Reviewer #2 (Public review):

Summary:

This paper studies the interplay of evolutionary and in-lifetime learning. The authors develop a neural network model in which initial weight configurations evolve under selective pressure, while fitness is determined by the network's performance after a learning period. They show that such a network displays very distinct learning dynamics from those trained by either gradient descent or genetic algorithms alone: in particular, they do not learn the task, but they show evidence of learning-to-learn and unusual representational structure.

Strengths:

(1) The writing, figures, and presentation of ideas were clear.

(2) The question of how evolution on initial weights combines with learning from within-lifetime experience to structure a learning trajectory seems interesting.

(3) The analysis of existing experiments was well-done, highlighting that though these networks did not really learn, they show latent learning structure that makes the network perform better from less data.

(4) The interplay between Baldwin & learning dynamics seemed novel and interesting, presenting many attractive puzzles.

Weaknesses:

First, the authors point to an important distinction between performing evolutionary selection on the weights pre- or post- lifetime training, the latter of which is Lamarckian. They argue, correctly, that their model is interesting because it selects on the weight initialisation, unlike, for example, Shuvaev et al. However, my understanding is that a long line of papers beginning perhaps with Hinton & Nowlan also do non-Lamarckian evolution: Hinton & Nowlan have unspecified weights (denoted '?' in the paper) that can be inherited and then learnt. Is this not exactly inheritance of initial conditions (in this case, whether learnable or not)? This novelty is a primary motivation of the paper, whereas to me it seems it was already apparent in Hinton & Nowlan, and developed further in what seems to be a long line of uncited literature (see next paragraph). As such, this paper's conclusions seem poorly positioned within the existing state of knowledge/literature.

Second, the algorithm is framed as novel, but I think it is a rediscovery. This framework is very close to MAML, in which an initial weight configuration is optimised by gradient descent to be good after a few steps of fine-tuning (Finn et al., 2017). The authors' approach differs in using a genetic algorithm to perform the training of the initial weights, avoiding some of the computational complexities of MAML, especially after long fine-tuning. In this, the authors have, I think, rediscovered ES-MAML, MAML where the inner optimisation loop is gradient descent, while the outer is genetic (Song et al., 2020). Other similar work is "Meta-Learning by the Baldwin Effect" (Fernando et al., 2018).

Further, within Fernando et al. there is a rich literature review, almost none of which are cited by the authors. I point especially to Keesing & Stork, 1990, which appears to show a strong dependence of Baldwin-like improvements on the amount of data, something this paper also shows but explores less thoroughly.

To summarise my critique thus far: I think the literature already answers the main concern of the motivation (i.e. non-Lamarckian neural network evolution and learning), I think it has already discovered this particular algorithm, and I think past work has more thoroughly analysed behaviours similar to those presented in this paper. Without positioning correctly within this literature, the more general contribution of the paper is hard to establish. The true novelty of the authors' analysis seems to be the emphasis on Saxe et al.-like learning dynamics and its interplay with the Baldwin effect, but I am not certain of this without knowing the literature better.

Regarding experiments, there was an interesting effect where the EC networks didn't learn but did show latent learning (Figure 2, Figure 3), which sped up later learning (Figure 4). There were a few details I was surprised by on which I would appreciate clarity:

(1) The main result has basically no headline learning under EC. This will clearly be very dependent on parameters (e.g. if you add or remove enough training steps, the algorithm becomes SGD/GA, which both show learning). It seems like a natural analysis would examine this (e.g. a plot of final performance of EC after 4000 generations with different per-generation learning budgets).

(2) It is then shown that after 4000 generations EC can learn very quickly to perform the semantic task perfectly, at least within 200 generations (Figure 4C, and perhaps much sooner, Figure 4D, Figure 4F last panel; it was hard to say. This and the previous point seem somewhat inconsistent; was it just that Figure 3 used only 100 fine-tuning steps while the perfect-task-performing networks in Figure 4 required somewhere between 100 and 200? This seems to point to extreme parameter dependence. More broadly, how should I square this inconsistency/near-inconsistency?

(3) Figure 3k, and especially Figure 4f bottom right panel, seem to show networks that correctly separate all stimuli but cannot classify them. Should I understand this as networks learning to just push apart all pairs of datapoints without structure?

(4) If I understood the genetic algorithm correctly, only three individuals from each population seeded the next generation. This seems another important parameter to tune, since I think it is far lower than standard evolutionary work, but I am not sure.

Finally, the paper most interested me as a neural network learning puzzle: how can the network perform so badly, yet lead to such different post-fine-tuning results? The paper pointed to these as 'distinct' learning phenomena without explaining what was causing those differences. The only way I could square these results in my head was as above: that the 4000 generations pushed the initial representation to represent all datapoints differntly, effectively changing the learning problem gradient descent faces from one with a lot of structure (the semantic task) that leads to stepwise learning, to one in which it was basically linear regression on a set of well separated stimuli without the structure necessary for stepwise learning. Since the paper focuses so much on learning dynamics, and studies a task where such things can be precisely probed, it would have been nice to pin down exactly what was happening slightly more.

Reviewer #2 (Public review):

Summary:

The authors use simulations and empirical data fitting in order to demonstrate that informing a decision model using noisy single-trial estimates of an underlying fixed non-decision time can guide the model to more reliable parameter estimates, especially when the model has collapsing bounds.

Strengths:

The paper is well written and motivated, with clear depth of knowledge in the areas of neurophysiology of decision-making, sequential sampling models, and in particular, the phenomenon of collapsing decision bounds.

Two large-scale simulations are run to test parameter recovery, and two empirical datasets are fit and assessed; the fitting procedures themselves are state-of-the-art, and the study makes use of a very new and well-designed ERP decomposition algorithm that provides single-trial estimates of the duration of diffusion; the results provide inferences about the operation of decision bound collapse - all of this is impressive.

Weaknesses:

This is an interesting and promising idea, but a very important issue is not clear: it is an intuitive principle that information from an external empirical source can enhance the reliability of parameter estimates for a given model, but how can the overall BIC improve, unless it is in fact a different model?

Comment on revised version.

Thanks to the authors for their responses and inclusion of additional analyses and simulations. Thanks, in particular for clarifying a crucial detail, that the ndt-informed model actually assumes, like the uninformed model, that there is no variability in the non-decision time, and the idea is that the variable single-trial measurements of non-decision time are noisy estimates of an underlying, constant ndt. The revised paper itself has not made this clear - for example, throughout the Intro, there is no statement that the behavioural model assumes a trial-invariant ndt, and line 231 still calls tau the 'mean' non decision time, implying there is a distribution rather than an invariant single value in the behavioural model.

One implication of the above is that if the lognormal sigma is purely measurement noise that does not relate to actual variation in the underlying decision process generating behaviour, then the HMP latencies should not relate to behaviour, e.g. shorter latencies predicting shorter RT. I assume that even if the authors did find such a relationship, the principle still stands that a model with fixed ndt is more accurately fit when there are single-trial ndt estimates whose mean provides a constraint on that ndt value, than without such measurements. Still, given ndt variability is a core feature of many decision models, the authors could comment on whether the strategy would work in theory for a model with ndt variability (in the behavioural part), where the single trial estimates would then presumably reflect a mix of measurement noise and genuine ndt variability.

Another more important implication is that since it is in fact the same model being compared with and without the HMP data guiding the fixed ndt estimate, the reason the fit quality improves with HMP-information is not because it is a better model per se (it is the same model) but because without the HMP guidance, the search algorithm somehow gets lost and fails to find the 'optimal' parameter vector. That is, the parameter vector (just the parameters that relate to the behavioural model itself, not the HMP lognormally-distributed noise associated with VEP measurements) identified as optimal in the HMP-informed version of the model exists in the parameter space of the model without HMP information, but it is just not found? I raised this implication before, and it is still not clear whether it applies. I'm sorry to press on it, but it is critical for readers to understand why it is that neural information can improve overall model fit. Again, the enhancement of parameter recovery (like in Nunez 2025) makes sense, but the enhancement of the "model's fit to behavioural data" does not, without pointing to a deficiency in the search algorithm / fitting procedure.

The authors state in their replies that the onset of bound collapse is set at accumulation onset and imply that setting it instead at stimulus onset could "mathematically resolve the issue" but they don't do it because it is implausible. It is in fact not only plausible but clearly evidenced in empirical data - collapsing bounds are implemented neurally through urgency signals, and these can begin to dynamically build toward threshold well before, let alone at, stimulus onset. There is nothing bizarre about this - we can prepare movements without sensory input, and indeed even if choosing actions based on a sensory discrimination, motor preparation can launch well before the sensory evidence (e.g. Stanford, Salinas et al 2010) and this in effect collapses the bound on cumulative evidence for triggering action before any evidence actually arrives. So, Urgency/bound-collapse does not need to be triggered by a stimulus; it can start in anticipation of the stimulus. It seems critical, therefore, for the authors to clarify this point - does re-defining the onset of the collapse at stimulus onset remove the trade-off and render unnecessary the neurally-informed ndt estimation?

Related to this, it is still not clear how bias in the estimation of nondecision time would not be a problem. What if, for example, it is the end of the N2 rather than the peak of the N2 that marks accumulation onset, and/or there is an additional fixed motor time that adds to the N2-based marker to make the full nondecision time that applies in the underlying decision process. By definition (and I think this is essentially what the authors' new simulations verify), because of the trade-offs, this bias would simply be absorbed in shifted estimates of theta and lambda describing the bound collapse function. But wasn't the whole point of the exercise to more accurately estimate those parameters? The obvious implication is that the parameter-estimation accuracy of the ERP-informed model is determined by the accuracy with which the proposed ERP marker directly pinpoints the full nondecision time without bias, but this is not at all obvious in the paper as written. Importantly, in the example scenario I describe above where accumulation onsets when N2 ends, there may still be a perfect correlation of N2 peak latency with underlying ndt across trials - they could still be very strongly "linked" statistically, but we can't know what size offset might be involved.

Reviewer #2 (Public review):

Summary:

The study presents an in-depth analysis of the peptide repertoire bound by a promiscuous chicken MHC molecule using mass spectrometry, x-ray crystallography and modelling. While the MHC can bind a very diverse set of peptides, the authors have found some new rules that govern peptide binding to this MHC that could help to build a predictive model to study the repertoire of pathogen-derived peptides.

Strengths:

The study uses a range of well performed experiment across multiple techniques and provides an in-depth analysis of the peptide repertoire, including peptide sequences, length, preferred residues, stability and MHC presentation.

Reviewer #2 (Public review):

Summary:

Cerebrospinal fluid contacting neurons (CSF-cNs) are GABAergic cells surrounding the spinal cord central canal (CC). In mammals, their soma lies sub-ependymally, with a dendritic-like apical extension (AP) terminating as a bulb inside the CC.

How this anatomy-soma and AP in distinct extracellular environments-relates to their multimodal CSF-sensing function remains unclear.

The authors confirm in the GATA3:GFP mice where these cells are labeled that CSFcNs exhibit prominent spontaneous electrical activity mediated by PKD2L1 (TRPP2) channels, non-selective cation channels with ~200 pS conductance modulated by protons and mechanical forces.

They investigated PKD2L1 pH sensitivity and its effects on CSFcN excitability. They uncovered that PKD2L1 generates both phasic and tonic currents, bidirectionally modulated by pH with high sensitivity near physiological values.

Combining electrophysiology (intact and isolated AP recordings) with elegant laser-photolysis, they show functional PKD2L1 channels localize specifically to the apical extension (AP).

This spatial segregation, coupled with PKD2L1's biophysical properties (high conductance, pH sensitivity) and the AP's unique features (very high input resistance), renders CSFcN excitability highly sensitive to PKD2L1 modulation. Their findings reveal how the AP's properties are optimised for its sensory role.

Strengths:

This is a very convincing demonstration using elegant and challenging approaches (uncaging, outside out patch of the AP) together to form a complete understanding on how these sensory cells can detect so finely the changes of pH in the CSF.

Weaknesses:

Not weaknesses, there are only minor requests to complete the beautiful study.

(1) The apical extension's response to removal of acidification is nicely illustrated in Figure 4C,G. There's something puzzling there: while the response to Glutamate is immediate, the channel responses to H+ is extremely delayed by 100ms - 2s, and even sometimes came in bursts separated by few hundreds of ms. H+ diffuse even faster than glutamate. Why is that?

I don't quite understand how the response is so delayed & how to explain the recurring bursts of channel opening in the figure panel ?

- The authors should show in Fig 4C,G the traces for 1-2 s before uncaging occurs so we can appreciate whether such events occur as well in baseline and discuss this further in revisions.

- Could the authors use a fluorescent pH sensor to monitor pH in the extracellular space and in the cell ?

- Could the authors investigate whether in the apical extension, PKD2L1 channels are mainly at the outer membrane in the apical extension OR whether many channels are located in inner membranes ?

(2) Suppl Fig 4 is very cool and should be moved to main figure. The coupling of Soma and AP is very tight, yet there is a clear difference in targeting of channels that respond to cues in the CSF. In the context of an intact spinal cord, we can wonder how and when the contribution from ASIC in the some would be relevant to physiology. Can the authors think of experiments with an intact central canal to test the sensitivity and condition of recruitment of pH sensing in the soma (ASIC) versus the apical extension (PKD2L1)?

(3) The Reissner fiber is missing after slicing the spinal cord. From our observations in fish, the fiber being under tension triggers lots of activity in CSF-cNs (Bellegarda et al Elife 2023) that also relies on PKD2L1 (Bohm et al NC 2016; Sternberg et al NC 2019). Could the authors discuss the contribution of the Reissner fiber to the PKD2L1 mediated modulation of CSFcN excitability ? Could the authors conceive a way to slice along the anteroposterior axis (sagitally) the spinal cord to keep the Reissner fiber in the central canal when recording CSF-cN apical extension ?

Reviewer #2 (Public review):

Summary:

This manuscript introduces HSSM, a Python-based toolbox for fitting cognitive models, with a specific focus on sequential sampling models. The toolbox brings together several components: a model construction interface, surrogate likelihoods, sampling tools, an inference backend, formula-based regressions, and tools for validation and visualization.

One of the key advantages is that HSSM relies on well-established open-source packages. This ensures both a robust foundation and also opens a potential for future (community-driven) development. While HSSM is not the first publicly available toolbox for fitting sequential sampling models, it introduces several novel features that will be very valuable to researchers in the field.

Strengths:

The biggest strength of HSSM is its flexibility and ease of use for hierarchical modeling. Many existing toolboxes work as closed systems that are hard to modify. In contrast, HSSM's modular design allows it to be used either as a stand-alone tool or to pick out specific components to integrate into existing pipelines. In addition, the toolbox combines simulation-based inference, surrogate likelihoods, and formula-based regression. This opens up a lot of new modeling possibilities and makes it easy to incorporate trial-by-trial neural or physiological covariates alongside standard RT and choice data.

Weaknesses:

The paper provides a high-level overview of the toolbox, rather than a didactic walk-through that shows how to use it in practice. Additionally, despite being framed as a broad toolbox for "neurocognitive modeling", HSSM currently focuses on sequential sampling models. While these models are widely used, they represent only a small slice of neurocognitive modeling as a whole. Additionally, the toolbox is currently in beta phase, and lots of planned extensions are not implemented yet. Finally, there is currently no information on general performance benchmarks.

Reviewer #2 (Public review):

Summary:

The generation of alternate stages in the life cycle of a single species requires vast remodeling of the cellular complement of the individual during metamorphosis from one stage to another. In this paper, the authors provide a detailed description of single-cell RNA-seq data derived from the planula stage of the hydrozoan model Clytia hemispherica and compare this to an expanded dataset from the medusa stage to assess changes in transcriptomic identity of cell types between these two phases of the life cycle. The paper further includes valuable TEM data illustrating fine anatomy of cell types present at both stages investigated, and documentation of the retention of epithelial polarity from the planula through to the polyp stage, using a reporter line.

Strengths:

The study provides a solid and convincing transcriptomic characterization of planula cell types (including in situ validations and a planula-to-polyp mapping of epithelial polarity), and introduces a potentially valuable method for evaluating cluster similarity.

Weaknesses:

The work suffers from insufficient documentation of methodological approaches and missing code, lack of clarity regarding clustering resolution and nomenclature (thereby hindering cross-referencing with prior papers), and unclear plans for public, fully annotated data release.

Full Review:

The single-cell transcriptomic data analyzed include both previously published and newly generated data: two additional medusa libraries and two additional planula libraries were generated and integrated with the data from https://doi.org/10.1126/sciadv.abh1683, and https://doi.org/10.1126/sciadv.adv1159. The original release of the planula dataset in their 2025 Science Advances paper did not include analyses of all cell types. Here the authors provide this analysis for the planula stage. However, as both the number of clusters and the nomenclature of the clusters changed, this leads to some confusion and inability to cross-reference the two papers. There is no explanation given for the re-processing of the planula dataset in the current paper, and the fact that only some of the data is new is buried in the supplement, which is not referenced in the main document, while the text within the main article suggests that the entire dataset is new. The fact that the dataset in the current analyses contains fewer cells than presented in their Science Advances paper further adds to this confusion. The current paper would benefit from greater transparency in the origin of the data analyzed.

The authors do try to apply the same nomenclature for the updated medusa dataset that is present in their 2021 Science paper. For example, the previously identified 'bioluminescent cells' are identified as 'gas-m8'. A look-up table that has all of the cluster id's cross-referenced would be useful (i.e. new: 8 = gas-m8 = previous: 28 = BC = "Tentacle GFP cells"). The inability to easily cross-compare with the published data is a major weakness of the current work and would benefit greatly from consistency between the three papers. Indeed, the clustering resolution is quite different across all three papers, and the current work does not adequately address how the clustering resolution was selected here. As an updated atlas, one would expect the entire transcriptomic diversity to be included here, so that the previous work can be transferred to the updated genomic mapping resource used in the current work. Nonetheless, presenting a unified nomenclature for moving forward would benefit the community as a whole and would increase the impact of the current work substantially.

The paper also includes new TEM data of the planula cell types. The authors attempt to correlate transcriptomic profiles with these anatomical data through in situ hybridizations that provide spatial distribution of the profiles. While the TEM data are valuable to catalog the presence of cells with different morphologies within the planula, the association with the transcriptomic profiles is somewhat speculative. These valuable anatomical data should be provided at a high enough resolution to zoom in and see the details, and further description could be provided. For example, the paper states that vacuolated cells are characteristic of the basal gastrodermal cells adjacent to the mesoglea; please identify the vacuoles in Figure 4f/h for the reader.

A novel method for reconstructing cluster similarity relationships is applied to grouping clusters into cell categories within the same life cycle stage, and also for matching cell types between stages. This is a valuable contribution to the field that is worthy of further evaluation. This is, however, difficult, as the methods for which DESeq2 was applied ("see code for details") are not present in the provided code, nor is it adequately described how the "binary matrix of marker gene presence/absence" was constructed. Similarly, there are additional details of other parts of the data analysis that are missing from the provided code, and the provided supplementary material is not referenced in the main document. More rigorous documentation of the methods is warranted.

The description of the transcriptomic profiles present in the planula is solid, and the attempt to associate these profiles with anatomic locations and putative morphology provides a foundation onto which further studies can be developed. Mapping of the planula ectoderm through to the polyp stage is also an important step forward in characterizing the life cycle, and the evidence for the retention of the oral/aboral ectodermal axis is convincing. The paper falls short in describing the updated medusa dataset and could benefit from a minor restructuring of the paper. Introducing the new medusa data only after the planula dataset is fully described would mediate the shallower treatment of the updated medusa dataset, where only 22 of the original 36 transcriptomic states are recovered. In this way, the focus will shift onto the cross-life cycle stage comparisons, and it could be argued that the lower resolution of the medusa dataset is justified in order to simplify the comparisons.

It will be essential that the datasets that are presented in this work be made available for public exploration in a fully annotated format. It is currently unclear how the authors intend to do this; however, there are many repositories available for this. The UCSC Cell Browser hosted at cells.ucsc.edu is one very good option if the authors do not wish to develop an interactive tool themselves. It is imperative that the gene annotations which correspond to the dataset, and the cluster annotations that are presented in this paper, are available and easily connected to the released dataset.

Reviewer #2 (Public review):

Summary:

This paper presents a large structural survey of extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs) in the olfactory sensilla of Drosophila melanogaster. Using high-pressure freezing and serial block-face SEM, the authors avoid many of the artifacts associated with conventional fixation and analyze more than 7,800 particles across 352 sensilla. The manuscript maps the distribution of these particles, describes their morphological heterogeneity, and examines their likely origins across different sensillum classes in both normal and degenerating tissue.

Strengths:

The strongest aspect of the paper is the imaging. Preservation is painstakingly controlled. The cryofixation appears to preserve the sensillum lymph in a more convincing native state than standard preparation methods, giving this work gravitas. Further, the authors characterized thousands of particles, further making this data strong.

The figures are strong. They are clear, easy to read, and generally well designed; I think people will use this paper as a model for how to present complex data in a concise and straightforward manner. The manuscript is careful in how it presents the dataset and does not overinterpret the descriptive observations. As an ultrastructural resource, this paper will be useful to the field. The identification of auxiliary support cells as major secretory sites, together with the striking accumulation of EVs in degenerating tissue, will provide a useful starting point for future work.

Weaknesses:

The main point that could use more clarification is the vesicle categorization. In particular, the distinction between "dense," "cargo-filled," and "double EVs" is not always easy to follow from a biological perspective. Some additional discussion of how the authors think these categories relate to one another, and whether they are intended as purely morphological groupings or as distinct biological classes, would strengthen the manuscript.

Reviewer #2 (Public review):

The authors investigate the mechanism by which a gasdermin pore-forming effector of the cartilaginous fish Callorhinchus milii, GSDMA/B (CmiGSDMA/B), is activated. This potentially provides information on the ancestral function of gasdermins, a class of proteins broadly important in human health and disease. By reconstituting components of this system in vitro using transfection models, they show that GSDMA/B is activated by cleavage by the caspase-1 homolog CmiCASP1, which directly senses lipopolysaccharide. This mechanism is broadly similar to the non-canonical pathway in mammals, wherein caspase-4/5/11 cleaves GSDMD upon cytosolic LPS sensing. The conclusions of these interactions are mostly well supported by data, but some aspects need clarification, and based on the experimental approaches, some of the broader interpretations have limitations that should be considered and further discussed.

A more detailed analysis and discussion on the differences between caspases with regard to their LPS-binding capacity would be valuable for comparison. The analysis of Figure 4A and 4B effectively shows that there are similarities between CmiCASP1 and some of the studied mammalian caspases. However, part of this analysis is to make the point that some caspases do not bind LPS, and it would benefit from the inclusion of additional relevant LPS-insensitive caspases to show the connection between the chondrichthyan caspase residues highlighted and LPS-binding dependence. Modeling the LPS binding site (such as in Figure 1F) would further help clarify whether these are appropriately positioned for coordination, or for non-conserved residues, if there are alternate binding modes thought to have biological relevance.

The authors note that two different cleavage products are formed, with variable function, which is of interest. The results of Figure 2b suggest that the 241A mutation (blocking the 30 kDa product) increases processing to the larger 35 kDa product, while the 288A mutation decreases processing of the 30 kDa product (also blocking the 35 kDa form). Paired with the lysis data (Figures 2C-2E), its not clear that the 35 kDa product is anything but inactive, but this is quite different from the observations in the experiments with each form (Figure 3N-3Q). A more detailed kinetic and stoichiometric analysis between full-length, N241, and N288 would be important for clarifying the potentially interesting observation of N288 inhibition of N241.

The mechanism of bacteriocidal activity proposed in the final model and by the experiments of Figure 5 would benefit from further development to support the claim. The experiments do not adequately address whether, during pyroptosis, there is release of N241-like fragments that can kill bacteria. Figure 3G would indicate that it stays in the cell, either in the membrane or mitochondria, and it's not clear there would be circumstances where it could be extracted from it to then target bacteria. Figure 5B might require additional explanation and analysis, but the appearance of similar colonies between conditions would appear to support that there is not measurable antibacterial activity. More rigorous support would come from differences in bacterial killing by knockout Callorhinchus cells, but a minimal step to demonstrating the relevance would be MIC assays, and connecting the effective concentration with one that could naturally occur in Callorhinchus.

Broadly, the methods of reconstitution of components of this system demonstrate the sufficiency of LPS for activating Casp1, and Casp1 for activating GSMDA/B. However, in more established models, it is clear that there are inhibitors, feedback mechanisms, alternative pathways, and regulation that could render these interactions irrelevant in Callorhinchus. For example, it's not clear where Casp1 and GSMDA/B are ever expressed in the same cell, at quantities sufficient for this mechanism, or that Casp1 doesn't induce more rapid death by acting on something other than GSDMA/B, or that Casp1 is irrelevant because GSDMA/B can be activated more readily by another mechanism. Therefore, while the insights into the evolution of the individual factors of GSDMA/B and Casp1 are interesting and of potential value to the field, reconstituting choice components by transfection of human HeLa and HEK293 cells introduces limitations to how far these experiments can be interpreted as a system. The abstract, for example, states this is a "pyroptosis pathway in cartilaginous fish". However, for all the interest of these data in the evolution of these proteins, the evidence falls short of this. It establishes a biological potential, but it's not clear this is an active pathway in fish.

Reviewer #2 (Public review):

Summary:

This paper presents theoretical and empirical insights into the use of multi-task batteries for precision functional brain mapping and offers practical guidelines for optimal task design. Specifically, the authors evaluate differences between single-contrast and multi-task localizers, explore data-driven strategies for battery selection, such as minimizing collinearity, and compare grouped and interspersed stimulus-presentation designs. Through a combination of simulations and analyses of empirical fMRI data, the study provides a systematic set of recommendations for improving the reliability and specificity of individualized functional mapping.

Strengths:

Traditional functional mapping has long relied on single-contrast localizers or resting-state fMRI. However, there is growing recognition that diverse batteries of general tasks can yield more detailed functional maps with higher signal-to-noise ratios (SNRs). This manuscript systematically evaluates these advantages using both simulations and empirical data. The contribution is timely and provides the community with not only a theoretical justification for multi-task designs but also practical tools, in the form of the MultiTaskBattery toolbox, for implementing them.

Weaknesses:

Although the results are robust, they are largely consistent with existing expectations in the field, and the conceptual novelty or "surprise" factor is therefore somewhat limited. Nevertheless, synthesizing these findings into a coherent set of design recommendations provides significant value to researchers.

Additionally, there appears to be a slight mismatch between the content of the manuscript and its designated article type. Although the manuscript was submitted as a "Tools and Resources" article, its extensive empirical analyses and theoretical evaluation make it read more like a "Research Article." I defer this categorization to the Editor's judgment.

Finally, the authors use inter-subject overlap as a primary metric for validating the accuracy of functional mapping (Figure 3). However, given that genuine inter-individual variability in brain organization is a central premise of precision mapping, greater overlap across subjects may not necessarily indicate more accurate individual-level localization. A more detailed analysis or discussion of how to distinguish measurement noise from genuine individual differences would make the paper more comprehensive and strengthen its overall contribution.

Reviewer #2 (Public review):

Summary:

Two types of Schwann cells (SCs) ensheath motor axons - myelinating SCs along the axonal length and terminal SCs (tSCs) that cover nerve terminals at the neuromuscular junction (NMJ). Therefore, the NMJ is, like other synapses, tripartite, with specialized presynaptic, postsynaptic, and glial cells. Many studies have shown that tSCs play roles in the development and function of the NMJ, but for some of these, interpretation is difficult because it is hard to manipulate tSCs without also manipulating myelinating SCs. To circumvent this problem, Kong et al. make use of a gene selectively expressed in tSCs, Col20a1 (Figure 1), to generate a knock-in mouse line, Col20a1-CreER, that gives them genetic access to tSCs. They cross this to Cre-dependent lines that mark tSCs with a red fluorescent protein (Figures 2 and 3) or ablate them by expression of diphtheria toxin along with the fluorescent protein (Figure 4). They show that ablation at postnatal day (P) 10 does not affect the overall structure or function of the NMJ (Figures 4 and 5). It does, however, affect some aspects of neuromuscular transmission over the following few weeks (Figures 6 and 7). Long-term effects cannot be studied by this method, however, because terminal SCs are replaced, presumably from the preterminal population (Figure 8).

Strengths:

The work is done to a high technical standard, including detailed characterization of the knock-in model. Results are presented clearly and illustrated beautifully. The finding that some early reports of synaptic alterations may result from concurrent loss of axonal SCs is important in rethinking the role of tSCs.

Weaknesses:

(1) The authors claim that tSCs are dispensable for some aspects of NMJ maturation, including synapse elimination (called pruning here), formation of "pretzel-like" postsynaptic topology, and generation of junctional folds in the postsynaptic membrane (lines 223 and 363). However, this conclusion is based on injection of tamoxifen to initiate tSC ablation at P10, which is necessary because Col20a1 is expressed in some preterminal SCs at earlier times. It presumably takes a few days for CreER to translocate to the nucleus and activate the toxin transgene, and some more time for the toxin to be generated and act. This is problematic because synapse elimination and other aspects of maturation mentioned occur during the first two postnatal weeks and are largely complete by P14. Therefore, one cannot conclude that these aspects "proceeded normally despite the loss of tSCs....".

(2) Effects on synaptic transmission are modest at best, being significant at a level of p<0.05 but not p<0.01 (Figure 6E, G, H and most of L). Effects on vesicle density are more robust (Figure 7).

(3) The authors use red fluorescent protein from the Col20a1 to label tSCs, and antibodies to S100b to label all SCs. This is appropriate in normal muscle and soon after tSC ablation. At later times, however, the NMJ is repopulated by S100+ Col20a1- SCs (Figure 8B). It is therefore important to show when this repopulation begins, because a modest recovery of SC coverage could have a big effect. For example, Figure 4C quantifies loss of NMJs with residual RFP+ cells but not S110+ cells; both should be quantified at this and slightly later stages.

Reviewer #2 (Public review):

Summary:

Genes associated with risk for a specific disease commonly have widespread expression and functions across the body. Surveying patterns in these effects may reveal novel mechanisms, organs, and systems implicated in a disease, amongst other associations that are truly independent. In this work, Husen and coauthors use the Human Protein Atlas to explore such associations in Alzheimer's disease (AD), Lewy body dementia (DLB), and Frontotemporal dementia (FTD). Focusing on human non-disease tissue expression may avoid the effects of disease progression obscuring initial vulnerabilities. However, associations in non-diseased tissues do not necessarily reflect mechanisms causally related to the diseases themselves.

The work describes patterns of enrichment of genes across tissue types, brain regions, and cell types. A relatively small set of classes of each show enrichment for disease. While neural signatures are unsurprisingly prevalent, these classes are largely distinct across the three diseases. Alzheimer's disease is linked to liver and central and peripheral immune cells, while DLB shows interesting enrichments associated with cilia, which are linked to an existing literature. Results from the drug repurposing approach are then presented, with 1777 drugs linked to protein products of any of the modules enriched by the risk genes using DrugBank, categorised according to key signatures.

The authors developed R code (the HPA GeneSet Explorer) to automate the production of multi-system summaries of the organs, brain regions, cells and gene modules associated with traits and diseases and associated gene sets, within the HPA. Risk gene sets for the three dementia types were derived from the GWAS Catalog.

Strengths:

While many studies of how risk genes contribute to disease take a narrow approach focusing on organs, cell types, and processes already associated with a disease, it is a sensible approach to start with a system-agnostic approach that assesses tissues that are not ostensibly affected by disease. Here, this approach reveals a range of associations for 3 neurodegenerative diseases, identifying disease-associated modules and drug candidates that might be prioritized for subsequent confirmatory inference across biological scales. Results highlight key organs and cell types, most of which have established associations with the disease. Perhaps the most intriguing results are the links of DLB to cilia-related processes, which can be linked to some prior reports of DLB/PD but are not a core element of current theories of pathogenesis.

Weaknesses:

A difficulty with broad, multi-dataset surveys of disease associations is the need to distinguish novel and robust patterns - even if they lack causal evidence - from those that are unsurprising or do not stand out statistically. The work is exploratory in nature, but it is often hard to know how strong the evidence is for particular observations reported.

The work combines nominal, FDR<0.1, and Monte Carlo-based inference (with no apparent multiple assessment control across all tested modules) p-values throughout the paper, with patterns of effects of nominal significance. In some places, modules appear to be retained if they meet any of these criteria, muddying inference. This makes it difficult to weigh the different reported associations. Results report numbers of risk genes showing nominal p<0.05 enrichment across gene modules and biological scales - it is difficult for the reader to determine null expectations for false positives here. Similarly, it is unsurprising that thousands of drugs can be linked to the risk genes and their signatures using nominal significance.

The results have limited mechanistic specificity. The modules identified often reflect biological processes implicated in the diseases. This provides some validation of the approach, but the modules are often broadly defined, providing little mechanistic insight. For example, many aspects of ciliary biology may overlap with DLB, but can the HPA provide more specific insight? More generally, it is difficult to determine how much relevance that enrichment in non-disease tissue has for disease processes. Similarly, it is hard to determine whether overlap of drug targets from DrugBank with these modules realistically increases their prioritization.

Methodologically, there could be more detail. The paper - in particular the methods - is partially presented as a tool/pipeline paper, but thorough descriptions of the HPA models that are employed and modules reported for the analyses should still be presented in detail. The drug repurposing approach is described in a couple of sentences without a precise reference to the tool or statistical methods.

Reviewer #3 (Public review):

Thapliyal, Gopinath, and Glauser show that starvation alters how C. elegans respond to noxious thermal stimuli. Using targeted neural ablation, mutant analysis, and live-cell functional imaging the authors demonstrate that hunger changes the properties of AWC sensory neurons, which sense noxious heat. The authors further show that effects of hunger on nociception require ASI neurons, which are known to respond to hunger and mediate effects of food deprivation on behavior. Finally, the study uses mutant analysis to implicate glutamate and specific neuropeptides in thermal nociception and in modulation of nociceptors by hunger-responsive neurons.

The study clearly shows a strong effect of hunger on nociception and documents a striking effect of hunger on the intrinsic properties of AWC sensory neurons, which respond to noxious heat. The study also clearly and compellingly demonstrates that ablation of hunger-responsive ASI neurons blocks effects of hunger on nociceptive AWCs. These data, which constitute the kernel of the manuscript, are striking and exciting. This revised manuscript analyzes effects of starvation on AWC physiology and clearly shows that starvation alters the way AWCs respond to thermal stimuli by decreases the probability that AWCs will be activated and increases the probability that they will be inhibited. New data also identify ASI-derived neuropeptides that are required for modulation of AWCs by starvation. This study reveals a mechanistic link between an animal's metabolic state and sensory processing and establishes modulation of AWC function as a powerful model to study the molecular basis of this link.

Reviewer #2 (Public review):

Summary:

The authors used whole-network imaging to identify sensory neurons that responded to the repellant 1-octanol. While several olfactory neurons responded to the initial onset of odor pulses, two neurons consistently responded to all the pulses, ASH and AWC. ASH typically activates in response to repellants, and AWC typically activates in response to the removal of attractants. However, in this case, AWC activated in response to the removal of 1-octanol, which was unexpected because 1-octanol is a harmful repellant to the worm. The authors further investigated this phenomenon by testing different concentrations of 1-octanol in a chemotaxis assay and found that at lower (less harmful) concentrations the odor is actually an attractant, but becomes repulsive at higher concentrations. The amplitude of the ASH response appeared to be modulated by concentration, but this was not true for AWC. The authors propose a model where the behavioral response of the worm is the result of integrating these two opposing drives, where repulsion is a result of the increased ASH activity over-riding the positive drive from AWC. The authors further tested this theory by testing mutants that ablated the AWC response (tax-4 or AWC::HisCl) or ASH response (osm-9 or ASH::HisCl). The chemo-silencing (HisCl) and tax-4 experiments were consistent with their hypothesis, while the osm-9 mutation had a limited impact on chemotaxis behavior, highlighting the potential role of osm-9-independent signaling in ASH in response to 1-octanol. While the interneuron(s) that integrate these signals to influence behavior were not identified, the authors did find that increasing concentrations of 1-octanol did increase the likelihood of AVA activity, a neuron which drives reversals (and hence, behavioral repulsion).

Strengths:

This was simple and elegant work that identified specific neurons of interest which generated a hypothesis, which was further tested with mutants that altered neuronal activity. The authors performed both neuronal imaging and behavioral experiments to verify their claims.

Weaknesses:

The authors note that other sensory neurons likely contribute to 1-octanol chemotaxis. Given the NeuroPAL data, it would have been nice to identify these other neurons as well. However, the reviewer is aware that this is tangential to the primary focus of this study.

Reviewer #3 (Public review):

Summary:

In the manuscript by Greter, et al., entitled "Targeted induction of gut-microbial metabolism acutely affects feeding patterns and clock gene expression in the host" the authors investigate whether acute exposure to a non-nutritive disaccharide (lactulose) promotes microbial metabolism that feeds back onto the host to impact circadian networks. The premise of the study is interesting, and the experiments are thoughtfully designed to dissect these relationships. The evidence presented generally supports the authors' conclusions regarding the impact of lactulose administration during the fasting period, which is intended to mimic a feeding-associated perturbation of the gut microbiota, and its comparison with lactulose administration during the fed state. The studies employ complementary model systems, including germ-free mice, mice colonized with a simplified three-member microbial community, and conventionally colonized animals. These approaches support the authors' conclusions regarding the relationship between diurnal rhythms of microbial fermentation and host circadian clock gene networks. Overall, the work provides a useful experimental framework for developing a deeper mechanistic understanding of how microbial fermentation products contribute to diurnal host-microbe interactions.

Strengths:

Attempting to disentangle nutrient acquisition from microbial fermentation and its impact on diurnal dynamics of gut microbes on host circadian rhythms is an important step for providing insights into these host-microbe interactions.

The authors utilize a novel approach in leveraging lactulose coupled with germ-free animals and metabolic cages fitted with detectors that can measure microbial byproducts of fermentation, particularly hydrogen, in real time.

The authors consider several interesting aspects of lactulose delivery, including how it shifts osmotic balance as well as providing calculations that attempt to explain the caloric contribution of fermentation to the animal in the context of reduced food intake. This provides interesting fundamental insights into the role of microbial outputs on host metabolism.

The authors employ complementary systems, including a simplified three-member microbial community, providing insight into the minimal set of functionally distinct community members necessary to promote the rhythmic production of fermentation products that can affect host physiology.

Residual limitations:<br /> Hypothesis and study framing: The manuscript still does not clearly articulate a specific, testable hypothesis. While the Introduction provides motivation and objectives (e.g., line 53 onward), it remains unclear what precise hypothesis was being evaluated. A more explicit statement would strengthen the conceptual framework of the study.

Interpretation of circadian gene expression changes: The authors have not fully reconciled the differing effects of lactulose treatment on circadian gene expression in the 3MM and SPF settings. In particular, it remains unclear how the increased expression of certain circadian genes observed in lactulose-treated 3MM mice, particularly Cry1, relates to the decreased expression seen in SPF mice, and how the reduction in Arntl expression observed in lactulose-treated SPF mice fits within the proposed model. The authors acknowledge that resolving these mechanistic differences is beyond the scope of the current study, but the limitation should be discussed more explicitly.

Reviewer #2 (Public review):

Summary:

In this manuscript, Koh and colleagues describe ADePT (Axially Decoupled Photo-stimulation and Two-photon Readout), a modular approach for combining patterned one-photon optogenetic stimulation with two-photon calcium imaging in independently controlled axial planes. The method relies on a digital micromirror device together with a motorized holographic diffuser to generate spatially confined stimulation patterns while imaging deeper neuronal populations. As proof-of-principle applications, the authors use the system to map excitatory and inhibitory functional connectivity in the mouse olfactory bulb by stimulating superficial glomerular circuits and recording responses from mitral and tufted cells in deeper layers.

This is a well-executed Tools and Resources manuscript. The technical implementation is described in considerable detail, the optical performance is systematically characterized, and the biological experiments provide convincing demonstrations of the types of circuit questions that can be addressed using the method.

Strengths:

The greatest strength of the manuscript is the comprehensive technical characterization of the optical system. The authors carefully benchmark the spatial resolution, axial confinement, registration accuracy, calibration procedure, and practical operating limits of the setup. I found the extensive optical benchmarking particularly helpful, as it gives readers a realistic sense of the operating regime and practical limitations of the approach.

Another strength is the high level of methodological transparency. The optical design, calibration procedures, stimulation strategies, and analysis pipeline are described in sufficient detail that an experienced laboratory could realistically evaluate whether the system is suitable for its own applications. This level of documentation is particularly appropriate for a Tools and Resources article.

A further strength is the clear positioning of ADePT relative to existing approaches. The authors are transparent about the trade-off between spatial resolution and implementation complexity: ADePT does not provide single-cell photostimulation, but offers flexible axial separation, a large stimulation field, and cellular-resolution two-photon readout in deeper planes without requiring a full holographic stimulation system. This defines a credible and potentially useful experimental niche.

The biological applications convincingly demonstrate the utility of ADePT. The experiments identifying sister mitral/tufted cells through selective glomerular stimulation and the mapping of heterogeneous inhibitory influences from DAT-positive interneurons illustrate the types of functional connectivity questions that become experimentally accessible with this approach. Importantly, the authors generally avoid overstating these biological findings and appropriately present them as proof-of-principle demonstrations of the technology.

Weaknesses:

The primary limitation is inherent to the method itself rather than the execution of the study. Because ADePT relies on one-photon patterned illumination, photo-stimulation remains restricted to relatively superficial structures and does not achieve single-cell spatial resolution. The authors appropriately acknowledge these constraints and clearly position the method within this operating regime. Consequently, ADePT occupies a useful niche for interrogating spatially organized functional units such as olfactory glomeruli or cortical barrels, rather than applications requiring single-cell precision or deeper tissue penetration.

Although the manuscript describes the approach as relatively simple and cost-effective, implementation still requires careful optical alignment, registration, calibration, and optimization. This does not diminish the value of the approach, but terms such as modular or accessible may better reflect the practical implementation than simple. Likewise, a brief bill of materials, approximate add-on cost, and indication of which components are essential versus substitutable would help prospective users assess the accessibility of the system.

Finally, the manuscript provides an impressive level of technical characterization, but much of the practical guidance for adopting the system is distributed across the Results and Discussion. Bringing together the principal limitations, recommended operating regime, expected calibration workflow, evidence for long-term alignment stability, and the circumstances in which ADePT is preferable to alternative approaches would further strengthen the manuscript as a community resource.

Reviewer #2 (Public review):

Summary:

The main contribution of this study is that brain activations related to linguistic context varied as a result of presentation speed, with the main finding that increased activity for coherent stories relative to other conditions was reduced in fast presentation relative to slow. The results thus challenge certain assumptions about the nature of the brain dynamics of language processing, with certain effects even disappearing under faster presentations, which may be related to the processing mode of the participant. The results continue to establish the viability of a parallel presentation design, which generally produces results congruent with those of the literature.

Strengths:

The study contains a somewhat novel presentation method, illustrating its viability. The results are bolstered by a strong sample size (N=33) and robust analytic techniques. The conclusions are measured and appropriate to the results, and the manuscript is exceedingly clearly written and accessible to readers.

Weaknesses:

The spatial specificity of the effects is hampered by the use of MEG, particularly with minimal structural MRIs for participants. Thus, the conclusions of the study in the spatial domain are tentative and more general than might result from other studies.

In addition, the general finding that faster presentation speed reduced activity overall (and eliminated it in the frontal cortex) appears to be somewhat contradictory to existing literature, which finds that sentences which are complex or difficult to process generally produce greater activation, particularly in the frontal cortex. These studies might be reviewed, and this (seeming) contradiction could be addressed.

Reviewer #2 (Public review):

Summary:

Chen et al. consider the activity of retrosplenial cortex (RS) neurons during performance of an open-field navigation task in mice. Using a Ca-++ transient imaging approach to examine activity, the authors claim to find tuning to distance of the animal to a hidden reward location. The question of tuning to distance in RS is of much interest of late, with other works making claims. In this respect, the present work is interesting in that it utilizes an actual navigational task that does not explicitly demand encoding of distance and does consider an open-field environment. I do have reservations concerning the robustness of distance tuning.

Strengths:

Testing of distance coding in open fields during performance of an actual navigational task.

Weaknesses:

Lack of robust evidence for distance coding and head direction coding.

Reviewer #2 (Public review):

Summary:

The vertebrate spinal cord receives inputs from many supraspinal regions. The authors used optical backfilling to trace neurons in the zebrafish larval brain sending axons to the spinal cord. With two-photon microscopy, they managed to render a comprehensive 3D map of these neurons and drew homologs with mammalian brain structures.

Strengths:

The main strength lies in the precise 3D mapping. The fact that most of the previously reported neuron groups have been confirmed by their approach is a solid endorsement of their methodology.

This study provides a comprehensive alternative anatomical reference framework for studying individual groups of supraspinal neurons with projections to the zebrafish spinal cord.

Weaknesses:

The whole approach could be enhanced by counter-staining their preparation to profile brain structures, including many nuclei more precisely.

Also, the backfilling approach does not reveal the full trajectories of axons, which is already available to some degree by ZExplorer Atlas.

Reviewer #2 (Public review):

In this work, Alamdari et al. present EvoDiff, which provides the capability to generate protein sequences directly in sequence space, using a discrete diffusion model. There are several versions. EvoDiff-seq is trained on UniRef50 sequences (~42 million), and EvoDiff-MSDA operates instead by using sequence alignment methods to generate new members of protein families. The authors demonstrate many modes of sequence generation, including unconditional and conditional, inpainting of disordered regions, and also generating scaffolding of functional motifs. Their evaluation is also multifaceted, covering foldability, folding self-consistency, language embeddings, secondary structure distributions, and experiments for a set of different scenarios.

The paper has many notable strengths. It is comprehensive in breadth, and the experimental component is distinctive, although I am not personally suited to review the rigor of that element.

I would suggest that the paper's results certainly support the conclusion that order-agnostic sequence generation can yield useful candidates for multiple conditional design tasks. I am not totally convinced that it necessarily establishes diffusion as a generally superior approach to other competitors, like the conventional protein language models- EvoDiff is certainly competitive, and I think that the demonstration of diffusion is nice. I also am not sure that it is fair to say that sequence alone is sufficient for the broad design capabilities claimed (other than the "in principle" statement).

I am overall quite supportive of the work and its demonstration, but I have a few comments for consideration in any revision.

(1) I did not work through all dates of everything, but it appears to me that there are several recent conceptual and methodological competitors. These include DPLM and ProtBFN - both of these seem to be after the first preprint of EvoDiff, but given the time gap, there probably deserves to be some additional discussion or comparison. I would say, ideally, they should offer direct benchmarking. If the authors are disinclined, then I would think they should just temper their claims of contemporary SOTA performance or general superiority. Instead, the paper would still remain valuable as an early and experimentally demonstrated sequence diffusion framework. I don't think it needs to be more than that.

(2) Related to the above, the manuscript should more carefully distinguish the demonstrated advantage of order-agnostic generation from the quality of unconditional generation. Regarding Figure 3, the authors argue that Evodiff's diffusion objective is necessary, but this does not seem to account for or address the LRAR baselines in Tables S1 and S3. Unless I am misunderstanding, the 640M LRAR model exhibits several better scores. The authors later suggest that EvoDiff's principal advantage is conditioning on arbitrary positions, which is valid, but that's a little different that what is being claimed. I suggest that the LRAR results should be shown or discussed alongside Figure 3, and then the authors revise to say that they have flexible conditional generation as the principal empirical benefit.

(3) I really like the IDR experiment, but I'm not sure it demonstrates that Evodiff can design functional IDRS generally. Cox15 is a favorable target because its mature sequence strongly identifies a conserved mitochondrial protein, and EvoDiff-MSA is supplied directly with its orthologous family. The eight tested sequences were also selected from hundreds of candidates using both DR-BERT and MitoFates, making the experiment a test of the full generation-and-prediction pipeline rather than of EvoDiff alone. Moreover, mitochondrial targeting is tested, but the disordered character of the generated sequences is not experimentally established. In any case, I think it would certainly be more convincing if there were other examples, with unrelated proteins or IDR functions. I would appreciate that this is again a step beyond what the authors might be compelled to do, but their claim could be simply more calibrated.

(4) The authors might benefit from explaining the advantages or complementarity of EvoDiff to other property-directed approaches for exploring sequence space. This has been done, for example, by using Bayesian optimization and genetic algorithms to tune properties of IDP condensates (DOI: 10.1021/acs.jpcb.8b03822). In my understanding, these are addressing a different problem from EvoDiff by optimizing sequences explicitly towards physical targets, while EvoDiff is a generative framework that can be used for sampling/inpainting/ etc. Is it clear how these strategies might be plausibly integrated? If so, that would be a relevant point of discussion and a potential advantage for EvoDiff.

Reviewer #2 (Public review):

Summary:

Studies in rodents have demonstrated that early life adversity (ELA) impacts many aspects of the exposed offspring's brain and behavior. Work in this field has traditionally focused on how<br /> stress in very early life can impact cognitive and emotion-related behaviors in the ELA-exposed offspring. By contrast, this manuscript focuses on how stress in a slightly later adolescent period can produce latent and intergenerational effects by impacting maternal caregiving from female offspring, as well as social outcomes of the next generation of animals born to ELA-exposed females. Specifically, the manuscript describes that female mice exposed to ELA in the form of social isolation in late adolescence show reduced pup-directed maternal behaviors, while self-directed behaviors remain intact. Offspring reared by these dams in turn show deficits in social behavior, which are linked to reduced activity in an excitatory connection between the medial cingulate cortex (mCg) and prelimbic cortex (PrL). Further, the authors find that social behavior can be rescued by chemogenetic activation of the mCg-PrL pathway in the offspring of ELA-exposed/stressed mice or recapitulated in control mice by chemogenetic inhibition of this connection. Importantly, co-housing ELA-exposed/stressed dams with experienced parous females during the early postpartum period restores pup-directed maternal behaviors in these mice and normalizes offspring social outcomes as well as mCg-PrL activity.

Strengths:

Strengths of the manuscript include the focus on an important and novel question about intergenerational effects of adolescent ELA transmitted via subsequent maternal care, and the use of multiple techniques to link circuit function to behavior, including slice electrophysiology and chemogenetics. While the findings that maternal care can influence offspring behavior and that experienced females can instruct and improve maternal care of less experienced mice are not novel, they add support to this important area of literature.

Weaknesses:

Weaknesses of the paper include the lack of validation that the viral chemogenetic paradigm was appropriately targeted in the brain and impacted the excitability of mCg to PrL projections as anticipated, the use of inappropriate statistical tests that do not account for non-independence of pups from the same litter or cells measured from the same pup or categorical versus continuous data, and the lack of important descriptions of methods or experimental paradigms in several places that altogether make it difficult to judge the rigor of the findings in its current state.

If these weaknesses are addressed, these findings will provide important information about circuit mechanisms underlying intergenerational effects of adolescent stress on social behavior in next-generation offspring.

Reviewer #2 (Public review):

Summary:

In this study, Nollet and colleagues sought to determine whether selective amyloid pathology confined to medial septal (MS) cholinergic neurons is sufficient to recapitulate the prodromal Alzheimer's disease-like phenotypes observed in global AppNL-G-F knock-in mice. To this end, the authors employed a cell-type-specific AAV-mediated approach to selectively express the familial AppNL-G-F allele in MS-ChAT neurons, and subsequently characterized sleep-wake architecture, EEG spectral features, cognitive function, emotional behavior, and histological changes over 13-14 months. By comparing these mice with global AppNL-G-F knock-in mice and with mice in which MS-ChAT neurons were selectively ablated via caspase expression, the authors found that cholinergic cell lesioning recapitulated most disease phenotypes, suggesting that cholinergic loss, rather than amyloid deposition, is a likely driver of these phenotypes.

Strengths:

The study has several notable strengths. First, the experimental design is rigorous and well-controlled, employing three complementary mouse models that enable elegant causal inference. The use of cell-type-specific APP expression is a powerful approach for distinguishing the contributions of MS-ChAT neurons and amyloid deposition. Second, the combination of multiple behavioral assessments, EEG spectral analysis using FOOOF parameterization, and detailed histological quantification strengthens the validity of the conclusions. Third, the finding that caspase-induced cholinergic lesions largely recapitulate the cognitive and REM sleep phenotypes, while amyloid pathology contributes additional features such as epileptiform spikes and astrogliosis, represents an important mechanistic dissection.

Weaknesses:

Despite the overall strength of the study, several limitations warrant consideration. First, the mechanism by which amyloid is "broadcast" from MS-ChAT terminals to distant brain regions remains unclear. The authors do not definitively determine whether the amyloid detected in hippocampal and cortical regions represents released soluble Aβ, transported APP fragments, or amyloid derived from degenerating axons. Second, while the authors demonstrate that MS-ChAT cell loss correlates with cognitive, emotional, and REMS deficits, the causal relationship among these phenomena and the specific circuits involved remains unresolved.

Reviewer #2 (Public review):

Summary:

To address how the CHIKV macrodomain contributes to replication dynamics in mammalian and insect hosts, the authors initially created two separate mutations in the highly conserved N24 residue, which is known to be critical for the CHIKV macrodomain's ability to erase ADP-ribose from target proteins. Interestingly, they could not produce a virus with a mutation in this residue without second-site mutations in an aspartic acid residue nearby (D31). However, when tested biochemically, these second-site mutations did not enhance the enzymatic activity of the protein, indicating that other enzyme dynamics, such as substrate binding, may be impacting these mutations. Mutations at this residue allowed the CHIKV to replicate in Vero cells and in mosquito cells, but they replicated poorly in IFN-competent human cells, indicating clear IFN-specific impacts on these viruses. Interestingly, they found unique impacts on virus dissemination and replication in live mosquitoes. While the N24A/D31N virus did poorly in vivo in all accounts, the N24D/D31H/N virus tended to infect both the bodies and heads of the mosquitoes better than the WT virus, though titers were reduced. The authors claimed, based on a DSF assay, that there were no real differences in ADP-ribose binding and thus suggested that these differences could be due to changes in substrate specificity, as the D31 residue resides in the substrate exit path, potentially tuning the virus to unique substrates in different species. The authors also produced crystal structures of the mutants to demonstrate the changes in the binding pocket caused by these mutations.

Strengths:

The authors have done a rigorous job of evaluating CHIKV macrodomain mutant viruses and the proteins' biochemical activities. The use of live mosquitoes is highly unique and provides important insights into the importance of the macrodomain in different species.

Weaknesses:

It is not clear if the interpretation of the ADP-ribose binding data is correct. It appears there are notable differences that could explain the results, though the authors chose to minimize the impact that these differences had on the results. The N24D-D31H/N proteins had at least a 1C degree difference in the thermal shift assay when compared to the N24A/D31N, single D31 mutants, and WT proteins, which is likely significant and could explain the dichotomous results between the two viruses in mosquito cells. Even the single N24D mutant had enhanced binding compared to the WT protein. Furthermore, as this virus has no enzymatic activity, one could hypothesize that enhanced binding to a substrate that is normally cleaved by the protein could certainly lead to alterations in phenotypic effects, whether good or bad. The authors should test the binding activity in a separate assay, such as an ITC assay, to determine if there are, in fact, binding differences or not. Having said this, it is likely that the impacts of these mutations on replication and transmission in human and mosquito cells are multi-factorial and could include both enhanced binding with altered substrate specificity amongst other activities.

Additionally, as both mutants had no detectable enzymatic activity but had quite different phenotypes in mosquitoes, I don't agree with the title stating that catalytic activity modulates dissemination and transmission potential in mosquitoes. It seems more likely that alterations in binding activity or substrate recognition (even suggested by the authors) impact these phenotypes in mosquitoes.

Reviewer #2 (Public review):

Summary:

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

Strengths:

I particularly like the identification of "super compensatory" substitutions that improve fitness across diverse genetic backgrounds and apparently reduce the sensitivity of the local fitness landscape to subsequent mutations. The work connects epistasis, protein stability, mutational robustness, and evolvability in a clear and potentially broadly relevant manner.<br /> The authors provide several complementary lines of evidence in support of this central conclusion. In particular, the new experimental validation of S189A is an important strength because it directly demonstrates that a predicted super compensator can buffer the effects of diverse deleterious substitutions, while analyses of additional DMS datasets from other proteins and assay systems suggest that the phenomenon is not restricted to the original His3p landscape.

Weaknesses:

The structural analysis currently relies primarily on correlations with RSA, weighted contact number, conservation, and Rosetta-predicted changes in folding or binding energy. For super compensators, the mechanistic evidence is largely limited to predicted stabilization and individual examples, such as the proposed salt bridge between 110D and R112. I believe that the newly developed structure-aware deep-learning approaches could provide useful information on the mechanism of super compensators. For example, an inverse-folding model such as ESM-IF1 could score complete multi-mutant sequences conditioned on the His3p backbone and test whether adding a super compensator restores sequence-structure compatibility across backgrounds. More recent multimodal mutation-effect or stability models could similarly be used to cross-check the Rosetta results, including models that explicitly support combinatorial mutations. I would not recommend simply comparing AlphaFold confidence scores between mutants, because current structure predictors are not necessarily sensitive to subtle mutation-induced energetic or conformational changes.

The manuscript states that the pipeline was applied to 217 ProteinGym datasets and concludes that super compensators are broadly distributed across proteins and assays. However, this central generalization is described in only a few sentences and is largely relegated to Figure S7. The Methods do not explain which datasets contained sufficient combinatorial mutants to calculate compensatory ability or buffering, how many genotype pairs or quadruplets were available per substitution, or how differences in assay scale and library design were handled. This point requires clarification because supercompensation is inherently a background-dependent property and cannot be established from single-mutant measurements alone. ProteinGym is widely used as a substitution-effect benchmark, and many of its constituent assays primarily contain single substitutions; for example, an analysis of an earlier ProteinGym collection reported that 76 of 87 assays contained only single substitutions. It is therefore unclear how the same compensatory-interaction pipeline could be applied uniformly to all 217 datasets.

The analysis of 335 His3p orthologs in Discussion is potentially very interesting, but co-occurrence between super compensators and putatively deleterious amino-acid states does not by itself demonstrate evolutionary compensation. Closely related species share substitutions through common ancestry, and both states could be associated with a particular lineage or ecological context. A tree-aware analysis would considerably strengthen this result. The authors could reconstruct ancestral states and ask whether acquisition of a super compensator tends to precede or accompany otherwise deleterious substitutions. Alternatively, they could use phylogenetically informed permutations that preserve substitution frequencies and shared ancestry.

Reviewer #2 (Public review):

This study addresses an important question in motor learning: whether algorithmic versus retrieval-based explicit strategies differentially shape implicit recalibration. The progressive experimental logic across three experiments is commendable, and the plan-based generalization account is a plausible and interesting interpretation. However, several methodological concerns limit the strength of the conclusions. I recommend the authors temper their claims accordingly, in the results/discussion section.

Concerns

(1) The retrieval group received 5 pre-exposure trials before main training began, which the algorithmic group did not. Faster RTs in the retrieval group could therefore reflect task familiarity from extra practice rather than efficient memory retrieval per se. I might have missed this, but I did not see performance data from these pre-exposure trials. The early training advantage in the retrieval group might be confounded with the 5 pre-exposure trials they received. Unless there is a direct comparison between the pre-exposure trials for the caching group and the first 5 trials of the algorithmic group, the claim that "storing and retrieving a memory from a short-term memory cache confers more rapid performance improvements than executing an algorithmic strategy" seems somewhat unwarranted.

The algorithmic group also visited the critical target approximately 40% of trials across 356 trials (about 140 trials?). McDougle & Taylor (2019) showed that 300 trials of practice with 2 targets is enough transition from algorithmic to caching strategies. It seems likely that the number of visits to the critical target here was sufficient for caching to develop in the algorithmic condition. This concern about caching in the algorithmic group has implications for the implicit recalibration measurements. As I understand it, the 7 exclusion blocks were distributed throughout training, and so, implicit recalibration was measured across both early and late practice. If caching emerged in the algorithmic group during late practice, then the generalization functions - averaged across all 7 exclusion blocks - conflate early algorithmic strategy and later caching. The broader generalization function observed in the algorithmic group may therefore be driven primarily by early exclusion blocks, while later exclusion blocks may increasingly resemble the retrieval group as caching develops. This is testable in the data: if generalization breadth in the algorithmic group narrows across the 7 exclusion blocks while remaining stable in the retrieval group, that would be consistent with a strategy transition occurring during training. The authors should either report exclusion block-by-block generalization functions separately for each group, or acknowledge that the averaged generalization functions may obscure a strategy transition in the algorithmic group.

(2) The error-clamp paradigm in Experiment 3 introduces two problems. First, it breaks the relationship between planned movement direction and feedback of movement direction, likely reducing the sense of agency over movement feedback (indeed, typical error clamp study instructions tell participants to ignore the movement feedback).

Reduced agency may itself suppress differences between algorithmic and caching conditions. First, if strategy type exerts its influence on implicit recalibration via the explicit plan - as the plan-based generalization account predicts - then severing the link between intended movement and feedback might close off the channel through which strategy could shape the implicit system, regardless of which strategy is used. Second, reduced agency could modify the explicit strategies themselves. For caching, the stimulus-response association might be reinforced by a consistent relationship between intended movement and observed outcome; the clamped feedback may make it more difficult to reinforce the cached response, weakening the stimulus-response association. For the algorithmic strategy, effortful mental rotation may depend on the perception that the computation meaningfully determines the outcome; as participants understand that clamped feedback does not depend on their behavior (although yes, the text-based "Excellent/Good Move feedback) does depend on their behavior, they may engage in somewhat less complete mental rotation. Both possibilities could contribute to convergence between groups in generalization. It is noted that the preserved RT difference between groups in Experiment 3 partially argues against a loss of effort under the algorithmic condition, but it does not rule out weakened formation of stimulation-response associations during caching.

Reviewer #2 (Public review):

In this study, the authors investigate the mechanisms underlying phosphatidylserine (PS) exposure during efferocytosis in Drosophila. They first show that Xkr promotes PS exposure and apoptotic cell clearance in both S2 cells and Drosophila embryos. As Drosophila Xkr lacks the canonical caspase cleavage site found in mammalian XKR proteins, the authors further explore the underlying mechanism by which Xkr regulates PS externalization. Through protein interaction studies, they identify TM9SF4 as an interacting partner of Xkr that regulates PS distribution and show that non-vesicular PS transport contributes to apoptotic PS exposure and efferocytosis. Using protein interaction studies, they further demonstrate that Xkr interacts with the lipid transfer protein dORP9 at ER-PM contact sites to facilitate non-vesicular PS transport to the plasma membrane. Loss of these proteins affects PS externalization and efferocytosis in Drosophila. Finally, using human cells, they demonstrate that human OSBPL8 interacts with XKR8 to regulate apoptotic PS exposure. Overall, the study supports a model in which Xkr promotes efferocytosis by facilitating lipid transport in addition to its role as a phospholipid scramblase.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate high-frequency oscillations (HFOs) in the prefrontal cortex during REM sleep. They identify a specific pattern where these HFOs occur in "chains" that are phase-locked to theta oscillations, primarily during the "phasic" periods of REM. The study contrasts these events with isolated HFOs and NREM ripples, suggesting a unique role for these chains in coordinating activity between the prefrontal cortex and the hippocampus. Most notably, the authors report that a specific subset of hippocampal cells-those that co-fire with the prefrontal cortex during these HFOs-increase their firing rates over the course of sleep, suggesting a potential mechanism for selective memory consolidation.

Strengths:

The study addresses an under-explored area of sleep physiology: the fine-grained temporal coordination between the cortex and hippocampus during REM sleep. The identification of HFO "chains" and their association with higher theta power provides an interesting framework for understanding how the brain might organize information transfer outside of NREM sleep. The observation that specific hippocampal populations show differential firing rate changes based on their participation in these HFO events is a striking finding that warrants further investigation.

Comments on revised version.

I do have one remaining concern, which is about their continued use of the term "reactivation" during REM sleep, whereas it still seems "activation" is more appropriate. The only place they show more Post vs. Pre activation is in Figure 6F/6G which includes NREM sleep where indeed reactivation is robust (but not the main focus of this paper). There is no evidence offered that the REM ensembles are not already "pre-configured" and active at similar levels (with similar activation patterns) during Pre sleep. Notably Louie and Wilson 2001 found greater "replay" during Pre than Post during REM. Also, the first half vs. second half comparisons (e.g. Fig 6C) could be more effectively performed in Figure 6A, showing that the same ordering persists across the periods. If this point were addressed, the significance of the findings could potentially increase.

Reviewer #2 (Public review):

Summary:

The manuscript by Qin and colleagues entitled "Pupil and Neural Dynamics Reveal Belief-Dependent Decision Making Under Ambiguity" examines decision-making under risk and ambiguity using pupillometry and EEG. The study employs a lottery choice task with three levels of ambiguity (zero, low, high). Participants were classified into three groups based on their choice behavior in a condition with risk and no ambiguity: ideal (choosing in line with objective expected values), aggressive (preference for investments), and conservative (preference against investments). The authors then compared behavior, pupil, and EEG results across these groups. The study concludes that individual beliefs about ambiguity are reflected in different behavioral strategies and neural correlates.

Strengths:

The combination of behavior, computational modeling, pupillometry, and EEG.

Weaknesses:

(1) It is unclear whether group definition is theoretically justified.

One general concern is that the strategy to form three distinct groups is not clearly motivated. The authors created the three groups, "aggressive", "ideal", and "conservative", based on the zero-ambiguity trials. However, as the authors state: "Ambiguity differs fundamentally from risk at both the physiological level (34; 6) and the behavioral level" (page 4). Under this assumption, it is questionable whether forming groups based on risk preferences is a useful strategy for studying ambiguity. What do we learn about ambiguity processing when group differences are primarily based on risk preferences? Might the present results partly be driven by risk preferences rather than ambiguity preferences? I recommend the following two points: (a) Clearly justify the reasoning behind the group approach; (b) Add an additional continuous analysis approach indicating whether the key results hold independent of the group definition based on risky decision-making.

(2) k-parameter.

The authors use the k-parameter that infers the expected high-payoff probability (e.g., page 11). On page 22, this is explained as: "the subjective value term K was assigned according to each participant's internal belief of the high-payoff rate under ambiguity, yielding a participant-specific estimate of expected value under uncertainty." I hope I have not missed anything, but I neither understood the role of this parameter nor how it was computed.

(3) How were individual beliefs and models computed?

A related but more general point is that it remained unclear how the authors computed internal beliefs and internal models in the study. The study contains many statements suggesting that the authors measured internal beliefs. For example:

a) Abstract: "We show that individuals adopt distinct decision strategies that reflect different internal beliefs about unknown outcomes."<br /> b) Page 3: "We then inferred subjective belief parameters that captured how individuals internally interpreted the ambiguous probability mass and examined how these beliefs related to choice behavior, arousal dynamics, and neural activity."<br /> c) Page 16: "Together, these findings show that ambiguity does not evoke a uniform behavioral or physiological response across participants with different decision-making styles; instead, individuals rely on distinct internal models and computational strategies when forming decisions under ambiguity."<br /> d) Page 16: "Taken together, these results show that ambiguity aversion is not a uniform psychological bias, but a set of heterogeneous belief-driven strategies that shape how ambiguity is represented and acted upon."<br /> e) Page 18: "Ambiguity processing, therefore, reflects distinct belief-driven pathways rather than a single canonical mechanism."

Based on the present data, analyses, and results, I don't think that the authors can draw these conclusions. Which analyses in the manuscript identify these internal beliefs, models, or strategies? How can we dissociate a unified strategy from a heterogeneous set of strategies based on the present results? My feeling is that the k-parameter might be related to this, but as explained above, I did not understand how it was computed and what it is supposed to reflect. The DDM analyses might also be targeted at this. However, it remains elusive how the DDM captures internal beliefs about ambiguity itself. My recommendation is that the authors more clearly explain (a) why the DDM is a useful model to study ambiguity, (b) what the different parameters exactly reflect about ambiguity processing, and (c) how the DDM captures internal beliefs and distinct belief-driven strategies in this context.

(4) Statistical tests.

4.1. Figure 2B: The authors summarize the number of participants with significant effects of ambiguity on choice behavior for each group. I recommend a statistical test at the second level that properly assesses the effects of ambiguity and group within a common statistical model. In my opinion, it is not enough to simply count the number of significant tests (from the first level) for each group.

4.2. Figure 2C: For the analysis of response times, the authors might want to consider reporting the main effects of group and ambiguity.

4.3. Figure 2D: The text on page 8 states that Figure 2D indicates that "aggressive investors showed no significant pupil modulation by ambiguity...". However, the figure and its caption indicate significant differences between ambiguous and non-ambiguous trials across all groups. Moreover, if the authors want to compare the groups, it is necessary to compare the groups to each other; a test against zero within each group would not be enough to demonstrate any group differences. In my mind, this would also be important for analyses in Figure 3C and D.

4.4. Strictly speaking, for the statistical tests, it would be necessary to take into account that participants completed multiple sessions (within-subject variance is different from between-subject variance). Currently, each session is treated independently (page 19: "Each individual completed one to three experimental sessions. For data analysis, each session was treated as an independent participant, yielding a total of 108 sessions.")

(5) Necessary quality control for pupillometry and EEG data.

The task was performed in a virtual reality environment with a head-mounted display. The task was not isoluminant, and, to the best of my knowledge, participants were not instructed to avoid eye movements. The authors applied a GLM to control for luminance effects in the pupil data. For EEG, they used ICA to remove ocular and muscular artifacts. While these methods are established, they are usually applied to more controlled paradigms optimized for EEG and pupillometry. To demonstrate high data quality despite these issues, it is necessary to present quality-control analyses. Can the authors please indicate how many blinks had to be removed from the data? Could the authors please indicate how many blinks were removed from the data? Can the authors please show trial-level data (after preprocessing) for a few subjects?

(6) Quality control for the DDM.

The manuscript lacks systematic posterior predictive checks and parameter recovery for the DDM results. It is important to validate that the model accurately captures the data. Currently, we only see the model parameters, but it remains unclear whether the model performs well on the current data set. Moreover, if the authors aimed to test different strategies using the DDM, it might be useful to perform systematic model comparison.

(7) Implications of the second experiment with collaborative task remain unclear.

To me, the link between the main study and the second experiment on leadership and team performance is not obvious. In my opinion, this topic is beyond the scope of the present paper. Linking the two studies more comprehensively based on deeper theoretical grounds would likely be better suited for an independent manuscript.

Reviewer #2 (Public review):

Summary:

Price et al. present new work providing insight into the function and mechanisms of mitochondrial-derived compartments (MDCs) in yeast. The Hughes lab previously established that these large ~micron-sized structures are formed under a variety of conditions including amino acid stress (rapamycin, conA, cycloheximide), alterations in mitochondrial metabolites and lipids, or acute expression of specific outer membrane proteins. These stressors lead to the sequestration of outer membrane proteins (that can include mistargeted inner membrane proteins) that extend or tubulate into large multilamellar structures that ultimately target the vacuole in an ATG5/Dnm1 dependent autophagy related pathway for degradation. Initially reported in aging yeast a decade ago, it has now been accepted as a mechanism to remove excess mitochondrial proteins as a pathway distinct from mitophagy or the extraction of stalled precursors from the import translocon.

In this study, the authors examined additional metabolic transitions they suspected would drive increased mitochondrial protein expression and promote MDC formation. Indeed, they show that glucose-restricted conditions (or a switch to galactose or incubation with 2DG) induced MDCs within 2 hours. This correlated with increased transcription/translation of mitochondrial precursors porin and OM45. Similar results were seen with osmotic shock, a process previously shown to induce mitochondrial gene expression. The metabolic or osmotic shift was shown to activate a yeast AMPK-type kinase called Snf1, which phosphorylates a key substrate Mig1 - an established repressor of mitochondrial gene expression. Loss of these pathways abolished the generation of MDCs under these conditions. As the key novel finding in the study, the authors explored the relationship/requirement for Snf1 and Mig1 using multiple approaches in different backgrounds and employing auxin-inducible degron tools for acute depletion. These data further support the hypothesis that excess mitochondrial outer membrane proteins result in MDC formation to facilitate their removal, at least transiently until the import machinery can adapt to the increased import demand. To test this more directly, they generated an inducible yeast strain to express a canonical transcription factor Hap4 that induces mitochondrial gene expression. In this system, induction of Hap4 expression also resulted in MDC formation. While not all previously reported MDC inducers act through Snf1/Mig1, the common feature is the transcriptional induction of mitochondrial protein expression.

Strengths:

The important aspect of this work is that the authors dissected the transcriptional signaling pathway that induces MDCs in a much more physiological metabolic transition, which complements the more common use of chemical compounds. They had previously shown that overexpression of individual outer membrane proteins could lead to MDCs, but here the Hap4 expression offers a new condition to show that the canonical induction of mitochondrial biogenesis leads to MDC shedding. Overall, the data are of high quality, the findings are clear, and the work provides important new insights into the regulation of MDC formation.

Weaknesses:

There are a few points that should be addressed.

(1) MDCs are almost exclusively monitored through GFP-tagged TOM70, and the authors do not show the inclusion of any endogenous cargo. The evidence for their fate in the vacuole is through the appearance of cleaved, free GFP after 6 hours that is dependent on ATG5, Dnm1, Pep4, etc. Can the authors demonstrate the appearance of MDCs without expressing any GFP tags and instead monitor known outer membrane cargoes? In the case of Hap4 expression, the proteomics identifies some very highly induced mitochondrial proteins, and there surely must be some with antibodies that can detect the protein by IF and Western blot.

(2) There is a very unexpected ~10X increased in a sporulation factor SPO21 upon induction of Hap4. I see no evidence of sporulation, and it's not long enough for stationary phase. Is the increased mitochondrial biogenesis driving a specific metabolic state of these cells that is signaling to other biology?

(3) It is important to understand the kinetics and stoichiometry of outer membrane loading that drives MDCs, and their transit to the vacuole. This is why it would be highly informative to monitor some endogenous cargoes (previous point). In the review the authors cite (NRMBC, Pfanner lab 2019), it was stated that the import machinery is not generally increased upon metabolic induction of mitochondrial gene expression. Therefore, (pre-MDCs) the field concluded that the import machinery has a very high capacity for the rapid biogenesis of newly synthesized proteins, along with regulation through the phosphorylation of import receptors (ie; the work of Meisenger). Consistent with this, the Hap1 proteomics did not show any increases in the core import machinery, while ETC subunits and a large swath of mitochondrial proteins were elevated over 2-fold (I looked carefully through the Excel sheet). Since MDCs are induced transiently about 2 hours after glucose deprivation, and fully dependent on de-repression of Mig1, the authors are right to imply that this is coupled to the import of newly synthesized proteins.

However, it seems to me that MDCs are being formed at very early stages of mitochondrial protein expression, not after they have necessarily "overloaded" the outer membrane. The Hap4 proteomics after 3.5hr of induction would suggest that the bulk of the mitochondrial proteins have been successfully inserted (no import failure) and are likely already functional (metabolizing). I'm trying to understand the percentage of the proteins that would be incorporated within MDCs, as the mitochondria appear to handle the bulk of their newly inserted proteins without issue. How can the authors adapt their "free GFP" assay to understand the stoichiometry of the transport of endogenous, newly imported outer membrane proteins to the vacuole?

(4) As a last theoretical point for discussion: Can the authors exclude that MDCs are not functional or play a signaling role? Given the emerging work on SPOTs (Lena Pernas), and from the new evidence from Craig Thompson's lab that there can be very specific functional mitochondria (oxidizing vs reducing), it is possible that MDCs are not simply there to be degraded. They last at least 3 hours, which is a long time for yeast (budding cycle 90 min, 3 hours in glucose deprivation). Taking the data presented here very objectively, there is no direct evidence that the cargoes within MDVs reflect any failure to import, or that they are damaged in any way. The deletions of Tom70/71 have way too many pleotropic effects and essentially demonstrate only that the MDC cargoes came from the mitochondria. It could be helpful if the discussion also positioned these MDC mechanisms within the context of other aspects of selective mitochondrial-related compartments that have been emerging in the literature.

Reviewer #2 (Public review):

The manuscript describes a numerical analysis of the domains of the T. cruzi cell surface containing different proteins. It has the potential to be of great interest.

I do not have the expertise necessary to comment on the image collection or analysis.

I have one concern: the amount of manipulation of the cells prior to fixation; these were clearly stated in the methods, which is good.

My concern is whether these manipulations prior to fixation alter the observations. The 'Labelling sialic acid acceptors' involves >6 centrifugations and >90 minutes incubation in PBS prior to fixation, and the 'immunostaining' protocol involves cells 'extensively washed with PBS' prior to fixation. I would like to suggest that the authors do controls in which they compare the pattern of anti-SAPA staining under four conditions.

(1) Cells fixed in culture by the addition of paraformaldehyde to 4%, followed by blocking and PBS washes.

(2) Cells fixed in culture by the addition of paraformaldehyde to 4% and glutaraldehyde to 0.2% followed by blocking and PBS washes.

(3) Cells fixed by the 'labelling sialic acid acceptors' protocol.

(4) Cells fixed by the 'immunostaining protocol'.

Reviewer #2 (Public review):

Summary:

In the submitted manuscript, Akter et al use a series of ferroptosis inhibitors in mesenchymal-like ovarian cancer cells and discover that the ferroptosis inducers induce cell death that is inhibited by pyroptosis inhibitors, namely YVAD-fmk and disulfiram, which inhibit pore formation by gasdermin D (GSDMD). Remarkably, the authors also saw the release of IL-1β in response to ferroptosis inducers. Unexpectedly, they did not observe the involvement of caspase-1 but rather observed that caspase-5 was activated in response to the ferroptosis inducers. Moreover, they found that caspase-5 directly cleaves GSDME in response to the ferroptosis inducers, establishing CASP5/GSDME as downstream executors of ferroptosis.

Strengths:

These findings are interesting because only CASP1 is known to induce IL-1β maturation, and their data suggest that CASP5 rather than CASP1, is responsible for IL-1β activation in the context of ferroptosis inducers. Notably, CASP3 is the only caspase reported to be able to cleave GSDME, so the identification of CASP5 as a driver of ferroptosis in this context is a significant finding. They genetically show that loss of CASP5 and GSDME knockdown inhibits cell death in response to the ferroptosis inducers ML162 and Erastin, which is evidence that they play a role in this context.

Weaknesses:

The major findings in this paper are interesting, but the data presented do not robustly support the claims made in this paper. For example, they claim that CASP5 is responsible for the activation of GSDME by cleaving it directly to induce cell death. They try to rule out the involvement of CASP1, ASC, and CASP4 using siRNA targeting these genes, but the knockdowns are incomplete, and the loading controls are inconsistent. They also claim they do not see GSDMD or CASP3 cleavage and activation but use negative data to make that claim. It is unclear if the antibodies used can detect cleaved GSDMD or CASP3 as they do not include a positive control to show that they can indeed detect these activation events if they were occurring. This needs to happen in the same experiment - they need to show in the same experiment with the same lysates that they can detect CASP5, GSDME and IL-1β activation but not CASP1, GSDMD, CASP4, or CASP3 activation. Of course, they should include agonists for positive controls of CASP1, CASP4 and GSDMD activation, which are lacking in the current manuscript.

Notably, the major evidence supporting a direct role for CASP5 cleavage of GSDME is one Coomassie gel using recombinant CASP5 and GSDME, but there were too many non-specific bands, and the full-length uncleaved protein could not be detected even in the untreated lanes. The authors need to show a gel where the protein can easily be identified and should also include a positive control protein like GSDMD to show the relative cleavage efficiency of GSDME compared to a known substrate. It would also be great to compare this to CASP3-mediated cleavage of GSDME. With recombinant proteins, calculating the catalytic efficiencies would be the best way to ascertain if this is biologically similar to other known substrates.

The way that ferroptosis is defined, it is caspase-independent, and pyroptosis is defined as gasdermin-mediated cell death. Given that these agents lead to activation of CASP5/GSDME, it would be more accurate to say that these ferroptosis inducers also induce CASP5/GSDME-dependent pyroptosis, as opposed to them being the executors of ferroptosis. This can be a distinct mechanism/pathway from the ferroptosis pathway, as multiple cell death pathways can be initiated in cells. Consistent with this, ferrostatin-1 also inhibited cell death, likely due to inhibition of the ferroptosis signaling cascade. It is unclear if this pathway is upstream of the caspases. How these ferroptosis triggers selectively activate CASP5 and not CASP4 to induce GSDME cleavage is a major unresolved question. Notably, it is also unclear if this biology is specific to the mesenchymal-like cells used in this study or if it expands to other cells.

Reviewer #2 (Public review):

This study examines the evolutionary context of the emergence of human speech. The authors address the widely held hypothesis that the expansion of the human vocal space, resulting from modifications of the vocal tract, was a key prerequisite for the evolution of spoken language.

To test this hypothesis, the authors quantified the acoustic space of human speech, non-linguistic vocalizations, and musical vocalizations and compared it with that of nonhuman primates, chimpanzees, bonobos, and chacma baboons.

The authors found that speech and song occupied significantly less volume in the acoustic space than human non-linguistic vocalizations. In addition, the acoustic-feature volume of speech and song was not statistically distinct from that of non-human primates. Accordingly, the authors conclude that the evolution of human speech did not depend on an expansion of the human vocal acoustic space.

I find the analysis presented in this manuscript highly convincing. It is conducted at a contemporary scientific standard, and the results provide strong support for the authors' conclusions. I particularly appreciate that the authors explicitly discuss the limitations of their approach. For example, they acknowledge that MFCCs cannot capture all aspects of acoustic structure.

I have only three minor comments:

First, the authors may wish to briefly summarize the main findings of the study by Anikin et al., as it represents the central reference for the present work. A concise summary in two or three sentences would help readers who are not familiar with that study.

Second, I would appreciate a brief explanation of why the authors chose this particular statistical approach.

Third, the authors could briefly mention that the Chacma baboon dataset provides a very comprehensive representation of the vocal repertoire of this species, although a small number of rare vocalizations are not included. I am not sure whether a similar limitation also applies to the chimpanzee and bonobo datasets, but if so, it would be useful to mention this as well.

Reviewer #2 (Public review):

Summary:

The aim of the authors was to measure starvation-induced and basal autophagy in vivo across several tissues and developmental stages. For this, they developed a novel mouse model expressing the GFP-LC3-RFP reporter. They also aimed to provide a more high-throughput method for autophagy flux measurements than assessment by imaging and developed an assay based on a microplate reader.

Strengths:

(1) Good validation of the mouse model. The knock-in strategy is well explained and illustrated.

(2) The model has potential to be applied to a wide range of research questions. The Cre-dependent expression allows for customization of KO timing, which will be beneficial in developmental studies.

(3) The authors presented consistent findings using two different methods to quantify autophagy, strengthening the robustness of their results.

(4) The authors demonstrated the validity of the high-throughput method (microplate reader).

Weaknesses:

(1) The comparison of neuronal populations in different areas of the brain is not ideal. In the cerebellum, Purkinje cells were chosen, which are rare and not representative of this tissue, as well as functionally very different from the neurons in the hippocampus and cortex that they were compared to.

(2) The explanation of the GFP-LC3-RFP construct and specifically if/how autophagosome formation can be measured and distinguished from flux could be clearer.

Conclusion:

The work presented is thorough, and the authors achieved their goals for this study. The effort used to further investigate unexpectedly high basal levels of autophagy in the brain is well appreciated and adds value to this paper. The conclusions of the authors are mostly very well supported by the data provided. The well-structured description of the results, along with clear figures, allows the reader to comprehend the authors' reasoning in reaching their conclusions.

The presented mouse model has great potential for a lasting positive impact on the research field of in vivo study of autophagy. The method of utilizing a microplate reader will also benefit future research where semi-high throughput is an advantage. Together, the information provided in this study not only presents new methodology that will allow the investigation of new research questions, but also provides novel information about in vivo autophagy flux at the selected developmental stages that opens up new follow-up research questions.

EP.99 故事线A: 预计 2026 年底达到 1000-1200 亿年化收入，对应的 IPO 估值预期超过 2 万亿美元——这将是历史上规模最大的 IPO。AI 公司的财务规模正在超越大多数传统行业巨头，速度令人咋舌。

Reviewer #2 (Public review):

In this study, Serrano et al. employed a combination of cell biological and molecular approaches to investigate the localization and regulation of Casein Kinase CK1 during the cell cycle using U20S cells. They show that CK1 dynamically localizes between the centrosomes and the nucleus but can be sequestered away from the centrosomes upon overexpression of its binding partner PER2. They provide evidence that CK1 WT but not a phospho-null mutant strongly accumulates in a hyperphosphorylated form upon inhibition of phosphatases (using Calyculin and Okadaic Acid) and thus conclude that CK1 tail phosphorylation protects the kinase from degradation. Using synchronized cells, they show that CK1 accumulates unphosphorylated in S-phase (APC/Cdh1 inactive) but phosphorylated at the G2-M transition. Immunostaining shows that CK1 localizes to the centrosomes during mitosis.

The manuscript has improved overall, but some sections are still inconclusive and require clarification.

Major comments:

Figure 1 is inconclusive. CK1 nuclear staining is highly similar in untreated cells and in cells treated with CHX + PF670462. The reduction in centrosomal staining in these cells is barely significant. However, the authors draw very strong conclusions from these data sets. In panel B, the cells appear to have been fixed incorrectly, and the anti-PCNT shows a strong background signal. Not convinced that immunofluorescence is the best approach to look at protein dynamics in vivo.

In Figure 2, panel B, the authors should co-stain the centrosomes of cells that co-express CRY1 and CK1, as some of these dots may represent the centrosomes.

Figures 3B, please provide information on the non-phosphorylable CK1a mutant (it is mentioned as a variant in which all serine and threonine residues in the C-terminal tail were replaced by alanine). Specify the number of sites mutated and their exact position. Is this non-phosphorylable CK1a version catalytically active? Treatment of samples with inactivated PPase should be used as a control.

Strengths:

The authors reveal that the activity and abundance of dephosphorylated and phosphorylated CK1δ are regulated in a cell cycle-dependent manner. This suggests that these different pools are associated with distinct physiological functions.

Weaknesses:

Unfortunately, some of the data are inconclusive, and there is no data/information linking the cell cycle regulation of CK1δ to its function during the cell cycle.

Reviewer #2 (Public review):

This study focuses on revealing the essential divergent function of the Acyl Carrier protein (ACP) in the deadliest human malaria parasite, Plasmodium falciparum. More precisely, using inducible KO, cellular and biochemical approaches, the authors determined that instead of a canonical role for ACP allowing the de novo synthesis of fatty acids in the apicoplast (essential relict plastid) of the parasite, the enzyme couples with pyruvate kinase II to generate nucleoside triphosphate to maintain parasite survival during blood stages. The study is novel, well-designed, providing interesting new data on Plasmodium and apicomplexa biology. The results convincingly support the major claim of the study. However, it is currently incomplete to support some claims on the essentiality of some apicoplast pathways.

In this study, Geher et al. focused on deciphering the role of the Acyl Carrier Protein (ACP) present in the relict non-photosynthetic plastid, i.e. the apicoplast of the most lethal human malaria parasite, Plasmodium falciparum. More particularly, they determined an essential function of ACP independent of its usual/typical function as the central protein for the normal function of the apicoplast Type II fatty acid synthesis (FASII) pathway. Rather, the protein seems to associate with the apicoplast Pyruvate Kinase II, together generating an essential nucleoside triphosphate (NTPs) source to fuel the apicoplast and parasite survival instead.

By generating a TetR-DOZY-based inducible KD line for ACP, they confirmed that the protein is indeed essential to maintain apicoplast integrity and parasite survival during asexual blood stages, as previously predicted and experimentally shown. They showed that ACP requires a biochemical modification, typically activating the protein for its function in the FASII pathway, i.e. binding of the 4-PP group by holoACP synthase. Then, they showed that the other enzymes of the FASII pathway are likely dispensable during the blood stage, as they were able to generate a KO line of the first enzyme of the pathway, FabD (which was predicted to be essential in P. falciparum). Based on a cell culture approach in a controlled culture medium, they further claimed that, unlike current evidence-based hypotheses, the FASII pathway (and thus a potentially FASII-linked ACP) has no role/activity during blood stages. Using a proximity biotinylation approach, they determined that ACP associates with the apicoplast pyruvate Kinase II (PKII), previously shown to generate NTPs in the apicoplast for energy and DNA/RNA maintenance (Xia et al. 2019), and not to fuel the FASII pathway as its main function in blood stages. Finally, they showed that the disruption of ACP induces the reduction of the presence/content in PKII in the parasite, as well as the drastic reduction of the apicoplast DNA and RNA content. Together, they concluded that the main function of ACP is indeed the NTP formation via its association with PKII, rather than its canonical role for the generation of fatty acids in the apicoplast.

This study is novel and focuses on a topic of particular interest in malaria biology, but also for most of the apicomplexa-related diseases, and beyond for plastid bearing orgnaisms and this unusual role for ACP. The study is well thought out with proper biochemical approaches that convincingly point to this association of ACP with PKII for NTP synthesis as a major function during P. falciparum blood stages.

Reviewer #2 (Public review):

Summary:

In the manuscript "Primordial Cardiomyocytes orchestrate myocardial morphogenesis and vascularization but are dispensable for regeneration", Sun et al. identify a novel marker of primordial cardiomyocytes and use it to visualize and ablate the population during development and regeneration. The role of the primordial layer has not been investigated because the tools to manipulate this population have not existed. The manuscript is straightforward, easy to understand, and addresses an important question that has not been explored.

While the manuscript provides important insights into the role of primordial CMs, backed by a convincing methodology, the authors should clarify their requirements for heart development and maturation. Specifically, is the primordial layer required for the fish to survive? Do primordial CMs regenerate when ablated during development, and do the defects observed (in trabecular and compact CMs and coronary vessels) resolve after 10 days post-treatment when they were detected?

Strengths:

The major strengths are the identification of a marker that enables manipulation of primordial cardiomyocytes and the tools generated by the team.

Weaknesses:

The major weakness is not considering the longer-term consequences of primordial layer ablation during development, as it is unclear whether the animals succumb to the acute cardiac defects observed or fully recover.

Reviewer #2 (Public review):

Summary:

As a member of DspB subfamily, PRRT2 is predominantly expressed in CNS and has been associated with various paroxysmal neurological disorders. Previous studies have shown that PRRT2 interacts with Nav and Cav channels, modulating channel properties and neuronal excitability.

In this manuscript, Lu et al. demonstrate that PRRT2 is a potent regulator of Nav channel slow inactivation, promoting the development of Nav slow inactivation and impeding the recovery from slow inactivation. This effect is highly conserved in PRRT2s across species as well as among DspB family members (TRARG1 and TMEM233). The authors further confirmed the interaction between Nav channels and PRRT2 in heterologous expression systems as well as in Prrt2-V5 knock-in mice. Prrt2-mutant mice, which lack PRRT2 expression, require lower stimulation thresholds for evoking after-discharges when compared with WT mice.

Overall, this is a well-executed and methodologically comprehensive study. This work offers valuable insight into the physiological functions of PRRT2 and reveals a potential pathogenic mechanism underlying PRRT2-associated neurological disorders.

The revised manuscript has addressed most of the concerns raised by the reviewers and has been substantially strengthened, although I still have several concerns regarding the discussion section.

Strengths:

(1) Overall, this is a well-executed and methodologically comprehensive study. The electrophysiological data strongly support the conclusion that PRRT2 is a potent regulator of Nav channel slow inactivation. The observation that this regulation is conserved in PRRT2 across species and among DspB family members raises the possibility that altered regulation of Nav channels may also contribute to the pathogenesis of TRARG1- or TMEM233-associated disorders.

(2) Co-immunoprecipitation assay performed using brain tissue from genetically modified Prrt2-V5 knock-in mice provides convincing in vivo evidence for the interaction between PRRT2 and Nav1.2 channels.

(3) Prrt2-V5 KI mice show markedly reduced PRRT2 protein expression and display phenotypes similar to those observed in Prrt2-mutant mice, supporting an important role of PRRT2 in regulating neuronal and network excitability.

Weaknesses:

(1) Nav1.6 is also highly expressed in cortical neurons and is widely regarded as a major contributor to action potential initiation and sustained high-frequency firing. Given that PRRT2 similarly regulates the fast and slow inactivation of Nav1.6 and Nav1.2 channels, the potential contribution of Nav1.6 regulation to neuronal and network excitability should be discussed.

(2) Slow inactivation is generally considered to develop over timescales ranging from hundreds of milliseconds to seconds or longer. Therefore, the statement in Discussion (Page 13, line 381-382) that "slow inactivation develops on a timescale of tens of milliseconds to seconds" may not accurately reflect the conventional kinetic definition of slow inactivation and should be clarified.

(3) Page 14, line 417-430: "question about how Nav channel slow inactivation is regulated in cells that do not express PRRT2".<br /> PRRT2 is unlikely to be the sole regulator of Nav channel slow inactivation. Other molecules and signaling pathways may regulate Nav channel and contribute to neuronal excitability. In addition, neuronal excitability can also be regulated through modulating other Nav properties, such as long-term inactivation or slow recovery from inactivation, as well as through modulating the activity of other ion channels, for example, Kv7.2 and Kv7.3 channels. Therefore, PRRT2-negative cells may utilize alternative mechanisms to fine-tune neuronal excitability. In its current form, this paragraph somewhat overstates the role of PRRT2 and would benefit from a more balanced discussion.

(4) Page 50, Figure 7-figure supplement 2: It would be helpful to include representative traces of the 1st and the last (20th) compound APs in panels B and C.

Reviewer #2 (Public review):

Summary:

In this paper, the authors describe the results of a longitudinal study of pertussis infection in mother/infant dyads in Lusaka, Zambia. Unlike many past studies, the authors assessed the infection status of individuals independently of whether they were symptomatic for a respiratory infection. As a result, this work represents one of the first studies specifically designed to assess asymptomatic transmission of pertussis. Using qPCR, the authors find strong evidence for the role of asymptomatic transmission from mothers to infants and also evidence for long-term bacterial carriage. This work represents an important contribution to our understanding of the global burden of pertussis. Also, it highlights the still under-appreciated role of asymptomatic transmission across many infectious diseases (including vaccine-preventable ones).

Strengths:

Unlike many past studies, the authors assessed the infection status of individuals independently of whether they were symptomatic for a respiratory infection. As a result, this work represents one of the first studies specifically designed to assess asymptomatic transmission of pertussis. Using qPCR, the authors find strong evidence for the role of asymptomatic transmission from mothers to infants and also evidence for long-term bacterial carriage.

Comments on revised version:

I appreciate the authors' attention to my comments during the revision process and still believe that their work represents an important contribution to our understanding of pertussis epidemiology. In most cases, the authors have done a thorough job of either addressing or responding to my comments. However, I do not believe the authors engaged sufficiently with two of the queries raised in my previous round of comments. The two queries were about the vaccination status of the mothers and engagement with literature on asymptomatic transmission. I still think they matter and ask the authors to consider them again.

I do not think the authors can rule out two alternative explanations: (1) recent introduction of pertussis and low vaccination coverage amongst study mothers, or (2) recent introduction of a breakthrough strain (either w.r.t. the vaccine or prior infection) and higher vaccination coverage/infection-derived immunity amongst study mothers. Depending on which mechanism was mostly driving the observed patterns in Zambia, i.e.,

a. long-running, widespread, unreported transmission;<br /> b. transmission started recently, and vaccination was low amongst study mothers;<br /> c. a breakthrough strain is causing the current rise (here we'd still want to know about vaccination status); and<br /> d. something else that I have not considered

would have implications for how the results are interpreted, and potentially far-reaching implications for the broader pertussis community. All of that is to say, I think the authors were too quick to dismiss these concerns (even if they disagree with my assertions).

In their reply, the authors largely dismissed concerns about not knowing the mother's vaccination status, stating in their reply that, "our findings strongly suggest ongoing pertussis transmission in this population. Based on this, we expect that mothers in our study who were not vaccinated would likely have some degree of infection-derived immunity."

However, they also stated that, "Zambia offers an evocative example of pertussis surveillance, where no cases have appeared in official WHO reports since 2009" and "As we noted above (and now address in our Discussion), widespread genomic surveillance and microbiological characterization of pertussis are sorely lacking across Africa."

I don't disagree with the authors' conclusion that pertussis is clearly spreading in Zambia. I also don't disagree that there's clearly evidence for minimally symptomatic, infectious mothers spreading infections to children. Both of these findings matter for Zambia and for our broader understanding of pertussis. However, I don't see how the authors can so confidently conclude that low vaccination rates, coupled with a recent introduction, high vaccination rates, coupled with a breakthrough strain, or high infection-derived immunity, coupled with a breakthrough strain, couldn't be what's driving the increase. The authors do hedge in places and also state in the discussion that their findings don't line up with expectations related to WP/infection-derived immunity, "This corresponds to a mean return frequency of one infection per 14.8 years, which is much shorter than the presumed duration of immunity from natural infection or the whole-cell pertussis vaccination used in Zambia (70, 71)." But, my read of the paper is that the authors are pushing way to ward for a preferred hypothesis that is not more favored than other alternatives.

Secondly, I asked about placing this work in the context of other studies on asymptomatic transmission, but realize that I did not list any specific papers. Two worth considering are Warfel et al. 2014 and Althouse and Scarpino 2015. Restating for the editor, the Warfel study found that WP facilitated rapid clearance in a non-human primate experimental infection study (admittedly with small sample sizes and many other caveats). Many took that as evidence that WP would also block transmission (admittedly experiments Warfel did not run). If the mechanism underlying the results in Zambia is that either WP or natural infection does not block transmission (in the absence of a breakthrough strain), that would upend many of the assumptions in pertussis research. While not incompatible with the Warfel et al. results, it would negate most of the importance of their finding that WP blocked transmission. From what I can see, the authors do not even cite Warfel et al. 2014, which is a serious gap regardless of whether the authors agree or disagree with the findings. A quick sidebar, the authors seem to duplicate Craig et al. 2020 10.1093/cid/ciz531, listing it as both citation 9 and 38.

In Althouse and Scarpino, they found evidence of a rise in asymptomatic/underreported/subclinical transmission following the switch from WP to AP. While not as directly relevant to the current study as the Warfel paper (so I leave it to the authors to decide whether citing this paper is important), Althouse and Scarpino discuss asymptomatic transmission at length and also assume that WP conferred strong protection against transmission, so their results (along with dozens and dozens of other studies assuming similar WP/infection-induced immunity protection and durability) would also need to be reinterpreted in the context of this study. The authors should engage with the implication of their results in the context of past modeling studies and what we think we know about vaccine-/infection-derived immunity.

Going back to my earlier points, unvaccinated mothers and the recent introduction of pertussis, or vaccinated/infection-induced immune mothers with a breakthrough strain, would both explain the current results and be compatible with Warfel et al., Althouse and Scarpino, and a sizable number of other studies. Instead, if transmission from WP- or naturally infected mothers is common (in the absence of a breakthrough strain), that would really change the landscape of pertussis epidemiology. The authors have not convinced me that they can make this conclusion. Hence, why I think it's important that the authors engage more actively with those hypotheses and with relevant debates in the pertussis literature on asymptomatic transmission. I think it's appropriate for the authors to present their preferred hypothesis, but, absent other data, they should also present plausible alternatives that are consistent with past publications.

References:

Althouse, B. M., & Scarpino, S. V. (2015). Asymptomatic transmission and the resurgence of Bordetella pertussis. BMC medicine, 13, 1-12.

Warfel, J. M., Zimmerman, L. I., & Merkel, T. J. (2014). Acellular pertussis vaccines protect against disease but fail to prevent infection and transmission in a nonhuman primate model. Proceedings of the National Academy of Sciences, 111(2), 787-792.

Reviewer #2 (Public review):

Summary:

The authors use an Evolve-and-Resequence approach in Drosophila to study the genomic basis of adaptation to long-term starvation. Replicated selection lines and control populations are sequenced and analyzed to identify signals of selection, which are then related to starvation-related phenotypes. The general experimental design is appropriate, and the combination of genomic and phenotypic data is a clear strength of the study.

Strengths:

The strongest aspect of the work is the experimental evolution framework combined with population genomic inference across replicate populations. The observed parallelism across replicates supports the robustness of at least a subset of the detected selection signals. However, several key methodological details are either unclear or insufficiently justified. In particular, both the maintenance of control populations and demographic assumptions are not fully described, and the treatment of structural variation (e.g., segregating inversions) is not sufficiently addressed. The phenotypic analyses are broadly appropriate and replicated but would benefit from access to raw data.

Weaknesses:

The human ortholog enrichment analysis is an interesting component of the study, but it should be interpreted more cautiously. As currently presented, it is based on correlational signals of differentiation and is therefore sensitive to potential confounding factors. While the analysis may point to intriguing patterns consistent with conserved genetic architecture, the evidence is not sufficient to support strong claims of conserved starvation/malnutrition-related polygenic adaptation in humans. Framing this component more explicitly as exploratory would strengthen the manuscript. In its current form, this analysis is somewhat less conclusive than the experimental evolution results in flies.

Overall, the study provides a useful dataset and a reasonably solid analysis of starvation adaptation in experimental Drosophila populations, but several methodological clarifications and a more balanced framing of the cross-species comparisons would strengthen the manuscript.

Reviewer #2 (Public review):

Summary:

Lee et al. is a comprehensive conservation genomics study that combines a chromosome-level genome assembly (sex-specific, too), population resequencing, coalescent species delimitation, and simulations to reassess the evolutionary status and conservation outlook of the Formosan landlocked salmon, Oncorhynchus formosanus. The authors showed a distinctive genome structure, replete with chromosome fusions and an unusual placement of the sex-determining gene sdY. Across sampling sites, they observed variable levels of genetic diversity, but in a way that was surprising given previous census numbers and conservation history. In particular, the authors report a previously unrecognised native population in Hehuan Creek, and conclude that Hehuan is more resilient to typhoon disturbance than the long-protected Qijiawan population - motivating stream-specific rather than range-wide conservation.

Overall, this is a well-written paper that combines a number of elements that are timely and relevant. It uses state-of-the-art techniques to reach its conclusions and is generally performed to a high standard. It describes a critically endangered species that poses its unique conservation challenges. There are a number of things to like, as well as some substantial shortcomings in this paper.

Strengths:

The genomic resource is excellent. The assembly is well validated (97.3% anchored to 25 scaffolds, 95.7% BUSCO), and the authors generated a separate male assembly specifically to resolve the sex-determining region, allowing XY-shared and Y-specific contigs to be distinguished on coverage rather than inference. This is truly well done, and at a high standard. The synteny evidence for telomere-to-telomere fusions involving at least 14 ancestral chromosomes, against two in O. m. masou, is convincing.

The Hehuan Creek result is the paper's most valuable contribution. Elevated heterozygosity, short and infrequent runs of homozygosity, and private alleles absent from the Qijiawan broodstock are difficult to reconcile with a purely reintroduced origin. The contrast with Luoyewei is a clean and useful cautionary case for hatchery supplementation.

Weaknesses:

(1) The species-rank claim is featured in the abstract, but it is made with any level of rigour in the paper. "New species" appears once, in the abstract (l. 32). The Results conclude only that O. formosanus is a distinct evolutionarily significant unit (ll. 188-191), which itself can be well-justified, but it's far from a taxonomic rank (see author's own ref 10). No species concept is explicitly named anywhere, and the taxon is referred to across the manuscript as a subspecies (l. 68), a "new species" (l. 32), and an ESU (l. 189) in turn.

(2) Gene flow is asserted, not tested, and two divergence estimates disagree twentyfold. The abstract reports "no detectable gene flow for ~50,000 years." That figure is a divergence time from BPP under the A00 model, which contains no migration parameter; a model that cannot fit gene flow cannot report its absence. Separately, Figure 1b shows a split at 1.15-5.09 Mya (Figure 1b), while the ddRAD coalescent places the same split at ~50 kya (Figure 1d). The explanation offered (ll. 417-421, "differing temporal sensitivity of genomic markers") is not a mechanism.

(3) The placement of sdY is unresolved, and the paper's own figures conflict with its text. Figure S10 and Table S6 both make O. formosanus chr13 homologous to O. m. masou chr32, whereas reference 28 - on which the authors rely - places the sdY contig on O. m. masou chr7, whose O. formosanus homologue is chr5 (Table S6). These cannot both be correct, and Figure S10's caption compounds the confusion by attributing chr13 to masou and omitting the chr32 track entirely.

(4) The population-viability model's stated mechanisms are contradicted by the authors' own supplementary tables. The Discussion attributes Qijiawan's vulnerability to "lower juvenile survival, decreased fecundity, and narrower terminal age class representation" (ll. 522-525). Table S10 gives Qijiawan higher age-0 survival (0.202/0.616 vs 0.184/0.615); Table S11 gives it higher fecundity at every reproductive age (7.68/19.27/7.86 vs 6.10/8.95/4.14); Table S9 gives it a broader terminal age class (5.0% vs 0.8% age-3 in November). The only parameter favouring Hehuan is age-1 survival - 0.087 (95% CI 0.000-0.180) versus 0.131 (0.093-0.187) under typhoon, and 0.054 (0.000-0.167) versus 0.087 (0.047-0.149) at baseline. Both Qijiawan intervals include zero and overlap Hehuan's, yet a reported extinction odds ratio of 4.48 rests on this difference.

(5) The two streams were not measured equivalently, and every asymmetry favours the conclusion.

Reviewer #2 (Public review):

In this manuscript, the authors dissect how Gβγ potentiates PLCβ3 signaling in cells. Using engineered crosslinking to stabilize a Gβγ-PLCβ3 complex, single particle cryo-EM, and cell-based functional assays, they identify map multiple putative Gβγ interaction surfaces on PLCβ3, including a previously unrecognized binding mode. Structure-guided mutagenesis supports the functional relevance of these interactions and suggests that Gβγ potentiation is not primarily mediated by PLCβ3 membrane recruitment, but instead enhances PLCβ3 activity after the lipase is already at the membrane.

Previous reconstitution work on membrane surface (Falzone & MacKinnon, 2023) proposed a recruitment/partitioning-centric model in which Gβγ increases PLCβ3 output largely by elevating its membrane surface concentration, whereas Gαq primarily increases catalytic turnover; under those reconstitution conditions, the two inputs can combine approximately multiplicatively. In receptor-driven cellular signaling, however, PLCβ3 is robustly recruited to the plasma membrane upon Gαq activation, which raises the question of whether Gβγ contributes mainly through additional recruitment or through a post-recruitment mechanism once PLCβ3 is already at the membrane.

This manuscript helps address that gap by using membrane-anchored PLCβ3 and complementary cellular readouts to separate "getting PLCβ3 to the membrane" from "boosting activity once PLCβ3 is already there." Their results argue that, in cells, membrane recruitment is largely dominated by Gαq·GTP, while Gβγ can further potentiate PIP2 hydrolysis after membrane association, consistent with a modulatory role at the membrane rather than primary recruitment.

Overall, the work provides a structural and mechanistic framework for Gβγ-PLCβ3 cooperation and helps clarify the basis of Gq pathway amplification.

Comments on revised version.

The authors have reasonably addressed my comments.

Reviewer #2 (Public review):

This manuscript, "Nerve Injury-Induced Protein 2 preserves lysosomal membrane integrity to suppress ferroptosis", identifies a previously unrecognized function of NINJ2 as a regulator of lysosomal membrane integrity and iron homeostasis, thereby suppressing ferroptosis. The authors demonstrate that NINJ2 localizes to lysosomes, interacts with LAMP1, limits lysosomal membrane permeabilization (LMP), stabilizes ferritin, and protects cells from ferroptotic cell death. They further extend these mechanistic findings to human cancer datasets, showing co overexpression and positive correlation of NINJ2 with ferritin genes in iron addicted cancers.

Overall, the study is conceptually interesting, technically solid, and integrates cell biology, iron metabolism, and ferroptosis in a coherent framework. The work expands the functional repertoire of the Ninjurin family beyond plasma membrane rupture and inflammation, which will be of interest to researchers in cell death, lysosome biology, and cancer metabolism.

Strengths:

(1) The identification of NINJ2 as a lysosome-associated protein that suppresses ferroptosis represents a meaningful advance beyond its previously described roles in inflammation, pyroptosis, and tumorigenesis.

(2) The work distinguishes NINJ2 functionally from NINJ1, reinforcing the idea that structurally related Ninjurins have divergent membrane-related roles.

(3) The study presents a logically connected pathway:<br /> NINJ2 loss → LMP → labile iron increase → ferritin degradation → ferroptosis sensitization, which is well supported by the data.

(4) The link between LAMP1, ferritin turnover, and ferroptosis is particularly compelling and timely given recent interest in lysosomal contributions to ferroptotic signaling.

(5) The authors use confocal microscopy, proximity ligation assays, biochemical IPs, iron measurements, protein half-life analyses, ferroptosis assays, and TCGA-based analyses, providing convergent evidence for their model.

(6) Use of two distinct cell lines (MCF7 and Molt4) strengthens generalizability.

(7) The integration of cancer expression datasets linking NINJ2 with ferritin expression in hepatocellular and breast carcinomas enhances translational relevance.

(8) Assigning NINJ2 a lysosomal protective function, distinct from NINJ1-mediated plasma membrane rupture is novel.

(9) Linking NINJ2 to ferroptosis regulation via lysosomal iron handling, rather than canonical GPX4 or system Xc⁻ pathways is also novel, along with proposing a NINJ2-LAMP1-ferritin axis as a buffering mechanism against iron-driven lipid peroxidation.

(10) These insights are not incremental; they reframe how NINJ2 may function at the intersection of membrane biology, iron metabolism, and regulated cell death.

Reviewer #2 (Public review):

Summary:

This study identifies IR20a-expressing gustatory neurons in Drosophila as a multimodal sensory population integrating amino acid (arginine) and low-salt signals through combinatorial IR20a/IR25a/IR76b receptor assemblies. The proposed model of peripheral-level signal integration and synergistic enhancement of feeding preference is potentially significant, as it expands current understanding of gustatory coding beyond single-modality labeled lines.

Strengths:

Overall, the findings are conceptually interesting and suggest a novel framework for multimodal taste integration, but some mechanistic interpretations remain incompletely supported by direct evidence.

Weaknesses:

(1) Although the authors demonstrate co-expression of IR20a, IR25a, and IR76b in the same GRN population, this evidence is insufficient to support the proposed model of distinct receptors coexisting within individual neurons. Additional molecular or structural data would be required to distinguish whether these subunits assemble into complexes.

(2) Given that IR76b has already been established as a sodium/salt sensing channel, the novelty of this study relies on the proposed role of IR20a in conferring multimodal integration and synergy. However, it remains unclear whether this represents a fundamentally new sensory mechanism or a re-interpretation of known IR76b-dependent salt responses in a different neuronal context.

(3) Line 127:<br /> -The statement that there is no overlap between IR20a-GAL4 and GR64f-LexA or GR66a-LexA is not sufficiently supported by the presented imaging data. In particular, the resolution and clarity of the confocal images in Figure 1 appear suboptimal, making it difficult to confidently assess co-localization. The authors are encouraged to provide higher-resolution images or additional quantitative co-localization analysis to substantiate this conclusion.<br /> -In addition, the images shown in Figure 1 F1-F2 suggest possible partial overlap between IR20a and GR66a signals, which appears inconsistent with the authors' statement of no co-expression. This discrepancy should be clarified.

(4) Lines 138-141:<br /> There appears to be a discrepancy between imaging and behavioral data: IR20a is reported as dispensable for arginine-evoked neural responses, yet IR20a mutants show significantly reduced attraction to arginine in behavioral assays. The authors should clarify how behavioral deficits arise in the absence of detectable changes in calcium imaging,

(5) The manuscript proposes that IR20a functions in combination with IR25a to mediate multimodal detection of arginine and low NaCl. However, the specific role of IR25a in this context remains unclear.

(6) The authors report that co-expression of IR20a and IR25a confers synergistic responses to combined arginine and NaCl stimulation, whereas the inclusion of IR76b abolishes this response (Figures 5E-K). This is an intriguing and potentially important finding; however, the mechanistic basis for this suppression is not clearly explained.

(7) The authors propose that IR56b mediates state-dependent modulation of low-salt preference. However, the current data do not clearly distinguish whether IR56b acts as a real nutrient state sensor or just functions as a downstream modulatory component within a broader feeding circuit. Additional evidence linking IR56b activity changes to upstream metabolic state signals would be necessary to support the interpretation that IR56b functions as a primary state sensor.

(8) The manuscript suggests that IR20a and IR56b define two parallel and functionally independent pathways mediating nutrient detection and state-dependent preference, respectively. However, this conclusion is not fully supported by the current dataset. While the two receptors are shown to be expressed in distinct neuronal populations, the possibility of indirect interactions or convergence at downstream circuit nodes has not been excluded. Given that both pathways ultimately influence feeding behavior, it remains possible that they converge at higher-order interneurons or shared neuromodulatory circuits.

(9) In the state-dependent feeding assays (Figure 6), using H2O as a control introduces a severe masking effect. Salt-deprived flies actively suppress pure water intake to avoid osmotic shock, which artificially inflates the Preference Index (P.I.) for salt due to the denominator effect. To cleanly isolate salt preference from the thirst/osmotic drive, the authors will need to utilize an "isosmotic sucrose vs. isosmotic sucrose + salt" paradigm (Jaeger et al., 2018, eLife; Puri et al., 2026, PNAS).

Reviewer #2 (Public review):

Summary:

This review constructs a novel theoretical framework to elucidate incomplete postmenopausal age-related lobular involution (ARLI) in the breast. Differing from the conventional view of persistent lobules as passive residual structures, the work innovatively defines them as an actively maintained senescence-immune reserve niche. It comprehensively integrates multidisciplinary evidence from breast epidemiology, stromal biology, cellular senescence and immune surveillance, as well as cross-tissue research findings, and identifies menopause as a core biological turning point regulating ARLI and relevant breast cancer risk, providing a new theoretical perspective for subsequent breast cancer risk assessment and preventive intervention research.

Strengths:

This study presents an original, logically rigorous, and well-organized research hypothesis. It innovatively breaks through the traditional cognitive perspective of ARLI and adopts a multidisciplinary and cross-tissue analytical approach to sort out relevant biological mechanisms systematically. The proposed theoretical framework is insightful, with good theoretical innovation and potential translational value for guiding breast cancer risk evaluation and targeted prevention strategies.

Weaknesses:

The manuscript currently serves primarily as a conceptual framework rather than a rigorously evidenced synthesis. Its central argument relies heavily on cross-sectional correlations and theoretical analogies to other organ systems, lacking operational definitions for the reserve state in human breast tissue.

Reviewer #2 (Public review):

Summary:

The authors identify KIN-G as an in vitro substrate for phosphorylation by TbPLK and show that several of the in vitro P-ated sites, including T310, overlap with P-ation sites seen in live cells. The authors further show that PLK-mediated P-ation inhibits KIN-G binding to microtubules in vitro, as does a KIN-G-T301D mutant, and that expression of a KIN-G-T301D Phospho-mimic in T. brucei phenocopies KIN-G RNAi knockdowns, producing defects in cell division, morphogenesis of the centrin arm, FAZ and other cellular structures, as well as misplaced cytokinesis furrow.

Understanding cytoskeletal rearrangements that drive cell division in T. brucei is an important and unresolved problem, so the work addresses important questions that are of great interest. PLK and KIN-G have previously been shown to be important for cell division and morphogenesis of cytoskeletal structures that drive cell division in T. brucei. The current work advances our understanding by suggesting a potential mechanism by which PLK and KIN-G might participate, namely through PLK-dependent P-ation to control KIN-G MT binding activity.

Strengths:

The authors use a rigorous combination of biochemistry, phosphoproteomics, cell biology, and mutant analysis to support their conclusion that PLK-mediated P-ation of KIN-G negatively regulates KIN-G microtubule binding and this may explain the observation that a KIN-G T301 phosphomimic mutant blocks cell division and perturbs biogenesis of cytoskeletal structures that drive cell division and morphogenesis. Combining rigorous and informative in vitro studies with mutant analysis in live cells is a great strength. The work is solid and important, though a few pieces are needed to fully connect the in vitro findings with the in vivo observations.

Reviewer #2 (Public review):

Summary:

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

The authors investigate the potential of fetal human RPE cells to be reprogrammed into retinal neurons using overexpression of pro-neural factors. While this is a critical knowledge gap in the field of retinal regeneration with significant promise for developing regenerative therapies, several methodological concerns impact the interpretation of results. Firstly, while the authors sought to evaluate factors that enhance RPE reprogramming when co-expressed with ASCL1, nearly all co-expression constructs tested failed to achieve appreciable expression of ASCL1, leaving a central hypothesis of this study largely untested (Major concern 1). Second, although the authors were able to detect a cluster of photoreceptor-like cells in their screen, they were unable to identify which reprogramming construct generated this cluster (Major concern 2). Finally, an essential control that definitively demonstrates the value of combinatorial transcription factor reprogramming is missing (Major concern 3).

In summary, the authors establish a valuable new paradigm for culturing and reprogramming fetal human RPE, and even more importantly, demonstrate successful reprogramming to neural fates. However, the discussion and interpretation of results needs to be modified significantly to make it clear that (i) the outcome of many co-expression paradigms remains effectively unknown/untested due to failed over-expression of ASCL1, and that (ii) the reprogramming construct giving rise to photoreceptor-like cells could not be conclusively identified from their initial screen.

Strengths:

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

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

Weaknesses:

Major concerns:

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

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

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

Biểu đồ phân tán (Scatter Plot) Khái niệm: Biểu diễn tập hợp các điểm dữ liệu "phân tán" trên mặt phẳng tọa độ.

Cách sử dụng: Đánh dấu các điểm bất thường (outliers), có thể thêm đường xu hướng (trendline) hoặc dùng màu sắc/kích thước điểm khác nhau để biểu diễn các nhóm đè lên nhau.

Sử dụng khi nào: Khi muốn khám phá mối quan hệ, mô hình giữa 2 biến liên tục (continuous variables).

Biểu diễn điều gì: Biểu diễn mối tương quan (Correlation), quan hệ và sự tập trung/phân cụm (Clusters/Outliers) giữa 2 biến.

Reviewer #2 (Public review):

Summary:

The authors investigated whether neurofeedback (NFB) training targeting spontaneous gamma oscillations (30-60 Hz) at the parieto-occipital region (Pz electrode) could reduce experimental pain perception. They randomized 88 healthy participants to active or sham NFB groups across two cohorts (44 each). Active NFB consisted of real-time feedback based on participants' own gamma power; sham NFB consisted of the preceding participant's gamma power. Participants completed three ~16-min sessions, and approximately 52% of active NFB participants showed increased gamma power in session 3 and were considered responders. Analyses restricted to these 23 responders (matched with 23 sham controls) showed reduced pain intensity, unpleasantness, and laser-evoked potential (LEP) amplitudes, with a significant negative correlation between gamma power and pain intensity after session 3.

Strengths:

(1) The distinction between spontaneous and stimulus-evoked gamma oscillations in pain processing is theoretically important.

(2) The rationale for targeting spontaneous gamma via NFB is clearly articulated.

(3) The study was sham-controlled, and the blinding was adequate.

(4) The authors commendably ran a second cohort (n=44) with simultaneous posterior neck EMG recording to address the critical concern of muscle artifact contamination of gamma, in response to a previous review

Weaknesses:

(1) The most critical issue is about the exclusion of non-responders from the analysis. I find this problematic, as the reasoning becomes circular (selecting the participants who managed to increased gamma and then asking whether gamma NFB influenced pain), effect sizes are inflated, and the selection itself may introduce biases. For example, the responders may differ from the non-responders with respect to other characteristics (better attention skills, better self-regulation, etc). It would be more principled to present the results for the entire sample and only present the responder analysis as a secondary analysis. In the preregistration, the responder-only analysis was not mentioned.

(2) Another critical point is about the causal claims made in the abstract, introduction, and discussion. Given that the current results provide only correlational evidence in a subsample, the language should be revised to avoid overinterpretation. If the authors can demonstrate a significant mediation effect (NFB group -> gamma change -> pain change), they may be able to argue that increases in gamma activity mediate the observed reduction in pain.

Minor points:

(1) For the sham procedure, the authors used the preceding participant's gamma data for feedback. This raises two questions: How was this handled for the first participant? Did the authors check the discrepancy between actual gamma and presented gamma in the sham NFB group?

(2) Was baseline gamma power comparable between groups?

Reviewer #2 (Public review):

Many insects possess extremely sensitive olfactory systems that can detect chemical signals from distances of several kilometers. For decades, the arms race between bats and insects has served as a prime example of acoustic co-evolution. The auditory adaptations of insects to echolocation have been well documented. Cricket has a multi-sensory predator recognition system with keen olfactory, tactile, and auditory senses. However, whether crickets can use the scent of bats to avoid them remains unknown at present. The authors hypothesized that cricket prey (Loxoblemmus equestris) might eavesdrop on predator bat (Scotophilus kuhlii) VOCs as an early warning. L. equestris is one of the prey species of S. kuhlii, and the authors demonstrated that the body odor of the insectivorous bat S. kuhlii triggers robust avoidance and electrophysiological responses in the cricket L. equestris, and that a single compound, (-)-limonene, is sufficient to elicit this avoidance in the laboratory and suppress calling in the field. Overall, this paper has a complete chain of evidence and should be a highly praised study.

Reviewer #2 (Public review):

Summary:

The authors introduce a K-mer-based method for profiling repeat content within a species, applied here to 1,142 A. thaliana genomes sequenced with short reads. This approach allowed them to bypass the challenges of genome assembly, particularly for repetitive regions, while still quantifying copy number variation. Their analysis identified >50 trans-acting loci regulating repeat abundance, enriched for genes involved in DNA repair, replication, and methylation. They also speculate on the role of selection in shaping genome repeat content, arguing that purifying selection tends to suppress alleles that promote repeat expansion.

The work presents a scalable way to extract meaningful insights from the large quantities of short-read datasets available. However, I have several concerns regarding the methodology, scope of claims, and interpretation of results.

Strengths:

The authors leverage a large dataset, >1100 samples, of A. thaliana. The scale of the study is impressive and clearly bolsters their findings. Additionally, this provides a framework for future, large-scale studies and offers a solid foundation for hypothesis generation. The k-mer-based method is generally practical for large-scale analysis and should be transferable to other datasets. Finally, the authors are commendably upfront about many of the project's limitations.

Weaknesses:

The decision to use k=12 is loosely justified. While the authors performed a sweep of k-mer lengths (from 5-20) and noted computational constraints, the choice is highly dataset-specific. Benchmarking across different k values with additional datasets (especially including other species) would strengthen confidence in the robustness of the method.

All analyses rely exclusively on the TAIR10 reference genome, which is incomplete and known to collapse certain repetitive regions. This dependence raises concerns that some repeats (especially recently expanded or highly variable ones) are systematically undercounted. With improved A. thaliana assemblies now available, testing the method against a more complete reference would alleviate these concerns.

The manuscript's conclusions are framed in very broad terms (e.g., "shaping genome evolution in plants"). However, the study is restricted to a single species, A. thaliana, which may not represent other plants. While the findings may suggest general principles, the claims in the abstract and conclusion should be moderated to reflect the study system more accurately.

The identification of >50 trans-acting loci enriched for DNA repair and replication genes is compelling, but the conclusions remain correlational.

Reviewer #2 (Public review):

Summary:

The authors aimed to determine the molecular mechanisms by which nuclear pore component NPP-3/NUP205 regulates chromosome localization in C. elegans embryos. Previous studies had shown that depletion of NPP-3 caused premature chromosome condensation and movement of chromosomes to the nuclear periphery. Peripheral location of chromosomes is also observed under respiratory stress conditions, suggesting that peripheral chromosome positioning could act as a protective response to stress conditions. How NPP-3 affects chromosome positioning was unknown. Here, the authors conduct a screen to identify factors that promote chromosome relocation to the periphery in npp-3-depleted embryos, identifying an important role for spindle assembly checkpoint components in this process.

Strengths:

Using cytological tools to visualise chromosomes and nuclear envelope markers, the authors show that, in addition to the peripheral location of chromosomes, NPP-3 depletion causes partial rupture of the nuclear envelope and premature chromosome condensation. By systematically co-depleting NPP-3 and factors required for heterochromatin association with nuclear lamina (CEC-4), telomere binding to nuclear envelope (SUN-1 and POT-1), proteins required for the nuclear rupture repair machinery (BAF-1 and LEM-2), kinetochore proteins and components of the spindle assembly checkpoint (SAC) (MDF-1 and MDF-2), the authors convincingly show that SAC components are required for peripheral relocation of chromosomes in absence of NPP-3. The study also provides convincing evidence that peripheral relocation of chromosomes in the absence of NPP-3 has functional implications as it causes transcriptional deregulation and premature relocation of SAC components from the nuclear envelope to chromosomes. Co-depletion of NPP-3 and SAC components accelerates progression through miotic prophase and increases the incidence of defects in chromosome segregation during mitosis. These findings demonstrate that SAC proteins play an important role in regulating chromosome positioning during prophase (at least in the absence of NPP-3) and that they can regulate cell cycle progression at earlier stages than previously thought.

Weaknesses:

The authors also propose that NPP-3 depletion causes DNA damage; however, the evidence presented to support this claim is not as strong as that presented for the effects mentioned above. Also, the premature condensation of chromosomes appears as a clear consequence of NPP-3 depletion, but this intriguing phenotype remains unexplored.

Reviewer #2 (Public review):

Summary:

STX is a non-steroidal, CNS-selective estrogenic compound with neuroprotective effects in stroke and Alzheimer's disease models, but its molecular target has remained unknown for nearly 20 years. In this study, the authors identify VDAC proteins as the direct mitochondrial targets of STX using chemoproteomics, single-cell qPCR, electrophysiology, and metabolic flux analyses. They further show that VDAC2 is the primary functional target in female POMC neurons, linking STX-mediated VDAC modulation to enhanced mitochondrial bioenergetics and neuroprotection.

Strengths:

This study is strengthened by its innovative chemoproteomic approach, in which the authors developed a novel bifunctional STX probe (BF-STX) containing a photo-crosslinkable diazirine group and an alkyne handle to capture transient STX-protein interactions in living cells. The experimental design is further reinforced by rigorous controls, including no-UV negative controls and competition assays with excess unlabeled STX, which provide convincing evidence that VDAC1, VDAC2, and VDAC3 are genuine STX-binding targets rather than nonspecific artifacts. Finally, the authors validate the STX-VDAC interaction using multiple complementary approaches, including chemoproteomics, single-cell qPCR, planar lipid membrane electrophysiology, and Seahorse metabolic flux analyses, providing strong mechanistic support for their conclusions.

Weaknesses:

While the study provides convincing evidence that STX directly modulates VDAC function, several limitations remain. Most experiments were performed in immortalized cell lines rather than primary neurons or in vivo models, limiting their physiological relevance. In addition, the exact structural binding site of STX on VDAC remains unresolved, and no loss-of-function experiments (e.g., VDAC2 knockdown) were performed to establish a direct causal link between VDAC2 and STX's bioenergetic and neuroprotective effects. The non-linear dose-response at higher STX concentrations also requires further investigation.

Reviewer #2 (Public review):

This manuscript addresses an important and timely question in TDP-43 biology by systematically identifying regulators of TDP-43 anisosome formation, with a particular focus on nuclear export via XPO1. Using a combination of unbiased chemical screening, genetic perturbation, and advanced imaging approaches, the authors propose that inhibition of nuclear export modulates the abundance and biophysical properties of TDP-43 anisosomes. They further strengthen their findings by introducing an additional model system, a semi-permeabilized in vitro assay, which provides mechanistic evidence that XPO1 activity prevents anisosome dissolution by retaining nuclear RNAs. The study is conceptually innovative and has potential relevance for neurodegenerative diseases characterized by TDP-43 pathology. Some minor concerns remain, mostly about experimental design of the newly added data.

Strengths:

(1) The study employs an unbiased, hypothesis-free compound screen to identify regulators of TDP-43 anisosome formation, which is a major strength and reduces confirmation bias.

(2) The authors combine chemical and genetic screening approaches, providing orthogonal validation of key pathways and increasing confidence in the biological relevance of top hits.

(3) The focus on biophysical properties of TDP-43 assemblies, assessed through imaging and FRAP, moves beyond simple presence/absence of aggregates and provides mechanistic insight into the biophysical states of TDP-43.

(4) The use of multiple experimental modalities, including live-cell imaging, FRAP, pharmacological perturbation, and transcriptomic analysis, reflects a technically sophisticated and ambitious study design.

(5) The authors attempt to extend findings beyond immortalized cancer cell lines by incorporating organoid models, demonstrating awareness of disease relevance and translational importance.

(6) The authors extend their study by incorporating a semi-permeabilized in vitro system, which provides compelling evidence that inhibition of nuclear export promotes the retention of nuclear anisosomes, an effect driven by the accumulation of nuclear RNAs.

Overall, the manuscript is clearly written and logically structured, making complex experimental workflows accessible and the central hypotheses easy to follow.

Weaknesses:

(1) The manuscript has significantly improved with the revisions. Some experimental procedures and method details, as well has statements remain incompletely described:<br /> a) What is the smear in Figure S1 after VLX treatment?<br /> b) The authors state that "The reduction in TDP-43 signal was not due to protein elimination.", however no data is provided to support that statement.<br /> c) The authors state that "TDP-43 shifts from phase-separated state to a soluble state ...", however no data is provided to support that statement.<br /> d) Why did the authors choose cow lover cytosol for this study?<br /> e) The experimental setup for supplementing with cytosol/ATP/GTP is unclear. A more detailed schematic would be helpful to understand at what stage in the experiment these factors were added. Which step of the protocol was performed at 37 {degree sign}C, which is indicated in the figure schematic but not described in the methods.<br /> f) In the organoid model, the authors mention that they observe similar levels of total TDP-43, however they do not provide quantification. Instead, they provide a graph that shows highly significant changes in nuclear TDP-43, which was not addressed in the text.

Additionally, some questions remain unclear:

(1) The anisosomes induced by ATP/GTP or cytosol are insufficiently characterized. It remains unclear whether these structures correspond to canonical ring-shaped anisosomes, and whether they exhibit dynamic (liquid-like) or more static (gel-like) properties.

(2) The contribution of the cytosol and ATP/GTP supplementation experiments to the overall narrative is unclear. While the findings are intriguing, their interpretation within the context of the study is not well articulated. In particular, the rationale for including cytosol is not sufficiently justified, given that ATP/GTP alone induces a pronounced effect, whereas cytosol alone does not.

(3) The authors should address why endogenous XPO1 does not co-localize with anisosomes, whereas overexpressed XPO1 does. This raises the possibility that the observed co-localization may be an artifact of non-physiological protein levels, which should be discussed.

(4) The iPSC-based model remains insufficiently characterized. While the authors propose that this system recapitulates the accumulation of liquid and solid aggregates resembling anisosomes, it is unclear whether this phenotype is robustly observed and whether KPT treatment effectively modulates it.

(5) The rationale for the selected treatment durations is unclear, and the timing appears inconsistent across experiments (ranging from 3 to 16 hours), including within experiments involving the same compound. This variability should be justified or standardized.

(6) Several figure legends require clarification:<br /> a) In the section stating "Collectively, our results suggest that the stability and dynamics of anisosomes are modulated by XPO1-mediated nuclear export ...", the cited figure appears to be incorrect. This should refer to Figure 5L rather than Figure 5J.<br /> b) Figure 1B: Please specify the number of replicates per concentration, the number of cells analyzed, and the model used for regression analysis. Additionally, the legend indicates a treatment duration of 15 hours, whereas Figure 1A states 24 hours.<br /> c) Figure 2G: The authors state "7 anisosomes per condition," but the graph displays only 4-6 data points. Please clarify what each data point represents.<br /> d) Figures 3B and 3G: Please clarify whether a defined threshold was used to determine a "reduction in anisosome number."<br /> e) Figure 4B: These do not represent biological replicates, as all samples derive from a single cell line; rather, they constitute independent experimental replicates.<br /> f) Figures 5B and 5H: The legend states "n = 3 biological repeats," but the number of data points shown appears higher. Please clarify.<br /> g) Figures 5K, 6C, and 6E: "Mean Fluorescence Intensity (MPI)" should be corrected to "MFI."<br /> h) Figure 6C: Please include the number of cells analyzed and provide relevant statistical measures (e.g., R², p-value).<br /> i) Figure 6D: The experimental timeline is unclear. Please specify the duration of incubation and the timing of each step.<br /> j) Figure 7B: Improved labeling is needed (e.g., clarification of "mean spot volume") to better align with the figure legend.

Reviewer #2 (Public review):

Summary:

The authors have made a convincing argument that the current system of in vitro translation using E. coli extracts can be significantly optimized to work with much lesser components, while maintaining activity. They have showcased their improved activity using not only physical but also functional readouts.

Strengths:

The experiments are designed in a very logical and easy to understand manner, which makes it easier not only to follow the paper, but also reproduce the results. Functional assays with the synthesized proteins are a good way to demonstrate functionality and applicability of the system. They also benchmark their system against a commercial kit to show superior performance of their system.

Weaknesses:

The production of the lysate requires special instrumentation, limiting accessibility.

Comments on previous version:

Thank you to the authors for addressing the concerns both textually and experimentally. This work has significant value.

Reviewer #2 (Public review):

Summary:

Chapman, Determan et al. investigate how pathogenic mutations in DNMT3A which cause of Tatton-Brown-Rahman Syndrome (TBRS) disrupt human cortical developmental processes using a comprehensive panel of human pluripotent stem cell models spanning DNMT3A loss-of-function severity. The authors aim to identify the cellular and molecular mechanisms underlying TBRS-associated brain overgrowth and intellectual disability, and to test whether mechanistic convergence exists between TBRS and other overgrowth-intellectual disability disorders (OGIDs) caused by mutations in EZH2 (Weaver syndrome) or PIK3CA pathway components. Their central conclusion is that GABAergic interneuron development is selectively vulnerable to DNMT3A mutation where reduced DNA methylation causes premature de-repression of neuronal and synaptic genes, driving precocious neuronal maturation and hyperactivity sufficient to disrupt neuronal network synchrony. This report adds to a growing literature supporting the vulnerability of GABAergic interneurons in NDDs and further provides a mechanistic view of this vulnerability potentially convergent across OGIDs. The mechanistic claims around H3K27me3 compensation and mTOR-based therapeutic convergence, while promising, rest on more preliminary evidence and would benefit from the distinction between correlation and mechanism being made more explicit in the text. Overall, this is a compelling study with rigorous experimental design and novel findings with potential impact across better understanding OGID pathophysiology.

Strengths:

(1) A major strength of this work is the breadth and rigor of the disease modeling approach. Four independent TBRS model systems are used in tandem: a patient-derived iPSC line with isogenic CRISPR-corrected control (R882H), a knock-in hESC model (P904L) with its wild-type isogenic, patient deletion iPSC lines (Del1/2), and CRISPRi knockdown models (G1/G2), collectively spanning a range of DNMT3A loss-of-function that correlates with phenotypic severity. This allelic series design substantially strengthens causal inference beyond what any single isogenic pair could provide.

(2) The multi-omic integration across matched developmental stages provides a strong mechanistic foundation for the cellular phenotyping and provides significantly enhanced novelty. RNA-seq, whole-genome bisulfite sequencing, and H3K27me3 CUT&Tag are combined in the same cell types and timepoints show that DNMT3A loss reduces CG methylation at neuronal and synaptic gene loci, leading to premature transcriptional activation.

(3) The selective vulnerability of ventral (GABAergic) versus dorsal (glutamatergic) progenitors is one of the study's most important findings. This lineage specificity is consistently observed across all model systems and in both 2D and organoid formats, where ventral NPCs show increased proliferation, premature neuronal gene expression, and increased neurogenesis, while dorsal NPCs are largely unaffected at the transcriptomic and cellular level despite exhibiting comparable DNA methylation changes. This adds to a body of emerging work showing GABAergic interneuron vulnerability in NDDs where ubiquitously expressed genes such as chromatin modifiers are perturbed and provides additional molecular insights into potential mechanisms of "resilience" of dorsal populations.

(4) The functional characterization follows a logical progression from single-neuron electrophysiology (demonstrating GABAergic hyperactivity with increased action potential amplitude and firing rate) to network-level analysis using high-density multi-electrode arrays. The HD-MEA experimental design - pairing TBRS or control GABAergic neurons with a constant background of control iGlut neurons - cleanly isolates GABAergic dysfunction as the driver of network hypersynchrony.

Weaknesses:

(1) The concomitant induction of proliferation and differentiation in TBRS V-NPCs is conceptually striking, since these are generally considered antagonistic developmental programs. The authors clarify that neuronal and synaptic gene de-repression is the more prominent direct consequence of mCG loss, while PIK3/AKT/mTOR pathway upregulation is not itself directly linked to differentially methylated regions, suggesting an indirect relationship between DNMT3A LOF and increased proliferative signaling. This framing is reasonable, but the mechanism linking DNMT3A mutation to mTOR activation remains unresolved, and the manuscript would benefit from being explicit about this gap. Relatedly, the rapamycin rescue, while demonstrated across multiple models including 904 and Del1 (Supplementary Fig. S3e-f), remains limited to proliferation readouts. Whether mTOR inhibition also rescues the downstream neurogenesis, maturation, or network phenotypes is an important open question that the authors appropriately frame as motivation for future work.

(2) The claim that H3K27me3 compensates for mCG loss is supported by prior work (Lii et al. 2022), which demonstrated increased PRC2 component expression and H3K27me3 gain at sites of DNA methylation loss in Dnmt3a knockout mouse neurons, and by data showing that PRC2 subunits (SUZ12, EED, EZH2) are significantly more highly expressed in D-NPCs than V-NPCs. Together, these findings provide a plausible molecular basis for why dorsal progenitors may be better equipped to maintain repression when DNA methylation is lost, and they make the EZH2 overexpression rescue in V-NPCs more interpretable. Yet, a formal distinction related to two competing, potentially underlying mechanisms, between active compensation, in which EZH2 is recruited to specific loci in response to methylation loss, and functional redundancy, in which higher baseline Polycomb occupancy in dorsal cells simply becomes the dominant repressive mark once mCG is reduced, has not been resolved.

Reviewer #2 (Public review):

Summary:

This work presents a spiking network model of traveling waves at the whole-brain scale in mouse neocortex. The authors use data from the Allen Institute to re-construct connectivity between different neocortical sites. They then quantify macroscopic traveling waves following stimulation of all layer 4 neurons in neocortex.

Strengths:

Overall, the results are interesting and shed new light on the dynamic organization of activity across neocortex of the mouse. The paper uses realistic neuron models specifically fit to intracellular recordings, demonstrating that traveling waves occur in the mouse neocortex with both realistic connectivity and realistic single-neuron dynamics. The paper is also well-written in general. For these reasons, the authors have generally achieved their aims in this work.

Weaknesses:

(1) Description of Algorithm 1: While the Methods section clearly explains the density parameter \rho, the statement on line 358 concerning the "ideal" average number of connections is a little unclear. The authors should explicitly clarify that \rho is a free parameter that can be adjusted to balance computational feasibility (for a given set of computational resources) and biological fidelity.

(2) Lines 102-103: The \rho parameter used here results in approximately 300 connections per neuron on average. The authors should state clearly that the number of connections per cell is the key determinant of computational feasibility (cf. Morrison et al., Neural Computation, 2005). The authors should also review neuronal density and synaptic connectivity in mouse neocortex and clearly reference density and connectivity in their model to the biological scales found in the mouse.

(3) Line 131: From the plots in Figure 2, it is not clear that the stimulus response is necessarily a rhythmic oscillation, in the sense of a single narrowband frequency.

(4) Line 217: Can the authors clarify how these findings relate to the results from Mohajerani et al. (Nature Neuroscience, 2013), or differ from them?

(5) Line 230: Because higher temporal frequency activity also tends to be more spatially localized, a correlation between PGD and temporal frequency could be an inherent consequence of this relationship, rather than a meaningful result.

(6) Line 247-248: It is not clear that the algorithm for generating connections between neurons presented here really relates to those for community detections. For example, in the case of the Allen Institute data, the communities are essentially in the data already.

(7) Line 284-285: The relationship between conduction delay is more direct than this sentence suggests. Conduction delay is fundamentally determined by the time required for action potentials to propagate along axons, making it intrinsically linked to anatomical distance.

(8) Line 287-288: The authors suggest at this point that they do not have enough information to estimate time delays due to axonal conduction along white matter fibers. However, experimental data from white matter connections typically includes information about fiber length, which does enable estimating conduction delays. These estimations have been previously implemented for Allen Institute connectome data in the mouse (Choi and Mihalas, PLoS Comput Biology, 2019) and human connectome data (Budzinski et al., Physical Review Research, 2023).

(9) Lines 294-295: Several methods do exist for detecting and characterizing wave dynamics in three-dimensional data (Budzinski et al., Physical Review Research, 2023).

Comments on revised version.

In this response and revised manuscript, the authors have addressed all points raised in the first round of review. In response to Point 2.7, however, is it not the case that the Allen dataset has the axonal lengths?

Reviewer #2 (Public review):

Summary and Strengths:

This in-depth genetic analysis of Zasp52 function in Drosophila indirect flight muscle (IFM) provides an interesting perspective regarding the role of a partially disordered region (IDR) in exon 15e. This exon seems to be exclusively present in IFM and contributes to the prevention of myofibril disintegration during aging, likely due to interactions of this region with Z-disc insertion and/or stability. The addition of an isoform (PR) that lacks exon 15e serves as a nice control to illustrate the necessity of exon 15e in muscle structure and function. Overall, the manuscript is exceptionally well-written, logical, with nicely controlled experiments and detailed statistical analysis that largely support the conclusions drawn by the authors. While exon 15e is clearly involved in preventing muscle degeneration, a solid role for thin filament stability is not clearly shown (as mentioned in the abstract). In addition, which regions/how the proteins of the IDR may contribute are unclear.

Reviewer #2 (Public review):

Summary:

This manuscript examines how the statistical properties of others' charitable donations shape subsequent giving using four preregistered experiments and computational modelling. The authors find that both the average level and variability of observed donations influence donation behaviour, and that individual differences in social information use are associated with psychopathic traits.

Strengths:

This is a well-executed paper on the important question of how social information shapes charitable giving. In my view, the combination of preregistered experiments, large sample sizes, computational modelling, and a multi-paradigm approach makes for convincing evidence. The progression across experiments, the use of real donation data rather than deception, the incentivized experiment 4, and the generalization to a second paradigm are all notable strengths. The introduction is clearly written and well-motivated - an enjoyable read. The experimental paradigm is thoughtfully designed, and the methods and supplementary materials are described in considerable detail. The computational modelling provides useful additional insights beyond the behavioural analyses.

As far as I could tell, the manuscript also adheres closely to the preregistrations. The primary hypotheses, experimental designs, exclusion criteria, and key analyses are all consistent with the preregistered plans. Deviations seem to consist of methodological improvements (e.g., mixed-effects models replacing ANOVAs), additional computational and robustness analyses, and therefore strengthen rather than weaken the manuscript. (NB: for transparency, I would appreciate a clearer distinction between preregistered and post hoc analyses, as well as a brief explanation for why some preregistered secondary analyses are no longer reported; see minor comments below).

Overall, I enjoyed reading this paper. I believe it will make a valuable contribution. My comments below are intended to further strengthen an already solid manuscript.

Weaknesses:

(1) The rationale for the social-information phase could be clarified further. Given the research question, I wondered why participants observed the five donations sequentially (and only briefly) rather than simultaneously. In particular, variance is arguably more difficult than the mean to encode and remember, and a sequential presentation may both obscure distributional differences and introduce primacy or recency effects. It would be helpful if the authors could better motivate this design choice, and indicate whether they examined possible order effects.

Relatedly, I felt somewhat uncertain about the purpose of asking participants to predict each donation before observing it. The prediction phase appears to play an important role in the computational model, but its theoretical role is not clearly introduced. Is it intended as a measure of participants' evolving beliefs about the descriptive norm, or primarily as a modelling device? Finally, were these predictions incentivized (e.g., for accuracy), and if not, how should readers interpret them?

(2) I would appreciate having the full experimental materials reproduced in the Supplementary Information. This would make it easier to understand what participants experienced during the task, including what they were told about the "other participants" whose donations they observed.

Minor points:

(1) The interpretations around domain-generality would be strengthened by reporting the association between social information use in the charitable giving task and in the BEAST. Currently, both measures are shown to correlate with psychopathy, but it remains unclear whether individuals who rely strongly on social information in one task also do so in the other. Reporting this correlation (or explaining why it cannot be meaningfully computed) would provide a nice and direct test of a domain-general tendency to use social information.

(2) It would help to explain more explicitly why the standard deviation of donations is theoretically interesting in its own right. The motivation for studying the mean seems immediately intuitive, whereas the motivation for focusing on variability could be elaborated on further in the Introduction.

(3) As I said above, I think the manuscript follows the preregistrations closely. Maybe I missed it, but it seems that prediction accuracy and reaction-time analyses were omitted. It would improve transparency further if the authors would briefly mention the preregistered secondary analyses that are no longer reported (and explain why they were omitted).

Reviewer #2 (Public review):

Summary:

The manuscript by Lau et al. investigates the mechanisms underlying IRF4 and BLIMP1 transcriptional activities during antibody-secreting cell fate decision. Both master regulators of plasma cell differentiation, these two transcription factors have distinct targets and non-overlapping roles. The authors used an in vitro culture system to generate antibody-secreting cells from human naïve B cells, and scRNA-seq, Crispr Cas9 editing, and Cut&Run to dissect the molecular mechanisms defining their specificity.

Strengths:

The experiments are overall well executed, and the manuscript is well written. The in vitro culture model appears to generate genuine human antibody-secreting cells. The identification of non-conserved nucleotides within the binding motifs that induce the specific binding of IRF4 or BLIMP1 is convincing, novel, and exciting.

Weaknesses:

The authors need to correct some overstatements and flaws to improve the manuscript.

In Figure 1f, the authors aimed to determine whether in their culture system the plasma cells emerged from the plasmablasts or directly from the activated B cells. First, it is noticeable that the distinction between plasmablasts and plasma cells relies here only on the expression of CD138. It does not include a higher capacity to secrete antibody or their proliferative state. In Figure 1e, the authors could have strengthened their distinction by showing the Ki67 staining at day 21 for both subpopulations. Second, this question does not seem to be related to IRF4 or Blimp1 activity, and thus one could wonder if it is relevant to this study. Finally, and most importantly, the design of the experiment appears flawed to me. The authors sorted cells at day 7 of culture based on their expression of CD20 and put the two subpopulations back for 14 more days. This culture system is a stepwise system, and it is not specified if the CD20+ cells were put back in the day 7 condition or the day 0 condition with the CD40L stimulation. Have both conditions been tested? This experiment also assumes that all B cells have equal potential to differentiate into antibody-secreting cells. What if it is not the case and some are anergic or have committed to the memory B cell fate during the first 7 days? Then the day 7 CD20+ fraction would be enriched in these cells. Moreover, this experiment didn't show that the plasma cell derived from the plasmablasts in the strict sense of the term, as the CD138+CD20- cells could be a mix of proliferative plasmablasts and immature plasma cells.

In Figure 3a and thereafter, the authors claimed that IRF4 acted earlier than BLIMP1, but both deletions strongly affected differentiation at day 7. IRF4 might have a stronger effect, but it does not mean that it had an earlier effect. To substantiate their claim, the authors would need to demonstrate that, at an earlier time point, deletion of IRF4, but not BLIMP1, results in defective differentiation.

In Figure 3b, the authors stated that in each individual KO the expression of the other transcription factor was lower. Given that there were no cells in the gate, it is puzzling to figure out how these expressions were compared.

In Figure 3c, on the UMAP the bottom right part of the activated B cell cluster does not appear to be attributed to any condition. How can it be? Besides, it is highly surprising that at D9 we cannot see any plasmablast on these UMAP, even in the control. Based on the G1/S and G2/M scores, none of the ASC represented were proliferating. Could the authors explain this strong discrepancy with Figure 1?

Another discrepancy exists between Figure 3b and c: Figure 3b depicted no IRF4- or BLIMP1-expressing cells in either KO, so what were the stunted PC and the BLIMP-KO PC reported in Figure 3c? What are the signature genes defining pre-PC and the score depicted in Supplementary Figure 3d, as the materials and methods only state that they are intermediate between PC and B cells? Could the authors show IRF4, BLIMP1 and some of their known target expression in these populations?

The authors claim that BLIMP1 is not needed to initiate the transition from pre-PC to PC, but in Figure 1, the intracellular staining showed that at day 7 the antibody secreting cells already expressed BLIMP1. This would rather suggest that BLIMP1, unlike IRF4, does not need to be maintained once the cell reaches a certain point.

Reviewer #2 (Public review):

Summary:

The authors tested whether the olfactory cues that drive attraction or avoidance behavior have diverged between surface‑dwelling and cave‑adapted strains of the Mexican cavefish Astyanax mexicanus. They use high‑throughput odor‑discrimination assays between known attractants and repellents by calculating an "odor index" per fish (=the difference in time spent in an odor zone versus a control zone). Further, hybrid crosses to probe heritability, starvation experiments to assess plasticity of odor perception, and whole‑brain pERK detection/mapping to link behavioral changes with known localized neural activity. The results support the hypothesis that the extreme cave environment has selected for an approach response to stimuli that are ancestrally aversive (like alarm or death odors) but in harsh environments can be used as guidance to the rare food sources in this ecosystem.

Strengths:

The odor index analysis is convincing, and the experiments for odor attraction/avoidance are robustly performed. The light-to-darkness shift reflected by avoidance to attraction in cavefish towards skin odors is compelling and carefully analyzed. The analysis of odor indices of three cave-dwelling populations in comparison to surface fish highlights a similar regime, yet with differences among the different populations, suggesting population-specific genetic variation.

Another strength of the paper is exactly this genetic inheritance study by generating F2 hybrids of cave-dwelling and surface-living individuals. The hybrids displayed a continuous range of odor indices for social, alarm, and death odors, indicating that these traits are heritable and likely based on additive genetic markers. Further, the authors uncovered a sexual dimorphism: only female cavefish exhibited approach behavior to social odors, whereas males remained neutral. This result aligns with known differences in olfactory organ morphology between sexes of other species from harsh environments.

Although limited in number, the neurophysiological correlation using whole‑brain pERK mapping after 10 min of odor exposure is convincing. The data revealed overlapping activation in the thalamus and pre‑optic region for food and decay odors, suggesting that these brain areas mediate the evolved attraction response to previously repellent stimuli.

Overall, the manuscript presents a concise story: cavefish have evolved attraction to alarm and death odors as a result of shifting from ancestral avoidance-driven to attraction by genetic changes and physiologically similar activation of specific neural circuits. The evidence is robust, with multiple independent experiments (behavioral assays, hybrid genetics, starvation experiments, and brain mapping) that collectively support the conclusions.

Furthermore, exposure to unpleasant odors can not only be tolerated but can even serve as a trigger for foraging. This plasticity demonstrates that genetic predispositions can be put into practice through active changes in physiology in species or organisms confronted with (drastically) changing environmental conditions.

Weaknesses:

I value that the authors are critical of their own data, indicating low numbers in the pERK/brain experiments. Yet this is a weak point as the statistical power is thus limited. However, their reasoning is careful, based on the results and not over-interpreting.

The layout/design of the ethograms (bout category plots) for both individual and population-wise are not easy to follow. Reworking these display items to convey the information is necessary.

Taken together, the manuscript uses odor perception and attraction/avoidance behavior studies to show that environmental changes (light-to-darkness) have an immediate impact on smell perception and behavior. Attraction to otherwise repellent odors is used by cavefish to likely adapt to harsh environments with low food sources. The manuscript convincingly demonstrates this plasticity, which is an interesting idea to follow up for other traits spreading among a population. This also underlines that a genome may be fixed and the blueprint for behavioral traits, but extrinsic cues can readily be adapted to change wired behavior even to the extreme as reported here: changing avoidance to attraction.

Reviewer #2 (Public review):

Summary:

In this short paper, a clever single-cell Strand-seq method was used to study the number and location of sister chromatid exchange events (SCEs) in cells after CRISPR/Cas9-induced DNA double-strand breaks (DSBs). Unique as well as multiple genomic loci were targeted. Cas9-induced cuts at unique genomic locations led to statistical enrichment of SCEs at the target site, whereas Cas9 targeted at repetitive targets revealed only mild enrichment of on-target SCEs unless analysis was restricted to a subset of cells with >8 SCEs per cell. Interestingly, reciprocal daughter-cell pair analysis revealed large-scale structural alterations on some chromosomes. Whereas disruption of DNA repair genes, including LIG3, LIG4, XRCC1, and XRCC4, did not measurably alter SCE frequency per cell within 24 hrs, consistent with delayed functional loss following editing and selection against essential genes. Together, these findings demonstrate that Cas9-induced DSBs are potent local triggers of SCE at unique loci and can be associated with structural alterations, highlighting the influence of lesion type and genomic context on recombination outcomes during genome editing.

Strengths:

The data in this paper represent a very rich resource of how parental DNA template strands are distributed in paired daughter cells after various treatments. Abnormalities observed in only one of such paired daughter cells provide a novel and exciting approach to study mechanisms of DNA instability and DNA repair at a genome-wide level in general and following Cas9-induced DSB in particular.

Weaknesses:

The effect of Cas9-induced DSBs in the cells that are used will depend on the cell cycle stage of the cells that are targeted, as well as the number of times cuts are made. The latter could happen before, during, and after DNA repair reactions on one or both alleles in a diploid cell. As a result, it is very difficult to extrapolate the mechanisms of DNA instability and DNA repair from the observed genomic rearrangements. Novel approaches are needed to limit the number and timing of Cas9-induced breaks to overcome some of these limitations. The language and logic in the paper can be improved, and some of the claims seem incorrect. For example, the abstract reads "A single Cas9 cut at a unique genomic locus led to strong local enrichment of SCE at the break site, reaching up to 41% in the same cell cycle and 17% in the subsequent division, indicating that DSB repair frequently engages non-local inter-sister repair." The evidence that only a single Cas9 cut was made is lacking (see my earlier comment); it is not clear how local enrichment or non-local inter-sister repair are defined.

Reviewer #2 (Public review):

Summary:

The authors use DRL to train a C. elegans connectome-based ANN to control stepping in a D. melanogaster body model. The resulting system can walk. This shows that one needs further constraints to derive biologically meaningful results from this approach.

Strengths:

The authors perform a very simple experiment with a clear outcome. The interpretation (or lack of interpretation) is a striking cautionary tale.

Weaknesses:

There is little analysis of precisely how robust this result is to parameter variation and network wiring. The worm also undulates in an oscillatory fashion. Thus, it is possible that the network is tapping into biologically meaningful motifs to generate oscillations for walking. As well, it would be useful to examine which heuristics one can use to determine whether modeling efforts are sufficiently constrained (i.e., how much biological data will be necessary to start obtaining fruitful, interpretable outcomes from DRL task optimization). For example, their "solution" using the worm connectome is not sparse (i.e., it uses many neurons). Perhaps a signature of a biologically-meaningful, interpretable result is one that is sparse?

Reviewer #2 (Public review):

Summary:

The authors showed the expression of TGR5 in hematopoietic compartments and that loss of TGR5 doesn't impair steady-state hematopoiesis. Notably, TGR5 knockout significantly decreases BMAT, increase the APC population and accelerate the recovery upon bone marrow transplantation.

Strengths:

The role of TGR5 is interesting.

Weaknesses:

Additional mechanistic studies would further strengthen the work and provide deeper insight into how TGR5 regulates the bone marrow microenvironment.

Reviewer #2 (Public review):

Summary:

Ma and colleagues presented a study on the characterization of brain-wide spatio-temporal impact of olfactory cortical outputs. They take advantage of multi-modal techniques on rats: fMRI, optogenetics and electrophysiology. In addition, they used cutting-edge analytical techniques and modeling to support and interpret their data. The main findings of the study are:

(1) Neurons in Olfactory Bulb (OB) predominantly activate primary olfactory network regions, while stimulation of OB afferents in Anterior Olfactory Nucleus (AON) and Piriform Cortex (Pir) primarily orthodromically activates hippocampal/striatal and limbic networks, respectively.<br /> (2) Non-specified adaptation or habituation mechanisms may play a significant role in modulating olfactory outputs over subsequent fMRI sessions.

(3) Artificially induced aging in rats induces profound modification in the functional interaction between olfactory cortices and multiple brain regions.

The results on AON are of particular interest because of the lack of functional information on this region, despite its recognized importance in shaping OB output and behavior (odor localization tasks).

Strengths:

The manuscript is very accurate. The figures are well-crafted, clear and provide much information with the most appropriate plots and graphics. The study's amount and data quality are remarkable, and the experimental size adequately addresses the scientific questions. I particularly appreciated the details in the description of the methods regarding the missing data and the size of the different animal groups. The supplementary data complete the leading figures and provide information at a single animal level.

Weaknesses:

(1) One of the main reasons the Piriform Cx is understudied in rodents is because of the proximity to air, which creates artifacts in fMRI images. This issue becomes more critical at ultra-high magnetic fields, but I would expect it also at 7T. One main achievement of this study is, indeed, the acquisition of fMRI data from Piriform, and this point should be highlighted by showing raw functional data from a rat. The best would be if an fMRI data sample for a rat, no matter which stimulation, is shared on a public repository, like Zenodo or similar. I am curious to check the quality of the BOLD data from such an 'enormous' field of view, particularly in the OB, with a single-shot sequence. Also, the visual inspection of raw data is essential to appreciate how many 0.5 x 0.5 x 1 mm voxels fit into AON, and others analyzed small brain structures, like the amygdala, etc. Was the amygdala entirely visible in BOLD, or did the air in the ear channel make an artifact partially shadowing it?

(2) Surprisingly, the only information missing in the methods is the post-surgery period and the time between two consecutive fMRI sessions. How much time was accorded to rats to recover from the surgeries, and what time interval between two scans? This information is crucial for interpreting the decrease in most BOLD responses in subsequent recordings. The supposed adaptation should fit into the known time frames for odor adaptation. Usually, fast adaptation does not last for days (and it should be measured within a single experiment: is it the case?), while for long-lasting adaptation the stimulus (odor or opto) should be maintained constantly ON. This does not seem to be the case in this study. The hypothesis, alternative to adaptation, of a less efficient light activation, for example, due to gliosis around the fiber tips, should be discarded with more evidence than the preservation of OB > Pir responses or acknowledged in the manuscript.

(3) The D-galactose experiments were conducted only after administering the aging molecule, with no baseline/reference data on the same animals. Then, comparisons were made with healthy rats, but the two groups not only can be discriminated with respect to D-galactose administration but also with age (10 VS 18 weeks). A control group for 18-weeks-old rats with no D-galactose treatment would better compare the D-galactose effect and avoid any potential bias from group comparisons of rats at different ages. Do you confirm that D-galactose was injected into each rat 56 times/days in a raw, or am I mistaken?

The updated version of the manuscript partially addresses the flaws of the original submission. Here are my general concerns:

(1) Overall, the revised version comes with a few modifications/additions and no new data. Apart from a new correlation analysis, the improvements are mainly discursive, often non-convincing, justifications of the authors' choices. This may reflect a lack of interest in a publication that, in the meantime, lost its original peer-review value. However, it should be acknowledged that the Authors made an effort to partially address the concerns raised by the reviewers.

(2) My main concern was the quality of fMRI recordings. In the revised version, the Authors provided a new figure with an example single-mouse fMRI data. However, the depicted regions of interest (ROIs) mostly cover the brain spots that I expected to be the most impacted by the BOLD artifacts caused by the proximity of the air and the big field-of-view. In addition, these ROIs do not appear to match the mouse anatomy shown above the functional data. As an example, the EPI images in the OB are almost entirely covered by the colored mask. The feeling is that the fMRI data was indeed poor, as I worried, and the lack of any public repository of raw data reinforces that feeling. To make this point clear: I do not think the findings are not true, but poor fMRI data quality might have hidden more insightful results and does not foster the use of fMRI to monitor the olfactory pathway, which lowers the impact of this article.

Reviewer #2 (Public review):

Summary:

Overall, the authors aimed to provide evidence that clarifies two debates within metacognition research concerning subjective confidence reports:

(1) Does the post-decision confidence report arise from the same process that drives the initial decision, or does a separate, independent process support confidence computation?

(2) How do we stop accumulating evidence for the post-decision confidence report? Is it based on a self-imposed time limit, or on accumulated evidence crossing a boundary?

For the investigation, the authors constructed four models (2 × 2 factorial) to compare each combination of processes to account for random-dot motion tasks data with speed/accuracy manipulations. The models are generally embedded in the drift diffusion model framework, retaining basic parameters such as drift rate, boundary separation, starting point, and non-decision time, while adding linearly collapsing boundaries to model the initial choice. For the single vs. distinct process dimension, the difference lies in whether post-decision evidence accumulation is referenced to the endpoint of the initial decision process or restarts from a new, freely estimated starting point. For the time- vs. boundary-based stopping rule dimension, the key difference is that post-decision evidence accumulation stops either at a deadline sampled from a normal distribution or when the accumulated evidence hits a collapsing boundary.

Based on model comparison, the boundary-based stopping rule clearly outperformed the time-based stopping rule. However, models with the boundary-based stopping rule performed similarly regardless of whether a single or distinct process was used. Here, the authors drew additional insights from EEG recordings during the task, focusing on the centro-parietal positivity (CPP), which has been proposed as a neural correlate of the evidence accumulation process. By simulating evidence accumulation trajectories (with additional assumptions) and comparing the patterns of those trajectories with observed ERP waveforms, the authors argued that the single-process model provided a better match to the CPP findings and was therefore preferred. This was specifically demonstrated by the model's superior ability to match the pre-response CPP amplitude differences conditioned on the post-decision confidence-related variables.

Strengths:

(1) The authors translated existing theories into computational models of decision-making and systematically compared different cognitive processes by assessing model fits to the data. This provides strong evidence supporting the idea that post-decision confidence reports could be better explained by boundary crossing rather than a self-imposed deadline to respond.

(2) Beyond model evidence, an important result is that CPP amplitude predicted confidence before the initial choice was reported, which is a unique prediction of the single-process model. The use of EEG as an independent validation measure provided additional evidence in favour of this model.

(3) Combining points 1 and 2, this study successfully addressed the two key debates with solid evidence to favour one theory over another.

(4) Another strength of this study is the data quality. The high number of trials provided a strong foundation for model inference as well as ERP analysis. The experiment also contained a speed-accuracy manipulation to evaluate model performance across diverse situations.

Weaknesses:

I have two main concerns around the modelling work and neural analyses, which in my opinion could have limited the interpretation of the findings. My responses here will be lengthier, but this reflects the nature of the modelling work rather than implying stronger criticisms than those suggested by the strengths discussed above.

(1) There are a few assumptions in the models that lack psychologically meaningful interpretations, and this study placed more effort into model comparison while lacking discussion of the cognitive processes inferred from parameter estimates.

To start, I think some of the parameterisations were not properly justified. For the boundary models, it is not very clear why the upper and lower boundaries were different and collapsed at different rates for confidence decisions, given that a single boundary parameter and collapse rate were used for the initial decision. This allows more flexible shifts in the model's predictions of confidence ratings without strong justification. Specifically, it is unclear why the boundary-single model has such an implementation while the boundary-distinct model was only equipped with one boundary parameter (a2, compared to a2up and a2down).

Similarly, the inclusion of metacognitive noise creates another layer of flexibility in the predictions of confidence ratings. In most existing evidence accumulation models with a diffusion process, noise comes from two sources: within-trial noisy evidence accumulation and across-trial variability (e.g., drift rate variability). Beyond these, such models almost always assume that the decision is made deterministically once the evidence reaches a specific boundary. The inclusion of metacognitive noise here sounds more like a noisy decision-to-action mapping.

I also have similar doubts about allowing the non-decision time parameter for confidence accumulation in the distinct model to be negative. The authors argued that confidence accumulation may begin during initial evidence accumulation. However, this is a flawed implementation, as the non-decision time was simply added to the evidence accumulation time rather than being incorporated within it. Allowing negative non-decision times may achieve similar predictions, but it is ad hoc.

The inclusion of a collapsing boundary mechanism in the post-decision confidence accumulator helped the model reach more diverse levels of accumulated evidence and ultimately improved predictions of confidence ratings. However, no strong argument is presented for this implementation beyond the observation that the model performs worse without it. The collapsing boundary mechanism has traditionally been interpreted as reflecting a sense of urgency. For the boundary models, I noticed that the collapse rate of the upper boundary differed significantly between speed and accuracy conditions, which is consistent with the urgency interpretation. Overall, I would like to see more discussion of the specific model mechanisms included by the authors, interpreted in light of parameter estimates.

(2) While the ERP findings provided external evidence and validation of the modelling results, I find the simulation practices not particularly useful and potentially misleading for naive readers. Specifically, the authors attempted to draw a parallel between patterns of simulated evidence accumulation traces and observed CPP waveforms. While the CPP has received support as a correlate of the evidence accumulation process, the DDM is by no means a neural model capable of generating predictions of neural observations. To my understanding, the superior fit of the boundary-single model was primarily due to the fact that pre-response CPP amplitude predicts post-decision confidence ratings. Therefore, as the boundary-distinct model did not connect the two phases of evidence accumulation, it would fail to account for this observation. I think this point could be clearly demonstrated without the need to introduce additional assumptions into the model simulations in order to directly compare averaged trajectories with averaged ERP waveforms. While the authors did not explicitly claim otherwise, this approach creates an illusion that the model can mechanistically account for ERP data. I would like the authors to provide explicit clarification on this point.

Appraisal:

Overall, the authors have provided solid evidence in support of their research aims. The findings contribute to longstanding debates with insights from model mechanisms and neural findings that should not be overlooked by future studies on this topic. This study also offers a good starting point for future model development and refinement in broader contexts of confidence reporting, such as paradigms involving simultaneous initial decisions and confidence judgements. The high quality of the behavioural and EEG data will make a valuable contribution to future research.

Reviewer #2 (Public review):

Summary:

The authors studied cognitive control and attention in response to mnemonic prediction errors (MPEs): situations in which the external reality violates internal memory-based predictions. The behavioral task first established strong versus weak predictions, and then either confirmed or violated these predictions. The authors examined markers of cognitive control (frontal theta) and attention (posterior alpha suppression, pupil response) while strong and weak predictions were confirmed or violated. They found increased cognitive control (frontal theta) for strong MPEs, which correlated with subsequent memory. Markers of attention (alpha suppression, pupil response) also accompanied strong MPEs but did not correlate with subsequent memory. Pupil response was investigated using an interesting approach that decomposes the response into different components, finding that different components respond earlier or later and show different correlations with MPEs and their strength. The authors also investigated how EEG, reaction time, and pupil responses correlated with one another, providing further insight into the mechanism underlying the response to MPEs. Together, the study points toward multiple control and attention mechanisms involved in MPE response and memory.

Strengths:

The study has a clear behavioral paradigm with multiple measures - behavioral, EEG, and pupillometry that offer an investigation into different aspects of MPE response and memory.

The study is also very comprehensive in looking at multiple phases in processing MPEs: the prediction phase (prior to the violation), the response to MPEs, and subsequent memory of MPEs, all within one study. Specifically, the link between neural mechanisms and subsequent memory is a major advancement, as most prior studies did not include this component. Mechanisms underlying subsequent memory of MPEs are theoretically important, as a primary function of MPEs is to promote learning and memory. As the authors mention, the different neural and pupillary signals are not robustly correlated, suggesting multiple mechanisms underlying MPE detections, which is interesting, offers avenues for future research, and can facilitate a better theory of how MPEs are processed in the brain. Finally, the decomposition of pupil response into different components and their correlation with behavior (RT during match/MPE detection) is interesting.

Weaknesses:

The methods are rigorous, and the data support the claims. The weaknesses are minor and are offered here as avenues for future research.

(1) The relationships the authors find between brain measures and pupil components were largely not specific to mismatches/matches. Thus, the specificity of this relationship is untested.

(2) The results with subsequent memory are important and address a major gap in the field that largely did not relate neural effects of MPE to subsequent memory. However, one major limitation of the study is that the authors did not test memory for matches. I understand the logic of avoiding testing matches. Because matches were repeated more times in the study, it's not a fair comparison and could change participants' overall criterion for old/new decisions. Future research could address this, e.g., by testing weak matches or potentially using a between-subject design.

Comments on revised version.

The authors addressed all my concerns. I appreciate the authors' thoughtful and detailed response.

Reviewer #2 (Public review):

The authors investigate the contribution of dorsal CA1 hippocampal dysfunction to cognitive impairments in the Cntnap2 knockout mouse model of autism spectrum disorder. Building on previous evidence implicating the hippocampus in episodic and relational memory processes, they combine trace fear conditioning, fiber photometry, optogenetic manipulation, a relational/declarative memory radial maze task, and cFos mapping to test whether altered CA1 function contributes to deficits in temporal binding and memory flexibility.

The study has several important strengths. First, the work addresses a relatively understudied aspect of autism-related cognition, namely hippocampal-dependent memory processes, whereas much of the literature has focused on social behavior, cortical circuits, or striatal dysfunction. Second, the authors employ multiple complementary approaches that converge on a coherent mechanistic hypothesis. The behavioral data demonstrate a reduced ability of Cntnap2 knockout mice to retain associations across long temporal gaps. Fiber photometry recordings reveal reduced dorsal CA1 activity during conditions that challenge temporal binding, and optogenetic activation of dorsal CA1 neurons during the trace interval is sufficient to rescue memory performance. Together, these findings provide strong support for a causal contribution of dorsal CA1 activity to temporal binding deficits in this model.

The second major strength of the manuscript is the extension of these findings to a more complex hippocampus-dependent memory task. The radial maze experiments indicate that Cntnap2 knockout mice show impaired memory flexibility and a greater reliance on egocentric learning strategies. The accompanying cFos analyses suggest altered recruitment of hippocampal and striatal networks during learning, providing a systems-level framework that may explain the observed behavioral phenotype.

Overall, the main conclusions regarding impaired temporal binding and reduced dorsal CA1 engagement are well supported by the data. The optogenetic rescue experiments are particularly compelling because they move beyond correlation and directly test causality. The manuscript therefore makes a meaningful contribution to our understanding of how hippocampal dysfunction may contribute to cognitive abnormalities associated with autism.

Weaknesses:

Some conclusions are necessarily more inferential than others. In particular, the interpretation that the observed behavioral phenotype reflects a broader shift from hippocampal-dependent declarative memory toward striatum-dependent procedural learning is supported primarily by cFos activity patterns and behavioral strategy measures. While the data are consistent with this interpretation, they do not directly demonstrate a causal reorganization of memory systems. Similarly, although the findings identify a mechanism in the Cntnap2 model, caution is warranted when extrapolating these conclusions to autism spectrum disorder more broadly; but I believe this caution is addressed in the discussion.

Despite these limitations, the study presents a coherent and well-executed body of work that provides novel mechanistic insight into hippocampal contributions to cognitive dysfunction in a widely used autism model. The findings should be of considerable interest to researchers studying hippocampal function, memory systems, and neurodevelopmental disorders.

Reviewer #2 (Public review):

Summary

This manuscript by Schilling et al. presents an important advancement in our understanding of how mTOR signaling regulates organismal aging. While the longevity-promoting effects of reduced mTOR activity have been extensively documented across species, the mechanisms by which mTOR communicates systemic metabolic information to regulate lifespan remain unclear. In this study, the authors provide strong evidence that longevity induced by reduced TORC1 signaling requires the bile acid-like steroid hormone dafachronic acid (DA) and its cognate nuclear receptor DAF-12. Furthermore, through a combination of transcriptomics and functional genomics, they identify the conserved short-chain dehydrogenase DHS-26/DHRS1 as a previously unrecognized downstream effector of this pathway. The work integrates genetics, lifespan analyses, sterol measurements, transcriptomics, proteomics, endogenous genome engineering, and comparative mammalian datasets. The resulting model, in which mTOR influences lifespan through regulation of endocrine steroid signaling, represents a conceptual advance that links nutrient sensing, metabolism, and organismal aging. Although several mechanistic questions remain unresolved, the study is comprehensive, technically rigorous, and likely to be of broad interest to investigators studying aging, metabolism, endocrine signaling, and cellular stress responses.

Strengths:

One of the major strengths of this manuscript is its conceptual novelty. Rather than reinforcing the well-established role of mTOR as a longevity regulator, the study proposes a specific endocrine mechanism that links reduced mTOR activity to increased lifespan through steroid hormone signaling. This advances the field beyond descriptive observations of mTOR-dependent longevity and introduces a model in which bile acid-like hormones function as systemic mediators of nutrient-sensing pathways. The idea that endocrine steroid signaling may serve as a downstream effector of mTOR provides a new perspective on how longevity signals are coordinated at the organismal level.

The genetic evidence supporting this model is particularly strong. In Figure 1, the authors use a series of epistasis experiments to demonstrate that mutations in daf-36, daf-9, and daf-12 suppress lifespan extension in raga-1 mutants. The DA supplementation experiments further strengthen the pathway ordering by rescuing longevity in hormone-deficient backgrounds while failing to restore lifespan in receptor-deficient animals. Importantly, the direct quantification of endogenous DA levels elevates the study by providing biochemical support for the proposed model.

The transcriptomic analyses presented in Figure 2 provide a valuable systems-level perspective on the interaction between mTOR and steroid signaling pathways. The observation that DAF-12 profoundly reshapes the RAGA-1 transcriptional program highlights the importance of steroid signaling in mediating the physiological consequences of reduced mTOR activity. The enrichment of metabolic, lysosomal, and peroxisomal pathways is consistent with established longevity-associated programs and generates a valuable resource for future mechanistic studies.

Figure 3 effectively integrates discovery-driven and hypothesis-driven biology. The authors use transcriptomic information to prioritize candidate genes and then perform a functional genomic screen to identify factors required for RAGA-1-mediated lifespan extension. This approach converges on DHS-26, which subsequently emerges as a central mechanistic component of the study. The progression from transcriptomics to functional validation is well executed.

In Figure 4, the generation of CRISPR-engineered dhs-26 deletion mutants and endogenous tagged reporter strains provides strong validation for DHS-26 function. The demonstration that dhs-26 deletion selectively abolishes RAGA-1-dependent longevity without substantially affecting wild-type lifespan strongly supports its role as a context-dependent mediator of mTOR signaling. Furthermore, the conservation analyses linking DHS-26 to mammalian DHRS1 provide biological context and enhance the broader significance of the findings.

In Figure 5, multiple independent experimental approaches converge on the conclusion that DHS-26 participates in DA-dependent lifespan regulation. The rescue of lifespan by DA supplementation, reductions in DA levels in raga-1;dhs-26 mutants, reporter-based analyses of DAF-12 activity, and proteomic profiling collectively support a mechanistic model. The proposed positive feedback relationship between DA/DAF-12 signaling and DHS-26 is intriguing and offers a plausible explanation for how endocrine signaling may amplify longevity-promoting responses. Finally, the incorporation of mammalian datasets showing regulation of DHRS1 by rapamycin and FXR signaling provides a promising avenue for future studies investigating conservation of this pathway.

Weaknesses:

Despite the many strengths of the study, important mechanistic questions remain unresolved. The most significant limitation is that the precise molecular connection between reduced mTOR activity and increased DA production remains unclear. While the genetic and biochemical data convincingly place DA/DAF-12 signaling downstream of mTOR, the study does not establish whether mTOR regulates DA biosynthesis, degradation, intracellular trafficking, sterol uptake, or hormone availability. The observed increase in endogenous DA levels is statistically significant but relatively modest, and the mechanistic basis for this increase remains speculative. Additional experiments examining sterol flux, enzyme activity, or intracellular sterol trafficking would substantially strengthen the proposed model.

The transcriptomic analyses in Figure 2 are informative but correlative. Because the RNA-sequencing was performed at a single adult time point, it remains difficult to distinguish primary transcriptional responses from secondary adaptive changes. Similarly, while pathway enrichment analyses identify plausible processes, they do not establish direct regulatory relationships. Additional temporal analyses or direct assessment of DAF-12 occupancy at candidate loci would strengthen mechanistic interpretations and help distinguish direct from indirect targets.

A major unresolved question concerns the biochemical function of DHS-26 itself. While the genetic evidence clearly establishes DHS-26 as an important regulator of RAGA-1-mediated longevity, its endogenous substrate and enzymatic activity remain unknown. The manuscript presents evidence linking DHS-26 to sterol metabolism, but direct biochemical characterization is lacking. Thus, the mechanistic model remains somewhat incomplete. Defining the substrates and products of DHS-26 activity would greatly strengthen the study and provide important insight into how this enzyme influences DA availability.

Another area requiring additional clarification is the proposed neuroendocrine role of DHS-26. The expression of DHS-26 in canal-associated neurons is interesting and raises the possibility that these cells participate in systemic longevity regulation. However, the current data do not establish whether DHS-26 functions autonomously within these neurons or whether expression in other cell types contributes to the observed phenotypes. Tissue-specific rescue or depletion experiments would strengthen the neuroendocrine model and help establish physiological sites of action.

Finally, the mammalian data presented in Figure 5 are supportive and suggestive of evolutionary conservation, but they remain correlative. While regulation of DHRS1 expression by rapamycin and FXR signaling is interesting, these observations do not yet demonstrate functional conservation of the longevity mechanism itself. Additional studies directly testing DHRS1 function in mammalian systems will be required before stronger conclusions regarding conservation can be drawn.

In summary, this manuscript provides a significant contribution to the aging field and introduces a model linking mTOR signaling, endocrine steroid hormones, and longevity. The study is comprehensive, technically sophisticated, and supported by multiple complementary approaches. Although some mechanistic questions remain open regarding the precise regulation of DA production, the biochemical function of DHS-26, and the extent of conservation, these limitations represent opportunities for future investigation. Overall, the work substantially advances our understanding of how nutrient-sensing pathways regulate aging and is likely to stimulate considerable interest within the fields of aging biology, metabolism, and endocrine signaling.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

General comment:

(1) Although there is already an incredible amount of data presented, one limitation of this study is the lack of cellular validation - do these drugs enter cells, restore interferon signalling, reduce viral loads, or alter Orfb9 localization?

(2) The logic of locking Orfb9 as a dimer is that the monomer binds Tom70 - thus, a more stable dimer means less monomer. In Figure 2, the Orfb9 homerdimer stabilization by compounds should reduce binding affinity to Tom70. A direct binding experiment measuring reduced Tom70 binding with compound treatment would better strengthen this claim.

Reviewer #3 (Public review):

Summary:

The manuscript takes a dynamical systems perspective on emotion regulation, meaning that rather than a simplistic model conceptualising regulation as applying to a single emotion (e.g. regulation of sadness), emotion regulation could cause a shift in the dynamics of a whole system of emotions (which are linked mathematically to one another). This builds on the idea that there are 'attractor states' of emotions between which people transition, governed by both the system's intrinsic characteristics (e.g. temporal autocorrelation of a particular emotion/person) and external driving forces (having a stressful week). Conceptually this is a very useful advance because it is very unlikely that emotions are elicited (or reduced) singly, without affecting other emotions. This paper is a timely implementation of these ideas in the context of a psychotherapeutic intervention, distancing, which participants were trained (randomised) to perform while watching emotion-inducing videos.

The authors' main conclusion is that distancing both stabilises specific emotional patterns and reduces the impact of external video clips. I would consider these results strong and believable, and to have the potential to impact models of emotion regulation as well as the field's broader views on the mechanisms of psychological therapies.

Strengths:

This paper has very many strengths: I would especially note the authors' very-well-matched active control condition and the robustness of their model comparison approach. One feature of the authors' approach in is that they explicitly add noise - not what you typically see in an emotion time-series analysis - which allows for participants to make errors in their own subjective ratings (a reasonable thing to assume); this noise can then be smoothed during filtering. In their model comparison approach, they explicitly test whether a true dynamical system explains emotion change/emotion regulation effect on emotions - demonstrating that both intrinsic dynamics and external inputs were needed to explain subjective emotion. Powerfully, they also used this approach to test the differential effects of the treatment groups (see below).

The main result seems quite robust statistically. Verifying the effects of the distancing intervention on emotion, the authors found an interaction between time (pre- to post-intervention) and intervention group (distancing vs. relaxation) suggesting that distancing (but not relaxation) reduced ratings of almost all emotions. Participants allocated to the distancing intervention also showed decreased variability of emotion ratings compared to those in the relaxation intervention (though note this interaction was not significant).

Using a model comparison approach, the authors then demonstrated that whilst the control group was best-explained by a model that did not change its dynamics of emotions, the active intervention (distancing) group was best-explained by a model that captured both changing emotion dynamics and a changing input weights (influence of the videos) - results confirmed in follow-up analyses. This is convincing evidence that emotion regulation strategies may specifically affect the dynamics of emotions - both their relationships to one another and their susceptibility to changes evoked by external influences.

The authors also perform analyses that suggest their result is not attributable to a demand effect (finding that participants were quicker during the control intervention, which one would expect if they had already decided how to respond in advance of the emotion question). I personally also think a demand effect is unlikely given the robustness of their control intervention (which participants would be just as likely to interpret as a mental health-enhancing training as distancing) and am convinced by the notion that demand effects would be unlikely to elicit their more specific effects on the dynamic quality of emotions.

Weaknesses:

The authors use an active control - a relaxation intervention - which is extremely closely matched with their active intervention (and a major strength). However, there was an additional difference between the groups: "in the group allocated to the distancing intervention, the phrasing of the question about their feelings in the second video block reminded participants about the intervention, stating: "You observed your emotions and let them pass like the leaves floating by on the stream." Therefore, some of the effects of distancing may have also been driven by different emotion regulation strategies, i.e. reappraisal, since this reminder might have evoked retrospective changes in ratings.

An unanswered question is exactly how distancing is producing these effects. As the authors point out, there is a possibility that eye-movement avoidance of the more emotionally-salient aspects of scenes could be changing participants' exposure to the emotions somewhat, which could vary by emotion, as the authors now discuss in their limitations.

Comments on revised version.

The authors have addressed my concerns.

Reviewer #2 (Public review):

Okray et al. identify a novel form of multisensory memory in Drosophila, where pairing reward with a color+odor together gives a stronger memory than color alone or odor alone. Remarkably, this multisensory enhancement occurs even if only one modality is used during testing (i.e. training color+odor, then testing odor alone gives a stronger memory than training odor alone, then testing odor alone), showing that the two modalities are persistently linked following training. The manuscript presents compelling behavioural genetic evidence that the normally visual-selective gamma-d Kenyon cells acquire a functional role in the retrieval of odor memories following odor+color training, and that this occurs via transfer from gamma-main KCs via the serotonergic interneuron DPM.

The key pieces of evidence supporting this conclusion are that olfactory retrieval of multisensory memories requires:

(1) synaptic output from gamma-d KCs during retrieval (but not training);

(2) synaptic output from gamma-main KCs during training and retrieval (whereas it's only required during retrieval, not training, for pure-olfactory memory);

(3) synaptic output from DPM during training and retrieval, and expression of the serotonin receptor 5HT2A in gamma-d KCs.

In the absence of physiological data, the exact nature of the gamma-d KCs' participation in olfactory retrieval following odor+color training remains unclear. For example, do the gamma-d KCs encode the odor identity (i.e., is there an odor-specific pattern of gamma-d KCs activated for a particular odor+color combination), or does their activity provide a general activity boost to other neurons (e.g. gamma-m) that encode odor identity? This will be interesting to address in future studies.

That being said, the behavioural data are clear and back up the authors' conclusion that signaling between KC subtypes via DPM underlies multisensory integration for multimodal memories in the fly mushroom body.

Reviewer #2 (Public review):

Summary:

Using the 5xFAD model in combination with GPR34 mice, the authors explore the function of microglia in the context of neurodegeneration. Using a broad spectrum of methodology, they show that DAM signatures are increased in KO 5xFAD mice. Using several KO clones of GPR34 KO iMGLs and another set of broad methodologies, the authors show that GPR34 is important for microglia homeostasis,<br /> phagocytosis, specifically of myelin. GPR34 KO iMGLs also show a distinct transcriptional response to myelin. Together, they propose that GPR34 limits microglial activation in neurodegeneration.

Strengths:

All methods are state-of-the-art, and the combination of mouse and human microglia responses is a particular strength.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

The authors set out to test the extent to which differences in learning capacity and experience contribute to behavioural variation in a genetically identical population under identical environmental conditions.

Strengths:

The authors developed and used a scaled-up version of a simple two-choice behavioural paradigm allowing them to test thousands of individuals across multiple genotypes. They then deployed clever and powerful statistical analysis methods and provided compelling evidence for a role of variability in learning in the expression of behavioural variation.

Weaknesses:

There are no major weaknesses, although some level of longitudinal analysis to strengthen the evidence for a strict definition of individuality would be a welcome extension of a future study. In addition, it would have been very interesting, although understandably beyond the current scope, to delineate a potential source of learning variability in the brain.

Reviewer #2 (Public review):

This study uses a combination of field sampling and manipulative experiments to test for facilitative impacts of pikas on yaks via suppression of a poisonous forb. The authors found that, when Stellera forbs were present, yak weight increases over the growing season were greater in the presence of pikas compared to in their absence. This occurred because, although pikas do not consume Stellera, they clip it and use it in nest/burrow construction, thereby decreasing its relative abundance in the plant community. Thus, overall, the study contributes to our understanding of how herbivores of different size classes indirectly affect each other via use of shared resources.

It is well known that large herbivores on grasslands impact smaller animals, but the reciprocal interaction is rarely tested. Thus, this study asks a valuable question, and the experiment is well-designed to test it. The authors also do a good of demonstrating the potential conservation impacts of their research.

Reviewer #2 (Public review):

Summary:

The authors identify KIN-G as an in vitro substrate for phosphorylation by TbPLK and show that several of the in vitro P-ated sites, including T310, overlap with P-ation sites seen in live cells. The authors further show that PLK-mediated P-ation inhibits KIN-G binding to microtubules in vitro, as does a KIN-G-T301D mutant, and that expression of a KIN-G-T301D Phospho-mimic in T. brucei phenocopies KIN-G RNAi knockdowns, producing defects in cell division, morphogenesis of the centrin arm, FAZ and other cellular structures, as well as misplaced cytokinesis furrow.

Understanding cytoskeletal rearrangements that drive cell division in T. brucei is an important and unresolved problem, so the work addresses important questions that are of great interest. PLK and KIN-G have previously been shown to be important for cell division and morphogenesis of cytoskeletal structures that drive cell division in T. brucei. The current work advances our understanding by suggesting a potential mechanism by which PLK and KIN-G might participate, namely through PLK-dependent P-ation to control KIN-G MT binding activity.

Strengths:

The authors use a rigorous combination of biochemistry, phosphoproteomics, cell biology, and mutant analysis to support their conclusion that PLK-mediated P-ation of KIN-G negatively regulates KIN-G microtubule binding and this may explain the observation that a KIN-G T301 phosphomimic mutant blocks cell division and perturbs biogenesis of cytoskeletal structures that drive cell division and morphogenesis. Combining rigorous and informative in vitro studies with mutant analysis in live cells is a great strength. The work is solid and important, though a few pieces are needed to fully connect the in vitro findings with the in vivo observations, as detailed below.

Weaknesses:

Overall, I find this work to be solid, and to provide an important addition to our understanding of mechanisms controlling cell division in T. brucei. The biochemistry, in particular, is rigorous and convincingly demonstrates PLK can P-ate KIN-G, altering its MT-binding ability. Analysis of phospho-mutants of KIN-G in live T. brucei support the conclusion that P-ation of KIN-G at T301 negatively affects KIN-G function in vivo. I think, however, that the results fall short of supporting the title, because, although the data convincingly show that PLK can phosphorylate KIN-G at T301 in vitro, and that T301 is P-ated in vivo, they do formally demonstrate (nor even test) whether PLK is the kinase responsible for this phosphorylation in vivo (experiments to address this seem quite feasible). I also do not see where the authors try to reconcile the absence of phenotype for KIN-G-T301A with the implied importance of KIN-G phosphorylation by PLK in cell division, which calls into question the need for P-ation of KIN-G-T301 in cell division. Suggestions for addressing these concerns are provided below.

My two main questions are:

(1) What is the biological relevance of KIN-G P-ation at T301?<br /> a. The authors report no defect for the KIN-G-T301A mutant, so what then is the need for T301 P-ation, if the cell gets along fine without it? One step toward addressing this would be to ask what fraction of KIN-G shows P-ation at T301. Although published studies indicate P-ation at T301, it isn't known what percentage of KIN-G in the cell is P-ated. One might anticipate, for example, that T301-P is a small minority of the population in asynchronous cultures and that T301 P-ation increases at specific cell cycle stages.<br /> b. Published work links PLK to cell division, FAZ elongation, etc... The current work suggests that one role of PLK is to P-ate KIN-G at T301. In contrast, however, the current work also indicates that P-ation of KIN-G at T301 is unnecessary for normal cell division, FAZ elongation, etc....<br /> c. Some experiments or at least commentary on points a and b above would strengthen the paper.<br /> - The authors have now addressed this question by assessing what % of KING is phosphorylated at T301 and adding commentary on this point in the revised paper.<br /> - I would suggest that the model (new figure 8) include a dephosphorylation step, as that is proposed by the authors in the text. Also include in the legend some commentary on the role of phosphorylation, which is the center point of this paper, but not currently mentioned.

(2) Is PLK the kinase that P-ates Kin-G T301 in vivo?<br /> a. The authors show PLK P-ates T301 (and other residues) in vitro, and that T-301 is P-ated in vivo. To bring the analysis full circle, it would be informative to examine KIN-G P-ation in a PLK mutant or upon inhibition of PLK with published inhibitors. This seems to be a very doable experiment with the tools available.<br /> - The authors have addressed this question by demonstrating that T301 phosphorylation is reduced upon treatment with a PLK inhibitor, thus supporting that PLK phosphorylated T301 in vivo. It is noted that one might consider an alternate kinase is also able to phosphorylate T301 in absence of PLK activity, as that could explain the relatively low (~27%) reduction in phosphorylation by PLK inhibitor treatment.

Reviewer #2 (Public review):

Summary:

The study finds that nutrient resorption efficiency in Phragmites australis shows no plastic response to salinity stress but is canalized by phylogeographic lineage, ecotype, and latitude. In a common garden with 110 genotypes, salinity induced stress, yet no plastic change occurred for N, P, or K resorption. The authors conclude that intraspecific variation is historical and geographic; thus, predictions of wetland nutrient cycling need to account for phylogeographic composition.

Strengths:

The core finding that NuRE shows no plastic response to salinity, but is instead evolutionarily canalized by lineage and latitude, challenges a key assumption of broad trait plasticity. This conclusion is firmly supported by a robust common garden design with 110 genotypes, rigorous multi-level stress validation, and element-specific resorption analyses. The work provides compelling evidence that intraspecific variation in this critical nutrient cycling trait is shaped by phylogeographic history rather than short-term acclimation. The implications for predicting wetland responses to salinization are significant, as ecosystem-level nutrient dynamics may be constrained by the genetic composition of plant populations.

Weaknesses:

The experiment covers only one growing season, with salinity applied in June and measurements in December. While the stress is clearly effective, longer-term or multi-year stress might reveal acclimation or epigenetic effects that are not captured. Given the author team's expertise in parental and transgenerational effects in clonal plants, this limitation is particularly relevant and warrants more thorough discussion in the manuscript.

The salinity treatment uses a single moderate level of 10 ppt, which does not allow assessment of whether more extreme stress might trigger a plastic response. A dose-response design across a gradient would have provided stronger inference about the threshold at which NuRE canalization might be overcome. Additionally, the ecotype analysis in Figure 4 applies only to Chinese populations, as classification was not available for non-Chinese populations, which should be stated more explicitly in the Results.

The variation partitioning shows latitude as a significant predictor, but the R² values are relatively low, indicating that much variance remains unexplained. The manuscript should avoid overinterpreting latitude's explanatory power and more openly acknowledge the role of unmeasured factors. The interpretation of slopes greater than 1 for the resorbed N:P versus green N:P relationship, labeled as "inverted limitation", also needs further explanation regarding its functional significance.

Reviewer #2 (Public review):

Summary:

The authors aim to address a major problem in brain network modeling: the high computational cost of simulating and fitting brain activity models, particularly for large samples, individualized models, and broad parameter searches. They introduce cuBNM, an open software package that uses graphics processing units to accelerate model simulation, fitting, and calculation of simulated brain activity features.

The manuscript is primarily a methods and software contribution, rather than a paper providing novel neurobiological insights. The authors demonstrate the tool using human imaging data, showing examples of group-level and individualized model fitting, comparisons between homogeneous and heterogeneous model parameterizations, and analyses of repeated-measurement stability and genetic influences of simulated features. They also provide speed and scaling tests to support the claim that the software can make large-scale and individualized brain network modeling more practical for the field.

Strengths:

A major strength of this work is that it addresses a clear computational bottleneck in brain network modeling. The authors provide an open software package that combines a user-friendly Python interface with an accelerated back-end, making large numbers of simulations and model fits more practical for other researchers.

The demonstrations are broad and relevant to real use cases. The authors show group-level and individualized model fitting, different optimization strategies, and comparisons between homogeneous and heterogeneous models, rather than limiting the paper to a narrow technical benchmark. The benchmarking and openness of the work further increase its value. The comparisons across hardware and network sizes give readers a practical sense of the tool's speed and scalability, while the availability of code, documentation, tutorials, and containers should make the method easier for the community to test and adopt.

Weaknesses:

(1) The benchmarking provides solid evidence for substantial speed improvements within the authors' implementation, but the generality of the performance claims is more limited. The largest reported speed-ups are measured relative to a single central processing unit thread, and the study does not fully benchmark cuBNM against other optimized brain modeling frameworks. This makes the results useful as evidence of strong acceleration in the tested setting, but less definitive as a general comparison across available implementations.

(2) The comparison between homogeneous and heterogeneous models is informative, but it is not fully controlled for model complexity. The best-fitting node-based heterogeneous model has more free parameters than the homogeneous and map-based alternatives, so its improved fit may partly reflect greater flexibility rather than a more biologically valid parameterization. As a result, the model comparison supports the conclusion that this parameterization fits better under the current setup, but not necessarily that it is generally superior or more biologically realistic.

(3) The reliability and heritability analyses are valuable demonstrations of what scalable individualized modeling can enable, but they do not establish the simulated features as validated biological mechanisms. Because these simulated features are derived from individualized structural and functional imaging data, their stability across repeated measurements and genetic influences may partly reflect information already present in the empirical inputs or fitting targets. These results therefore support a more cautious conclusion: the simulated features retain stable and genetically structured variation, but their biological interpretation remains model dependent.

(4) The empirical demonstrations are narrower than some of the broader claims made in the manuscript. Most analyses rely on one human imaging dataset, one cortical parcelation, one main brain model, and a specific fitting objective, while broader claims refer to diverse populations, dense networks, high-dimensional models, and biological applications. The current results show that cuBNM is a useful and scalable tool in the tested setting, but the extent to which the findings generalize across datasets, model classes, network resolutions, or clinical contexts remains to be established.

Reviewer #2 (Public review):

Summary:

In vivo glia-to-neuron conversion emerges as a potential regeneration-based therapeutic strategy for neural injuries and diseases. However, controversies exist in this exciting field, largely arising from the non-stringent methods employed for analyzing in vivo neuronal conversions. The study by Li et al. directly addressed this controversy regarding Neurod1-mediated microglia-to-neuron conversion. They took advantage of two transgenic mouse lines to specifically express Neurod1 in the microglia of adult mouse brains. Results from immunohistochemistry, in vivo live-cell imaging, and scRNA-seq convincingly demonstrate that microglia cannot be converted in vivo to neurons by ectopic Neurod1 expression under both normal and injury conditions. Instead, it induces microglia death, consistent with their earlier findings. These solid results, though negative, are critical additions to the field and further support that stringent lineage tracing methods are essential for studying in vivo cell reprogramming. Overall, the studies are rigorously designed and executed. Only minor issues need to be dealt with.

Reviewer #2 (Public review):

Summary:

In this study, the authors set out to determine how a contact-dependent bacterial antagonistic system contributes to the ability of specific bacterial strains to persist within a complex, native gut community derived from wild animals. Rather than focusing on simplified or artificial models, the authors aimed to examine this system in a biologically realistic setting that captures the ecological complexity of the gut environment. To achieve this, they combined controlled laboratory experiments with animal colonization studies and sequencing-based tracking approaches that allow individual strains and mobile genetic elements to be followed over time.

Strengths:

A major strength of the work is the integration of multiple complementary approaches to address the same biological question. The use of defined but complex communities, together with in vivo experiments, provides a strong ecological context for interpreting the results. The data consistently show that the antagonistic system is not required for initial establishment but plays a critical role in long-term strain persistence, an insight that moves beyond traditional invasion-based views of microbial competition. The observation that transferable genetic elements can confer only temporary advantages, and may impose longer-term costs depending on community context, adds important nuance to current understanding of microbial fitness.

Weaknesses:

Overall, the study is not a lack of evidence, but a deliberate trade-off between ecological realism and mechanistic resolution, which leaves some causal pathways open to interpretation.

Comments on revised version.

The authors have addressed all previous concerns thoroughly and satisfactorily.

Reviewer #2 (Public review):

This study by Jaykumar and colleagues seeks to expand the field's appreciation of insulin responses in the brain, specifically by implicating WNK kinase function in various neuronal responses, ranging from behavioral / memory changes to GLUT4 trafficking to the cell surface with subsequent glucose uptake. This revised study is now comprehensive and presents a logical and reasonably documented cascade of molecular interactions responsible in part for GLUT4 trafficking under the regulation of WKK and insulin. Additional data allow the authors to dissect a plausible WNK/OSR1/SPAK-sortilin pathway for the modulation of GLUT4 trafficking, in part by capitalizing on an overlay of various techniques and systems. The data - much of it in vivo or ex vivo - showing a potential role for WNK function in brain glucose utilization remains a compelling part of the story, with the dissection of the signaling cascade and a potential role for sortilin in mediating WNK function via effects on GLUT4 cellular localization now more convincing.

Initially, the group shows that oral WNK463 treatment - an inhibitor of WNKs broadly - in mice augments a number of memory readouts. These findings fit within the context of the overall story the authors present: that WNK function is critical to brain glucose utilization, which impacts learning. Multiple approaches are used to show that WNK463 treatment, i.e. inhibition of WNKs, increases glucose uptake, including labeled 2-deoxyglucose uptake in vivo in the brain and in isolated synaptosome, and uptake in ex vivo hippocampal slices. These findings are solid and consistent. With the exception of some relatively minor comments regarding the data presentation made to the authors and now fully addressed, the findings showing that WNK463 treatment increases GLUT4-mediated glucose uptake and surface localization of GLUT4 are reasonable, with the hippocampal slice data being particularly relevant.

While the details of the WNK signaling cascade is dense, in the revised application one clearly appreciates the molecular interrogation and interactions the group is dissecting, supported by the use of multiple models. With the additional findings, these systems and the data now reinforce each other, presenting a strongly documented overall story.

A limitation of the study with the initial submission was the authors' reliance upon a single pharmacological tool (WNK463) to inhibit WNK kinases. WNK463 apparently has substantial specificity for WNKs and WNK463 treatment lessened OSR1 phosphorylation (a WNK substrate). Nevertheless, the cohesiveness of the findings in terms of the broader pathway engagement (GLUT4 trafficking, glucose uptake) is consistent with the author's proposed mechanisms and conclusions. The authors have additionally addressed this concern in the revised manuscript with more information supporting the specificity of WNK463 as well as the multiple approaches to confirm the effect of WNK463 on the WNK signaling pathway of interest.

The final few paragraphs of the discussion that weave the author's findings into the field more broadly, including Sortilin function and neurological disorders, are appreciated. Additional clarity in the Methods section is also helpful.

Reviewer #2 (Public review):

Summary:

This study is quite thorough, tackling this NO-dependent UV avoidance circuit with both breadth and depth. There are several novel discoveries throughout, but the whole package represents perhaps even more than the sum of these parts.

Strengths:

The presentation of the work is compelling. The introduction sets up the question and the state of the field very nicely. The discovery of the non-canonical NO receptor pathway in the ciliary photoreceptors is fascinating and will likely open up new avenues for future research into NO-pathways in different species. The use of genetic and pharmacological manipulations of circuit components was well thought-out. The authors applied different experimental techniques expertly throughout the study so that they could develop a comprehensive view from the molecular to the behavioral levels.

Weaknesses:

The authors have done an excellent job revising and explaining their model. No important weaknesses remain, in my opinion.

Reviewer #2 (Public review):

Summary:

The manuscript "Quantifying microbial fitness in high-throughput experiments" provides a comprehensive analysis of the various approaches to quantifying fitness in microbial evolution, focusing on three primary factors: encoding of relative abundance, time scale of measurement, and the choice of reference subpopulation. The authors systematically explore how these choices impact fitness statistics and provide recommendations aimed at standardizing practices in the field. This manuscript aims to highlight the impact of differing fitness definitions and the methodologies utilized for analysis and how that can significantly alter interpretations of mutant fitness, affecting evolutionary predictions and the overall understanding of genetic interactions in the experiments.

Strengths:

The choices for quantifying fitness in evolution experiments are critical and highly relevant given the increasing prevalence of high-throughput experiments in evolutionary biology. The authors methodically categorize fitness statistics and their implications, providing clarity on a complex subject. This structured approach aids in understanding the nuances of fitness measurement. The manuscript effectively highlights how different choices in fitness measurement can influence fitness rankings and the understanding of epistasis, which is important for modeling evolutionary dynamics.

Comments on revisions:

The authors have comprehensively addressed all previous comments and suggestions. In particular, the addition of the new methods section: 'A guide to calculate pairwise relative fitness under the logit encoding from bulk competition data' - significantly improves the clarity of the implementation and helps in the overall interpretation of the framework.

Reviewer #2 (Public review):

This paper describes application of the "GLM-Spectrum" mass univariate approach to examine the effects of age on M/EEG power spectra. Its strengths include promotion of the unbiased approach, suitable for future meta/mega-analyses, and the provision of effect sizes for powering future studies. These are useful contributions to the literature. What is perhaps lacking is discussion of limitations of this approach, in comparison to other methods.

An analogy is the mass univariate approach to spatial localisation of effects in fMRI/PET images. This approach is unbiased by prior assumptions about the organisation of the brain, but potentially also less sensitive, by ignoring that prior knowledge. For example, a voxelwise univariate approach is less sensitive to detecting effects in functionally homogeneous brain regions, where SNR can be increased by averaging over voxels. In the context of power spectra, the authors' approach deliberately ignores knowledge about the dominant frequency bands / oscillations in human power spectra. This is in contrast to approaches like FOOOF and IRASA, that explicitly parametrise frequency components. I am not saying these methods are better; I just think that the authors should acknowledge that these approaches have advantages over their mass univariate approach (in sensitivity and interpretation; see below). I guess it is a type of bias-sensitivity trade-off: the authors want to avoid bias, but they should acknowledge the corresponding loss of sensitivity, as well as loss of interpretation compared to model-based approaches (i.e., models that parameterise frequency; I don't mean the statistical models for each frequency separately).

An example of the interpretational loss can be seen in the authors' observation of opposite-signed effects of age around the alpha peak. While the authors acknowledge that this pattern can arise from a reduction in alpha frequency with age, this is an indirect inference, and a direct (and likely much more sensitive) approach would be to parametrise and estimate the peak alpha frequency directly for each participant, as done with FOOOF for example (possibly with group priors, as in Medrano et al, 2025, EJN). The authors emphasise the nonlinear effects of age in Fig 2A, but their approach cannot test this directly (e.g. in terms of plotting effects of age on frequency, magnitude, width for each participant), so for me, this figure illustrates a weakness of their approach, not a strength.

Then I think the section "Two dissociable and opposite effects in the alpha range" in the Discussion section is confusing, because if there is a single reduction in alpha peak frequency and magnitude with age, then there is only one "effect", not "two dissociable" ones. If the authors do want to claim that there are two dissociable age effects within the alpha range, then they need to do a statistical test, e.g., that the topographies of low and high alpha are significantly different. This then reveals another limitation of the mass univariate approach - that space (channel) is not parametrised either - so one cannot test for significant channel x effect interactions within this framework, as necessary to really claim a dissociation (e.g., in underlying neural generators).

While the authors show that normalisation of each person's power spectra by the sum across frequencies helps improve some statistics, they might want to say more about disadvantages of this approach, e.g., loss of sensitivity to any effects (e.g. of age) that are broadly distributed across majority of frequencies, loss of real SI units (absolute effect sizes) (as well as problems if normalisation were used for techniques like FOOOF, where the 1/f exponent would be affected).

Please give more information how artifactual ICs were defined. This may be important for cardiac artefacts, since Schmidt et al (2004, eLife) have pointed out how "standard" ICA thresholds can fail to remove all cardiac effects. This is very important for effects of age, given that age affects cardiac dynamics (even though the focus of Schmidt et al is the 1/f exponent, could residual cardiac effects cause artifactual age effects in current results, even above ~1Hz?).

Please could the authors clarify the precise maxfilter arguments, and explain what "reference" was used for the "trans" option - e.g., did the authors consider transforming the data to match a sphere at the centre of the helmet, which might not only remove some of the global power differences due to different head positions, but also be best for generalisation of the effect sizes they report to future studies (assuming the centre of the helmet is the most likely location on average)? And on that matter, did head positions actually differ by age at all?

Comments on the latest version:

I am happy with their revised version.

Reviewer #2 (Public review):

Summary:

The authors train E-I recurrent networks with short-term synaptic plasticity on a sequential delayed match-to-sample task, comparing regular versus jittered sample timing. They report a small accuracy gain under rhythmic input, a more separable population geometry during encoding, organization of internal oscillations around the dominant input frequency, a preference for temporal order over feature encoding, and improved decodability and persistence of stimulus information in both activity and synaptic efficacy. A delay-period perturbation shows synaptic efficacy contributes more than activity to maintenance.

Strengths:

The model is well-specified. Dale's law, the STSP formulation, the training objective, and the hyperparameters are all reported clearly enough to reproduce, and code is shared. The statistical machinery is appropriate, with cluster-permutation tests for the spectral analyses and across-network sign-flip tests rather than naive pooling. The temporal-order versus stimulus-direction dissociation in Figure 4C is the most interesting result. The negative association between phase locking and direction selectivity is non-trivial and argues against a simple global-gain reading, and it connects to Liebe et al. 2025. The serial-position decoding curves and the synaptic-versus-neuronal perturbation are well-motivated tests of the maintenance claim.

Weaknesses:

The behavioral effect is very small. Match accuracy is 0.991 versus 0.987, and non-match is 0.973 versus 0.969, on networks already at the ceiling. The entire mechanistic analysis is built to explain a roughly 0.4 percentage point difference, and the paper does not establish that this difference is functionally meaningful rather than a marginal byproduct of the timing manipulation. The IOI-dependence result meant to support it is weak, with an R-squared of 0.071 at a p-value of 0.029 on n of 67.

The core spectral and phase results are close to definitional and should be framed that way. The regularity index R is computed from IOI variability, the dominant frequency f0 is computed from the same IOIs, and the oscillatory metrics in Figures 3 and 4 are then measured relative to f0 and correlated against R. This shows that more regular input produces internal phase progression closer to the input-derived reference frequency, partly restating the input statistics rather than uncovering an independent network mechanism. The phase-locking-increases-with-regularity finding is the clearest case. This does not invalidate the analyses, but the manuscript currently reads them as a mechanism when much of the signal is built into the measurement.

The only genuinely causal manipulation is the delay-period shuffle, and it is underpowered at n of 15. Its main conclusion, that synaptic efficacy matters more than activity for maintenance, largely recovers prior STSP results (Mongillo et al. 2008, Masse et al. 2019) rather than establishing something specific to rhythm. The result the authors most want, that disrupting synaptic state removes the rhythmic advantage, is predicted in the Discussion but not tested.

The authors should add a control that breaks the circularity (a held-out f0/phase reference, or shuffling R against the metric) and run the causal STSP-disruption test that is mentioned in the Discussion.

The oscillatory framing is stronger than the model supports. Phase locking to a periodic input can reflect temporal predictability or repeated preparation without self-sustained entrainment, and the authors acknowledge this once but then use entrainment-style language throughout. The signals are extracted from firing-rate units and should not be read as LFP or EEG oscillations.

The authors should show raw single-unit and population activity so readers can verify the oscillations before the filtered pipeline. The delay perturbation largely recovers Mongillo 2008 / Masse 2019 rather than anything rhythm-specific, and the relationship to Liebe et al. 2025 should be addressed in the Results.

Appraisal and impact:

The authors largely achieve their stated aim of describing how temporal regularity constrains recurrent dynamics in this model, and the temporal-order preference is a useful prediction. The reach of the conclusions exceeds the evidence in two places: the functional importance of the behavioral effect and the degree to which the phase results are independent of the input construction. With the framing corrected and one causal test added, this would be a useful contribution to the modeling literature on timing and working memory rather than a definitive account.

Reviewer #2 (Public review):

Summary:

This work investigated differences in the temporal dynamics of neural populations in frontal orienting fields (FOF) and anterior dorsal striatum (ADS) in rodents during an auditory change detection task. The relative roles of these two regions have been studied previously and have been shown to play a role in the accumulation of evidence, with FOF converting this evidence into a categorical decision. By focusing on the temporal dynamics of neurons in these regions, the authors identified a subpopulation of neurons within FOF that displayed an abrupt ramping of activity near the time of decision commitment. Both FOF and ADS contained subpopulations exhibiting ramping activity aligned to stimulus onset. This is an interesting finding, suggesting that FOF contains a subpopulation of neurons that transforms accumulating evidence from other subpopulations in ADS and FOF into an action.

Strengths:

The conclusions of this paper are mostly well supported by data.

Weaknesses:

(1) In the neural analysis, the authors use a technique in which the weights of a linear decoder are used to define a feature vector for each neuron. These weights are used to measure the overall contribution of a neuron in decoding time (from stimulus or decision commitment). Interpreting decoding weights in this way is technically not correct (Kriegeskorte and Douglas, "Interpreting encoding and decoding models"), as a large weight in a decoder is not necessarily indicative of a large effect. Weights in decoding models can become large in order to cancel noise. Alternative analyses, for instance, treating the time series of each neuron as a feature vector, could have supported the conclusions from this technique.

(2) In this same analysis, it appears that the abrupt change in response in FOF at the time of decision commitment is coming from a single subpopulation of about 130 neurons. In the example session (Figure 8J), there is a clear outlier (the neuron in the top right corner). A closer inspection of the single neuron responses in this group would strengthen the results to confirm the abrupt change in mean population response is not coming from a relatively small number of neurons and sessions.

(3) The significance of the dynamical motif corresponding to transient bumps was unclear. For example, when looking at Figure 6K-L, I do not see any neuron groups that exhibit a clear transient bump. I would characterize all groups as ramping, with some groups showing steeper ramps. It would be helpful if the figure displayed the fraction of variance explained by PC2 so that it would be clear how much variance the bump motif is contributing. Given that there was no discussion of the functional relevance of this second motif, interpretation of this result is unclear.

(4) The finding that FOF contains subpopulations which slowly ramp during the trial as well as a subpopulation which acts like a switch that abruptly turns on at the time of decision commitment is interesting and significant and presents several computational questions. For example, is this subpopulation a non-linear readout of the more slowly ramping populations? The approach based on constructing a feature vector for each neuron, projecting these vectors into a low-dimensional subspace, and partitioning into subpopulations is insightful and allowed distinguishing these different computational functions within a single region (FOF). However, I found this particular result to not be clearly stated and obscured by other seemingly less significant results (e.g., existence of the transient bump motif) and other less interpretable analyses (e.g., subspace re-alignment).

Reviewer #2 (Public review):

Summary:

The authors investigated the role of hypothalamic CRH neurons in social behavior. They performed fiber photometry recordings in mice from CRH neurons and showed that novel conspecifics trigger stronger and more prolonged responses compared to familiar conspecifics and objects. The activity of CRH neurons appears to be related to risk assessment, as interactions with juvenile unfamiliar mice (lower-risk conspecifics) trigger responses similar to those of familiar adult mice. Behaviorally, CRH neurons were linked to increased anogenital investigation of unfamiliar compared to familiar mice. Optogenetic suppression of CRH neurons decreased anogenital sniffing of unfamiliar conspecifics.

Strengths:

The manuscript is elegant, and the results are compelling. The approaches are well justified, and the methods are validated (eg: Arch inhibition).

The findings substantiate the role of CRH neurons in responses to stress and uncover the involvement of these neurons in the assessment of social risk.

Weaknesses:

These are not weaknesses, just some observations: It is somewhat surprising that CRH neurons respond similarly to familiar and unfamiliar objects; it would be good to have more insights into that aspect.

Similarly, the novel context by itself is expected to lead to increased activity of CRH neurons (based on data from the last author's lab as well as other labs in the field). It is somewhat surprising (and interesting) that the novel environment did not affect the magnitude of CRH responses to unfamiliar conspecifics.

Reviewer #2 (Public review):

Summary:

Shimogawa et al. studied the effect of lysine acetylation at different sites in the alpha-synuclein (aS) sequence on the protein-membrane affinity, seeding capacity in the test tube and in cells, and on the structure of fibrils, using a range of biophysical methods. They use non-canonical amino acid (ncAA) mutagenesis to prepare aS lysine acetylated variant at different sites.

Strengths:

The major strength of this paper is the approach used for the production of site-specific lysine acetylated variants of aS using ncAA mutagenesis, as well as the combination of a range of biophysical methods together with cellular assays and structure biology to decipher the effect of lysine acetylation on aS-membrane binding, seeding propensity, and fibril structure. This approach allowed the author to find that lysine acetylation at positions 12, 43, and 80 led to lower seeding capacity of aS in the test tube and in cells, but only acetylation at lysine 80 did not affect aS-membrane interaction. These results suggest that lysine acetylation at position 80 may be protective against aggregation without perturbing the proposed functional role of aS in synaptic plasticity.

Weaknesses:

SDS is not a good membrane model to investigate the effect of lysine acetylation on aS membrane-binding because it is a harsh detergent and solubilizes membranes. Negatively charged vesicles or vesicles made of a mixture of lipids mimicking the lipid composition of synaptic vesicles are more accepted in the field to study aS-membrane interactions. The authors used such vesicles for the FCS experiments, and they could be used for the initial screening of the 12 lysine acetylated variants of aS.

Reviewer #2 (Public review):

Summary:

In this study, the authors propose the existence of an AQP4-positive tanycyte-associated canal system in the hippocampus and suggest that this system participates in waste clearance and contributes to Alzheimer's disease pathology. Using histological, ultrastructural, immunohistochemical, and RNA-based approaches, the manuscript attempts to reinterpret amyloid-β plaques and tau-associated structures as components of a tanycyte-derived waste-internalization system. The work is conceptually ambitious and raises observations that may stimulate discussion regarding glial organization and waste clearance in the diseased brain.

Strengths:

A strength of the manuscript is the combination of imaging modalities and anatomical observations across mouse and human tissue. Some of the reported morphological features are intriguing and may warrant additional investigation. The study also attempts to integrate structural observations with broader hypotheses regarding neurodegeneration and Alzheimer's disease.

Weaknesses:

The central interpretation depends almost entirely on identifying the observed hippocampal structures as tanycytes, and the evidence supporting this conclusion remains insufficient. Tanycytes are classically associated with ventricular regions in circumventricular organs, particularly in the third ventricle and median eminence region, yet the manuscript does not provide sufficiently specific anatomical or molecular evidence to convincingly distinguish the described structures from astrocytic, ependymal, radial glial-like, oligodendroglial, myelin-associated, vascular-associated, or degenerative elements. The marker profile used throughout the study, particularly the reliance on AQP4 labeling and Luxol-positive structures, is not sufficiently selective to establish tanycyte identity, especially in pathological tissue where reactive glial changes may occur.

This becomes particularly important because the manuscript repeatedly interprets Luxol-positive and myelin-associated structures as tanycytic processes or "myelin-derived tanycyte protrusions," despite tanycytes not being known to produce myelin. Alternative explanations are not sufficiently explored. Some of the canal-like structures shown in Figure 4 also resemble vascular profiles, and additional vessel markers would be necessary to exclude this possibility.

Several of the proposed structures and mechanisms are also difficult to reconcile with established cell biology and neuroanatomy. The introduction of new terminology such as "tanysomes," "waste receptacles," and "toroids" further extends the interpretation beyond what is currently demonstrated experimentally.

The discussion and integration of the existing literature on tanycytes are also insufficient. Tanycytes themselves are not clearly introduced; the manuscript does not adequately discuss what is currently established regarding tanycyte anatomy, ventricular localization, morphology, and function. Foundational literature defining tanycyte biology, including work from the Prévot group or others, is largely absent despite its central importance to the field. Because the manuscript proposes a substantial departure from established neurobiological concepts, it is particularly important that previous literature be discussed comprehensively and critically. The current version does not sufficiently contextualize the proposed model within the existing literature on tanycyte, AQP4, glymphatic, and Alzheimer's disease, making it difficult to evaluate what is genuinely novel versus what is merely being reinterpreted. It is also not entirely clear what is genuinely new here compared with the authors' previous work, particularly reference 11, which appears to present a highly similar conceptual framework.

More broadly, several of the manuscript's mechanistic conclusions extend well beyond the available evidence. The proposal that amyloid-β plaques and tau pathology represent hypertrophic tanycyte-derived waste structures is provocative and potentially interesting, but currently remains largely correlative and speculative. At several points, it becomes difficult to distinguish direct observations from broader mechanistic interpretation. The manuscript itself acknowledges that the proposed glial-canal hypothesis contradicts the current understanding of nervous system organization and states that ultrastructural serial-section analysis would be required to unambiguously determine the origin of the myelinated profiles described. This point is critical because the study's central conclusions depend on the assumption that these structures are tanycyte-derived. At present, this interpretation remains insufficiently demonstrated, which substantially limits the strength of the broader pathological and mechanistic conclusions proposed throughout the manuscript.

Although access to human material is understandably limited, the study appears to include only one male and one female AD patient, making it difficult to assess the reproducibility or frequent these structures are across individuals and pathological conditions. The manuscript would benefit from clearer characterization of prevalence, reproducibility, and variability across samples.

Overall, the manuscript presents an unconventional and thought-provoking model that may stimulate discussion. However, the evidence currently provided does not convincingly establish tanycyte identity for the described hippocampal structures, and several of the broader disease-related interpretations would require substantially stronger anatomical and molecular evidence before the proposed model can be convincingly supported.

Reviewer #2 (Public review):

Summary:

The study introduces an innovative device designed to collect non-invasive saliva samples from animals using disposable cassettes with odor attractants and filter paper. The authors aimed to validate this tool for pathogen monitoring, specifically by detecting pathogen RNA in animal models. While the concept is compelling and the problem statement well-framed, the validation of the device for pathogen detection was not achieved. For example, the rabies virus was not detected in the chosen model, and results were limited primarily to FeLV. The work highlights the potential of saliva-based sampling for microbiota analysis, but the rationale for virus selection and the experimental design require further clarification. Overall, the study presents a novel approach with promise, though its current scope is better suited to microbiota monitoring rather than pathogen surveillance.

Strengths:

The innovative design of the device, which enables non-invasive saliva collection through disposable cassettes with odor attractants, represents a creative and practical advance in sampling methodology. The authors undertook an extensive experimental effort, generating a substantial amount of data that highlights the feasibility of saliva-based monitoring. The rationale for exploring saliva as a medium is valid, and the work successfully shows that the device can be applied to microbiota profiling, where the strongest results were obtained. This methodological innovation could be valuable for expanding non-invasive approaches to animal health monitoring.

The authors acknowledge that metabarcoding sequencing has limitations; however, the study could be refocused on the microbiota in general rather than on pathogen detection. They could give greater prominence to the taxonomic composition of microorganisms in saliva using high-throughput sequencing. That is where they obtained the most results.

Weaknesses:

Despite the enormous experimental effort undertaken, the results fall short of the expected success of the proposed test. The rationale and criteria for virus selection are not clearly explained, leaving the experimental design insufficiently justified.

The central aim of validating the device for pathogen detection was not achieved, particularly in the case of the rabies virus. The mouse infection model used for the rabies virus does not seem to adequately replicate the natural course of the disease. This could explain, at least in part, the negative results obtained.

Of the three viruses evaluated, satisfactory results were obtained only for FeLV, and the sample size remains limited. According to the literature reviewed, this virus is not common in wild cats, so the applicability of the results would appear to be limited primarily to domestic cats.

The collected samples were stored at −80 {degree sign}C for later analysis, which likely contributed to the high Ct values observed with the device. The need to store samples at low temperatures may be a limitation to applying this technique in wildlife sampling scenarios where access to dry ice or liquid nitrogen tanks may be difficult.

Stating that the device can be used for pathogen monitoring in wild animals is not desirable, since the viruses for which results were obtained are not relevant in wild animals. On the other hand, claiming that this is a tool for monitoring diseases in endangered species is also misleading. Endangered species are typically scarce and therefore would not be the reservoirs that these surveillance efforts should target. In fact, groups such as wild rodents would be a better target for monitoring zoonotic pathogens.

Reviewer #2 (Public review):

This study examines how curl in the retinal flow field can be used as a control variable for estimating and controlling the heading of a moving observer. The basic idea (which is not entirely new, see Matthis et al. 2022) is that translation along a path with eccentric gaze (meaning that the subject is not heading toward the point they are looking at) produces a pattern of optic flow on the retina with a rotational component around the point of fixation (which can be captured by the mathematical "curl" operator). The sign and magnitude of retinal curl varies with heading relative to the point of fixation, such that curl can be used as a control variable to steer rightward or leftward to move toward the fixated target. The authors perform behavioral experiments and show that there are biases in perceived heading that seem to be largely governed by retinal curl. They also show that a simple controller model can use curl to steer toward a target, and they provide a neural network model that provides a biologically-plausible implementation of the controller (although there are some questions about that).

There is a core of interesting work here that I think can be important to the field. However, there is a lack of clarity on several important fronts, including design of the behavioral experiments, presentation of the behavioral data, conceptual framing of what curl can and cannot do, etc. Equally importantly, the manuscript is not written in a manner that will make it accessible to most vision scientists. I consider myself to be pretty knowledgeable about optic flow, and I had to read most of the manuscript 3 or 4 times to be able to understand the bulk of it. And my experience is that most vision scientists do not understand optic flow well, so I fear that most of the readers that the authors should want to reach would struggle to understand the work. As written, this is mainly going to make an impact on a handful of optic flow gurus. Thus, this manuscript is going to need a major overhaul to clarify important issues and make this more accessible.

Major issues:

(1) The manuscript contains inconsistent, if not misleading, messaging about what information retinal curl does, and does not, provide regarding heading estimation. In the Abstract, the authors state: "We propose an alternative: the visual system utilizes retinal curl directly to estimate heading, rendering the explicit recovery of the FOE unnecessary." Based on my understanding of the rest of the manuscript, I find this statement to be a misrepresentation for two main reasons:<br /> a. To "directly estimate heading" relative to what? When not qualified, most people interpret "heading" to mean an observer's heading relative to the world (or some allocentric reference frame). But retinal curl only gives information about an observer's heading relative to the point on which their eyes are fixated. Moreover, that point of fixation will change every few hundred milliseconds in natural viewing, so the retinal curl will change with each new fixation even as heading relative to the world remains unchanged. So, I think most readers would grossly misinterpret the claim that retinal curl can be used "directly to estimate heading". Indeed, in the authors' controller model, the initial heading needs to be given and then the controller can work. But from where does the visual system get the initial heading, since it does not come from curl? These issues are left hanging. Thus, while curl can provide a very useful input for steering toward a fixated target, other signals are needed to estimate heading relative to the world. This has to be made much clearer early on, and a conceptual schematic diagram might help. Also, the authors generally do not specify the reference frame of the variables they are talking about, leaving lots of room for misinterpretations. It should be clear each time they are talking about a variable, such as heading, whether it is relative to the fixation target, body, world, etc.<br /> b. It seems to me that retinal curl will depend on other variables, in addition to heading relative to the fixation target. For example, it seems to me that the magnitude of retinal curl will depend on self-motion speed, the depth structure of the scene, the angle of elevation of the fixated target, and perhaps others. This is not discussed at all, and many readers would get the misguided impression that there is a 1:1 mapping from curl to heading (relative to fixation). If I am right that this is not correct, it means that retinal curl can tell the observer whether to steer right or left to move toward the fixated target, but it cannot tell them how much to steer. Indeed, in the authors' controller model, there is a free parameter that calibrates curl to angle. It makes sense that this works to fit trajectory data that are given from a fixed environment, but it is unclear how the brain would use retinal curl to control steering when these other variables are uncertain or changing unpredictably. Moreover, how does the system change the mapping from curl to steering command as the location of fixation changes relative to the current heading? These are issues that need to be brought up in framing the problem and discussed at some length. If the authors can show mathematically that retinal curl is only dependent on heading (relative to fixation) and not any of these other variables, it would be very valuable to show the equations for this relationship.

(2) The description of the behavioral experiment and presentation of behavioral data leaves a lot to be desired.<br /> a. First, it is stated (line 158) that "Participants continuously reported their perceived direction of self-motion while maintaining fixation on the yellow dot." Again, reference frame is completely unspecified. Participants were reporting their perceived heading relative to what? The fixation target? The world? What exactly were the instructions given to the subjects to perform the task? Based on the description of how perceived paths are computed (line 166-), it seems to be presumed that subjects are reporting their heading relative to the world because those angles are then converted into x and z coordinates in what I presume is a world-centered reference frame. But how do we know that subjects are accurately reporting their heading relative to the world? What if they are biased in their reports by the location of the fixation target relative to the scene, or by some other reference signal? Is it possible for the authors to rule out the possibility that perceptual biases seen in the unaltered curl condition result from observers not fully adopting the assumed reference frame of the task? If this cannot be firmly excluded, it seems to create problems for the rest of the study.<br /> b. I also feel that there is a mismatch between what the behavioral task requires and what the controller model does. Subjects are apparently asked to report their heading relative to the world, but the controller model only controls their heading relative to the point that they are fixating. I understand how this is resolved in the model, but I think this type of distinction is buried and will not be apparent to most readers. Again, the reference frames of what is being measured and controlled need to be specified explicitly in all parts of the paper, and the authors needs to explain how the system would combine curl-based control with some other measures of (at least initial) heading for world-centered heading to be computed. All of the assumptions need to be clearly specified.<br /> c. Second, I found it frustrating that the authors never present raw perceptual data from the observers. Rather, in Figure 2, we see reconstructed trajectories that are perfectly smooth with no indications of noise whatsoever. Since these paths are computed from the perceptual reports, there must be some noise inherent in them. The figures should represent this uncertainty somehow, and it should be explained how these perfectly smooth trajectories are obtained.

(3) "...the magnitude of retinal curl in the fovea can specify the body trajectory relative to gaze (Matthis et al., 2022)." The main idea put forward by the authors here seems to overlap heavily with this statement that they attribute to Matthis et al. 2022. While I think this paper still adds importantly to the topic, the authors do not discuss how their findings are different from those of Matthis et al. 2022, why they are an important extension, etc. Readers should not have to go read this other paper to have any idea how the present findings are placed in importance relative to the literature.

(4) The analysis and treatment of eye movements is extremely weak. The authors discarded trials for which gaze deviated from the fixation point by more than 3 degrees (which is a LOT given that the eye speeds are generally in the neighborhood of 0.5 deg/sec), and they provide basic stats on the distribution of positions. But this largely misses the point: it is not small position errors that are likely to matter, but rather velocity errors. Even a small amount of retinal slip of the target while it is being pursued will cause image motion that is going to alter the optic flow field around the fixation target. So, for example, the retinal curl field may no longer be centered on the fixation target. How do we know that some of the perceptual biases are not influenced by image motion resulting from imperfect tracking of the fixation target? This needs to be analyzed and discussed.

(5) I found the sections of text comparing the separate and joined fits (starting line 287) to be a bit too rosy. The authors show the separate fits in the main text, and it is not very surprising that these fits are good given that the model has 30 parameters, and these data are pretty low dimensional. The authors only show the joined fits in the supplement, and they say that they are almost as good as the separate fits (indeed they are better in a model comparison sense, but this is 30 parameters vs. 2 parameters). However, when I look at the fits of the joined model in the supplement, I don't find them to be very impressive. In particular, the model grossly misses the data for the straight paths for several subjects (e.g., id5, id6, id8, id10). And fitting the straight paths would presumably be easiest. This implies that the joined model is really missing something and that fitting the curved paths interacts strongly with fitting the data for different fixation target locations on the straight path. I think that the authors should discuss the results a bit more soberly and tone down their conclusions here.

(6) The section of the paper on neural simulations (starting line 387) has a few weaknesses. First, why are only straight paths simulated here? This does not seem to provide a very rigorous test of the model. Second, it is awkward that the simulation results are presented in units of pixels, rather than degrees. Third, the authors seem to downplay the fact that the neural estimates of heading seem to oscillate rather wildly (over a range of hundreds of pixels, whatever that means, see especially Fig. S16). It was far from clear to me how an estimate of heading with these large oscillations is useful. It would seem to require that heading estimates are integrated over substantial lengths of time to be reliable. It was therefore unclear how the model produces such smooth paths from these oscillating estimates.

Comments on revised version.

Overall, the authors have done a responsible job of responding to the comments of my previous review, and the manuscript is substantially improved. There are a few points on which I still do not completely agree with the authors, and I think these are important to document for the record:

(1) Introduction: "Pure visual decomposition should function regardless of 3D depth or whether the rotation stems from an active eccentric fixation." Perhaps in a world of noiseless perfect computation, this might be true. But I generally disagree. When there is more depth structure in an environment, then translation of the observer is generally going to create greater motion parallax. That is a fact that I don't think can be disputed. And greater motion parallax should help to decompose optic flow into components related to translation and rotation (the latter of which is not depth dependent), especially when there is noise in estimating location motion vectors.

(2) Related to point #9 of my previous review: I had asked why the authors believed that retinal curl was computed in area MSTd. Their response is that previous studies (i.e., Graziano et al. 1994) show selectivity to spiral motion stimuli in MSTd. That is true, but those studies typically placed the spiral stimulus centered on the MSTd receptive field, hence they were not presenting something like retinal curl as defined here. So, I think it is still an open question as to where in the brain retinal curl is encoded, and from which areas it would be possible to decode retinal curl from population responses.

(3) Related to point #10 of my previous review: I had asked about biological plausibility of the gaze-centered inhibition signal in the model. The authors' response is that parietal neurons show gain fields in which response depends (usually monotonically) on eye position. This is true, but it is not a trivial jump from gain fields in individual neural responses to a gaze-centered inhibition signal, and I think the authors should have been more forthcoming about the lack of an established neural signal that directly signals what they want in their model.

(4) The authors point out that the perceptual biases they measure take a few seconds to emerge and they attribute this to temporal integration. But in their curl manipulations, they temporally average over a 2.4 second window in computing the curl signals that they use to cancel or over-cancel curl. So, it is not clear whether some of the delay in the behavioral effects might result from their computations.

(5) Related to point #13 of my previous review: I had asked about empirical evidence for the assumption of a relationship between the heading preferences of MSTd neurons and their receptive field locations. In response, the authors state that such a relationship is built into the Layton and Browning (2014) model. While that is a precedent, citing another model as a response to a question about empirical evidence is not a convincing response. If there is no empirical evidence to support such a relationship, it would have been better for the authors to acknowledge this.<br /> Given the way that the eLife review model works, it is not necessary for the authors to address these comments, but I think they should be included in the public review record.

Reviewer #2 (Public review):

Here, the authors record dopamine release using fast-scan cyclic voltammetry in the nucleus accumbens/ ventromedial striatum (VMS) while rats perform variants of a go/no-go task. Two versions are self-paced, in that the rat can initiate a trial by nosepoking at the odor port at any time once the ITI had elapsed, whereas the other two require the rat to wait for a cue-light before responding. Two "long" variants also require either more lever-presses on go trials, or a longer nosepoke time for no-go trials, and also incorporate "free" trials in which the rat is rewarded for just heading straight to the food tray. The authors find that dopamine levels increase more during the response requirement for go than no-go trials, indicating a role for invigorating to-be-rewarded actions. Dopamine levels also steadily increased as rats approached the site of reward delivery, and the authors demonstrate quite elegantly that this was not due to orientation to the food tray, or time-to-reward, or action initiation, but instead reflects spatial proximity to the rewarded location. Contrary to previous reports, the authors did not discern any differences in dopamine dynamics depending on whether the trials were cue- or self-paced, and dopamine release did not scale with effort requirements.<br /> The manuscript is well-written and the authors use figures to great effect to explain what could otherwise be a hard-to-parse set of data. The authors make good use of the richness of their behavioral data to justify or negate potential conclusions.

Reviewer #2 (Public review):

Summary:

The manuscript from the Rice lab by Gangadharan et al., submitted to eLife, investigates the polymerization mechanism of the yeast microtubule polymerase Stu2. The lab has published a number of articles demonstrating the structural basis by which the two TOG domains of Stu2 each bind free tubulin heterodimers and has developed a tethered polymerization model by which the TOG domains drive polymerization by shuttling those tubulin subunits onto the microtubule plus end. A second model was proposed by Nithianantham et al. (eLife, 2018) based on a closed - to - open transitional state in which Stu2 unfurls and loads two longitudinal associated tubulin heterodimers onto the microtubule plus end. While the second model is not directly tested, the current work aims to further characterize/model the tethered polymerization model using a kinetic framework developed by developed by Breitsprecher et al. for Ena/VASP actin polymerization activity, using a model that is enzymatic (EMBO J., 2011). The general architecture and function of Ena/VASP on actin polymerization versus Stu2 on microtubule polymerization is a reasonable relation and hits upon, as the authors note, potential convergent mechanistic evolution across distinct cytoskeletal networks. The model effectively treats tubulin as the substrate, and the polymerized microtubule plus end as the product. If Stu2 is "enzymatic" in this framework, the model predicts it would behave with Michaelis-Menten kinetics, that there would a Vmax, and polymerase activity would either be "affinity limited" by TOG:tubulin affinity (KD) and/or "kinetically limited" by TOG:tubulin association (Kon) and transfer of tubulin to the microtubule plus end (Kt). The authors find that the Brietsprecher model works well for Stu2 activity, and that Stu2 best aligns with a "kinetically limited" model. The work is interesting and adds to the growing elucidation of the Stu2 microtubule polymerase model. While yeast microtubule polymerases are somewhat distinct in their architecture, there is significant overlap that findings from the manuscript can be utilized to inform the mechanisms of larger, more complex microtubule polymerases such as human ch-TOG.

Strengths:

The manuscript invokes the enzymatic model of Breitsprecher et al. used for Ena/VASP and conducts an elegant series of (mostly established) experiments to determine whether Stu2 microtubule polymerase activity aligns with the model - which they conclude does align, supported by the data/results obtained.

Weaknesses:

The authors used biolayer interferometry to measure TOG:tubulin affinity. The affinities obtained were significantly higher affinities than the lab obtained in an earlier publication using analytical ultracentrifugation. While differences in buffer and salt conditions may underlie these differences, additional runs using comparable buffer systems, or use of a third independent assay to measure affinities would have added rigor.

The discussion could be expanded to better compare and contrast the results with both existing polymerase models introduced in the introduction, as well as expanded to look at reversible enzymatic activity (microtubule depolymerization at low to zero tubulin concentrations) and microtubule plus versus minus end activity.

Comments on revised version.

The revised submission has addressed these comments adequately.

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

Comments on revised version:

The revised version has addressed my major points raised in the original review.

Reviewer #2 (Public review):

Uphoff and colleagues present the results of a study focused on characterizing the binding of SVEP1 to TIE1 along with Angiopoietin-2. Starting with computational prediction of SVEP1 binding to TIE1, the authors identify the region of SVEP1 that serves as a high-affinity ligand for TIE1. Advanced studies identify a weak secondary binding site within SVEP1 that appears to be sufficient but not necessary for its interaction with TIE1 based on in vivo rescue experiments. The most novel contribution of the manuscript seems to be the identification of angiopoietin-1 and -2 as co-factors that seem to enhance the binding of SVEP1 with TIE1 and impact downstream AKT signaling. They propose a complex in which SVEP1 binds to TIE1 and ANG2.

Although the first set of results is essentially confirmatory, the identification of ANG-2 as a "co-factor" enhancing the binding of SVEP1 to TIE1 and associated downstream signaling (i.e., Figures 3 and 4) is novel and is of interest. However, the manuscript and its conclusions would greatly benefit from some clarifying details and additional experiments to ensure rigor and support specific claims.

Comments on revised version:

I have no further comments. The authors have addressed my concerns.

Reviewer #2 (Public review):

Summary:

This study aims to test the hypothesis that microsaccades are linked to the shifting of spatial attention, rather than the maintenance of attention at the cued location. In two experiments, participants were required to judge an orientation change at either a validly cued location (80% of the time) or an invalidly cued location (20% of the time). This change was presented at varying intervals (ranging from 500 to 3,200 ms) after cue onset. Accuracy and reaction times both showed attentional benefits at the valid versus invalid location across the different cue-target intervals. In contrast, microsaccade biases were time-dependent. The authors report a directional bias primarily observed around 400 ms after the cue, with later intervals (particularly in Experiment 2) exhibiting no biases in microsaccade direction towards the cued location. Noteworthy, it would have been interesting to observe whether directional biases in microsaccades are also evident when compared to a neutral condition. The authors argue that this finding supports their initial hypothesis that microsaccade biases reflect shifts in attention, but that maintaining attention at the cued location after an attention shift is not correlated with microsaccade direction.

Strengths:

The results are straightforward given the chosen experimental design. The manuscript is clearly written, and the presentation of the study and its visualisations are of a high standard.

Weaknesses:

The link between attention and microsaccades has been the subject of extensive research over the past two decades. The authors present a potential solution to the conflicting past findings, arguing that attention should be considered a dynamic process that can be broken down into an attention shift and a sustained attention phase. To differentiate between the two components, the authors varied the interval between the onset of the attention cue and the test stimulus. It would have been nice to use a theory-driven criterion (or an independent measure), in addition to their data-driven approach, to distinguish between these components of a dynamic attention concept. Moreover, it is important to note that the current experiments take a purely correlational approach.

Reviewer #2 (Public review):

Summary:

This manuscript addresses an important and mechanistically interesting question: whether plasma membrane glutamate transporters contribute only to slow clearance of ambient glutamate or whether they can rapidly shape synaptic signaling during high-frequency auditory activity. This manuscript provides important evidence that EAAT-mediated glutamate uptake is not merely a slow background clearance mechanism but is essential for maintaining reliable synaptic transmission and linear stimulus-intensity coding in ventral cochlear nucleus T-stellate cells during sustained auditory nerve activity.

Strengths:

The finding that EAATs may be required for rapid, local control of glutamate during high-frequency auditory nerve activity is interesting and could have broad relevance to auditory processing. The electrophysiological evidence is generally strong, particularly the use of patch-clamp recordings, stimulus trains, partial versus complete EAAT blockade, and comparison with bushy cell/endbulb synapses. The comparison between T-stellate cells and bushy cells/endbulb synapses strengthens the manuscript. The authors demonstrate that EAAT blockade disrupts coding in T-stellate cells but has little effect on bushy cell spike transmission, supporting a cell-type- and synapse-specific role of glutamate uptake.

Weaknesses:

However, some mechanistic conclusions, especially the specific contribution of neuronal versus glial EAATs and the absence of glutamate crosstalk between auditory nerve inputs, rely mainly on pharmacological and indirect electrophysiological inference and would be strengthened by additional anatomical, genetic, or direct glutamate-sensing evidence.

(1) Clarification of DL-TBOA concentration.

The authors used bath application of 200 µM TBOA and 25-50 µM in the other experiments, stating that "sub-maximal concentrations (25-50 µM)". The authors should provide a clearer rationale for why different concentrations were used across experiments rather than a fixed concentration.

The reversibility of DL-TBOA effects should be demonstrated by washout experiments. In addition, potential off-target effects of DL-TBOA on postsynaptic receptors, intrinsic membrane excitability, or presynaptic release (e.g., PPR measurement) should be carefully considered. It would also be useful to test the effects of the submaximal DL-TBOA concentrations (25-50 µM) on membrane potential and inward currents, shown in Figure 1, to determine whether these concentrations depolarize the membrane potential in current-clamp mode or induce inward currents under voltage-clamp conditions.

(2) Potential contribution of altered intrinsic excitability.

In Figures 3B and 3C, DL-TBOA appears to induce additional action potentials even immediately after the first stimulation, whereas Figures 6 and 7 suggest that the first EPSC is not substantially altered. This raises the possibility that the enhanced firing may partly result from a modest depolarization caused by background glutamate accumulation or from other changes in intrinsic membrane properties after drug treatment. To address this, the authors should provide a quantitative analysis of physiological parameters under submaximal DL-TBOA conditions, including spontaneous action potential frequency, resting membrane potential, input resistance, and spike threshold.

(3) Spillover/ crosstalk between AN-fiber-synpases.

The authors should provide more explanation of how altering the number of active auditory nerve fibers demonstrates the absence of glutamate spillover/crosstalk between bouton synapses. Strong stimulation likely recruits more AN fibers, but it may also change release probability, axonal synchrony, or stimulation spread. The authors should more clearly justify the interpretation that strong stimulation recruits additional independent AN fibers rather than altering release probability or activating fibers with different intrinsic properties.

(4) Interpretation of glial versus neuronal EAAT contributions.

The authors claim that both neuronal and glial transporters contribute to rapid uptake using pharmacological approaches. The pharmacological data demonstrate that glial EAATs play a major role in glutamate clearance at T-stellate cell synapses. The strong increase in EPSC decay time and synaptic charge after UCPH-101/DHK application supports the conclusion that glial transporters contribute substantially to limiting glutamate accumulation during sustained auditory nerve activity. However, the conclusion that neuronal EAATs contribute directly should be stated with some caution. The evidence for neuronal EAAT involvement is indirect and depends on the pharmacological specificity and completeness of glial EAAT blockade. The conclusion would be strengthened by additional evidence, such as EAAT subtype expression/localization in T-stellate cells or auditory nerve terminals, transporter current recordings, immunohistochemistry, or genetic manipulation of neuronal EAATs. In addition, fitting the decay phase with a double-exponential model may help determine whether glial and neuronal EAATs contribute over distinct temporal windows.

Reviewer #2 (Public review):

This manuscript describes the fascinating phenomenon of growth hormone (GH)-independent growth occurring in the mother during pregnancy. This growth was most pronounced in dwarf mice that are lacking the receptor for growth hormone-releasing hormone (GHRH) and therefore showing isolated GH deficiency. However, the pregnancy-induced growth could also be observed in wild-type mice, suggesting that it is a normal part of the maternal adaptation to pregnancy. The study falls short of identifying the mechanism(s) driving this pregnancy-induced growth response, but it certainly reveals a novel insight into maternal physiology. The authors have completed a range of experiments in mice to prove that, as well as being GH independent, the pregnancy-induced growth also did not require GH signaling in the liver (i.e. not another pregnancy-specific ligand operating through the GHR to promote IGF). They also provided complementary data from a population of humans with untreated isolated GH deficiency that are broadly consistent with the hypothesis. While it is important to consider the significant species differences between rodents and humans, both in terms of growth physiology and also in terms of evolution of placental somato-mammotrophic hormones, this unique population are a valuable resource and adds credence to the study. Overall, I find this a compelling research story, but disappointingly unfinished. There are some areas where additional information could improve the ability to interpret the data, and some additional concepts that could be considered in the discussion. There are also areas where additional experiments might provide important insights. However, I think that such suggestions can be considered as appropriate for future research, rather than delaying consideration of the current manuscript.

Main comments:

(1) Data in Figure 1 are remarkable - not so much the growth in pregnancy in the wildtype mice, because while elevated GH is well known in pregnancy, but growth in the dwarf mice is indicative of GH-independent growth. From these data, it seems that there is good evidence that growth in pregnancy is an adaptive function. However, it is possible that growth is achieved in dwarf mice and that in wildtype mice may have been mediated through different mechanisms. The dwarf mice showed an increase in liver and plasma IGF1, suggestive of an additional ligand driving IGF in pregnancy. One could hypothesize that such an effect could be mediated by an additional pregnancy-specific ligand activating the GH receptor. In humans, placental growth hormone could be such a ligand, but as far as we know, there is no placental GH in mice. In contrast, the wildtype animals showed suppression of liver and circulating IGF1, and low levels of pSTAT5 in the liver during pregnancy. These data (in Figure 5) are very surprising. Given the high circulating GH in pregnancy, as well as high placental lactogen (which would be expected to activate STAT5 in the liver through the Prlr), the low levels of pSTAT5 are unexpected and would seem to indicate some sort of acquired insensitivity to GH. Is this entirely driven by down-regulation of STAT5b protein, or could there be activation of other, negative regulators of STAT signalling, such as SOCS? What is causing such a profound suppression of STAT5? Regardless of the mechanism, this suggests that pregnancy-induced growth in wildtype mice is independent of circulating IGF1 (potentially a different mechanism or in addition to that seen in IGHD mice).

The data shown in Figure 6 are a major strength of the study, showing that the pregnancy-induced changes are not specific to one particular transgenic model, but still occur in a variety of models affecting GH through different approaches. Given the pregnancy-specific nature of the changes, however, it seems an oversight not to have evaluated the role of placental lactogens. Prlr is highly expressed in the liver, but the function of this hormone in the liver is not well established. Could the extremely high levels of PL be mediating this growth response? Given the low expression of STAT5 in the liver and the fact that plasma IGF1 is not markedly elevated, it seems more likely that this growth response may be mediated by locally produced IGF1 in target tissues.

I think these possibilities could be addressed by an expanded discussion of species variation in placental hormones, to highlight that humans have expansion of the GH locus, but rodents have expansion of the prolactin axis (see Soares, M. J. The prolactin and growth hormone families: pregnancy-specific hormones/cytokines at the maternal-fetal interface. Reprod Biol Endocrinol 2, 51, 2004). Importantly, placental GH and chorionic somatomammotropins (CSM) in humans are all variants of the GH gene, but CSM have preferential activity at Prlr. This seems to be a fundamental species difference in pregnancy biology, but has been interpreted as an example of convergent evolution, with conservation of prolactin and GH-like functions at the maternal-fetal interface, mediated by different mechanisms, likely contributing to the metabolic adaptations of the mother (see Newbern D, Freemark M. Placental hormones and the control of maternal metabolism and fetal growth. Curr Opin Endocrinol Diabetes Obes. 2011; 18: 409-416). While the preceding function has focused on explaining the evolution of placental lactogens (either prolactin or GH variants), the present data suggest that there are also mechanisms to maintain growth in pregnancy, independent of GH (even in the absence of a placental GH).

(2) The human data are very interesting, and my initial impression was that it seemed unlikely to be the same phenomenon. Was there any real evidence for "growth" in pregnancy? Pubertal maturation of long bone growth might be expected to prevent further growth in adulthood. However, these issues were appropriately discussed, and it seems well justified to evaluate this unique population of women with IGHD who underwent pregnancy. It would be very interesting to know if these women experienced elevated IGF1 during pregnancy, indicative of placental GH contributing to growth. Mechanistically, this might be more like the dwarf mouse situation of IGHD, that the situation in wildtype mice (associated with liver insensitivity to GH and low IGF1).

(3) It would be useful to include investigations that isolate the effects of pregnancy and the placental hormones. Such studies could include evaluating growth in pseudopregnant mice with IGHD (pregnancy-like changes in hormones but lacking the placental contribution) and in IGHD animals that experience pregnancy but not lactation (pups removed at birth). I accept that this might be too large an additional study to add for the present manuscript.

(4) It is an important and translationally relevant observation that pregnancy increased the risk of long-term weight gain, and that after the first pregnancy, the pregnancy-induced growth response was more directed to promoting fat deposition. Does this provide any mechanistic insight? Could a metabolic adaptation result in growth?

Reviewer #2 (Public review):

Summary:

This study uses neurofeedback to modulate alpha-band activity and examines how this influences pain-related processing. The question is timely and methodologically elegant, because it addresses whether noninvasive modulation of ongoing oscillatory activity can causally shape pain perception and/or expectation-related processes.

Strengths:

The use of neurofeedback as a tool to modulate alpha activity is a major strength, because it provides a noninvasive and conceptually clean approach to probing the functional role of oscillatory brain activity. The design is also attractive because it links neurophysiological regulation to a psychologically meaningful outcome, namely pain processing. Further, the induced changes were also related to different EEG microstates and ERP components during the processing of the pain stimulus, and therefore the authors demonstrate a clear relation between preparatory prestimulus states and stimulus processing.

The manuscript appears to address an important and clinically relevant question, and the idea of testing whether alpha regulation can alter pain-related responses is of high interest for systems neuroscience and pain research.

Weaknesses:

Methodologically, it is unclear what alpha values were used in the analyses. It is stated that alpha was extracted within 2s windows of the 16s long feedback period. However, the values change across this period. Which value is used for the correlation with the pain ratings and all other analyses? Using the average across the 16s could reflect large values in the first half and low values in the final half, but for the relationship between alpha and pain, the last segments should be more relevant. If the initially elevated alpha activity subsides several seconds before the onset of the pain stimulus, it is difficult to see how it could influence subsequent pain processing.

Related, after the 16s feedback period, a fixation period is used with a 3-5s length. If alpha band activity is relevant for the consecutive pain processing, the amount of alpha in this period should be relevant. The authors should demonstrate that the induced alpha change during the feedback period remains stable during the fixation period and that the activity in this period is related to pain processing.

Further, it should be noted that the alpha band modulations related to alpha band training were accompanied by significant effects in other frequencies. Therefore, a clear relationship between alpha and behavioral pain ratings is not the only interpretation. Correlations with other frequencies or combinations of frequency band modulations should be incorporated to allow a more precise interpretation. Furthermore, in the sham feedback group, an increase in alpha band activity was observed (p=0.06), and the small difference in the pain intensity rating may be related to a clear outlier in the Sham group (Figure 4a).

In both groups, a main effect of training, regardless of sham or real feedback, was reported with a small difference between groups. But the main modulator seems to be related to the instruction to modulate the neural activity, and this large effect should be discussed in more detail regarding, for example, possible attentional processes.

A further central concern is that the visual feedback signal (the ball movement) may generate expectations that are not specific to alpha activity and that these expectation processes modulate the pain processing (ball down may indicate more pain). It is well known that intensity cues can generate expectations about upcoming perceptions, and the used feedback signal with an increasing or decreasing visual curve clearly signals what intensity should be expected. Therefore, it is important to show that the amount of positive (ball up) and negative visual displays is matched between the sham and real feedback group. Further, the authors should report whether the final ball position can predict the latter pain rating in both groups or differentially. Following this interpretation, alpha band activity is not directly related to pain processing but only serves as a signal that is transformed to a visual stimulus that then generates expectations.

Finally, the manuscript would benefit from a more explicit analysis of whether individual alpha changes are related to pain ratings within each subject. If higher alpha is truly linked to reduced pain perception, this should be visible at the participant level during learning of the neurofeedback procedure. Relatedly, there is no learning period incorporated, and usually participants are not able to regulate their alpha activity from the first trial on. The authors should include an analysis of the development of alpha band activity over learning and a relation of these individual alpha values and the corresponding pain ratings.

I cannot find a link to the preregistration in the current manuscript.

In summary, a "causal" relation of alpha activity with pain perception -that is mentioned several times in the manuscript- is not fully supported by the present results

Reviewer #2 (Public review):

Summary:

The study tests whether male/female-related information is represented in a form that generalizes across faces, bodies, and gender-associated objects. Using within- and cross-category MVPA, regression RSA, comparisons with fine-tuned CNNs, and connectivity analyses, the authors identify a right middle temporal gyrus region whose patterns generalize across the three stimulus classes. They conclude that this region provides a category-general, mid-level representation of gender and acts as a neural hub.

Strengths:

The question is novel and important, while the logic of the study is straightforward. Examining faces, bodies, and objects within the same participants provides a useful extension beyond the predominantly face-based literature. Cross-category decoding is also a stronger test of shared information than simple anatomical overlap between within-category maps. The combination of MVPA, RSA, computational modelling, and connectivity analysis is ambitious, and the replication of the CNN layer profile with both AlexNet and VGG16 is a useful characterization of relevant information.

Weaknesses:

(1) The construct labelled "gender" is not equivalent across stimulus classes. For faces and bodies, the male/female label is intended to track a property of the depicted person, albeit one inferred imperfectly from appearance; for objects, masculinity or femininity is not an intrinsic property of the object but a culturally contingent association that may vary across observers and contexts. Treating both as levels of a single binary factor risks conflating person-category information with gender-stereotypic object associations and interpreting their common neural discriminability as evidence for one abstract concept of gender. The term "object gender" could also be confused with grammatical gender in some languages (e.g., French or German).

(2) The CNN analysis does not isolate the shared male/female component. The authors correlate the complete six-condition neural RDM with the complete CNN RDM. However, rMTG also carries substantial information about whether an image is a face, body, or object. Consequently, the peak correspondence with Conv4 may reflect category structure rather than the representation that supports cross-category male/female decoding. The current analysis does not establish that shared gender-related information specifically depends on mid-level features.

(3) The connectivity interpretation is overstated. PPI measures task-dependent covariance; it does not establish information transmission, directionality, or an upstream-to-downstream processing sequence. The reported face-body connectivity similarity is also small (r=.08). Also, describing rMTG as a "hub" is not justified without network-centrality measures, lesion evidence, or causal perturbation.

The authors partly achieve their aims. The results provide credible evidence that patterns in rMTG contain information that generalizes across binary male/female-labelled faces and bodies and masculine/feminine-associated objects. They do not yet establish a genuinely abstract representation of gender, a specifically gender-related correspondence with intermediate CNN layers, or a neural hub that transmits information through a directed network. With more precise framing and targeted reanalysis, the study could make a useful contribution to research on social vision and cross-category representation.

Reviewer #2 (Public review):

Summary:

The authors extend the ORGaNICs framework (a recurrent circuit that dynamically implements divisive normalization) to connected cortical areas with explicit top-down feedback. Because the network has a known analytical fixed point that coincides with (or closely approximates) the normalization equation, the authors can linearize about that fixed point and derive closed-form expressions for the power spectral density, inter-areal coherence, and communication subspaces. Using a two-area instantiation (V1 & V2) with a single fixed parameter set and no data fitting, they show the model reproduces: (i) contrast-response functions with steeper slope V2; (ii) gamma-band power and coherence peaks that shift to higher frequency with contrast; and (iii) a low-dimensional inter-areal communication subspace that is lower-dimensional than the within-area subspace. They derive parallel predictions of what happens by changing model parameters: feedback gain enhances inter-areal and suppresses within-area communication, and normalization is necessary for both the oscillatory dynamics and the reduced subspace dimensionality. A three-area extension (V1&V4, V1&V5/MT) is used to argue that differential top-down feedback can dynamically route functional connectivity.

Strengths:

(1) Analytical tractability: Deriving power spectra, coherence, and communication-subspace structure in closed form from a known fixed point is genuinely valuable.

(2) Conceptual unification: Framing coherence and communication subspaces as arising from the same normalization-driven dynamics is an elegant and useful contribution.

(3) Breadth from few assumptions: A large range of phenomena (contrast gain, gamma dynamics) emerges from normalization-based model assumptions.

(4) Biological grounding: The mapping of model variables onto identified cell types connects the abstract computation to known cortical microcircuitry.

(5) The prediction that input-gain versus feedback-gain modulation produce distinct spectral signatures gives experimentalists a clear way to test the framework.

Weaknesses:

(1) Comparisons are qualitative, not quantitative: The theory/experiment panels are visual side-by-side comparisons. There is no quantitative goodness-of-fit for any predictions.

(2) The simulations use τ ≈ 1 ms for all cell types, which the authors acknowledge is unrealistically short; realistic values would shift the gamma peaks to lower frequencies.

(3) Divisive normalization is a special case and is recovered exactly only for the identity recurrent matrix (self-normalization). Some statements that the circuit implements divisive normalization exactly need softening.

(4) The element-wise (multiplicative) interaction in the modulator dynamics is not tied to a specific cellular mechanism.

Reviewer #2 (Public review):

Summary:

The authors provide the first thorough profiling of neurons in Tribolium characterized by the expression of the transcription factor foxQ2, which will be useful for developmental neurobiology. They use state-of-the-art methods convincingly to not only identify the neurons, but also to further characterize them anatomically and neurochemically.

Strengths:

Thorough and meticulous application of state-of-the-art anatomical methods in a non-standard laboratory organism.

Weaknesses:

No weaknesses were identified by this reviewer.

Comments:

I don't really have any major suggestions at all. Loved the work.

There is only one tiny nitpicking aspect:

P21: "Biogenic amines are involved in learning and memory and setting arousal threshholds (Davis, 2023), which are functions performed by the mushroom bodies and related to the function of the central complex in goal directed navigation, respectively."

MBs mainly process olfactory memory. At least in Drosophila, most other kinds of memories are being supported elsewhere.

https://pubmed.ncbi.nlm.nih.gov/10454381/

such as, e.g., visual pattern learning in the CX

https://pubmed.ncbi.nlm.nih.gov/16452971/

or motor learning in motor neurons

https://pubmed.ncbi.nlm.nih.gov/38779314/

or ventral ganglion, antennal lobes, and median bundle for place learning:

https://pubmed.ncbi.nlm.nih.gov/10706599/

If the authors focus on MBs, this sentence ought to reflect the fact that the function of the MBs is much narrower than the current sentence appears to suggest.

Reviewer #2 (Public review):

Summary:

This work provides an open-source, Python-based, graphical user interface for curating the detection and classification of dentate spikes (DSs) from hippocampal local field potential (LFP) recordings. The tool may also be used to detect, but not classify, sharp wave-ripples (SPW-Rs). The tool utilizes previously published Python packages for loading LFP files and creating experiment-specific probe objects. Detection and classification parameters are clearly defined and logged in a parameter file before starting processing. Once LFP data has been mapped to the probe object, event detection occurs across all channels. DSs are detected as qualifying peaks in the filtered DS band LFP, while SPW-Rs are detected as qualifying peaks in the filtered ripple band amplitude envelope. An initial curation step allows visualization of the LFP, instantaneous current source density (CSD), and depth-by-frequency band power plots for determining the approximate channel locations of key anatomical regions (i.e., CA1, the hippocampal fissure, and the hilus of the dentate gyrus). The optimal channel for detection is further refined in the next step by comparing event waveforms and quality metrics across channels. Artifacts and noisy waveforms can also be manually excluded during this step. Finally, DSs detected from the optimal channel are classified by computing the CSD profile around events and then clustering the first two principal components of all CSDs. The authors claim that this customizable tool will standardize DS detection and classification.

Strengths:

Toothy's detection and classification algorithms are appropriate and well-validated in the literature. The ability to change many parameters, the CSD calculation method, and clustering algorithm is helpful for precise replication of methodology that has varied previously. Default parameters optimized for mouse recordings provide a standardized starting point for rodent researchers.

The authors' commitments to transparency and user-friendliness are to be commended (e.g., clear instructions, defined and logged parameters, multiple visualization options, etc.) and are likely to be appreciated by new users. Researchers with little-to-no coding experience should find this tool especially powerful for jumpstarting their own DS analyses.

While not the focus of the paper, the capability to detect SPW-Rs provides an additional use case for Toothy and streamlines simultaneous analysis of SPW-Rs and DSs.

Weaknesses:

I encountered unexpected errors while trying to load LFP data into Toothy for testing, indicating that the "data ingestion" stage of Toothy requires minor code revision.

Toothy's utility for recordings that do not produce an LFP depth profile is unclear. According to the authors, Toothy allows probe designs with irregular spatial sampling (e.g., tetrodes) to be used. However, recording from a linear probe with electrodes spanning from approximately the hippocampal fissure to the hilus of the dentate gyrus is required for Toothy's full functionality. For example, Toothy uses a DS type classification algorithm that relies on sufficiently sampled CSD depth profiles that tetrode recordings cannot provide. As such, usage is currently restricted to detection only for certain recording setups.

The documentation on Toothy's output could be improved. Specifically, the work does not state which files different data are saved to or list the properties saved per detected event. Furthermore, the work does not discuss the potential importance of DS properties that are saved besides those related to the timing of the DS and its type.

Reviewer #3 (Public review):

In this manuscript, the authors investigate the role of attention in foveal processing during a naturalistic task. They record neural activity from extrastriate visual areas V4 and inferotemporal cortex, as well as from the lateral prefrontal cortex, in macaques performing a free-gaze visual search task. In this task, animals searched for a face or house target among multiple complex stimuli, with no constraints on eye movements. Unlike classic studies of visual attention, which often rely on controlled fixation, this work examines neural activity in both foveal and peripheral receptive fields during naturalistic eye movements.

The main question addressed by the authors is how feature-based attention is distributed and coordinated across foveal and peripheral visual fields during active search, and how this attentional processing influences saccade behavior. The authors show that foveal units in visual areas exhibit feature-based attentional enhancement, with stronger responses when a fixated stimulus is a target compared to when the same stimulus serves as a distractor. Peripheral units in visual and prefrontal areas show both feature-based and spatial attentional modulation, consistent with prior work. Finally, the authors show that attentional modulation depends primarily on stimulus category rather than response magnitude, with neurons showing similar enhancement for all images within the target category regardless of how strongly individual images drive the cell.

There are several notable strengths of this paper including:

(1) Disentangling feature-based and spatial attention during naturalistic vision remains a central challenge. This paper tackles both simultaneously, parsing neural populations by object selectivity (face-selective, house-selective, non-selective) and RF position (foveal vs. peripheral).

(2) The unconstrained search task (Fig. 1A) moves beyond the dominant fixed-gaze, cued-attention designs (Zhou & Desimone, 2011) to study attention as it operates during natural behavior, with sequential fixations and voluntary saccades.

(3) The scale of the multi-area recordings is a major strength and is well aligned with current trends in primate and human neuroscience toward large-scale, multi-area recordings. Simultaneous recordings from visual and prefrontal areas, comprising over 4,900 foveal units and more than 1,500 peripheral units, enable meaningful cross-area latency comparisons and area-specific analyses of attentional modulation. This study builds on the authors' previous analyses of this dataset by expanding the scope to show that feature-based attention generalizes across neuronal classes and operates on categorical identity rather than response magnitude.

(4) The combination of simultaneous multi-area recordings and a rich behavioral paradigm provide a dataset that is well suited for population decoding, cross-area interaction analyses, and trial-by-trial prediction of saccade choices, which could substantially deepen mechanistic understanding beyond the largely univariate comparisons presented here.

While the data broadly support the paper's main conclusions, several issues limit the strength of the mechanistic interpretation and should be taken into consideration:

(1) Receptive field size is not explicitly quantified and may confound foveal-peripheral comparisons. Units are classified as foveal or peripheral based on responsiveness to the cue versus the search array (Methods, p. 17), but the manuscript lacks essential information about receptive field sizes, eccentricities, and the number of search stimuli falling within each receptive field and related proper controls. This is critical because receptive fields in visual area V4 at foveal eccentricities are relatively small (Gattass et al., 1988; Desimone & Schein, 1987), whereas receptive fields in inferotemporal cortex can span several degrees to tens of degrees and often include the fovea (Op de Beeck & Vogels, 2000; DiCarlo & Maunsell, 2003; Zoccolan et al., 2007). Given the 2{degree sign} × 2{degree sign} stimulus size, multiple search items could potentially fall simultaneously within peripheral receptive fields. This introduces a potential confound, as attentional modulation is known to be strongest when multiple stimuli appear within a single receptive field (Reynolds et al., 1999). Although the authors acknowledge this issue for visual area V4 (p. 17), it is neither quantified nor controlled for. Without explicit receptive field mapping relative to the search array, comparisons between foveal and peripheral units, as well as between visual areas, are difficult to interpret cleanly.

(2) Attentional modulation is difficult to dissociate from saccade planning and decision-related signals. The free-gaze paradigm enhances ecological validity but introduces a temporal confound: mean distractor fixation durations are approximately 156 ms (p. 9), while attentional effects emerge between 137 and 170 ms after fixation onset (Fig. 2). As a result, the reported attentional modulation coincides with preparation of the subsequent saccade. Neural activity measured in the primary analysis window (150-225 ms; p. 19) therefore likely reflects a mixture of visual, attentional, motor planning, target recognition, and behavioral relevance signals, all of which are known to modulate responses in visual areas at similar latencies (e.g., Chelazzi et al., 1998). Moreover, target fixations (~257 ms) and distractor fixations (~156 ms) occur on fundamentally different behavioral timescales, which may inflate apparent foveal attentional effects. While the authors suggest that these timing differences support the idea that foveal feature-based attention facilitates prolonged fixation on target stimuli, this interpretation is not fully supported by the current analyses. That said, the saccade-aligned analyses of peripheral units (Fig. S3) partially mitigate this concern by demonstrating that feature-based modulation persists through saccade execution.

(3) The "attention-out" condition for spatial attention lacks directional control. In the spatial attention analyses (Fig. 4D-F), the "attention-out" condition appears to include all fixations followed by saccades directed away from the receptive field, regardless of saccade direction. This differs from classic spatial attention designs, which typically use controlled anti-saccades or saccades to fixed locations opposite the receptive field (e.g., Moore & Armstrong, 2003; Gregoriou et al., 2009). Saccades directed toward locations adjacent to, but outside, the receptive field may still partially engage spatial attention mechanisms near the receptive field via broad attentional fields or motor preparation gradients (Bisley & Goldberg, 2010). In addition, the "attention-out" condition likely contains a heterogeneous mixture of trials in which the stimulus in the receptive field is either a target or a distractor, since feature-based attention effects are derived from this same pool of trials. As a result, spatial and feature attention effects are not fully orthogonal, and variance related to feature attention may already be embedded in the spatial attention baseline.

[Editors' note: the authors have provided responses to each of these points.]

Reviewer #2 (Public review):

Summary:

Song et al. comprehensively analyzed the SHIV-infected macaque B cell repertoires and commonalities among their antibody responses, despite their diverse genetic background. They suggest these studies would inform HIV-1 vaccine design.

Strengths:

This study is well-designed and used proper analysis methods, and the figures are clear and effectively presented.

Weaknesses: