenvector topology should be addressed too.

Comments on revised version.

I appreciate the authors' revisions and their clear efforts to improve technical transparency throughout the manuscript. I believe the manuscript could have been more strongly improved were the authors more constructive than defensive in their responses, but the manuscript is nevertheless more clear.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors build a connectome-scale resource for studying mitochondria in the Drosophila Hemibrain. The authors segment and classify millions of mitochondria, quantify their size, abundance, appearance, and spatial relationship to synapses across thousands of neuron types, and extend neuPrint so that these organelles can be queried together with neuronal connectivity. The analyses identify substantial mitochondrial variation across cell types, systematic differences between pre- and postsynaptic mitochondrial organization, associations with neurotransmitter class, and changes in mitochondrial properties with synaptic fan-out.

Strengths:

The scale and accessibility of the resource are the major strengths of this work. The mitochondrial annotations are integrated with an established connectomic dataset and can be queried through neuPrint, which should make the study useful to researchers beyond the immediate biological questions addressed here. The manuscript also describes the underlying data model and how mitochondrial information is linked to neurons and synapses.

The manuscript compares mitochondrial organization across multiple levels, including individual cells, cell types, brain regions, synaptic polarity, neurotransmitter class, and synaptic fan-out. Several descriptive results are particularly clear, including the tendency for mitochondria to be closer and larger at presynaptic than postsynaptic sites and the identification of cell-type-specific patterns.

The biological interpretations are generally placed in the context of prior experimental literature on synaptic energy demand, calcium handling, and mitochondrial positioning. In many places, the authors appropriately use cautious language such as "may," "possible," and "consistent with," which helps distinguish mechanistic hypotheses from direct measurements. The cited literature therefore provides a reasonable biological framework for interpreting the structural observations, even where the corresponding physiological variables are not measured in this specimen.

The authors also acknowledge important limitations of the Hemibrain sample, including incomplete neurons, regional differences in reconstruction quality, and the fact that the analysis represents a single physiological snapshot. The public code and data infrastructure further increase the utility and reproducibility of the study.

Weaknesses:

The dataset has extraordinary cellular coverage but derives from one Hemibrain specimen. Claims about "natural ranges," generality across Drosophila neurons, or organizational principles extending beyond this specimen should therefore be phrased with some caution. This is particularly relevant for cell types represented by only one or a few instances. For example, MBON13 is represented by a single cell in this dataset, so its unusually high mitochondrial investment is well supported for that instance, but variability of this property within the cell type cannot be assessed here. This is consistent with the authors' own acknowledgement in the Discussion that detailed conclusions for individual cell types may require larger numbers of cells.

The mitochondrial appearance classes support several biological conclusions, particularly the differences associated with brain region and neurotransmitter class. The manuscript describes how dark, medium, and light training labels were generated and reports strong validation of mitochondrial detection and nearby-mitochondrion recall, but I could not find a corresponding independent validation of the three appearance labels themselves. Some assessment of classification reproducibility, for example using a held-out manually assessed set, a confusion matrix, or inter-annotator agreement, would strengthen confidence in conclusions that depend on these categories.

Reviewer #2 (Public review):

Summary:

Electron microscopy (EM) offers resolution of neuronal ultrastructure not available through any other technique, but it has a limited "field of view". Also, an EM study's value is limited by the ability to identify neuron types within that field of view. This data set largely overcomes both limitations, first, through the analysis of contiguous blocks of SEM-FIB data spanning almost half a millimeter in extent, serially milled at an exceptionally fine 8 nm to create isotropic 8 nm voxels, and, secondly, through efforts that identified most neurons within the sectioned volume. In overcoming the usual limitations, the authors can offer an overview of the capacity of different classes of neurons to control the size and distribution of their mitochondria - a non-trivial outcome considering the essential role of mitochondria in generating cellular ATP and neurotransmitter precursors. The science community is doubly served by the authors' website "neuPrint" which is accessible to the public, where the data can be visualized and interrogated according to various hypotheses. As it stands, this study fails to reach its otherwise exceptional potential as analysis errors throw a shadow over the findings.

Strengths:

(1) This is the first comprehensive analysis of mitochondrial morphometrics and ultrastructural positioning across a substantial portion of an insect brain. The adult fruit fly brain was previously milled at 8 nm intervals and imaged at 4 nm/pixel (Scheffer et al., 2020), offering an unprecedented data set for mining mitochondrial parameters relevant to neuronal function (this manuscript).

(2) The value of the data set has been amplified through systematic identification of neuronal identities and likely neurotransmitter types. The numbers of mitochondria (>6,000,000), neurons (>20,000), and neuron types (5,555) included in the study are similarly unprecedented.

(3) The language of the manuscript is straightforward and accessible.

Weaknesses:

(1) The data describing mean median (mean of the medians derived from many neuron types) mitochondrial volume (Fig.2B) and mean median length (Fig.2C) are not internally consistent, i.e. an ellipsoid with volume 0.15 cubic microns requires a longest axis of at least ~0.66 microns to remain a valid prolate shape (i.e., for the long axis to exceed the two minor axes); the reported value of 0.55 microns falls well short of this, indicating the two panels are not describing a geometrically coherent object. This inconsistency can be reconciled only by looking at the supplementary data, where the data are reported for each cell type and the relevant column is titled "Median miitochondrion long axis (r0)" (sic). Here it is evident that the parameter being reported on each plot in Figure 2C is in fact the radius of the long axis and not mitochondrion "length" as currently stated.

(2) However, even with the correction referred to above, the mean median mitochondrial diameter, i.e., minor axes, would be 0.5 microns, a robust mitochondrion for otherwise fine central processes, and double the diameters reported from C.elegans motor neurons (Riboul et al., 2024) and interneurons within the optic lobe of the miniature parasitic wasp (Fischer et al., 2017). While it is a dubious exercise to reverse engineer possible ellipsoid body dimensions from the mean of medians of both longest axis and volume, the inconsistency remains when referring to the mean of medians of smaller groups of neurons in the supplementary data.

(3) The estimates of mitochondrial volume density are uncommonly high, i.e., 4%-24%, with a mean median of 10%. These values are 2-3 fold greater than estimates from comparable EM studies, e.g. 4-6% for C.elegans motor neurons (Riboul et al., 2024); 4-6% for Drosophila larval motor neuron terminals (Justs et al., 2022); and 4-8% for lamina-medulla and medial-medulla interneurons in the miniature parasitic wasp (Fischer et al., 2017). Estimates from rat brain are similarly small, not exceeding 8% (e.g., Santuy et al., 2018), leading to concerns that, just like the two-fold error in reporting mitochondrial lengths, a two-fold error has propagated in mitochondrial volume density.

(4) The internal inconsistency in reported mitochondrial axis length can be identified from the manuscript's own numbers alone, using simple arithmetic. By contrast, the plausibility of the resulting mitochondrial minor-axis diameter (~0.5 microns), and of the reported mitochondrial volume density (4-24%, median 10%), can only be assessed by comparison with the wider literature - where comparable EM-based estimates across several species consistently fall in the 0.18-0.24 micron (diameter) and 4-8% (density) range, respectively. Both this literature comparison and the simple arithmetic used to establish the axis-length inconsistency were available to a reviewer. The other parameters reported throughout the manuscript are inscrutable, i.e., not readily checked by arithmetic or by comparison with existing literature. Nevertheless, the errors and implausibilities identified above throw a shadow over every other piece of data.

(5) Putting errors to one side, if the authors wish to report values in an unprecedented range, they need to do so with due diligence and defend the novelty of their data in the context of precedence in the field.

Assessment of whether aims were met:

The authors did not achieve their aims, but those aims remain well within their reach, as processing data sets is generally trivial relative to the physical act of repeating experiments and recapturing ideal experimental conditions.

Discussion of the likely impact:

This manuscript has great bones, and should it receive the attention/rigor it deserves, it is likely to become the go-to reference for mitochondrial morphometrics and positioning across whole central neurons in Drosophila, specifically, and in insects in general.

Several of the findings in this study, should they persist through reanalysis, are likely to be impactful. A particularly interesting finding is that the size of the closest mitochondrion is larger at pre- rather than post-synaptic compartments (line 309; Figure 7). The dogma in neuroscience, mostly stemming from studies on vertebrate neurons and partly supported by the misguided use of fluorescence microscopy to assess mitochondrial size and distribution, is that mitochondria are larger in dendrites than in axons. The ability to assess a compartment as either pre- or post-synaptic in the present study gets to the heart of the matter about pre- vs post-synaptic dependence on mitochondrial function without getting drawn into arguments about more macroscopic cell structures that may or may not be preserved between insects and vertebrates. This finding is worth discussing in the context of the mouse data from Sager et al., 2026, Science, but it is a discussion that requires consideration of the relative volume of the pre- vs post-synaptic compartments, and whether mitochondria have the same opportunity to closely attend postsynaptic compartments.

A second finding, and one that might have received more attention in the discussion, was the observation: "KC presynapses with smaller median distances to mitochondria have larger nearest mitochondria, ... . For KCs and MBONs, the mitochondrial volume is inversely correlated with the distance". There are several avenues here that might have been explored. Firstly, in the context of data processing, and secondly, in the context of bioenergetics, the implication of greater intermittence of input at any particular postsynapse, and less intermittence of output at any particular presynapse. Lastly, implications with regard to the relative reliance on mitochondria for neurotransmitter synthesis between pre- vs post-synaptic compartments, an implication that might be addressed through reference to the particular neurotransmitters of each neuron class and whether that neurotransmitter is directly dependent on neighboring mitochondria for precursors.

Reviewer #3 (Public review):

Summary:

This manuscript presents a resource describing the morphology and distribution of mitochondria across a large part of the central brain of Drosophila. In addition to providing a valuable resource for the community, the authors analyze their dataset in great detail. These analyses reveal interesting differences in mitochondria across cell types and brain regions with regard to morphological parameters, their spatial relationship to pre- and post-synapses, differences in mitochondrial characteristics between neurons that use different neurotransmitters, and between synapses with multiple postsynaptic partners.

Strengths:

The main strength of the paper is the large size of the dataset comprising ca. 6 million segmented mitochondria distributed across more than 20000 neurons. This dataset provides enough statistical power to detect differences in mitochondrial characteristics between brain regions and cell types. An additional strength is the easy accessibility of the data through the neuPrint web interface, enabling the community to explore the dataset and perform further analyses.

Weaknesses:

The study is primarily descriptive. While this is not necessarily a weakness, the manuscript could more clearly discuss potential applications of the dataset beyond pure characterization. Potential improvements include neurotransmitter predictions, cell type classification, or sites of gap junction coupling.

Conclusion:

Taken together, I consider this manuscript to provide a highly valuable resource and analysis that will be useful to researchers interested in mitochondria and neuronal cell biology in Drosophila. Furthermore, the dataset provides potential for researchers to link mitochondrial characteristics to neuronal physiology.

Reviewer #4 (Public review):

Summary:

Longden et al. present a comprehensive analysis of all the mitochondria in the hemibrain dataset of the Drosophila brain. They quantify variations in mitochondrion size, spatial distribution, and proximity to pre- and post-synapses that reveal stark differences between cell types and brain regions. Similar to findings from other model systems, their results indicate that mitochondria are larger and closer to presynapses than to postsynapses. They also show that inhibitory neurons have more mitochondria by volume than excitatory neurons. Furthermore, they have made all this data available through neuPrint, making it a valuable resource for the broader research community. The evidence for their conclusions is compelling based on the unprecedented breadth of analysis across cell types and brain regions.

Strengths:

The comprehensive nature of their analyses across all of the mitochondria in the dataset enables Longden et al. to describe each observation in the context of individual neurons, cell types, and brain regions. The authors use this analysis structure to highlight cell types that possess unique mitochondrial metrics, such as the Kenyon cells

The reconstructions and results from this paper have been incorporated into neuPrint, making these data accessible to a wider set of researchers and scientific investigations.

Weaknesses:

The motivation for this study could be enhanced with a more general overview in the introduction. Answering some of the following questions would provide more context as to the interpretation of their results and the value of extending neuPrint to include mitochondria. How does the distribution of mitochondria in a cell enable function? What is the biological implication of having mitochondria near a synapse? What do we know about mitochondria from other modalities, and how does the information from the connectome extend or corroborate what we already know?

Similarly, this paper would benefit from more contextualization of their results within the broader study of mitochondria, both in the field of fly neuroscience and connectomics across species. A few of these studies are rightfully referenced; however, a more direct discussion of how the results from Longden et al. sit within the current landscape would highlight the value of this effort and broaden the readership of the manuscript.

Reviewer #1 (Public review):

This study addresses an important question: whether dopaminergic activity in Drosophila functions as a general value signal across behavioral contexts. Its main strength is the breadth of the experimental program, combining multiple DAN driver lines, several operant paradigms, rescreening, and more targeted follow-up experiments. The cross-paradigm design is well suited to test whether self-stimulation effects generalize.

The main weakness is that several conclusions extend beyond what the experiments directly establish. The genetic tools define populations rather than the same identified neurons at single-cell resolution. In the key time-course experiments, the data more clearly support a reduction of initial avoidance toward neutrality than a consistent aversive-to-appetitive sign reversal. Similarly, the non-contingent comparison uses continuous pre-exposure rather than a stimulation-history-matched open-loop condition; it therefore shows that simple non-contingent exposure is insufficient, but does not isolate agency itself as the causal variable.

The general claims are also constrained by incomplete overlap among the initial screens and by design changes between the original and follow-up joystick experiments. Overall, the study provides useful evidence that the behavioral effects of DAN activation are weak, context-dependent, and time-dependent, while the claims about neuronal identity, positive valence, agency, and a general dopamine "common currency" require more cautious framing.

Reviewer #2 (Public review):

Summary:

Broadly, the authors use a wide range of lines targeting dopaminergic neurons in different behavioral paradigms to determine their role in behavior.

Strengths:

There is a comprehensive use of lines and assays by a single group, such that internally consistent comparisons can be made.

Weaknesses:

It is not clear to what extent these broad assays will provide concrete answers that enable a "unified hypothesis" of how dopamine functions in behavior.

The manuscript has a feel of being a mix of an original paper, commentary, hypothesis-generating outlook, a focused summary, and a broad review of the field. As someone outside this field, I find that it has its advantages and disadvantages. For example, the final sentence of the Abstract, despite multiple readings, is not obvious to someone outside the field. Plus, due to the large number of references for any one view, it is not clear which work is saying exactly what. For example, which of the fly papers on dopamine clearly say they favor the "common currency" model? Do these references also use this term? Even for a reference that occurs multiple times (Jovanoski and Waddell, 2025), it's not clear what this paper actually proposes.

Also, what does "a reporter for a specific goal" mean (Line 16 of Abstract, and lines 1986-1987 of Discussion)? They provide a nice example of a tethered fly and what happens when it is suddenly released, vs a free-flying fly that has entered a maze--what is the difference here in terms of dopamine function and the fly's "goal" and how does the fly know the difference? When a fly is free flying in a maze, how does it know that it has just been released from a tether, or whether it has entered a maze as it was freely flying outside it?

And for their "unified hypothesis", how does a fly know that it is in "controls its environment" versus "lack of control"?

In sum, I found this paper to be valuable in bringing up the different views of dopamine signaling. However, it was a bit unclear at the end as to what dopamine is actually doing in the fly.

Reviewer #3 (Public review):

Summary:

Dopaminergic neurons (DANs) are known to play a role in conveying rewarding and punishing signals that influence future behaviour. This study uses optogenetic manipulation of a range of subpopulations of DANs to assess their influence in several operant learning paradigms, such that the animal's own behaviour determines the DAN activation. The results show surprisingly inconsistent effects and do not support the widespread assumption that specific DANs consistently convey either positive or negative valence. In one case, the apparent valence of the DAN activation switches over a time frame of minutes from aversive to attractive, and the relevant circuits are further dissected to show this effect is dopamine-dependent, for a specific DAN subset, and contingent on operant control of behaviour.

Strengths:

This study has two major strengths. The first is to address the relatively neglected field of operant learning in Drosophila. The detailed unpicking of learning circuits in the Drosophila brain that has been made possible by neurogenetics and other advances has almost exclusively focussed on classical conditioning paradigms, but these are often not representative of the real behavioural situations that animals face. Cross-comparison of the behavioural results of identified neuron activation in a wider range of paradigms is very welcome. The second is the use of multiple paradigms and a wide-ranging screen to survey the effects across many lines (targeting different DAN populations), including many that have been previously tested in classical conditioning. A further strength is the useful review (in the introduction and discussion) of existing research that already raises problems with the view that a single dimensional positive and negative "value" (as conceived of in standard reinforcement learning theory) can be directly mapped to the activity of two broad clusters of DANs in the brain.

Weaknesses:

The main weakness is that it is difficult from the manuscript to understand what the authors believe is the underlying mechanism by which DAN activation should affect behaviour in operant paradigms, and how this should relate to the better-known role of DAN activity in classical conditioning. For example, they refer to Heisenberg and Wolf's 1991 distinction between operant activity and operant learning. That paper proposed a mechanism involving a goal, some degree of random variation in action, comparison of sensory input to a goal, correlation of the efference copy of the action to the output of this comparison, with the strength of this correlation subsequently shaping the action probabilities. It would help to be explicit about what DAN activity is assumed to represent in this scheme - the outcome of comparison to a goal? The actual assumptions behind the work seem more simplistic: if different groups of DANs 'signal' reward or punishment, animals should persist in actions that activate the reward DANs and desist in actions that activate the punishment DANs. This might be justified by the standard way that innately rewarding/punishing stimuli are operationally defined in learning theory. But what brain circuitry is assumed to underlie the variation and choice of actions, and how is this influenced by DANs?

Perhaps what is most clearly shown is that avoidance behaviour to activation of innately punishing sensory neurons is not directly a consequence of downstream activation of specific DANs, such that DAN activation can be substituted to produce the same behavioural phenomena (at least for the DANs tested here). Indeed, this is how the core results are summarised at the start of the discussion. But should this have been expected? There are a lot of assumptions needed to connect the expected effect of a reinforcement signal in classical conditioning to its effect on operant behaviour that are not made sufficiently explicit.

Appraisal:

The authors met their aim to carry out a (fairly) comprehensive test of the effects of optogenetic activation of a variety of DAN subpopulations under several operant paradigms that differ in relevant respects (group vs. individual testing, freely moving vs. tethered). The lack of consistent results across these paradigms, and generally weak effects (with limited statistical analysis) make it difficult to interpret whether the outcome here is mostly 'noise', but perhaps this is an example where the failure to find a clear effect (as predicted) is itself informative. The paper appropriately describes the limitations (such as the mixed range and specificity of DAN subsets manipulated across the paradigms) which were probably unavoidable given the tools available.

Likely impact on the field:

This work should have an impact in countering assertions that dopamine neurons in the fly brain can be understood as (collectively) conveying a single dimension of valence, from positive to negative, across all behavioural and learning situations. It also provides strong motivation to more clearly understand the nature of operant learning and how/where it might be implemented in the insect brain.

Reviewer #1 (Public review):

Olmstead et al. present a single-cell nuclear sequencing dataset that interrogates how hippocampal gene expression changes in response to distinct physiological stimuli and across circadian time. The authors perform single nucleus RNA sequencing on mouse hippocampal tissue after (1) kainic acid-induced seizure, (2) exposure to an enriched environment, and (3) at multiple circadian phases.

The dataset is rigorously collected, and a major strength is the use of the previously established ABC taxonomy from Yao et al. (2023) to define cell types. The authors further show that this taxonomy is largely independent of activity-driven transcriptional programs. Using these annotations, they examine activity-regulated gene expression across neuronal and glial subclasses. They identify ZT12, corresponding to the transition from the light to the dark period, as transcriptionally distinct from other circadian time points, and show that this pattern is conserved across many cell types. Finally, they test how circadian phase influences activity-dependent gene expression by exposing mice to an enriched environment at different times of day, and report no significant interaction between circadian phase and enriched environment exposure.

Comments on revised version.

The authors have addressed the comments from the first round of review.

Reviewer #2 (Public review):

Olmstead et al. present ACT-DEPP, a single-nucleus RNA-sequencing atlas of the mouse hippocampus. The atlas profiles transcription after environmental enrichment, after kainate-induced seizure, and at four circadian time points, and all nuclei are registered to the Allen Brain Cell taxonomy. The authors report three main findings. First, seizure and novel experience induce transcriptional programs that differ greatly in magnitude. Second, ZT12 is a transcriptional inflection point shared across cell types. Third, induction of classical immediate early genes (IEGs) is broadly maintained across circadian phases, with only a few genes showing circadian-activity interactions.

The study is ambitious in scope, and the dataset is rigorously collected. Registering the data to the ABC taxonomy makes them easy to reuse and to integrate with other brain-wide resources. In the revision, the authors substantially strengthened the analyses and refined their interpretations. Overall, the descriptive conclusions are well supported.

Locomotor activity was not recorded, so some ZT-dependent effects may partly reflect behavioral state as well as intrinsic clock regulation. The authors clearly acknowledge this in the Limitations section. With relatively few replicates per ZT, small circadian-activity interactions may go undetected. The pathways highlighted in Fig. 6I-J are best viewed as hypothesis-generating. These points are appropriately framed and do not detract from the value of the work. This study will be a useful reference for investigators studying IEG biology, experience-dependent plasticity, and circadian regulation in the brain.

Reviewer #1 (Public review):

Summary:

Doronin et al. performed wide-field calcium imaging to investigate the cortex-wide activity dynamics in mice after the administration of the psychedelic DOI.

Strengths:

The lab has extensive experience with this approach. The analyses are not trivial, but here the authors progressively step through the rich data to identify salient effects. The observed effects at the level of cortical networks will be of great interest to the field, both for basic scientists interested in circuit mechanisms of psychedelics and for human researchers who may want to reverse translate from humans to mice.

Weaknesses:

The analyses were extensive, but the choices made for the analyses are quite specific, and there are places where other analyses may be more appropriate and logical. For example, instead of comparing activity with serotonergic axonal density, compare with Htr2a transcript because the receptor is the drug target. Moreover, data were combined for day 1 and day 5 for an unclear reason. Furthermore, a few statements in the text did not seem fully supported by the results and may be over-interpreted.

Reviewer #2 (Public review):

Summary:

In this paper, the authors report a quantitative change in the functional connectivity between cortical domains in mice, due to the influence of the psychedelic, DOI. Additionally, they report that specific cell types (and circuits) within the brain may be involved in the changes in the observed functional connectivity. This will have important implications - both for basic neuroscience and with potential translational implications.

Strengths:

This is a fascinating result - especially since the work on psychedelics has predominantly been focused on humans - and therefore a deeper understanding of cell-type-specific responses due to psychedelics has remained poorly studied. Given that psychedelics result in a variety of distinct changes within the circuit - both affecting plasticity and the steady-state dynamics - it is critical to characterize these changes within a system where we can measure changes in individual cell types.

Weaknesses:

The authors attempt to model the types of analyses that have been performed in humans within mice. Perhaps because of their attempt to translate from one system to another, the quantitative justification for each of the specific analyses that have been performed appears unclear within the mouse system. This makes some of their analyses, as currently structured, appear incomplete, and we have detailed the resulting questions in the following comments:

(1) The concept of a "cortical domain" is used in the abstract and is a fundamental component of the paper. We were not familiar with the concept prior to its use in this paper and, given the importance of the concept, it would be helpful to clearly delineate what the authors define as a domain at the outset, so that readers can build their expectations. Further, both AL and PM have been used as acronyms for secondary visual areas (which the authors are very familiar with). This leads to further confusion for the reader. As such, it would be helpful to clearly delineate that these are distinct acronyms for a distinct class of spatial feature within the brain - even within the abstract.

(2) The authors define the AL and PM cortical domains through the use of a threshold on the seed correlation maps. They chose a threshold of 35%, but it's not clear why that threshold was chosen - and what would occur if the authors used a different threshold instead. Would the boundaries change significantly, and would the rest of their results then be affected by this change?

(3) The authors report that the behavioral events that show a maximally distinguishable response in the PM and AL domains are behavioral fidgets. However, the authors do not define either (a) the set of all possible behavioral events which they compared against (and how those behavioral events were assayed), and (b) the quantitative definition of fidgets that was used. This is particularly critical since the authors use the fidget response as a metric of responsiveness in the rest of the paper.

(4) The authors report that the AL and PM domains correlate with the projection patterns of specific neuromodulatory systems. Given the (apparently) arbitrary threshold used in the definition of the AL and PM domains, and the fact that the authors mention that they examined "some of the major neuromodulatory systems", it is unclear how to interpret this result.

(5) The authors claim that the DOI-induced increase in the correlation between the cortical domains is 5-HT2A dependent, and their evidence for this depends on a block of the 5-HT2A receptor via ketanserin, in the presence of DOI. However, the result that they report from ketanserin is a decrease in the correlations below baseline, and not simply a removal of the effect observed in DOI. This suggests that the effect of DOI on the network cannot be simplistically explained through 5-HT2A dependence, and requires some further characterization and / or explanation.

(6) The authors introduce the concept of "amplitude of activity variation". However, they do not define the specific quantification that they are using in the text. Additionally, their justification for the use of the concept is also unclear. Why should the activity of input variation change "on first approximation"? If this is a quantifiable prediction, what is the approximation? Increasing the precision of their introduction of the concept would greatly aid the reader in interpreting the results in Figure 4.

(7) The authors report that EEG responses do not directly reflect the opposing responses observed via widefield calcium imaging. They conclude that "the opposing effects we observe in calcium activity are difficult to relate to psychedelic-induced EEG effects in humans". However, their apparent effects are also not observed in mouse EEG recordings. As such, their claim is unclear - are they reporting that calcium recordings are not reflected in EEG responses, or is there a specific effect present in mice that is absent in humans?

(8) The authors compare the widefield response resulting from stimulation of specific populations of cortical and subcortical neurons prior to and under the influence of DOI. They claim that the resulting changes provide "candidate circuit mechanisms" for the effects observed during DOI. However, the observed changes due to stimulation do not match the specific changes observed during DOI, making it hard to justify their claim. Indeed, one could hypothesize that the observed changes are only due to downstream effects in a secondary population, only indirectly stimulated through the authors' paradigm. As such, clarification of their results and increased precision of their claims would be helpful. As one example, the authors could provide a model for how the specific changes that they observe during stimulation may directly result in the domain-specific changes observed due to DOI.

(9) Following up on question (8), it would also be helpful for the authors to characterize the space of stimulation paradigms within the selected neuronal populations. How specific are the changes that they observe to the specific stimulation paradigm used? Are these stimulation paradigms biologically plausible? And, if not, what precisely do we learn through this stimulation?

Reviewer #1 (Public review):

Summary:

This study by Weber et al. investigates how artificial activation or inhibition of dopaminergic neurons innervating the Drosophila larval mushroom bodies in the context of learning paradigms influences individual properties of acquired memories. Similar approaches have been undertaken in the past (e.g., in the adult); however, the reduced complexity of the larval system makes this study valuable, especially as it eases the possibility for informed modelling approaches, as done here. A special aspect shown here is that artificial training overrides the need for context typically observed for recall of larval aversive memories.

Strengths:

This study adds insights into how the larval mushroom bodies store memories. The behavioral data using genetically precise manipulation of identified neurons is combined with computational modelling. Information on the physiological basis of the underlying mechanism is not provided. In light of the claim of ‚temporal precise optogenetic manipulation' and the authors ‚manipulating the intrinsic physiology' of DANs, this is important and should be stated clearly. While this reviewer agrees that the ‚temporal precision' is provided for memory phases, this might not be true for physiological parameters, especially when using tools like ChR2-XXL.

Thus, this study offers interesting behavioral and cellular insights; however, this reviewer also recommends specifying claims on temporal and physiological precision more carefully.

Weaknesses:

(1) The authors should be more precise about temporal and physiological windows/properties. It is recommended to discuss the published properties of the optogenetic tools used and how this might influence the obtained results, especially when comparing the results to 'naturally' reached learning after pairing stimuli in the absence of optogenetic intervention.

(2) The authors should comment more directly as to why pairing odors with activation or inhibition of DANs does not lead to measurable learning (Figure 1).

(3) A key finding is that DL DAN activation leads to context-independent memories (as also somewhat indicated in the title). To make this claim, the 'natural' training experiments should be conducted in parallel, or these potential limitations should be mentioned. However, if the authors state that the results using regular conditioning approaches are so clear that this is not necessary, the should state that.

(4) Potential caveats of activating chloride channels over prolonged time periods should be mentioned.

(5) The appropriate literature showing odor-evoked synaptic depression to MBONs (e.g., cited in lines 431/432) in the context of learning should be cited throughout.

Reviewer #2 (Public review):

Summary:

This study investigates how the timing of dopaminergic signals influences associative learning and memory in Drosophila larvae. Using optogenetic activation and inhibition of the dopaminergic neurons DAN-f1 and DAN-g1, the authors examine their roles during different phases of aversive olfactory learning and memory testing. The study reports that manipulating these neurons can alter behavioral performance not only when the manipulation is paired with punishment, but also during non-punished periods and during memory testing. The finding that behavior induced by artificial activation of these neurons can be expressed across different test conditions is also interesting. Overall, the study provides valuable observations concerning the timing-dependent roles of dopaminergic signals in associative learning.

Strengths:

A major strength of the study is the systematic manipulation of the same dopaminergic neurons at different stages of the behavioral paradigm. This provides a useful framework for examining whether dopaminergic signals have functions beyond their conventional role as reinforcement signals during training. The study also combines optogenetic activation and inhibition with behavioral assays and measurements of neuronal responses. Several of the observations are potentially informative for understanding how dopaminergic signals contribute to memory formation and expression in a context- and trimming-dependent manner.

Weaknesses:

The main weakness is that several conclusions are stronger than warranted by the current evidence. The optogenetic control experiments do not fully distinguish the effects of light, genotype, and opsin expression, and some experimental and control genotypes already differ in naïve behavior.

In addition, some conclusions rely on whether individual groups differ from zero rather than on significant differences between experimental groups. This affects key claims concerning loss of memory formation or expression.

The comparison between optogenetic reinforcement and natural punishment is also confounded by a fixed training order, making it difficult to separate reinforcement strength from recency or interference effects.

Finally, the proposed role of baseline dopaminergic activity is not directly supported by physiological measurements in the relevant neurons under the experimental conditions.

Overall, these issues make the evidence for several of the main mechanistic conclusions incomplete.

Reviewer #3 (Public review):

Summary:

Weber et al. focused on a punishment learning paradigm in Drosophila larvae using primarily high salt as an aversive stimulus. They examined the DL1-cluster of dopaminergic neurons (DANs) consisting of four neurons, mostly important for encoding such punishment. Using temporally precise optogenetic activation and inhibition during both training and testing, the authors show that particularly DAN-f1/g1 activity can bidirectionally alter memory strength and valence of high salt learning. Depending on the timing of the manipulation, artificial DAN activation could also strengthen, weaken, or abolish expression of learned behavior without altering innate preferences. By combining behavioral experiments with computational modeling, the authors developed a connectome-based circuit model in which activity of the DL1 compartment causes output neurons (MBONs) to recruit reward-like DANs via an intrinsic feedback loop. In contrast, models based on independent compartmental plasticity for reward and punishment failed to reproduce the memory valence reversals observed in behavioral experiments. Thereby, the study identified an important feature of the larval mushroom body circuit relying on intercompartmental feedback signals to process learned signals and provide appropriate behavioral outputs.

Strengths:

(1) The study benefits from temporal precision across both memory acquisition and retrieval phases using an optogenetic approach. DAN-f1/g1 function is addressed thoroughly and manipulated across several paradigms, convincingly validating that optogenetic manipulations specifically alter learning but not innate preferences.

(2) The study benefits from treating the DL1 cluster as functionally heterogeneous. By examining individual neurons during retrieval, the authors solidify a prominent role for DAN-c1 in memory retrieval and DAN-f1/g1 in aversive reinforcement.

(3) Computational modeling nicely complements the behavioral results, showing that simpler circuit architectures fail to reproduce the valence reversals observed during behavioral experiments. The success of the cross-compartmental feedback model therefore narrows the range of plausible circuit explanations and supports the authors' circuit-level rebalancing theory.

Weaknesses:

(1) The PI is calculated from the number of larvae occupying each side of the arena after a 5-minute period of free exploration. This is a reasonable summary and standard method to measure the behavioral results, but it provides limited information about how preferences and memory retrieval dynamics may develop over time. While standard in the field, trajectory-based parameters as odor approach behavior could differ between the experiments, so not all information can be retrieved.

(2) Their model argues for cross-compartment interaction in the mushroom body circuit, which is, however, not corroborated experimentally. Silencing individual DL1 neurons during the testing phase had little behavioral effect aside from DAN-c1, leaving the specific retrieval roles of DAN-f1 and DAN-g1 somewhat unresolved, particularly given that their simultaneous inhibition was shown to enhance memory recall.

(3) Another limitation concerns how neuronal dynamics are represented in computational models: whereas the four DL1-DANs are represented individually, the four-neuron pPAM cluster is modelled as a single unit. Together with the limited temporal resolution of the calcium signals, as well as the unknown output dynamics in MBONs, the current model simplifies the cellular organization and physiological dynamics underlying the proposed circuit mechanism. The response parameters of the four DL1-DANs and the pPAM cluster were fitted to previously measured calcium responses evoked by brief high-salt or fructose stimulation. Although these fits reproduce the distinct temporal profiles and stimulus sensitivities observed experimentally, it remains uncertain how faithfully such stimulus-evoked responses capture DAN dynamics.

(4) Blue light activation used in the optogenetic paradigm adds an additional (aversive) stimulus that might interfere with the behavioral outcome. While the authors included all reasonable controls and point out the benefits of being consistent with their previous work, it does add a potential confounder.

Reviewer #1 (Public review):

Summary:

This study investigates the structural basis of neuromodulatory transmission in the cerebral cortex. Using genetic labelling, correlative light and electron microscopy (CLEM), and focused ion beam scanning electron microscopy (FIBSEM), the authors reconstruct cholinergic axons originating from the basal forebrain and serotonergic axons from the raphe nuclei in the mouse somatosensory cortex, and compare their ultrastructural organisation with that of glutamatergic and GABAergic axons.

The study provides detailed quantitative evidence that cholinergic and serotonergic axons have an axonal architecture fundamentally different from that of classical fast-transmitting glutamatergic and GABAergic axons. In particular, vesicles in cholinergic and serotonergic axons are distributed more diffusely along the axon and are only weakly associated with morphologically identifiable synaptic specialisations. These findings provide strong structural support for the idea that volume transmission represents a major mode of signalling by these neuromodulatory systems in the cortex.

Although the paucity of conventional synaptic contacts made by cortical neuromodulatory axons has been reported previously, including in classical work by Descarries and colleagues, the present study provides substantially more detailed and quantitative three-dimensional evidence. The study also reveals characteristic differences among neurotransmitter systems in vesicle density, vesicle distribution relative to synaptic membranes, and vesicle morphology.

Strengths:

A major strength of this study is the combination of genetic labelling, CLEM, and high-resolution FIBSEM, which allows molecularly identified cholinergic and serotonergic axons to be reconstructed without the electron-dense reaction products used in conventional immunoelectron microscopy. This enables detailed analysis of vesicles and membrane specialisations along extended axonal segments.

Another important strength is that the analysis is not restricted to individual boutons or varicosities. Instead, the authors analyse vesicle distributions continuously along reconstructed axons and compare them quantitatively with glutamatergic and GABAergic axons. This provides a more comprehensive assessment of the relationship between vesicle distribution and morphologically identifiable synaptic sites.

Overall, the data are convincing and support the authors' main conclusions. In particular, the finding that most vesicles in cholinergic and serotonergic axons are spatially dissociated from identifiable synaptic specialisations provides strong structural evidence for predominantly non-synaptic or volume transmission. The work should therefore be of broad interest to researchers studying neuromodulation, cortical circuitry, and connectomics.

Weaknesses:

I do not identify any major methodological weakness. The manuscript is well prepared, and the experimental results generally support the authors' conclusions. I have only a few points that could further improve the interpretation and discussion.

First, in the Introduction, ionotropic receptors are emphasised in the context of rapid actions of acetylcholine and serotonin. However, these neuromodulators can also produce relatively rapid effects on cortical neurons through G-protein-coupled receptors. This could be acknowledged, for example, with reference to Gulledge et al. (2009) and Tanaka & North (1993).

Second, the implications of the findings for volume transmission could be discussed somewhat more explicitly. If acetylcholine and serotonin are released extrasynaptically and are not immediately degraded or removed, they may diffuse through the extracellular space and activate receptors located at some distance from the release site. Thus, their actions need not be restricted to cellular elements immediately adjacent to vesicle-rich regions of the axon. A brief discussion of the sequence from action-potential-dependent Ca²⁺ entry and transmitter release to extracellular diffusion and activation of spatially distributed receptors would strengthen the interpretation.

Reviewer #2 (Public review):

Summary:

This is an important, timely and extremely well-conducted study, providing 3D EM data of the highest quality for identification of modulatory axons in the neocortex. The field of connectomics, in which ever larger 3D EM volumes are being acquired, has so far focused on glutamatergic and GABAergic synaptic circuits in the cerebral cortex. These obviously constitute the major fraction of synapses and are known to form high-specificity networks. However, it has been clear that modulatory systems originating in subcortical structures are an important additional defining property of neocortical circuits. Without a clear identification of these specific axon types, their identification in 3D EM data has so far not been possible at scale.

Strength:

The present study addresses this issue and resolves the problem of identifying modulatory axons in 3D EM data of the cerebral cortex. Given the fast pace of development in the field of connectomics, such high-quality information is extremely relevant and of substantial impact for the field. The study is conducted beautifully, and the figures are very clear.

Slight weakness:

For the quantitative analyses, it would be highly appreciated to plot the raw data distributions as scatter plots. This is particularly important because, for many readers, these data will serve as a benchmark for classifying and identifying such synapses. Thus, any parameter distributions that are (close to) disjunct will be extremely informative. Along those lines, even at the level of the already extracted parameters, it would be very interesting to see whether the authors can already train a simple classifier that would allow the identification of cholinergic and serotonergic synapses or axons in the low-dimensional parameter space that has already been sampled.

Reviewer #3 (Public review):

Summary:

This paper uses correlative FIB-SEM to reconstruct cholinergic and serotonergic axons in mouse somatosensory cortex. The authors wanted to examine how these neuromodulatory axons are organized at the ultrastructural level, specifically whether their vesicles are concentrated at synaptic structures like fast transmitters (glutamate and GABA) or distributed differently. They compare the two types against glutamate and GABA axons in the same datasets, and found that the synaptic vesicles are distributed more homogeneously in the neuromodulatory axons, supporting volume transmission.

Strengths:

The main strength is the dataset. FIB-SEM gives isotropic resolution, so vesicles can be counted and positioned accurately in 3D. The correlative light-EM approach lets them identify genetically labeled axons without using electron-dense stains that would obscure internal structures. This is a clear improvement over previous EM studies that relied on immunolabeling and only looked at varicosities.

The analysis is quantitative and systematic. They don't rely on identifying boutons or varicosities as the unit of analysis. Instead, they sample along the axon centerline at regular intervals, which avoids the bias of only looking at swellings. This is a cleaner way to ask where vesicles actually sit relative to synapses and other structures.

The comparison with glutamate and GABA axons is important. It shows that the differences are not just about neuromodulatory axons being "less synaptic" in a graded way, but qualitatively different in how vesicles are organized. Glutamate and GABA axons concentrate vesicles at synapses; ACh and 5-HT axons don't.

Weaknesses:

An intrinsic weakness of the EM approach is the missing of molecular information. The paper finds vesicles to be distributed homogeneously regardless of potential "post-synaptic" contact elements. However, protein machinaries for vesicle release might still statistically bind to certain postsynaptic elements.

Reviewer #1 (Public review):

Summary and strengths:

This manuscript examined whether realized thermal and aridity niche limits were associated with extinction risk across more than 23,000 terrestrial vertebrate species. The authors found that climatic niche limits were phylogenetically conserved and that species with lower upper thermal limits were more likely to be threatened, while relationships with aridity niche limits were weaker and less consistent. They then used simulations based on current extinction-risk patterns and found taxon-specific changes in the distribution of climatic niche limits and loss of phylogenetic diversity under simulated future extinction scenarios.

The study addresses an important question at an impressive taxonomic scale, and the underlying dataset and analyses have the potential to provide valuable insights into how climatic niche limits relate to patterns of extinction risks.

Weaknesses:

(1) One main concern is a disconnect between the central framing of the study and what the analyses can establish. The manuscript proposes that climatic niche limits increase species' vulnerability to climate and land-use change, yet extinction risk is measured using overall IUCN threat status, which integrates pressures from many different causes, including exploitation (e.g. hunting; pet trade), disease, and invasive species. For several of these threats, there is no clear mechanistic basis for why climatic niche limits should influence extinction risk. Consequently, the observed association does not establish that climate or land-use change is the underlying driver of the relationship between climatic niche limits and extinction risk. Analysing whether climatic niche limits are associated with extinction risk from climate- and habitat-related threats would provide a more direct test of the proposed mechanism.

(2) The treatment of Data Deficient species is another important concern. The authors classified Data Deficient species as Not Threatened in their analysis. However, previous studies have shown that many may in fact be threatened (Borgelt et al., 2022, Communications Biology; Jetz and Freckleton, 2015, Philosophical Transactions of the Royal Society B). This issue is particularly important here given the large number of Data Deficient species, especially among reptiles and amphibians, for which extinction risk may itself be underestimated (Caetano et al., 2022, PLoS Biology; González-del-Pliego et al., 2019, Current Biology). Treating these species as Not Threatened could therefore influence the reported relationships and should be evaluated through additional analyses.

(3) The role of the phylogenetic diversity analysis in addressing the central question is also unclear. The primary question concerns the relationship between climatic niche limits and extinction risk, whereas the subsequent analysis examines phylogenetic diversity loss following simulated extinctions. Disproportionate losses of phylogenetic diversity following non-random extinction are already well studied (e.g. Hughes et al., 2022, Current Biology; Oliveira et al., 2020, Global Ecology and Biogeography; Davis et al., 2018, PNAS). The authors should therefore clarify what this analysis adds beyond existing work and how it provides a meaningful extension of the climatic niche-extinction framework.

(4) The interpretation of the future extinction scenarios is also stronger than warranted by the simulations. The authors appropriately acknowledge that these scenarios do not explicitly incorporate climate or land-use change, but subsequently interpret the results as suggesting that climatic niche constraints shape future extinction pathways and that climatically vulnerable species will be disproportionately affected. Identifying species with lower climatic niche limits as climatically vulnerable is reasonable within the framework of the study. However, because the simulations are based on present-day extinction-risk transition probabilities rather than future climate- or land-use-driven processes, they do not establish that future climate or land-use change will preferentially drive the extinction of these species.

Overall, the manuscript addresses an important question and provides potentially valuable data and analyses. However, several of the central conclusions currently extend beyond what the analyses can directly support. Addressing the issues above would substantially strengthen the manuscript and clarify the scope of its conclusions.

Reviewer #2 (Public review):

Summary:

The manuscript presents a comprehensive and useful evaluation of the association between extinction risk and niche limits. If these results were clearly articulated, then the manuscript would make a valuable contribution to the literature. However, I struggled to understand the manuscript, as the terms and methodology are not clearly defined. I realize that methods do not belong in the main text, but as written, it is often very difficult to understand what was calculated.

To give an example. In the abstract, the main result is: "Species with colder upper thermal (niche) limits are more likely to be classified as threatened across taxa." In the 1st paragraph of the discussion, we read: "We show that... species with narrower and colder thermal niches face greater threats to extinction...". These are three different variables: colder upper thermal limits, narrower thermal niches and colder thermal niches. I really did not see where these were all defined in the manuscript, why so many different variables are considered and why the results are not consistent. I think this is likely a case of insufficient care in term definition and usage, but I found it rather difficult to follow the manuscript as a result. Clearer short explanations of terms (including a sense of how they were calculated/what they actually mean) and consistent use of terms would make the manuscript much more understandable.

Next, there are extra bits of information - for example, much of a paragraph in the introduction devoted to dispersal, many references to land-use (though I did not see that land-use was considered in this manuscript), and overly long discussion paragraphs that are highly speculative (or at least lack citations).

Finally, while I am a big proponent of identifying macro-ecological patterns, I would appreciate a critical discussion of what assumptions are made (i.e., everything about a microclimate; no consideration of extremes, etc.) and what the approach may be missing. This is not to invalidate the work, but to give the reader greater confidence that such broad-scale analyses are informative and are not glossing over too much species-specific ecology or finer spatial or temporal scale heterogeneity.

In summary, I think the paper has great potential but would benefit from a very conscious and careful revision.

Reviewer #1 (Public review):

Summary:

Sensory preconditioning protocols have been used to understand how the brain controls aversive or appetitive behavioral responses towards stimuli that have never been directly reinforced. In this study, the authors combine pharmacological NMDA blockade in a specific brain region previously involved in sensory preconditioning (i.e., perirhinal cortex) and different versions of a light-tone sensory preconditioning task in rats (i.e., different number of associations during preconditioning, distinct temporal separation between stage 1 and stage 2, stimuli order) to better understand how the integration between separated stimuli occurs. Overall, the main conclusion of the study is that the number, order, and time determine how distinct experiences are encoded and stored in the brain, which clearly advances the understanding of how these complex cognitive processes are regulated by the brain.

Strengths:

(1)The manuscript is very well written, and the methodology is well detailed, fully aligned with previous work by the research group.

(2) Results from each pharmacological manipulation and/or protocol configuration are clear, demonstrating the robustness of their findings.

(3) The overall conclusions are very interesting and clearly advance our understanding of higher-order conditioning processes.

Weaknesses:

(1) The Discussion is overly redundant with the Results and should be rewritten to place the findings more clearly within the existing literature and add specific important topics for the field (e.g., type of stimulus presentation, other brain circuits involved, etc).

(2) Before-after and sex-disaggregated data should be presented to better show statistical differences between periods with or without cue during the test and between sexes.

(3) The authors should broaden their coverage of recent literature on light-tone higher-order conditioning beyond their own previous work.

Reviewer #2 (Public review):

Summary:

In a series of experiments, the authors addressed the effect of the number of CS1-CS2 pairings, their order, and the time between their pairings and the second phase (CS2-US) in a sensory-preconditioning procedure. They hypothesized that:

(1) A higher number of CS1-CS2 pairings would favor the integration of CS1-CS2 and CS2-US associations via chaining at Test of CS1, as the CS2 develops a stronger prediction of the absence of CS1 in Phase 1, thus preventing the formation of a CS1-US association in Phase 2 and a chaining mechanism at Test of CS1 (CS1-->CS2-->US). In contrast, when the order is reversed (CS2-->CS1), a higher number of pairings should not change the mechanism of integration, as CS2 becomes a predictor of the presence of CS1. This would preserve the retrieval of CS1 and the formation of a CS1-US association in Phase 2 (mediated learning), while weakening the CS1-->CS2 link required for chaining at Test.

(2) A longer interval between the two phases of sensory-preconditioning would disrupt the retrieval of the order of the CS1-->CS2 association. Consequently, CS2 is no longer a good predictor of the absence of CS1, and the latter gets associated with the US in Phase 2, thus favoring a chaining mechanism at Test of CS1.

(3) A reminder of the CS1-CS2 order after a long interval between the two phases of sensory-preconditioning should restore the retrieval of the order of the CS1-->CS2 association, thus reversing the effect of time, and favoring a chaining strategy at Test of CS1.

To distinguish the two mechanisms, the authors blocked NMDA receptors in the PRh with DAPV before Stage 2. If integration occurs through a mediated CS1-US association formed in Stage 2, DAPV should reduce freezing to CS1 at test; if it occurs through chaining at test, DAPV should have no effect.

Strengths:

The strategies developed by the authors to address this issue are elegant and well controlled. While the sensory-preconditioning procedure has been extensively investigated, the mechanisms underlying the integration of separately acquired associations remain unclear. This study determines - in a convincing manner - the conditions under which a conditioned response is expressed via a chaining or mediated learning mechanism.

Weaknesses:

The role of NMDA antagonism is somewhat ambiguous throughout the manuscript. Does it prevent retrieval or acquisition or both?

(The Introduction (lines 98 to 101) refers to retrieval, while the Results (lines 128 to 132) and Discussion (lines 329 to 331) refer to acquisition.

Reviewer #1 (Public review):

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

Summary:

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

Strengths:

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

Comments on previous version.

The authors have revised their manuscript to clarify that molecular analyses have not yet occurred and have resolved the technical/publication issues with the figures. I look forward to seeing these tools used in future publications to answer important questions in Blastocystsis research.

Reviewer #3 (Public review):

Summary:

The primary objective of this study was to establish a practical and functional framework for propagation of stable transgenic cell lines of Blastocystis, a common animal gut microeukaryote. Although the work focused on Blastocystis ST7-B, a subtype with relatively low prevalence in humans, this choice is justified by its association with more frequent negative health effects. Beyond their relevance to the medical field, the methodological advances described here have the potential to also expand cell biology studies of this anaerobic organism, including its unusual mitochondria and redox metabolism.

Strengths:

Prior to this work, genetic tools for Blastocystis were very limited, relying on a single strong promoter-terminator combination. The authors successfully expanded the available promoter set across a range of expression strengths by testing two dozen variants in luciferase-based assays. Critically, they developed an integrated workflow from a modular transgenic construct design to an expanded inventory of molecular components (promoters, reporters), optimized DNA delivery, stepwise antibiotic resistance-mediated clonal selection and propagation, and to reporter validation. The evaluation of several anaerobiosis-compatible labeling strategies for live (and fixed) cell optical imaging will be particularly useful, with the SNAP-tag system appearing especially promising for Blastocystis.

Reviewer #1 (Public review):

Summary:

This is an interesting study that addresses whether mitochondrial DNA (mitoDNA) variants impact telomere length (TL), which may be relevant to potential maternal inheritance of TL in offspring. The study addresses this question using an innovative cybrid model approach in which mitochondria from donor platelets from 7 individuals that vary in TL and differ in mitoDNA variants are fused with 143B cells that lack mitochondria. MitoDNA variants that exhibited reduced complex I activity showed telomere shortening in cybrids, and increased telomere dysfunction. Interestingly, these phenotypes could be reduced with NAC antioxidant and NAD+ supplementation, suggesting that ROS and oxidative DNA damage at telomeres contributed to the telomere shortening. They further showed that cybrids with lower levels of ROS correlated with longer TL in the lymphocytes of the mitochondrial donors.

This study has many strengths, including the use of cybrid technology, which allows for analysis of how mitochondria and mtDNA variants from various human donors influence telomere length when introduced into the same genetic background of 143B mitochondria deficient cells. Other strengths include the use of human donors that vary in leukocyte telomere length, and analysis of multiple endpoints of telomere integrity and mitochondrial function in the resulting cybrids. This study provides convincing evidence that mtDNA variants influence TL, through a mechanism involving mitochondrial derived ROS, potentially causing telomeric oxidative damage. These results may help explain prior work demonstrating a link between mitochondrial function and telomere length regulation during embryogenesis and development. The data are robust, and the manuscript is well written. Overall, the results support the conclusions and will likely have an impact on the field of mitochondrial function and telomere length regulation, with important implications for development, aging, and aging-related diseases.

Given the heterogeneity in TL and mtDNA variants in the human population, the conclusion could be further strengthened by increasing the number of donors and cybrids analyzed. Other minor weaknesses stem from the limited number of samples. For example, the study presented limited telomere FISH metaphase analysis of chromosome end fusions, telomere damage, and telomere attrition rates due to low sample numbers. Furthermore, the proposal that acute oxidative stress was induced by a metabolic shift from glycolysis to OXPHOS in vitro is based primarily on assumptions from prior work, rather than direct demonstration in the current study. However, results with the NAC antioxidant combined with NR strongly support this assumption. Overall, the findings from this study are convincing and provide a solid foundation for expanding this analysis in the future to include a greater number of samples from donors for cybrid analysis.

Reviewer #2 (Public review):

Summary:

The authors aim to determine whether mitochondrial genotype influence telomere length. By generating cybrids harboring different mitochondrial backgrounds, the authors seek to establish a mechanistic link between mitochondrial status and telomere biology.

Strengths:

A major strength of the study is the use of cybrid technology, which provides a great approach to investigate the role of mitochondrial DNA independently of the nuclear genome. The authors also employ multiple complementary assays to assess telomere-related phenotypes associated with mitochondrial dysfunction. Together, these experiments generate an interesting dataset that will be of value to researchers interested in the intersection between mitochondrial biology, genome stability, aging and development. These results also build on previous work supporting roles for ROS/mitochondria in driving telomere shortening.

Overall, this study provides interesting evidence linking mitochondrial background to telomere biology. The cybrid models represent a useful resource for the field and the work raises important questions regarding mitochondria-telomere communication.

Comments on revised version.

In my view, authors have answered satisfactorily my concerns.

Reviewer #3 (Public review):

Strengths:

Mahieu and colleagues ask whether mitochondrial genetic variation helps explain differences in human telomere length. They selected seven donors from a Flow-FISH reference cohort of 491 individuals and compared their mitochondrial backgrounds in cybrids sharing the same nuclear background.

The combination of telomere and mitochondrial assays is a strength. The association between low complex I activity and telomere shortening, together with the NAC/NR rescue in Cyb2, provides useful evidence linking mitochondrial function to telomere maintenance. The correlation between donor telomere length and cybrid ROS is also interesting, although the cybrid results are more persuasive than the claims about inheritance.

Weaknesses:

(1) The authors have clarified that the seven donors include intermediate telomere lengths. Their analyses excluding donors #1, #3 and #6 are reassuring, since the correlation persists in each case. However, these results appear only in the response, and the full leave-one-out analysis is not reported. The small, selected sample still limits how broadly the correlation can be interpreted.

(2) The revised manuscript makes the distinction between the cybrid and donor findings clearer. However, CI activity shows no significant association with donor telomere length. The proposed PARP1 mechanism also remains untested: the authors acknowledge that they did not establish whether PARP1 is required for the NAC/NR rescue, but still describe its role too strongly.

(3) The authors appropriately acknowledge that the ATP6 A177T association is inconclusive (p = 0.153). Some inheritance claims remain stronger than this warrants. The family observations are too limited to separate mitochondrial from nuclear or shared environmental effects, and the cybrid experiments do not isolate ATP6 A177T from the other variants transferred with it.

Reviewer #1 (Public review):

Summary:

Epileptogenesis involves deficits in GABAergic inhibition mediated by chloride-permeable GABA(A) channels, which are caused by elevated intracellular chloride. These changes in chloride homeostasis result from increased activity of the NKCC1 chloride importer and loss of function of the KCC2 chloride extruder. The authors used organotypic hippocampal slice preparations from mice expressing the genetically encoded chloride indicator Clomeleon as a model of posttraumatic epileptogenesis to show that these changes are mediated by the WNK-SPAK/OSR1 pathway. Blockade of the WNK-SPAK/OSR1 pathway with WNK463 alleviated epileptic activity. These findings provide important insights into the mechanisms of epileptogenesis and open new avenues for treating epilepsy.

Strengths:

The conclusions are well supported by the detailed pharmacological analysis.

Comments on revised version.

The authors addressed all the recommendations in their revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors investigate whether inhibition of the WNK-SPAK/OSR1 pathway using the allosteric inhibitor WNK463 improves neuronal chloride homeostasis and suppresses epileptiform activity in organotypic hippocampal slice cultures. Using SuperClomeleon imaging combined with extracellular field recordings, they demonstrate that WNK463 accelerates recovery of intracellular chloride following chloride loading, reduces interictal chloride accumulation, and progressively suppresses recurrent ictal-like discharges. Pharmacological inhibition and siRNA-mediated knockdown of NKCC1 and KCC2 are then used to investigate the contribution of these transporters to the anti-ictal effects of WNK463.

Strengths:

The study addresses an important question in the field of chloride homeostasis and epilepsy and combines complementary experimental approaches. In particular, the distinction between baseline chloride measured in the presence of TTX and activity-dependent interictal chloride accumulation provides a useful conceptual framework for interpreting previous studies of WNK-SPAK inhibition. The imaging, electrophysiological, and pharmacological data are internally consistent and support the conclusion that WNK463 alters chloride dynamics and substantially suppresses ictal-like activity in this model.

Weaknesses:

The revised manuscript addresses several concerns raised in my initial review. The authors have broadened the discussion of KCC2 dysfunction and added further context on therapeutic targeting, as well as a useful discussion of the limitations of the organotypic hippocampal slice model.

My main remaining concern is the mechanistic interpretation of the data. The authors have appropriately softened several statements and now acknowledge that membrane potential and the GABA reversal potential were not directly measured. However, strong mechanistic conclusions remain in the revised manuscript, including statements that the anti-ictal effects of WNK463 are "primarily mediated" by reduced NKCC1 and enhanced KCC2 transport activity and are "mainly due to" NKCC1 inhibition and KCC2 activation. These conclusions go beyond what is directly demonstrated, as WNK463-induced regulation of NKCC1 and KCC2 was not directly measured in this preparation. Lee et al. (2022) provides important support for this mechanism, demonstrating reduced KCC2-T1007 phosphorylation and enhanced KCC2-dependent chloride extrusion following WNK463 treatment. However, these findings were obtained using different experimental preparations, including cultured cortical neurons and an acute 4-AP seizure model. The current experiments therefore support a contribution of NKCC1 and KCC2, but do not directly establish coordinated regulation of these transporters as the primary mechanism underlying seizure suppression. This is also relevant to the simultaneous NKCC1/KCC2 inhibition experiments, where the overall anti-ictal effect of WNK463 is reduced but not completely abolished.

The statistical reporting has also been improved, but the experimental unit remains unclear for analyses involving multiple cells recorded from individual slices. The authors clarify the use of repeated-measures analyses, but it remains unclear whether individual cells or slices were treated as independent observations and how multiple cells from the same slice were accounted for statistically.

Reviewer #1 (Public review):

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

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

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

This is an interesting manuscript that aims to dissect the molecular mechanisms underlying biomolecular condensation of the NUP98::KDM5A oncoprotein. The work is solid, and the results are well explained and presented in a logical order.

Reviewer #2 (Public review):

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

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

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

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

Reviewer #1 (Public review):

Summary:

Jacobs et al. report how acute psilocybin influences value-based decision-making and associated neural correlates in the medial prefrontal cortex (mPFC) of rats performing a probabilistic two-choice task with reward probability varying in blocks. The authors highlight that acute psilocybin does not influence basic outcome-sensitive choice strategies (i.e., win-stay/lose-shift), but find that psilocybin increases choice variability and optimal choices in low-uncertainty contexts. Using RL modeling, the authors chose one of five models examined because it represented the behavior around transitions. This recapitulates their behavioral results, showing changes in choice variability (entropy) specifically under high-uncertainty conditions and improving asymptotic performance under low uncertainty. Single-unit recording from mPFC during the task revealed that psilocybin may enhance the representation of reward-preferring neurons and suppress non-reward-preferring neurons, with no changes to outcome encoding and total/relative value signals largely intact. The authors interpret these findings as evidence that psilocybin rebalances mPFC processing to favour learning from positive outcomes, potentially offering a candidate neural coding mechanism for psilocybin's purported ability to increase cognitive flexibility acutely. While the research question is highly significant, the experimental design, analysis, and interpretations have critical shortcomings.

Strengths:

Understanding how psilocybin influences behavioral effects like reward learning and cognitive flexibility is critical to understanding its potential use in the clinic. The behavioral, neural, and modeling studies use a powerful set of approaches that are useful for understanding this complex problem.

Weaknesses:

(1) Design and sample size were major concerns. Only 8 rats were used, and the primary comparison is within-subject psilocybin vs. the immediately preceding "baseline" day rather than a fully counterbalanced or randomized drug/vehicle design. Since psilocybin sessions always followed baseline sessions (never the reverse), order effects, practice effects, or day-to-day drift cannot be fully excluded. Additionally, a saline control was only available for 5 of 8 animals, on a haphazard schedule ("due to unanticipated technical issues"), so the claim that effects are pharmacologically specific to psilocybin rather than to injection/novelty stress rests on a small, incomplete control dataset.

(2) There are a number of major concerns regarding behavioral confounds. Results about the number of trials performed in each condition seem buried in the methods instead of being provided with the rest of the behavioral data. Critically, the results show a 40% reduction in the number of trials completed. This is important to highlight and consider in the context of reduced motivation/hunger, and also in terms of the associated neural activity. Additionally, the percentage of rewarded trials is not reported - this is important for the interpretation of the behavioral results. For example, is the increased choice variability following psilocybin treatment measured by entropy advantageous? Does this result in more or fewer rewarded trials?

(3) Any differences in neural data are greatly diminished by the behavioral differences. Given the extraordinary long latency (averages of up to 20s) to reward receipt, it is unclear how the neural data in Figure 3 referencing reward is meaningful given the time frame is a maximum of 1.5s post-response. How can neurons be labeled non-reward-preferring based on their activity well before they retrieved the reward? Further, it is unclear how the post-response neural activity should be interpreted in the context of this long and variable latency to retrieve reward. The abnormally long latency to retrieve a reward is not discussed, especially in the context of response latencies on the order of 2 to 4s. Furthermore, the difference between groups (7 versus 20 s) should be discussed because this may represent a difference in motivation/hunger/response drive between groups and influence the interpretation of both behavioral and neural data.

Additionally, it is unclear how to interpret the neural data given the offset timing between groups because of the large behavioral differences. For example, given the decrease in latency to respond following the psilocybin treatment, the neural data shown (aligned to the response; e.g., Figure 6A, B) is largely offset from the time of trial initiation. There is no discussion of what is occurring behaviorally during the latency, which may be the cause of the different neural signatures.

(4) For the RL Modeling, there is circularity in the author's approach to choosing a model - namely, based on the "posterior predictive ability to recover psilocybin effects". The author's process for arriving at Model 3 as the specific "preferred" model over the intermediate options (particularly Model 4 with stickiness) is not well-described. The validation step (of recovering the key psilocybin behavioral transition signatures) is circular and not sound evidence for model adequacy rather than independent confirmation. I would want to see the actual PSIS-LOO comparison table (with SEs and Pareto k diagnostics) from Supplemental Figure 2A before fully accepting that Model 3 was clearly the best-justified choice rather than simply the most convenient one given convergence constraints. There was no prior predictive check with the parameters being chosen from the literature. Additionally, the Bayesian parameter effects rely on a relatively lenient 80% HDI threshold (rather than 95%).

(5) Some minor concerns:

a) The order of the Type I and II neurons appears to be reversed in this sentence based on the rest of the results/figures. "These neurons had distinct temporal patterns, showing reward-related increases immediately at action or later in the action/outcome epoch and are depicted as Type I and Type II, respectively".

b) Connecting results from Figures 3 and 4 would be interesting; in other words, showing how Type I/II neurons look in Figure 4 plots.

c) In Figure 1H, are the post-transition trials collapsed across high and low next blocks, since it seems from the depicted trial order that low uncertainty blocks can be followed by either a high uncertainty block or another (reversed) low uncertainty block.

d) The methods by which the authors arrived at the binned value neurons are not well-described, and more clarity on how authors arrived at relative and total value estimates would be helpful.

Reviewer #2 (Public review):

Summary:

The authors set out to examine how psilocybin acutely alters learning in rats in a reward-based probabilistic choice task. Additionally, they asked how psilocybin acutely alters encoding of decision variables in the prefrontal cortex (PFC).

Strengths:

The behavioral paradigm is robust. Thorough and rigorous computational analyses suggest that psilocybin may differentially modulate the impact of rewarded and unrewarded outcomes and enhance exploration under uncertainty. Single-unit neural activity in PFC suggests a potential mechanism for these effects, with psilocybin inducing opposing effects on encoding of rewarded and unrewarded actions. These findings are potentially exciting and important.

Weaknesses:

The major weakness of this work is the lack of a saline-treated control. All conclusions rest on analyses that are limited to within-animal comparisons between a baseline session and a later session in which rats were injected with psilocybin prior to session start. There is no control for the injection stress or passage of time. The manuscript mentions that 5 of 8 mice were also tested in a session following a saline injection, but because data were lost for some animals, this was not pursued. Figure S1G reports very limited analysis of this data to suggest that there is no effect. At a minimum, this must be analyzed in full (behavioral and neural data) and included in the manuscript to support the central claim that the effects reported are indeed attributable to psilocybin.

Beyond this, it would be of interest to examine if the effect of psilocybin is restricted to the acute session or if any effects persist.

Reviewer #3 (Public review):

Summary:

Current theories suggest psilocybin promotes meta-learning, modifies learned associations, and promotes flexibility, contributing to its transdiagnostic clinical relevance. However, the effects of psilocybin on neural state dynamics during learning and decision-making are unclear. Jacobs et al. use a rat model to examine how psilocybin changes prefrontal cortex encoding during a value-based probabilistic choice task. The authors found psilocybin increased high-value choices when uncertainty was low and increased choice variability when uncertainty was high, compared to baseline performance. A reinforcement learning model found psilocybin decreased side bias and increased reward learning while decreasing the unrewarded learning rate. Encoding of rewarded actions was enhanced in the mPFC while unrewarded actions were attenuated.

Strengths:

The neural correlates and computations underlying the impacts of psychedelics are poorly understood, and this study examines these through well-established decision-making approaches. These data show changes in mPFC activity during a dynamic task, allowing for analysis across different contexts and events. The data presented provide some support for leading theories on psilocybin's therapeutic use in promoting meta-learning and flexibility. The combination of techniques, including decision-making, computational modeling, and neural recordings, is appropriate, powerful, and needed to make sense of the underlying change in experience triggered by psilocybin.

Weaknesses:

An unfortunate weakness in the design that limits the interpretability of the findings is a lack of control data under saline administration, and a lack of comparison of psilocybin to saline rather than baseline. Psilocybin performance is compared to a single baseline session, which lacks the effect of an injection that would be present in a saline control. There is some evidence that mild stress can itself impact outcome encoding. Supplemental data in this manuscript suggest there is no significant difference in probability of selecting the optimal choice on low uncertainty trials between baseline and saline; however, the direction of impact of the saline injection is in the same direction as psilocybin in the main figures. This means that the mild stress of simply receiving an injection of saline may not be totally neutral with regard to these measures, and may contribute to the effects attributed to psilocybin at both a behavioral and neural level.

Reviewer #1 (Public review):

Summary:

The paper presents an elegant task designed to investigate humans' ability to generalize knowledge of learned graph structures to new experiences that share the same structure but are built from different stimuli. Using behavior and MEG recordings, the authors test evidence for neural representation and application of structural knowledge.

Comments on revised version.

The authors have addressed my concerns and have largely toned-down the overstatement of their findings which was pervasive in the previous version of the manuscript. However, the revised title still seems rather misleading given the findings presented. I suggest the authors revise this to: 'A neural mechanism for transfer of abstract structural knowledge in humans'.

Reviewer #2 (Public review):

The authors track structural role representations during stimulus processing, which is relevant to study how prior experience shapes new learning. They employ a sequence learning task based on graph factorization and record brain activity using MEG to investigate whether graph factors are reused during learning and inference in a new graph. MEG data were recorded during passive stimulus presentation, while behavior is assessed in a subset of probe trials testing either experienced or inferred successions in the graph. The authors report neural similarity among stimuli conditional on their role in subprocesses, and a correlation between evidence for role representations and performance in experience probes, but not in inference probes.

The interpretation of "knowledge transfer" in the title and abstract is, however, constrained by the absence of a corresponding relationship with performance in the inference probes, which provide the behavioral measure of transfer. While the authors discuss why role representations might relate more closely to experience than inference probes in this task, this distinction makes the link between the neural findings and transfer weaker than the current framing suggests. (E.g., "was positively related to behavioral success in subprocess knowledge transfer" in the abstract could be misleading)

More broadly, the framing of the results as a mechanism for structure transfer or structural scaffolding appears stronger than warranted by the evidence. The findings indicate representations consistent with reusable roles, but do not establish that these representations functionally facilitate learning and transfer. The claims should align with this distinction between evidence for representational reuse and structural scaffolding.

Also, apart from abstract roles, simpler categorical grouping of different stimuli belonging to the same subprocess could produce increased similarity.

Relatedly, although the MEG-behavior (experience probes) correlation is significant for Component 6 but not Component 4, the difference between the two correlations is not significant. Interpreting the correlation as selective to the structurally relevant graph is thus difficult to statistically substantiate.

Overall, the study provides a well-designed paradigm for investigating compositional representations during structural generalization at high temporal resolution. However, the findings do not provide sufficient evidence to frame them as a mechanism for structure transfer.

Reviewer #3 (Public review):

Summary:

This study investigates how task components can be learned and transferred across different task contexts. The authors designed two consecutive sequence learning tasks, in which complex image sequences were generated from the combination of two graph-based structural "building blocks". One of these components was shared between the prior and transfer task environments, allowing the authors to test compositional transfer. Behavioral analyses using generalized linear models (GLMs) assessed participants' sensitivity to the underlying structure. MEG data were recorded and analyzed using classifications and feature representational similarity analysis (RSA) to examine whether neural similarity increased for stimuli sharing the same relational structure. The paper aims to uncover the neural dynamics that support compositional transfer during learning.

Strengths and weaknesses:

I found the methods and task design of this paper difficult to follow, particularly the way stimuli were constructed and how the experimental sequences were generated from the graph structures. These aspects would be hard to replicate without some clarification. I appreciate the integration of behavioral and neuroimaging data. The overall approach, especially the use of compositional graph structures in sequence learning, is interesting and could be used and revised in further studies in compositionality and transfer learning. I appreciated the authors' careful interpretation of their findings in the discussion. However, I would have liked a similar level of caution in the abstract, which currently overstates some claims.

Comments on revised version.

No further comments. I thank the authors for their diligence in addressing my comments.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

I appreciate the authors' efforts to address the remaining issues raised by the reviewers and agree that it becomes clearer by refocusing the study away from learning towards effects of Ma2 cell activation on motor execution. I also appreciate added clarifications throughout the manuscript.

That said the study still relies heavily on measuring changes throughout "performance refinement", which is just another word for learning. Given their task design, it is hard to assess any learning or refinement over the course of training. This makes it hard to interpret why certain changes in neuronal activity occur at the defined sessions. The main conclusion thus is that there is "a complex interplay between Mα2 activity, oscillatory brain dynamics, and motor function", as the authors state as well in their discussion. I would encourage a further focus or restriction of the data presentation and interpretation on motor execution and limit any plasticity findings on training session 6, where the reaching task properly starts, such as in the study the authors reference (10.1016/j.neuron.2021.07.030).

Relatedly, the authors cannot say that "enhancing Mα2 cell excitability improved the performance of a motor task relying on an already refined skill (Figure 6C)" (lines 1134-1136). Since there is no improvement in success ratio throughout the training prior to CLZ injection (Supp Fig. 7A), there is no evidence for learning or skill refinement in the period where the pellet is presented on the plate.

Regarding the Ca2+ image analysis, the baseline subtraction approach the authors use only appropriately reduces part of the technical variations seen with 1p Ca2+ imaging. It only partially accounts for differences in GCaMP expression or different illumination across cells, or slower task-independent fluctuations in neuronal activity. Given the goal of the analysis is to show activity modulation in the peri-trial interval around prehension onset, some version of trial-wise baseline normalization (which is commonly used) is necessary and would likely result in less confusing results.

Minor:

- Unclear which session traces in figure 1I are from.

- Label in Supp Fig. 7A should say "switch from spoon to plate".

Reviewer #1 (Public review):

In the previous revision the authors addressed 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.

Comments on revised version.

In this revision the authors addressed my remaining points.

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.]

The authors have substantially strengthened the manuscript in response to my comments. In particular, they have improved the treatment of non-canonical calls, added further illustrative examples at the level of individual RNA variants, repeated pathway enrichment using an appropriate expression-matched background, assessed treatment-associated cell viability, and moderated several biological claims. The revised manuscript is now most compelling as a technical benchmarking and best-practice resource for high-confidence prediction of a common RNA chemical modification from nanopore direct RNA sequencing.

The matched-input comparison of two methods, use of unmodified RNA controls, and inhibitor treatment are important strengths. Together, these experiments provide solid support for the central conclusion that careful filtering and appropriate controls can substantially reduce likely false-positive calls and yield a reproducible set of high-confidence predictions. The added analyses also provide useful illustrations of the capacity of long-read sequencing to assign predictions to individual RNA variants, although they do not establish broad superiority over the short-read comparison method.

Reviewer #2 (Public review):

The revised manuscript has improved substantially. The authors have addressed the main concerns raised during the previous round by introducing an IVT-based calibration framework, evaluating background calls across probability thresholds, examining basecalling mismatches, and replacing "stoichiometry" with the more appropriate term "predicted modification proportion." The expanded discussion of non-DRACH predictions is also useful and appropriately balanced. Overall, the study now provides a valuable practical framework for Dorado-based RNA modification analysis.

Reviewer #1 (Public review):

Summary:

In this manuscript, Ding et al. use genetic mouse models to demonstrate that atrial trabeculation is more dependent on Tie1/Tie2 signaling than ventricular trabeculation. With additional experimentation that would support the current claims, the results may hold significant value, as atrial trabeculation remains an understudied phenomenon in cardiac biology with potential implications for atrial cardiomyopathy and atrial fibrillation.

Strengths:

Detailed characterization of atrial versus ventricular trabeculation across different developmental timepoints, and the use of appropriate animal models to address the scientific question at hand.

Comments on revised version.

I thank the authors for their detailed comments on the reviewers' comments. There however remain some issues that should be properly addressed to make the manuscript stronger.

(1) The authors state that Tie1 and Tek levels are noticeably higher in atrial vs ventricular endocardial cells. Can the authors provide the appropriate statistics to give strength to this claim, since the Supplemental Figure 1F does not indicate robust chamber-specific differences? As normal statistics (t-test, Mann-Whitney, ANOVA) cannot be applied to raw RNAseq data, the authors should collect independent samples and perform qRT-PCR analyses on these.

(2) When Tek is depleted in endothelial cells at E12.5, ventricular defects are also observed at later stages. Presumable, this could mean that earlier depletion of Tek, prior to initiation of ventricular trabeculation, might have resulted in equally severe atrial and ventricular defects. Similarly, they observe that, when Tie1 is deleted early on (tamoxifen administration at E10.5), combined with the heterozygous deletion of Tek, both atrial and ventricular defects are observed. While their postnatal studies do suggest a differential impact of Tie2 on atrial vs ventricular trabeculation (or the maintenance thereof), a more thorough analysis is needed to truly support the authors' claim that Tie1 and Tie2 synergistically impact atrial trabeculation more dramatically than ventricular trabeculation. However, to give weight to this claim, they should perform EC-specific depletion of Tie1 and Tie2 (or at least 3 out of 4 alleles) at different developmental stages, where tamoxifen is administered prior to ventricular or atrial trabeculation.

(3) Earlier reports indicate that a part of Tie1 null mutants (app. 15%) die in utero. Did the authors also observe this phenomenon in their mutant lines, and did these animals have more severe atrial defects?

(4) Although the reviewers did not request detailed mechanistic insights into the molecular mechanisms that drive Tie1-mediated atrial trabeculation, several of the reviewers' requests (e.g. YAP/TAZ staining) were unfortunately not performed. This is a shame, as peer review is instrumental to strengthen manuscripts and increase their validity. It would have therefore been nice if the authors had at least made an attempt to address some of outstanding reviewers' questions.

Reviewer #2 (Public review):

Ding et al. examine the role of TIE1 in cardiac chamber morphogenesis using genetic mouse models targeting Tie1, Tek, or both, and analyzing endocardial cell-mediated chamber formation across multiple embryonic developmental and postnatal stages, supported by analysis of published single cell datasets and new bulk RNA seq analyses of murine cardiac tissue. The authors find that Tie1 and Tek expression is higher in atrial than ventricular endocardial cells. Notably, endothelial TIE1 is required for atrial trabeculation at E12.5, but is less critical in ventricular trabeculation. TIE1 also acts synergistically with TIE2 during atrial trabeculation. While Tie1 deficiency alone does not cause defects at E10.5, combined heterozygous deletion of Tek disrupts both atrial and ventricular development at E10.5. This synergy is further supported by analyses at later embryonic stages and in postnatal hearts.

Strengths:

The study is well designed, clearly written, and supported by high-quality figures. The performed experiments demonstrate a previously unrecognized role for TIE1 in cardiac development and identify synergistic control of cardiac morphogenesis by TIE1 and TIE2. This synergy is consistent with the previously identified roles of TIE1 and TIE2 in venous development and with TIE1 involvement in angiopoietin-dependent postnatal vascular and lymphatic remodeling. Together, these findings support a role for TIE1 as a contributor to ANGPT1-TIE2 signaling during heart development.

Weaknesses:

The manuscript does not include direct mechanistic studies; however, RNA seq analysis of atria and ventricles showed reduced expression of Tek, Dll1, and Notch1 upon Tie1 deficiency in developing hearts. Although no direct mechanistic experiments are performed, potential mechanisms based on previously published work such as TIE1-TIE2 heterodimer formation, effects on endothelial junctions, migration, survival and venous development are discussed in the context of TIE1-TIE2 synergy.

Reviewer #3 (Public review):

Summary:

Ding et al. investigate the roles of TIE1 and TEK (Tie2) in mouse cardiac development, with a particular focus on atrial trabeculation. The authors employ multiple genetic models, including Tie1ICDflox/flox (with Cdh5-CreERT2), a knockout-first allele (EUCOMM, Tie1 tm1a/tm1a), and a Tek deletion model.

Based on the dataset from Feng et al. 2022 Nat Commun, the authors report increased expression of Tie1 and Tek transcripts in atrial endocardial cells compared to ventricular cells at embryonic day (E) 14.5. Loss of Tie1 leads to early atrial trabeculation defects detectable at E12.5, whereas ventricular defects appear later and are less pronounced at E14.5. Chamber-specific RNA sequencing reveals stronger transcriptional changes in atrial tissue.

Conditional deletion of Tek results in a similar phenotype, with more pronounced atrial defects. Combined deletion of Tie1 and Tek (Tie1 ΔICD/ΔICD; Tek+/-) leads to earlier and more severe defects in both atrial and ventricular trabeculation and results in embryonic lethality around E12.5, suggesting a synergistic interaction between the two genes.

Conditional endothelial deletion of Tie1 combined with heterozygous global Tek at later embryonic stages allows analysis at later time points and again shows more severe defects in atrial trabeculation. Postnatal analysis of this model reveals reduced heart-to-body weight ratios and potential mild atrial abnormalities.

Strength:

(1) The authors address chamber-specific signaling mechanisms underlying atrial versus ventricular trabeculation, an area of high developmental and clinical relevance.

(2) The study provides a comprehensive temporal analysis across multiple embryonic stages.

(3) The use of multiple genetic models strengthens the overall conclusions and allows comparative interpretation.

(4) While focusing on trabeculation, the authors also include observations on coronary vessel development, increasing the broader relevance of the work. The findings are therefore of interest to the wider cardiovascular research community.

Weakness:

(1) Limited mechanistic insight<br /> While the authors aim to investigate underlying mechanisms, the current study is largely descriptive and based on mRNA expression and genetic interaction analyses (Tie1/Tek co-deletion). Direct mechanistic insights into signaling pathways remain limited. However, the dataset provides a valuable foundation for future mechanistic studies, which should be more clearly acknowledged in the discussion.

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:

There is prior literature showing a robust relationship between sulcal interruptions in the posterior occipital temporal sulcus (pOTS) and reading ability. The goals of this study were to extend these findings to children examined longitudinally as they become better readers, and to examine the underlying white matter properties in individuals with and without pOTS sulcal interruptions. To do this, the authors collected longitudinal structural, diffusion, and behavioral data in 51 children (TP1 age 5.5, TP3 age 8.2 years).

First, the authors found that the gyral gap was consistent across time within the subject. This is expected, as they state in the introduction that sulcal patterns are typically established in utero. Next, they found that children with an interrupted pOTS have higher reading scores (across a variety of measures) at timepoint (TP) 3 than children with continuous pOTS, and this was specific to the pOTS, as no associations emerged for the anterior OTS or MFS; this is again expected from prior literature. They then found that the binary presence of this gap, but not anterior OTS or MFS predicted T3 reading performance. Further, they found that a subsample of the lowest readers at TP1 did not have differences in reading score by gyral gap, but that this difference emerged at TP3. Additionally, the gyral gap at TP1 is similar to variance TOWRE 3 reading skills as some behavioral measures at TP1. Examining underlying white matter in a smaller subset of children, the authors found higher MD in children with an interrupted pOTS vs. those with a continuous pOTS, which was contrary to their hypothesis, and higher local connectivity for interrupted, aligning with their hypothesis, but this difference was no longer present when accounting for TP3 reading scores. The authors conclude that structural properties, in this case, the gyral gap, may guide neural plasticity for reading.

Strengths:

This paper has an interesting set of longitudinal data to examine the perhaps changing relationship between sulcal interruptions in the pOTS with reading scores. I commend the authors on data collection and attention to detail in the anatomical analyses.

Reviewer #2 (Public review):

Summary:

This manuscript examined the impact of sulcal morphology on reading development. A very specific feature on the ventral surface of the brain was identified, namely the presence of an interruption in the posterior portion of the left occipitotemporal sulcus (pOTS). Compared to children with a continuous pOTS, children with an interruption at age 5 years had better reading ability at age 8. This was a large effect measured in 43 children. Surprisingly, this morphological feature was a better predictor of reading ability than measures of pre-literacy cognitive skills, such as phonological awareness. The effect was tested and reproduced across several different measures of reading ability. The authors hypothesised that the mechanism underlying this benefit related to greater local connectivity, which confers a computational advantage. This was demonstrated using analysis of diffusion-weighted imaging data in 29 of the children obtained at age 8.

Strengths:

The novelty of the manuscript is threefold: (i) the measure was made in children who were pre-literate (previous work was in older children and adults); (ii) longitudinal brain imaging and behavioural data were analysed; and (iii) diffusion data were analysed to test a hypothesis about the underlying mechanism.

The manuscript is exceptionally well written. The methods are detailed and easily reproduced. The approach is thoughtful and meticulous. All possible alternatives appear to have been considered. Where possible, further analyses have been done to address these alternatives. For example, the testing of the specificity of the sulcal interruption to left pOTS was an important addition. None predicted reading skills.

Reviewer #1 (Public review):

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

Summary:

In this work, T. Wijerathne et al. investigated and reported the agonistic effect of Yoda1 and Yoda2 over PIEZO2 function using patch clamp electrophysiology, Ca2+ imaging, and molecular dynamics. They find that Yoda1 sensitizes PIEZO2 to membrane tension, can induce Ca2+ influx, and decreases its inactivation to a lesser degree than it does to PIEZO1 channels. Additionally, their data shows that Yoda2 sensitizes PIEZO2 channels to membrane indentation to a greater extent, but it has a weaker effect on channel inactivation than Yoda1. Interestingly, they report that a mutation in a conserved arginine between PIEZO channels can be used to abolish PIEZO1-mediated Ca2+ flux in response to Yoda molecules. As a whole, the results presented here should be put into perspective against previous and future works involving systems where both PIEZO1 and PIEZO2 might be expressed. This is especially true for works where Yoda1 has been used as a basis for determining the absence of PIEZO2.

Strengths:

The authors use multiple techniques to investigate how Yoda molecules affect the three most important biophysical aspects of PIEZO channels that, when changed, result in pathophysiological responses: a) sensitivity to mechanical stimuli, b) Ca2+ entry, and c) channel inactivation. Lastly, they find a specific amino acid/region that could be exploited for drug design and/or development.

Reviewer #2 (Public review):

Summary:

This manuscript challenges the long-standing assumption that Yoda1 and Yoda2 are PIEZO1-selective activators. Using patch-clamp electrophysiology and calcium imaging in HEK293TΔPZ1 cells overexpressing PIEZO2, the authors demonstrate that Yoda1 potentiates PIEZO2 stretch-activated currents to a similar extent as PIEZO1 and slows PIEZO2 poking-current inactivation (albeit with lower efficacy). They further show that the more potent analog Yoda2 affects PIEZO2 at nanomolar concentrations and use mutagenesis and molecular dynamics simulations to propose that Yoda2's benzoic acid group forms a transient salt bridge with R1724 in the putative Yoda binding pocket, explaining its enhanced potency.

Strengths:

The authors are established Piezo/biophysics experts; the study is highly important, technically competent, and carries significant implications for the reinterpretation of prior work that used Yoda compounds as PIEZO1-selective probes.

The core finding that Yoda1 modulates PIEZO2 stretch currents is convincing and important.

Reviewer #3 (Public review):

Summary:

The manuscript reports that Yoda1 and Yoda2 agonize PIEZO2 in a manner similar to PIEZO1, increasing open probability and stretch sensitivity, but the mechanism underlying this sensitivity is incomplete. Mutagenesis was shown exclusively in PIEZO1, with no corresponding mutagenesis in PIEZO2, so the proposed mechanism in PIEZO2 is inferred by homology rather than directly tested. All experiments use mouse PIEZO2, and the human ortholog should be used before generalizing the proposed reinterpretation of the field.

Strengths:

The pressure-clamp electrophysiology demonstrating a shift in half-activation pressure for PIEZO2 is compelling evidence in support of the central claim.

Reviewer #1 (Public review):

Summary:

The study investigates the Drosophila non-visual light receptor rhodopsin7 with regard to its role in light information processing and resulting consequences for behavioral patterns and circadian clock function. Using behavioral, in situ staining, and receptor activation assays together with different fly mutants, the authors show that rhodopsin7 is an important determinant of activity under and response to darkness, which likely signals via a pathway distinct from other, visual Drosophila rhodopsins. Based on phylogenetic analysis, the authors further discuss a potentially conserved functional role of non-visual photoreceptors like rhodopsin7 and the mammalian melanopsin light information processing and circadian clock modulation.

Strengths:

The manuscript follows a very clear structure with all investigations logically building onto each other. Background information and methodology are provided in appropriate detail so that readers can fully understand why and how experiments were conducted. It is further praiseworthy that the authors provide the details that allow also non-experts in the field to fully understand their approaches. Experimental work was conducted in a highly standardized manner, and also considered potential "side-aspects" like the consequences of temperature cycles and changed photoperiods. The detailed and clear description of the obtained results makes them very convincing, with (almost) all observable patterns being addressed.

By highlighting the evolutionary old phylogenetic position of rhodopsin7 and its conservation across numerous clades, the authors provide strong reasoning for the relevance of their work, also pointing out the similarities to the mammalian melanopsin. The postulated hypothesis regarding protein structure and functioning, as well as the role in light information processing and behavioral and circadian clock modulation are well based on the authors' observations, and speculative aspects are correctly pointed out.

Weaknesses:

Where the manuscript still has potential for improvement is the discussion, which in its current form does seem slightly self-contained and does not fully integrate the findings of previous studies on Drosophila rhodopsin7. As the introduction specifically points out that previous findings have been contradictory, this seems like a missed opportunity. Further details on this are provided in the recommendations below.

Similarly, the manuscript currently lacks a discussion of the possible relevance of rhodopsin7 (and other non-visual light receptors in other organisms) in the context of a species' environment and lifestyle, i.e., what is the relevance/benefit of having rhodopsin7 in the fly's everyday life? While this clearly involves speculation, when done carefully, it can elevate the paper's relevance from a primarily academic to a societal one.

An additional point concerns the title and abstract, which postulate rhodopsin7 roles in contrast vision as well as motion and brightness perception. Contrast remains poorly defined in the text, leaving it ambiguous whether it refers to bright/dark contrasts, e.g., along edges, or the temporal contrast that results from dark pulses (startle response). While the latter seems to apply here, the former is likely more intuitive. Thus, this aspect should be rephrased (also in the title) or properly clarified early on. Regarding motion detection, this is backed up by the optomotor response results, but the findings stand somewhat isolated from the other results, lacking a clear connection aside from general visual processing. Lastly, brightness perception is mentioned in the abstract, but never again, possibly due to inconsistent phrasing throughout the manuscript.

Reviewer #2 (Public review):

Summary:

This is a very interesting paper bringing new and important information about the poorly understood rhodopsin 7 photoreceptive molecule. The very ancient origin of the gene is revealed in addition to data supporting a signaling pathway that is different from the one known for the canonical rhodopsins. Precise expression data, particularly in the optic lobe of the fly, as well as clear behavioral phenotypes in responses to light changes, make this study a strong contribution to the understanding of the still-debated function of rhodopsin 7.

Specific comments

(1) Title and abstract: Contribution of Rh7 to circadian clock regulation

a) It is not that clear to me what rhodopsin does in terms of circadian regulation (even though its function might be circadianly regulated). The clear role in the light/dark distribution of activity might not be circadian per se, but mostly light/dark- driven and there is no evidence here for a role in the entrainment of the clock.

b) The authors should cite Lazopulo, which nicely shows that Rh7 has an important role in peripheral neurons to allow flies to escape from blue light (see below).

(2) Figure 2 C

The finding showing that Galphaz but not Galphaq can trigger signaling from light-excited Rh7 is a very intriguing finding to better understand Rh7 function. Since Galphaz is related to Gi/o, it would be interesting to test those, for example, by expressing RNAi with Rh7-gal4 and testing the Light-dark or light-off response behavior.

(3) Figures 3-4

The change in the locomotor activity distribution between light and dark in LD conditions provides a nice assay for Rh7 function. Since Lazopulo et al. (2019) have shown that wild-type but not Rh7 mutants do escape from blue light, it would be important to compare and discuss these LD behavior data with the Lazopulo results. Precisely, is this nighttime preference linked to blue light?

The expression data are really nice and show that Rh7 is mostly a non-retinal photoreceptor. However, the paper would be strongly reinforced by correlating this with the LD behavior. The LD phenotype should be tested in flies with Rh7 expression rescued under Rh7gal4 control (as done for the startle response). This is important to show whether the expression pattern is likely responsible for the described Rh7 function in LD. If L5 and or M11 drivers are available, they should be used to rescue Rh7? Since expression in some clock neurons is shown, the rescue experiment should also be done with a clock neuron driver.

In the same line, can the LD phenotype (or startle response phenotype of Figure 4) be restored by expressing Rh7 under ppk control, as shown for the blue light avoidance phenotype by Lazopulo et al?

Finally, the Rh7 "darkfly" rescued flies should be tested in LD.

Reviewer #3 (Public review):

Summary:

While our knowledge regarding visual opsins is largely very good, a lot more uncertainty exists around the role of non-visual opsins. Using the power of the Drosophila melanogaster model system, Kirsh et al. investigate the role of the non-visual opsin Rhodopsin7 (Rh7). Expression analysis, based on Rh7-Gal4>UAS-GFP and HRC in situ staining, reveals strong expression in the optic lobes and somewhat weaker, but nevertheless extensive expression in the brain. An investigation of motor activity reveals that loss of function leads to an altered day and night rhythm, specifically decreasing activity during the dark phase. These flies were also less sensitive, but still responsive to a light-induced startle response and showed deficiencies in the optomotor response. To further investigate how Rh7 may modulate these responses, inspired by the Dark line of flies (which were kept in the dark for ~1400 generations) and which has accumulated C-terminal related losses, the authors conducted rescues with an intact and a C-terminal-deficient Rh7 and were able to pinpoint that region as an important driver of related behavioral shifts. These findings are particularly intriguing as Rh7 represents an ancient opsin with phylogenetic and mechanistic parallels to mammalian melanopsin.

Strengths:

The paper is well-written and contains high-quality data with appropriate sample sizes, and the conclusions are well supported.

Weaknesses:

No weaknesses were identified by this reviewer, but the following recommendations are made:

(1) The authors should clarify exactly what tissues were taken for the comparative qPCR. This is particularly interesting in terms of the retina. Since Rh7 appears not to be expressed within the photoreceptor cells of the retina, this raises the important question as to which cells it is expressed in. To address this important question, it would also be helpful to include an expression analysis of the retina itself (by extending the RH7-GFP expression patterns and/or adding HCR in situ of the ommatidia array). The cell types of the retina are very well classified, and some evidence already exists for Rh7 expression in support cells (e.g., Charlton-Perkins et al., (2017); PMID: 28562601). This study has a unique opportunity to investigate this further by adding these critical data for a more complete picture of Rh7.

(2) Mammalian opsins should be included in the phylogenetic analysis illustrated in Figure 2A and indicate their position on the tree. This will allow readers to better put the authors' statements regarding the intermediate position of Rh7 into perspective. In addition, note that the distinction between red and deep red is easy to miss regarding the Rh7 cluster. Perhaps the authors could use a more distinct colour scheme, for example, orange and deep red.

(3) More details should be provided on the optomotor response experiments. Specifically, specifications of the frequencies used for the optomotor response are needed. Results show a relatively large level of variation, which may be due to different angular perspectives that flies may have had while viewing the stimulus. If possible, provide videos as examples, as they will make it clearer to viewers how much flies could move around in the setup (from the methods, it seems they could move within the 2.2 of the 3 cm diameter of the arena, which would lead to substantial differences in the visual angle of the viewed grating.

Reviewer #1 (Public review):

Summary:

The authors have addressed many of the concerns. In sum, this study examined how ErbB4 signalling in parvalbumin-positive (PV) interneurons contributes to olfactory bulb (OB) microcircuit function and olfactory behaviour in mice. Using RNAscope, single-cell RT-PCR, conditional Erbb4 deletion, awake in vivo recordings, slice electrophysiology and optogenetics, the authors report that ErbB4 was expressed in PV interneurons of the EPL and IPL, and that its loss impaired odour discrimination and dishabituation, reduced GABAergic transmission onto mitral cells, and elevated ongoing LFP power. The experiments were well-designed, but several central claims were not yet adequately supported by the data as presented.

Strengths:

Pretty comprehensive set of experiments.

Weaknesses:

(1) Figure 1. The investigation time of the AAV-Cre-GFP group varied considerably across isoamyl acetate concentrations. Were there any differences between the AAV-GFP and AAV-Cre-GFP groups at the 0 (mineral oil) condition? Since the threshold was defined by a comparison against mineral oil, a baseline difference would propagate directly into the threshold estimate and could reduce exploratory drive rather than an olfactory deficit.

(2) Previous papers have reported that ErbB4 is expressed in neuroblasts of the SVZ and RMS, and that disruption of NRG1-ErbB4 signalling may decrease GABAergic neuron number and alter distribution (Anton et al., Nat Neurosci 2004; Ghashghaei et al., PNAS 2006). The authors showed in Figure 2L that ErbB4 expression in the OB was already decreased at P7, suggesting reduced neuron number and disrupted neuronal distribution. The authors should therefore quantify the number and laminar distribution of the different neuronal types in the OB. Cresyl violet staining showed that gross lamination was preserved, but could detect changes in cell density or in the relative proportions of interneuron subtypes.

(3) Figure 4C. First of all, there were no indicators marking the sessions, so we had no idea what state the mice were in. Also, why did the odour-evoked MFRs of PV-Erbb4⁺/⁺ and PV-Erbb4⁻/⁻ mice show opposite trends during the 0-2 s window in the first session - were there any differences in the behaviour of the two groups during this session? As the mice were head-fixed on a floating ball, locomotion and sniffing should have been monitored, since both strongly influence M/TC firing.

(4) Figure 4K-L. The authors did not explain why spontaneous and odour-evoked MFR were so much different among the "no response" M/TCs. Did this mean that ErbB4 knockout in PV interneurons disrupts the function? This would imply a tonic rather than a stimulus-locked disinhibition, which is a different claim from the one made in the Discussion. In addition, the authors stated that panel 4L indicates "the distribution of types of odour-evoked responses was significantly altered in PV-Erbb4⁻/⁻ mice compared with PV-Erbb4⁺/⁺ mice, suggesting compromised odour detection" - what exactly does this mean? A shift in the proportions of response categories is also compatible with a changed baseline firing rate altering the classification itself.

(5) Figure 5. The power of all frequency bands in PV-Erbb4⁻/⁻ mice was higher than in PV-Erbb4⁺/⁺ mice, including the gamma band. However, Tan et al. (2022) reported that inhibition of ErbB4 kinase activity decreases LFP power and GABA release, which is inconsistent with the present data. This discrepancy should be addressed explicitly. A uniform elevation across all four bands was atypical for a PV-dependent mechanism and could reflect a global scaling difference; separating periodic from aperiodic components and reporting normalised power would help.

(6) Photocurrent amplitude and mCherry⁺ PV cell density was not compared across genotypes; Figure 9-figure supplement 1B-C showed photocurrents for only one group. Since the authors themselves reported in Figure 7-figure supplement 1 that PV interneurons in knockout mice reduced mEPSC frequency and amplitude and increased AP frequency, the presynaptic population was clearly not in an equivalent state. The reduced oIPSC could therefore result from differences in ChR2 expression level or in the number of labelled cells. Photocurrent curves and mCherry⁺ cell density must be provided for both groups.

Reviewer #2 (Public review):

The authors aimed to understand how the transcription factor Erbb4 contributes local circuit dynamics and affects performance. This is an interesting question because Erbb4 is largely localized to PV cells in the olfactory bulb (OB) and upregulated following exposure to an olfactory discrimination task.

Strengths:

They have now validated their local, PV-specific knockout strategy, which more directly tests their hypothesis.

The inclusion of optogenetically evoked presynaptic responses from PV cells onto M/T cells also strengthened the conclusion that this synapse was altered.

The authors also now included slice recordings from PV cells, which helps to form a more comprehensive picture of the circuit.

Weaknesses:

(1) The data used to support their spike sorting approach is now included but is difficult to evaluate. In Figure 4 - supp 1B, they show what appears to be mean + sem of concatenated waveforms from one tetrode, but this is not explained anywhere. Moreover, the timescale seems to be off by an order of magnitude. It also seems that the spike widths in panel E are bimodally distributed, with many of the half-widths being more consistent with fast-spiking cells. There is a high density of PV cells in the same layer where they record M/T cells, so is it not possible that some of the units were PV cells (note: this was suggested in the previous review as a potential exciting direction to further validate their findings)? It is still unclear how the authors confidently record from only M/T cells when there is no mention of how other units (e.g., fast-spiking cells) are excluded. The methods state that only M/T cells can be recorded from this layer, which is an oversimplification and appears at odds with the apparent diversity in waveforms and responses.

(2) Upon review, the authors acknowledge that the PV-Erbb4+/+ and PV-Erbb4-/- mice were recorded in separate batches where the control mice were exposed to unwanted artefacts related to a solenoid valve. While we don't know how much that change influenced the results, even with careful analysis, it does draw attention to a major weakness in study design where controls and experimental mice are not run simultaneously. The presence of an artefact from the solenoid valve (perhaps a noise?) immediately before odor presentation for controls, but not experimental mice, should be considered by the reader when it comes to evaluating the odor-evoked responses.

Overall, I feel the manuscript is much improved and more solidly demonstrates that Erbb4 expression in PV cells plays a major role in regulating local OB circuit dynamics and influences odor processing. The remaining weaknesses surrounding the in vivo single unit recordings from PV-Erbb4+/+ versus PV-Erbb4-/- mice reduce my confidence in the validity of the results, but the general agreement of the in vitro work is more re-assuring.

Reviewer #1 (Public review):

[Editor's note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The inclusion of a summary schematic in this revision significantly improves the manuscript. We have also modified the last sentence of the original editorial assessment.]

Summary:

The authors demonstrate the stereoselective role of D-serine in 1C metabolism showing that D-serine competes with L-serine and inhibits mitochondrial L-serine transport. They observe expression of 1C metabolites in their metabolomics approach in primary cortical neurons treated with L-serine, D-serine and mixture of both. Their conclusions are based on the reduction in levels of glycine, polyamines and their intermediates and formate. Single cell RNA sequencing of N2a cells showed that cells treated with D-serine enhanced expression of genes associated with mitochondrial functions such as respiratory chain complex assembly and mitochondrial functions with downregulation of genes related to amino acid transport, cellular growth and neuron projection extension. Their work demonstrates that D-serine inhibits tumor cell proliferation and induces apoptosis in neural progenitor cells highlighting the importance of D-serine in neurodevelopment.

Strengths:

D-amino acids do not merely function as ligands at receptors but have underlying roles in signaling and metabolism. These roles are just beginning to be uncovered. The authors elucidate the metabolic role of D-serine in the context of neuronal maturation by its suppression of mitochondrial L-serine availability for SHMT2 and 1C flux. This is the strength of the manuscript. The implications for the metabolic role of D-serine in neurons is a highlight and underlines its roles in neuronal metabolism.

Comments on previous version.

This reviewer is satisfied by the effort made by the authors based on the prior comments raised.

Reviewer #2 (Public review):

Summary:

This study by Suzuki et al. reports an interesting stereo-selective role of D-serine in regulating one-carbon metabolism during neurodevelopment to adapt the functional transition, probably through the competition with mitochondrial transport of L-serine. The authors provide a multi-layered set of evidence, including metabolomics, enzyme assays, mitochondrial transport competition and functional assays in immature/neural progenitor cells, to build up a conceptual integration of D-serine as both a neurotransmitter and a metabolic regulator in central neural system, which raises a broad potential interest to the neuroscience and metabolism communities.

Strengths:

This work provides a conceptual advance that D-serine is not only serves as a traditional neurotransmitter in central neural system but also critically contributes to metabolic regulation of neural cells. The authors performed solid metabolomic assays to validate the suppressive effect of D-serine on one-carbon metabolic pathway, providing some evidence that D-serine competitively inhibits mitochondrial serine transport, but not directly impairs SHMT2 enzymatic activity. All these data indicate a critical role of D-serine synthesis during neural maturation and suggest a potential translational strategy for targeting serine metabolism in neural tumors.

Comments on previous version.

My previous concerns have been appropriately addressed or discussed in this revised version of manuscript. I have to say that, at this stage, I have no further questions.

Reviewer #3 (Public review):

Summary:

This manuscript presents a comprehensive and well-executed investigation into the metabolic role of D-serine in the central nervous system. The authors provide solid evidence that D-serine competitively inhibits mitochondrial L-serine transport, thereby impairing one-carbon metabolism. This stereoselective mechanism reduces glycine and formate production, suppresses cellular proliferation, and induces apoptosis in immature neural cells and glioblastoma stem cells. Developmental analyses further reveal a physiological enantiomeric shift in serine metabolism during neurogenesis, aligning with the transition from proliferation to maturation. Overall, the study bridges developmental neurobiology, cancer metabolism, and amino acid transport, uncovering a previously unrecognized metabolic function of D-serine beyond its role in neurotransmission.

Strengths:

(1) The discovery that D-serine inhibits one-carbon metabolism by competing for mitochondrial L-serine transport-rather than through enzymatic inhibition or receptor-mediated signaling-represents a significant and previously underappreciated mechanism. This finding has broad implications for understanding metabolic regulation during neurodevelopment and offers potential relevance for targeting metabolic vulnerabilities in cancer.

(2) The authors integrate metabolomics, mitochondrial transport assays, molecular dynamics simulations, genetic and pharmacologic perturbations, transcriptomics, and both in vitro and ex vivo models. The breadth of experimental approaches, combined with the coherence of the findings across systems, provides strong support for the central conclusions and enhances the overall impact of the study.

(3) The temporal shift in D-/L-serine levels during neurodevelopment is elegantly linked to the transition from proliferative to mature neuronal states. The selective vulnerability of neural progenitors and tumor cells-contrasted with the resistance of mature neurons-highlights a biologically meaningful and potentially targetable metabolic distinction.

Reviewer #1 (Public review):

Summary:

This study by Tsuji et al. explores a mechanical threat model in Drosophila using air puffs as a stimulus. The authors first establish the paradigm and show that air puffs induce cardiac deceleration along with increased locomotion. They then identify dopamine as a key regulator of this response and go on to map the underlying circuit. In doing so, they pinpoint two pairs of DA-WED neurons as critical players. They carefully used intersectional strategies to achieve relatively clean labeling of these neurons, which helps ensure that the observed effects can be attributed specifically to DA-WED neurons. They further show that DA-WED neurons are both required and sufficient to drive cardiac deceleration, and that their activity increases in response to air puff stimulation. These neurons also contribute to the locomotor response. Directly inducing cardiac deceleration via optogenetic manipulation of cardiomyocytes also increases locomotion, suggesting a link between cardiac state and behavioral output.

Strengths:

Overall, the experiments are thoughtfully designed, well controlled, and clearly presented. The figures are easy to follow, and the conclusions are generally well supported by the data. The manuscript is also clearly written, with a discussion that acknowledges potential caveats and outlines future directions. The genetic tools, behavioral paradigm, heart rate measurement approaches, and stimulation methods introduced here will be valuable resources for the community. few minor comments:

Comments on revised version:

I thank the authors for responding to my questions, they have thoroughly addressed my comments. I am therefore supportive of publication of the manuscript.

Reviewer #2 (Public review):

Summary:

The authors study cardiac deceleration during threat responses in Drosophila. Particularly, it focuses on identifying the neuronal control of this deceleration. Using behavioral and cardiac tracking and analysis, genetics and calcium imaging, they identify two pairs of dopaminergic neurons involved in cardiac deceleration during air puff responses.

Strengths:

The study is overall well done and the paper clearly written. Particularly the work on identifying the two pairs of dopaminergic neurons involved in cardiac deceleration using a series of drivers and generating new ones is rigorous and extensive. Finally, the authors manipulate heartbeat to investigate how it influences threat responses. Although the evidence that heartbeat deceleration-induced increase in locomotion remains partial, the study reveals important insight in the relationship between locomotion, cardiac activity and neuromodulatory system during threat responses.