Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

(1) Clarification of the Superpixel-based image analysis

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

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

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

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

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

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

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

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

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

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

(4) Motion of the epiblast

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary and Significance:

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

Strengths:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

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

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

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

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

Reviewer #3 (Public Review):

Summary:

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

Comments on the introduction:

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

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

Comments on the results section:

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

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

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

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

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

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

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

Comments on the discussion:

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

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

Comments on revised version.

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

(1) A primary and external validation dataset.

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

(3) A wide range of data was investigated.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Comments on revised version:

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

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

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

Reviewer #3 (Public review):

Summary:

This paper addresses a fundamental gap in bone biology: our near-complete ignorance of the in vivo dynamics of calvarial bone marrow adiposity (BMA) at population scale. The authors developed an elegant artificial neural network trained on simulated data to automatically localize and quantify the bone marrow layer within standard T1-weighted MRI head scans; scans originally acquired to study the brain but harboring rich, unexploited information about adjacent bone. Applying this method to over 33,000 individuals from the UK Biobank, they accomplished three things that had never been done before: (1) they precisely quantified the sex-dimorphic age trajectory of calvarial BMA, including the dramatic post-menopausal rise and the protective role of hormone replacement therapy; (2) they performed the first well-powered GWAS of this trait, identifying 41 genome-wide significant loci including six sex-specific ones, with SNP heritability of 31.5%; and (3) they revealed significant genetic correlations and overlap between BMA and traits including bone mineral density, Parkinson's disease, and general cognitive ability, a finding made all the more intriguing by the recently described direct vascular channels connecting calvarial bone marrow to the meninges. Integration of GWAS genes with single-cell RNA-sequencing data from mesenchymal lineage cells further illuminated which genes govern lineage commitment to the adipogenic pathway versus lipid loading in mature adipocytes.

Comments on revised version.

The reviews raised substantive points across three domains, and the authors engaged with every one of them seriously and thoroughly.

On the validation of T1-weighted MRI as a measure of BMA: Reviewer 2 raised the strongest concern, arguing that T1-weighted signal intensity had never been formally validated as a quantitative fat-fraction measure in the calvarium. The authors responded with both a principled scientific argument and new data. They assembled existing literature demonstrating that T1-weighted signal is an established semi-quantitative proxy for marrow fat in multiple skeletal sites (Loevner et al. 2002, Shen et al. 2013, Zhang et al. 2020), and provided additional comparative analyses against quantitative T1 relaxation maps, multiple intensity normalization strategies (KDE, WhiteStripe, GMM, FCM, Z-score), DEXA-derived bone mineral density, and osteoporosis status. The biological coherence of their findings, recapitulating known sex and age profiles, identifying genes already established in cell and animal models of BMA biology, and estimating heritabilities consistent with twin data constitutes powerful, convergent evidence for construct validity. Their point that a semi-quantitative measure of a highly variable, well-demarcated biological signal can outperform a perfectly precise measure of a poorly defined entity is methodologically sound and well-argued.

On sex differences and the role of Hyperostosis frontalis interna: Reviewer 1 raised the clinically astute concern that Hyperostosis frontalis interna (HFI), a condition of inner table thickening prevalent in up to 49% of postmenopausal women, could confound calvarial BMA measurements and drive apparent sex differences. The authors performed a dedicated new analysis, stratifying BMA-BMD associations by sex and age group. They demonstrated that (1) the BMA-BMD association is robust in both males and females, (2) it remains stable across age groups, and (3) the neural network trained on simulations incorporating wide anatomical variation including inner table thickness is inherently resistant to moderate inner table thickening. Given that HFI is restricted to the frontal bone, which represents only a fraction of the calvarial surface, and that severe cases are rare (ICD-10 prevalence ~0.02% in the UK Biobank), the authors make a convincing case that this does not materially bias their results. Their suggestion that the method could itself be used in future work to study the genetic architecture of HFI is a nice forward-looking addition.

On genetic correlation interpretation and cross-trait pleiotropy: Reviewer 1 asked for clarification of the vertical versus horizontal pleiotropy distinction and for formal Mendelian randomization to support the possible causal effect of BMA on cognition. The authors appropriately clarified the conceptual framework in the revised text and, rather than overstating a causal claim without the supporting analysis, responsibly softened the language to "may be consistent with the hypothesis that BMA could have a causal effect on cognition." This is scientifically honest and appropriate.

On mouse scRNAseq and its relevance to humans: The authors acknowledged that the results section had not explicitly stated the mouse origin of the scRNAseq data, corrected this, and provided a well-justified rationale for the relevance of mouse mesenchymal lineage data to human BMA biology, which is a well-established and widely accepted model system in this field.

On GWAS replication: The claim that the study lacked replication was addressed by clarifying the a priori separation of discovery (white British, n=33,042) and replication (non-white British, n=4,958) samples, with 62% of significant discovery SNPs and 95% of lead SNPs replicating in the correct direction.

Overall Assessment:

This is a technically innovative, scientifically rigorous, and biologically meaningful paper. The method is genuinely novel, the sample size is among the largest ever applied to this phenotype, the genetic findings are well-powered and well-replicated, and the integration across imaging, genetics, and single-cell transcriptomics is exemplary. The authors have engaged with every substantive reviewer criticism in good faith, producing new analyses where appropriate and defending, and convincingly, with findings that were challenged without adequate basis. The revised manuscript is strengthened throughout.

This paper opens a new window quite literally, through the skull - into bone marrow biology at a scale and resolution that has never been achieved before.

Reviewer #3 (Public review):

Summary:

This manuscript presents a comprehensive and well-executed investigation into the metabolic role of D-serine in the central nervous system. The authors provide solid evidence that D-serine competitively inhibits mitochondrial L-serine transport, thereby impairing one-carbon metabolism. This stereoselective mechanism reduces glycine and formate production, suppresses cellular proliferation, and induces apoptosis in immature neural cells and glioblastoma stem cells. Developmental analyses further reveal a physiological enantiomeric shift in serine metabolism during neurogenesis, aligning with the transition from proliferation to maturation. Overall, the study bridges developmental neurobiology, cancer metabolism, and amino acid transport, uncovering a previously unrecognized metabolic function of D-serine beyond its role in neurotransmission.

Strengths:

(1) The discovery that D-serine inhibits one-carbon metabolism by competing for mitochondrial L-serine transport-rather than through enzymatic inhibition or receptor-mediated signaling-represents a significant and previously underappreciated mechanism. This finding has broad implications for understanding metabolic regulation during neurodevelopment and offers potential relevance for targeting metabolic vulnerabilities in cancer.

(2) The authors integrate metabolomics, mitochondrial transport assays, molecular dynamics simulations, genetic and pharmacologic perturbations, transcriptomics, and both in vitro and ex vivo models. The breadth of experimental approaches, combined with the coherence of the findings across systems, provides strong support for the central conclusions and enhances the overall impact of the study.

(3) The temporal shift in D-/L-serine levels during neurodevelopment is elegantly linked to the transition from proliferative to mature neuronal states. The selective vulnerability of neural progenitors and tumor cells-contrasted with the resistance of mature neurons-highlights a biologically meaningful and potentially targetable metabolic distinction.

Weaknesses:

(1) While the authors attribute D-serine's metabolic effects to competition with mitochondrial L-serine transport, the specific identity of the transporter(s) mediating this process remains undefined. This represents a meaningful mechanistic gap, as the central conclusion depends on D-serine limiting mitochondrial L-serine availability to inhibit one-carbon metabolism.

(2) The effective concentrations of D-serine used in vitro (IC₅₀ ≈ 1-2 mM) exceed typical brain levels (~0.3 mM). While the authors acknowledge this, a more focused discussion on whether higher local D-serine concentrations could arise in specific microenvironments-such as synaptic compartments, tumor niches, or pathological states-would help contextualize the in vitro findings and strengthen their physiological relevance. For example, disruptions in D-serine clearance or altered expression of serine racemase and transporters in disease contexts could lead to localized accumulation. Moreover, differences between extracellular and intracellular D-serine pools-and the mechanisms governing their regulation-may further influence its metabolic impact in vivo.

(3) While the manuscript focuses on neural stem/progenitor cells and neural tumors, it remains unclear whether the anti-proliferative effects of D-serine are specific to neural lineages or extend to other highly proliferative non-neural cell types. A brief discussion addressing this point would help clarify the scope of D-serine's metabolic impact and whether its mechanism of action reflects a unique vulnerability in neural cells or a more general feature of proliferative metabolism. This distinction is particularly relevant for assessing the broader therapeutic potential of targeting mitochondrial L-serine transport.

Reviewer #3 (Public review):

Summary:

Submitted to the Tools and Resources series, this study reports on the use of a single-domain antibody targeting the nucleoporin Nup84 to probe and track NPCs in budding yeast. The authors demonstrate their ability to rapidly label or pull down NPCs by inducing the expression of a tagged version of the nanobody (Fig. 1).

Strengths:

This tool's main strength is its versatility as an inexpensive, easy-to-set-up alternative to metabolic labelling or optical switching. This same rationale could, in principle, be applied to the study of other multiprotein complexes using similar strategies, provided that single-chain antibodies are available.

Weaknesses:

This approach has no inherent weaknesses, but it would be useful to verify in the future that this pulse labelling strategy can also be used to detect assembly intermediates, structural variants, or damaged NPCs, e.g. NPC clusters formed in some nucleoporin mutants.

Overall, the data clearly shows that Nup84 nanobodies are a valuable tool for imaging NPC dynamics and investigating their interactomes through affinity purification.

Comments on revised version.

None at this stage.

Reviewer #3 (Public review):

Summary:

The study aims to elucidate the dual molecular mechanisms of the RNA-binding protein MATR3 in oocyte growth and maturation. The authors propose that MATR3, highly expressed in growing oocytes (GOs), regulates oocyte quality through two pathways: epigenetically, by recruiting KDM3B to remove the repressive H3K9me2 mark at the Gdf9 locus to activate transcription; and post-transcriptionally, by binding Rdx mRNA to maintain microvillus structure for GDF9 secretion. This mechanism ensures oocyte-granulosa cell communication and female fertility. The study also explores the link between MATR3 and human oocyte maturation arrest (OMA).

Strengths:

The study proposes an innovative dual-mechanism model encompassing "epigenetic transcriptional activation and cytoskeletal regulation," which not only expands the functional understanding of RNA-binding proteins in chromatin regulation but also reveals the coordination between nuclear transcription and organelle structure. By integrating scRNA-seq and LACE-seq, the authors constructed a comprehensive regulatory network for MATR3, identifying both key targets and numerous potential molecules, thereby providing rich resources for future mechanistic studies. Furthermore, the inclusion of oocyte samples from human OMA patients directly links the basic findings to clinical reproductive disorders. Despite the limited sample size, this approach demonstrates strong translational potential.

Weaknesses:

The partial phenotypic improvement achieved by exogenous GDF9 supplementation suggests that the downstream effector pathways may involve a more complex network regulation, implying that the current interpretation of GDF9 central role could be further explored. Regarding the developmental abnormalities of granulosa cells in the conditional knockout model, their pathological origins require in-depth analysis to determine whether they represent primary alterations or secondary adaptive responses resulting from the loss of oocyte signaling.

Reviewer #3 (Public review):

Summary:

This paper focused on how to navigate the complex decision-making process of whether to go into human trials. This is a critical topic considering the well-documented challenges in replicating and translating findings. While these are two distinct topics (i.e., replication and translation), they are related, and the authors simulated many conditions to assess the utility of replication assessment metrics.

Strengths:

A major strength of the study is the detailed approach to identifying relevant conditions and metrics, and to providing rich results that outline the strengths and weaknesses of each metric. Any simulation study is challenged by trying to identify the most relevant variables of interest, and this study provided sound justification for its chosen variables of interest. While this study does not make a strong recommendation (which I see as a strength), it does provide a comprehensive overview of the various metrics and conditions that were investigated.

Conclusion:

This paper provides a much-needed investigation and discussion of how decisions are made when assessing whether to go into human trials. This is an important topic that productively challenges the status quo, considering documented challenges in replication and translation in biomedical research.

Reviewer #3 (Public review):

Summary:

In this manuscript, Flamholz et al. sought to determine whether consistent and significant interactions exist between the gut microbiome and disease pathology in sickle cell disease (SCD). By sequencing and analysing metagenomes from faecal samples collected from 98 SCD patients and 46 control subjects, they identified community-level shifts in both the bacterial and proviral gut microbiome of SCD patients. They further reported correlations between the proviral microbiome and multiple blood cytokines, whereas similar associations were not observed for the bacterial microbiome. Based on these findings, the authors propose the existence of a viral-immune axis in SCD pathophysiology and targetable functional alterations in the gut microbiome.

Strengths:

This work includes the largest SCD cohort analysed to date, enabling analysis with relatively strong statistical power. In addition to profiling the bacterial microbiome, the study also examines the gut proviral microbiome, thereby providing a more comprehensive investigation of the topic. The newly generated metagenomic dataset will also be valuable for further meta-analysis by the wider community. Overall, the authors have largely achieved their aims.

Weaknesses:

However, this study represents a single-centre cross-sectional investigation, and most findings remain correlative in nature. In particular, the claim that the study identifies targetable functional alterations in the gut microbiome for disease treatment may be somewhat overstated. Although the reported functional module changes in SCD patients are intriguing, additional mechanistic and/or longitudinal evidence would be required before these features can realistically be considered targetable.

Reviewer #3 (Public review):

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

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

Reviewer #3 (Public review):

Summary:

This manuscript presents openretina, a Python-based platform designed to facilitate collaborative retinal modeling across datasets, laboratories, species, and recording modalities. The package provides standardized model architectures, evaluation metrics, and analysis tools, while also integrating several publicly available retinal datasets. The authors further demonstrate the platform through examples of in silico analyses and model benchmarking.

Strengths:

(1) Emphasis on standardization and reproducibility. Retinal modeling has become increasingly dependent on deep learning approaches, yet datasets and evaluation procedures remain fragmented across laboratories. By providing a unified framework, the authors lower barriers to entry and create opportunities for more systematic comparisons of models and datasets.

(2) The manuscript is clearly written, and the examples effectively illustrate the range of analyses supported by the platform.

(3) The benchmarking results are useful, particularly because they reveal substantial remaining gaps between current model performance and explainable variance ceilings.

Weaknesses:

Not a weakness per se, but rather a limitation, is that the manuscript focuses on software infrastructure rather than new biological or computational insights. While this is appropriate for a resource paper, some of the scientific examples, such as the gradient-field analysis of ON-OFF cells, function more as demonstrations than as rigorous validations of novel hypotheses. It might be useful to add a few sentences discussing potential scientific projects that can be immediately facilitated by the openretina (the current text in the Discussion focuses more on advancements in the technical/social aspects of science that will be supported by openretina).

Overall, this is a valuable and timely resource that is likely to benefit the retinal and computational neuroscience communities.

Reviewer #3 (Public review):

Summary:

In this manuscript, Williams et al. combine optogenetics, whole-cell electrophysiology, local field potential recordings, large-scale voltage imaging, and computational modeling to investigate the cellular and circuit mechanisms underlying theta-nested gamma oscillations in superficial medial entorhinal cortex (mEC). The authors propose that fast-spiking interneurons receive strong gamma-frequency excitatory drive and provide rhythmic inhibition onto principal neurons, supporting a pyramidal-interneuron network gamma (PING) mechanism. They further report cell-type-specific differences in gamma phase locking, spatial clustering of subthreshold voltage signals, and a network model reproducing several observed features, including interneuron bursting and gamma-cycle skipping in excitatory neurons.

Strengths:

The study is technically sophisticated and addresses an important question in entorhinal circuit function. The combination of intracellular recordings, voltage imaging, and computational modeling is a clear strength.

Weaknesses:

Several key conclusions developed from experimental results require additional raw data, statistical support, clearer methodological description, and more cautious interpretation. The computational modeling focuses primarily on stellate cells, whereas the experimental results suggest an important role for pyramidal neurons in PING dynamics. This creates inconsistency between theory and experiments.

Reviewer #3 (Public review):

Summary:

Following previous work that demonstrated a relationship between higher homeostatic cytosolic calcium and lower retinal ganglion cell (RGC) apoptosis following injury to their axons, McCracken et al. investigated whether homeostatic calcium levels of the endoplasmic reticulum (ER) or mitochondria provide additional insights into the mechanisms by which calcium influences RGC survival. Their study reveals that homeostatic mitochondrial calcium shows a similar positive correlation with RGC survival. Despite that correlation, pharmacologic or genetic methods to lower mitochondrial calcium improved, rather than reduced, the survival of injured RGCs, while a genetic approach intended to increase mitochondrial calcium resulted in more RGC loss. These findings highlight the complexities of calcium regulation in modulating neuronal survival and raise important questions of how homeostatic levels of mitochondrial calcium affect stress responses that themselves can be either neuroprotective or neurodegenerative.

Strengths:

This study tackles an intriguing hypothesis that differences in calcium ion homeostasis in specific organelles may contribute to differences in survival of various RGC subtypes after optic nerve injury. This is a technically demanding question, and a primary strength of this work is its attention to, and meticulous reporting of, appropriate controls and, where applicable, seemingly contradictory results. Among these are careful evaluation of the effects of drug (or vehicle) delivery and genetic manipulations with and without injury and over extended time courses. The combination of thoughtful pharmacologic and genetic approaches makes for a thorough analysis of a challenging set of questions. The result is a study that provides a helpful perspective on the complicated roles that calcium, and especially mitochondrial calcium, can play across neuronal insults, neuronal types, and neuronal subtypes.

Weaknesses:

Given the paradoxical results, it would be helpful to have a clearer picture of how strongly the overexpression and knockdown of MCU altered the mitochondrial calcium levels. There may be potential for extraordinarily strong effects that would need to be tuned by using different shRNAs or promoters to more closely align with the observed differences between surviving RGCs and those that die. The investigation includes a relatively small number of resilient RGC subtypes, using the markers SPP1 and TBR2, raising questions of how generalizable the trend is between mitochondrial calcium levels and RGC resilience. The analysis and implications of Figure 3D might benefit from including not only the provided 50:50 split between "high" and "low" but also views of the data after splitting into thirds, fourths, and perhaps even fifths. The authors' inference that higher homeostatic calcium in more resilient RGCs may result in chronic mitochondrial stress is intriguing and worthy of more experimental investigation than is currently provided.

Reviewer #3 (Public review):

Summary:

The article by Chen et al. describes the comprehensive metabolic profiling of DP16 mice, a Down syndrome model that carries a duplicated segment of the mouse chromosome syntenic to human chromosome 21. The authors note that this model is superior to previously used models, based on genetics, as ~65% of the chromosome 21 orthologues. The metabolic phenotypes also appear to be more consistent with those observed in humans with Down Syndrome. The study lays the groundwork for a more detailed genetic dissection of dosage-sensitive genes that contribute to the metabolic deficits observed in Down Syndrome.

Strengths:

There is an enormous amount of data in this manuscript, and the methods are described with adequate attention to detail. A strength of the manuscript is that both male and female mice were analyzed, so that concordant and discordant phenotypes were identified. Both males and females had evidence of insulin resistance. Transcriptomic and metabolomic data revealed impaired pathways for lipid metabolism, a pro-inflammatory state, reduced mitochondrial health and oxidative stress. Although the effects of a high-fat diet on weight gain were divergent, this diet caused worsened insulin resistance in both males and females.

The discussion is excellent. Limitations of the study are well described. This reviewer does not identify any critical missing data.

Reviewer #3 (Public review):

This is a short and punchy manuscript that nicely summarises the 4 structures that are determined and provides a basis for the differences seen for acetylation sites shown for RNAPII activity.

The authors build on previous biochemical work that determined the functional outcomes of H3 core acetylation, adapting an assay they have previously used extensively to investigate RNAPII transcription on nucleosomes and, indeed, even H3 N-terminal tail acetylation. This assay is as such well set up and has a wealth of confirmatory previous studies from this lab and the authors are careful not to overanalyse their results, leading to robust and well-considered results. The structures are determined to a high resolution, allowing the interpretation put forward about side chain orientations, with clear densities shown for the regions of interest.

Further discussion or experiments would strengthen the conclusions further:

(1) The conclusion on the role of H3K56Acetylation could be strengthened, especially as the results are somewhat counterintuitive. It is conceptually surprising that acetylation near the entry/exit DNA that destabilises this region also leads to a reduced stall propensity at the dyad but has a limited effect at SHL5? While it can be explained by the clash at the dyad pause being reduced, the more direct effect of DNA breathing amplification would be expected to have a larger effect at SHL 5. Indeed, the density for DNA at SHL5 appears to be weaker in Figure 2A, suggesting the entry/exit DNA flexibility is amplified past this region.

Perhaps another assay that looks more directly at the flexibility of the entry/exit DNA would be useful, either through restriction enzyme-mediated cleavage or FRET (DNA ends and H2AK119 labels), providing stronger evidence of this effect. MNase is rather indirect and similar to the RNAPII assay itself.

Similarly, were the authors surprised by the modest effect (less than 2-fold) in transcriptional pause at SHL 0 for the K122Ac? Presumably, based on the model in Figure 4, this would be expected to be the area with the largest effect? The results of K56Ac and K122Ac almost seem swapped to what would be expected in Figure 1H. Further discussion of this observation would be useful.

(2) Could the local weakening of DNA, especially at the dyad, be observed in the cryo-EM structures? Perhaps comparison of local resolution estimation differences in this region compared to unmodified would be useful.

(3) Caution should be taken, and discussion should include that the structural data presented is after extensive processing. Many nucleosome averaging classes were discarded in the 3D classification steps (nicely summarised in Table 1 as "particles for 3d classification" and "particles in final map"). Indeed, it is likely that higher DNA flexibility particles would be thrown away during this processing step. This can be observed for K56Ac DNA ordering, for example, in Supplementary Figure S4, yellow and cyan classes from the round of 3D classification look to be high resolution and have a higher order of DNA, so there has been some selection here. How was this done? While this is not fully quantifiable, it gives an idea of the extent of wrapping. We would suggest discussing the methodological limitations and showing the models after the first auto refinement to see if the features discussed on end flexibility and dan ordering are retained.

(4) Di Cerbo et al. (reference 13) showed acetylation at K64 alters salt stability and affects transcription. Why do the authors think there is a discrepancy, albeit with different assays? Direct reference and discussion of this in the text should be included.

(5) Why was H3.2 used, while this is relatively abundant in mouse cells, human protein was used, and this appears to be less common than H3.1 and H3.3. We are sure that the effect is not likely to be substantive on structure (as shown by the Kurumizaka lab previously), but should be addressed in the text

Reviewer #3 (Public review):

Summary:

In this work, the authors set out to characterise how encounter states between antibodies and antigens evolve during affinity maturation through molecular dynamics simulations and Markov state modeling. They demonstrate how early glycan-mediated interactions increased association rates rather than modifying the final bound state.

Strengths:

The computational approach is backed up by experimental results and allows for visualising otherwise too short-lived association states, thus allowing to discriminate between different lineages.

Weaknesses:

The figures and captions are not always clear about what they are trying to show. The choice of CVs is not sufficiently discussed.

Reviewer #3 (Public review):

In this manuscript, the authors strived to develop a highly efficient drug survival assay for in vitro cultured human malaria parasites P. falciparum. This was done by generating a transgenic P. falciparum line using a creLox strategy that allows detection of (presumably) viable parasites by a β-lactamase assay. To estimate the Limit of quantification of the recombined P. falciparum NF54i-lacZ, the authors ultimately designed a protocol in which viable parasites are detected by the luminescence of β-D-galactoside generated by β-lactamase within the transgenic parasites. For this, the parasite must be incubated with rapamycin for 120 hours to induce CreLox recombinase, which places β-lactamase under an active promoter. Using this assay, termed MULTI-i2, the author shows interactions between two antimalarial drug pairs that were previously demonstrated by another assay. In the case of pyronaridine and piperaquine pair, the NULT-i2 assay generated some additional insights compared to the previous assay, presumably by virtue of including more concentration datapoints. In conclusion, the authors argue that the MULTI-i2 assay is much less resource-intensive and time-consuming and can be applied on a large scale at a much lower cost and with the highest efficiency.

Overall, the data generated in this manuscript are clear and well represented, and I am convinced that MULTI-i2 provides yet another of many drug assays for malaria parasites and could be put to good use. However, I struggle to fully appreciate the merit of his study, as the manuscript reads more like a technical document than a scientific study.

I particularly lack an understanding of the strengths and weaknesses/limitations of the MULTI-i2 methodology and, thus, its applicability. I also do not fully appreciate the need for such an elaborate luminescence-based experimental setup. It would be good if some of these issues were addressed.

Specifically:

(1) The whole assay is based on detecting parasites by luminescence after 120 hr (5 days) after drug exposure. During that time, presumably the parasites that survived the drug pressure regrow to a detectable level and, at the same time, perform efficacious CreLox-based recombination to produce β-D-galactoside for detection. Is this necessary? How superior is this detection method to other methods, such as Fluorescence-assisted Cell Sorting (FACS), etc? Moreover, the 5-day growth-CreLox-β-D-galactoside production could introduce a series of confounding effects. In my view, more studies (beyond comparisons with a single existing method) would be useful for understanding this entire process.

(2) Related to that above, how would MULTI-i2 perform in case of drugs that do not necessarily kill all parasites, such as artemisinin? In the case of artemisinin, it is becoming evident that at least a small fraction of the parasite revives after treatment via a temporary dormancy state. This has, in fact, also been shown for other drugs such as mefloquine, pyrimethamine, etc. Would such a situation produce a range of false readings? In general, in its current state, it is hard to see what the limitations of this method are, which makes it hard to decide whether to use it for a particular application.

(3) Given the stated cost and labor efficiency of MULTI-i2, it is disappointing to see only two applications for two drug pairs: atovaquone/proguanil and piperquine/pyronaridine, for both of which their interactions were already known. The manuscript would benefit greatly if the authors demonstrated more drug interactions and identified (and ultimately validated) new ones. This would certainly make MULT-i2 method more attractive. In particular, it would be nice to see if one could use MULTI-i2 for studies of triple combinations as enthusiastically suggested.

(4) Throughout the manuscript, the authors claim that MULTI-i2 is considerably less expensive and can be done much faster than previous methods. In my view, this is not exactly a scientific argument. The cost of an assay depends heavily on the cost of reagents and labor, which are subject to market price fluctuations. The efficiency and time consumption can very much depend on laboratory organization, etc. Unless the author could specifically demonstrate where and how these assays are cheaper and faster, I suggest not discussing this.

Reviewer #3 (Public review):

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

This is an important extension of their prior work in mice and represents an elegant translation of those previous findings to humans, where future work can inform theories of e.g. psychiatric diseases that are believed to involve disordered predictive processing. For the most part, the authors are appropriately circumspect in their interpretations and discussions of the implications. The paper in its current form represents an important addition to the literature.

The authors have included analyses of the auditory mismatch using temporal electrodes, referenced to Cz (and therefore should exhibit a mismatch positivity). This added data clearly and convincingly shows that the sensorimotor mismatch is, indeed, stronger than the passive auditory MMN.

Comments on latest version:

The authors added useful points to the discussion and also included time frequency analyses to the paper formally, which strengthens the translational potential, in addition to the bolstering their claims slightly.

Reviewer #3 (Public review):

Summary

This manuscript reports that protocells derived from wall-deficient B. subtilis proliferate well when densely packed but fail to divide and eventually lyse when isolated. The authors attribute this density-dependent proliferation to mechanical shearing between growing neighbors, which deforms cells and increases the likelihood of membrane stalk formation and subsequent scission, enabling division without any dedicated molecular machinery. Through a combination of quantitative imaging, membrane tension measurements, and Cellular Potts Model simulations, the authors make a compelling case that self-generated mechanical stresses are critical for sustaining population growth in protocolonies. The findings have implications for understanding the lifestyles of primitive life forms, L-form bacterial pathogenesis, and the design of synthetic cells.

Strengths

The central finding is both surprising and counterintuitive: crowding is not just tolerated by protocells but is required for sustained population growth. The mechanism the authors propose is interesting: mechanical shearing between growing neighbors deforms cells, increasing the likelihood of membrane stalk formation and thus division, all without dedicated molecular machinery. Conceptually, this is a type of biophysical "scaffold" (Jacobeen et al. 2018, Nat. Phys.; Day et al. 2022, Biophys. Rev.) in which key elements of a Darwinian loop, namely a life cycle involving growth and reproduction, are provided "for free" by physics, enabling open-ended Darwinian evolution that can eventually bring these life cycle components under developmental control. Such scaffolds, both biophysical and ecological (Black et al. 2020, Nat. Ecol. Evol.; Libby & Rainey 2013, Phys. Biol.), are likely key mechanisms in the origin of life and in evolutionary transitions in individuality, and this paper provides a nice example of how they can work in a protocell context.

The combination of experiments and modeling works well. The membrane tension measurements are the strongest piece of evidence for the proposed mechanism, showing directly that tension is elevated in protocolonies and concentrated at cell-cell interfaces. The Cellular Potts Model captures the key experimental features. The discussion is nicely balanced, particularly the note about Gram-negative L-forms, whose rigid outer membrane may preclude this mechanism, which is a testable prediction for future work. I would suggest the authors also discuss the connection to biophysical scaffolding, as I think this is conceptually important and would help situate their work within a broader framework for understanding how primitive life cycles can arise from physical processes (see also Zamani-Dahaj et al. 2023, Genes; Hammerschmidt et al. 2014, Nature).

Weaknesses

The surface-volume balance analysis is central to the argument, and it depends on the assumption that cells have a fixed thickness of 0.8 µm, taken from the width of walled cells. But these are wall-deficient cells, which are mechanically quite different, and their thickness could plausibly vary during growth or under compression. I think the paper would benefit from either a direct measurement of cell thickness or a sensitivity analysis showing how η responds to plausible variation in this parameter. If the results are robust, that would put the analysis on much firmer ground.

The positive correlation between cell shape deformation and division rate (Figure 3C) is central to the proposed mechanism, but I think the paper needs to be more careful about the jump from correlation to causation. The authors propose that deformation increases the likelihood of membrane stalk formation, leading to scission. That is plausible, but an alternative is that cells with higher local growth rates both deform more and divide more frequently, with the two outcomes driven independently by the same underlying cause. The paper does show that average volume growth rates are indistinguishable between aggregated and isolated cells, which argues against a simple "faster growth explains everything" interpretation, but this does not rule out local variation within protocolonies driving the correlation. I think the most convincing experiment would be to apply external mechanical stress to isolated cells and see if that alone can drive division, decoupling deformation from growth. I realize that this may be technically very difficult, but at a minimum, the paper should acknowledge this as an alternative hypothesis.

The Cellular Potts Model has quite a few free parameters (Table S1), and it is not clear how tightly these are constrained by the data. A sensitivity analysis would go a long way toward showing that the results are robust and not overly dependent on specific parameter choices.

In any case, this is a strong paper with a cool finding and an interesting mechanistic explanation. I think it will be of broad interest, particularly to people thinking about the origins of life and synthetic cell design.

Reviewer #3 (Public review):

Summary:

The study by Bhojappa et al. brings new and interesting elements about the stability of the septin ring and the crosstalk between septin and actomyosin ring assemblies. The study focuses on the four kinases associated with the septin ring, Elm1p, Gin4p, Hsl1p and Kcc4p. Elm1 and Gin4 show the strong knock-out phenotypes, whereas Hsl1p and Kcc4p show the weak knock-out phenotypes. The Elm1p/Kccp1p and Gin4p/Hsl1p pairs show similar timing at the bud neck. While these kinases share redundant functions, Gin4 appears to have a unique interaction with the BAR domain protein Hof1, revealing a novel direct interaction between the septin and actomyosin rings. Interestingly, the kinase activity of Gin4 is not required for its role in septin organisation and AMR constriction. The last part of the manuscript shows an original protein tethering protocol used to show that Hsl1 and its membrane binding ability are required for phenotype rescue of gin4null cells.

Comments on revised version:

I thank the authors for their thorough and thoughtful response to my review. The revised manuscript clearly reflects their efforts to provide rigorous and high-quality science. Addressing the concerns raised in the review required significant effort, but the improvements in the manuscript make it clear that the work was well worth it.

Reviewer #3 (Public review):

Summary:

This manuscript provides a comprehensive characterization of the Plasmodium falciparum protein LSA3, combining biochemical, genetic, and in vivo approaches. The authors convincingly demonstrate that LSA3 is expressed during liver stage infection and that disruption of the gene leads to a modest but reproducible reduction in liver stage parasite load in humanized mice.

Strengths:

Their biochemical and cell biological analysis of blood stages provides strong evidence that LSA3 is exported to the infected erythrocyte, and the detailed analysis of its PEXEL motif processing is well executed.

Weaknesses:

The study suggests LSA3 as one of only two known P. falciparum PEXEL proteins contributing to this stage, although there is no evidence for the export beyond the vacuolar membrane. Several key conclusions, particularly regarding antibody specificity, localization in liver stage parasites, and the interpretation of the phenotypic data, are not fully supported by the current experiments.

Comments on revised version.

I appreciate the authors' efforts to revise the manuscript and to clarify several aspects of the study.

However, I remain concerned that some conclusions extend beyond the data presented. In particular, the authors acknowledge in their rebuttal letter that antibody specificity in liver stages could not be validated and that cross-reactivity cannot be excluded. Consequently, the localization data shown in Figure 5 cannot currently be considered definitive evidence for liver stage localization of LSA3 itself.

Similarly, the revised manuscript appropriately states that LSA3 was not detected beyond the PVM in liver stages and that export into the hepatocyte remains unresolved. Nevertheless, several statements continue to imply a role for liver stage protein export. At present, the possibility that a domain of LSA3 may face the host-cell side of the PVM remains speculative and is not supported by direct experimental evidence.

The liver stage fitness phenotype is convincing and supports the conclusion that LSA3 contributes to normal liver stage development. However, the current data do not establish the developmental process affected nor connect the phenotype to export beyond the PVM.

I therefore recommend that the manuscript consistently distinguish between (i) demonstrated export of LSA3 during blood stage infection and (ii) the unresolved localization and trafficking of LSA3 during liver stage infection. I would also encourage the authors to consider revising the title to better reflect the findings presented, as the current title may be interpreted as demonstrating liver stage export, which has not been shown.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

Comments on revised version.

The authors have answered the questions in the revised manuscript, no further comments.

Reviewer #3 (Public review):

Summary:

The manuscript by Zilinskas et al seeks to understand the mechanisms underlying the ability of Mtb to suppress Th17 differentiation. As Th17 responses are needed for protective immunity against TB, this is an important topic of investigation. They use Mtb mutants that lack eccC1 (from ESX-1 locus) and fadD28 (encoding PDIM) and implicate a Tbet-dependent pathway by which Mtb modulates Th17 differentiation. The mechanism by which ESX-1/PDIM function to impact Th17 differentiation is, however, unclear, which limits the novelty of the results.

Strengths:

Understanding how Mtb limits Th17 differentiation has implications for vaccine development. Comparative study of KO mice and Mtb mutants is a strength.

Weaknesses:

(1) Addressing several questions related to the Tbet KO mouse experiments would strengthen the study. Do the Tbet KO mice have elevated IL-4/5/13 (which has been previously reported in non-TB studies) in addition to IL-17? The lack of Th17 cells in the IFNg KO compared to the Tbet KO may reflect a difference in timing, since only 3-week data are shown; earlier and later time points would provide a better interpretation. The authors do not present any data on neutrophil infiltration in WT vs Tbet KO vs IFNg KO mice. Since IL-17 is known to be important for recruiting neutrophils to the lung, neutrophil data are important for clarifying the mechanism underlying the CFU outcomes.

(2) While IL-23 is important for sustaining IL-17 production, IL-6, TGF-b and/or IL-1β are necessary for Th17 polarization. What were the levels of these cytokines in DCs in the lung? (Fig 5). Additionally, Tbet-deficient DCs exhibit impaired activation of antigen-specific Th1 cells and have reduced IL-12 production. Given the data showing higher IL-17 levels in Tbet KO mice, the authors should provide information on the DC phenotype (IL-23, IL-6 etc) in the Tbet KO experiments.

(3) The mechanism by which ESX-1/PDIM function to impact Th17 differentiation is not clear. While data showing a role for ESX-1 and PDIMs in inhibiting Th17 responses is interesting, there is no insight into the potential mechanism of action. Fig 3 showing reduction in IFNg+ CD4 T cells after infection with eccC1 and fadD28 mutants suggests that this outcome is due to a lower bacterial load relative to WT Mtb at the 3-week time point. Since IFNg is known to suppress IL-17, the higher levels of Th17 cells could be due to the reduction in IFNg due to the attenuated growth of the mutants.

Reviewer #3 (Public review):

Strengths:

Mahieu and colleagues address an interesting and underexplored question: whether non-pathogenic variation in the mitochondrial genome contributes to the inter-individual variability of human telomere length (TL). Using a Belgian Flow-FISH reference cohort (n=491) to identify donors at TL extremes, they generate transmitochondrial cybrids from platelets of seven donors of distinct mtDNA subhaplogroups and characterize the resulting cells with a broad and well-executed toolkit (TRF, TeSLA, ddTRAP, EPR-based mitoROS, Seahorse with permeabilized-cell ETC dissection, LC-MS metabolomics, telomeric PAR-FISH). The most compelling finding is that cybrids derived from donors with low complex I (CI) activity undergo rapid telomere shortening during the glycolysis-to-OXPHOS transition of cybrid formation, and that this is largely prevented by co-treatment with NAC and the NAD⁺ precursor nicotinamide riboside, supporting a model in which CI sustains the NAD⁺ pool required for PARP1-mediated repair of oxidative damage at telomeres. The authors further report an inverse correlation between donor lymphocyte TL and mitoROS in the corresponding cybrids, and provide preliminary evidence that the K1a-defining ATP6 A177T variant (m.G9055>A) may be enriched in long-telomere individuals.

Weaknesses:

(1) Statistical support and donor sampling for the central in vivo correlation (Figure 4C).

The inverse correlation between donor lymphocyte TL and cybrid mitoROS (R²=0.794, p=0.007) is the principal in vivo claim of the paper, but it is built on seven donors deliberately selected from the extremes of the Flow-FISH distribution. Sampling at the tails of the outcome variable can substantially inflate apparent correlation strength and significance. I would encourage the authors to (i) explicitly state this sampling structure where the correlation is introduced, (ii) report a leave-one-out sensitivity analysis to confirm the relationship is not driven by one or two donors (Cyb3 and Cyb6 appear to anchor the line), and (iii) where feasible, extend the analysis to additional donors with intermediate TL to test whether the relationship holds across the full distribution. Even a modest expansion (e.g., 4 to 5 additional donors at P25 to P75) would substantially strengthen this central claim.

(2) Reconciling the cybrid CI / TL relationship (Fig 3B) with the absence of a CI / TL relationship in donor lymphocytes (Figure 4A).

Figure 3B shows a strong correlation between CI activity and TL in cybrids (R²=0.87), while Figure 4A shows no correlation between donor CI activity (measured in the same cybrids) and donor lymphocyte TL. The authors acknowledge this, but the manuscript subsequently builds toward a CI-centric model of in vivo TL regulation, which seems to outrun the data. The most internally consistent interpretation is that the cybrid CI phenotype reports a sensitized in vitro response to the acute oxidative stress of the metabolic shift, rather than a steady-state determinant of leukocyte TL. I would suggest reframing the abstract, significance statement, and Discussion to make this distinction clearer. The in vitro CI / NAD⁺ / PARP1 axis is a strong finding on its own, while the in vivo role of CI activity (as opposed to ROS more broadly) is not yet established here. Donor #1's profile (very long lymphocyte TL, low CI activity, severe shortening in cybrids, no telomere inheritance in offspring) is informative in this regard and could be discussed more directly as a case that helps delineate where the cybrid model does and does not recapitulate in vivo biology.

(3) The K1a / ATP6 A177T inheritance claim.

The proposal that K1a (and specifically ATP6 A177T) contributes to maternal inheritance of long telomeres is intriguing but currently rests on three pedigrees (one of which, donor #1, does not support the hypothesis) and a chi-square test that does not reach significance (p=0.153, Figure 4F). The supporting evidence is also limited by the fact that platelet-mediated mitochondrial transfer delivers donor mitochondrial proteins, lipids, and residual mtRNA in addition to mtDNA, making it difficult to attribute the cybrid phenotype of donor #6 specifically to the ATP6 A177T variant. I would recommend either: (a) extending the genotyping screen to additional unrelated donors and, if feasible, confirming the effect of ATP6 A177T through an isogenic approach (e.g., mtDNA base editing in a clean background), or (b) softening the relevant statements to "suggestive trend warranting larger studies," and presenting the K1a observation as hypothesis-generating rather than supportive. The Ashkenazi-centenarian connection raised in the Discussion is an excellent direction for follow-up and could be framed accordingly.

Reviewer #3 (Public review):

In this manuscript, the authors aim to understand the function of the transcription factor CHOP, which is known to promote cell death during severe stress in the ER. The authors note that CHOP is induced during less severe stress, but its functional output is not well understood in these cases. Here, they study the effects of conditional knockouts of CHOP in hepatocytes of mice challenged with chemical inducers of ER stress.

Tunicamycin (an ER stress inducer) injection leads to the upregulation of CHOP and lipid accumulation in the liver, but no significant cell death in the experiments outlined here. Conditional knockout of CHOP results in a number of differences in the way hepatocytes respond to stress, notably resulting in lower steatosis.

There are two main findings supported by the data presented here. First, the authors show that CHOP suppresses the expression of ONECUT, a master regulator of hepatocyte differentiation and metabolism, during ER stress. They show by ChIP-seq that CHOP binds to the promoter region of this gene, and by RNA-seq that ONECUT expression is suppressed by ER stress in a CHOP-dependent manner. Many predicted targets of ONECUT1 were also suppressed by ER stress in a CHOP-dependent manner, though they were not bound directly by CHOP. The data support a model where CHOP down-regulates hepatocyte metabolism and identity via regulation of ONECUT1. This is a new and interesting finding, perhaps explaining the steatosis phenotype of livers that accompanies ER stress, although this was not tested directly.

The second main finding of this paper is that CHOP deletion leads to an interesting assortment of effects on genes related to the ER stress response and integrated stress response (ISR). As expected, based on prior work, CHOP deletion led to more phosphorylation of eIF2alpha (CHOP is known to upregulate the phosphatase for this translation factor). However, unexpectedly, this did not cause increased expression of ATF4 (a transcription factor whose upregulation during stress is dependent on eIF2alpha phosphorylation) and its downstream targets; in fact, CHOP deletion had the opposite effect on these. In other words, CHOP seems to both turn off the initiating signal for the ISR (namely, eIF2alpha phosphorylation) and also promote the downstream signaling events that rely on this initiating signal. It makes sense that cells would do this, as restoring translation would be important for realizing the effects of the massive changes in gene expression initiated by ER stress, and yet this would exacerbate stress in the short term, so it would be counterproductive to also turn off the entire stress-regulated program. Having a factor (perhaps CHOP) that coordinates these two events makes sense. It will be interesting in future work to understand the mechanisms behind this regulation.

Finally, CHOP deletion led to less activity of other aspects of the ER stress response, notably IRE1 (determined through measurement of XBP1 splicing and RIDD of Bloc1s1). This is explained by the continued phosphorylation of eIF2alpha in these knockouts, as the continued attenuation of translation would lessen the burden of misfolded proteins in the ER. Somewhat confusingly, the same pattern is not seen in downstream targets of XBP1. Less splicing, coupled with perhaps less translation of the spliced mRNA, should result in less active transcription factor and lower expression of its target genes in the CHOP KO. This is not observed in Figure 2, although the more global gene expression analysis suggests that all stress-dependent gene expression changes were weaker in the CHOP KO livers.

The authors characterize the effects of CHOP, promoting restoration of protein synthesis and the accompanying exacerbation of stress while preserving the signaling that should relieve ER stress, as a switch from an acute to chronic phase of ER stress. This is mirrored in their analysis of ATF6 in a similar series of experiments. Although this is an interesting framework for thinking about the stress response, whether CHOP is the key factor or a supporting actor in regulating this transition will require a better understanding of the mechanisms involved.

Reviewer #3 (Public review):

Summary:

In this article, the authors performed targeted lesions in cortical areas involved in forelimb control of rhesus macaques. Using a reaching task with kinematic tracking, they compared kinematic variability (as a proxy for dexterity) and reaching speed (as a proxy for strength) before and after cortical (n=7) and magnocellular red nucleus (RNm) (n=2) lesion. Changes in these movement metrics were related to the location and extent of the lesions, reconstructed from histology. The authors report that lesions with a large component in New M1 had a pronounced effect on kinematic variability, whereas lesions with a large component in posterior Old M1 primarily affected reaching speed. Lesions of the RNm were performed in two animals approximately seven to eight weeks before the cortical lesion. By themselves, RNm lesions produced a significant but small reduction in reach speed. They also magnified the effect of the subsequent cortical lesions.

Strengths:

(1) For non-human primate (NHP) research, this is a large cohort.

(2) The behavioural analyses are clear and precise.

(3) The additional red nucleus lesions in two monkeys provide unique complementary information.

Weaknesses:

(1) Description of injuries. As described and reported in the current result section and figures, readers do not have a clear understanding of the lesion extent and location.

(2) Lack of formal correlative analyses between lesion characteristics and behaviour. Currently, it seems that the conclusions are based on impressions between some aspects of the lesions and precise kinematic measures.

(3) The data will be of interest to a large community of researchers working on brain injury and stroke recovery, as well as cortical motor control. There are, however, some major methodological issues in the current version of the manuscript that prevent a clear evaluation of the findings and potential contribution of the work in the field. These issues need to be addressed to support the conclusions.

Reviewer #3 (Public review):

This manuscript addresses an interesting and timely question: whether regional glymphatic clearance in the human cortex is spatially coupled to neural activity and whether a mismatch between activity and clearance may help explain regional vulnerability to amyloid-β deposition. The authors use intrathecal gadolinium-based glymphatic MRI in 96 participants, derive cortical influx and clearance maps, integrate these with Allen Human Brain Atlas transcriptomic data, and then relate regional clearance to resting-state fMRI measures in a smaller subgroup. They further compare the resulting activity-clearance mismatch map with an open-source ¹¹C-PiB amyloid PET dataset. The overall concept is attractive because it attempts to connect glymphatic physiology, neuronal activity, and proteopathy at the regional level of the human brain, an important and understudied area.

The main strength of the study is the use of direct intrathecal contrast-enhanced MRI to generate cortical maps of glymphatic tracer dynamics. This is a technically demanding approach and provides a richer spatial readout than indirect MRI proxies of glymphatic function. The authors show that the cortical tracer signal increases from 4.5 h to 15 h and then decreases by 39 h, allowing them to interpret the early signal as reflecting influx and the persistent signal at 39 h as impaired clearance. They further identify regional patterns, with faster influx in medial prefrontal/insular areas and slower clearance in dorsal prefrontal and parietal surface regions. The analysis is visually clear, and the use of cortical gradients is a useful way to reduce complex regional data into interpretable spatial axes.

The multimodal integration is also interesting. The transcriptomic analysis suggests that regions with faster glymphatic clearance are enriched for synaptic organisation and neuronal activity-related pathways, while regions with slower clearance show enrichment for metabolic and mitochondrial pathways. The cell-type enrichment analysis further implicates excitatory and inhibitory neurons, oligodendrocyte lineage cells, microglia and, to a lesser extent, astrocytes. This provides a plausible biological bridge between regional neural activity and clearance function, and the sensitivity analysis using ReHo in addition to fALFF is a useful robustness check.

However, the manuscript should be more careful in its causal interpretation. The study is cross-sectional and largely correlative in space. The finding that regions with higher spontaneous neural activity tend to show better glymphatic clearance is intriguing, but it does not establish that neural activity drives clearance in these participants. Conversely, it remains possible that better tissue integrity, vascular function, CSF access, cortical geometry, vascular density, or disease composition jointly influence both fMRI measures and tracer clearance. The authors do acknowledge some of these limitations, but the abstract and discussion should more consistently frame the findings as associations rather than evidence of an activity-clearance mechanism in humans.

The most important limitation is the small size of the fMRI subgroup. Although the whole glymphatic MRI cohort includes 96 participants, the key activity-clearance analysis is based on only 15 individuals, including 11 with peripheral neuropathy and 4 with motor neuron disease. This is a very small and clinically heterogeneous sample on which to build a central conclusion about regional neural activity and glymphatic clearance. The authors show that the 39 h PC map in the fMRI subgroup resembles the whole-cohort map, which is helpful, but this does not address whether the fALFF-clearance relationship is robust at the individual level. The paper would be strengthened by reporting subject-level stability, leave-one-out analyses, and whether the association persists after excluding the four motor neuron disease cases.

A second major concern is the interpretation of the amyloid analysis. The ¹¹C-PiB map is derived from an external open-source Alzheimer's disease dataset, not from the same participants who underwent glymphatic MRI and fMRI. Therefore, the association between activity-clearance mismatch and amyloid burden is a spatial correspondence across group-average maps, not an individual-level relationship. This is valuable for hypothesis generation, but should not be presented as evidence that a mismatch in the present cohort predicts amyloid deposition. The authors should clearly state that this analysis tests whether mismatch regions overlap with known amyloid-prone cortical regions, rather than directly linking mismatch to amyloidosis in individual participants.

The definition of "mismatch" also needs clarification. The text defines the mismatch index as the negative absolute difference between z-fALFF and z-39h PC, and states that higher scores indicate greater mismatch. Because the index is negative, values closer to zero would normally indicate a smaller absolute difference rather than a greater mismatch. This should be checked carefully and corrected if necessary. More broadly, because a higher 39 h PC indicates worse clearance, the interpretation of match and mismatch categories is not intuitive. The authors should provide a clearer schematic and ensure that the mathematical definition, biological interpretation and figure labelling are fully aligned.

Several technical confounds require more attention. Intrathecal gadolinium MRI is influenced by CSF dynamics, posture, sleep, circadian timing, renal clearance, age, intracranial pathology, and potentially diagnosis-specific differences. The authors acquired scans at fixed time points and noted that patients slept as usual, but individual sleep duration, sleep quality, posture, and daytime activity were not objectively measured. Given that the central claim concerns glymphatic clearance, these are not minor confounders. The authors should consider adjusting for age, sex, diagnosis, vascular risk factors, and relevant clinical variables where possible, and be more explicit about how heterogeneous disease indications may influence cortical tracer kinetics.

The statistics are generally good. However, many correlations are performed across 400 cortical parcels, which are not independent biological samples. The paper would benefit from clearer separation between participant-level inference and region-level spatial inference. For example, the fALFF-clearance and mismatch-amyloid analyses are regional map correlations, not correlations across individuals. This should be clearly stated throughout. The authors should also report effect sizes and confidence intervals more consistently, and explain how multiple comparisons were controlled across transcriptomic, cell-type, fMRI, ReHo and amyloid analyses.

The transcriptomic analysis is useful but should be presented as indirect. AHBA data come from six post-mortem brains; only the left hemisphere was used, and the donors were healthy and younger than the clinical cohort. Therefore, these data capture intrinsic regional gene-expression patterns rather than disease-state expression in the same individuals. The authors should avoid implying that the transcriptomic findings directly explain glymphatic function in their participants. The current discussion partly acknowledges this, but the framing in the abstract and results could be more cautious.

There are also several points of presentation that should be improved. The manuscript should consistently distinguish glymphatic influx, glymphatic clearance, CSF tracer retention, and waste clearance. A 39 h residual gadolinium signal is a useful proxy for delayed clearance, but it is not the same as direct measurement of amyloid or tau clearance. The language around "waste clearance" and "amyloidosis" should therefore be precise. The authors should also clarity whether "higher clearance" corresponds to lower 39 h PC across all analyses, as this inversion is easy for readers to misinterpret.

Reviewer #3 (Public review):

Summary:

In this manuscript, Noirot-Gross et al take an open-ended approach to elucidate the c-diGMP-associated protein complexes in Pseudomonas fluorescens. Starting with 10 cyclic d-GMP putative proteins, they use a combination of genome-wide two-hybrid system followed by CRISPRi-mediated exploration of phenotypes to describe the cyclic di-GMP-associated regulation of biofilm formation, and how it relates to other functions. Overall, this work presents an excellent example of how genome annotations can be further confirmed with the use of integrated functional genomic approaches. Some areas of improvement can be applied to this manuscript to enhance readability and provide a clearer distinction between confirmatory results and new findings, which are provided below:

Strengths:

(1) The authors have explored their findings extensively and provide a comprehensive view of the topic.

(2) The combination of genome-wide explorations of protein-protein interactions with the more focused phenotypic exploration of the interactions found provides a solid framework for the work presented.

Weaknesses:

(1) Overall goal of the work:

While articles that describe open-ended approaches can be comprehensive and descriptive in nature, the authors should have a main overall goal, which can guide the reader through the main and most compelling findings at the end. As written, the overall goal is not clear. The network perspective is interesting, and the focus on biofilm formation appears in the title. Why P. fluorescens? How is cyclic di-GMP-mediated regulation of biofilm formation in P. fluorescens different from P. aeruginosa? Why would it be studied?

(Positive or negative regulation of biofilm formation?)

(2) Abstract:

The abstract is very well written and guides the reader to the DipA as a hub protein in the network. From further reading, the article could clarify whether this finding is confirmatory or novel (does DipA play a similar role in P. aeruginosa?) It would be appropriate to mention the role of DipA in other Pseudomonas species from the beginning, and not only in the discussion session.

(3) Introduction:

The introduction is nicely written. An area of improvement could be giving more attention to protein interactions as relevant to c-di-GMP. The authors could consider an independent paragraph starting with line 84-85 "Protein-protein interactions involving DGCs, PDEs, and target effectors are crucial in establishing localized signalling through the generation of local pools of c-di-GMP", expanding on this particular aspect with an example of localized signal, after explaining that localization could help decipher specific function within the network of DGCs and PDEs. Then go into connecting biofilms with c-di-GMP and protein-protein interactions, using the example of GcbC and LapD.

(4) The rationale of choosing 10 PDEs could be clarified. The nice diagrams shown in the supplementary table could be used as part of Figure 1, so the reader understands why these proteins were used, and what is known about them (for example, add them as Figure 1a).

(5) Figures 1b and 2 convey the same information as in Figure 1a. They could be removed without affecting the understanding of the article.

(6) CRISPRi and Figure 3. Figure 3 shows the methodology of CRICPR phenotypic screening. A diagram showing the CRISPRi system in P. fluorescens could help the non-expert reader. While the choice of 23 proteins related to the emerging hub DipA is clear, the choice of the other 33 genes could be better explained. Are these proteins already related to biofilm formation? Where are they part of the network detected? How about the other 14 SBW25 genes? The authors could clarify the rationale of the choices. Figure 4 could be combined with Figure 3 or moved to the supplementary material.

(7) Figures 5, 6 and 7 represent solid network analysis of the findings. Still, they could be improved in clarity on the main findings. The authors conclude at the end of section 3.2.3 that there are networks that exert a "positive role" and a "negative role". The authors could show that in the figures, explaining what those roles are: more biofilm structural coding genes? positive or negative regulation of biofilm formation?)

Reviewer #3 (Public review):

Summary:

Hall et al. use a multi-omics approach to investigate the responses of bovine alveolar macrophages to Mycobacterium tuberculosis infections and the underlying mechanisms that are shared between bacterial family members. The study is of particular importance for multiple reasons, including the impact of bovine infections on food supply (and resulting economic impacts) as well as the use of bovines as a large animal model to investigate the effects of M. tuberculosis infection. The authors isolated bovine alveolar macrophages and exposed them to infection with M. bovis, M. bovis BCG, irradiated M. bovis, or M. tuberculosis. 24 hours post-infection, samples were analyzed by RNAseq, ChIP-seq, and ATAC-seq to look at alterations in the transcriptome and epigenome. Through fluorescence imaging-based analysis, the authors show equivalent infection (bacterial uptake) in all groups, except for the irradiated M. bovis. Principal component analysis of the transcriptomic data demonstrated strong segregation for the M. bovine-infected cells compared to the other groups, which had some intermixing in the PCA. Among the differentially expressed genes, the cytokine IL36G was significantly upregulated across all four groups. This is significant as this cytokine enhanced autophagy in macrophages and subsequent M. tuberculosis killing activity. To further investigate the transcriptional changes, ChIP-seq and ATACseq were utilized to investigate chromatin changes in the form of differential affinity binding sites (DABS) and differential open chromatin regions (DOCR). TPMRSS2, a protease that plays an important role in multiple types of infections (tuberculosis, COVID, etc.), was found to be a significant DOCR in both the M. bovis and M. tuberculosis challenge groups, conferring enhanced TPMRSS2 expression in both of these groups. Using the integrative approach between all the omics data, the authors found that CD274, which encodes the PD-L1 protein, was upregulated in all four groups. PD-L1 is known to play an immunosuppressive role, and PD-L1+ macrophages have been shown to create "cold" microenvironments that would likely favor the mycobacterium. Lastly, SNP analysis found variants in four genes (ERBB4, LRCH1, MRTFA, RNPC3) that could serve as susceptibility genes.

Strengths:

Overall, this study demonstrates that challenge with M. bovis elicits a more extensive remodeling of chromatin and subsequent gene expression changes in macrophages compared to the other closely related strains. The work demonstrates the power of functional genomics and its utility in investigating the underlying changes that can affect responses to infections and subsequent outcomes. While the study lacks functional validation, the cohesive dataset is quite compelling, and from it, the authors draw conservative conclusions and are frank about their study limitations.

Weaknesses:

Lack of functional validation of some of the targets found.

Reviewer #3 (Public review):

Summary:

This work takes a precision neuroscience approach to examining how different task demands elicit lateralized vs. bilateral activity in large-scale brain networks. Using ~35-150+ minutes of resting state data per person, the authors identify individualized networks and map their degree of laterality. They find that all association networks are bilateral, with some networks. such as the language network (left) and fronto-parietal B network (right), showing some marked degree of laterality. A sentence reading task evokes activity bilaterally in the language networks, while working memory load during an n-back task evokes bilateral activity largely in fronto-parietal, dorsal attention, and salience networks. Interestingly, though, the authors find that when digging into the N-back task and contrasting blocks that have rhyming words vs. faces, this elicits a more lateralized pattern of activity; left activation for rhyming and right activation for faces. This lateralization is seen across multiple association networks, not just the language or fronto-parietal networks. These findings are then replicated in another precision data set.

Strengths:

This work has several notable strengths. This study boasts a lot of data for each individual, allowing them to examine individualized functional networks and task activations. Given the marked individual differences in laterality (see Figures S4 and S5), a group-averaged network atlas and group-averaged activation maps would likely muddle some of the interesting effects.

The authors also use an elegant approach for individualized network estimation, multisession hierarchical Bayesian modeling, or MSHBM. MSHBM allows vertex-based functional connectivity to steer individualized networks while also incorporating meaningful priors to identify comparable networks across individuals. This is a nice approach combining elements of a common atlas across individuals with data-driven approaches.

The authors have a rich working memory N-back task with four different stimuli types with which they can examine processing different types of stimuli.

A major strength of this work is the replication. The authors take their surprising findings and replicate the entire experiment in another sample. The replication sample notably has even more data per person than the discovery sample, allowing for robust and precise estimation of individual networks and task activity.

Weaknesses:

I'd like to frame this section as 'unsolved challenges' rather than 'weaknesses'.

One of the strengths of this paper is that the working memory task that incorporates so many different stimuli and conditions also poses a caveat for interpreting the task stimuli effects. The 'word' condition requires multiple cognitive demands - working memory and rhyming/phonological processing. This makes it a little hard to draw complete parallels between the word and face conditions. It does, however, provide a useful backdrop to explain why there are such strong left laterality effects for the word condition. I would expect that explicit phonological processing that is required for a rhyming task would elicit more left lateralized activation than the sentence processing task, as fluent readers are likely not using explicit phonological processing for sentence processing. This could explain why, for the sentence reading task, they find more bilateral activation, but a task that specifically targets phonological processing (i.e., the 2-back word condition) would evoke more left lateralized activation above and beyond the activation supporting working memory, which is held constant across both the 'words' and 'faces' conditions. Ultimately, I believe this supports the broad idea of this work - that large-scale association networks can show lateralization, even beyond language network boundaries. However, the fact that there is a dual-task load that evokes phonological processing for the word condition is important for contextualizing these findings.

When looking at plots of individual differences, it is clear that some people are more bilateral than others in certain networks (and sometimes overall). Because of the amount of data per person in this precision study, it naturally limits the overall sample size (N=29). This makes it difficult to interrogate individual differences in laterality and what might predict this effect across people.

Reviewer #3 (Public review):

Summary:

The manuscript by Murrell et al. describes a high-throughput approach for evaluating food foraging strategies in mice. Building on their prior publication describing the technical aspects of the Feeding Experimentation Device 3 (FED3), this study demonstrates the utility of the FED3 in evaluating decision-making in mice. The authors identify key differences in male and female foraging strategies that could not be accounted for by total food consumption or overall food motivation. Given the rapid adoption of the open source FED3 platform, this work is likely to be of broad interest and utility in the field.

Strengths:

(1) The use of cost-effective, open-source devices like FED3 provides substantial value to the scientific community. Validation of appropriate conditions for using this equipment is an important step toward broad adoption.

(2) The authors implement a simple but elegant experimental design for studying food-motivated decision-making behavior. This approach could be applied to a wide range of preclinical disease models in future studies.

(3) The study is well-powered to evaluate sex as the primary experimental factor (62 females, 74 males), allowing the authors to make convincing claims about differences in strategy. Additionally, the dataset provides a useful benchmark for power analyses in future studies involving more complex experimental designs.

(4) The figures are clear and generally easy to interpret the primary findings.

(5) The conclusions are appropriate and not overstated

Weaknesses:

(1) A major strength of this study is the potential utility for new investigators trying to implement cognitive behavioral tasks in mice. However, the present version provides limited background on the rationale for selecting the bandit task and on prior work applying it in similar contexts. Including additional background and discussion would better contextualize the approach for other groups considering adopting it in their own studies.

(2) Some methodological details surrounding the initiation of the experiment could be clarified. Specifically, it is unclear if mice transitioned directly from standard housing conditions (group housed, standard chow) to the study conditions (single housed, FED3-based probabilistic learning), or intermediate acclimation/training steps were used, such as autoshaping, free access to new food pellets, or FR1 training. A more detailed experimental timeline (for example, see Figure 1 from PMID 39710132) would address this concern.

(3) The authors evaluated multiple probabilistic conditions (100%, 90%, 80%, 70%, 60%), but ultimately focused on the 80% condition for this study. A more detailed explanation for how this conclusion was reached would be useful for future researchers working under different experimental conditions (i.e., age, strain, genotype, disease model) where other probabilistic conditions may be more appropriate.

Reviewer #3 (Public review):

Summary:

In "Design of a minimal, allosteric, and ATPase-like machine using mechanical linkage," Omabegho introduces a simplified model of an allosterically-regulated molecular machine. The model machine takes inspiration from simple ATPase motors, specifically myosin or dynein, and is meant to capture the interactions between enzyme, ligand, and substrate that empower these enzymatic biomolecular machines. The model system attempts to specifically model the inhibitory allosteric regulation relevant to machine operation, following work on mechanical features of allostery in protein-analogue elastic network models. After the introduction of the model machine, the manuscript discusses the primary cycle by which the substrate serves to displace and then cyclically replace the ligand (roughly corresponding to a "recovery" and "power" stroke, respectively, in their biomolecular machine inspiration), as well as cataloguing other futile cycles or individual chemical pathways the machine may traverse. After an exhaustive enumeration of all possible states and transitions of the model machine, the actual mechano-chemical system is mapped to an abstracted stochastic chemical reaction network, which is finally studied numerically in more detail. We believe this model system is novel, interesting, and, importantly, interpretable and could prove to be useful in future modeling and rational design of simple bio-mimetic nanoscopic systems.

Strengths:

(1) Omabegho introduces a novel chemo-mechanical model system that captures the core behavior of a simple ATPase molecular machine. The model is relatively simple in construction, but, as the manuscript demonstrates, it can display very rich behavior depending on various competing chemical timescales and mechanisms. These features suggest that this system could, in fact, serve as a useful starting model if adopted by the wider community.

(2) A key feature to highlight is the mechanistic interpretability of the model. By cutting to the seemingly core functional details of an ATPase-like machine, Omebegho is able to algorithmically produce an exhaustive listing of all allowed chemical states and all possible transitions between them, enabling the study of all possible mechanistic pathways and the relative frequency of each observed path. Further, Omebegho produced very clear visualizations of these states, transitions, pathways, and cycles that again facilitate the interpretability and utility of the model. This interpretability is key to our suggestion that this model could be more widely adopted as a clear ATPase analogue model for study by the broader biophysical community.

Weaknesses:

There are some issues to note.

(1) First, although the model is inspired by ATPases, the cycle in question is not a model even for the significantly abstracted form presented in Supplemental Section 1.1, as noted in the manuscript. The allosteric regulation utilized in the model does not mandate that the ligand (a stand-in for actin, for example) displaces both products (stand-ins for ADP and P). Rather, one product (P) dissociates spontaneously, and the other, larger one (ADP) is allosterically displaced. We recognize mandating this small further detail would presumably complexify the model (i.e., it may require an additional tile in the enzyme construction), but it would also lead to a more accurate, while still simple and interpretable, picture of the biological molecular machines in question.

(2) Much as we recognize and applaud the model's structure as simple and interpretable, when trying to actually study the chemical dynamics and observed dynamical behaviors, individual chemical rates of various binding and unbinding processes appear to be quite finely tuned. There is some attempt at studying the behavior of this model in various parameter tuning regimes, but the large number of model parameters makes a truly complete numerical study prohibitive. Given this difficulty, it would be instructive to know if a comparison could be made to the actual motivating biological systems and any observed chemical rates in the experiment. Further, given the desired cyclic behavior, it would be quite interesting to see to what degree this behavior is robust to various parameter choices. Again, preliminary work was done in the manuscript by biasing rates or numerical siloing experiments, but a far more exhaustive study is certainly worthwhile.

(3) Perhaps our biggest critique of the manuscript is that, although it incorporates both mechanical and chemical aspects into the model system construction, all mechanical aspects of the model simply function to limit allowed state transitions. From our understanding, all mechanical aspects of the model are, in fact, abstracted away during any simulations. This modeling choice, of course, retains some mechanical inspiration while making the resulting system more tractable, but it is ultimately not an actual mechanical model. ATPases, especially motors such as myosin and dynein, are inherently mechanical, their fundamental features being the conversion of chemical fuel into mechanical motion. A fuller treatment, perhaps in future work, should include these physical degrees of freedom in simulation, thus truly tracking the interplay between physical enzyme mechanics and chemistry. The absence of actual mechanics as yet, beyond a strict allosteric restriction, is an inherent limitation of the model.

Reviewer #3 (Public review):

This paper, by Brian Chen and collaborators, adapts the highly alternatively spliced Dscam1 gene locus for use in a system for simultaneous multi-transgene expression in a variety of insect species. Specifically, they show that the hypervariable Dscam1 exon 4 region maintains its alternative splicing when placed in a UAS expression vector, and that each of the twelve exon 4 alternates can be replaced with an exogenous gene such that co-expression of up to twelve proteins can be achieved. Since the co-expression of more than a few proteins simultaneously is difficult, this represents a significant advance with multiple use cases. The authors validated the technique by assessing expression in vitro and in vivo, and by rewiring Drosophila sensory neuron axons by simultaneously expressing several cell surface receptors within the neuron. Overall, this is a clearly written paper that describes a potentially important new system. I have no major criticisms.

Reviewer #3 (Public review):

Summary:

The authors aimed to identify new molecular pathways that could enable long-range signaling through the cerebrospinal fluid (CSF), focusing on a specialized class of neurons called CSF-contacting neurons (CSF-cNs) in larval zebrafish.

Strengths:

Anatomical validation of transcriptomic candidates using HCR, providing high-resolution spatial mapping of chemoreceptor expression in CSF-contacting neurons and neighboring spinal cord cells. The work broadens the potential understanding of CSF-cNs and offers a resource for future functional investigations of CSF-mediated signaling.

Weaknesses:

The principal limitation of the study is that the conclusions remain largely transcriptomic and inferential. Although HCR convincingly validates mRNA expression, no protein-level evidence is provided to demonstrate receptor translation or subcellular localization, leaving uncertainty regarding functional receptor availability at the CSF interface. Moreover, the study does not establish whether the proposed ligands are present in the relevant CSF microenvironment or engage the identified receptors in vivo. As such, the functional significance of the proposed chemosensory pathways remains speculative.

Reviewer #3 (Public review):

This manuscript reports a striking sex-specific effect of the daf-2(e1370) mutation on C. elegans lifespan. The authors show that male daf-2 mutants exhibit dramatically extended lifespan relative to wild-type males, wild-type hermaphrodites, and daf-2 hermaphrodites. The study also demonstrates increased lipid accumulation in these long-lived males, which is increased further over time, improved late-life motility, enhanced oxidative stress resistance, and a requirement for the downstream effector of daf-2, daf-16, for the longevity phenotype.

The interest of the work is the magnitude and consistency of the lifespan effect. The authors report large increases in both median and mean lifespan in daf-2 males across independently replicated experiments. This is further supported by healthspan analyses and their finding that male daf-2 mutants maintain improved motility and stress resistance, which argues against the interpretation that lifespan extension merely reflects prolonged frailty. The genetic epistasis experiment demonstrating loss of the longevity phenotype in daf-2;daf-16 double mutants provides evidence that the effect depends on canonical insulin/IGF-1 signalling.

The main limitation is that at least the first figure is rather an incremental increase on previous work examining the lifespan of daf-2 males, although the authors do indeed show that the effects can be much larger (or more 'plastic') than those previously published. While these findings are potentially important, the manuscript would certainly benefit from a more extensive discussion of how the results compare with prior studies of daf-2 mutants and male longevity, including possible explanations for the apparent discrepancies.

The epistasis experiment shows that this exceptional longevity requires the expression of daf-16. However, in contrast to the initial experiments (Figure 1) that show three replicates of the lifespan experiment (the standard in lifespan work in this model), it appears that the daf-2;daf-16 experiment has only been performed once.

In addition, the lifespan data for hermaphrodite daf-2 mutants appear somewhat unusual. Although the mean lifespan is increased, the median lifespan is reported to be only modestly greater than that of wild-type hermaphrodites. I know that this mutant can give lifespan curves that look like this, but either the use of another allele or of the experimental conditions and how these values compare with previously published daf-2(e1370) datasets would help readers interpret the magnitude of the male-specific effect.

The lipid phenotype is intriguing. It would be interesting to expand this to examine somatic vs embryonic fat. In addition, I noted that in the methods section, the authors use palmitic acid to stop the male worms 'fleeing' the plates; is it possible to rule out the possibility that the daf-2 mutants are simply eating and metabolising/storing this fatty acid barrier differently than their wild-type counterparts? This would be worth considering and controlling for, particularly as male C. elegans have been shown to have dramatically altered metabolic transcriptional profiles. If indeed this increased lipid is responsible for the extreme longevity of the daf-2 mutant males, it would be desirable to try to link this mechanistically to the phenotype.

Overall, the evidence convincingly supports the conclusion that male daf-2(e1370) mutants are exceptionally long-lived under the conditions tested and that this phenotype requires DAF-16. The work has the potential to make an important contribution to understanding sex-specific regulation of ageing, although further contextualisation within the existing literature would strengthen the manuscript.

Reviewer #3 (Public review):

Summary:

The authors investigate the structural brain basis of congenital aphantasia, a condition characterised by a lifelong absence of voluntary mental imagery. They test two competing accounts: one predicting structural differences in early visual pathways, the other predicting differences in higher-order frontotemporal and cingulate systems. To do this, they combine four complementary structural imaging approaches: white-matter microstructure profiling along anatomically defined tracts, tractography seeded from functional regions of interest, whole-brain structural network analysis, and cortical thickness mapping. The main finding is that white-matter differences are selective for frontotemporal and cingulate pathways and absent in early visual pathways, which the authors interpret as support for the higher-order account.

Strengths:

The multi-modal design is a genuine strength: running four independent analyses increases the chance of detecting real effects and of identifying false positives that appear in only one stream. The statistical choices within each analysis are appropriate. Permutation-based correction with a threshold-free method is well-suited to the tract-level comparisons. The use of Bayes factors to quantify evidence for null results, rather than simply reporting non-significant tests, is particularly valuable here, since the absence of visual pathway differences is central to the argument. The robustness checks across multiple brain parcellations for the network analysis strengthen confidence in those findings.

Weaknesses:

The main limitation concerns the relationship between two of the analysis streams. The measure used to weight structural connections in the network analysis is calibrated to match fiber density estimates derived from the same diffusion signal that drives the white-matter microstructure differences. If the two groups differ in tissue organisation in certain pathways (which the microstructure analysis suggests they do), that difference will feed into both measures. The authors should acknowledge this dependency when discussing convergence across analyses.

More broadly, the imaging metrics used throughout (measures of fiber organisation and weighted connection counts) reflect what the diffusion model captures from the tissue and cannot be directly read as measures of axon number or connection strength. This is a known limitation of the field, but it is relevant to the strength of structural claims made in this paper.

The network analysis is presented without comparison to a null network. Without this, it is hard to know whether the node-level differences reflect specific network topology or simply follow from overall differences in connectivity weight or density between groups.

The study runs four separate discovery analyses on the same 36 participants, each corrected within itself but with no control across analysis streams. At 18 participants per group, this is exploratory work. Some of the language used in the abstract and discussion, like "first comprehensive characterization" and "selective structural phenotype", reads as more definitive than the data support at this sample size. Framing the results as hypotheses to be replicated would make the paper stronger.

The paper frames the results as distinguishing between two competing accounts. The positive evidence for the higher-order account is clear. The absence of differences in visual pathways is a different kind of result: it means such differences were not detected in this sample, not that visual pathways are uninvolved. The discussion at times moves toward that stronger conclusion, which the data do not support.

The cortical thickness analysis finds one cluster in the predicted direction, while the other analyses each return multiple effects. One cluster in a whole-brain search with 18 participants per group is not strong evidence and should not be presented as equivalent to the other results.

Effect sizes are reported without confidence intervals throughout. With 18 participants per group, the uncertainty around those estimates is large, and confidence intervals would give readers a more accurate sense of what can be concluded.

Reviewer #3 (Public review):

Summary:

In this study, the authors reanalyzed choice, RT and gaze datasets collected from human subjects performing a food-choice task. They show that models that posit a causal role for attention in shaping the decision-making process fail to account for empirical observations in the data. These include the attentional drift diffusion model (aDDM) and models that derive attention-choice associations from an optimal policy. The authors show that a model that assumes that gazes are directed towards the chosen option after decision commitment captures more (but not all) empirical findings, suggesting that attention may reflect decisions once they are made instead of contributing to their formation. However, this post-decision-gaze (PDG) model failed to capture all aspects of the data, suggesting that gaze may reflect both decisional and post-decisional operations, and existing models are still missing some features of the gaze-directing process. The authors provide convincing evidence that post-decision gaze explains a number of empirical findings in this task.

Strengths:

(1) The analyses are generally appropriate, and the conclusions are supported by the data.

(2) The study was rigorous, as the authors considered a number of alternative possible models for behavior, and evaluated their performance based on a wide range of qualitative predictions (as opposed to exclusively relying on model comparison).

(3) The proposal that gaze may largely reflect post-decisional processes is interesting, and as far as I am aware, novel.

Weaknesses:

There was limited discussion about why one might allocate attention post-decision. I would have appreciated more discussion on the potential functional consequences or implications of post-decision gaze.

Reviewer #3 (Public review):

Summary:

The authors studied social aspects of antibiotic resistance by co-cultivating antibiotic-resistant and sensitive Enterococcus faecalis (an important pathogen) as biofilms to assess the extent to which sensitive cells can take advantage of the protection provided by resistant cells against both a beta-lactam antibiotic and in the presence of a B-lacatamase inhibitor. By quantifying the proportion of each cell type using fluorescence microscopy, they conclude that protection is provided equally in the biofilm and planktonically, and that the biofilm is completely unstructured with regard to the locations of the two cell types. A mathematical model is then used to show that no spatial information is needed to recapitulate the results and that the protective effect can be described completely by the growth rates of the two cell types and the affinity of the β-lactamase to the antibiotic and inhibitor. The strength of evidence is difficult to assess due to unclear descriptions of some methods, and the significance of the findings is limited by the experimental setup, where antibiotics were added very close to the time of inoculation.

Strengths:

The co-cultivation of antibiotic-resistant and sensitive bacteria allows for exploration of the social aspects of antibiotic resistance. Fluorescently-tagged strains allow for unambiguous tracking of the two cell types. The simultaneous analysis of biofilm and planktonic cells enables insight into whether these different growth modalities are influenced by social aspects of antibiotic resistance. In analyzing the structure of the biofilm, the use of a null model with randomized cell positions allows for an accurate determination of whether the observed data are due to some effect; however, as noted below, there is a caveat to this analysis. The broad observation that biofilm and planktonic populations are linked is generally supported by the data; however, this result is closely tied to the experimental setup used. The development of a mathematical model that can recapitulate results from a second set of data with values obtained from fitting a different set of data shows robustness of the model for using it to explain the results.

Weaknesses:

The observed results are tied very closely to the experimental setup of adding antibiotics very close to the time of inoculation, but this connection is not discussed. The described 'population inversion' effect is better described as frequency-dependent selection for resistant cells, but frequency-dependent selection is not discussed. Confocal microscopy was used to quantify the relative proportion of antibiotic-resistant and sensitive cells in the biofilm; however, it is unclear if the entirety of the Z stacks was used to determine these proportions. This is also the case for the analysis of whether the sensitive/resistant cells are non-randomly distributed in the biofilm: it is unclear whether the vertical distance between cells was taken into account. The authors claim that biofilm and planktonic bacteria are protected equally by the presence of resistant bacteria; however, Figure 1a and b seem to clearly show that the proportion of sensitive cells is higher in the planktonic cells compared to biofilm cells when started from an equal frequency inoculum, meaning this is not always the case. The mathematical model is used to confirm the result that no spatial components are needed to describe the results; however, this is mostly linked to the initial setup of the experiment, where antibiotics are added at the time of inoculation, and no biofilm could form before the outcome of the antibiotic-cell interactions was concluded.

Reviewer #3 (Public review):

Summary:

The authors investigate mRNA targets of the nonsense-mediated decay (NMD) pathway in astrocytes and link the dysfunction of NMD in astrocytes to aberrant synaptic transmission that has downstream effects on behavior. Specifically, they find a link between the aberrant synaptic transmission with elevated spontaneous calcium signaling in astrocytes, and functionally they demonstrate that manipulating astrocyte calcium signaling with CalEx modulates astrocyte calcium signaling towards wildtype levels and improves anxiety behavior. They investigate the astrocyte calcium signaling changes in Upf2 conditional knockout mice in several brain regions that have been linked to anxiety behavior, including the hippocampus and prefrontal cortex. They also observe aberrant astrocyte calcium signaling in the visual cortex, demonstrating that dysfunction of the NMD pathway in astrocytes has widespread effects on synaptic transmission in various brain regions. This work identifies, through RNA-Sequencing, potential mRNA targets of NMD in astrocytes, and shows that pathway enrichment of these targets highlights calcium signaling. Altogether, this work highlights the importance of the basic cellular process of NMD in astrocytes, which are known to have extensive local translation of proteins in their perisynaptic processes. NMD may be particularly important in astrocytes due to their intimate association of processes with neuronal synapses, and the authors suggest that alterations to NMD function in astrocytes may be an important avenue for future investigation in neurodevelopmental disorders.

Strengths:

Altogether, this work is a critical foundation for future research into astrocyte contributions to neurodevelopmental disorders. The authors do a thorough characterization of astrocyte conditional Upf2 knockout mice in several brain regions. They present a complete story that connects molecular events (NMD pathway regulation of mRNA degradation) to astrocyte regulation of circuit activity to organismal behavior. The electrophysiological analysis is thorough, and the manipulation of calcium activity ties astrocyte calcium activity to anxiety behavior. The RNA-sequencing dataset is useful to the scientific community and provides a resource of candidate molecules that might be dysregulated in neurodevelopmental disorders.

Weaknesses:

The study suffers from some overstated claims and a lack of statistical rigor in some experiments, as detailed below.

(1) The title states that "Astrocytic Nonsense-mediated mRNA decay regulates calcium signaling to support synapse function and restrain anxiety". The term "restrain anxiety" implies that the NMD pathway has a direct effect on a molecular switch to control anxiety. Anxiety behavior is a complicated process, controlled by many biological phenomena and synaptic transmission in the circuit as a whole, and is not directly linked to a specific NMD mRNA target. This title is overstating the findings of the study.

(2) In general, the first figures (1-2) suffer from low power (N = 3) and statistical rigor. The statistics are inflated by analyzing individual fields of view and per-cell data rather than performing the statistics on the average of biological replicates. It is preferable to show the biological replicate data so that readers can observe the natural biological variability between replicates.

(3) The claim that astrocytes have decreased engulfment of synapses in the Upf2 conditional knockout mice is not strongly substantiated by the data. The resolution of confocal microscopy and the static nature of histological images make it difficult to measure synaptic engulfment as an active process. Additionally, the metric of quantifying the % occupancy of PSD95 puncta within the total astrocyte volume may be skewed due to overall differences in cell size (shown in Figure 1). There is not much discussion of how a decrease in astrocyte engulfment of synapses may lead to decreased synapse number. To the contrary, one might expect decreased engulfment to result in increased synapse density.

(4) The authors use Gfap as a marker to count astrocyte cell number and assess if there are changes in cell number between genotypes (Figure S6). However, Gfap does not label all astrocytes in the cortex and, in fact, is rather an aberrantly expressed marker in conditions of inflammation, as opposed to the hippocampus, where Gfap is basally expressed in all astrocytes. In the cortex, there seems to be a trend for reduced Gfap in the conditional knockout mice, which may suggest differences in astrocyte molecular signatures rather than cell numbers. Another astrocyte marker, like Aldh1L1, will be more accurate to assess this question histologically.

(5) The authors state that "Preventing abnormally high basal calcium activity in NMD-deficient astrocytes restores normal excitatory synapse function...". However, this claim is not substantiated by the data. CalEx manipulation certainly shifts the input-output curve but does not restore to wildtype baseline levels (Figure 6E). Additionally, synapse number does not appear to be restored to wildtype levels (Figure 6D - although the p-value for this comparison is now shown). The investigators do observe improvements in anxiety phenotypes, suggesting there is some modulation of circuit activity, but the claim that CalEx manipulation restores baseline synaptic transmission is not supported.

Reviewer #3 (Public review):

Summary:

In the presented work, the authors investigate spectral staining in axons of the sciatic nerve, where the MPS has been detected before using STED microscopy. They employ 3D-dSTORM in tissue sections and analyze the data, measuring localization of clusters on the axon perimeter and the relative distribution of those. From these data the conclude that large gaps in spectrum localizations exist and that clusters around the axon exist that are spaced at 200nm.

Major Comments:

(1) The presented data are at times overinterpreted, and the discussion lacks a critical view of the data. For example, the statement "...Unlike previous suggestions from qualitative evidence in cultured neurons (REfs), βII‑spectrin distribution in MPS segments of peripheral nerves is discontinuous, with extensive stretches of the perimeter lacking βII‑spectrin." is quite strong, given it is based on immunofluorescence staining and dSTORM microscopy in tissue. Absence of evidence of staining is not evidence of absence.

(2) The authors claim in the abstract that "The number of these clusters scales linearly with the axonal perimeter, maintaining a constant membrane occupancy of ~20% across varying axon diameters." Again, this is from a cut through an axon, while measuring the density of clusters on the perimeter. If they claim area occupancy, an area should be imaged, and the dots (clusters) should be measured in surface coverage in a 2D projection of the axonal surface.

(3) In general, this reviewer suggests being a bit more moderate in statements such as: "These findings challenge simplified models of the MPS based on cultured systems and demonstrate that the MPS in peripheral nerves is composed of discrete structural units." These statements are bold from the relatively few measurements in a single method and a single viewpoint. Especially when considering that techniques such as dSTORM depend extremely highly on labeling density, and apparent clustering of localization is highly prone to misinterpretation. If the authors desire to make such statements, working with endogenously labeled protein would be warranted. The authors should at least hedge such statements.

(4) If the authors want to make statements about general organization, why do they not compare adjacent cuts through the axon? If there are continuous spectrin filaments, the clusters should appear at the same site across repeated cuts through the axon.

Besides this, this reviewer welcomes the effort that has been made to establish dSTORM in tissue sections and to investigate the MPS in native tissue.

Reviewer #3 (Public review):

Marantos et al. showed that for some coliphages, the energetic state of the bacterial host cell has a strong impact on whether phage infection is initiated. The authors drew this conclusion from the observation that there are more free phages remaining in the medium after infection of arsenate-azide-treated cells as compared to after infection of untreated cells. These data were analyzed and reported both as ratios of the treated vs. untreated conditions and using a mass-action kinetic model of phage-cell collision in the infection mixture. The data supported the findings that for four phages infecting Escherichia coli bacteria, namely, phages λ, 𝜙80, m13, and T6, the phages are less likely to initiate infection if the host bacteria are energy depleted. However, for phage T5, the authors found that their infection propensity is not impacted.

As I have stated in the first submission of this manuscript, the data presented by the authors clearly supported the principal conclusion of the study. The five phages chosen by the authors represent different viral lifestyles and infection mechanisms, highlighting the potential applicability to other Escherichia coli phages. Finally, the authors successfully use a classic mass-action model of phage-cell collision to interpret their data. The simplicity of their experimental assay, combined with the use of this mathematical model, offers other investigators who study phage-bacterial interactions in other contexts a potentially useful toolkit to examine infection in general, and specifically, the dependence of phage infection on the host's metabolic state.

Comments on revised version.

In this revised version, the authors have successfully resolved all of my comments. I appreciate that the main text has been majorly revamped, which greatly helps the readers follow the motivation behind the experiment and analyses, and interpret the data. I agree that the revised terminology choice "commitment to infection", instead of the previous interchangeably used "adsorption"/"entry", is much more logical, considering the experimental data. I also commend the authors for writing the modeling part in a very clear, pedagogical, and instructive manner. Overall, I believe that this manuscript will be valuable to those who are interested in phage-bacterial interactions.

Reviewer #3 (Public review):

This manuscript investigates the role of PolK in cisplatin repair. While in general it is considered that polK is not involved in the repair of cisplatin-induced DNA damage, the authors show that in a very specific scenario, namely cisplatin-resistant head and neck cancer cells, loss of PolK causes cisplatin sensitization, implying a role in cisplatin repair by polK in these cells. It is also implied that these cells acquire cisplatin resistance by overexpressing polK, but this is not really investigated. The authors then go on to show that DNA replication in the presence of cisplatin is affected by the loss of polK in these cells and also identify USP18 as a potential polK interactor in these cells with a similar phenotype. They claim that polK and USP18 form a pathway that allows cisplatin tolerance in these cisplatin-resistant head and neck cancer cells. The findings are interesting and useful to the field; however, the manuscript, in its current form, has several issues. Most importantly, the mechanism of USP18 has not been investigated. In addition, the manuscript does not flow fluidly, and instead, various experiments are put together without a clear logic. Some of the claims are not substantiated by the data shown.

(1) The experiments in Figure 1 using a few cell lines from various types of cancers are not enough to conclude that polK expression is specifically induced by cisplatin in some types of cancers but not others. Since the focus of this study is head and neck cancer, the authors should show the expression of PolK after cisplatin treatment in more head and neck cancer cell lines, and not just the two investigated.

(2) It is unclear to me why the authors include H357-S in their experiments. If the idea is that these cells acquire resistance because they overexpress polK, then the authors should investigate this by exogenously overexpressing PolK in H357-S cells and test if these cells are cisplatin resistant.

(3) In addition, the authors should create the polK knockout in H357-S cells as well and include it as a control in their experiments.

(4) Page 6, line 28: the comet assay does not measure DNA degradation, but rather DNA breaks.

(5) Figure 4B: How does the overexpression of PolK mutants compare to endogenous PolK expression? It is important to assess if this expression is similar or of much higher magnitude.

(6) Page 9, line 22: "For such a function, the catalytic domain of PolK becomes dispensable, whereas its interaction with PCNA is sufficient to drive efficient replication". I do not understand what data the authors used to make this claim. The interaction and colocalization studies should be performed with the PIP mutant. Similarly, this mutant should be used in the HU DNA fiber assays.

(7) It is unclear how USP18 acts. What are its substrates? Chk1/2, BRCA1, BRCA2? This needs to be investigated. The impact of PolK on this activity needs to be assessed as well (is PolK needed for USP18-mediated de-ubiquitination of these DSBR proteins?). As it stands, the manuscript does not address the mechanism of USP18 in DNA repair, which is billed as the main finding of the paper.

(8) Do PolK and USP18 interact directly? Experiments using recombinant proteins would be useful to address this.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors have undertaken an investigation of differences between two mammalian species, the brown rat and the crab-eating macaque, in the mechanisms supporting a well-established model of long-term Hebbian synaptic plasticity, Schaffer collateral to CA1 Long-term potentiation (LTP) in the hippocampus. LTP has been long-studied and deeply characterised due to its potential importance in modeling a strong candidate process for the central mechanism of learning and memory. LTP was first discovered in lagomorphs (rabbits), but has since been much more widely studied in rodents (mostly rats and mice), and there has been some complementary work revealing LTP in non-human primates and even in humans, revealing largely overlapping canonical mechanisms of induction, expression, and maintenance. More specifically, this study puts a particular focus on the fascinating associative features of this form of lasting synapse-specific modification, in which a synaptic input can be stimulated with a relatively weak induction protocol that will not produce lasting plasticity on its own, but can undergo lasting LTP if paired with stronger stimulation on a separate synaptic input to the same neuron. This associativity mechanism is particularly attractive within the Hebbian synaptic plasticity framework as it provides a candidate mechanism for associative forms of learning in which stimulus-stimulus, stimulus-reward, stimulus-punishment, or action-outcome associations are formed. A particularly attractive feature of this associative LTP is that there can also be a substantial time-lag between the strong stimulation of one pathway and the weaker stimulation of the other synaptic input, which only undergoes lasting LTP by hijacking the proteins synthesized as a result of strong stimulation elsewhere. This observation has led to the famous tagging and capture hypothesis as an explanation of how such synapse-specific change can be achieved on both stimulated inputs but not on other synaptic inputs, given the potential requirement for cell-wide protein synthesis. This theory, for which there is very strong experimental evidence, posits that a protein tag is left at synapses that have been stimulated with sufficient vigor in recent history, serving as a key mechanism to ensure that those weakly stimulated synapses will undergo change when a larger-scale LTP event occurs due to stronger stimulation elsewhere within a relevant time window. Again, this idea is attractive as it can explain how we might form associations between events that occur slightly separated in time. The manuscript goes on to show that an induction protocol that is particularly physiologically relevant, theta burst stimulation, produces this tag and capture associative effect in ex vivo slices of Macaque hippocampus, much more readily than in side-by-side ex vivo slices of rat hippocampus. Moreover, the manuscript delves into the importance of well-characterised LTP maintenance mechanisms, including PKMzeta and BDNF, which are key factors that ensure that altered synaptic change is maintained for long periods of time despite substantial molecular turnover in the neuron. The observation in this manuscript is that a degree of redundancy for these mechanisms exists in the primate species but not the rodent species, as both mechanisms need to be inhibited to return LTP to baseline in the Macaque, but only one needs to be inhibited to have that effect in the rat. A major emphasis of this study is that there may be a step-wise difference in associative learning mechanisms between rodents and primates that may contribute to their differing cognitive capacities, although I believe a lot more evidence would be required to reach that conclusion.

Strengths:

The strengths of this study are that it is technically very proficient and is from a laboratory that has a long history of seminal work on synaptic tagging and capture. The cross-species comparison, particularly involving non-human primates, is also very hard to achieve, and a major strength here is the side-by-side comparison of slices from rat and monkeys. Further strengths of the study are the use of a number of experimental strategies, including both observation and intervention, to demonstrate differential involvement of LTP maintenance mechanisms. A final major strength is conceptual, as it is undoubtedly useful not only to identify shared mechanisms of plasticity between commonly used model organisms and either humans or much more closely related species such as old world monkeys, but also to reveal differences that have the potential to contribute to differences in memory/cognition.

Weaknesses:

The findings of this study are a very useful building block for understanding how generalisable mechanisms of LTP are. However, arriving at really substantial conclusions from these findings is challenging, as there are a number of variables that are unaccounted for in this study that may explain the differences that have been observed between rats and monkeys. One example of a potential confound to these interpretations is that rats are nocturnal/crepuscular animals, and macaques are diurnal animals. Thus, to undertake a like-for-like comparison, it would be necessary for the rats to be on a reversed light-dark cycle to ensure that the wake cycle of the rat (dark) is being compared with the wake cycle of the monkey (light). It is possible that the authors have done this, but it is not mentioned in the methods section. The reason this is important is that there is a substantial body of work indicating that different mechanisms are at play in hippocampal LTP during wake and sleep. Transcripts and proteins related to synaptic function are dramatically differentially regulated during sleep-wake cycles, and phosphorylation states of key proteins involved in plasticity are also altered. Moreover, synaptic tagging and capture are specifically disrupted by sleep deprivation. Perhaps the authors have already considered this factor and appropriately reversed the light-dark cycle of their rat subjects, in which case a clarification in the manuscript would be useful. Nevertheless, I have used this as an example because there is a variety of potential confounds that may explain the difference between SC-CA1 TBS LTP in rats and monkeys, e.g., circadian rhythms, degree of enrichment, natural light vs indoor lighting, diet, degree of inbreeding, strain, etc. Thus, to make strong conclusions about the potential for differences in plasticity rules/mechanisms and how those may contribute to differences in cognition, I think it would be necessary to compare a wider variety of species, including a good representation of each order (e.g., nocturnal rats and diurnal squirrels, new and old world primates) and not just a single exemplar. I understand, of course, that this is really pushing the boundaries of practicality, but I see no other way to make a strong conclusion or to generalise to mechanisms or properties of plasticity in rodents vs primates. Thus, while I believe the manuscript presents really admirable work, I am not sure the findings are at all easy to interpret.

Reviewer #3 (Public review):

Summary:

The authors conducted a large-scale replication effort of lab-based biomedical experiments with an emphasis on the country of origin and who conducted the replication experiments. The authors aimed to understand this context in both the outcomes produced, but also in the approach. Finally, the authors aimed to conduct multi-lab replications to provide richer data from the replications. Overall, the authors find replication rates that are like other large-scale replication efforts in the biomedical space. The authors provide rich detail into the three experimental techniques that were the focus of this effort, potential moderators of replication success, and challenges in conducting replications and coordinating a large-scale crowd-sourced effort.

Strengths:

The paper is outstanding in being transparent and calibrated in how the results are presented. While the authors were challenged by mundane aspects (e.g., difficulty with logistics), unexpected aspects (e.g., COVID pandemic), and very insightful aspects unique to conducting replications (e.g., experimental issues). The authors also provide variation in how they present the results, including confirmatory, multiverse, and exploratory analysis. A unique strength for this study is the rich in-depth insights about the process and interpretation of conducting replications, including predicting replication success in the lab-based biomedical space.

Weaknesses:

The study has weaknesses that the authors acknowledge in their discussion, such as lower number of replications than originally planned that limited the intended effort to compare multiple experiments with multiple attempts against a single original experiment. Another weakness is the limited discussion connecting these findings to the Brazilian research ecosystem.

Reviewer #3 (Public review):

Cotto and colleagues integrated data analysis with mathematical modeling to examine extended-spectrum beta-lactamase (ESBL)-producing E. coli in France. While ESBL prevalence has risen globally, it has stabilized at approximately 6-8% across Europe. Established risk factors for ESBL carriage include prior antibiotic exposure and travel to high-prevalence regions, most notably South-East Asia. The dataset incorporated information on ESBL-producing E. coli and travel history in young children, and the model was calibrated to ECDC surveillance data on ESBL across Europe, supplemented by literature-derived parameters on antibiotic use, E. coli biology, and transmission dynamics. The authors report that ESBL-carrying strains exhibit a 14% fitness cost in community transmission relative to susceptible bacteria, yet are cleared 23% less frequently. ESBL carriage was strongly associated with factors that prolong gut colonization. Both antibiotic treatment rates and transmission efficiency were identified as key determinants of community-level ESBL prevalence.

Strengths:

The study addresses a clinically and epidemiologically important topic. The integrated modeling approach is methodologically sound and well-suited to disentangling the relative contributions of transmission and antibiotic selection pressure.

Weaknesses:

Several concerns regarding the data used in this study warrant consideration. First, model calibration relied on ECDC surveillance data pooled across multiple European countries, several of which have substantially lower antibiotic consumption than France (ECDC ESAC-Net Annual Epidemiological Report, 2024). Given that antibiotic use is a primary driver of ESBL selection, ESBL prevalence is likely to be heterogeneous across these settings. Calibrating to a geographically diverse dataset risks introducing systematic bias into parameter estimates that may not be representative of the French context. The authors should repeat the analysis using France-specific data, or, where this is not feasible, restrict the calibration dataset to countries with comparable antibiotic consumption profiles. Second, the travel exposure data may be insufficient to adequately capture importation dynamics from South-East Asia, as the cohort consisted exclusively of young children, a demographic less likely to travel to high-prevalence regions than older age groups. This may result in an underestimation of travel-associated importation as a contributor to community ESBL prevalence, and the generalizability of these findings to the broader population should be interpreted with caution.

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.

Weaknesses:

(1) In the single-channel recordings (Figure 1a), it's unclear how many channels were present in those patches. After applying -60 mmHg pressure, multiple channels would be activated (as seen in Figure 1e). The number of channels in the patch and their inactivation rate could significantly influence the open probability in such experiments. To overcome this, in the original Yoda1 article (Syeda, Ruhma, et al. eLife 2015), no additional pressure was used. Additionally, the reported open probability comparison (n=7 Yoda1 vs n=17 DMSO patches) has an SEM nearly as large as the effect itself (0.30 {plus minus} 0.11), consistent with a small number of outliers driving this. The underlying mean open and shut times are reported without any statistical test; only the derived open probability receives a p-value. Additionally, in Figure 1a, the Yoda1 condition noise is different from the control. This should be stated if noise filtering was applied and how, given that this could affect open probability analysis.

(2) The calcium imaging data in Figure 2 raise significant concerns regarding the chemical activation claim. The calcium-boosted solution (30 mM Ca2+) is not physiological and appears to be generally stressing cells rather than specifically activating PIEZO2: the control condition under CBS already shows an elevated signal, consistent with cells being unwell at this calcium concentration, and adding Yoda1 on top of this shifted baseline raises further questions about specificity rather than confirming it. Separately, it is unclear why DMSO alone produces measurable PIEZO2-associated calcium influx in HBSS, a result that is not addressed in the text. Figure 2 should clearly indicate when DMSO/Yoda1 perfusion was initiated, and y-axis labels are missing from panels A and B.

(3) In the poke experiments, an activation threshold should be calculated and reported, and amplitude data (e.g., peak current versus indentation depth) should be shown rather than only inactivation tau values. It is also unclear why mClover3- and N-GFP-tagged constructs were used in these experiments, since electrophysiological recording already confirms channel expression without requiring a fluorescent tag.

(4) For inactivation kinetics (Figure 3b), the authors use unpaired comparisons across separate cells, whereas the deactivation experiments (Figure 3c) use paired; it should be applied to the inactivation experiments as well. Deactivation kinetics for PIEZO2 itself should be shown. If the claim is that Yoda1 acts on PIEZO2 through the same mechanism proposed for PIEZO1, then a PIEZO1/2 chimera should be expected to show a corresponding effect on deactivation tau; instead, this chimera is reported as completely Yoda1-insensitive despite both parental channels being Yoda1-sensitive, as shown in this study.

(5) Given that this reflects a different experimental paradigm for Yoda EC50, PIEZO1 should be included within Figure 4b. Additionally, EC50 bar plots should be present on this figure. The inactivation time constant for PIEZO2 without Yoda1 is inconsistent across figures, below 20 ms in Figure 3b but above 20 ms in Figure 4c.

(6) Finally, the modeling is performed exclusively on PIEZO1, whereas the manuscript's central focus is PIEZO2. It is therefore unclear whether the proposed structural mechanism, including the basis for Yoda2's reduced efficacy on PIEZO2, can be directly extrapolated to PIEZO2.

Reviewer #3 (Public review):

This paper reveals that the neuronal protein PRRT2, previously known for its association with paroxysmal dyskinesia and infantile seizures, modulates the slow inactivation of voltage-gated sodium ion (Nav) channels, a gating process that limits excitability during prolonged activity. Using electrophysiology, molecular biology, and mouse models, the authors show that PRRT2 accelerates entry of Nav channels into the slow-inactivated state and slows their recovery, effectively dampening excessive excitability. The effect seems evolutionarily conserved, requires the C-terminal region of PRRT2, and is recapitulated in cortical neurons, where PRRT2 deficiency leads to hyper-responsiveness and reduced cortical resilience in vivo. These findings extend the functional repertoire of PRRT2, identifying it as a physiological brake on neuronal excitability. The work provides a mechanistic link between PRRT2 mutations and episodic neurological phenotypes.

Comments:

(1) The precise structural interface and the molecular basis of gating modulation remain inferred rather than demonstrated.

(2) The in vivo phenotype reflects a complex circuit outcome and does not isolate slow-inactivation defects per se.

(3) Expression of PRRT2 in muscle or heart is low, so the cross-isoform claims are likely of limited physiological significance.

(4) The mechanistic separation between trafficking of PRRT2 and its gating effects is not clearly resolved.

(5) Additional studies with Nav1.6 should be carried out.

Comments on revised version.

These comments have been addressed in the revised version.

Reviewer #3 (Public review):

This paper tackles an underexplored dimension of whisker-based texture sensing: while surface coarseness encoding has been extensively characterized in rodents, the mechanical and neural basis for stickiness sensing has not previously been examined. The authors make two intertwined contributions that together represent a substantial advance: a methodological one - a 3D whisker tracking pipeline operating at 4000 fps, capable of capturing torsion, roll, and out-of-plane whisker motion - and a scientific one - a first characterization of how whisker mechanics and primary trigeminal afferent responses differ between surfaces of high and low stickiness. The work is technically solid, the dataset is large, and the question is well motivated both by the multidimensional nature of tactile texture perception and by the practical advantages of the whisker system for studying touch mechanics.

Strengths.:

The 3D tracking system is a timely advance over existing tools, particularly in its handling of non-planar whisker shapes and the full automation required for the sub-millisecond resolution needed to detect stick-slip events. The mechanical dataset is extensive. The finding that whisking against silicone expands the sampled whisker strain space and produces stronger but less frequent stick-slip events is clearly demonstrated and internally consistent with the proposed mechanism of greater strain accumulation before frictional release - a physically intuitive result. The open release of the tracking code considerably increases the value of this work to the broader community.

Weaknesses:

A few aspects of the paper, if sharpened, would considerably strengthen the evidence and the clarity of the conclusions.

The central claim - that "stickiness information is available to the whisker system" - does not capture the precision of what the paper demonstrates. As stated, the finding is close to guaranteed: any variation in surface friction will produce some change in whisker mechanics, so the presence of mechanical differences between materials is expected rather than surprising. The more valuable question the paper is well positioned to answer is which specific dimensions of the whisker mechanical response are most informative about surface stickiness. The paper reports effects on strain distribution breadth, stick-slip amplitude, and stick-slip rate, but does not synthesize which of these - or which sub-dimensions (bending, twisting, or rolling) - carry the most discriminating information. Identifying the salient dimensions of the mechanical response and relating them to the proposed frictional mechanism would sharpen the paper's conclusions substantially.

A related but distinct limitation is the absence of direct force measurements during whisker-surface contact. The authors acknowledge this openly, and I recognize it is not easily remedied within the current experimental setup. It does, however, constrain interpretation: without knowing the actual forces generated at the whisker-surface interface, the assumed stickiness ordering of the tested materials cannot be validated, and - importantly - the relative contribution of surface friction and material compliance to the observed mechanical differences cannot be determined. This is an important direction for future work in this area.

The paper argues carefully that 2D tracking is insufficient for capturing the full mechanical picture of whisker-surface interactions, and the figure currently in the supplementary material (Figure S2) makes this case convincingly through multiple analyses. This argument is the core justification for the paper's methodological contribution and deserves a place in the main manuscript. Furthermore, while the mechanical case for 3D over 2D tracking is well made, it has not yet been tested at the neural level: the regression model used to predict neural firing incorporates 3D variables, but its performance is not compared against an equivalent model restricted to 2D variables. Such a comparison would directly demonstrate whether torsion and roll - the signals inaccessible to 2D tracking - carry neural predictive value, and would elegantly unite the paper's methodological and scientific contributions.

Finally, the three-dimensional plots in Figure 3 are the paper's primary representation of its main mechanical result, and there is a real opportunity to make them considerably more informative. The whisker deformation probability distributions (panel B) are rendered in 3D from a single viewing angle, making it difficult to assess the shape or anisotropy of the distributions - and in particular to see which dimensions expand most for silicone relative to the other materials. This is precisely the information needed to identify the most salient dimensions of the stickiness signal, and two-dimensional representations would make it directly readable.

Reviewer #3 (Public review):

Summary:

The manuscript by Elley and colleagues describes experiments on the effects of synthetic torpor on ex vivo heart ischemia. The key aspect of the study was the use of viral-vector mediated manipulation of the hypothalamic medial preoptic area (MPA) in rats. They used AAV-CaMKIIa-hM3D(Gq). The authors report that chemogenetic activation of the MPA prior to an ex vivo heart ischemia-reperfusion insult induces cardio protection against infarct size that is independent of prior in vivo hypothermia. Phosphoproteomic analysis of cardiac tissue suggested changes in cell survival and death pathways.

Strengths:

This study has important strengths. The idea is novel. The experimental design is appropriately rationalized and fascinating. The manuscript is written and presented concisely.

Weaknesses:

The study has important weaknesses in the experimental design and validation of the model.

(1) The study is based on the use of a DREADD-designed viral vector (AAV-CaMKIIa-hM3D(Gq) -mCherry) that is activated by 2 mg/kg IP injection of CNO. The rationale is to putatively activate the MPA. The authors show no evidence for chemogenetic activation of neurons in the MPA. This could be done using a variety of different approaches, even phosphoproteomics.

(2) The stereotaxic injections are difficult to precisely and locally place, particularly bilaterally. Figure 2F is only a schematic. It would be better to show actual low magnification brain sections (bregma +0.12 to -0.48) from a representative rat to show the placement of the AAV.

(3) The control rats were injected with AAV-CaMKIIa-EGFP. Why was EGFP used instead of mCherry for the control?

(4) Ideally, a mutant non-activatable variant of AAV-CaMKIIa-hM3D(Gq) should have been used for a better control.

(5) The authors should comment on whether there is any neurotoxicity in the MPA associated with the forced AAV expression of hM3D-Gq.

(6) Is there any inflammatory pathology seen in the MPA with AAV transduction?

(7) There are no experiments to show that the systemic torpor is specifically associated with the MPA region. Experiments should be done with injections of AAV-CaMKIIa-hM3D(Gq)-mCherry placed in other brain regions, for example, the nearby nucleus accumbens.

(8) The mapping of the distribution of neurons responsible for synthetic torpor is not mechanistic enough and is not directly to the point. While excitatory and inhibitory markers are examined, a more interesting and deeper approach would have been to use glutamate receptor antagonists to manipulate the torpor response.

(9) The ischemia and reperfusion aspects of the Lagendorff method need to be clarified. The isolated hearts are already ischemic after their removal from the rat. The reperfusion aspect is caused by reflow of blood to generate oxidative stress, but in the ex vivo model, is there really reperfusion injury?

(10) The authors show that whole animal oxygen consumption is reduced in the torpor state. The measurement is crude and most likely reflects the inactivity of the animal's skeletal muscle in the torpor state. A more relevant and direct experiment would be to do oxygen consumption (or Seahorse) assays on extracts of the isolated hearts.

(11) The authors report that the synthetic torpor induces bradycardia. There is no follow-up on this important observation. The MPA-heart connection is not analyzed. (A) Is the link through cardiovascular centers in the brainstem? (B) Is the torpor-induced bradycardia mediated through increased parasympathetic or decreased sympathetic autonomic tone? Pharmacological experiments could also be done.

Reviewer #3 (Public review):

Summary:

This manuscript combines widefield calcium imaging, electroencephalography, 2-photon imaging, and immunohistochemistry in mice to re-demonstrate that electroconvulsive stimulation (ECS) induces a seizure followed by cortical spreading depolarization, as previously shown. The putative novel finding - which is not unexpected - is that ECS is also correlated with increased expression of the immediate early gene cFOS, although this has also been shown previously. The authors speculate that CSD drives cFOS expression, which might contribute to the therapeutic effects of ECT; however, experiments performed do not provide causal evidence for this hypothesis. Instead, the authors use expression of cFOS - a nonspecific activity-dependent gene induced in various pathological and non-therapeutic contexts - as a proxy for plasticity and/or therapeutic effect. Hence, overall, the significance of the findings is limited and primarily serves to replicate prior work, with the evidence evaluated as incomplete.

Strengths:

The experiments are generally well executed from a technical perspective.

Main Weaknesses to be addressed in revision:

(1) The main findings of this paper are replication experiments of prior work, and thus, the novelty and significance of this manuscript are relatively limited.

- It is already known that the mean frequency of ECT-induced seizures decays between peak and offset in humans (Stuiver et al. Clin Neurophysiol. 2026 Jan:181:2111439. doi: 10.1016/j.clinph.2025.2111439) and mice (Murakami et al. J Pharmacol Sci 2008 Jan;106(1):78-83. 10.1254/jphs.FP0071453), which the authors re-demonstrate in Figure 1.

- It has already been demonstrated that ECT in mouse models induces lateralized CSD waves in a manner that depends on stimulation parameters and the initial evoked response during stimulation (Rosenthal et al. Nat Comm. 2025 May 18;16(1):4619. doi: 10.1038/s41467-025-59900-1); the authors replicate this in Figures 1, 2, 3, 6.

- It is already widely established that EEG and calcium signals are highly concordant in mouse brain physiology, as shown in Figure 1. It is already known that CSD propagates from supragranular to granular and infragranular layers (Zakharov et al. Epilepsia. 2019 Dec;60(12):2386-2397. doi: 10.1111/epi.16390) as shown in Figure 4.

- It is already known that CSD waves induce cFOS expression (e.g., Dell'Orco et al. Front Cell Neurosci. 2023 Dec 14:17:1292661. doi: 10.3389/fncel.2023.1292661; Hermann and Hossman. Neuroscience. 1999 Jan;88(2):599-608. doi: 10.1016/s0306-4522(98)00249-8) as the authors replicate in Figure 5.

Minimally, the authors should revise claims regarding novelty, as the manuscript, as written, is misleading to a reader not familiar with the field. There is limited innovation in re-demonstrating that these events are seizures and that they involve spreading depolarization.

(2) The authors frame their hypothesis that CSD could be a potential mediator of the therapeutic effects of ECT, but they do not measure therapeutic effects or directly test this hypothesis. The principal advancement of the paper is showing that ECT-induced CSD triggers hemisphere-specific cFOS expression as a proxy of plasticity. However, it is already known that CSD induces cFOS expression (as noted above). The observation that cFOS expression was induced only by CSD, not by the initial seizure, is likely a byproduct of the greater activity induced by CSD than by seizure. cFOS expression is nonspecific to plasticity or therapeutic effects and can be triggered by many non-therapeutic interventions. The cFOS data thus do not meaningfully measure therapeutic plasticity. The authors also selectively cite references suggesting that EEG metrics such as seizure duration predict positive therapeutic outcomes, but this link is controversial and not well established in the clinical literature.

Minor Weaknesses:

(3) For the n=3 mice used for concurrent 2P imaging with microprism implant, these animals also had ChrimsonR co-expression, but there are no optogenetic studies described in this paper, which is confusing. Yet, this co-expression introduces a significant confound, as GCaMP6 emission (525/50nm band in this study) will overlap substantially with the ChrimsonR excitation spectrum. Thus, the fluorescence emission used to image these neurons may be optogenetically activating them at the same time. Please explain.

(4) Incision of the cortex for implantation of a prism is a significant cortical injury that likely induces CSD instantaneously and may change the propensity for CSD in subsequent recordings. Please comment on this limitation and address how much time elapsed after surgery before imaging.

(5) Method details are missing or insufficiently described for location, titer, and injection strategy for 2-photon experiments.

(6) Given the wide range of parameters used for ECS in mice and ECT in humans, the authors should provide tables for what stimulation parameters were used for each recording. These protocols were chosen manually rather than randomly or systematically, which introduces confounding factors into analyses that use parameters as an independent variable.

(7) While much of the cFOS staining after unilateral CSD shows hemisphere-specific asymmetry, several regions (piriform cortex, amygdala, thalamus) do appear to have bilateral cFOS expression. Please comment on this.

(8) The discussion states: "If CSD accounts for plasticity effects, triggering a CSD in a non-seizure context may be sufficient to elicit therapeutic effects. This is supported by the clinical success of ultra-brief stimulation treatments that do not cause seizures, such as rTMS with accelerated protocols, which achieves treatment efficacy on par with ECT for major depressive disorder". Are the authors implying that TMS induces CSD? What evidence supports this idea?

(9) This statement - "Assuming psychosis is the result of thalamocortical coupling that is too weak in frontal areas of the cortex" (lines 583-585) - may be overly speculative.

Reviewer #3 (Public review):

In the present study, the authors describe the development of new tools and imaging strategies to assess the concomitant development of excitatory and inhibitory synapses in dissociated neuron cultures. To this end, they generate fluorescently tagged constructs of excitatory and inhibitory synapse marker proteins using either conventional overexpression or CRISPR-based strategies. They then image these marker proteins over a timespan of 15 hours to assess synaptic dynamics at different developmental timepoints. Based on their data, they conclude that excitatory and inhibitory synapse development occur in concert to maintain a functional balance despite individual synapse turnover.

Overall, this study addresses an interesting question, i.e., the interplay between the development of excitatory and inhibitory synapses, which has important implications, particularly for neurodevelopmental disorders in which the balance of excitation and inhibition is disrupted. The experiments are technically solid and well-executed, and the individual images are highly compelling.

Comments on revised version:

The authors have fully addressed my concerns, and this is now a strong manuscript for the synaptic field.

Reviewer #3 (Public review):

Summary:

Using fMRI, the authors demonstrate that human temporal voice areas (TVA) respond not only to human vocalizations but also to those of other primates, particularly chimpanzee calls, which share acoustic features with human voices. These findings provide compelling evidence for cross-species vocal processing in the human auditory system and carry important theoretical implications for understanding the evolutionary underpinnings of speech perception.

Strengths:

The study offers a valuable comparative design, rigorous acoustic and phylogenetic modeling, and consistent evidence that bilateral anterior TVA regions respond more strongly to chimpanzee vocalizations than to other species' calls. The inclusion of both great apes and monkeys provides a rare cross-species perspective.

Weaknesses:

Minor limitations include the acoustic-phylogenetic confound (which the authors partially address with additional analyses), the lack of non-vocal controls to establish true selectivity.

Overall, the methods, data, and analyses broadly support the claims, with only minor weaknesses that do not undermine the main conclusions. The findings are valuable for the subfield of auditory neuroscience and comparative cognition, with solid evidence supporting the primary claims.

Comments on revised version.

After revision, this work has shown great improvement in data analysis, figure organization, and writing. I have no further suggestions.

Reviewer #3 (Public review):

Summary:

The work investigates how the foraging behaviour of Drosophila larvae depends on resource quality, valence, and heterogeneity in the foraging environment. A specific focus of the work was to study how foraging decisions depend on the prior experience of alternative resource patches in the same environment. Moreover, the work presents computational models (drift diffusion models) that recapitulate foraging decisions, and whose parameters appear to depend on resource quality and environment statistics, providing potential insights into the dynamics of the decision-making process.

I am not familiar with previous literature on foraging decisions in Drosophila, but I was specifically consulted to comment on the computational modelling. Therefore, my comments will mostly focus on the modelling aspects.

Strengths:

In my understanding, the two strengths of the current study are that:<br /> (1) it uses non-volatile resources, providing better control of the available cues that could guide foraging decisions, and<br /> (2) it tracks foraging behaviour over an extended period of time (3h), generating a rich dataset of foraging behaviour in the same environment.

Overall, the study appears to have been carefully conducted.

Weaknesses:

The computational modelling currently provides limited additional value beyond the empirical results. There are no prior hypotheses that are addressed by the computational models. Given the flexibility of DDMs, fitting foraging times is expected to be feasible. The question is whether the fits provide mechanistic insight. The main insight appears to be that describing foraging times in a homogeneous environment requires a single free parameter (drift rate), while the heterogenous environment requires a second parameter (leak). However, the effective complexity of the model is higher than the stated parameter count suggests, as each patch quality is fit with a different drift rate, which does not generalise across environments: in the heterogeneous environment, the drift rate differs substantially across fructose concentrations, whereas in the homogeneous environment, the same concentrations yield nearly identical drift rates. Counter their claims, the authors also do not systematically explore the effect of specific prior foraging experience on computational parameters, but only contrast model fits to environments with different statistics, in which prior experiences will be generally different. Overall, at the moment these modelling results have a rather descriptive character, and provide very little insight into the underlying computational principles that drive foraging decisions.

A second weakness is that the study does not report the detailed results of the statistical tests, and it seems that the authors interpret several differences that are not marked as statistically significant in the figures. Furthermore, the model comparisons do not account for different degrees of freedom of the models, and the goodness of fit values alone are insufficient to conclude that one model is better than the other (rather than overfitting).

Reviewer #3 (Public review):

Summary:

The authors optimize continuous-time linear recurrent networks driven by noisy input, computing the gradient of decoding performance numerically and analytically. Optimizing for stimulus discriminability after a delay, with a penalty on firing rate, they find networks that adopt what they call high-dimensional rotational dynamics. They argue that these outperform attractor and feedforward models on noise robustness and energetic cost, and resemble state-of-the-art state-space models. They then fit a targeted dimensionality reduction model to prefrontal recordings from monkeys performing a spatial working memory task and argue that the population structure matches the rotational solution.

Strengths:

The evolution of the dynamics throughout learning is a nice observation, as are the analytical calculations, although I am not sure they are new since there is a fair share of work on the learning dynamics of linear networks.

Weakness:

I see many weaknesses. I will classify them into five groups.

(1) Strawman comparison and no clear definition of what is rotational. The paper is centered on comparing a trained model with two models meant to represent "attractor dynamics" and non-normal dynamics. Both are picked as the weakest member of their class.

I use quotation marks for "attractor dynamics" because I am not sure a linear system with an eigenvalue equal to zero is a representative model for the class. This is a particular linear instantiation of the line attractor from Seung 1996, but most attractor models are nonlinear and far more robust to noise, and they are robust through error correction that this linear model does not have. Even modern continuous attractors (Rivkind and Darshan) are very robust to noise through multiple mechanisms. So what the authors picked as an "attractor model" is a limited zero-eigenvalue case that, of course, will drift. "Attractor networks are highly susceptible to noise" is therefore true only of the toy they built, not of the class.

Second, what they call a non-normal model is in fact a feedforward chain, the extreme of non-normality. There are degrees of non-normality in any matrix, and the homogeneous delay line is the corner that requires the largest firing rates. This is not representative. See Daie et al., which has a skip and recurrent structure, or Stroud, which is not a pure chain. So the feedforward chain was also picked as a strawman, chosen so that the energetic cost they then complain about is guaranteed.

This brings me to the real problem in this section. "Rotational" is never defined. If it means complex eigenvalues, then it is a spectral property of any non-normal matrix, and "rotational versus feedforward" is not a dichotomy; it is two regions of the same continuous space of non-normal connectivity. Their own Figure 2C shows the network passing continuously through an attractor, then feedforward, then rotational during optimization. If these are points on a continuum, then "rotational dynamics is optimal" is just a statement about where the optimizer lands under this particular loss and input normalization, not the discovery of a new dynamical class. They need to define the term operationally and show the solution is qualitatively, not just quantitatively, different from non-normal feedforward. I do not think it survives that test.

This brings me to the references.

(2) The dynamical mechanisms of working memory have been studied for more than two decades, and I am surprised how much directly relevant work is missing. First, Druckmann and Chklovskii 2012, where a linear system produces stable encoding from oscillating modes. This is essentially their result more than a decade earlier, and it is not cited. They also miss Murray et al. on stable encoding and heterogeneous timescales in data. They oversimplify the attractor picture; for example, Pereira-Obilinovic et al. 2023 show you can have genuinely stable attractors. They do cite Daie et al., but they ignore its central claim, that non-normality is the underlying mechanism, which is more troubling than not citing it because it means they read it and did not engage. Overall, the references are idiosyncratic, missing relevant work, and not engaging the results of papers they cite.

This brings me to the third point.

(3) Novelty and the relationship to Stroud and Orhan. Those papers take a similar optimization approach and find that, depending on the task parameters, the optimal solution is non-normal, non-normal plus attractor, or attractor. My impression is that what this work calls rotational is just the dynamics of a strongly non-normal A, selected here by the firing-rate regularizer. They never clarify the connection with Stroud. Is the only difference the energy penalty?

The way to settle this is quantitative, and they have the handle and do not use it: report the Henrici departure-from-normality of their optimized A and place the solution inside Stroud's regime structure.

There is also a tension they leave implicit. In Stroud, the early loading direction is orthogonal to the late persistent readout, and that orthogonality is the source of dynamic coding. This paper's subspace alignment result (Figure 5G, H) shows exactly this early-to-late orthogonalization in both model and data, and then presents it as evidence for the rotational account and against Stroud's hybrid. You cannot reproduce a Strout's stim vs. decoder orthogonality and claim it against Strout's without doing more work.

(4) I did not understand the SSM section, and I think it should be cut. Is this a result? Either "SSM" just means a linear dynamical system, in which case it is trivial since every linear network here, including the LMU is an SSM, or it means the network matches a fixed-connectivity model like the LMU, which it does not seem to either. So in what sense is it a result?

(5) The data analysis is one section, and the analysis could be described as feeling somewhat like an afterthought on a very rich dataset. The coding structure they show for the rotational model also looks like the Stroud non-normal-plus-attractor model to me. They even state that the hybrid reproduces the cross-temporal subspace. What are the quantitative, cross-session metric that discriminates rotational from the non-normal-plus-attractor hybrid? Is it eyeballed trajectories?

Reviewer #3 (Public review):

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

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

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

Strengths:

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

Reviewer #3 (Public review):

Summary:

The authors developed a new Agbl5 KO allele by extending the deletion to the N-terminus of CCP5 to investigate its function in mouse ependymal cells and trachea.

Strengths:

They show that the KO mice exhibit severe hydrocephalus due to disorganized and mislocated basal bodies. Additionally, they present evidence of both impaired beating coordination and a reduction in ciliary beating.

The manuscript is well-written, and the experiments are convincing.

Comments on revised version.

The authors have taken all of my comments into account and have revised their manuscript to my satisfaction.

Reviewer #3 (Public review):

Summary:

In this study, Bonanno et al. show that after a lesion of the corticospinal tract (CST), rehabilitation running in a complex wheel drives improvement in skilled forelimb performance in mice. Mice with unilateral CST injury can perform gross motor tasks (locomotion) at the same level as the non-injured mice, but injured mice still have deficits in another task involving fine motor control. Thus, it is well-suited to test the efficacy of locomotion-based rehabilitation in fine motor control. Mice that voluntarily engaged in the rehabilitation protocol improved in the fine motor control task more than those mice that did not perform any rehabilitation. Highlighting the role of rehabilitation in the recovery of motor function after the lesion.

The authors aimed to study rehabilitation-driven intact CST sprouting to supraspinal areas. They identified one area in the motor medulla where rehabilitation significantly changes the projection density from the intact cortical spinal neurons. Interestingly, this area has ipsilateral connections and thus could be a pathway to convey motor commands from the intact corticospinal tract to the denervated area. However, as the authors acknowledge in the discussion, they only found a correlation between the change in the synaptic projections from intact CST to the medulla and the recovery. Future work should study if indeed the area of the motor medulla identified here increases its ipsilateral projections to the denervated area, confirming the re-routing of motor commands from the intact cortico spinal tract to the denervated area. The paper is strong and, in general, claims are supported by the data.

Strengths:

In this study, Bonanno et al. show that after a unilateral corticospinal tract lesion (CST), locomotion rehabilitation can improve motor function and improvements generalized to tasks that require fine motor control. Moreover, it identifies a potential pathway that could be used for the intact corticospinal tract to convey motor commands to the denervated area. The pathway identified here could become a target for rehabilitation therapies.

Weaknesses:

As the authors acknowledge in the discussion of the study, the main limitation of this study is that the reorganization observed at the motor medulla is only correlational. Thus, it is possible that the adaptation to running with an injured limb of the intact CST to adapt to an injured limb rather than a re-routing of the intact CST inputs to the denervated area underlies the synaptic changes observed in the motor medulla.

The statistical analysis could be better described.

The generalization of skilled movement is limited to only locomotion tasks.

Reviewer #3 (Public review):

Summary:

The authors present an extensive review of the literature on normative grid cell theory, asking what kind of cost function might be minimized by the entorhinal grid cell code. The authors show which of the main features of grid cells emerge from combinations of terms in a cost function that optimizes for spatial fidelity, biological plausibility, and path integration. They conclude by outlining potential future directions for the field.

Strengths:

The structure of the review makes it particularly useful for researchers who are familiar with grid cells but not necessarily with normative models. Equations are kept to a minimum and are usually explained conceptually.

Weaknesses:

I identified one main weakness, related to the fact that the introduction to experimental results around grid cells and what they allow us to conclude is less nuanced than the rest of the review. However, since this is not the main focus of the manuscript, I consider this a secondary limitation.

The review organizes the current literature on the subject within a coherent conceptual framework, helping to define possible paths forward for the field.

Reviewer #3 (Public review):

Summary:

The authors provide a highly valuable and thoroughly documented pipeline to accelerate the processing and spike sorting of high-density electrophysiology data, particularly from Neuropixels probes. The scale of data collection is increasing across the field, and processing times and data storage are a growing concern. This pipeline provides parallelization and benchmarking of performance after data compression that helps address these concerns. The authors also use their pipeline to benchmark different spike sorting algorithms, providing useful evidence that Kilosort4 performs the best of out the tested options. This work, and the ability to implement this pipeline with minimal effort to standardize and speed up data processing across the field, will be of great interest to many researchers in systems neuroscience.

Strengths:

The paper is very well written and clear. The accompanying GitHub and ReadTheDocs are well organized and thorough. Benchmarks are exceptionally well applied to support the authors' claims, and it is clear that the pipeline has been very thoroughly tested and optimized by users at the Allen Institute for Neural Dynamics. The pipeline incorporates existing software and platforms that have also been thoroughly tested (such as SpikeInterface), so the authors are not reinventing the wheel, but rather putting together the best of many worlds. In the latest revision, the authors add a nice analysis showing that compression mostly affects the lowest SNR units. This is a great contribution to the field and it is clear the authors have put a lot of thought into making the pipeline as accessible as possible.

Weaknesses:

None noted. The authors have addressed all previous questions and requests for clarification.

Reviewer #3 (Public review):

Summary:

SpeB is a cysteine protease secreted during infection by Streptococcus pyogenes (Spy). SpeB has been extensively investigated for its role in pathogenesis, which involves proteolytic processing of both Spy virulence factors and host proteins. Regulation of speB expression is complex and includes growth phase regulation, a quorum-sensing system, the transcription factor RopB, and the global regulatory system CovRS (CsrRS). Guerra et al now attempt to refine the current model of regulation of SpeB expression, focusing on the Spy protein Vfr, which has been suggested previously to act as a negative regulator of SpeB expression. In the current study, neutrophil lysates (representing proteases released during NETosis) are shown to degrade Vfr and to relieve repression of SpeB. At high cell density, SpeB itself also degrades Vfr, which may allow autoregulation of SpeB expression. These observations are unsurprising as the broad protease activities of both neutrophil proteases and SpeB are well known. Nonetheless, the data presented fill in additional details in our understanding of the complex regulation of an important Spy virulence factor.

Strengths:

(1) Construction of a GFP reporter strain provided a facile methodology for tracking speB promoter activity in a variety of experimental setups.

(2) A Vfr deletion mutant was a useful tool to investigate the role of Vfr in SpeB regulation, and mutants in speB and ropB were important controls.

(3) Experiments using neutrophil lysates in vitro, as well as in vivo studies of mice depleted of neutrophils with anti-Ly6G or in PAD4-/- mice (that cannot form NETs) support the hypothesis that neutrophil proteases derepress speB expression by degrading Vfr.

Weaknesses:

(1) The introduction and all the experiments in Figure 1 focus on CovRS, which turns out to be largely tangential to the overall story developed by the rest of the study. On the other hand, the complex and well-studied regulation of speB expression by RopB and the SIP quorum-sensing system is only minimally described. A better framing would be a more detailed introduction to the current model of speB/RopB/SIP/quorum sensing/growth phase regulation. CovRS could be introduced later as its relevance is really just to show that neutrophil lysates or NETs do more than simply providing LL-37, which signals through CsrS, as another regulator of speB expression.

(2) Vfr, as the central focus of the paper, also deserves a more thorough introduction to provide context for the study. For example, reference 19 (Shelburne et al, 2011) showed reduced transcription of speB in a vfr mutant, an effect that could be complemented by expressing vfr or a 39-aa N-terminal fragment in trans. That study presented evidence that the N-terminal peptide binds to RopB, which may prevent RopB from upregulating SpeB expression. Do the authors concur with that model? As it stands, the discussion and model in Figure 1A imply a direct regulatory effect of Vfr on speB expression rather than an indirect one through regulation of RopB. If direct regulation of speB by Vfr is a consideration, it should be investigated more thoroughly, e.g., by promoter-binding assays, CHIP-seq, etc.

(3) Use of single-cell flow cytometry generally confirmed results observed in batch culture. The authors also comment repeatedly on the heterogeneity of individual cell fluorescence representing both speB and has operon expression. However, the reason(s) for heterogeneity in gene expression are not explored, e.g., differences in individual cell growth rate in batch culture, variable loss of reporter plasmid during infection experiments, etc).

(4) Lines 116-118 and Figure 3C: Incubation of recombinant Vfr with Spy Dvfr reduced SpeB expression, but the degree of suppression is modest compared to that seen in wild-type Spy. How does the concentration of rVfr added compare to that present in the culture fluid of wild-type Spy? (Also, the concentration of rVfr used is unclear: the figure says 3 µg/ml and the legend says 0.3 mg/ml, i.e., 300 µg/ml).

(5) Lines 125-126: "...the Vfr structure contains several potential protease SpeB cleavage sites..." The role of Vfr in degrading SpeB could be clarified by identifying the predicted cleavage products, e.g., by mass spec, after co-incubation of the two recombinant proteins.

(6) Lines 122-124: "Notably, speB expression in Spy Dvfr is unaffected by LL-37 or MgCl2, further validating its [Vfr's?] dominance over CovRS regulation." This statement is an oversimplification and is potentially misleading: LL-37 is degraded by SpeB (Nyberg et al, JBC 2004), which likely explains why the addition of LL-37 fails to signal through CovRS to repress SpeB in Spy Dvfr since SpeB is produced continuously in that strain. By contrast, SpeB is only produced during the stationary phase in the wild type, so LL-37 remains active throughout the exponential phase and represses SpeB expression. The response to the CovRS ligand MgCl2 is similar (or greater) in Spy Dvfr compared to wild type (Figure S2C).

(7) Lines 153-154 and Figure 6E: Growing wild type Spy in the presence of neutrophil lysates with or without a protease inhibitor stimulated or repressed speB expression in a manner consistent with degradation (or not) of Vfr. It would be confirmatory and informative to do the same experiment with the Spy Dvfr strain.

(8) Clarity of writing could be improved, particularly by eliminating pronouns of indefinite reference (it, its, this) in contexts in which the subject is ambiguous (examples at lines 62, 89, 111, 114, 115, 123, 183, 190, 193, 204, 205, 210, 217, 221, 222, 224).

Reviewer #3 (Public review):

Summary:

The authors aimed to overcome the challenges associated with complex, conventional prokaryotic cell-free protein synthesis (CFPS) systems, which require up to thirty-five components, by developing a streamlined and efficient E. coli CFPS platform to encourage broader adoption. The main objective was to reduce the number of reaction components from thirty-five to seven, while also developing an accessible 'fast lysate' preparation protocol that eliminates time-consuming runoff and dialysis steps. The authors also sought to demonstrate the robustness and translational quality of this streamlined system by efficiently synthesising challenging functional proteins, including the cytotoxic restriction endonuclease BsaI and the self-assembling intermediate filament protein vimentin.

Strengths:

This study presents several key strengths of the optimised E. coli cell-free protein synthesis system in terms of its design, performance and accessibility.

- The reaction mixture has been dramatically simplified, with the number of essential core components successfully reduced from up to thirty-five in conventional systems to just seven.

- The "fast lysate" protocol is a significant advance in terms of procedure.

- The system's ability to synthesise challenging, functional proteins is evidence of its robustness.

Weaknesses:

(1) Title: "A simplified and highly efficient cell-free protein synthesis system for prokaryotes".

- This title is misleading since one would expect a simplified and highly efficient cell-free protein synthesis system to yield similar protein levels compared to current cell-free protein synthesis systems. What this study shows is that the composition of cell-free protein synthesis systems can be simplified while maintaining a certain level of protein synthesis. Here, optimisation does not involve maintaining protein synthesis yield while simplifying the cell-free protein synthesis system; rather, it involves developing a simplified cell-free protein synthesis system. As mentioned in my comments below, this study lacks a comparison of protein levels with a typical cell-free protein synthesis system.

- What do the authors mean by "highly efficient"? Highly efficient compared to what experimental conditions? If one is interested by the yield of protein synthesis, is this simplified system highly efficient compared to current systems?

(2) Figure 1, 3-5:

- What do relative luciferase units represent? How are these units calculated?

- In this system, the level of expression depends mainly on the level of NLuc transcripts and the efficiency of NLuc translation. How did the authors ensure that the chemical composition of the different eCFPS buffers only affected protein translation and not transcript levels? In other words, are luciferase units solely an indicator of protein synthesis efficiency, or do they also depend on transcription efficiency, which could vary depending on the experimental conditions?

- How long were the eCFPS reactions allowed to proceed before performing the luciferase activity measurement? Depending on the reaction time, the absence or presence of certain compounds may or may not impact NLuc expression. For example, it can be assumed that tRNA does not significantly affect NLuc levels over a short period of time, and that endogenous tRNA in the lysate is present at sufficient concentrations. However, over a longer period of time, the addition of tRNA could be essential to achieve optimal NLuc levels.

- The authors show that tRNA and amino acids are not strictly essential for the expression of NLuc, likely due to residual amounts within the cell lysate. However, are the protein levels achieved without added amino acids and tRNA sufficient for biochemical assays that require a certain amount of protein? It is important to note that the focus here is on optimising the simplicity of the buffer rather than the level of protein expression. In fact, the simplicity of the buffer is prioritised over the amount of protein produced. This should be made clear.

- How would the NLuc level compare if all the components were optimised individually and present in an optimised buffer, compared to a buffer optimised for simplicity as described by the authors?

(3) Line 71, Streamlining eCFPS: removal of dispensable components. This title is misleading because it creates the false impression that proteins can be produced in vitro without the addition of certain compounds. While this is true, the level of protein produced may not be sufficient for subsequent biochemical analyses. This should be made clear.

(4) Figure 2: In the legend, change "(A) Protein expression levels of the eCFPS system measured at varying concentrations of KGlu and MgGlu2" to "(A) Protein expression levels of the eCFPS system using an Nanoluciferase (NLuc) reporter DNA measured at varying concentrations of KGlu and MgGlu2".

(5) Lanes 302-303: "The thorough optimization of the seven core components was a critical step in achieving high protein expression levels". What are "high expression levels"? Compared to what?

Comments on revised version.

The authors have adequately addressed my previous concerns.

Reviewer #3 (Public review):

Summary and Overall Evaluation:

This is an elegant paper addressing an important question: whether spatial location is automatically activated during the recall of object memories. Building on prior work that relied on trained or repeated stimuli, the present study uses unique objects with one-time encoding across four spatial locations - a meaningful advance in ecological validity. The experimental design is clean, the data analysis is well-executed, and the reported effects, while small, are intriguing and open up interesting questions about the role of spatial structure in visual memory. Overall, this is a solid contribution, and my comments below are intended to help the authors strengthen the paper further.

Major Comments

(1) Incidental encoding.<br /> Was the memory task fully incidental - that is, were participants unaware that a subsequent memory test would follow encoding? This seems important for interpreting the automaticity claim that is central to the paper's contribution, and should be clarified explicitly.

(2) Spatial extent of the analysis - higher visual regions and negative pRFs.<br /> The analysis appears restricted to regions V1-V3. Have the authors examined higher visual areas as well? This seems like an important omission given that object memory likely engages regions well beyond the early visual cortex. Relatedly, recent work by Adam Steel and colleagues suggests that spatially tuned negative pRFs may play an important role in memory. Have the authors considered examining these? Expanding the analysis in these directions could substantially enrich the findings.

(3) Mechanism - retinotopic or spatiotopic?<br /> The paper makes a compelling case that spatial structure supports memory, but the nature of that spatial structure deserves more discussion. Are the effects retinotopic or spatiotopic in nature? The current design may not be able to fully dissociate these possibilities, but this distinction is theoretically important, and the authors should engage with it directly. Even a careful discussion of what the current data can and cannot tell us on this point would be valuable.

(4) Relationship between encoding failure and retrieval failure.<br /> For trials where memory performance is worse, and the encoding models fail, is there a systematic relationship between how the pRFs fail at object retrieval versus spatial retrieval? In other words, are the pRFs wrongly tuned in the same way at both stages? This analysis could provide meaningful insight into whether object and location retrieval draw on shared spatial representations.

(5) Object shape and spatial mapping.<br /> Real-world objects vary considerably in surface structure and shape, which may affect how cleanly they map onto a specific spatial location. Was this considered in the analysis? What was taken as the correct or peak location for each object, and how was this defined when objects extended across space? Apologies if this was addressed in the methods and I missed it.

(6) Time course of pRF activation.<br /> Is there a way to examine the time course of pRF activation within a trial? Do the spatially tuned responses arise immediately upon retrieval, or do they build up over time? Even a preliminary analysis of this would be of considerable theoretical interest, as it would speak to whether spatial reinstatement is an early automatic process or a later, more deliberate one.

(7) Effect size and functional significance.<br /> The authors acknowledge that the reported effects are very small, which I appreciate. However, this does raise genuine questions about functional significance that I think deserve a more direct response. One approach that would help contextualize the spatial effects would be to compare their magnitude to that of another feature - object identity, for example - to give readers a sense of the relative importance of spatial versus non-spatial information in memory representations. I recognize this may not be straightforward with the current design, but even a brief discussion of how one might benchmark the spatial effects would be helpful.

(8) The attention account.<br /> I found the discussion of attention less than fully convincing. The authors appear to argue against an attentional interpretation of the spatial effects, but it is not clear why participants wouldn't attend to the encoded location during retrieval - particularly in a design with relatively few retrieval cues, where spatial location may be one of the most useful available. The attention account thus seems difficult to rule out on the basis of the current data, and the discussion should engage more seriously with this alternative rather than setting it aside.

(9) Later-remembered versus later-forgotten objects - BOLD signal.<br /> Were later-remembered objects associated with stronger overall BOLD responses during encoding compared to later-forgotten objects, or was the effect specific to the pRF modelling? Clarifying this would help readers understand whether the spatial effects are part of a broader pattern of stronger encoding or something more specific to the spatial reinstatement mechanism.

Reviewer #3 (Public review):

Summary:

TDP-43 proteinopathy is broadly found in neurodegenerative diseases. This manuscript investigates how nuclear export influences the biophysical properties of TDP-43. The authors use a combination of chemical screening and genome-wide siRNA screening to identify pathways that modulate TDP-43 liquid-to-solid transitions. Overall, the study employs a broad array of approaches and addresses an important question in TDP-43 pathobiology. The identification of nuclear export as a central regulator is compelling and conceptually aligns with the emerging view that TDP-43 nucleocytoplasmic trafficking is a major defect in neurodegeneration.

Strengths:

This work integrates chemical and genetic screening to identify novel modifiers. The candidates were validated in both reporter cell lines and iPS-differentiated organoids. The findings support the nucleocytoplasmic transport is important for the biophysical properties of TDP-43.

Comments on revised version.

The manuscript has been improved with more data and clarification. The RNase T1 treatment experiment suggests that RNA is required for anisosome integrity. However, this does not directly demonstrate LMB increases nuclear RNA availability as changes in protein composition or other RNA-dependent mechanisms may also contribute. The conclusion and discussion need to be edited to consider these alternative scenarios. Overall, as most of the evidence remains indirect, the manuscript should avoid overinterpretation regarding the mechanisms underlying TDP-43 phase transition and aggregation.

Reviewer #3 (Public review):

Summary:

This is a well-written manuscript that describes three robust and complementary computational approaches to unravel the sequence determinants of membrane insertion, specifically of intrinsically disordered regions (IDRs) containing aromatic-centered insertion motifs.

Strengths:

A robust, multifaceted computational approach employing aromatic-centered model membrane-insertion peptides, which provides critical insights into the determinants of membrane insertion.

Weaknesses:

I only have specific concerns about some of the models used for this purpose.

(1) Membrane composition and lipid shape characteristics: The authors chose to use a model membrane bilayer of a distinct lipid composition, POPC: POPS: PI4,5P2 (70:25:5 molar ratio), for their all-atom simulations of the various model peptides. While this may be pertinent for some of these peptides, it is not for many, such as sequence 2 derived from Drp1, which preferentially binds target conical lipids such as cardiolipin (CL) and phosphatidic acid (PA). The rationale behind using PI4,5P2, which can induce positive membrane curvature when sequestered, versus CL and PA, which both induce negative membrane curvature, is not explained.

(2) Parallel vs. perpendicular peptide orientation of sequence 2 in peripheral Drp1-lipid interactions: On page 11, the authors state that their simulation results of sequence 2 derived from Drp1 "contrasts with a transmembrane orientation proposed by Mahajan et al." However, upon review, a transmembrane orientation for this region has never been proposed anywhere. Drp1 is a peripheral membrane protein that reversibly binds CL- and PA-containing membranes via its intrinsically disordered variable domain containing an aromatic-centered WRG motif. Indeed, the model presented in Figure 9 of Mahajan et al. displays a peripheral and parallel orientation of the transiently helical WRG-containing motif rather than a transmembrane (i.e., across the bilayer) orientation. While the authors can distinguish between a parallel vs. perpendicular orientation of this sequence relative to the plane of the membrane bilayer surface from their simulations, suggesting that previous studies indicated a transmembrane orientation for Drp1 is disingenuous and misleading. The term "transmembrane" should be removed or replaced, as it presents a wrong image.

(3) Mutational analysis of W vs. F in membrane insertion of W-centered insertion motifs and vice versa: The PPM-based workflow suggests that F-centered sequences have the highest membrane insertion properties as opposed to W-centered ones. A W552F mutation in the WRGML sequence of Drp1 was, however, found to impair function. How do the authors rationalize this? A cross-mutational analysis of W vs. F in W-centered motifs and F-centered motifs is warranted.

Reviewer #3 (Public review):

Summary:

Forbes et al. present a new approach for identifying cis-regulatory elements in large genomes. Using Parhyale hawaiensis, a crustacean with a large genome (~3.6 Gb, comparable in size to the human genome), the authors show that current methods for identifying cis-regulatory elements, effective in smaller genomes, are markedly inefficient in organisms with large genomes. To address this limitation, they combine bulk ATAC-seq and single-cell (sc) ATAC-seq to identify chromatin regions that are either ubiquitously accessible or specifically accessible in particular cell types. They further integrate comparative genomics across multiple Parhyale species (P. hawaiensis, P. aquilina, and P. darvishi), selected at appropriate phylogenetic distances (20-95 million years divergence), to pinpoint conserved open chromatin regions likely under functional constraint.

Using this strategy, the authors predict a set of ubiquitous and cell-type-specific cis-regulatory elements. Importantly, they validate these predictions using rigorous transgenic reporter assays, convincingly demonstrating that their approach can successfully identify functional regulatory elements where previous methods had failed.

Strengths:

The approach introduced by Forbes et al. is conceptually straightforward, efficient, and readily transferable to other organisms. The validation experiments show not only that a substantial proportion of the predicted elements are functional, but also that the method is capable of identifying both ubiquitous and cell-type-specific regulatory elements. Given that the identification of regulatory regions remains a major bottleneck in understanding the molecular mechanisms underlying processes of development and regeneration, this work has the potential to make a significant impact in developmental and regeneration biology, particularly for studies involving non-model organisms with large genomes.

An additional strength is the demonstration that only the genome of the focal species requires high-quality sequencing and assembly. In contrast, species used solely for comparative analysis can be sequenced at low coverage without assembly, substantially reducing costs and increasing the accessibility of the approach.

Weaknesses:

While the method is effective in identifying regulatory elements that are active ubiquitously or in differentiated cell types, it failed in detecting elements associated with developmentally regulated genes. This may be due to trivial reasons, such as a very low level of expression of the selected genes. However, as acknowledged by the authors, it may also indicate inherent challenges in identifying regulatory elements associated with developmentally dynamic gene regulation, compared to those associated with genes expressed in differentiated cell types.

A second limitation, also acknowledged by the authors, is the absence of chromatin conformation capture data, which would help link distal regulatory elements to their target genes. This limitation may be particularly relevant for developmentally regulated genes, where long-range regulatory interactions may be critical.

Addressing these limitations will be an important direction for future work. Nonetheless, the approach as presented in this manuscript represents a key contribution that sets the stage for further methodological advances in the identification of cis-regulatory elements in large genomes.

Reviewer #3 (Public review):

This manuscript investigates computational mechanisms underlying increased risk-taking behavior in adolescent patients with suicidal thoughts and behaviors. Using a well-established gambling task that incorporates momentary mood ratings and previously established computational modeling approaches, the authors identify particular aspects of choice behavior (which they term approach bias) and mood responsivity (to certain rewards) that differ as a function of suicidality. The authors replicate their findings on both clinical and large-scale non-clinical samples.

The main problem, however, is that the results do not seem to support a specific conclusion with regard to suicidality. The S+ and S- groups differ substantially in the severity of symptoms, as can be seen by all symptom questionnaires and the baseline and mean mood, where S- is closer to HC than it is to S+. The main analyses control for illness duration and medication but not for symptom severity. The supplementary analysis in Figure S11 is insufficient as it mistakes the absence of evidence (i.e., p > 0.05) for evidence of absence. Therefore, the results do not adequately deconfound suicidality from general symptom severity.

The second main issue is that the relationship between an increased approach bias and decreased mood response to CR is conceptually unclear. In this respect, it would be natural to test whether mood responses influence subsequent gambling choices. This could be done either within the model by having mood moderate the approach bias or outside the model using model-agnostic analyses.

Additionally, there is a conceptual inconsistency between the choice and mood findings that partly results from the analytic strategy. The approach bias is implemented in choice as a categorical value-independent effect, whereas the mood responses always scale linearly with the magnitude of outcomes. One way to make the models more conceptually related would be to include a categorical value-independent mood response to choosing to gamble/not to gamble.

The manuscript requires editing to improve clarity and precision. The use of terms such as "mood" and "approach motivation" is often inaccurate or not sufficiently specific. There are also many grammatical errors throughout the text.

Claims of clinical relevance should be toned down, given that the findings are based on noisy parameter estimates whose clinical utility for the treatment of an individual patient is doubtful at best.

Comments on revisions:'

The authors adequately addressed my comments and I find the manuscript substantially strengthened.

Reviewer #3 (Public Review):

In this revised manuscript, White et al. aimed to understand the wound-induced syncytia formation behavior in wound repair of Drosophila melanogaster pupal notum. For this purpose, the authors characterized two different types of adherens junctions' outcomes during syncytia formation around the wound region - border breakdown versus apical shrinking which appear to happen in different time points and for different time durations. The authors characterized cell-cell fusion events using cytoplasmic, junctional and nuclear markers. They determined that about half of the cells within 70 um radii from the wound undergo cell-cell fusion. They studied wound induction on the border between control epithelia and pnr domain suggesting that Atg1 is required for post-wound syncytia formation and wound closure. They showed that during wound closure syncytia gradually invade the wound leading edge mostly by radial fusion events. The data suggests that intercalation of cells from the leading edge slows down the wound closure process. They propose that cell fluidity of syncytial cells plays a role in wound closure speed. Finally, the authors showed that actin is concentrated to the front edge of syncytia located in the wound leading edge. The authors described some aspects of syncytia formation during wound closure using different approaches. Some clarifications are needed as described below.

Major suggestions:

(1) Introduction, page 4. The examples of developmental syncytia formation of invertebrates and vertebrates are confusing. The authors may want to make the examples clear and add additional examples. Currently, readers may assume that C. elegans cell fusions occur only in the hypodermis - other structures can be mentioned like the vulva, pharyngeal muscles, glia, tail. In addition, the authors may want to add injury-induced fusions like the C. elegans' PLM and PVD neurons (Ghosh-Roy et al., 2010; Newman et al., 2015; Oren-Suissa et al., 2017).

(2) In cases where it is not clear whether fusion has occurred or whether mononucleated cells were ejected from the leading edge, membrane markers can be used. Page 6. Lines 96-99. The authors may want to use a membrane marker like RFP-PH driven by the epithelial cell promoter.

(3) Pages 8-10. The authors may want to clearly explain that apical junctions shrinking is a post fusion event. That the apical shrinking is caused by the expansion of fusion pores and the migration of apical junctions towards the basolateral domain. This is something that was clearly shown during physiological epidermal cell-cell fusion in C. elegans by Mohler et al., 1998 and 2002. A cartoon showing the process of cell-cell fusion, pore expansion and apical junction dynamics would make the manuscript much clearer.

(4) Page 9. Line 170. "...as these cells represent fusion initiation events (fusion pore) but were unable to productively stabilize and expand the site of fusion and so returned to the diploid state." The authors may want to make clear that this is an assumption that needs to be tested. Live imaging using a membrane marker may resolve whether a reversible fusion pore was generated.

(5) Page 11. It is not clear whether Atg1 is directly required for cell fusion, or that autophagy is required for efficient cell fusion or both Atg1 and autophagy participate in the fusion process.

(6) Page 12. Line 235. "Indeed, we observed that several hours after wounding, the entire leading edge was occupied by syncytia." This observation is based only on the adherens junction marker. Can they test basal cell membrane marker? Is it possible that the mononucleate cell in the leading edge is under the two syncytia?

Reviewer #3 (Public review):

Summary:

This manuscript describes the development of engineered NK-92-derived extracellular vesicles (EVs) displaying CD19scFv for targeted treatment of systemic lupus erythematosus (SLE). Using a CD19scFv-LAMP2B fusion strategy, the authors generated EVs intended to selectively target pathogenic B cells in the MRL/lpr lupus mouse model. The study reports reductions in CD19⁺CD20⁺ B-cell populations, improvements in proteinuria and renal histopathology, decreased inflammatory cytokines and autoantibody levels, reduced splenomegaly, and improved survival outcomes following treatment. The work aims to position engineered EVs as a cell-free alternative to CAR-T/CAR-NK therapies for autoimmune disease treatment. While the concept is interesting and potentially translational, the study currently lacks sufficient methodological rigor, EV purification standards, mechanistic validation, and comprehensive characterization to fully support many of the claims presented.

Strengths:

(1) The study addresses an important unmet clinical need in systemic lupus erythematosus and explores an innovative cell-free therapeutic strategy.

(2) The concept of combining CAR-like targeting approaches with engineered EVs is interesting and potentially translational.

(3) The manuscript includes both in vitro and in vivo experiments, including functional renal assessments, immune profiling, histopathology, and survival studies.

(4) The authors attempt to evaluate multiple disease-associated readouts, including proteinuria, cytokines, autoantibodies, splenomegaly, and survival outcomes, which strengthens the overall biological relevance of the work.

(5) The use of engineered NK92-derived vesicles as a scalable alternative to CAR-NK therapy represents a potentially attractive therapeutic platform.

(6) The in vivo therapeutic observations in the MRL/lpr lupus model are encouraging and warrant further mechanistic investigation.

Weaknesses:

(1) The EV isolation strategy is not sufficiently rigorous for defining the isolated particles as "exosomes" according to current International Society for Extracellular Vesicles/MISEV guidelines. The precipitation-based workflow without density gradient purification or SEC raises major concerns regarding EV purity and identity.

(2) No direct validation was provided demonstrating successful surface localization or functional accessibility of CD19scFv on EV membranes.

(3) The characterization of EVs is incomplete and insufficient. Additional positive/negative EV markers, purity metrics, and orthogonal characterization methods are required.

(4) The absence of density gradient ultracentrifugation is particularly concerning, given the systemic injection of EV preparations into mice, as contaminating soluble factors and non-vesicular particles may contribute to the observed therapeutic effects.

(5) The manuscript lacks adequate mechanistic studies explaining how engineered EVs mediate B-cell depletion or immune modulation.

(6) The in vitro functional assays are weakly designed, particularly the use of A549 cells for evaluating CD19-targeted vesicle function.

(7) Important methodological details are missing, including EV normalization strategies, flow cytometry gating controls, blinding procedures, and randomization approaches.

(8) Several figures, particularly TEM and western blot images, are of low quality and difficult to interpret.

(9) The study does not sufficiently exclude the possibility that observed therapeutic effects result from contaminating soluble immune mediators rather than EV-specific activity.

(10) Broader immune profiling is lacking despite the systemic immune complexity of SLE.

(11) The statistical analysis section includes tests that are not reflected in the Results section, creating concerns regarding data presentation and consistency.

(12) Overall, while the concept is interesting, the manuscript currently falls short of the experimental rigor expected for high-impact translational EV studies.

Reviewer #3 (Public review):

Summary:

This manuscript presents a multimodal circadian intervention ("LiFE") that combines short photoperiod exposure, time-restricted feeding, and scheduled exercise and examines its effects on circadian activity structure, SCN rhythmicity, sleep, glucose regulation, cognition, and Alzheimer's disease-related phenotypes in mice. The study is ambitious in scope and conceptually appealing. In wild-type mice, the authors report that LiFE consolidates activity rhythms, enhances SCN PER2::LUC amplitude, increases sleep, lowers baseline glucose, reduces glycemic variability, and improves novel object recognition. They then extend the paradigm to 5xFAD and 5xFAD/PS19 mice, where the effects are more modest and mostly trend-level, with limited evidence for improved behavior or reduced pathology.

Strengths:

Overall, the work is interesting and potentially important because it moves beyond single-zeitgeber manipulations and tests the idea that combining multiple entrainment cues may produce broader physiological benefits than light, feeding, or exercise alone. The WT dataset is the strongest part of the paper and provides evidence that the combined intervention changes circadian organization and metabolic physiology.

Weaknesses:

Alzheimer's disease claims are considerably less convincing than the title and framing suggest. The manuscript would be stronger if the authors more clearly separated the robust conclusions in WT animals from the preliminary, underpowered, and largely non-significant findings in the disease models. In its current form, the paper contains substantial merit, but several interpretive and methodological issues should be addressed before publication.

Reviewer #3 (Public Review):

The paper is titled "DUAL: Deep Unsupervised Simultaneous Simulation and Denoising for Cryo-Electron Tomography." The authors provided two closely related code branches: one for denoising and one for missing-wedge correction. However, I did not find the simulation component. This is important, as the authors state that "the simulation branch provides learning-based cryo-ET simulation to generate synthetic tomograms indistinguishable from experimental ones."

In addition, no pre-trained models were provided. Given that the authors indicate that all training data are publicly available, sharing trained models together with references to the corresponding datasets would significantly facilitate evaluation of the reported performance.

The provided instructions are quite minimal and do not currently support reproduction of the reported findings. Compared with other cryo-ET software packages, the documentation is insufficient for installation and practical use. The software also does not consistently support standard cryo-ET file formats, particularly during inference for denoising and missing-wedge correction. In particular, volume preparation (in the first notebook of either pipeline) expects MRC input, whereas inference requires NPZ input. This inconsistency makes me believe that the shared code is not tested, and likely is a new wrap up that does not correspond to the version used to generate the results in the paper.

I also found the denoising workflow difficult to interpret. The notebooks require a "clean" target volume as input, but it is not explained how such a volume should be obtained. It is unclear whether any clean volume may be used or whether this should be simulated based on what the user expects to contain in the input. The logic about this introduced prior is not clear. Additionally, it is not clear whether the default configuration parameters provided in the notebooks correspond to those used in the paper or are intended as illustrative examples. I had requested the exact configurations used to produce the reported results to avoid ambiguity.

After many hours of trial, debugging, and experimentation, I was able to train a model for missing-wedge correction using the default parameters, although the process was slow and memory-intensive. However, despite sustained effort over two days, I was not able to perform inference using the trained model. Full-volume inference fails due to shape mismatches, as the network is trained on fixed-size 3D patches but does not support whole-volume inputs. Patch-based inference also fails at the stitching stage due to incompatible output dimensions, even when using standard volume sizes (e.g., 1024 × 1024 × 400 voxels) that work correctly during patch preparation.

While less central, I also found the training time to be close to prohibitive. The notebook sets the number of epochs to two for a toy example and notes that more epochs are required for real experiments. In practice, training for a single tomogram required approximately 16 hours of computation on two high-end GPUs to reach only six epochs, and likely more would be required (100s?). Due to the inference issues described above, I was not able to evaluate the trained model.

Reviewer #3 (Public review):

The study demonstrates the effectiveness of a cost-effective closed-loop feedback system for modulating brain activity and behavior in head-fixed mice. Authors have tested real-time closed-loop feedback system in head-fixed mice two types of graded feedback: 1) Closed-loop neurofeedback (CLNF), where feedback is derived from neuronal activity (calcium imaging), and 2) Closed-loop movement feedback (CLMF), where feedback is based on observed body movement. It is a python based opensource system, and the authors call it CLoPy. Authors also claim to provide all software, hardware schematics, and protocols to adapt it to various experimental scenarios. This system is capable and can be adapted for a wide use case scenarios.

Authors have shown that their system can control both positive (water drop) and negative reinforcement (buzzer-vibrator). This study also shows that using the closed-loop system, mice have shown to better performance, learnt arbitrary tasks and can adapt to changes in the rules as well. By integrating real-time feedback based on cortical GCaMP imaging and behavior tracking authors have provided strong evidence that such closed-loop systems can be instrumental in exploring the dynamic interplay between brain activity and behavior.

Reviewer #3 (Public review):

Summary:

This study investigates how phasic and tonic pain modulate behaviour in a free-operant foraging paradigm. The authors apply a computational modeling approach to the behavioural data to quantify the decision value of phasic pain, as well as the degree to which tonic pain reduces motivational vigour. EEG assessments showed, e.g., reduced signal power at alpha and beta frequencies in tonic pain conditions compared to no-tonic-pain conditions, but no association between these neural measures and motivational vigour. The authors conclude that tonic and phasic pain serve different motivational functions, with phasic pain acting as a punishment signal promoting avoidance and tonic pain reducing motivational vigour.

Strengths:

The experimental paradigm is highly innovative. Assessing human behaviour in a naturalistic yet highly controlled setting represents a promising approach to pain research. Notably, assessing pain magnitude implicitly, via its motivational value, offers insights about the overall pain experience that are not usually accessible via common pain ratings.

Reviewer #3 (Public review):

Summary:

These studies are based on previously published work that showed that deletion of expression of the Sox17 gene in the testis essentially deleted the formation of the Sertoli valve in the Rete testis. The authors extended this work by constructing a vector that resulted in increased Sox17 expression by Sertoli cells and enhanced formation of the Sertoli valve in both wild type and Sox17 knockout mice. The work provides strong evidence supporting the requirement for Sox17 expression to allow formation of the Sertoli valve.

Strengths: The general approach was to express Sox17 from a Tg mouse that expressed Sox17 from Sertoli cells. This Tg mouse was bred into both the WT and the Sox17 KO mouse. The Sertoli valve was enhanced in both the WT/Tg mouse and KO/Tg mouse, showing that ectopic Sox17 could compensate in the Sox17 Ko and act in a concentration-dependent manner in the WT mouse. The results are strong and support the conclusions from the authors. The results were as expected from the original paper describing the KO of Sox 17. These results strengthen these conclusions and provide ideas for additional conclusions. These studies were technically challenging, and the authors provided a very solid manuscript.

Weaknesses:

The authors refer several times to high or low expression, but it all appears to be based on immunohistochemistry, and there is no real quantification using PCR, for example. The process used for cell quantification lacks a rationale for why certain numbers were assigned.

Reviewer #3 (Public review):

Summary:

The authors aimed at studying the genetics of interactions between individuals, notably the genetic architecture of indirect genetic effects. For that, they mobilized a technique known as "genome-wide association" study. GWASs are typically formalized as linear mixed models (LMMs) with fixed effects to identify the oligogenic component of the genetic architecture (usually SNPs tested one by one, as done here), and with random effects to quantify the overall contribution of the polygenic component of the genetic architecture (using a kinship matrix). They used an LMM with a few corrections and improvements from one of their already-published model, assessed it on data they had already simulated in a previous work, and applied it to three datasets generated and originally analyzed by others, focusing only on direct genetic effects. The results on simulated data confirmed that it was necessary to adapt their previous model. The results on real data confirmed the presence of negative correlation between direct and indirect genetic effects (for two out of three species), as was already known from other studies. They found a few SNPs with significant, indirect effects, which led them to identify candidate genes, but they did not validate them.

Strengths:

The main strength of the manuscript lies in the question tackled by the authors, i.e., related to indirect genetic effects, with the ambition to go beyond the estimation of overall effects towards the distinction between polygenic and oligogenic components of genetic architecture. They also found, in an apple dataset, a significant IGE SNP that also happens to be in a DGE-associated region.

Weaknesses:

(1) Overall, the authors do not engage sufficiently with the existing literature, and do not provide strong evidence that their approach is more powerful or more interpretable than others. Hence, this work seems rather incremental.

(2) The authors used an LMM that corresponds to a previous LMM they already published in 2021, with a few changes that appeared more like corrections than improvements. Their model raised several questions.

(3) First of all, their previous model included the polygenic component of direct genetic effects (modeled as random with a kinship matrix), but not the polygenic component of indirect genetic effects. As a consequence, the initial model did not allow both direct and indirect genetic effects to be correlated, although this correlation is the hallmark of the topic: a negative correlation can lead to selection on direct effects only to deliver a negative genetic gain (Griffing, 1967). This was corrected in their new model here, so that it is similar in this respect to the other models. They highlighted that, on simulated data, their new model could "infer a trade-off between DGEs and IGEs", but that was the very goal of introducing the correlation parameter, so it was reassuring at least to know that they could estimate it on simulated data. On real data, they found evidence for it being negative, which was already the case in Cappa and Cantet (2008) for a tree species, in Haug et al (2023) for annual crops, in Montazeaud et al (2023) for A. thaliana, etc. They tested for significativity but did not provide any confidence interval. They showed the proportion of variance explained by the covariance, but did not discuss the sign or magnitude of this correlation.

(4) Although the authors included a correlation parameter between DGE and IGE in their updated model, they did not specify if the residual errors were correlated, too. In fact, they did not even specify a distribution for them. It is already known that allowing for correlated errors may not change the estimates (Haug et al, 2021), but in some settings it can be important (Bergsma et al, 2008).

(5) In appendix S4, they say that the "ordinal" model (I am not sure of what they meant by this word) "defines polygenic DGE and IGE by random effects without fixed effects for each SNP". However, this is not correct; see Baud et al (2021), for instance. In any LMM, it is straightforward to include a single fixed effect for a given SNP, and to do it one SNP at a time. Moreover, they claimed that "compared to the ordinal model (Equation S4), the proposed model (Equation 1) is more extensible to incorporate SNP-wise fixed effects while distinguishing variance-covariance matrices", without providing more evidence than this statement.

(6) The authors seemed keen to convince us that the fact that their model is analogous to the Ising model of ferromagnetics was an advantage in itself. But why would it be? Beyond the mere analogy, it should be a matter of modelling choice, and thus be clearly motivated. For instance, they chose to assess the strength of the association between the trait in the focal individual (y_{k_i}) and the average (dis)similarity between the focal individual and all its neighbors (in neighborhood k), calling the latter "indirect genetic effect". Moreover, it is not clear if what they called "IGE" is \beta_{q,2}, u_2, both, or also \beta_{q,12}, etc? Furthermore, they should have used another term as this is not the same as the "indirect genetic effects" of the other models. In these models, what is called the indirect genetic effects can be modeled as depending on group size (see Hadfield and Wilson, 2007; Bijma, 2010). In which sense would the approach of the authors be better? How does it relate to the other models? Do they have more power? Is their term more interpretable?

(7) Another way in which the authors' model may be different from the other models is in the way it models interactions between direct genetic effects and aggregate (dis)similarity between focal and neighbors. At the level of the polygenic components, other models simply have a (DGExIGE) term capturing the deviations from the additivity of DGE + IGE (e.g., Wright, 1985, in the multispecific context). Here, the authors indeed mentioned "interactions between polygenic DGEs and IGEs" and introduced the K_12 matrix, but it is not clear how different (or similar) it is from the more classical (DGExIGE) term. At the level of the oligogenic component, the authors introduced \beta_{q,12}, but it is not clear, to me at least, how it relates to K_12 and K_21.

(8) The authors checked their model on simulated data for various levels of correlation between u_1 (GE) and u_2.

(9) It is not clear why they have higher absolute errors with negative covariance than with a positive one.

(10) As a causative IGE SNP, the authors considered one with a beta_{q,2} significantly different from 0. However, they also have two other coefficients, beta{q,_}1 and beta_{q,12}, for each SNP q. How is the FDR in RAINBOW controlled in such a case? This is not detailed.

(11) In their simulations, the causative IGE SNPS were also causative DGE SNPs. However, this may increase power. From the manuscript title, one could assume that the authors' goal was to distinguish between the SNPs that are both DGE and IGE, versus the ones that are IGEs only.

(12) From what I understood, the authors first estimated the (co)variance components once and for all on the model without any SNP, and they then used the values to fit the GWAS model one SNP at a time. This assumes that the inclusion of SNP effects modeled as fixed would not change anything regarding the (co)variance components, but this is not warranted.

(13) The authors applied their model to three datasets of perennial plants.

(14) They only used their model and did not provide evidence that their model gave a significant improvement compared to other models, such as the one of Baud et al (2021).

(15) In Figures 3, 4 and 5, having an indication of which cases have a significant correlation between u1 and u2 would have helped.

(16) Concerning the Aspen dataset, it is not clear why the authors claimed that "the negative effects of neighboring genotypes were amplified as trees matured" as the PVE_cov in Figure 3 in 2015 are not systematically more negative than those of Figure 3 in 2014.

(17) When discussing their results, the authors should engage more with the literature estimating DGE-IGE correlations (see some of the references above).

(18) Concerning the apple dataset, they mentioned that "metabolite accumulation in ripening fruits may be facilitated by volatile chemicals, such as ethylene", but they did not find any evidence for significant IGE SNPs localized close to a gene involved in ethylene production. Claiming that these are testable hypotheses should have been made earlier, in the introduction, than a posteriori in the discussion.

Reviewer #3 (Public review):

This study addresses a fundamental and long-standing question in neurotrophin biology, how cellular context shapes the interpretation of a single trophic message, and tackles it with a technically demanding and well-executed single-cell mass cytometry approach. By simultaneously measuring 19 signaling effectors and 18 identity markers across a developmental gradient of spinal cord cell types, the authors substantially expand our understanding of BDNF signaling and provide a compelling demonstration of the limitations inherent to bulk biochemical readouts, which average across heterogeneous populations and obscure the discrete subpopulation behavior that the present data reveal.

The finding that only 47-75% of cells respond at peak activation, that maturation state dictates both the magnitude and the qualitative "signature" of the response, and that identical receptor stoichiometries can yield divergent outcomes across cell types collectively constitute an important conceptual advance. The proposed framework of "prepared competence" is thought-provoking and likely to stimulate follow-up work.

That said, several aspects of the data interpretation deserve more critical discussion. My specific comments are detailed below.

(1) Interpretation of TrkB-independent ERK activation (lines 194-196).

The authors state that the residual pERK induction observed in TrkB-negative ("None") cells and the incomplete suppression of pERK by K252a support the established notion that BDNF signaling is not mediated solely through TrkB. This interpretation is presented without sufficient mechanistic detail and, in its current form, is difficult to follow. If BDNF-induced ERK activation is not mediated by TrkB, which alternative receptors could account for it? Does this reflect signaling through p75NTR, transactivation of other receptor tyrosine kinases, or another mechanism altogether? Likewise, the partial resistance of pERK to K252a is interpreted as evidence of an additional regulatory layer, but the underlying activity is not specified. Is the authors' hypothesis that a distinct pool of ERK is engaged independently of Trk activity? If so, what kinase activity is proposed to drive it? These results are intriguing yet puzzling and merit a more critical and explicit discussion of the candidate mechanisms.

(2) The "progenitor paradox" in light of prior work on PC12 cells (lines 207-208).

The observation that TrkB-expressing progenitors remain insensitive to BDNF is presented as a paradox and interpreted through the lens of impaired internalization. This interpretation would benefit from explicit discussion in the context of the classical work on PC12 cells (Segal and colleagues, among others), which established that plasma membrane-restricted Trk receptors engage the Ras-MAPK pathway with rapid, short-duration kinetics that drive proliferation rather than differentiation, whereas internalized Trk receptors sustain MAPK signaling and promote differentiation. Under this framework, the apparent signaling silence of progenitors could, in fact, reflect transient plasma membrane signaling that the time points sampled in the present study (5 min onward) may not capture. The single-cell mass cytometry approach used here is, in principle, well-suited to resolving such rapid kinetics, and the authors are encouraged to address this possibility, both as an alternative interpretation of their data and as a potential extension of the study.

(3) Astrocyte responsiveness and the TrkB isoform issue.

The authors report that astrocytes are highly responsive to BDNF and exhibit robust ligand-induced depletion of surface TrkB, which they interpret as evidence of signaling-competent full-length TrkB (TrkB-FL) on these cells. However, it is well established that astrocytes predominantly express the truncated isoform TrkB-T1, which lacks the intracellular kinase domain and is thought to function in BDNF capture, clearance, and recycling at synapses rather than in canonical downstream signaling. The robust phosphorylation events observed in astrocytes are therefore difficult to reconcile with TrkB-T1-mediated signaling alone. Could these responses instead reflect transactivation of other receptors through neuron-astrocyte crosstalk, for instance, via ligands released by neurons in response to BDNF? Because the authors explicitly state that their antibody cannot distinguish TrkB-FL from TrkB-T1, this limitation directly impacts the interpretation of the astrocyte data and of the proposed isoform-switch hypothesis for progenitors. This caveat is briefly acknowledged but deserves more thorough discussion, ideally with explicit consideration of the alternative interpretations outlined above.

(4) Pathways resistant to K252a inhibition.

The authors note that K252a fails to fully abolish pERK induction in several lineages, but the specific pathways, differentiation states, and receptor stoichiometries that remain K252a-resistant are currently insufficiently described. A more systematic description would strengthen this section. In addition, it would be helpful to discuss whether the residual signal could reflect the proximity of the response to the detection threshold rather than a genuinely K252a-insensitive pool of activity. More broadly, K252a is a broad-spectrum tyrosine kinase inhibitor with well-documented off-target effects, and the present study relies on this single pharmacological tool to define Trk-dependence. The limitations of this approach, and the desirability of complementary inhibitors or genetic perturbations in future studies, should be acknowledged in the Discussion.

(5) The 12-hour trophic deprivation paradigm as a potential confounder.

All cells in the present study are trophically deprived for 12 hours prior to stimulation. This is a methodologically convenient choice, but sustained deprivation is not a neutral starting point: it activates stress-responsive pathways (JNK, p38, autophagy), alters receptor surface trafficking, and can sensitize cells to subsequent stimulation. Several of the reported observations - including the apparent synergy of p75NTR with TrkB on stress markers (p-c-Jun, p38) and the strong induction of trophic effectors immediately upon BDNF addition - could be amplified, or qualitatively altered, by the prior deprivation state, which does not reflect baseline in vivo physiology. The Rescue control, with complete medium, partially addresses this concern but is non-specific. The authors should explicitly acknowledge this limitation and, ideally, discuss the extent to which their conclusions about cell-type-specific signaling competence depend on the deprivation paradigm.

(6) Direct comparison of pseudobulk data with conventional bulk biochemistry.

The pseudobulk reconstruction of the single-cell data is presented as recapitulating canonical BDNF responses, but this comparison relies on general agreement with the published literature rather than on a direct, parallel measurement in the same cultures. Given that the central conceptual contribution of the manuscript rests precisely on departures from the bulk biochemical view of BDNF signaling, an explicit side-by-side comparison of the pseudobulk profile against a parallel bulk Western blot from sister cultures - for at least a subset of key markers such as pERK, pAkt, and pCREB - would substantially strengthen the validation of the platform. Such a comparison would reassure the reader that the discrete subpopulation behavior reported here is genuinely biological, and not in part a consequence of methodological differences between mass cytometry and conventional biochemistry (e.g., differences in fixation kinetics, epitope accessibility, or sensitivity to low-abundance phosphoproteins).

(7) Manuscript organization and balance between main and supplementary figures.

The manuscript presents an exceptionally rich dataset, but the current organization - seven main figures supported by thirteen supplementary figures, several of which are explicitly labeled as extensions of main-text figures - makes it difficult to follow the argument without continuous cross-referencing between documents. I would encourage the authors to consider a substantive reorganization with the following suggestions: (i) Figure S2 and Figure S3, which respectively define the threshold-based "responsiveness" criterion and assess its robustness, are foundational to the central 47-75% responsiveness claim and would be better integrated into the main text, for example as additional panels of Figure 2; (ii) the methodological and quality-control components of Figure S1 and Figure S2 would be more naturally placed within the Methods section; and (iii) the four "Extension" figures (S4, S7, S12, S13) contain considerable redundancy with the corresponding main figures and could be consolidated, with only the most diagnostic panels retained. Concurrent trimming of the denser main figures (Fig. 4, 5, and 6 each carry six or seven panels) would further improve readability.

Reviewer #3 (Public review):

Summary:

This narrative review provides a clear, well-structured, and comprehensive synthesis of intracerebral recording work on the neural correlates of consciousness. It is written in an accessible manner that will be useful to a broad community of researchers, from those new to iEEG to specialists in the field.

Strengths:

The manuscript successfully integrates methodological and theoretical perspectives and offers a balanced overview of current sometimes contradicting evidence. As such, the manuscript is important as call for a concernted better exploration of NCCs using iEEG in the future.

Comments on latest version:

The current version of the manuscript is clear and complete. Kudos to the authors for their thorough revisions.

Reviewer #3 (Public review):

Summary:

This work shows experimentally and computationally that single CA1 neurons can perform mismatch detection on patterned CA3 inputs and that STP and EI balance underlie this detection.

Strengths:

It has been known that STP can enhance the EPSP when the corresponding presynaptic input exhibits abrupt changes in firing rate. This work provides experimental evidence and further computational support for the hypothesis that the basic computation through STP is useful for detecting abrupt changes in the spatial pattern of synaptic inputs at the Schaffer collaterals. Further, their results indicate the novel view that mismatch detection is most efficient when gamma-frequency bursting inputs exhibit mismatches between theta cycles. The authors included novel results in the revised manuscript to show that the effective frequency range of gamma oscillation is broad, including both slow and fast gamma bands.

In the initial submission, the dependence of mismatch detection performance on model parameters and experimental settings, such as pattern overlaps and other network parameters, was not sufficiently explored. In the revised manuscript, the authors extensively studied these points and summarized the novel results in Fig. 9. Furthermore, the authors clarified that jitters in input spikes can improve detection performance in some cases. These results show the robustness of their results against variations in external and internal conditions.

Weaknesses:

While this study shows an intriguing example of combined experimental and computational studies, some analytic results, for instance, regarding the complex contributions of jitters to detection performance, could have clarified the underlying mechanism deeper and further strengthened the manuscript.

Reviewer #3 (Public review):

Summary:

This paper describes new insights into the effects of type-I and type-II LRRK2 inhibitors on HEK293T cells that over-express GFP-labeled LRRK2-I2020T. Using correlative light microscopy and cryo-electron tomography, a type-I inhibitor leads to the extensive decoration of microtubules with LRRK2, which is not seen for a type-II inhibitor. Subtomogram averaging reveals that LRRK2 binds to the microtubules in a closed-kinase conformation, with density for the N-terminal arms.

Strengths:

The paper is well written; the CLEM and cryo-ET appear to be done to a high standard. Consequently, I have only minor comments.

Weaknesses:

The resolution of the subtomogram averages is somewhat limited, but the authors have adequately limited the number of degrees of freedom in the fitting of their atomic models by only allowing rigid-body transformations of separate parts of LRRK2.

The authors should include FSC curves between the rigid-body fitted atomic models and the various sub-tomogram average maps.

Comment on the current version from the Reviewing Editor:

I do note that Ext Data Fig 8 does not yet contains the requested model-vs-map FSC curves. I guess this is an oversight and trust that the authors will remedy this during the production process. They might also want to explain what the black, red, green and blue FSC curves are in the current figure (or only show the black (solvent-corrected FSC) curve, together with the requested model-vs-map curve.

Reviewer #3 (Public review):

This manuscript examines how locus coeruleus (LC) activity relates to hippocampal ripple events across behavioral states in freely moving rats. Using multi-site electrophysiological recordings, the authors report that LC activity is suppressed prior to ripple events, with the magnitude of suppression depending on ripple subtype. Suppression is stronger during wakefulness than during NREM sleep and least pronounced for ripples coupled to spindles.

The study is technically sound and addresses a timely and important question regarding how LC activity interacts with hippocampal and thalamocortical network events across vigilance states. While the findings are interesting, they remain observational in nature.

Reviewer #3 (Public review):

Summary:

This novel study asks whether meaning-based guidance of overt attention, well-established in humans through the "meaning map" framework, extends to non-human primates. The authors recorded eye movements from two rhesus macaques freely viewing naturalistic indoor scenes and modeled fixation selection using DeepMeaning maps, Itti-Koch salience maps, and center proximity. They report that scene meaning robustly predicts fixation selection after controlling for salience and center bias, that meaning and salience interact competitively rather than additively, and that the influence of meaning is modulated by scene familiarity and attentional engagement. The cross-species extension of the meaning map approach is a valuable contribution, and the Bayesian GLMM framework with variance partitioning is well-suited to the question.

Strengths:

(1) The cross-species extension itself is novel and well-motivated. Nobody has applied the meaning map framework to NHP gaze behavior before. Even with the interpretive caveats I raise below, creating this methodological bridge between human scene perception research and NHP circuit neuroscience is a valuable contribution.

(2) The statistical framework is strong. The Bayesian GLMM with posterior distributions, HDIs, and probability of direction is more informative than frequentist alternatives. The variance partitioning with ΔR² is the right approach for disentangling predictor contributions. Random intercepts for scene are appropriate. The convergence diagnostics (R-hat = 1.00, ESS > 8000 across all models) are exemplary.

(3) Transparent individual-subject reporting. With N = 2, reporting each monkey separately rather than pooling or averaging is the correct choice, and the authors do this consistently. The individual differences are visible because the reporting is honest.

(4) The experimental design is excellent. 200 scenes is a substantial stimulus set by NHP standards. The inclusion of both familiar and unfamiliar environments, the repeated-viewing design for reliability estimation, and the 5-second free viewing window that yields ~15 fixations per trial all reflect thoughtful design.

(5) The familiarity and engagement analyses go beyond the basic demonstration. Even with the limitations we identified, asking how behavioral context modulates the meaning-gaze relationship is more ambitious than simply showing that the correlation exists. These analyses generate testable predictions for future work.

(6) Data and code sharing commitment. The authors plan to release raw data, preprocessing, and analysis code on OSF and GitHub.

Weaknesses:

(1) The authors' central claim is that meaning-based attentional guidance is an "evolutionarily conserved component of primate vision." This claim rests on the finding that macaque fixation patterns correlate with DeepMeaning maps. However, DeepMeaning is trained on human ratings of local scene meaning using a vision-language transformer (CoCa) pretrained on billions of human image-text pairs. What the model captures, then, is the spatial distribution of visual structure that humans judge to be semantically informative. The authors acknowledge that DeepMeaning represents "structured visual representations of scene regions containing identifiable objects and informative relationships" (lines 261-262), but this acknowledgment actually highlights the problem: regions containing identifiable objects and informative spatial relationships would plausibly attract fixations in any visual system with object-selective neurons and a bias toward structured content, regardless of whether the observer is processing "meaning" in any semantic sense. That is, the correlation between macaque gaze and DeepMeaning maps is consistent with shared object-level visual processing, but doesn't uniquely implicate shared semantic processing. The critical adversarial test from Hayes & Henderson (2022a)-where meaning maps detected the removal of semantic content via diffeomorphic scrambling while deep saliency models did not-has not been applied to macaque viewing behavior. Importantly, such a test would require new data collection (showing monkeys scrambled scenes), which may not be feasible. A more tractable approach with the existing data would be to compare DeepMeaning against some other model that captures mid-level visual structure without semantic supervision, though this would be a weaker test. Given these constraints, I would ask the authors to (a) acknowledge this limitation explicitly and temper the evolutionary conservation claim accordingly-for example, framing the result as evidence that macaques and humans share attentional biases toward visually structured scene regions, with the semantic interpretation remaining an open question-and (b) note the diffeomorphic scrambling experiment as an important future direction for establishing whether macaque attention is guided by semantic content per se.

(2) The familiar/unfamiliar scene comparison confounds long-term familiarity with systematic differences in scene content. Familiar scenes are photographs of the vivarium and laboratory; unfamiliar scenes are restaurants, bedrooms, kitchens, and offices. These two categories almost certainly differ in visual complexity, object density, spatial layout, clutter, and the types of objects present. The familiar environments (vivarium caging, lab equipment) are likely more spatially repetitive and lower in object diversity than, say, a restaurant or residential kitchen. Any difference attributed to "familiarity" could therefore reflect these systematic content differences. The negative interaction between meaning and familiarity (Monkey V: β = −0.19; Monkey I: β = −0.19), which the authors interpret as familiarity broadening exploration, could instead reflect the fact that vivarium/lab scenes have a different distribution of meaning values or a different relationship between meaning and salience than human domestic environments. The authors should address this confound directly. At minimum, comparing the distributions of meaning and salience values across the two scene categories would help the reader evaluate whether the familiarity effect can be separated from content effects. Ideally, the authors would include a subset analysis using only scenes matched on feature distributions or include scene-level summary statistics of the meaning and salience maps as covariates in the familiarity model.

Reviewer #3 (Public review):

I believe that the paper is excellent and very well executed. I have several reservations about the meaning of the tonic component of the feedback responses and about the more general interpretation from a computational standpoint. These aspects may not require extensive adjustments, but some key points could be discussed or better justified:

(1) It is true that most papers view adaptation as a trial-by-trial update and that several models summarise motor errors by a scalar quantity for a model fit. The importance of feedback control in visuomotor control has also been overlooked, as several studies explicitly instructed not to correct. I also agree about the fact that the temporal aspects of sensory encoding and control are often neglected in motor adaptation studies. However, there have been some developments about adaptive control in the context of force field learning to express the error signal and learning rule based on continuously evolving state variables as those formulated in online control models (Crevecoeur et al., 2020, eNeuro 7(1); Kalidindi and Crevecoeur, 2023, Curr Opin Neurobiol, 83, 102810). Could the authors consider discussing whether this framework could or not be consistent with the current dataset?

(2) The choice of a cursor jump may require more in-depth justification. From an experimental standpoint, it is clear from the authors' data that a cursor jump does evoke an aftereffect and hence the developments are clearly validated empirically. The nature of the adaptive response is less clear: indeed, cursor jumps can be represented as an external perturbation to a variable that may be independent of the hand (e.g. Kasuga et al., 2022, J Neurophysiol, 127 (2), 354-372). In contrast, a visuomotor rotation requires a change in state space representation parameters (it is not clear which ones) that is more closely related to the update of an internal model. Could the authors explain why they believe that a learning response to a cursor jump is consistent with adaptation in general?

(3) The relationship between the tonic component of the feedback response and the learning response is very clear from an experimental perspective again. However, I would suggest being very cautious about the interpretation of this effect. My concern is that it is not clear that this tonic response is irrelevant from a behavioural standpoint, and I am left wondering what the correlation with the learning response truly means. Indeed, in real-life conditions, there should be no net force produced in the end during a static phase, as the force during stabilisation is by definition zero; only the net force produced against constant external loads is required. There can be co-contraction but not net resultant force, unless external forces are applied. So if the tonic response vanishes in real conditions, should there be no learning response? This aspect is also relevant if one attempts to generalise the findings to force field learning: since velocity-dependent force fields vanish during stabilisation, how can there be a tonic component?

Reviewer #3 (Public review):

Summary:

The authors used a gene regulatory network inference-based clustering approach with existing scRNAseq data sets from cadaveric donors with T1D, auto-antibody positive, and non-diabetic donors and found a regulatory network associated with b-cell survival that is associated with increased expression of genes controlled by interferon regulatory factor 1.

Strengths:

Using established data sets of RNAseq previously performed, the authors identify an interesting population of surviving b-cells in T1D that express a key antiviral transcription factor (IRF1), antiviral genes such as GBPs and iFIT, and decreased expression of a limited number of genes that have been associated with the identity of b-cells.

Selective expression in T1D and not observed in islets from control or auto-antibody positive donors.

Expression changes, TFs identified are also identified in human islets treated with cytokines.

The lack of changes in genes associated with ER stress or the response of endocrine cells to ER stress.

Weaknesses:

The authors do an excellent job of identifying characteristics of the donors/islets in the methods; however, this needs to be addressed in the Figure Legends and Results. Specifically, the length of exposure to cytokines is critical in evaluating the comparisons made in this study.

Is it possible to evaluate sex as a variable in this analysis, and if yes, does one still observe similar changes in identity gene expression and IRF1-dependent gene expression?

Length of disease and evidence for the C3 populations? Does one observe the C3 population in alpha cells of islets with long-standing disease or in the samples that had too few b-cells to perform the analysis? Temporally, 24 h was used for ATACseq and 48 h for cytokine treatment. These are very late exposures, suggesting that secondary and tertiary effects are being compared.

Activation of stress response genes has been correlated with impaired cytokine signaling in islets (human and rodents), limiting the number of endocrine cells that are cytokine responsive. Was this observed in the authors' analysis?

Recent studies have identified induction of antiviral and antibacterial genes in islets in response to short exposures to IL-1, TNF, IFN's that are consistent with the C3 expression profile observed by the authors. While this work has mostly been performed in rodent islets, it has also been observed in human islets, and may be useful in comparing additional transcripts that may contribute to the observed profiles.

Reviewer #3 (Public review):

Summary:

Through micro-electroencephalography, Hight and colleagues studied how the auditory cortex in its ensemble respond to cochlear implant stimulation compared to the classic pure tones. Taking advantage of a double implanted rat model (Micro-ECoG and Cochlear Implant), they tracked and analyzed changes happening in the temporal and spatial aspects of the cortical evoked responses in both normal hearing and cochlear-implanted animals. After establishing that single trial responses were sufficient to encode the stimuli properties, the authors then explored several decoder architectures to study the cortex ability to encode each stimuli modality in a similar or different manner. They conclude that a) intracranial EEG evoked responses can be accurately recorded and did not differed between normal hearing and cochlear-implanted rats; b) Although coarsely spatially organized, CI-evoked responses had higher trial-by-trial variability than pure tones; c) Stimulus identity is independently represented by temporal and spatial aspect of cortical representations and can be accurately decoded by various means from single trials; d) and that Pure tones trained decoder can't decode CI-stimulus identity accurately.

Strength:

The model combining micro-eCoG and cochlear implantation and the methodology to extract both the Event Related Potentials (ERPs) and High-Gammas (HGs) is very well designed and appropriately analyzed. Likewise, the PCA-LDA and TCA-LDA are powerful tools that take full advantage of the information provided by the cortical ensembles.

The overall structure of the paper, with a paced and exhaustive progress through each step and evolution of the decoder is very appreciable and easy to follow. The exploration of single trial encoding and stimulus identity through temporal and spatial domains is providing new avenues to characterize the cortical responses CI stimulations and their central representation. The fact that single trials suffice to decode the stimulus identity regardless of their modality is of great interest and noteworthy. Although the authors confirm that iEEG remains difficult to transpose in clinic, the insights provided by the study confirm the potential benefit of using central decoders to help in clinic settings.

Weakness:

The conclusion of the paper, especially the concept of distinct cortical encoding for each modality, is unfortunately partially supported by the results as the authors ignored fundamental limitations of CI related stimulation.

First, the authors stimulated in a Monopolar mode which, albeit being clinically relevant, notoriously generates a high current spread in rodent models. Comparing the averaged BF maps for iEEG (Fig-2A, C), BFs ranged from 4 to 16kHz with a predominance of 4kHz BFs. The lack of BFs at higher frequencies might reveal a potential location mismatch between the frequency range sampled at the level of the cortex (low to medium frequencies) and the frequency range covered by the CI inserted mostly in the first turn-and-a-half of the cochlea (high to medium frequencies). Looking at Fig-2F (and to some extend 2A) most of CI electrodes elicited responses around the 4kHz regions and averaged maps show a predominance of CI-3-4 across cortex (Fig-2C, H and Sup Fig. 3) from areas with 4kHz BF to areas with 16kHz BF. It is doubtful that CI-3-4 are located near the 4kHz region based on Müller's work (1991) on the frequency representation in the rat cochlea. Moreover, Supplemental figure 3 shows that only a couple of CI electrodes are predominately represented at the level of the cortex. Thus, it seems possible that current spread ended stimulating indistinctly higher turns of the cochlea or even the modiolus in a non-specific manner, greatly reducing (or smearing) the place-coding/frequency resolution of each electrode, which in turn could explain the coarse topographic (or coarsely tonotopic according to the manuscript) organization of the cortical responses.

Second, although the authors acknowledge that post-lingual CI users always have an adaptation period, their conclusion is based on measurements that are relatively "early" in the CI-use timeline so to speak since iEEG were collected a) acutely right after mono-aural implantation and stimulation, b) under anesthesia, c) using unmodulated pulse train fixed at 900pps regardless of the electrode used and thus lacking any temporal information shifts in relationship to electrode cochleotopic placement. Basically, all CI electrodes had the same rate whereas you would expect basal CI electrodes to be amplitude modulated at higher frequencies than apical electrodes.

As much as the reviewer likes the overall approach with the use of PCA-LDA and TCA, and agrees that information transfer seems inexistant at time of measurement, authors should be more careful in their strong conclusion that two distinct encoding exist. The non-overlapping between sound and electric stimulation representations might exist only transiently and this should be acknowledged a bit more in the discussion. Without repetition of iEEG measurement at later period with chronic use of the CI, it is not possible to definitively claim that two distinct, non-overlapping coding co-exist at all times.

Nevertheless, the reviewer wants to reiterate that the study proposed by Hight et al. is well constructed, relevant to the field and that the overall proposal of improving patient performances and help their adaptation in the first months of CI use by studying central responses should be pursued as it might help establish new guidelines or create new clinical tools.

Reviewer #3 (Public review):

Programmed DNA elimination (PDE) is a process that removes a substantial amount of genomic DNA during development. While it contradicts the genome constancy rule, an increasing number of organisms have been found to undergo PDE, indicating its potential biological function. Single-cell ciliates have been used as a prominent model system for studying PDE, providing important mechanistic insights into this process. Many of those studies have focused on the excision of internally eliminated sequences (IES) and the subsequent repair using non-homologous end joining (NHEJ). These studies have led to the identification of small RNAs that mark retained or eliminated regions and the transposons that generate double-strand breaks.

In this manuscript, Nagao and Mochizuki examined the other type of breaks in ciliates that are healed with telomere addition. They specifically focused on the sequences at the ends of the germline (MIC) chromosomes, which have received relatively less attention due to the technical challenges associated with the highly repetitive nature of the sequences. The authors used the Tetrahymena model and developed a set of new tools. They used a novel FISH strategy that enables the distinction between germline and somatic telomeres, as well as the retained and eliminated DNA near the chromosome ends. This allows them to track these sequences at the cellular level throughout the development process, where PDE occurs. They also analyzed the more comprehensive germline and somatic genomes and determined at the sequence level the loss of subtelomeric and telomere sequences at all chromosome ends. Their result is reminiscent of the PDE observed in nematodes, where all germline chromosome ends are removed and remodeled. Thus, the finding connects two independent PDE systems, a protozoan and a metazoan, and suggests the convergent evolution of chromosome end removal and remodeling in PDE.

The majority of sites (8/10) at the junctions of retained and eliminated DNA at the chromosome ends contain a chromosome breakage sequence (CBS). The authors created a set of mutants that modify the CBS at the ends of chromosome 4R. CBS regions are challenging for CRISPR due to their AT-rich sequences, making the creation of the 4R-CBS mutants a significant breakthrough. They used the FISH assay to determine if PDE still occurs in these mutant strains with compromised CBS. Surprisingly, they found that instead of blocking PDE, its adjacent retained DNA is now eliminated, suggesting a co-elimination event when the breakage is impaired. Furthermore, in biparental mutant crosses, no PDE occurred, and no viable progeny were produced, indicating that the removal of chromosome ends is crucial for proper PDE and sexual progeny development. Overall, the work demonstrates a critical role for 4R-CBS in separating retained and eliminated DNA.

Reviewer #3 (Public review):

Summary:

This paper describes new insights into the effects of type-I and type-II LRRK2 inhibitors on HEK293T cells that over-express GFP-labeled LRRK2-I2020T. Using correlative light microscopy and cryo-electron tomography, a type-I inhibitor leads to the extensive decoration of microtubules with LRRK2, which is not seen for a type-II inhibitor. Subtomogram averaging reveals that LRRK2 binds to the microtubules in a closed-kinase conformation, with density for the N-terminal arms.

Strengths:

The paper is well written; the CLEM and cryo-ET appear to be done to a high standard. Consequently, I have only minor comments.

Weaknesses:

The resolution of the subtomogram averages is somewhat limited, but the authors have adequately limited the number of degrees of freedom in the fitting of their atomic models by only allowing rigid-body transformations of separate parts of LRRK2.

The authors should include FSC curves between the rigid-body fitted atomic models and the various sub-tomogram average maps.

Reviewer #3 (Public review):

Summary:

This manuscript, "Estimating bone marrow adiposity from head MRI and identifying its genetic 2 architecture", brings together the groups of Drs. Kaufmann and Hughes in a tour de force work to develop an artificial neural network that localizes calvaria bone marrow in T1-weighted MRI head scans, with the goal of studying its composition in several large MRI datasets, and to model sex-dimorphic age trajectories, including the effect of menopause.

Strengths:

Bone marrow adiposity is a very active tissue with far-reaching implications for tissue crosstalk and human health than we had initially recognized. Although MRI has been used to measure BM, studies such as the one by these two groups are still lacking whereas very large datasets are analyzed using advanced AI machine learning tools coupled with genetic studies and a specific pathology. The groups had to develop new methods and new AI machine-learning tools for the imaging analyses.

Weaknesses:

Some aspects of the work that authors could add additional clarification.

(1) Imaging Limitations: The authors provide an excellent overview and references supporting the use of MRI as a method for assessing marrow fat, particularly with some specific modifications. However, MRI images can be affected by various factors, including the presence of other tissues as well as specific MRI settings, which are much harder to precisely control when using different datasets.

(2) The specific density of cranial bones as it relates to the types of bone marrow: Cranial bones are extremely dense structures, which naturally interfere with MRI imaging. While it is thought that cranial bones have mostly "red bone marrow", this is only true for a short time in humans. How sensitive is their system in differentiating between red and yellow BM?

(3) Both items above are further complicated by aging, but aging is not a linear event as we have learned. There are specific bursts of aging in humans around the age of 45 and early 60s. How do the system and model predict or incorporate these peaks of aging? It seems from the data shown that aging is reflected more as a linear phenomenon. Is this because additional aging datasets are needed?

(4) The authors describe in richness of detail their AI learning programming and how it extracted the data from datasets. The authors also show some important correlations with specific genes, SNPs. What is not clear is how conditions such as anemia for example. An expected finding would be that patients with chronic anemia have lower bone marrow (BM) signal intensity on MRI scans than healthy people. This is because the signal intensity of BM depends on the fat-to-cell ratio in the tissue. Furthermore, patients with a host of musculoskeletal disorders ranging from osteopenia to osteoporosis, sarcopenia, and osteosarcopenia will also have altered MRI scans. When using such large datasets how did the authors control or exclude these pathological conditions, or were all these conditions likely present?

(5) Some of the genes and SNPs although significant showed very small correlations. What is their likely physiological significance?

(6) The authors could use this excellent manuscript to expand their discussion to include the need for studies like theirs to be also complemented by multi-OMICS studies that will include proteomics and lipidomics of BM, bones, and muscles.

Reviewer #3 (Public review):

Summary:

This study examines the role of RNF25 in translational quality control. Previous work indicated that RNF25 is activated by ribosomes stalled with defective elongation or termination factors bound in the A-site. Here, the authors provide evidence that RNF25 is activated by other treatments that evoke ribosome stalling, including amino acid starvation, where the A-site may be empty, leading to ubiquitination of RPS27A in a manner requiring the ISR collision sensor Gcn1, but not EDF1 and ZAKα, involved in the RQC and RSR surveillance pathways. They present some evidence from polysome profiling that RNF25 and its ubiquitination of RPS7A help resolve ribosome collisions and support translation elongation in basal conditions. They further show that KO of Gcn2 increases RPS27A ubiquitination in basal conditions, but not in amino acid-starved cells, and that RPS27A ubiquitination was reduced on overexpressing the WT RWD domain of Gcn2 but not a variant harboring substitutions of residues predicted to bind Gcn1. Based on these findings, they propose a model that, in response to ribosome stalling induced by various stresses, Gcn1 recruits RNF25 via the latter's RWD domain to ubiquitinate RPS27A and thereby resolve ribosome stalling and promote continued elongation. If collisions increase even further, GCN1 recruits GCN2 instead of RNF25 to elicit the ISR.

Strengths:

The data is convincing that a variety of triggers leading to diverse stalled ribosomal states, including amino acid limitation, can activate RNF25, suggesting that activation of this pathway does not require the presence of trapped protein factors in the ribosomal A-site but is a more general response to ribosome collisions. It is also convincing that Gcn1 is required for RNF25 activation under all of these conditions, which is consistent with previous findings that Gcn1 is required for RNF25 function in the presence of trapped elongation or termination factors. The finding that EDF1 and ZAK are not needed for RNF25 activation in amino acid starvation conditions is of interest for EDF1, given the recent claim that it is required for full ISR activation.

Weaknesses:

The evidence presented from polysome profiling that RNF25 helps resolve naturally occurring ribosome collisions in basal conditions is not compelling, as eliminating RNF25 could be increasing the rate of initiation rather than increasing stalled ribosomes as the means of increasing the P/M ratio. The Rps27A-K113R mutation could have the same effect of increasing initiation, which could have been obscured by inhibiting the ISR with ISRIB.

The evidence that RNF25 competes with Gcn2 for Gcn1 binding is also not compelling. While it's convincing that Rps27A-Ubi is elevated in basal conditions on eliminating Gcn2, loss of GCN2 would be expected to increase ribosome loading on mRNAs, potentially elevating the frequency of collisions and thereby stimulating RNF25 activity indirectly.

It's also quite puzzling and left unexplained why they observed no further increase in Rps27A-Ubi on -Arg/-Lys starvation in the cells lacking Gcn2. Why wouldn't -Arg/-Lys starvation lead to further stalling and RNF25 activation in the absence of Gcn2? (Since Gcn2 KO increases Rps27A-Ubi in the presence +Arg/+Lys conditions, it can't be that Gcn2 is required for RNF25 function.) The same puzzling and unresolved observation was made in the cells lacking DRG2. One possible explanation for this conundrum is that low-level RNF25 abundance limits further activation.

The quantitative effects of overexpressing the Gcn2 RWD domain on Rps27A-Ubi, constituting their other evidence presented to support the competition model, are quite small in magnitude.

Reviewer #3 (Public review):

Significance of the findings and the strength of evidence:

The article presented by Yang et al. describes the development of protein binders targeting the C-terminal domain of the protein DELE1, which is involved in the mitochondrial integrated stress response (mitoISR) pathway. It was shown earlier that DELE1 is imported into the mitochondria and cleaved by the inner mitochondrial membrane protease OMA1, resulting in an N-terminal and C-terminal domain, the latter being transported back into the cytosol, where it interacts and activates the kinase HRI. HRI, in turn, phosphorylates eIF2α, resulting in selective translation of mRNAs encoding proteins involved in stress signalling, such as the transcription factor ATF4. ATF4 activates expression of genes involved in amino acid balance, redox homeostasis and proteostasis. The C-terminal domain of DELE1 (DELE1CTD) was structurally and functionally characterized by earlier by cryo-EM by Jie Yang and co-workers. These studies suggest that it forms an octamer with D4 symmetry consisting of two tetramers arranged in a tail-to-tail arrangement. In this octamers two interfaces were identified, one between the monomers in the tetramers and one connecting the tetramers to form the octamer. In this earlier work, it was also shown by mutational studies that interrupting the first interface has an impact on the OMA1-DELE1-HRI-eIF2α-ATF4 pathway upon mitochondrial stress in human cells. To this end, the authors concluded in the current manuscript that it might be interesting and also of therapeutic interest to develop a protein binder that binds DELE1 and disrupts oligomer formation. The authors set up a de novo protein design approach using RFdiffusion to design a protein scaffold and ProteinMPNN to design the side chains to create protein binders targeting the α-helix α1 in DELE1CTD that is directly involved in the formation of the first interface forming the tetramer. As I am not an expert in protein design, I cannot judge the quality of this data. The candidates were evaluated by AlphaFold3 to confirm complexes formed between the designs and DELE1CTD. In the end, 12 designed protein binders were selected for further analyses. These proteins were recombinantly produced in E. coli and purified. The proteins DELE1 full-length (DELE1fl) and DELE1CTD were produced as MBP-fusion proteins to improve solubility and stability. Co-expression studies with mbp-delet1CTD revealed that 11 out of the 12 binders co-eluted with MBP-DELE1CTD from a size-exclusion chromatography column, indicating complex formation. Without the presence of the binders, MBP-DELE1CTD elutes as a higher oligomer, suggesting that the binders interfere with oligomerisation. Further analyses included the impact of the presence of selected binders on stress-induced ISR. The authors found that different binders had a slightly different impact on the outcome upon treatment with stressors, and also compared two different stressors. This was concluded by assessing the ATP4 protein level by immunoblotting. The interaction of selected binders with DELE1CTD was subsequently confirmed by co-immunoprecipitation experiments. To evaluate whether the impact of the binders is restricted to mitochondrial stress studies, eliciting endoplasmic reticulum stress showed no effect on ATF4 levels. The presence of the binders furthermore impaired recovery of tubulated mitochondria following mitochondrial stress induction, resulting in more fragmented mitochondria. The authors determined a crystal structure of one binder at a resolution of 2.6 Å and performed AlphaFold3 predictions to model the complex between binders and DELE1CTD. The interface is characterized by many hydrophobic residues. From this data, they concluded some interface mutants and tested those concerning their impact on the interaction. Indeed, mutation of these hydrophobic side chains to charged residues interfered with complex formation. Finally, the authors show that binder binding to DELE1CTD does not interfere with the binding of HRI kinase. Overall, the methodology applied is state-of-the-art, and the manuscript is well-written. The design of protein binders targeting DELE1 involved in mitochondrial stress signalling is interesting for basic science to study stress signalling, but also therapeutically. However, as ISR has a positive impact on disease development and ageing, but also a negative one, depending on the degree of activated ISR, a therapeutic use would need to be precisely applied. The study has some weaknesses, and particularly the structural data seems to have severe issues.

Reviewer #3 (Public review):

Summary:

The authors track cortical activity across the dorsal cortex of head-fixed mice for up to ten weeks following bilateral eye removal, asking how the cortex reorganizes over an extended period after vision loss. They report a rapid and long-lasting reversal of the normal relationship between movement and visual cortex activity, together with a delayed, weeks-long window of enhanced slow-wave activity during rest and a persistent reorganization of large-scale cortical correlations.

Strengths:

The longitudinal scope is the work's strength. Tracking the same animals over a ten-week window after sensory loss is technically demanding and rarely done, and it yields a temporal picture that short studies cannot provide. The observation that the movement-related activation of the visual cortex inverts within a day and only partially recovers over weeks is striking and has not been documented at this timescale. The analysis is internally consistent across two protocols (short- and long-term) and frames the changes by behavioral state, focusing on rest versus movement. This is a useful analysis that the field has not systematically applied to studies of deprivation.

Weaknesses:

The manipulation is unusually severe: removing both eyes eliminates patterned vision, non-image-forming light input, and all residual retinal signals abruptly and irreversibly, in contrast to the milder and often reversible manipulations the discussion draws on. Without a sham-surgery control, the early effects cannot be cleanly separated from the surgery itself.

The language of "plasticity" runs ahead of what the data actually measure, since the study quantifies spontaneous activity and pairwise correlations but does not assess receptive fields, evoked responses, synaptic changes, or the causal manipulation of any candidate circuit. The discussion nevertheless attributes findings to specific interneuron circuits, molecular pathways, and thalamocortical reorganization, none of which are tested in this study.

The imaging method also constrains what can be claimed: widefield calcium signals are dominated by superficial-layer and excitatory output and cannot resolve the cell-type-specific mechanisms invoked in the discussion. Because the key findings lie in the low-frequency band where vascular contamination is greatest, the hemodynamic correction, particularly in the deprived state, where vascular tone itself may be altered, deserves more validation than it currently receives.

Finally, the presentation relies heavily on group-level heatmaps in the main figures, with raw traces, spectrograms, and per-animal trajectories at the key inflection points (day 1, week 1, week 10) largely absent. This makes it difficult to judge whether the reported patterns are coherent across animals.

Reviewer #3 (Public review):

Summary

In this paper, the authors have 5 human subjects learn to play Super Mario Bros while undergoing fMRI for 15 hrs each. They compare a reinforcement learning (RL) model (PPO), an imitation learning (IL) model, and a vision model (ResNet) in their ability to play the game, match human behavior, and, critically, explain human brain activity.

The key findings can be summarized as follows:

(1) RL, IL, and vision models explain similar amounts of variance in the BOLD signal (Fig 2a), with a significant but small trend of RL > IL > ResNet (Tab 1).

(2) Untrained models with the same architecture explain a smaller but very similar amount of variance (Figure 2a, Table 1).

(3) The brain maps across all models (and layers) are strikingly similar, with the strongest effects in visual, parietal, and motor regions (Figures 2b, 2d; Supplementary Material II).

(4) Behavioral and neural performance are correlated across model checkpoints (but not levels), such that later checkpoints in training have better behavioral and neural encoding performance (Figures 3 & 4), although the neural effect plateaus pretty quickly.

(5) Out-of-distribution performance is quite poor, both behaviorally (Figure 5a) and neurally (Figure 5b).

I believe this work will be of interest to neuroscientists, cognitive scientists, and AI researchers alike. There has been a growing trend in neuroscience to adopt AI models as cognitive models of complex perception and action, while at the same time, AI researchers are increasingly looking at the brain for inspiration. The key finding of this paper -- that these models fail to generalize to out-of-distribution levels -- questions the core assumptions of this whole enterprise.

Strengths:

Unlike previous studies applying machine learning to naturalistic game-play, the authors take great care to make sure their models are evaluated on an equal footing, using equivalent or similar architectures/number of parameters and training data.

While the number of subjects (5) is relatively small, the amount of data per subject (15 hours) is impressive, which is important for fitting the imitation learning & ResNet models and for obtaining reliable encoding performance for each individual subject. The authors employed a train/val/test split and held out sets, the gold standard in the literature.

Overall, the paper was well-written and easy to follow. The figures clearly illustrate the main findings.

Weaknesses:

(1) Missing statistical tests

I think the main weakness of the paper is that many of the claims are qualitative in nature and lack appropriate statistical tests, for example:

- "The conv3 layer has the highest brain encoding score";<br /> - "Robust association between task performance and brain encoding" ;<br /> - "Level patterns strongly predict brain encoding";<br /> - "Brain encoding performance was severely degraded";<br /> - "Effect of training on brain encoding was apparent".

While these effects are indeed qualitatively visible in the figures, it is unclear which of these differences are significant (with the notable exception of Table 1). I believe the paper would benefit substantially if these effects were quantified and every claim were supported by the appropriate statistical tests. As an example, with the exception of Table 1 and the corresponding paragraph, I could not find any p-values in the results section.

(2) Missing model performance and human-likeness

Also absent from the results is an assessment of model performance on the task and similarity to human performance/behavior. From Figures 3 and 4, we can see that the game score of PPO is around 500-1000 - how does that compare to the humans? We can also see that the imitation scores for IL are around 0.4-0.7, but what does that mean? Such results would be crucial to assess if the models have indeed learned to play the games and/or imitate the humans, and therefore, whether they would be good candidates as cognitive models (before even looking at brain activity). At minimum, plotting the human versus model game scores (see e.g. Tomov et al. 2023 Neuron, Figure 2) would be helpful; or, if you'd like to dig deeper, showing that human actions are more valuable or more likely under those models (see e.g. Cross et al. 2022 Neuron, Figure 2). It might also be helpful to look at imitation scores for the RL model and game performance of the imitation model -- I suspect they will both be bad, but they can at least serve as informative baselines for their counterparts.

(3) Possible undertraining

Relatedly, one possible explanation for why the Untrained model does so well is that all the models may be effectively undertrained. For example, while there are no training curves in the paper, it seems from the spacing of the checkpoint game scores (x-axis on Figure 3c) that the RL model may not have converged yet (it would be helpful if those were somehow colored by training epoch). Showing training curves would be helpful (i.e., something similar to Figure 3a, except with performance on the y-axis).

Additionally, it would be great to provide more details regarding the PPO training protocol. How many episodes? How many steps per episode? How many steps for all of the training? Similarly, for the imitation learning model: batch size, number of epochs, optimizer, scheduler, etc.

(4) Mysterious poor encoding performance of Untrained and ResNet models on the held-out set

Critically, and related to that, I'm a little confused about the Untrained model results on the held-out set (Figure 5b, top row on the right). Why should those be any different from the test set results with the Untrained model (Figure 2a, right, fourth row from the top)? It makes sense why the other models are worse on the held-out set -- they have never been trained on any frames from those levels. However, the untrained model has not been trained on *any* frames from *any* levels, including the test set and the held-out set.

The same is true for the ResNet model, which is pre-trained on a completely separate data set and yet similarly shows worse performance on the held-out set compared to the test set.

This cannot be explained by the ridge regression, which has no parameters or hyperparameters fitted on either the test set or the held-out set.

The big discrepancy in the untrained model & ResNet results between the test and the held-out set makes think that there is something substantially different about the levels in that held-out set; that they are truly out of distribution compared to the other 20 levels (e.g., maybe they're the last 2 hardest levels and look completely differently? e.g. ResNet proxy in Fig 5c shows worse performance than the mean, which is indicative of an anti-correlation). Alternatively, it may be some issue with the analysis pipeline. The poor generalization results are central to the claims of the paper, so I believe this should be clarified.

(4) Brittleness conclusion rationale

I'm not quite on board with the author's rationale that "[poor model performance on the out-of-distribution levels] demonstrates that the models we tested are limited in scope and may not provide a valid inference of brain-like processing, as human behavior remains robust and generalizable across levels".

For one, unlike the models, humans were actually trained on those levels, so it would not be surprising if they perform just as well on them as on the other levels (but do they? Again, it would be great to see some behavioral data from the humans and the models).

Second, as the authors themselves show, task performance and human-likeness do not really correlate with neural encoding across levels (Fig 4a & b, respectively), so even if model performance remained "robust and generalizable" on the held-out levels, that will not necessarily translate to good neural encoding.

Thirdly, and perhaps most importantly, unless the test set and held-out set were sampled exclusively from the practice phase when the subjects have mastered all the levels (that doesn't seem to be the case, but the authors should clarify), then the humans are continuously learning, which means that their own internal representations of the game are evolving. That's not the case for the models, which I assume are in "inference mode" when their representations are extracted for neural encoding. That is, their weights are frozen. So there's a fundamental mismatch between the mode in which humans are operating (continuously learning and executing) and the mode in which the models are operating (just executing). While this is true for all the levels, it may partially account for the discrepancy in the held-out set specifically.

Reviewer #3 (Public review):

Summary:

The authors describe a mathematical and computational approach used to compute stresses and cellular deformations in a multicellular spheroid embedded in a fiber network. This approach is then used to predict stress and cellular anisotropy distributions in "solid-like" and "fluid-like" spheroids. Simulations show that shear stresses in solid-like spheroids are large and concentrated at the boundary of the spheroid, yet cells do not align with the direction of the largest shear. Conversely, shear stresses in fluid-like spheroids are smaller and uniformly distributed in the spheroid. In this case, cellular elongation is more likely to be aligned with the direction of the largest shear stress. The model and simulations also predict a nonlinear stress-strain relationship that is indicative of strain stiffening. This strain-stiffening is more pronounced in fluid-like spheroids. In an extension of the preliminary polyhedral vertex model, in which cellular interfaces are shared, the authors incorporate mechanical cell-cell interactions via adhesion springs between neighboring vertices. Using this extension, they show that cell breakout is more likely to occur in fluid-like spheroids, where cells are more likely to elongate and stiffen, allowing for larger forces to be exerted on the surrounding fiber network. Furthermore, the authors state that anisotropic cell-cell adhesion is required for multicell streaming during breakout.

Strengths:

The modeling and computational approach used in this research is this work's biggest strength. Treating the embedded spheroid as a set of polyhedra, where each polyhedron represents a single cell, is a mechanically robust, yet still tractable way to model multicellular spheroids in three dimensions. Starting with expressions for constraining cell volume and surface area as well as a surface energy term, the authors derive an expression for an averaged stress tensor for each polyhedron. This allows the authors to approximate the stress in each polyhedral cell that is caused by cellular deformations during mechanical interactions with the extracellular fiber matrix. This is a clever and robust approach that is based on fundamental mechanical principles that allow one to make reasonable predications about the mechanical state of the spheroid under a variety of conditions.

Weaknesses:

The weakness of the manuscript is the exposition. There are significant pieces of critical information missing from the manuscript that would make the presented work significantly more understandable and better support the authors' claims. Most importantly, many necessary details of the model are missing. I was able to get a better understanding of some of these details by reading the authors' earlier work (ref [10] in the submitted manuscript), and for this reason, I do feel that this work has value. However, several descriptions must be added for the paper to be more readily understandable. These include (1) a better explanation of what drives motion, in particular in the case where no external fiber network is present. (2) What physically distinguishes fluid-like spheroids from solid-like spheroids? Simply stating the value of the parameters s0 with no explanation is not sufficient. (3) An explanation of how histograms in Figure 2 are calculated is necessary. Are these histograms based on one simulation or several simulations? (4) The experimental results are briefly mentioned, but significantly more connection between these results and the numerical results of the cell breakout model is needed. (5) The description of the model that incorporates variable cell-cell attachments and cell breakout is very terse and needs more detail. Moreover, while the description of the results of this model is strong, the figure that illustrates cell breakout (Figure 5) is difficult to interpret. Addressing these and other issues will make the current manuscript, which presents an interesting model and result, much stronger and easier to read.

Reviewer #3 (Public Review):

In this manuscript, the authors explore a molecular basis for hypermethylation of histones in epithelial cells infected with the obligate intracellular bacterial pathogen Chlamydia trachomatis. This is of particular interest given that Chlamydia is known to drastically alter host cell gene transcription, and histone hypermethylation would suggest a new way by which Chlamydia interferes with gene expression of its host. Histone methylation was previously implicated in the introduction of dsDNA breaks in infected cells, and the chlamydial effector NUE was reported to methylate histones, but the role of this modification in dictating host cell gene transcription has been unexplored. The authors use a suite of tools to approach this question, including various -omics techniques, genetic approaches, and biochemical assays. Overall, the manuscript provides many interesting pieces of data, though some of them are difficult to reconcile, which may reflect methodological hurdles that are not fully addressed in the current version of the manuscript. My major concerns regard the rationale/interpretation for various mechanistic experiments and that the heterogeneity of the histone hypermethylation phenotype is not addressed which I believe may explain some apparent inconsistencies in the results.

Using an immunofluorescent approach, the authors show that a subpopulation of the nuclei in Chlamydia-infected cells (~10-20%) exhibit high amounts of methylated histone species. This occurs during the late stages of infection, near the time when Chlamydia would lyse the host cell and positively correlates with bacterial burden. Accordingly, halting chlamydial growth blocks the onset of histone hypermethylation. Exogenously supplying cofactors for histone demethylases, the low activity of which is implicated in the histone hypermethylation phenotype, reduces histone hypermethylation. In general, these data are compelling and raise interesting questions about the role of histone methylation in governing chlamydial egress from infected cells. Interestingly, these behaviors seem to arise independently of NUE, the secreted chlamydial histone methyltransferase, supporting the notion that a metabolic reprogramming may underlie the hypermethylation phenomenon.

As noted above, the authors propose that hypermethylation arises due to decreased demethylase activity in infected cells. However, the data do not conclusively support this interpretation. For example, the approaches used to probe demethylase activity rely on (i) a direct biochemical measure of demethylase activity, (ii), pharmacological inhibition of demethylase, and (iii) heterologous expression of a specific demethylase. With the exception of (i), these approaches would be expected to alter histone methylation regardless of the source. That is, inhibition of demethylases should increase histone methylation regardless of whether the source of methylation is increased methylase or decreased demethylase activity. Similarly, overexpression of a demethylase would be expected to reduce cognate histone methylation arising either from increased methylase or decreased demethylase activity.

Moreover, the authors report that the effect of the demethylase inhibitor on histone hypermethylation is significantly potentiated by infection, suggesting that infected cells have greater methylase activity than uninfected cells, because the latter barely respond to the presence of demethylase inhibitor. In other words, a dramatic increase in histone methylation in the presence of demethylase inhibitor is most parsimoniously explained by increased methylation (no longer being removed by demethylase), not decreased demethylation (which would be analogous to treatment with demethylase inhibitor). The authors do not directly assay methylase activity. These concerns extend to the rationale used to justify experiments with infected mice, which the authors treat with the demethylase inhibitor.

The authors perform experiments to characterize the consequence of hypermethylation genome-wide. Because the authors do not enrich for those cells which exhibit histone hypermethylation, the results reflect the mixed population, and therefore presumably dilute out important signal related to the phenomena under investigation. For example, the proteomic analysis of post-translational modifications identifies only one methylated histone species, whereas the immunofluorescent approach shows consistent effects across five different methylated histone species. Moreover, the chromatin immunoprecipitation analysis indicates that there is unexpectedly a lower density of methylated histones at regions which are also enriched in uninfected cells. The authors argue that this suggests increased methylation is happening "outside" of these histone-dense regions, but direct evidence in support of this claim is lacking.

In sum, this paper provides compelling evidence in support of the notion that histones are hypermethylated at various residues late in chlamydial infection, that this process is modulated by known cofactors of demethylases, and is the result of high levels of bacterial replication in the cell. That histone hypermethylation governs host gene transcription during chlamydial infection suggests a relatively novel mechanism by which Chlamydia subverts the host cell to establish a replicative niche or egress to infect a new cell. The information obtained regarding the methylation status of host proteins and host gene transcription controlled by a metabolic cofactor during infection will be a useful resource for other researchers. However, in the current version of the manuscript, the mechanistic basis for these behaviors is relatively unclear.

Reviewer #3 (Public review):

Summary:

Comay, Solovey, and Barttfeld aim to provide a unified computational account of confidence in reinforcement learning by distinguishing value confidence-the certainty associated with latent value estimates-from decision confidence-the confidence that a particular choice is correct. Across new experiments and reanalyses of previously published datasets, they argue that value confidence is best described by Bayesian posterior precision, that this form of confidence adaptively reduces decision noise as learning progresses, and that decision confidence is better captured by a hybrid model combining Bayesian probability correct with a more global estimate of value certainty. They further propose that individual differences in the relative weighting of these components define "confidence phenotypes" that predict task performance, exploration-exploitation behavior, and metacognitive accuracy.

Strengths:

A major strength of the study is that it addresses an important conceptual distinction that is often blurred in the confidence literature. The paper usefully separates uncertainty about latent environmental states from confidence in an action derived from those latent beliefs. This distinction is especially important in reinforcement learning, where uncertainty is not merely a retrospective judgment about accuracy but can directly shape future sampling, learning, and action selection. The manuscript is therefore well positioned to bridge work on Bayesian confidence in perceptual decision-making with work on uncertainty-guided learning and exploration.

A second strength is the authors' use of multiple datasets and model comparisons. The claim that value confidence tracks Bayesian uncertainty is supported across tasks in which participants explicitly report confidence in value estimates, including datasets where reward variance is manipulated. The latter manipulation is particularly useful because it helps distinguish a Bayesian uncertainty account from simpler models based only on the number of observations. The finding that value confidence modulates the softmax slope and thereby promotes more exploitative choices as uncertainty decreases is also theoretically coherent and supported across several datasets, including a preregistered replication.

The manuscript's most interesting and potentially impactful contribution is the hybrid model of decision confidence. The authors show that a model based only on Bayesian probability correct captures confidence on correct trials better than on incorrect trials, whereas adding an "overall value confidence" term improves the fit. This is a useful result because it suggests that confidence reports in reinforcement learning may not be a pure readout of decision-level discriminability, but instead may combine decision-specific evidence with more global latent-state uncertainty. This could help explain why human confidence often deviates from ideal Bayesian predictions, especially on error trials.

Weaknesses:

However, the interpretation of the hybrid model remains the main weakness of the paper. The second term, overall value confidence, is not equivalent to the precision of the decision variable. It can dissociate from decision difficulty: two options can be far apart but individually uncertain, or nearly identical but individually well estimated. The authors appear to recognize this issue and have reframed the term as "overall value confidence" rather than decision-variable precision. This is a useful clarification, but the conceptual role of the term still requires sharper treatment. In its current form, it is sometimes described as part of a unified confidence computation, but it may be more accurately understood as a biasing or contextual signal that modulates reported confidence without necessarily improving decision calibration.

A related concern is model identifiability. In many reinforcement-learning tasks, probability correct and overall value confidence both change systematically over the course of learning. As a result, the hybrid model may gain predictive power partly because it captures generic time-on-task or learning-progress effects, rather than because participants explicitly combine two separable uncertainty signals. The manuscript would be stronger if it more clearly demonstrated that the two latent variables are distinguishable in the behavioral data, for example, through model recovery, parameter recovery, cross-validated prediction, and analyses of the correlation between latent regressors across task conditions and individuals.

The link between the decision rule and confidence model also deserves more scrutiny. The authors use value confidence to modulate decision noise in the choice model, and then use a related global value-confidence term in the confidence-report model. This creates an appealing unified architecture, but it also raises the possibility that the same latent variable is doing multiple kinds of explanatory work. The paper would benefit from a clearer separation between uncertainty as a driver of choices, uncertainty as a determinant of confidence reports, and uncertainty as an inferred latent variable extracted from the same behavioral data.

From a computational neuroscience perspective, the manuscript would also benefit from a more explicit discussion of how these confidence quantities might be represented neurally. The current model treats value confidence, probability correct, and overall value confidence as scalar latent variables available to the observer. Yet uncertainty-related computations may be represented nonlinearly in neural population activity rather than as explicit scalar readouts. Work on nonlinear neural decoding and population codes has shown that task-relevant variables can be carried by nonlinear statistics of neural activity, especially when nuisance variables obscure mean tuning, and that behavioral choices can reveal whether such nonlinear information is efficiently decoded. This literature provides a useful framework for connecting the present behavioral model to possible neural implementations of value and decision confidence.

Overall, the authors largely achieve their goal of demonstrating that value confidence and decision confidence are computationally dissociable in reinforcement learning. The evidence for Bayesian value confidence is strong, and the evidence that confidence-guided exploitation improves the account of choice behavior is convincing. The evidence for the hybrid account of decision confidence is promising but would be strengthened by additional analyses clarifying model identifiability, the interpretation of the overall value-confidence term, and the conditions under which the model makes distinct predictions from simpler time-, value-, or evidence-based alternatives. The paper is likely to be useful for researchers interested in computational models of confidence, metacognition, and adaptive behavior under uncertainty.