Reviewer #1 (Public review):

Summary:

This manuscript addresses how internally generated evaluative signals can arise during self-guided learning in the absence of external reward. Using zebra finch song learning as a model system, the authors propose that tutor-song memorization and vocal performance evaluation are not separate processes, but instead emerge from a shared local circuit that learns to predictively cancel tutor-song-related auditory input. The comparison across several candidate circuit architectures, the quantitative comparison to experimental calcium imaging data, and the decomposition of the learned recurrent connectivity into modes shaping the error landscape are all strong aspects of the work. The final demonstration that the learned error signal can guide a downstream reinforcement learning agent also provides a useful proof of principle.

Strengths:

The idea that tutor-song memorization and performance evaluation can emerge from a shared predictive-cancellation circuit is interesting, and the combination of circuit modeling, comparison to experimental data, and error-landscape analysis is compelling.

Weaknesses:

(1) A central conclusion of the manuscript is that the E→I→E model best matches experimental data. This establishes model fit, but it does not yet explain why E→I and I→E plasticity are important for tutor-song cancellation and error-signal formation. Does E→I plasticity primarily teach the inhibitory population to represent tutor-song-related excitatory activity? Does I→E plasticity then implement the negative image required to cancel expected excitatory responses? Does the closed E/I loop primarily control gain, shift the minimum of the error landscape, or both? A useful analysis would be to compare models in which only E→I synapses are plastic, only I→E synapses are plastic, both are plastic, or neither is plastic.

(2) The analysis in Fig. 5 does not yet explain how the identified modes arise from the specific E→I/I→E plasticity mechanism. For example, are the landscape modes mainly produced by E/I gain-control dynamics? Are the memory modes related to an inhibitory negative image of the tutor song? Are these modes localized to particular blocks of the recurrent connectivity, such as E→I or I→E weights, or are they distributed across the full network?

(3) The manuscript emphasizes the emergence of sparse population error codes. However, in Fig. 6, the downstream actor-critic model uses the population mean excitatory activity as a scalar negative reward. This compresses the high-dimensional sparse population response into a single scalar. If the downstream system only uses the mean response, why is a sparse high-dimensional error code functionally important, beyond matching the observed response distribution? Conversely, if the sparse population pattern contains richer information about the direction or structure of vocal errors, how might downstream reinforcement pathways read out this information?

The manuscript should clarify whether sparsity is proposed to have a functional role in motor learning, or whether it is primarily a biological feature of the evaluative circuit. This point is particularly important because the broader framing of the paper concerns internal evaluative signals, whereas the final reinforcement learning demonstration uses a scalar reward.

(4) The actor-critic model in Fig. 6 is useful because it demonstrates that the learned error signal contains enough information to guide motor learning. However, the reinforcement learning module is attached downstream of the auditory circuit and is highly simplified. Therefore, it remains somewhat ambiguous whether Fig. 6 should be interpreted as a circuit model of song learning or as a demonstration of sufficiency. The latter interpretation seems appropriate and valuable, but the manuscript should state this more explicitly. The central contribution appears to be the bootstrapping of an internal evaluative signal, rather than a complete model of sensorimotor song learning. Clarifying this distinction would prevent overinterpretation of the actor-critic results.

Reviewer #1 (Public review):

Summary:

Marschall et al. develop a theoretical framework for analyzing multi-task dynamics in nonlinear recurrent neural networks (RNNs). In the RNN model, recurrent connectivity is a linear superposition of multiple non-overlapping low-rank components, each corresponding to a separate "task". Each task is an autonomous dynamical system that does not incorporate external inputs. The "multi-task computation" setting examines whether multiple tasks (dynamical systems) can operate concurrently or how a network can switch between tasks.

Within this framework, the authors show that when connectivity consists of two low-rank components implementing two tasks (a limit cycle and a bistable attractor), the network exhibits winner-takes-all dynamics, resulting in only one task being active and the other one suppressed. A task consistently dominates this competition when the magnitude of its low-rank connectivity ("task strength") exceeds that of the other task. When one task is dominant and many other tasks with weaker low-rank connectivity are present, increasing the number of tasks destabilizes the dominant-task dynamics, leading to chaotic fluctuations in network activity. Supported by the dynamical mean-field theory analysis, the authors show that as the dominant-task strength increases, the network transitions through three dynamical regimes: chaotic spontaneous activity, chaotic task-selected dynamics, and non-chaotic task-selected dynamics. The theoretical analysis additionally predicts how latent task-related dynamics manifest in single-neuron activity and how the dimensionality of population activity changes across the three dynamical regimes.

The results are interesting, the analyses and simulations are rigorous, and the text is clear and easy to follow. Overall, this study is a significant and timely contribution to the literature on low-rank RNNs, an influential model class for low-dimensional neural dynamics in computational neuroscience.

Main comments:

(1) In this modeling framework, only one dominant task can be selected while all other tasks are suppressed. In contrast, several previous studies constructed RNNs (either through gradient-descent optimization or reservoir computing) that simultaneously generate outputs for multiple tasks across the corresponding task-specific readouts. Of course, what counts as a task is arbitrary, and one could view the dynamics of a reservoir network as implementing a single high-dimensional "task" with multiple readouts. Nevertheless, it would be helpful to explicitly clarify the distinction and similarities between the current modeling framework and networks that simultaneously solve multiple tasks.

(2) The role of external inputs in task selection appears to be underdeveloped. It is only briefly examined in Fig. S4, with the conclusion that external inputs aligned with the task subspace cannot enable selection of the desired task. However, previous multi-task RNN models (e.g., optimized through gradient descent) are clearly able to switch across many tasks using external inputs. In these models, external inputs modulate RNN activity along specific directions, shaped through gradient descent, to select the relevant task for each input. In contrast, this study only considers inputs aligned with the m-direction (left loading vector in the low-rank connectivity for a task). Such input cannot enhance the corresponding task's activity through recurrent amplification (Fig. S4). Yet, low-rank RNN theory predicts that inputs aligned with the n-direction (task's right loading vector) are selectively amplified by the recurrent dynamics. Why inputs aligned with the n-direction were not examined? More broadly, focusing only on inputs aligned with either m- or n-direction appears too narrow, as trained multi-task RNNs indicate that task selection through external inputs is possible, but may require input directions different from m and n.

(3) It is unclear what the reason is for the task interference: overlaps between task loading vectors for different tasks or other nonlinear effects? This issue is especially prominent in the analysis of task capacity (Fig. 2D and Fig. 4A). The text states that the loading vectors are independent across tasks, i.e. there is zero expected overlap between loading vectors of different tasks (line 126). For a network of N neurons, a rank-R task requires 2R loading vectors. Under the assumption of independence, the maximum number of tasks is P = N/(2R). Then α=P/N can be at most 1/(2R). In the simulations, it is chosen R=2, such that alpha can be at most 1/4 for the loading vectors to remain independent. However, the range of alpha reaches up to 1 in Fig. 2D and Fig. 4A, suggesting that loading vectors are no longer independent across tasks for larger alpha. Is the linear dependence between loading vectors (i.e. overlap across tasks) the reason for the task-1 component norm to drop sharply around alpha=1/4 in Fig. 2D? More broadly, is it possible to isolate the contribution of overlap in task loading vectors versus other nonlinear effects?

(4) In the current version of the paper, it is difficult to understand how the analysis based on the dynamical mean-field theory in Methods explains the key observations from the numerical simulations. For example, the mechanism underlying the transition from the spontaneous state to chaotic task-selected state, and to the non-chaotic task-selected state remain opaque. Further, it would be helpful to specify which section in Methods is being referred to at each mention throughout the main text. Finally, Fig. 7 and Eqs. (11-13) clearly state the input sources that drive single unit activity, non-dominant task latent states and dominant task latent state. The decomposition is potentially very informative, but its implications are not discussed in sufficient detail. It would be helpful to provide an intuitive explanation of how contributions of these input sources evolve as connectivity strength of one task increases, and which of them eventually leads to the loss of stability of the previous network state.

(5) The text states that the results can be easily extended to include task-specific inputs and outputs (lines 117-119). However, such an extension does not seem to be straightforward and requires additional explanations. The dynamical mean-field theory analyses here are stationary and describe the steady-state of network dynamics. In contrast, common input-output tasks typically involve transient dynamics, in which time-dependent external inputs keep changing the RNN flow field, and steady-state is never reached. Under these transient conditions, it is unclear whether the same conclusions apply. For example, if a network is at a low-activity baseline when a task-input begins to drive activity in the corresponding task subspace, it is unclear whether the activity in other task-subspaces would grow sufficiently fast to cause interference, or whether such interference would not be observed. Thus, a more detail analyses are necessary to support the extension of the results to input-driven transient tasks beyond autonomous dynamical systems.

(6) When task strength is the same for all tasks, what determines which task will win the competition? Is it frozen noise in connectivity such that one task always wins, or do initial conditions determine which task wins, based on which task's activity grows faster?

(7) Does the theory require the activity of all neurons to operate in the saturating part of nonlinearity? For example, the text states "increased activity reduces the gain factor <Φ'(t)>" (line 169). This statement is only true when Φ'<0. For sigmoid nonlinearity used in the paper, Φ'>0 when firing rate is small. If a substantial fraction of neurons in the network is near the rest state, would the theory still apply? Similarly, this statement does not hold for ReLU nonlinearity, and it is unclear how the theory applies to ReLU networks in Fig. S3. The paper states that the results are not specific to the choice of nonlinearity (lines 176-178). However, the dynamics being studied (bistability and limit cycles) both operate on the saturating part of the nonlinearity. Could the authors clarify the assumptions on the nonlinearity for the theory to apply?

(8) Is it possible to interpret the results in Fig. 4? What does this dependence on the overlap matrix mean? Is there an intuition for this particular dependence, or is it just an observation without general interpretation?

(9) The results in Fig. 5E appear underdeveloped and somewhat arbitrary. It is unclear how the dependence of dimensionality on the recording time would change as a function of time spent in a task. If this time is long, then the curve grows slowly and total dimensionality is high. If this time is very short, then the network may not have sufficient time for all task variables to grow sufficiently large to contribute significantly to the total variance. Thus, the grows may be faster and the total variance may saturate at a lower value. Hence, it is unclear whether there will be always a qualitative difference from the spontaneous activity curve. Furthermore, since only one of two curves is measured, what quantitative criteria should be used to determine whether it is consistent with task switching or spontaneous state?

(10) On line 368: "For sufficiently large number of tasks, the dimensionality associated with sequential task selection can greatly exceed that of the spontaneous state (Fig. 5E inset)" - it seems that Fig. 5E inset shows the opposite that the dimensionality of spontaneous state can saturate at a very high value for large N, exceeding the dimension of task-switching network in the main plot. Although it is hard to say, since the inset has many lines with only two labels and no ticks on axis, so it is unclear what exactly does it show.

(11) Related to discussion on line 368-370: In a task-switching state, would the switching between tasks also be reflected in behavior? In addition, the time-correlation functions would not be stationary in task-switching state, i.e. they would change over time, whereas they will be stationary in the spontaneous state. Thus, could the two mechanisms be dissociated in experiments using behavior or metrics beyond dimensionality?

(12) On line 385, could the authors provide more details on how they envision the two potential mechanisms-synaptic plasticity and targeted neuromodulation-to reinforce a task-specific low-rank connectivity pattern? If neuromodulation changes the gain of individual neurons, this modulation corresponds to scaling of connectivity by a diagonal matrix, not strengthening of a specific low-rank component. Short-term facilitation or depression also modulates synaptic strength depending on the activity of the presynaptic neuron, thus also scaling connectivity by a diagonal matrix. It is unclear how these two mechanisms could produce a specific low-rank modulation.

Reviewer #1 (Public review):

Summary:

The authors present MiPS, a platform combining DMD-based patterned illumination, automated microscopy, retrained DeLTA segmentation, and mother-machine microfluidics to selectively inhibit or eliminate cells based on dynamic phenotypes. The system enables targeted UV or red-light illumination in real time using segmentation-informed projection masks, allowing selective enrichment directly within mother-machine devices. The manuscript demonstrates proof-of-concept enrichment of mCherry cells from mixed GFP/mCherry populations, characterizes off-target effects, and performs computational simulations of iterative enrichment rounds. Overall, the engineering and systems integration are impressive, and the platform has strong potential for applications in directed evolution, biosensor optimization, and dynamic phenotype-based selection workflows.

Overall, I believe the work is suitable for publication after minor revisions and clarification of several aspects of the manuscript. In particular, the paper would benefit from additional context in the Introduction and Methods sections, clearer positioning relative to existing platforms, improved figure readability/captions, and a more careful revision of the English throughout the manuscript.

Major comments:

(1) The manuscript should better position MiPS relative to recent microscopy-based and DMD-enabled selection/control systems, particularly Lugagne et al., Nature Communications (2024), DOI: 10.1038/s41467-024-46361-1. That work also combines mother-machine microfluidics, DeLTA-based real-time image analysis, and DMD projection. The key distinction here appears to be physical selection/enrichment through targeted killing rather than optogenetic control, and this difference should be stated more explicitly.

(2) The manuscript currently compares MiPS mostly to FACS/MACS. However, the more relevant comparison may be recent image-based and microfluidic photoselection systems. A dedicated comparison table discussing throughput, temporal phenotyping, iterative selection, dynamic phenotype tracking, and enrichment capabilities would strengthen the paper.

(3) The enrichment experiment in Figure 4 represents a relatively simple classification problem (GFP vs mCherry). Since the proposed applications involve subtle continuous phenotypes, it would considerably strengthen the manuscript to include at least one experiment selecting for high vs. low expressors within a single fluorescent reporter population.

(4) The strongest enrichment result (~170-fold enrichment in Figure 5) is entirely simulation-based. Since the manuscript already states that ~45 min is sufficient between rounds for growth evaluation, a real 2-3-round enrichment experiment seems feasible and would substantially strengthen the platform's practical relevance. This experiment appears realistic within a relatively short time investment.

(5) The bimodal distributions in Figure 2 suggest that a fraction of cells may be stress-resistant rather than simply surviving randomly. It would be useful to discuss whether repeated rounds could progressively enrich UV-resistant subpopulations.

(6) The manuscript repeatedly uses the term "killed," although the data shown in Figures 2 and 4 mostly demonstrate strong growth arrest/inhibition. Please clarify how the cutoff of division rate <0.4 h⁻¹ was selected and whether an independent viability assay was performed.

(7) The off-target analysis in Figure 3 is one of the strongest parts of the paper and should probably be emphasized more. The conclusion that the dominant effects are global rather than local is interesting, but additional discussion about optical scattering, ROS diffusion, or device-wide coupling effects would strengthen the interpretation.

(8) UV exposure is inherently mutagenic in E. coli, and untargeted cells still receive a substantial fraction of the UV dose at high targeting fractions. Please discuss whether the MB/red-light modality may be preferable in applications where preserving genotype integrity is important.

(9) The manuscript discusses that methylene blue (MB) improves the on:off target ratio, but MB also appears to reduce baseline growth by ~40% even without red-light exposure. This is potentially important for iterative selection workflows. Please discuss whether this effect is reversible after washout and how rapidly cells recover.

(10) The manuscript states that the retrained DeLTA model used ~3,000 annotated fluorescence images, but no train/validation/test split or segmentation performance metrics are reported. Since segmentation directly impacts phenotype classification and projection targeting, these details are important for reproducibility.

(11) The manuscript would benefit from a stronger Methods description regarding DMD calibration, alignment procedures, projection accuracy validation, and computational timing requirements for the real-time analysis pipeline.

Significance:

General assessment: This is a creative and technically impressive study that combines mother-machine microfluidics, automated microscopy, real-time image analysis, and DMD-based photoselection into a unified platform for dynamic, phenotype-based enrichment. The strongest aspects of the work are the systems integration, the quantitative characterization of off-target effects, and the conceptual demonstration that dynamic microscopy-derived phenotypes can be linked to physical enrichment workflows.

The main limitations are that the biological validation remains largely proof-of-concept and the most compelling enrichment results are currently simulation-based rather than experimentally demonstrated across multiple rounds. In addition, the manuscript would benefit from stronger positioning relative to recent image-based and DMD-enabled microfluidic control systems.

Advance: The study extends the field of single-cell microfluidics and image-based selection by introducing a platform that links longitudinal microscopy measurements directly to physical enrichment decisions within mother-machine devices. To my knowledge, the combination of iterative feedback-driven selection, DMD-based targeted elimination, and dynamic phenotype tracking in this context is novel.

The closest related systems appear to be recent DMD-enabled mother-machine platforms for real-time optogenetic control, particularly those reported by Lugagne et al. (Nature Communications 2024, DOI: 10.1038/s41467-024-46361-1). However, MiPS introduces a distinct conceptual advance by using patterned illumination for selective enrichment/elimination rather than gene-expression modulation alone.

The advance is primarily technical and conceptual, with potential downstream applications in directed evolution, synthetic biology, biosensor engineering, and dynamic phenotype screening workflows that are difficult or impossible to implement using FACS alone.

Audience: The work will likely be of strongest interest to researchers working in synthetic biology, microfluidics, single-cell analysis, systems biology, bioengineering, and automated microscopy. It may also be of broader interest to communities developing dynamic phenotype screening technologies, closed-loop biological control systems, and next-generation directed evolution platforms.

The audience is likely specialized but multidisciplinary, spanning both engineering-oriented and biology-oriented researchers. The methods and conceptual framework may also influence future development of automated selection systems beyond the specific mother-machine context.

Expertise - My expertise includes: Microfluidics, Synthetic biology, Single-cell systems, Automated microscopy, Real-time image analysis, Bioengineering platforms, Dynamic phenotype characterization.

Reviewer #1 (Public review):

Summary:

The study by McKim et al (eLife-RP-RA-2024-102684) seeks to provide a comprehensive description of the connectivity of neurosecretory cells (NSCs) using a high-resolution electron microscopy dataset of the fly brain and several single cell RNA seq transcriptomic datasets from the brain and peripheral tissues of the fly. They use connectomic analyses to identify discrete functional subgroups of NSCs and describe both the broad architecture of the synaptic inputs to these subgroups as well as some of the specific inputs including from chemosensory pathways. They then demonstrate that NSCs have very few traditional presynapses consistent with their known function as providing paracrine release of neuropeptides. Acknowledging that EM datasets can't account for paracrine release, the authors use several scRNAseq datasets to explore signaling between NSCs and characterize widespread patterns of neuropeptide receptor expression across the brain and several body tissues. The thoroughness of this study allows it to largely achieve its goal and provides a useful resource for anyone studying neurohormonal signaling.

Strengths:

The strengths of this study are the thorough nature of the approach and the integration of several large-scale datasets to address shortcomings of individual datasets. The study also acknowledges the limitations that inherent to studying hormonal signaling and provide interpretations within the context of these limitations.

Reviewer #1 (Public review):

Summary:

This study extends the authors' prior work on transgenic nhomie/homie boundary pairing (Fujioka et al. 2016 PLoS Genetics), which showed that these elements - corresponding to the eve TAD's left and right boundaries - can pair with endogenous copies over large genomic distances (142 kb here), bridging a linked reporter gene to endogenous eve enhancers for long-range gene activation (shown again here in Figure 1). Physical pairing was previously confirmed (Chen et al. 2018 Nat Genet) and further resolved by Micro-C (Bing et al. 2024 eLife), supporting the hypothesized "stem-loop" or "circle loop" topologies used to explain homie/nhomie directional pairing (shown here for nhomie in Figures 2-3). The authors recently showed that a Su(Hw) binding site is required for homie-mediated reporter gene activation by eve enhancers (Fujioka et al. 2025 Genetics); here, they extend this finding to nhomie (Figures 4-6), further showing that Su(Hw) motifs are required for Micro-C-detectable looping between transgenic and endogenous eve boundaries (Figures 7-9). Finally, they show that while cis-pairing over 142 kb is highly specific to homie/nhomie elements, transvection between homologous transgene insertions is more permissive (functioning with the Su(Hw)-bound gypsy insulator) but still shows some specificity (failing with the CTCF-bound Fab8 boundary, Figures 10-11).

Strengths:

The question of how pairs of loci can specifically physically pair over relatively long genomic distances is an interesting fundamental question. The study's strengths are the clarity and meticulous interpretation of the results, and the authors' conclusions are compelling.

Weaknesses:

A major weakness is that some figures reproduce previously published findings; in some cases it is unclear whether the same fly lines were used, and in others, the lines differ only slightly from those used previously (e.g., a shorter version of the Homie transgene than the one used previously). Most conclusions in this manuscript have already been published elsewhere. As a result, the paper does not report a genuine new discovery, and only incrementally advances our understanding of boundary pairing.

Reviewer #1 (Public review):

Summary:

Mast cells have previously been reported to play an important role in bacterial immune defence and act protectively in sepsis. However, many of these findings were based on studies using Kit mutant mice. In this study, the authors conducted a detailed investigation using mast cell-deficient Cpa3 Cre-Master mice. As a result, the authors found that the Cpa3 Cre-Master mice exhibited responses similar to wild-type mice in terms of bacterial immune defense. This suggests that the observed phenotype is not due to mast cell-dependent bacterial immune defense, but rather is associated with dysbiosis of the gut microbiota.

Strengths:

Mast cells have long been reported to play an important role in the protective response against sepsis, and their function in infection defense has been demonstrated. However, Kit mutant mice have been reported to exhibit impaired peristalsis, and several mast cell-specific genetically modified mouse lines have since been developed and examined in detail. This study presents an important finding by logically demonstrating that the exacerbation of sepsis in Kit mice is due to alterations in the gut microbiota, and that the phenotype previously thought to be mast cell-dependent was, in fact, not.

In addition, the experiments were carefully designed using mice with matched genetic backgrounds. These findings underscore the importance of microbiota composition in interpreting immune phenotypes and highlight the need for co-housing controls in mutant mouse studies.

A major strength of this work is the robustness of the CLP data, generated over eight years by three independent researchers across two institutions with large sample sizes, lending strong support to the conclusions.

Weaknesses:

The study assesses only a limited subset of gut bacterial species, leaving the extent to which E. coli expansion contributes to the observed phenotype unclear. Moreover, in the cohousing experiments, there is no evidence provided to confirm successful microbiota normalization between groups. A more detailed analysis of the microbial composition would be necessary to strengthen the reliability of the findings.

It is also important to note that Cpa3-deficient mice exhibit not only mast cell depletion but also defects in basophils and T cells. These additional immunological alterations may counterbalance one another, potentially masking phenotypic changes and complicating interpretation.

Furthermore, it remains to be determined whether the altered gut microbiota observed in KitW/Wv mice is a consequence of impaired intestinal motility, whether a similar phenotype is observed in KitW-sh/W-sh mice, and whether comparable results occur in SCF-deficient models. Addressing these questions would provide greater clarity on the contribution of mast cells versus secondary factors in the observed phenotypes.

Given that KitW/Wv mice exhibit impaired peristalsis, is the observed increase in E. coli a consequence of this dysfunction?

Previous studies with BMMC reconstitution experiments have indicated that mast cells are a source of TNF-how does this align with the current findings?

Comments on revised version.

The authors have made substantial efforts to address the concerns raised in the previous review, and the revised manuscript has been substantially strengthened by the additional microbiome analyses. In particular, the new data provide a more comprehensive characterization of the intestinal microbiota in both Kit mutant and mast cell-deficient mice and strengthen the interpretation that the microbiota alterations observed in Kit mutant mice are associated with Kit deficiency rather than mast cell deficiency per se. Although Enterobacteriaceae were significantly increased based on the unadjusted Welch's t-test, this difference did not remain statistically significant after correction for multiple comparisons. The authors appropriately acknowledge this limitation in the revised manuscript. Overall, I consider the major concerns regarding the microbiome analysis to have been adequately addressed.

Reviewer #3 (Public review):

In this work, Bryant, et al. investigate genetic interactions between non-essential members of the outer membrane protein biogenesis pathway and other genes in the genome using a transposon-directed insertion sequencing (TraDIS) approach in E. coli K-12. The authors identify interactions with other components of the envelope including LPS, peptidoglycan, and enterobacterial common antigen biogenesis, and they tie these interactions to specific members of the outer membrane biogenesis pathway. Although many of these interactions are known and have been previously investigated in the field, the study provides several synthetic phenotypes that could be useful for further investigations.

The strengths of the paper include the unbiased, TraDIS approach, and follow up on the interactions observed. The interactions with genes of unknown function also are of interest as they may suggest experiments to find the functions of these genes. Although the paper could better address the relation of its findings to existing literature, the work in the paper is well controlled and the findings will be of interest to the field.

Reviewer #1 (Public review):

Summary:

This study aimed to determine whether bacterial translation inhibitors affect mitochondria through the same mechanisms. Using mitoribosome profiling, the authors found that most antibiotics, except telithromycin, act similarly in both systems. These insights could help in the development of antibiotics with reduced mitochondrial toxicity.

They also identified potential novel mitochondrial translation events, proposing new initiation sites for MT-ND1 and MT-ND5. These insights not only challenge existing annotations but also open new avenues for research on mitochondrial function.

Strengths:

Ribosome profiling is a state-of-the-art method for monitoring the translatome at very high resolution. Using mitoribosome profiling, the authors convincingly demonstrate that most of the analyzed antibiotics act in the same way on both bacterial and mitochondrial ribosomes, except for telithromycin. Additionally, the authors report possible alternative translation events, raising new questions about the mechanisms behind mitochondrial initiation and start codon recognition in mammals.

Weaknesses:

All the weaknesses I previously highlighted were adequately addressed.

Reviewer #1 (Public review):

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

Summary:

D. Fuller et al. set out to study the molecular partners that cooperate with ATG2A, a lipid transfer protein essential for phagophore elongation, during the process of autophagy. Through a series of experiments combining microscopy and biochemistry, the authors identify ARFGAP1 and Rab1A as components of early autophagic membranes, which accumulate at the periphery of aberrant pre-autophagosomal structures induced by loss of ATG2. While ARFGAP1 has no apparent function in autophagy, the authors show that RAB1A is implicated in autophagy, although the precise mechanisms are not explored in the manuscript.

Strengths:

The work presented by Fuller et al. provides new insights into the composition of early autophagic membranes. The authors provide a series of MS experiments identifying proteins in close proximity to ATG2A, which is a valuable dataset for the field. Furthermore, they show for the first time the interaction between ATG2A and RAB1A both in fed and starved conditions, which extends the characterisation of the pre-autophagosomal structures observed in ATG2 DKO cells.

Reviewer #1 (Public review):

Summary:

King and colleagues generated a mouse with a point mutation in IL21R and investigate the influence on IL-21-mediated T and B cell activation and differentiation. They find that mutant mice show a reduced T and B cell response with CD4 T cell differentiation into T follicular helper cells being primarily affected.

Strengths:

The authors combine in vitro and in vivo analysis, including bone-marrow chimeric mice.

Weaknesses:

The effect of the IL21R EINS mutant does not specifically affect STAT1, as clearly shown in Figure 1 H, I. Particularly at lower doses of IL21 which may be more relevant in vivo, the effects are very similar. A second key weakness is the very small Tfh response, a not very clear PD-1 and CXCR5 staining to identify Tfh and a lack of a steady-state (prior to immunisation) comparison of Tfh numbers in the different mouse strains. The latter makes it impossible to know what fraction of the response is antigen-specific.

Comments on revised version.

Thank you for responding to some of my suggestions/comments.

Unfortunately, these responses do not address the concerns raised by the other reviewer and me (see 'weaknesses'). The argument that statistical analysis shows that there is no difference in p-STAT3/5 does not make any sense - it simply reflects the absence of sufficient statistical power.

No further experiments have been performed, and the authors didn't appropriately adjust the conclusions to reflect the limitations of the study.

To provide an example, the authors decided to keep the title 'An IL-21R hypomorph circumvents functional redundancy to define STAT1 signalling in germinal center responses' although both reviewers clearly highlighted that no conclusion about STAT1 can be made and the authors responded that 'We agree that further experiments are needed to definitively show that the effect is attributable to reduced STAT1 activation alone. Rescue experiments will be a focus of future experiments.'

These rescue experiments (and additional analysis of the Tfh response) are essential to allow firms conclusions to be made.

In its current form, the manuscript is unfortunately misleading.

Reviewer #1 (Public review):

Summary:

This manuscript is an excellent follow-up to your 2022 study, in which Sox17 expression was localized to the rete testis and shown to be required for proper formation of the Sertoli cell valve (transition region). By using Nr5a1-Cre to drive conditional deletion of Sox17 specifically in rete testis cells, you demonstrate that testis weights remain normal at 2 weeks of age but become significantly reduced by 8 weeks in Sox17-cKO males. At the later time point, the seminiferous epithelium is severely disrupted, with apparent arrest of spermiogenesis: the epididymal lumen is essentially devoid of sperm, and most tubules lack elongated spermatids.

Strengths:

Clearly shows the role of Sox17 in Sertoli cells being important to the SV function. The SV (transition region) between the rete testis and seminiferous tubules remains an understudied domain of testicular biology. The present work, together with your prior study, highlights intriguing mechanisms operating in this specialized niche.

Weaknesses:

The available data do not fully explain either the developmental assembly of the Sertoli valve or the precise consequences of its functional disruption. These studies are nonetheless valuable precisely because they raise more questions than they answer; the conceptual implications are thought-provoking.

Reviewer #1 (Public review):

Summary:

T cells that recognize lipids-CD1c are frequent in circulation; however, their role in infection is unclear. This study aims to understand how Mtb infection can shape the responses of CD1c-specific T cells. CD1c is expressed in MTB granuloma, but in lower amount than in nearby inflamed tissue. Mtb infection downregulates the expression of CD1c on monocyte derived DCs. Single cell RNA sequencing revealed the cytotoxic program inherent to the lipids-CD1c-specific T cells. Using an in vitro APC system where CD1c expression remains intact upon Mtb infection, the authors suggest that these T cells react better to Mtb-infected than uninfected Cd1c-expressing APC and reduce Mtb burden in infected cells. Therefore, Cd1c downregulation could be an immune evasion strategy used by Mtb.

Strengths:

This study asks an important question. The single cell transcription analysis suggests the inherent cytotoxic program of lipid-CD1c cells and provides insights into their phenotypic and potential functional profiles. Function experiments suggest that these autoreactive T cells can react to Mtb infection, adding to the paradigm of infection control by these non-conventional T cell population. In summary, this study reports potential role of lipid-CD1c specific T cells in Mtb infection., and the evidence overall supports the conclusions.

Weaknesses:

(1) The study engineered THP-1 cells that lacked endogenous beta2 microglobulin (beta2m) and stably expressed chimeric CD1c-beta2m (CD1c-THP-1 cells). T cells isolated from healthy donors showed stimulation when mixed with CD1c-THP-1, suggesting the presence of CD1c reactive T cells in healthy individuals (Fig. 1). It appears that antibody staining profiles for different markers are not from the same experiment (Fig. 1). It is better to show all the representative antibody staining from a single experiment. And a quantification of MFI values from at least three independent experiments should be shown with statistics. Further, in each of the representative antibody profile, unstained/isotype control should be incorporated. The characterization of beta2m KO cells - using DNA sequencing and western blotting - should be shown in supplementary.

(2) Next, using immunohistochemistry, the study suggests expression of CD1c in lung biopsy from TB patients, and that CD1c expression is reduced on primary MoDCs upon Mtb infection (Fig. 2).

(3) To assess CD1c-reactive T cell cytotoxicity in Mtb infection, the authors generated CD1c-T cell lines (from T cells from healthy individuals) using two different approaches. In one, CD1c-specific T cells were expanded upon mixing with CD1c-THP-1 cells, were sorted using CD1c-endo tetramer, and expanded. In another approach, CD1c-specifc T cells were sorted (using CD1c-endo dextramer) directly from blood-derived T cells and were expanded. The line obtained using one of the approaches to assess cytotoxicity towards CD1c-THP-1 cells, uninfected or infected with Mtb. It is seen that CD1c-T cells exert cytotoxicity towards infected, and not towards uninfected, CD1c-THP-1 cells (Figs. 3,4).<br /> The authors should clearly indicate the T line, obtained from which one of the two approaches, was used for the cytotoxicity assay, and what was the result with the line obtained from other approach. Fig. S4 shows the sorting strategy and final profile of the T line obtained after CD1c-endo tetramer-based sorting and expansion. This figure is mistakenly referred as supplementary fig. 3 in the caption of Fig. 3; should be corrected. Further, to complete the Fig. S4, the intermediate step of sorting scheme, which shows negative and positive fraction as defined by streptamer binding, should be included. And Y-axis of left-most lower-most panel (no tetramer) should be labelled. The Profile of the T line shows three distinct population: tetramer-low, tetramer-intermediate, and tetramer-high. The authors should comment on this, particularly on the tetramerlow population that can be CD1c-negative cells that have non-specific weak binding to the tetramer.<br /> The profile of the T line obtained from the other approach should also be shown, and the experiments that used those should be clearly indicated.

(4) To assess whether the elevated response of CD1c-T cells is mediated by the T cell receptor (TCR) that they harbour, the authors performed single T cell sequencing for TCR. They could identify 11 αβ pairs, two of them (named as EM1 and EM2) constituted 10 out of the 11 αβ pairs. It is surprising that such a shallow sampling (despite of the filtering mentioned in Fig. S9) such a high degree of clonal expansion and yielded two context-relevant TCR sequences. These TCRs were indeed DC1c specific, as indicated by their ability to upregulate CD69 upon engagement with CD1c (Fig. 5C). However, they respond very weakly to CD1c-THP-1 cells - the difference (THP-1 KO versus CD1c-THP-1 cells) is small for EM1, and the upregulation of CD69 per se is very weak (within the noise range) for EM2 (Fig. 5D). Nonetheless, the difference shown is statistically significant for both EM1 and EM2.

(5) The study further indicate that the CD1c-specific T cells are enriched for the marker associated with their cytotoxic functions, and they perform slightly better in controlling Mtb growth in culture.

The authors are suggested to carefully go through the manuscript a couple of times so that supplementary figures are referred appropriately and accurately.

Reviewer #1 (Public review):

Summary:

This study asks whether synapses formed by the same broad neuronal class (excitatory pyramidal neurons, PN) adapt their presynaptic organization in a cortex-specific manner, comparing prefrontal cortex (PFC) with primary somatosensory cortex (S1). The authors combine sophisticated electrophysiology (paired recordings and extracellular minimal stimulation), pharmacological perturbations of presynaptic Ca²⁺-secretion coupling, bouton Ca²⁺ imaging, and mechanistic modeling. Across two prominent excitatory connections (Layer 5 (L5) PN-L5PN and L2/3-L5PN), they provide convergent evidence that mature PFC synapses operate with looser Ca²⁺ channel-release sensor coupling than their S1 counterparts.

Overall, the study provides an appealing mechanistic link between synaptic nano/micro-architecture and cortical-area specialization. The idea that PFC synapses retain a more "plasticity-favoring" presynaptic state, while primary sensory cortex emphasizes reliability and timing precision, is potentially impactful for how we think about circuit computation and plasticity across cortical hierarchies.

Strengths:

A major strength is the multi-pronged experimental strategy. The paper first establishes robust, area-dependent differences in synaptic efficacy, reliability, timing, and short-term plasticity (facilitation prevailing in PFC versus depression in S1), using both paired recordings and minimal extracellular stimulation paradigms. The coupling interpretation is then directly supported by differential sensitivity to EGTA (and appropriate positive-control effects of fast chelators). Finally, volume averaged calcium signals are reported to be similar across areas, arguing against trivial explanations based on gross differences in calcium influx, and the modeling provides a quantitative framework for interpreting the observed chelator effects.

Weaknesses:

Limitations are minor and concern interpretation/clarity rather than core results. Some key inferences rely on indirect readouts (chelator sensitivity, fluctuation analysis-derived parameters, bouton-averaged calcium signals), each of which carries assumptions and potential confounds that should be discussed more explicitly. In particular, the, repatching paradigm for the paired-recording EGTA experiment, though very impressive, and the limited number of extracellular calcium conditions used for fluctuation analysis (three concentrations) can influence quantitative estimates and the confidence intervals around them.

Reviewer #2 (Public review):

Summary:

The study aimed to assess the associations between meteorological drivers and influenza is important although not new. The authors used 6 years of surveillance data and deep learning models, combining distributed lag non-linear models (DLNM) with Bayesian-optimized LSTM neural networks for predictive modeling. The key interest in this area is to explore the subtropical locations, where influenza is less common and circulates year-round. The authors further claimed that such an association could be able to provide an early warning in the community.

Strengths:

Study design based on a prospective cohort to analyse the data for retrospective outcomes.

[Editors' note: The Reviewing Editor has assessed the revised article without further input from the original reviewers. The authors have made considerable efforts to address the methodological and reporting concerns raised by the initial peer review, resulting in a substantially revised manuscript.]

Reviewer #1 (Public review):

Summary:

This work explores the relationship between sexual conflict and species diversification in a clade of small water striders. They use multiple phylogenetic methods to establish a potential phylogenetic tree and explore incomplete lineage sorting and introgression. They then compare their calibrated species tree with phenotypic measures of many species to try to infer the relationship between species diversity and sexual conflict. They show evidence for both ILS and introgression within the broader clade. They also find evidence that male leg diversity is associated with speciation, potentially due to sexual conflict, and that male genital grasping structures as well as female anti-grasping traits have less evidence for an association with speciation. This paper seems like a generally rigorous and interesting addition to the literature on sexual conflict and speciation, and I believe the conclusions are well supported with only a few minor concerns with methodology.

Strengths:

This paper verifies results using multiple methodologies to ensure robustness to changes in software use, and presents convincing evidence for the hypotheses tested.

Weaknesses:

(1) Lines 299-304: For the categorization of sexual conflict traits, were categories chosen by a blinded participant or by a researcher who knew the species they were observing? If any of these traits are subtle, this could add bias to the categorisation of traits.

(2) Lines 325-327: It is unclear to me if you control for phylogenetic relationships in this model. Diversification rate could be lineage-specific regardless of sexual conflict, so it seems like potentially including phylogenetic relationships in a model would control for that. And similarly, lines 331-333, I may be mistaken, but it sounds like you are using lm() to model a binary outcome (presence/absence), but lm() doesn't do logistic regression as far as I know, so it may be better to model this as a logistic regression (controlled for phylogeny) using glm().

(3) There are a few areas where the reporting of inference or statistics could be improved, and throughout the manuscript there are often mentions of 'significant' without any measure of uncertainty such as confidence intervals (example on lines 449-451). In lines 385-390, because the intervals are so wide, I would suggest saying these clades diverged between X-mya and Y-mya, rather than giving an actual estimate. It seems to me like giving a specific date is a bit overconfident when the authors have such wide intervals. On line 436, I think the authors should report confidence intervals for their 5mya estimate. In lines 455-456, what is this correlation and what are the authors' uncertainties around the estimate?

(4) Lines 549-545: When the two possible explanations for this result are reported, it seems like the authors are saying that because they can't think of how to test this possibility, the other possibility is more likely. But I don't think that is an argument against the first possibility.

(5) Lines 591-602: This paragraph confuses me. I thought that much of the introduction and Figure 1 seemed to be putting forth that the terminal segment complexity was a conflict structure that we were interested in. However, here it is stated that it does not strongly influence mating success, so is it necessarily a conflict trait? Is it demonstrated that the leg structures influence mating success?

Reviewer #2 (Public review):

The authors initial goal was to demonstrate loss of PG during the slow sporulation process of Myxococcus xanthus, with examination of the PG degradation products in order to implicate possible enzymes involved. Upon finding a predominance of LTG products, they examined sporulation in strains lacking each of the 14 candidate LTGs encoded in the genome, leading to the identification of two sporulation-linked LTGs. An extensive characterization of the roles played by these LTGs. One LTG is responsible for the slow sporulation PG degradation, while another is required for the rapid sporulation process. Interestingly, the "slow" LTG seems to provide an important regulatory brake on the rapid enzyme. Single molecule fluorescent tracking of these enzymes was used to develop a model for their interaction with PG that mimics their observed activity. The rate of PG synthesis activity was also shown to impact the rate of PG degradation, suggesting potential interplay between the synthetic and degradative enzymes.

Strengths:

The genetic analysis to identify sporulation-linked LTGs and their effects on growth sporulation, and spore properties was well done and productive. The fluorescence microscopy to track LTG mobility, presumably tied to activity, produced a convincing argument about the mechanism of regulation of one LTG by another. The authors have responded well to most points of the previous review.

Weaknesses:

While the impact of LTGs on sporulation was clearly demonstrated, the PG analysis that resulted in the study of LTGs raised some important unanswered questions. The analyses suggest that the PG is degraded to quite small fragments, which would normally be lost during the purification of PG. The conclusions concerning the PG degradation during sporulation needs to be clarified, as described below. The authors suggest a "new mechanism of sporulation" when they have actually simply identified an important factor (PG degradation by LTGs) within a complex "process of sporulation". This needs to be reflected also in title of the paper.

Reviewer #1 (Public review):

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

Meiotic recombination at chromosome ends can be deleterious, and its initiation-the programmed formation of DSBs-has long been known to be suppressed. However, the underlying mechanisms of this suppression remained unclear. A bottleneck has been the repetitive sequences embedded within chromosome ends, which make them challenging to analyze using genomic approaches. The authors addressed this issue by developing a new computational pipeline that reliably maps ChIP-seq reads and other genomic data, enabling exploration of previously inaccessible yet biologically important regions of the genome.

In budding yeast, chromosome ends (~20 kb) show depletion of axis proteins (Red1 and Hop1) important for recruiting DSB-forming proteins. Using their newly developed pipeline, the authors reanalyzed previously published datasets and data generated in this study, revealing here-to-fore-unseen details at chromosome ends. While axis proteins are depleted at chromosome ends, the meiotic cohesin component Rec8 is not. Y' elements play a crucial role in this suppression. The suppression does not depend on the physical chromosome ends but on cis-acting elements. Dot1 suppresses Red1 recruitment at chromosome ends but promotes it in interior regions. Sir complex renders subtelomeric chromatin inaccessible to the DSB-forming machinery.

The high-quality data and extensive analyses provide important insights into the mechanisms that suppress meiotic DSB formation at chromosome ends.

Comments on latest version:

I have checked the authors' responses and the revised analyses. I think they have adequately addressed my main concerns, particularly regarding the quantitative analyses of the chromosome fusion and SK1/S288c comparisons. I have no further comments and am content for you to proceed.

Reviewer #1 (Public review):

Summary:

The Voltage-Dependent Anion Channel 1 (VDAC1) is the most abundant β-barrel protein in the outer mitochondrial membrane and the main conduit for metabolite and ion exchange between the cytosol and mitochondria. Its oligomerization has been proposed to control mitochondrion-mediated apoptosis, making it a prime target for therapeutic intervention in diseases associated with excessive cell death, such as neurodegenerative disorders and autoimmunity. VBIT-4 is a small molecule developed to inhibit VDAC oligomerization and has shown therapeutic potential in various preclinical models. Despite its widespread use, the mechanism of action of VBIT-4 has not yet been fully elucidated. In this paper, Ravishankar et al. combine a suite of biophysical approaches with computer simulations to demonstrate that VBIT-4 forms water-permeable defects in membrane bilayers without any detectable effects on VDAC1 channel properties or oligomerization. Furthermore, cytotoxicity assays revealed identical VBIT-4 IC50 values in wild-type and VDAC1-KO cells, indicating that its activity does not depend on VDAC1. Collectively, these findings cast significant doubt on the widely held assumption that VBIT-4 is a specific inhibitor of VDAC1 oligomerization. Instead, it appears that VBIT-4 functions as a membrane-active compound.

Strengths:

This is a carefully conducted and well-written study that highlights potential side effects of VBIT-4, a compound that has been used to study the role of VDAC1 in a range of physiological and pathological conditions. The work is of interest to a broad readership by showcasing the importance of a systematic assessment of drug-membrane interactions to identify potential off-target membrane-driven effects of small molecules that may be mistakenly attributed to the inhibition of specific proteins. Its strength lies in the variety of complementary approaches the authors used to rigorously challenge the effect of VBIT-4 on VDAC1 organization and function. Overall, the experimental data are compelling and of high quality.

Weaknesses:

The authors used high-speed atomic force microscopy (HS-AFM) to study the impact of VBIT-4 on VDAC1 oligomerization in real time at nanoscale resolution. Toward this end, they adsorbed POPC:POPE:cholesterol membranes reconstituted with or without VDAC1 on mica. This revealed that addition of VBIT-4 produced small perforations in the bilayer that were independent of VDAC1. In the absence of VBIT-4, VDAC1 showed the characteristic honeycomb topography that the authors described in a previous study (Ref. 17). To quantitatively assess whether VBIT-4 affects VDAC1 organization, they analyzed protein compaction within clusters using inter-protein distance measurements. This analysis revealed no significant difference in VDAC1 organization between control conditions, 1 uM and 10 uM VBIT-4, supporting a model in which VBIT-4 primarily perturbs the lipid matrix rather than VDAC1 assemblies. This conclusion is based on the assumption that VDAC channels retain some lateral mobility in bilayers adsorbed onto mica, for which the manuscript does not provide direct evidence. In their rebuttal, the authors cite previous studies indicating that VDAC channels and beta-barrel proteins with larger extracellular domains exhibit measurable lateral diffusion in supported lipid bilayers formed on mica. Based on this, they conclude that their methodology does not constitute a limiting factor for lateral diffusion. It would be appropriate to cover this point and cite the corresponding references in the manuscript.

Reviewer #1 (Public review):

Summary:

In this study, Sidwell and Rothenberg report a genetically rigorous study whose central finding - reduction of Bcl11b at the positive selection stage reroutes CD8 T cells to the T cell virtual memory (TVM) fate - is well supported by the convergence of elegant mouse models (WT, Bcl11bΔEnh, Bcl11b+/-, Bcl11bR3S). In wildtype mice, Bcl11b acts as a transcriptional repressor that limits the reprogramming of CD8 T cells to the TVM cell type. Reducing Bcl11b dosage partially relieves this repression, resulting in increased development toward the TVM fate. Their analysis reveals several interesting aspects: 1)<br /> Strengths:

Factors that drive CD8 TVM fate are important and relatively poorly understood. This study makes the unexpected and interesting observation that Bcl11b gene dosage impacts TVM during T cell selection in the thymus. The conclusions are strongly supported by multiple independent lines of evidence.

Weaknesses:

The direct genomic targets impacted by Bcl11b heterozygosity were not identified.

Reviewer #1 (Public review):

Summary:

This work builds a theory to implement planning trajectories towards a goal in a known environment, inspired by analyses of prefrontal neural recordings. Unlike standard neural architectures for this task, such as value-based learning and successor representations, their proposed theory is able to adapt to novel goal locations within-trial. The key to the theory is that future times and locations are represented by disjoint groups of neurons. The recurrent connectivity between groups of neurons selective to specific future time and locations reflects the learned knowledge of the task. Finally, the authors show that standard networks trained on the task approximate their proposed theory.

Strengths:

The structure of the work is clear, and the article is very well written, which is particularly noticeable given the consequential amount of results presented. The authors are able to link their theory to experimental findings in neural recordings. The reverse-engineering of trained recurrent neural networks is very thorough, by analyzing both dynamics and connectivity. The assumptions and predictions of their model are clearly stated.

Weaknesses:

I believe the article shows no major weaknesses. There are important points that are beyond the scope of this study, that may limit its impact. For instance, while very little previous literature has linked planning to RNNs, their proposed theory of "space-time attractors" in RNNs is about input-driven stable attractors. The authors clarify this in the text, highlighting differences between classic attractor networks and the mechanism studied here. Related to this, an assumption of this mechanistic theory is that rewards are flexibly rerouted to the corresponding neural populations after every action, which seems difficult to implement biologically. Both aspects are discussed in the current manuscript.

Reviewer #1 (Public review):

Summary:

The immune system represents a source for melanocyte-extrinsic determinants of melanoma. Multiple immune cell types, including natural killer cells and CD8+ cytotoxic T lymphocytes, destroy cancer cells directly, and this anti-tumor activity is widely thought to eliminate many nascent tumors before they become clinically detectable. Regulatory T (Treg) cells, which are defined by expression of the transcription factor Foxp3, function as critical suppressors of lymphocyte activation in both homeostatic and disease contexts. Intratumoral Treg cell accumulation has been associated with disease progression in the clinic, and Treg cell depletion inhibits melanoma outgrowth in transplantable models of the disease.

However, the role of Treg cells during the early, premalignant stage of melanocyte expansion has not been examined. To study the interplay between incipient melanoma and cutaneous inflammation, the authors subjected an autochthonous murine model of melanoma [LSL-BrafV600E;Ptenfl/fl;Tyr::CreERT2 (BPT)mice] to three distinct inflammatory immune perturbations. Each of these perturbations accelerated premalignant melanocyte outgrowth, which was unexpected given that both Treg cell depletion and DNFB treatment markedly enhanced conventional T (Tconv) cell infiltration into the skin. Detailed analysis of each inflammatory response revealed a shared cellular and molecular signature comprising myeloid infiltration, characteristic cytokines and tissue remodeling factors, and vascular permeability. Altogether, the results support the hypothesis that oncogenic mutations in melanocytes along with altered immune response and inflammation synergistically drive melanomagenesis.

Strengths:

(1) The use of the three distinct inflammatory immune perturbations, such as transient Treg cell depletion, acute UV- B irradiation, and 2,4-dinitrofluorobenzene (DNFB)-induced contact hypersensitivity.

(2) The re-examination of the role of Treg cells in transplantable models of tumor growth by Subcutaneous (s.c.) implantation of syngeneic B16F10 melanoma cells as widely used to study anti-tumor immunity and to interrogate the effects of Treg cells on tumor suppression.

(3) The use of the LSL-TdTomato mice for measuring the TdTomato fluorescence in each immune cell type to assess uptake of melanocyte antigen.

(4) scRNA-seq data document a broad and rapid myeloid inflammatory response in Treg cell-deficient skin.

Weaknesses:

(1) The expression of inflammatory mediators Il1b, Il6, and TNFa, and angiogenic mediators Hif1a and Ang2 in all of the three models of immune perturbation has been verified at the transcript level by qRT-PCR, and it remains to be determined whether it correlates with the same at the protein level.

(2) scRNA-seq data to profile the diversity of immune cells (CD45+) in the ear skin of the DNFB-treated contact hypersensitivity model are currently missing.

(3) The authors indicate that at least 2 prior studies directly implicated inflammatory macrophages in the melanocyte proliferation response. However, no attempts were made for the identification of the UVB-driven factors underlying myeloid recruitment by which these cells activate melanocytes.

(4) It is very surprising to observe that altered immune responses, such as enhanced Tconv cell priming and activation in Treg cell-deficient skin, failed to antagonize mutant melanocyte outgrowth in the BPT model. While UVB-induced skin inflammation differs somewhat from the response to Treg cell depletion, both feature the infiltration of tissue remodeling macrophages and vascular instability. The findings that contact hypersensitivity can promote the expansion of non-malignant BRAF (V600E)Pten- Het melanocytes have interesting implications for benign hyperpigmentation conditions, such as post-inflammatory hyperpigmentation, Riehl's melanosis, and melasma.

Reviewer #1 (Public review):

Summary:

This study described the genomic features of K. pneumoniae in tissue samples from children in Zambia who had died from pneumonia, where K. pneumoniae was believed to be the causative organism. This was a substudy of a previous study which described more broadly the aetiologies of community-acquired pneumonia in this population, using minimally invasive tissue sampling.

In this study, the authors used culture-independent molecular tools to characterise the K. pneumoniae genomes from post-mortem tissue samples from 7 children who had died of pneumonia. They described a diversity of strain lineages in the 7 children, with different capsular types and virulence profiles. Notably, all strains had a wide array of antimicrobial resistance genes. This is probably not surprising given the propensity of this organism to acquire AMR genes, but it is concerning in isolates from community-acquired infections.

The study also highlights the value of using advanced culture-independent molecular techniques, and the scope of data that can be obtained. In settings where culture is not always available, storage and subsequent remote analysis of these samples is an option to better understand the microbiology (although expense is still a barrier, and culture-based microbiology should not be completely neglected).

Strengths:

Ascribing aetiologies in respiratory infections is not (yet) an exact science, and there is likely to always be some doubt about whether an organism (whether identified by culture or nucleic acid detection) is a true pathogen. However, the authors have used a robust combination of histology, microbiology, radiology, clinical assessment and verbal autopsy, and this is probably as good as we can get it at present.

The molecular techniques employed and the analysis were robust and technically sound. There are some areas where there is inconsistency between the results from two samples from the same patient, and this is likely due to the lower number of reads - important information for future similar studies. It highlights the potential limitations of this technique.

The data obtained (albeit from a small sample set) are consistent with the other data from Africa, which provides support for the value and reliability of the technique itself.

Weaknesses:

The major weakness is the small sample size. The 7 patients analysed in this study are a subset of the children in the larger study who had been identified as having died from K. pneumoniae respiratory infection. So while the findings of this study highlight the potential role of this organism as a respiratory pathogen and provide some insight into the distribution of lineages in the community, the results don't really allow for major changes to clinical or diagnostic practice as yet. The data highlight a potentially under-recognised problem and would be useful to inform future studies.

A minor weakness is more related to the journal layout, where methods are presented last. The results are not as easy to follow without reviewing the methods - in particular the description of histopathological findings and results of PCR on the biopsies. Until one realises that 6 biopsies had been taken from each of the deceased children, and a subset of these biopsies used for the study, the results seemed confusing.

Reviewer #1 (Public review):

Summary:

Overall, this is an interesting and well-written manuscript on a fascinating question in a "charismatic" model system.

Strengths:

(1) The Introduction is concise, though it might be helpful to the non-specialist reader to learn a bit more about what is known about the social control of somatic growth across diverse species (including humans), which would help to make this work more generally interesting.

(2) The experiment is well designed.

(3) The data collected are comprehensive.

(4) The complementary analysis of both feeding and aggression/submission data with and without known social roles is a neat idea and compelling!

Weaknesses:

The authors have addressed my concerns quite well. They now discuss the HPA/stress axis in some detail and also examined the extent to which growth, food intake, agonistic behavior, and/or gene expression patterns are coordinated across P1 vs P2 pairs. While still not ideal, they now provide a reasonable rationale for using whole bodies for the transcriptome analysis. Finally, the Discussion has been streamlined and is easy to follow.

Reviewer #1 (Public review):

Summary:

This article purports to show that ML-SA8, a synthetic activator of the lysosomal TRPML1 channel, results in AMPK activation and glucose uptake in hepatocytes, and that this action has therapeutic potential for metabolic disease. The final figure shows that glucose levels are improved in db/db mice, although it is not entirely clear whether this is due to an effect on the liver, on other tissues, or on glucose production or uptake. The earlier figures try to make the case that SA8 causes activation and GLUT4 translocation and glucose uptake in liver cells; however, these data are not convincing. GLUT4 is expressed at such low levels in liver that it is likely not physiologically important. The authors use a fluorescent glucose analog to measure glucose uptake, and this molecule has been shown to enter cells largely by fluid phase endocytosis. Overall, this reviewer finds the premise misguided and the data unconvincing.

Strengths and Weaknesses:

The initial figures show phosphorylation of AMPK on Thr172, but no downstream effects are shown. Usually, to convincingly show that AMPK activity is increased, it would be appropriate to immunoblot phospho-ACC or some other substrate. This is minor.

Lines 135-148: GLUT4 is not expressed at levels that are significant for physiology in liver cells, and its function in liver is not particularly relevant. The authors cite references 38-40 to support that it may be expressed at low levels in liver, but no knockout studies have been done to show that this expression is physiologically important.

Figure 1e is not convincing. No controls are included to show the specificity of the antibody for immunofluorescent staining. No intracellular GLUT4 is visible in the unstimulated samples.

In Figure 1f, again, the data are not convincing. The bands seem too sharp for GLUT4, which has 12 membrane-spanning domains as well as an N-linked glycosylation, so that it usually runs as a smear.

Figure 1h. Data are not convincing. 2-NBDG is not a valid approach to measure glucose uptake. 2-NBDG enters cells largely via fluid phase endocytosis, and its accumulation is independent of known GLUT inhibitors such as cytochalasin B (Yazdani et al., MBoC 2022; PMID: 35921166; see also PMID: 42287154). The idea that such a bulky derivative of glucose could enter the transporter channel is not compatible with known structural data.

Supplementary Figure 5 uses 2-NBDG glucose uptake again. This reviewer is not convinced that the data reflect transporter-mediated glucose uptake, as suggested by the authors. As well, although palmitate treatment of cells can cause an insulin-resistant-like phenotype in some cell types, this is not characterized in the present work. Finally, as noted, one would not expect hepatocytes to exhibit insulin-responsive glucose transport. Glycogen synthesis is the main insulin-regulated step that might be affected.

The data in Figures 2b,c,f,g,k,l are not convincing. Again, 2-NBDG is used.

For the glucose consumption measurements in other panels of Figure 2, the methods section states that cells were cultured in 10 mM glucose. What volume was used? It is difficult to believe that a monolayer of cells would consume very much of the glucose that is present in the culture medium. Data are shown as a percent of controls, and look reasonable, but it would be helpful to include absolute as well as relative units.

In Figure 2, in experiments using the TRPML1 KO cells, no panel is shown to demonstrate knockout. The authors cite a previous paper for the construction of these cells, but the control immunoblot should still be shown here.

In Figure 3, controls are missing in the BAPTA experiment in Figure 3a (only SA8-treated cells were treated with BAPTA and with EGTA). Again, it would be helpful to have p-ACC or some other readout of AMPK activity, and not just AMPK phosphorylation. 2NBDG is again used in this figure.

Line 212-213 the text states "considering our finding that TRPML1-mediated Ca2+ release is essential for AMPK activation." This has not been shown. The work uses chelators and does not necessarily indicate a role for TRPML1. The drug may be specific, as suggested by the authors, but the way this phrase is worded is too strong. As well, AMPK was shown to be phosphorylated, but full activation towards its various substrates has not been shown.

Figure 4cd suggests that GLUT4 expression is increased by 2 or 3-fold in the liver of DB+SA8-treated mice, compared to controls. This may be the case, but its abundance is still likely ~1000-fold less in liver compared to skeletal muscle or adipose tissue. This reviewer is still not convinced that this is physiologically relevant. The images in Supplementary Figure 8 suggest a larger increase, but it remains uncertain whether the staining really represents GLUT4.

Data showing that blood glucose and HbA1c are reduced in SA8-treated mice are reasonable, and GTTs and ITTs are shown. Unfortunately, there are no insulin concentrations, and it remains uncertain whether glucose production is reduced or uptake is increased (or if both effects are present).

In the discussion, the authors again state that GLUT4 is present in the liver and that it regulates hepatic glucose homeostasis, and they cite reference 63. This review article does not argue that GLUT4 acts in the liver to regulate hepatic glucose homeostasis, but that its actions in muscle and fat have secondary effects on the liver.

Reviewer #1 (Public review):

Summary:

This work by Beaudet and colleagues aims at exploring the effect of phosphorylation on the formation of tau envelopes and consequently on axonal transport both in vitro on reconstituted microtubules and in human excitatory neurons derived from IPSCs.

The authors found that a relatively widely used construct in which 14 serine or threonine residues often hyperphosphorylated in Alzheimer's disease are mutated to alanines (phosphodeficient) increases the density of tau envelopes compared to wildtype tau whereas a phosphomimetic (same residues mutated to glutamic acid) reduces envelopes density both in vitro and in human excitatory neurons derived from IPSCs.

By analysing the trafficking of different kinesins (KIF1a and KIF5C), they observed different effects of tau phosphorylation status on the movement of these two motors.

They then analyse transport of lysosomes by employing live imaging of lysotracker in human excitatory neurons derived from IPSCs transfected with wildtype, phosphodeficient or phosphomimetic tau observing that phosphodeficient tau seems to reduce transport of lysosomes while phosphomimetic increases transport compared to wildtype tau.

Strengths:

(1) The work aims to study a novel and underexplored topic in the tau field, tau envelopes, and investigate their relevance to Alzheimer's disease pathology.

(2) Experiments are well conducted and of high quality.

Weaknesses:

Relying only on in vitro reconstituted microtubules and human neurons derived from IPSCs leaves some doubts about the relevance of these results for Alzheimer's disease considering the embryonic state of IPSCs-derived neurons, but the authors clearly discuss this point.

Reviewer #1 (Public review):

Summary:

This work presents a flexible spike-sorting framework that allows users to run, swap, and benchmark individual modules commonly used in spike sorting. The paper argues that "opening the black box" is essential for understanding which components drive performance differences and for making progress toward more accurate and transparent spike sorting.

Using this modular benchmarking pipeline, the work identifies electrode drift as a primary bottleneck for accurate sorting, and introduces an end-to-end sorter ("Lupin") that combines the best-performing modules and is reported to be on par or maybe even outperform existing spike-sorting packages on their benchmark. While the modules forming Lupin were chosen based on a single benchmark, end-to-end evaluation of different sorters now use multiple benchmarks, including different available datasets.

Overall, this is a strong tool/resource contribution with clear potential to accelerate spike-sorting development and enable more rigorous comparisons. While not the main point of the paper and therefore less important, remaining claims regarding Lupin outperforming other sorters are not well supported. Lupin does not necessarily outperform all other sorters in real data if you consider 'finding most good units' more important than decreasing false positives - something that quality metrics post-sorting can take care off.

Strengths:

This work has high community value and practical utility. The effort to make benchmarking and spike sorting modules accessible and standardized is substantial, and likely to be broadly useful.

Treating spike sorting as a set of interchangeable modules is a useful approach to some extent, and it enables targeted improvements rather than 'new sorters' popping up which are difficult to fully understand.

Implementing this resource within SpikeInterface, an already widely used tool, will facility uptake and community contributions.<br /> Overall, I am positive about this manuscript as a resource paper. The core framework is compelling and timely.

Weaknesses:

(1) I appreciate the use of automatic curation tools to define the quality of units obtained from different spike sorters. However, a discussion on what is considered a good trade-off is missing. For example, one could argue that as long as you apply these curation tools post-sorting, finding more good-quality units is more important than keeping the number of false positives low, as these can be filtered out at a later stage by these curation methods. The manuscript currently seems to argue the balance between high number of good and low number of false positives is more important. This needs to be discussed and rationalized more explicitly, rather than using terms like 'clearly outperforms' and 'good trade-off'.

(2) Although I agree that overfitting to specific data is a general problem with spike sorting (as you can also change parameters of individual sorters), and perhaps this is even required for the best results, I still miss an explicit discussion of this.

(3) While the end-to-end evaluation is a very good addition, defining the best strategy for each module (which in turn led to choices what to use for Lupin) is still based on one benchmark only. This remains a weakness.

Cmments on revised version:

Previously identified weaknesses in limited support for Lupin being superior to other spike sorters, especially using only a single benchmark, have been largely addressed by 1) making superiority of Lupin less of a point in the manuscript and 2) adding more simulations and real datasets. Also, clarification on serial versus iterative spike sorting has been added to the discussion.

Reviewer #1 (Public review):

Summary:

The authors utilize both human iPSC-derived RPE and human fetal RPE cultures to interrogate the effect of various types of commonly used cell culture media on several key biological- and disease-relevant RPE properties. These include a comparison of RPE morphology, polarity, transepithelial electrical potential, lipid metabolism, autophagy, and targeted metabolomic profiles across 6 different media compositions.

Strengths:

This manuscript is very well written, and data are presented in a well-organized manner. The authors address media composition as a fundamental variable that will influence the interpretation of assays performed in RPE cell cultures, particularly metabolic studies. Figure 6 provides a useful summary of the study's findings across commonly used media types, and the manuscript's discussion offers insight into which media may be best suited to address specific experimental questions. Overall, this manuscript will not only serve as an important resource for vision scientists utilizing RPE culture models, but it also serves to remind the broader cell biology community of the importance of considering the potential (confounding) experimental effect(s) of various culture media and to consider tailoring the selection of culture media types to the specific experimental question.

Weaknesses:

(1) While the authors report that iPSCs were obtained from several healthy patients and that at least two clones were generated from each individual, it is not clear to this reviewer whether the experiments with each culture media type were performed on the same set of iPSC-RPE in each case. The authors mentioned iPSC differentiation variability as a limitation, but it would be helpful to understand (and quantify) the experimental variability that may exist with the same culture media using iPSCs from different patients and/or separate iPSC clones from the same patient.

(2) Since a major purpose of the manuscript is to highlight how cell culture conditions influence RPE biology and metabolism, it would be helpful to also report whether Mycoplasma testing was performed and confirmed to be negative across all cell lines.

(3) The effect of culture media on mean RPE area and hexagonality was compared in this study. Interestingly, Figure 1E demonstrates higher mean RPE cell area but also substantial variability in cell area for media 2 (MEM-alpha and B27) and media 4 (HPLM and B27). It would be helpful to include a discussion of the potential biological implications of variable cell area across these 2 media types.

(4) The authors speculate that FBS-containing media may encourage a more mesenchymal or de-differentiated state. This could be experimentally determined by interrogating mesenchymal markers (alpha-SMA, fibronectin, etc) by immunoblot and/or immunofluorescence microscopy, similar to how the RPE markers were evaluated in Figure 1.

(5) It would be helpful for the discussion section to include a comparison of key differences (where they exist) between iPSC-RPE and fetal RPE across culture media types.

Reviewer #1 (Public review):

Summary:

Thach et al. report on the structure and function of trimethylamine N-oxide demethylase (TDM). They identify a novel complex assembly composed of multiple TDM monomers and obtain high-resolution structural information for the catalytic site, including an analysis of its metal composition, which leads them to propose a mechanism for the catalytic reaction.

In addition, the authors describe a novel substrate channel within the TDM complex that connects the N-terminal Zn²⁺-dependent TMAO demethylation domain with the C-terminal tetrahydrofolate (THF)-binding domain. This continuous intramolecular tunnel appears highly optimized for shuttling formaldehyde (HCHO), based on its negative electrostatic properties and restricted width. The authors propose the hypothesis that this channel facilitates the safe transfer of HCHO, enabling its efficient conversion to methylenetetrahydrofolate (MTHF) at the C-terminal domain as a microbial detoxification strategy. Experimental data that shows an involvement of TDM in the reaction of HCHO with THF is less convincing.

Strengths:

The authors provide convincing high-resolution cryo-EM structural evidence (up to 2 Å) revealing an intriguing complex composed of two full monomers and two half-domains. They further present evidence for the metal ion bound at the active site and articulate a hypothesis for the catalytic cycle. Substantial effort is devoted to optimizing and characterizing enzyme activity, including detailed kinetic analyses across a range of pH values, temperatures, and substrate concentrations. Furthermore, the authors validate their structural insights through functional analysis of active-site point mutants.

In addition, the authors identify a continuous channel for formaldehyde (HCHO) passage within the structure and support this interpretation through molecular dynamics simulations. These analyses suggest an exciting mechanism of specific, dynamic, and gated channeling of HCHO. This finding is particularly appealing, as it implies the existence of a unique, completely enclosed conduit that may be of broad interest, including potential applications in bioengineering.

Weaknesses:

Although the idea of an enclosed channel for HCHO is compelling, the experimental evidence supporting enzymatic assistance in the reaction of HCHO with THF is less convincing. The linear regression analysis shown in Figure 1C demonstrates a THF concentration-dependent decrease in HCHO; however, it is well established that HCHO and THF can react spontaneously in a non-enzymatic manner, raising the possibility that the observed effect does not require enzymatic involvement. I appreciate the authors' clarification that the data in Figure 1 were not intended to demonstrate enzymatic channeling or catalytic involvement in the HCHO-THF reaction, and that the assay does not distinguish between changes in HCHO production and downstream consumption. The authors' revised statement "Overall, these findings suggest that TDM-mediated TMAO demethylation generates HCHO, which can subsequently react with THF, potentially linking TMAO breakdown to one-carbon metabolism." is appropriately cautious and leaves open the mechanism underlying this process, which is consistent with the evidence presented.

Overall, the authors were successful in advancing our structural and functional understanding of the TDM complex. They suggest an interesting oligomeric complex composition which in my opinion should be investigated with additional biophysical techniques.

Additionally, they provide an intriguing hypothesis for a new type of substrate channeling. However, additional kinetic experiments focusing on HCHO and THF turnover by enzymatic proximity effects are required to strengthen this potentially fundamental finding. If this channeling mechanism can be supported by stronger experimental evidence, it would substantially advance our understanding and knowledge of biologic conduits and enable future efforts in the design of artificial cascade catalysis systems with high conversion rate and efficiency, as well as detoxification pathways.

Comments on revised version.

It is unfortunate that no additional experimental evidence supporting the proposed channeling mechanism could be provided. In the absence of such evidence, I remain somewhat hesitant to regard the evidence as "solid" rather than "incomplete," particularly given that the authors themselves acknowledge that the current experiments do not establish enzymatic channeling. However, the rest of the manuscript is stronger despite this limitation.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers and the authors have satisfactorily answered the previous reviewer's comments.]

Summary:

They use cultures of insulinoma MIN6 cells that form spheroids in a micro-patterned PEG-hydrogel to measure Ca2+ oscillations in multiple cells simultaneously.

Strengths:

They demonstrate that insulinoma spheroids are formed in multi-well plates and that Ca2+ imaging can be performed on them.

Reviewer #1 (Public review):

Short overview:

This work demonstrates that exposure to the odor diacetyl in C. elegans first induces the expression of genes that act in the DHAP (dihydroxyacetone phosphate) - glycerol shunt, followed by induction of the flavin-containing monooxygenase fmo-2. This diacetyl exposure also increases the survival of starved animals. However, the mechanism through which diacetyl is sensed by the animal does not involve any of the known receptors of diacetyl, and it is unknown whether any sensory neurons are involved in these responses.

Summary:

This manuscript shows that diacetyl exposure induces metabolic remodeling in C. elegans, where genes in the DHAP (dihydroxyacetone phosphate) - glycerol shunt pathway are activated. This leads to a secondary activation of the flavin-containing monooxygenase fmo-2, which is a longevity-promoting gene upon dietary restriction. The activation of fmo-2 is consistent with the authors' observations that diacetyl exposure increases the survival of starved animals, but not of fed animals. The authors also show that diacetyl exposure improves the animals' resistance to hyperosmotic stress, which is consistent with an increase in glycerol and glycerolipids in these animals. Together, the authors show that a volatile odor or odors can induce gene expression changes that promote metabolic changes and survival under certain environmental conditions.

Strengths:

Through transcriptomics, metabolomics and lipidomics of wild type, with or without diacetyl, and phenotypic analyses of mutant or RNAi-treated animals, the authors delineate a mechanism through which an odor or odors remodel metabolism and increase survival. They show that genes of the DHAP-glycerol shunt are activated during diacetyl exposure, and that these in turn activate fmo-2, which is known to promote longevity under different stressors, like food deprivation. The authors also show that an upstream transcription factor, the co-activator MDT-15, is required for the diacetyl-mediated initial activation of the DHAP-glycerol shunt, and thereby of fmo-2. The involvement of MDT-15 and, to a lesser extent, one of its partner nuclear hormone receptors (NHRs), NHR-49, might also explain the lipidomic changes that accompany diacetyl exposure.

Weaknesses:

The authors tested the involvement of the ODR-10 receptor, which is the known receptor for attractive concentrations of diacetyl, the same concentrations used in this study. Surprisingly, ODR-10 is not required for any of the DHAP-glycerol shunt or fmo-2 induction. In addition, they found that loss of sensory cilia (in the background mutation of the NHR daf-12) enhances diacetyl-mediated fmo-2 induction, which suggests that wild-type sensory cilia inhibit fmo-2 and likely synergizes with the DAF-12 receptor. Considering that chemosensory receptors, like ODR-10, are normally localized to the ciliary endings, their observations suggest a different mechanism through which the animals process this odor. There are at least two possibilities.

One, diacetyl might affect the animals by permeating their cuticles. However, the authors did not test if any neurons are involved in their phenotypes. The AWA neuron, where ODR-10 is expressed, is required to sense attractive concentrations of diacetyl. Thus, what happens when the AWA neurons are ablated or lost?

Two, diacetyl is both light- and temperature-sensitive and can break down into two other odors, acetoin and 2, 3-butanediol. Acetoin is sensed by the chemoattractive AWA neurons (Siddiqui et al, eLife 2024, 101936.1), but not by ODR-10 (Zhang et al., PNAS 1997, vol 94, pp 12162-12167). Thus, this odor would have a different receptor. Although C. elegans chemosensory receptors have been found within the cilia, there is a formal possibility that some of the sensory receptors will also be found on other parts of some sensory neurons, like the dendrites, as in Drosophila (Joseph and Carlson, Trends Genet 2016, vol 31, pp 683-695). Regarding the other diacetyl derivative, 2, 3-butanediol, it has not been shown to elicit any chemosensory responses in C. elegans (Siddiqui et al. eLife 2024, vol 13, RP101936), but 2, 3-butanediol has been linked to the microbiome of fmo knockout mice (Said et al., Metabolomics 2025, vol 21, 170). Thus, it is possible that it is the breakdown products of diacetyl that elicit the responses the authors see.

Finally, the authors' data contradict the observations made by Park et al (Aging Cell 2021, vol 20, e13300). Park et al previously showed that diacetyl exposure reduces the survival of food-deprived animals, although this phenotype is also independent of ODR-10 or of the SRI-14 receptor for aversive concentrations of diacetyl.

Reviewer #1 (Public review):

Summary:

This work investigated the associations between abstraction and metacognition in the context of reward-guided learning and transdiagnostic symptom dimensions in a sample of N = 249 participants. Participants completed a reward-guided learning task and confidence judgements. Transdiagnostic dimensions used to examine associations with abstraction and metacognition were based on a large existing dataset. Findings showed that in the examined sample, a Compulsive Hypersensitivity dimension was negatively associated with abstraction and metacognitive sensitivity, while a Social Withdrawal dimension was positively associated with metacognitive sensitivity. These data add to the existing literature on associations between metacognition and transdiagnostic symptom dimensions and extend previous work on the association between abstraction and these dimensions.

Strengths:

(1) The study addresses an interesting research question and uses a transdiagnostic approach. While part of the research question is a replication (metacognition), the additional inclusion of an abstraction parameter is highly valuable.

(2) Methodologically, the study is strong. Specifically, the implementation of an experimental task to estimate parameters, the hierarchical Bayesian modelling, the parameter recovery analyses, the bootstrap regression (including corrections for multiple comparisons) and the control of relevant covariates and response tendencies are quite impressive.

(3) The Open Science approach is laudable in general. The study was preregistered and provides open data and open code. Deviations from the preregistration are transparently reported (e.g., bootstrap regression, exclusion criteria). In this vein, the high number of robustness analyses provided in the supplements is very much appreciated.

(4) More generally, the extensiveness of the supplements is particularly valuable.

(5) Finally, it is very useful that the discussion takes into account potential alternative explanations of the findings.

Weaknesses:

(1) High number of exclusions:

As the authors mention (also in their limitations section), more than half of the participants had to be excluded (only 249 out of 512 participants remained), which is substantial. Specifically, 203 participants were excluded because they failed attention checks, 58 failed comprehension questions on the confidence scale, 25 had a reading time of the instruction page below 5 seconds, and 68 showed a performance that was too poor (the sum is probably higher than 263 because these overlap). This high number of excluded cases resulted in a substantially smaller analysed sample than planned (corresponding to approximately 77% power instead of 90%) and potentially limited both statistical power and generalisability. It might even be the case that highly impulsive individuals were excluded systematically. That is, the exclusion criteria may themselves be associated with psychiatric symptoms, so the analysed sample may no longer (fully) represent the target population. It would be interesting to see whether the results also hold when including the excluded participants (because excluded and included participants differ in attentional and impulsivity-related symptoms). A sensitivity analysis would be beneficial.

(2) Deviations from preregistration:

While the preregistration of the study is positive in general, some aspects raise doubts here. First, there have been quite a few deviations from the preregistration. For instance, the primary outcome variable for abstraction has been changed (initially: proportion of blocks in which the Abstract RL model had a better fit than the Feature RL model; instead: mean posterior responsibility of the Abstract RL model across blocks), and this appears to have influenced the findings (e.g., Supplementary Figures: 8 vs. 9). Generally, these deviations introduce additional researcher degrees of freedom and therefore warrant a more detailed justification (or replication in future studies). Second, it appears that the preregistration was uploaded only to an OSF folder and not formally registered with a timestamp. However, while an update to the document has been made according to the metadata, the content still seems to be the same as the original one (created on Jun 11, 2025).

(3) Combined measurement of choices and metacognition:

Choice behaviour and metacognition were not measured independently (i.e., they were measured using a single slider). This challenges the interpretation of results concerning metacognitive sensitivity, as the authors note themselves in the limitations section (even if the effect of starting position was small). It should be clarified whether responses exactly at the centre of the slider were possible or not (and what range the slider had).

(4) Generalisability of findings:

It is questionable whether transdiagnostic factors derived from a Japanese population can be transferred to the sample in this work, especially because it seems to be composed of a heterogeneous international sample comprising participants from multiple countries (South Africa, United States, United Kingdom, Poland, others). While it is appreciated that the authors validate the transdiagnostic factors through a new exploratory factor analysis and by correlating item loadings across samples, the correlations are only moderate and point at least to some degree of variation. In addition, for factor 2 (social withdrawal?), the correlation seems to be mainly existent due to two clusters of items, challenging the validation approach in itself. Also, apart from the correlations themselves, the absolute values of the item loadings appear to vary largely. Considering this, the conclusion that "transdiagnostic symptom dimensions appear broadly consistent across samples (even across countries)" (p. 9) definitely goes too far.

(5) Parameter recovery analysis: The parameter recovery analysis is appreciated; however, the recovery of the learning rate in particular seems to be rather low (r=.40). This raises concerns regarding the validity of the parameter estimates.

(6) Effect sizes: The reported regression coefficients appear relatively small in magnitude (i.e., betas of the associations between metacognition/abstraction and transdiagnostic dimensions appear to be in the range around .02-.04). If these are standardised estimates, the corresponding effect sizes are relatively small. If not, I recommend reporting standardised effect sizes in addition.

Overall, the study provides relevant replications and new insights into the association between reward-guided learning behaviour (abstraction, metacognition) and transdiagnostic symptom dimensions. It should be noted that effect sizes appear to be rather low, however. The analytic approach is generally strong. At the same time, the high number of exclusions, the limitations regarding the validation of the transferred transdiagnostic factor structure, and the limited recovery of the learning rate parameter clearly raise important questions regarding the robustness and generalizability of findings. Given this, any interpretations regarding potential therapeutic implications (e.g., p. 10) should be made with caution.

Reviewer #1 (Public review):

Howell et al investigate the functional capacities of CD16dim CD56dim NK cells, including their activity against HIV-1-infected T cells. The authors provide an extensive characterization of the functional activity of different NK cell subsets derived from peripheral blood. CD16 is an important receptor expressed on NK cells, and previous studies have demonstrated that CD16 expression changes depending on the activation status of NK cells - one important regulator of CD16 expression is proteolytic shedding/cleavage of CD16 on activated NK cells by the metalloprotease ADAM17. In vitro activation of NK cells, for example in response to K562 cells or other target cells, results in a rapid downregulation of the expression of CD16 on the surface of NK cells, unless an ADAM17 inhibitor is added. This is an important point to consider in the interpretation of the presented results. Overall, the manuscript includes many data in nine figures plus supplemental figures, and would benefit from some focusing of the results.

(1) Figure 1<br /> The observation that CD16dim NK cells responded more strongly by degranulation to K562 cells and HIV-1-infected cells could be due to the shedding of CD16 following activation. In other words, more strongly activated NK cells express higher levels of CD107 but also shed CD16, resulting in higher CD107 expression in CD16low NK cells. The authors should investigate this, for example by performing the degranulation assays shown in Figure 1 in the presence and absence of an ADAM17 inhibitor.

(2) Figure 2<br /> The authors sorted CD16dim and bright NK cells for these experiments and observed higher lysis of HIV-1-infected CD4+ T cells. Important controls should be included in these experiments - how strong was the lysis of HIV-1-uninfected CD4+ T cells by these different NK cell subsets? It also appears that the results shown were derived using NK cells from one donor, and "representative of two independent sort experiments performed with separate donors, each yielding similar results". Why are the authors now showing the respective data? One or two experiments appear too few to come to these conclusions. To support the broad conclusions drawn by the reviewers, the experiments should be performed in a larger number of individuals.

(3) Figure 3<br /> It appears that experiments were performed again using bulk NK cell populations, and superior degranulation and killing frequencies by CD16dim NK cells might reflect different levels of activation again, as described above for Figure 1. The same applies to Figure 4 - lower degranulation events in CD16bright NK cells are consistent with lower activation of these cells, resulting in less CD16 downregulation. Also, it is not clear to the reviewer why CD107a expression and killing frequencies decrease with higher effector-to-target ratios (Figure 3).

(4) Pages 19-25<br /> It would be helpful if the authors could provide some conclusions regarding their findings - it is very difficult for the reader to follow the many reported frequencies and p-values. What does this actually mean? Overall, the results appear to follow prior observations that licensed (KIR3DL+) NK cells respond more strongly than unlicensed (KIR3DL1neg) NK cells. The consistent observation within these different subanalyses that CD16dim NK cells degranulate more than CD16bright NK cells is probably the result of activation-induced CD16 downregulation in these assays, as mentioned above. Providing two-way ANOVA analysis results for these very many observations would furthermore require, in the opinion of the reviewer, adjustments for multiple comparisons.

(5) Figures 5 and 6<br /> These figures demonstrate that NK cell-mediated activation by HIV-1-infected cells depends on NKG2D ligands and can be inhibited by blocking this interaction - this is consistent with data presented by the Barker group and others previously, and does not provide new information.

(6) Figure 7<br /> The authors extended their functional analyses of NK cells to ADCC function. It is very well established that CD16 is downregulated in the context of ADCC following activation of NK cells. Consistent with this, higher degranulation is observed by CD16dim NK cells.

(7) The data using ADAM17 inhibition in the final figures of the manuscript<br /> These data are of interest, but should be presented in a more structured way. First of all, does the addition of ADAM17 inhibitors change the overall proportion of CD16bright and dim NK cells following activation, independent of whether these cells degranulate or not? Overall, the proportion of CD16dim NK cells that degranulate appears to be reduced in the presence of the ADAM inhibitor, which is consistent with reduced CD16 shedding and maintenance of CD16 expression on activated NK cells - and this is supported by the increase in CD107a-positive NK cells that express CD16 (Figure 8a). Overall, the differences between CD16bright and dim NK cells in their level of activation appear to disappear in the presence of an ADAM17 inhibitor, based on the data shown in Figure 8b, suggesting that CD16 downregulation is occurring in response to activation of NK cells as a consequence of CD16 shedding, and can be inhibited by an ADAM17 inhibitor.

Taken together, many of the data presented in the manuscript are consistent with the very well-established downregulation of CD16 expression on activated NK cells, suggesting that the observed association between reduced CD16 expression on CD56dim NK cells and enhanced effector functions is a consequence of higher activation of these NK cells.

Reviewer #1 (Public review):

This work identifies two lectin receptor-like proteins as cell wall -plasma membrane anchors that contribute to persistent attachment sites maintained during plasmolysis. The authors propose that these attachment sites are important for plant resistance to hyperosmotic stress. Although the existence of persistent attachment sites between the cell wall and the plasma membrane, particularly evident in plasmolysed cells, was recognised long ago, the molecular components tethering the two cellular components remain largely unknown. Therefore, the findings presented by Arico et al. address an important question in plant cell biology.

Through a screening of potential anchors, they found that the overexpression of fluorescently tagged LecRK-I.9, LecTM, AGP18 and AT14A in Nicotiana benthamiana increased the density of Hechtian strands in plasmolysed cotyledon epidermis cells. They show that for LecRK-I.9* (* indicates kinase-dead version) and LecTM, this effect depends on the Lectin domain. Focusing on LecRK-I.9*, the overexpressed Lectin domain localized to cell walls, accumulating in certain foci. The authors interpret this as a possible preference for certain cell wall composition. I find these results convincing and the methodology robust.

The second part of the manuscript, however, relies on interpretations that, in my opinion, are not fully supported by the presented evidence. The authors move to Arabidopsis thaliana and show that overexpression of both LecRK-I.9* and LecRK-I.9*ΔLec fluorescent reporters also localizes to the plasma membrane and Hechtian strands in plasmolysed cells, although the density of Hechtian strands is not quantified. Thus, it is unclear whether LecRK-I.9* promotes Lectin domain-dependent strong cell wall-plasma membrane attachment sites in Arabidopsis.

The authors focus on the formation of big signal clusters at the plasma membrane in response to hyperosmotic treatment. They studied the dynamics of the clusters upon treatment application and observed increased mobility of LecRK-I.9* compared to LecRK-I.9*ΔLec and a plasma membrane marker. They then analysed the abundance and size (not the mobility) of these clusters on different plasma membranes facing cell walls that have or are predicted to have different mechanical and chemical properties, identifying differences between LecRK-I.9* and LecRK-I.9*ΔLec. Although the reduced mobility of LecRK-I.9 relative to LecRK-I.9ΔLec is consistent with an interaction between the lectin domain and the cell wall, it does not by itself demonstrate that the observed clusters correspond to CW attachment sites. Moreover, the relation between the clusters and Hechtian strands (bona fide cell wall-plasma membrane attachments) is not explored. Likewise, the differential clustering observed on different cell faces is intriguing but could have different interpretations.

Finally, the physiological relevance of the proposed anchoring mechanism is supported by osmotic stress assays, but the scoring method relies on manual classification of resistant seedlings and could benefit from a more objective quantitative readout.

In summary, although I find all these results valuable, I find that the methodology is not completely adequate and that several aspects of the data interpretation require additional support or clarification before the central conclusions can be fully justified.

Reviewer #1 (Public review):

Summary:

The present report describes an investigation into the use of machine learning techniques to improve cross-racial/ethnic performance of brain models of cognitive function. The authors tested several approaches to boost prediction of NIH cognitive toolbox scores using brain imaging data (function, structure) for minoritized (Black) participants in the ABCD Study sample compared to white (majority) participants. Structural (e.g., volume) measures showed the greatest performance gap, and a balanced weighting method showed the greatest performance gain across features. The authors conclude that supervised domain adaptive methods can improve models for cognitive prediction and mitigate cross-racial/ethnic performance disparities.

Strengths:

This investigation makes some headway into issues by identifying computational methods that may help to improve some models for limited outcome variables (i.e., general cognitive performance). Addressing racial/ethnic disparities in brain imaging research has significant implications for generalizability of findings and for the practical utility of imaging findings in the wider population. A comparative approach to evaluate the improvements in a "prediction gap" across various methods could have benefits for neuroimaging beyond racial/ethnic disparities. The use of the ABCD Study, given its deep phenotyping of individuals, is also a benefit.

Weaknesses:

Despite its strengths, there are several large conceptual and related methodological issues that impact its conclusions and the overall utility of the approach. The sample selection approach limits insight into likely drivers of the performance gap (e.g., socioenvironmental disparities known to exist between groups and associated with neurodevelopment), and in so doing ignores a critical component of understanding racial brain differences, particularly in relation to cognitive functions. Further, while the relative gaps in performance of a single cognitive score across features are well described, the actual performance (and therefore relative benefit to these techniques) is unclear. Specific examples include the following.

(1) The overarching conceptual issue with the manuscript is a lack of engagement with a substantial and growing evidence base on the drivers of racial disparities in brain imaging which impact model performance. Racial/ethnic groups in the US (and other regions of the world) are not equivalent in terms of developmental environments that shape brain function and structure (see Harnett et al., 2023, Neuropsychopharmacology; Ricard et al., 2023, Nature Neuroscience; Cardenas-Iniguez & Gonzalez, 2024, Nature Neuroscience for some overview here). The socioenvironmental disparities inherent to race in the US further shape cognitive development and brain associations with cognitive performance (e.g., Marek et al., 2025, Science). The framing of the manuscript focuses almost exclusively on broad sampling issues, and in doing so treats racial/ethnic variability as if it reflects statistical abnormality rather than a critical component of understanding human brain development. This lack of contextualizing racial disparities significantly impacts the overall utility of the proposed approach and the conclusions of the manuscript.

(2) In relation to the above, another conceptual issue in this approach of using a majority to inform minority brain associations with cognitive variables is an assumption that minority brain patterns should match the majority, rather than developmental stressors inducing alternative brain-weighting to predict outcomes. This framework does not assess this possibility and may in fact obscure such an outcome, limiting our inferences into neurodevelopment.

(3) Another conceptual/methodological issue here is the use of "matched groups" for analysis. The specifics of matching are fairly vague, but given the description one would assume the w/B groups are matched on a number of behavioral/socioenvironmental variables, which is a significant issue for interpretability and applicability. As noted, w/B groups in the US (and the ABCD Study) differ substantially across variables; matching has the likely consequence of creating a highly non-generalizable sample, particularly when the minority group is restricted to N = 10.

Reviewer #1 (Public review):

Summary:

In this manuscript, Flamholz and colleagues use metagenomic sequencing to profile the microbiome of individuals with sickle cell disease (SCD), the most common genetic blood disorder in the world. To build on previous studies that found dysbiosis in SCD, this manuscript aims to examine whether changes in either bacterial species or bacteriophages correlate with inflammatory hallmarks of the disease. The authors claim that sickle cell dysbiosis does not correlate with inflammatory hallmarks of the disease including aged neutrophil numbers, a cell type previously highlighted in preclinical sickle cell microbiome work.

Strengths:

The primary strength of this paper is the investigation into disease associated changes in bacteriophages. This is an entirely novel idea in the sickle cell field, and based on the current results, may be an important, under-recognized disease hallmark. It is unclear, however, if phages are "the chicken or the egg" in terms of sickle cell inflammatory profiles; do these increases in phage number simply result from other disease process or are they in anyway contributing to disease pathophysiology?

Weaknesses:

The authors addressed many of the initial manuscript weaknesses in their revision. In particular, they have softened language regarding sickle cell dysbiosis and its causative role in disease pathology. This is particularly appropriate given the lack of correlation between dysbiosis and immune factors in this single-center study.

Reviewer #4 (Public review):

Summary:

The current study tested the effects of repeated sessions of tDCS targeting the DLPFC on procrastination behavior. The main outcome is that anodal versus sham DLPFC tDCS reduces procrastination behavior on both a short-term and a long-term scale up to six months after the stimulation sessions.

Strengths:

The current study tests competing models of procrastination with state-of-the-art high-definition transcranial electric stimulation. The study assesses stimulation effects on procrastination on both a short-term and a long-term scale, suggesting that repeated stimulation of the prefrontal cortex reduces procrastination on a time scale of up to six months.

Comments on revised version.

The manuscript has already been reviewed and revised before, and it seems that the quality of the manuscript has substantially improved as a result of this revision process. I agree with the other reviewers that one must be cautious with drawing conclusions regarding the cognitive mechanisms underlying this effect, as many different cognitive functions are implemented by the DLPFC. The effect sizes are surprisingly large, but I am satisfied with the reasons provided by the authors for the large effect sizes.

The authors successfully addressed my previous concerns on the manuscript.

Reviewer #1 (Public review):

Summary:

This interesting paper demonstrates that transgenic over-expression of sphingosine 1-phosphate receptor 1 (S1PR1) on neutrophils alters their phenotype, resulting in (1) accumulation of neutrophils in blood, spleen, lung, and liver; (2) a shift in homing receptor expression with reduced CXCR2 and elevated CXCR4; (3) altered transcriptional profile with an increase in "G5c" neutrophils and reduced "module scores" for apoptosis and inflammatory response; (4) reduced ROS production upon fLMP stimulation; and (5) altered responses to bacterial and viral infections of the lung. It raises many interesting questions about how S1P signaling regulates neutrophil biology, and hence will be the basis of future studies. These include: (1) What is the physiological role of S1PR1 signaling in neutrophils? Although there is no dramatic effect on numbers upon S1PR1 loss, is there an effect on any of the other parameters measured? (2) What is unique about the lung that S1PR1 over-expression is particularly impactful there? (3) What distinguishes the bacterial context in which S1PR1 over-expression is maladaptive from the viral context in which S1PR1 over-expression is protective? and (4) Can treatment with an S1PR1 agonist mimic S1PR1 over-expression? As a possibly related question, when in neutrophil development does S1PR1 signaling function to shift the phenotype?

Strengths:

(1) A comprehensive characterization of S1PR1-transgenic neutrophils.

(2) Opens many interesting areas of investigation.

Weaknesses:

Although some characterization of the neutrophil-specific Mrp8-Cre is done, most of the experiments use the more widely expressed LysM-Cre. The redistribution phenotype is much stronger with LysM-Cre than with Mrp8-Cre, so it is unclear what effects are attributable to a cell-intrinsic role of S1PR1, even in studies of neutrophils analyzed ex vivo.

Reviewer #1 (Public review):

This is an interesting paper, with the primary finding being that localizing MreB or PBP2 to the cell poles in E. coli is primarily demonstrated via an aggregate formed by expressing M. xanthus MreB.

I have 2 main concerns:

(1) First, the authors should clarify and adjust their interpretation of FDAA incorporation: As written, the authors interpret FDAA incorporation as being caused by incorporation during PG polymerization. However, in E. coli, FDAA incorporation does not result from the elongation of PG strands or their initial 4-3 crosslinking by DD transpeptidases following polymerization, but rather by the remodeling of L,D-transpeptidases.

Thus, it is not accurate to refer to the FDAA incorporation as PG elongation, but rather the modification of crosslinks from 4-3 to 3-3 crosslinks at that location. To claim a link to PG polymerization, other experiments or substantial explanations are needed.

(E. coli Cells Incorporate FDAAs by L,D-TPases in a Growth Independent Manner) - https://doi.org/10.1021/acschembio.

(2) Second, the evidence provided that this is polar elongation is not sufficient to prove elongation. In all images claiming polar growth, the FDAA focus appears as a single spot, which corresponds to the MreB aggregate visible in bright-field. That might indicate incorporation, but it does not demonstrate polar elongation. To prove this, the authors should do different-length pulses of FDAAs and demonstrate an increasing length of labeled PG along the cell. Cells with one focus should show increasing length; polar foci should elongate from both ends. I find the current 2-color labeling insufficient, as BADA labels the entire cell.

Small points:

(3) Lines 350- 302: "In this case, as the nonpolar region is no longer the growth zone, the established cylindrical PG structure is sufficient to maintain cell width, where MreB filaments become nonessential." Does polar elongation give robustness to rod shape? The authors should include an analysis of cell width and its variation within a cell and between cells.

(4) In the abstract: "This reprogrammed growth mode bypasses the requirement for MreB filaments, highlighting a plasticity of the Rod system that suggests polar elongation may have emerged through the evolutionary loss of MreB." This argument should not be made without evolutionary analysis or reference to such work indicating this is the case.

(5) 365-367 "PG-depleted spheroplasts can spontaneously regenerate rod shape through curvature-dependent localization of MreB filaments [21, 57]". This should be amended. The Billing paper did indeed study spheroplasts, but the Hussain paper used teichoic acid-depleted cells that still had a cell wall.

Reviewer #1 (Public review):

Summary:

In this manuscript, Green et al. attempt to use large-scale protein structure analysis to find signals of selection and clustering related to antibiotic resistance. This was applied to the whole proteome of Mycobacterium tuberculosis, with a specific focus on the smaller set of known antibiotic-resistance-related proteins.

Strengths:

The use of geospatial analysis to detect signals of selection and clustering on the structural level is really intriguing. This could have a wider use beyond the AMR-focussed work here and could be applied to a more general evolutionary analysis context. Much of the strength of this work lies in breaking ground into this structural evolution space, something rarely seen in such pathogen data. Additional further research can be done to build on this foundation, and the work presented here will be important for the field.

The size of the dataset and use of protein structure prediction via AlphaFold, giving such a consistent signal within the dataset, is also of great interest and shows the power of these approaches to allow us to integrate protein structure more confidently into evolution and selection analyses.

Comments on revised version.

All my comments from the previous round of reviews have been addressed.

Reviewer #1 (Public review):

The authors sought to devise a model of the song system that captures the essential features of that neural circuitry and couple it to a behavioral model that captures the challenges of motor learning while remaining tractable. They seek to use this model to explain known features of song learning and relate them to the general problems associated with learning via gradient ascent. Their syrinx model uses two control parameters-air sac pressure and syringeal labial tension-to generate birdsong-like spectrograms. Normalized spectrograms generated by the syrinx model are compared to a target spectrogram by computing Pearson's correlation coefficient. This correlation coefficient quantifies the performance of the model, and the goal of learning is to maximize it. The syringeal model, while simplified, is complex enough to generate multiple local maxima in the correlation coefficient within the 2D control space with wide variation in the magnitude of the maxima, making it challenging to find a good optimum via gradient ascent. They also use a more abstract motor model where local maxima are generated by randomly placing Gaussians within the 2D control space. These two approaches to modeling motor space are a major strength of this work.

The neural model is extremely generic and consists of units (each representing a population of excitatory and inhibitory neurons) with continuous-valued outputs ("firing rate") ranging from -1 to +1 with sigmoidal activation functions. Premotor HVC simply generates a fixed temporal sequence that drives activity in RA (analogous to the primary motor cortex) that constitutes commands to the motor controller that translates RA output into 2D control signals for the syrinx. A second, indirect, pathway from HVC to RA is represented by a single node ("BG"); in songbirds this pathway consists of 3 structures (one of them quite complex and heterogeneous) with recurrent connections (i.e., from LMAN back to Area X). Learning is primarily driven by performance-modulated Hebbian plasticity in HVC-BG connection weights. There is also Hebbian plasticity in HVC-RA weights that are in effect trained by the RA activity patterns driven by BG-RA connections.

I worry that this model is too simplified to capture essential features of the song system (and cortico-basal ganglia circuits more generally). That problem is most acute in the way the authors model (or fail to model) the anterior forebrain pathway (AFP) through X, DLM, and LMAN; their model in effect reduces the AFP to just LMAN. I do not think that invalidates this study, but there is a significant danger that this model will end up missing the mark in some important way relative to a more realistic model. However, there is another flaw that comes close to doing that-the connection weights between the units are allowed to vary from -1 to +1. That means, for example, that the connections between HVC and RA units can be inhibitory and can flip between excitation and inhibition during learning. I can imagine some hand-wavy justifications for this (e.g., the connection becomes "inhibitory" because excitation to inhibitory neurons becomes stronger than that to excitatory neurons within the unit), but I can't easily imagine one that I would find persuasive.

A key feature of this model is "synaptic volatility" in the connections between HVC and BG. In songbirds, performance often improves steadily throughout the day, then deteriorates after sleep, a feature which the authors suggest is an important method for avoiding getting stuck on relatively low-performing local optima. I find this suggestion to be intriguing and reasonably persuasive. To implement this in their model, after a simulated "day" of practice, the HVC-BG connection weights are partially randomized (synaptic volatility). There is nothing intrinsically wrong with this idea, but the authors imply that there is experimental support for this phenomenon, which they relate to "continuous remodeling of the cortico-striatal synapses with volatility over hours to days." None of the papers cited really support this kind of synaptic randomization. However, given the simplicity of the authors' neural model, the HVC-BG weights are probably the only place this randomization can be implemented. The authors' implementation does not just add noise to HVC-BG weights "overnight"; it is also scaled inversely by the magnitude of accumulated weight change through the day. The authors do not appear to provide a justification for this aspect of their synaptic volatility.

If we accept the authors' model as detailed and accurate enough for their purposes, it does explain several features of song learning and relates them to solving general problems of learning through gradient ascent. This is particularly true of the daily deterioration of performance and how that relates to escaping local maxima. They show that their model reproduces some of the known effects of lesioning the motor (HVC-RA) and anterior forebrain (HVC-BG-RA) pathways and how those effects depend on the current stage of song learning. They also explore the implications of delayed maturation of the HVC-RA pathway, exemplified by a gradual increase in HVC-RA weights. However, it is not clear that this actually happens; the one paper they cite in support of this contention (Mooney and Rao, 1994) shows no such thing (it does show that HVC axons enter RA later in development than LMAN axons do). Moreover, it's not clear how essential this could be given that some songbird species continue to show profound vocal plasticity throughout their lives and presumably long after their HVC-RA pathway has fully matured. The authors compare their dual pathway model to a single pathway model (an AFP-only model, in effect); a very illuminating comparison that demonstrates the advantage of the dual pathway. They close by showing that dual pathway performance is robust under variation of key parameters.

Reviewer #1 (Public review):

Summary:

This study presents an interesting behavioral paradigm and reveals interactive effects of social hierarchy and threat type on defensive behaviors. However, addressing the aforementioned points regarding methodological detail, rigor in behavioral classification, depth of result interpretation, and focus of the discussion is essential to strengthen the reliability and impact of the conclusions in a revised manuscript.

Strengths:

The paper is logically sound, featuring detailed classification and analysis of behaviors, with a focus on behavioral categories and transitions, thereby establishing a relatively robust research framework.

Comments on revised version.

I think the authors have addressed all of my comments.

Reviewer #1 (Public review):

Summary:

In this work, the authors investigate the mechanisms of low-frequency synaptic depression at cerebellar parallel fiber to interneuron synapses using unitary recordings that allow direct quantification of synaptic vesicle release. They show that sparse stimulation can induce robust synaptic depression even in the absence of substantial vesicle consumption, and that this depressed state is rapidly reversed when stimulation frequency is increased. To account for these observations, the authors propose a model in which low-frequency depression reflects a redistribution of vesicles within the readily releasable pool, in particular a reduction in docking site occupancy due to vesicle undocking.

Strengths:

I found the experimental work to be of high quality throughout. The use of simple synapse recordings to count individual vesicle release events is particularly powerful in this context and allows questions to be addressed that are difficult to approach with more conventional approaches. The demonstration that low-frequency depression can occur independently of prior vesicle release, together with the rapid recovery observed during high-frequency stimulation, places strong constraints on possible underlying mechanisms and represents a clear strength of the study.

The modeling framework is clearly laid out and helps organize a broad set of observations across stimulation frequencies. Several of the experimental tests appear well motivated by the model, including the recovery train experiments, the analysis of failures, and the use of doublet stimulation. Taken together, the data provide a coherent phenomenological description of low-frequency depression and its relationship to vesicle availability within the readily releasable pool.

Weaknesses:

The major concerns raised during the initial review have been successfully addressed through substantial revisions of both the manuscript and the presentation of the model. The distinction between experimental observations and model-based interpretation is now considerably clearer, and the discussion more appropriately reflects the explanatory nature of the modeling framework.

Reviewer #1 (Public review):

Summary

This manuscript addresses an important question in auditory neuroscience and neuroprosthetics: whether cortical responses to cochlear-implant stimulation resemble those evoked by natural acoustic stimulation, or whether electrical stimulation engages a distinct cortical population representation. The authors use high-density intracranial EEG recordings in rats to compare responses to pure tones in normal-hearing animals with responses to single-channel cochlear-implant stimulation in deafened animals. They combine analyses of event-related potentials, high-gamma activity, trial-by-trial variability, PCA/TCA-based dimensionality reduction, and decoder-based measures of stimulus information.

Strengths

A major strength of the study is the question it addresses. Understanding how electrical cochlear stimulation is represented centrally is highly relevant for cochlear-implant design, fitting strategies, rehabilitation, and broader theories of sensory neuroprosthetics. The comparison between acoustic and electrical stimulation, including within-animal comparisons in a subset of cases, is valuable because it directly asks whether implant-evoked cortical activity can be interpreted within the same framework as normal acoustic responses.

The methodological approach is also a strength. Dense cortical surface recordings provide simultaneous access to spatial and temporal features of auditory cortical responses. The combination of PCA, TCA, and decoder analyses gives complementary views of the data, and the information-transfer analysis provides an interesting way to test whether representations learned from acoustic stimulation generalise to electrical stimulation.

The revision has improved the manuscript considerably. The use of mixed-effects models better matches the partially paired experimental design. The expanded Methods improve reproducibility. The revised cohort descriptions and figure legends make the experimental design easier to follow. The clarification of Figure 8 strengthens the interpretation of the poor cross-modal transfer result, and the more cautious framing of spatial organisation better reflects the data.

Weaknesses

The main remaining limitation concerns experimental validation. The authors now clearly state that deafening was not verified with ABRs, hair-cell counts, or behavioural confirmation in the animals used for the main iEEG dataset, and that support for deafening efficacy in this cohort relies on prior validation of the same procedure in separate cohorts. This transparency is welcome and improves the manuscript, but the lack of direct validation in the main cohort remains a limitation, particularly given the importance of complete deafening and reliable cochlear implantation for interpreting implant-evoked cortical responses.

Appraisal

This does not undermine the main conclusions, but it should be kept in mind when interpreting the strength of the cochlear-implant comparisons. Overall, the revised manuscript is much stronger and more appropriately framed than the previous version. The study addresses an important problem, uses useful analytical approaches, and provides convincing evidence that acoustic and acute cochlear-implant stimulation evoke cortical responses with poor representational transfer.

Likely impact of the work on the field

The work is likely to be of interest to auditory neuroscientists, cochlear-implant researchers, and neuroengineers. Even where some conclusions require caution, the dataset and analytical framework may be useful for future studies aiming to relate central neural responses to implant programming, perceptual learning, or closed-loop neuroprosthetic strategies.

Comments on revised version.

The revised manuscript is substantially improved. The authors have clarified the methodological details, improved the statistical treatment of partially paired data, provided a clearer account of the animal cohorts, clarified the Figure 8 cross-modal decoding framework, and moderated several claims about spatial organisation and perceptual interpretation. Importantly, the manuscript now distinguishes more clearly between non-random spatial organisation, coarse cochleotopic structure, and sharply graded tonotopy or cochleotopy.

The results support the conclusion that acoustic and cochlear-implant stimulation evoke cortical responses with different properties. In particular, acoustic responses support better single-trial stimulus decoding than cochlear-implant responses, and decoders trained on acoustic responses generalise poorly to implant-evoked responses. The evidence for spatial organisation is more nuanced: the cochlear-implant condition shows evidence of coarse, non-random spatial structure, but not strong evidence for consistent, sharply graded cochleotopy. Overall, the revised manuscript makes a valuable contribution, provided that the conclusions remain framed around acute cortical responses and poor representational transfer rather than definitive claims about long-term perceptual experience.

Reviewer #1 (Public review):

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

Summary:

This study aimed at replicating two previous findings that showed (1) a link between prediction tendencies and neural speech tracking, and (2) that eye movements track speech. The main findings were replicated which supports the robustness of these results. The authors also investigated interactions between prediction tendencies and ocular speech tracking, but the data did not reveal clear relationships. The authors propose a framework that integrates the findings of the study and proposes how eye movements and prediction tendencies shape perception.

Strengths:

This is a well-written paper that addresses interesting research questions, bringing together two subfields that are usually studied in separation: auditory speech and eye movements. The authors aimed at replicating findings from two of their previous studies, which was overall successful and speaks for the robustness of the findings. The overall approach is convincing, methods and analyses appear to be thorough, and results are compelling.

Weaknesses:

Eye movement behavior could have presented in more detail and the authors could have attempted to understand whether there is a particular component in eye movement behavior (e.g., blinks, microsaccades) that drives the observed effects.

Reviewer #1 (Public review):

The idea behind this paper is to have an alternate, reliable and quantitative approach to assess cell-cell metabolic heterogeneity. This study tries to achieve that using translationally-coupled energetic responses to metabolic stress. This is interesting because, in general, most quantitative measurements of metabolic outputs are 'bulk' and average for many cells. To overcome this, many recent studies use some read-outs of translation (presuming that translation is the single major energy sink in cells - however, this is objectively correct only in rapidly proliferating cells). That said, the authors take an interesting approach - to use two clickable methionine analogs, and assess baseline vs metabolically coupled translation within the same cell.

The highlight is the methodology development where two distinct, clickable CMAs are used (to replace methionine in proteins). The labelling and approach are clever, and can be useful if carefully used. But there is going to be a challenge in using this, since the depletion of methionine itself (required for labelling), and a bias towards incorporation in some proteins (because these reagents are not highly permeable) will make it challenging to obtain precise metabolic state information - which otherwise can be obtained directly and far more precisely using a combination of other methods (ATP/flux measurement, respiratory capacity, translation rates etc). I therefore only make broader comments in my review below - to help structure this study better, clearly identify key limitations (and there are several that are clearly seen), and better clarify what MARBL may be useful for.

(1) These reagents used for MARBL are not highly cell permeable/transported into cells, and largely work on the surface proteome. Which means the measurements related to changes in translation are indirect - quantified based on changes on the surface proteome (seen with labelling), and not the entire proteome.

(2) A major limitation - which will confound any interpretation- is the need to use methionine-free media; this can be a problem beyond protein synthesis/met incorporation since this will almost instantly deplete SAM pools in cells. There is little data provided on the impact of using this approach on SAM pools (time kinetics, how quickly SAM pools are affected, how much of the impact on metabolism comes from purely that, etc).

This is important to establish because (i) of the continuous, very high flux of SAM -> SAH (eg. in the folate pathway, other methylations), and a constant need for SAM synthesis from methionine. This information will set the limits of capabilities of this method, as well as help delineate how much you can interpret results related to metabolic states between compared cells/states, etc. The labelling process is ~2 hours, while effects on SAM can be seen within minutes of methionine starvation in media, in metabolically active cells.

(3) What is the effect on overall adenylate charge/ATP due to shifting to methionine-free media + label addition? How does it vary between the cells tested (suspension vs adherent)? This should be established before data related to Fig. 2. Does it correlate with extent of AHA incorporation?

The primary conclusion that this method suitably reflects overall changes in energetics comes from the titration of 2DG/glycolytic inhibition.

(4) Relatedly, if this label incorporation experiment is carried out (for ~2 hrs), and subsequently there is a washout/replacement with fresh, methionine-supplemented medium, (how quickly) do the cells recover and restore their energetic allocations?

(5) One possible advantage of a system like this can be to address questions in single cells/study cell metabolic heterogeneity. However, these are best done if the attaching moiety has a (selective) fluorescence increase and/or other read-out that can be quantitatively obtained at a single cell level. Largely, using AHA or HPG effectively only leads to bulk estimates (which can be sub-sorted towards single-cell estimates indirectly). This means that this method cannot really be used to study cell-cell metabolic heterogeneity effectively - compared to far simpler approaches, for example using a mitochondrial potentiometric dye with high fluorescence, or reporters for glycolytic activity, etc. This would also be related to Figure 5 - at best, this approach may complement existing approaches towards identifying heterogeneous sub-populations of cells.

However, I do agree that MARBL is flexible, stable, and can be internally normalised and used through flow-based platforms. It can supplement existing approaches to perturb bioenergetics, and also supplement existing approaches to understand metabolic state in live cells, particularly in suspension cells.

Reviewer #1 (Public review):

Summary:

The authors investigate whether systematic manipulation of GABA-A and NMDA receptors influences large-scale cortical neuronal timescales and transient network dynamics. 57 healthy male participants completed placebo, lorazepam and D-cycloserine sessions in a double-blind, within-participant, cross-over design. The authors estimated timescales from the knee frequency of the aperiodic component of MEG power spectra, and identified transient large-scale cortical networks using a time-delay embedded hidden Markov model (TDE-HMM) analysis. The authors report that lorazepam increases estimated timescale across multiple cortical areas, with particularly pronounced changes in states interpreted as frontal default-mode network (DMN) and dorsal attention network (DAN). These changes include increased fractional occupancy of the DMN and decreased for DAN. D-cycloserine did not significantly affect neuronal timescales.

Strengths:

The study has several notable strengths:

(1) The pharma-MEG study is well designed and executed (e.g., a crossover design, collecting subjective, cardiovascular and additional control measurements).

(2) Neuronal-timescales maps are validated against previously published cortical timescale and hierarchy maps.

(3) TDE-HMM findings are examined using alternative numbers of hidden states and different preprocessing pipelines.

(4) The manuscript is generally clear and well written.

Weaknesses:

Nevertheless, several aspects of the primary neuronal timescale measure and the TDE-HMM analysis require further validation, and several points should be clarified:

(1) A central concern is whether the reported measure of neuronal timescale, on its own, is sufficient to support the interpretation assigned to it. Lorazepam has been shown to alter several spectral parameters, including oscillatory peaks and the aperiodic component of power spectra, in ways that could influence knee-frequency fitting. The manuscript, however, does not provide sufficient information about fit quality (across participants, parcels, and conditions), parcels within each participant/condition with identifiable knees, or the sensitivity of the results to the fitting range and peak settings (i.e., FOOOF parameters). Importantly, Figure S3 indicates that neither lorazepam nor D-cycloserine significantly affects knee frequency, although the neuronal timescale measure is mathematically derived from that knee frequency (i.e., tau = 1/(2*pi*f_knee)). Although such a result is mathematically possible, the pattern is unusual and requires explanation because timescales are not measured independently, but rather derived from the knee frequency. Furthermore, the neuronal timescale maps show a relatively homogenous increase across the cortex under lorazepam (Figure 2A), whereas the knee-frequency maps show a heterogeneous spatial pattern, with increased knee frequency under lorazepam in frontal regions, and decreased in occipital and temporal regions (Figure S3). A widespread significant effect appearing only after inversion could reflect a genuine effect, particularly in parcels with low knees to begin with. However, it could also arise if a small number of extremely low fitted knee frequencies (below 1Hz?) produce very long timescale estimates and disproportionately affect the statistical analysis.<br /> The authors should address this discrepancy by presenting the distributions of knee-frequencies and neuronal timescales, and their ranges. Additional information about outliers and the robustness of the findings to extreme fitted values would also be valuable.

(2) A second concern relates to the TDE-HMM analysis. The model identifies states from the covariance matrix of time-delayed time-courses. Consequently, states are differentiated partly on the basis of their spectral and temporal characteristics. Knee frequencies for each state are then estimated from the power spectra of the same states used to define them. Differences in neuronal timescales across states may therefore be expected, at least in part, simply by the way the states were inferred. This point weakens the claim that cortical states are independent, and "operate on distinct timescales". A clearer separation between state definition and neuronal timescale estimation would be needed to establish that the reported state-specific differences are not partly an expected consequence of the fitted model. This could be addressed using, e.g., cross-validation or simulations. This concern is less substantial for the drug-related changes in neuronal time scale within individual states.

(3) The result and methods sections provide insufficient detail and statistical reporting for the TDE-HMM analysis. In the result section (page 10), the authors report only the *range* of fractional occupancies across all states. A range of 1% to 48% is substantial. It is therefore important to determine whether some states occupied only 1% (corresponding to ~3sec) while others occupied a much larger proportion. The authors should report the mean fractional occupancy of each state, together with its SD and range across participants.

(4) The authors should explain the apparent discrepancy between the relatively homogeneous increase in neuronal timescales under lorazepam across the complete recording (Figure 2) and the heterogeneous state-specific changes (both increases and decreases; Figure 4B). Could this pattern be explained, at least in part, by the differences in fractional occupancy of individual states? This provides an additional reason to report state-specific occupancy values in greater detail.

(5) On page 13, 2nd paragraph, the authors state that the findings indicate that the frontal DMN is an important driver of the global prolongation of neuronal timescales, based on the strong similarities to the time-averaged timescales. However, Figure S5 appears to show that the spatial correlation between state-specific and time-averaged timescales is as high for State 8 and is similar for State 2. The current statement therefore appears to be an overinterpretation. Demonstrating that the correlation for State 3 is significantly larger than the correlations for the other states would provide stronger support for this claim.

(6) Spatial maps are presented inconsistently throughout the main manuscript and in the supplementary. Specifically, some figures only show thresholded maps (at p<0.05 or p<0.001; e.g., Figure 4b), whereas others show unthresholded maps. Reporting only the number of parcels exhibiting a significant effect, without presenting the corresponding thresholded maps, makes it difficult to evaluate the spatial distribution of the reported changes. At least for the main findings (e.g., lorazepam effects on neuronal timescales), I recommend presenting both thresholded and unthresholded maps within the same figure.

(7) Figure 3: The rationale for presenting the mean power between 3-30 Hz is unclear. The authors should explain why this metric was selected to represent each state. Visual inspection suggests that the state-specific power spectra differ across several dimensions, including the aperiodic exponent, offset, alpha power, etc. Characterizing states using these parameters may be more informative. Each of these features could potentially also influence the estimated knee frequency.

(8) The conclusion on page 17 (and similar statement in the intro): "...these findings provide causal evidence that microscale synaptic inhibition directly shapes both local neuronal timescale organization... ") appears to be overstated based on the evidence presented. Although the lorazepam manipulation supports a causal effect of the drug on MEG-derived cortical timescales and network dynamics, the study does not directly measure microscale synaptic inhibition or establish a direct mechanistic link across scales. The phrasing should therefore distinguish the observed pharmacological effects from the inferred role of GABAergic inhibition and be phrased more cautiously.

(9) The method section lacks several critical details, including: criteria for excluding MEG channels and noisy segments (see comment below), details on source reconstruction (e.g., number of vertices used to project the sensor-level data), procedures used to generate the null distributions for the spatial autocorrelation preserving permutation test, transition probability analysis and more. Relatedly, the preprocessing pipeline of the MEG data appears to be based on manual inspection for the removal of channels, ICA components and noisy segments. This procedure is inherently subjective. The authors should provide details on the exact criteria used to make these decisions. Critically, the authors should also report the duration of usable resting-state data remaining after segment rejection. The Methods section should provide sufficient detail to allow readers to evaluate the validity of the study and reproduce the analysis as closely as possible. In its current form, it does not do so.

Reviewer #1 (Public review):

Summary:

The authors used extracellular field potential recordings and two-photon imaging to monitor neuronal network activity and intracellular chloride concentration ([Cl-]i) in organotypic hippocampal slices from mice expressing the genetically encoded chloride fluorophore Clomeleon. These slices were used as a model of acute traumatic brain injury and epileptogenesis in vitro. The study provides evidence that blocking the WNK-SPAK/OSR1 pathway with WNK463 alleviates epileptic activity, and that this anticonvulsant effect involves suppression of the chloride loader NKCC1 and enhancement of chloride extrusion via KCC2. Overall, this is a solid study with a comprehensive pharmacological analysis.

Strengths:

The conclusions are well supported by the detailed pharmacological analysis.

Weaknesses:

The only weakness I see is the absence of cellular-level electrophysiology, which precludes interpretation of the imaging data in the context of GABA action polarity.

Reviewer #1 (Public review):

Summary:

The authors developed a free flight perturbation assay that induces roll instability. They set out to understand how steering muscles control and stabilize roll instability. In addition to the previously reported changes in wing amplitude, they find that a pure roll perturbation induces changes in stroke deviation. They silence motor neurons of individual steering muscles to show that silencing any one muscle alone is not sufficient to disrupt the recovery from roll perturbations. Through aerodynamic modelling, they argue that this occurs despite the fact that each muscle alone can induce changes in wing amplitude and/or stroke deviation. They suggest that this robustness to roll perturbation may be due to redundancy in the motor control program. Finally, they confirm redundant projections from the published haltere connectome study and show that indeed muscles which produce similar effects on wing kinematics receive redundant connections from the haltere afferents.

Strengths:

This study's strength lies in integrating findings from several adjacent areas in insect flight control research. The authors combine steering muscles physiology, wing kinematic quantification, aerodynamic modelling, and sensory (haltere) control of wing kinematics. This integrated approach provides a comprehensive discussion of the mechanisms that may underlie recovery from roll perturbations.

Weaknesses:

The biggest weakness is that, although the authors generate a very plausible and interesting hypothesis, much of the supporting evidence already occurs in existing datasets. In fact, a distributed or redundant many-to-one muscle control for flight kinematics is not a new idea. It has been suggested wherever researchers have examined muscle control for wing kinematics, across studies in flies, moths and other insects (Heide and Gotz, 1996; Balint and Dickinson, 2001; Lindsay et al, 2017; Melis et al, 2024, etc; Wood et al., 2024, etc). Therefore, it is not particularly surprising that Drosophila employs a multi-muscle strategy for roll stabilization. Although it is interesting to see that inhibition of even the phasic muscles (b3/ I2) alone did not disrupt the recovery, further experiments are required to address how these muscles contribute to wing kinematics responsible for roll control. Unfortunately, although the new evidence provided in this manuscript strengthens the idea of redundant muscle control, it does not test it directly.

Reviewer #1 (Public review):

Summary:

Tullo et al. address an important and currently unresolved mechanistic question: does the prion-like spreading of alpha-synuclein (aSyn) generalize across three biological factors: host genotype, preformed-fibril (PFF) species, and disease epicentre (brain region); and can the resulting neurodegeneration be predicted computationally? Using a longitudinal design, adult M83 A53T-hemizygous mice and wild-type littermates received intrastriatal human- or mouse-PFF or PBS, with in vivo brain MRI at 7T, motor testing, survival and weight followed to 120 days post-injection. A parallel experiment seeded human-PFF or PBS into the hippocampal dentate gyrus. Atrophy was quantified by deformation-based morphometry, brain-behaviour coupling by partial least squares, and spread was simulated with a Susceptible-Infected-Removed agent-based model constrained by the Allen mouse connectome and SNCA expression. The authors conclude that aSyn-associated atrophy generalizes across genotype and fibril species but is anatomically distinct for the two epicentres, emphasizing regional vulnerability. This is a technically strong and ambitious study, reflecting a substantial and well-executed research effort.

Strengths:

This is a technically accomplished and ambitious study from a group with clear expertise in mouse neuroimaging and network modelling. The study addresses a real knowledge gap, relevant for mechanistic explorations of alpha-synucleinopathies: genotype and fibril inoculum species have rarely been compared head-to-head, and the relationship between aSyn propagation and downstream atrophy outside the striatum has been under-examined so far. The central hypothesis, that regional vulnerability constrains aSyn-associated neurodegeneration, together with the first attempt to model aSyn-induced atrophy computationally in rodents, is conceptually and methodologically valuable and translationally relevant.

The longitudinal dataset is unusually rich (687 in vivo scans), and both the data and the analysis pipeline are openly shared (OpenNeuro ds007671, Zenodo, GitHub), which is extremely important for reproducibility and of clear value to the community. The work uses a multi-modal methodology in which the same phenomenon is examined across anatomical MRI, multivariate brain-behaviour modelling, and a mechanistic simulation, and is the first to model aSyn-induced atrophy computationally in mice.

Another strength is the consistent incorporation of sex as a biological variable throughout, including sex-stratified survival, behavioural, and voxelwise atrophy analyses. This allowed the authors to identify sex differences in disease progression, notably in survival and symptom onset, while indicating that the core PFF-induced atrophy pattern was largely preserved across sexes.

The core descriptive findings, that PFF-induced atrophy and motor impairment are reproducible across genotype and fibril species, and that striatal and hippocampal seeding yield distinct anatomical signatures, are convincingly supported and represent a very valuable advance.

Weaknesses:

The strongest mechanistic and epicentre interpretations would benefit from some additional support, although the study's core findings are robust.

First, the framing centres on aSyn propagation, but the sole in-cohort readout is MRI-derived atrophy assessment; no aSyn/phospho-Ser129 pathology is shown for these animals. The atrophy-propagation link remains inferential.

Second, the computational model's fit is reported as the peak correlation across simulation time steps and is not yet benchmarked against null or baseline models, so it is somewhat difficult to determine how much the connectome and dynamics contribute beyond gene expression alone; parameter provenance is also not described in the text.

Third, the epicentre difference is well supported empirically at matched inoculum, but the computational comparison (SIR) is so far inoculum-mismatched: the striatal model was evaluated against mouse-PFF atrophy while the hippocampal model used human-PFF. A matched striatal human-PFF map is already available, so this could be reconciled without new data. The reduced hippocampal vulnerability despite higher hippocampal SNCA expression also remains unexplained.

Appraisal and impact:

The authors largely achieve their aims, and the generalization of atrophy across genotype and fibril species, together with the epicentre-specific anatomy, is well supported by a strong and openly available dataset. The more mechanistic conclusions - aSyn propagation specifically, connectome-driven vulnerability, and epicentre-determined resistance - would be strengthened where feasible by pathology validation, model benchmarking, and completing the already-available matched computational comparison, and should be interpreted with corresponding caution. Even so, the combination of a large longitudinal imaging resource, a factorial in vivo design, and the first rodent computational model of aSyn-related atrophy makes this a valuable contribution that is likely to be a useful reference and methodological template for the synucleinopathy and network-neurodegeneration communities.

Reviewer #1 (Public review):

Summary:

HIV can persist in brain microglia despite ART and is linked to ongoing neuroinflammation and altered cellular function.

Strengths:

The authors demonstrate an innovative cell-type-specific analysis of human postmortem brain tissue from aviremic and viremic people with HIV. It uses FANS, bulk and single-nucleus RNA-seq to show that HIV DNA is concentrated mainly in microglia and that inflammatory and synaptic abnormalities persist despite ART.

Weaknesses:

The evidence is exploratory, based on a small, heterogeneous postmortem cohort. Therefore, the findings are suggestive rather than definitive.

The study would be stronger if a larger cohort, particularly more HIV-negative controls, were included. Also, less heterogeneity would reduce confounding from co-infections and terminal illness. The findings would also be better supported by longitudinal or matched peripheral data, protein-level validation, and direct evidence of viral activity rather than proviral DNA alone. A larger sample size would improve statistical power and make the cell-type differences more reliable.

Proper Name Place Name Hospital for wounded soldiers

Proper Name Character Name Former classmate of the narrator, reluctant to volunteer for the military. Died early

Proper Name Character Name Strict commanding officer of the second company referred to as "The Prussian"

Proper Name Character Name Former Schoolmaster of the narrator and some of the soldiers

Proper Name Character Name Another soldier who was recently injured and now at the hospital

Proper Name Character Name Cook of the first company

Proper Name Nickname nickname of Heinrich the camp cook

Proper Name Character Name The cook for the rest camp

Proper Name Character Name Trades the narrator for chewing tobacco; referred to as the leader of the group

Proper Name Character Name Peasant who thinks about his life before the war

Proper Name Character Name Same age as the narrator and peat digger

Proper Name Character Name Working alongside Müller at a rest camp same age as narrator

Proper Name Character Name Narrator and military volunteer

Proper Name Character Name Working alongside Tjaden in the rest camp heavy reader and former classmates with the narrator

Proper Name Character Name Lance Corporal, described as the clearest thinker, former classmates with the narrator

Proper Name Character Name Bearded, and former classmates with the Narrator

Proper Name Nickname Referring to the narrators strict commanding officer

Reviewer #1 (Public review):

Summary:

The authors are trying to characterize the sources of H. pylori in an island system. They find that, like the humans, the bacteria are admixed, but there is no correlation within the island between human ancestry and bacterial ancestry.

Strengths:

The study has taken particular care to characterize the humans from which isolates were obtained. Thus, it is a particularly convincing demonstration of a bacterial "melting pot".

Weaknesses:

The GWAS is highly confounded by population structure. In particular, there is a large group of strains that lack the cag pathogenicity island, and also differ in frequencies of other genes. So it's not clear that these differences are other than in cag status.

Figure 1 seems very inconclusive.

Reviewer #1 (Public review):

This interesting manuscript challenges the current interpretation of the well-established stop signal reaction time task (SSRT), commonly used in many research areas. SSRT has been traditionally thought of as primarily a measure of inhibitory control. In this work, the authors argue that this is influenced significantly by sensory and motor transmission times, and that these low-level processes may systematically confound SSRT estimates both in individual groups and also in many clinical populations.

Conceptually, this raises an important and significant question regarding the construct validity of one of the more widely used behavioral measures of response inhibition. The authors provide a clear theoretical framing and importantly address the overlooked assumptions in SSRT modelling, the underexplored source of variability.

Further, direct evidence separating peripheral sensory and motor contributions from central inhibitory processes is certainly needed, as well as a more balanced interpretation of prior methodological refinements in the field. Is a correlation between T0 and SSRT sufficient to conclude that SSSRT may be predominantly driven by peripheral delays? What proportion of SSRT variance remains unexplained after one accounts for T0? If T0 and inhibitory processes share neural processing speed, does that mean they covary? If one corrects T0, do the group differences become smaller or perhaps disappear?

Furthermore, how robust is the T0 estimate in noisy environments of all sorts and especially across modalities (visual and motor)? The authors argue that this relationship is clearer in "good quality data"; how do you define that, and what happens with noisy data? For instance, the authors refer to a range of clinical disorders such as ADHD and PD where noise is abundantly present, partly due to the disease itself or due to treatment.

In conclusion, this is certainly a thought-provoking and potentially influential contribution in the literature that raises important questions about the interpretation of the stop signal reaction time task.

Reviewer #1 (Public review):

Summary:

In this paper, Chen et al. identified a role for the circadian photoreceptor CRYPTOCHROME (CRY) in promoting wakefulness under short photoperiods. This research is potentially important as hypersomnolence is often seen in patients suffering from SAD during winter times. The mechanisms underlying these sleep effects are poorly known.

Strengths:

The authors clearly demonstrated that mutations in cry lead to elevated sleep under 4:20 Light-Dark (LD) cycles. Furthermore, using RNAi, they identified GABAergic neurons as a primary site of CRY action to promote wakefulness under short photoperiods. They then provide genetic and pharmacological evidence demonstrating that CRY acts on GABAergic transmission to modulate sleep under such conditions.

Weaknesses:

The authors then went on to identify the neuronal location of this CRY action on sleep. This is where this reviewer is much more circumspect about the data provided. The authors hypothesize that the l-LNvs which are known to be arousal promoting may be involved in the phenotypes they are observing. To investigate this, they undertook several imaging and genetic experiments.

While the authors have made improvements in this resubmitted manuscript, there are still multiple concerns about the paper. I think the authors provide enough evidence suggesting that CRY plays a role in sleep under short photoperiod. The data also supports that CRY acts in GABAergic neurons. However, the identity of the GABAergic neurons involved in this CRY dependent sleep mechanism remains unclear. Similarly, whether l-LNvs are the target of this GABA mediated sleep regulation under short photoperiod is not fully demonstrated. The data presented suggests that but does not conclusively prove it.

Reviewer #2 (Public review):

Chong Wang et al. investigated the role of H3K4me2 during the reprogramming processes in mouse preimplantation embryos. The authors show that H3K4me2 is erased from GV to MII oocytes and re-established in the late 2-cell stage by performing Cut & Run H3K4me2 and immunofluorescence staining. Erasure and re-establishment of H3K4me2 have not been studied well, and profiling of H3K4me2 in germ cells and preimplantation embryos is valuable to understanding the reprogramming process and epigenetic inheritance.

(1) "The authors' assertion that H3K27me3 did not change from GV to MII stage (Author response) is noted; however, this data was not provided. To validate the technical success of the CUT&RUN protocol in MII oocytes-a stage characterized by chromatin condensation and low DNA input-it is essential that the authors provide their internal H3K27me3 data as a positive control.

Without showing that a stable mark (like H3K27me3) can be successfully mapped in their MII samples, the 'disappearance' of H3K4me2 peaks cannot be distinguished from a technical failure of the assay at this developmental stage. Furthermore, I remain skeptical of the inclusion of the first polar body as a DNA quantity, as polar body chromatin is often undergoing degradation and may not reflect the epigenetic state of the oocyte itself.

(2) I remain concerned by the inconsistencies regarding KDM1A (LSD1) expression. The authors claim in the text and Figure 4A that KDM1A is 'rarely expressed' during mouse embryonic development. However, their own Extended Data Figure 3A shows high expression of KDM1A in oocytes, 4-cell, and 8-cell stages. Both figures reportedly use published RNA-seq data. The authors must explain how the same gene in the same developmental stages can appear 'rarely expressed' in one figure and 'expressed' in another.

(3) Page 6 (Line 161-165) The H3K4me2 demethylases KDM1B (LSD2) were also highly expressed in growing oocytes but showed decreased expression in MII oocytes (Figure S3A, see also revised heatmap). We have re-analyzed the expression data and corrected the heatmap normalization; the revised Figure S3A now accurately shows that Kdm1b is highly expressed in growing oocytes, with lower levels in 8- week and MII oocytes, consistent with its role in maternal imprint establishment.

The expression data in growing oocytes are missing in Fig S3A.

(4) The authors' proposal to use transcriptome data to confirm TCP specificity is insufficient (Author reply). Since TCP is a small-molecule inhibitor of protein activity, its primary effect is not the reduction of mRNA transcripts, but the inhibition of the enzymes themselves.

To support their claim that the observed H3K4me2 resetting and developmental arrest are specifically due to KDM1B inhibition, I recommend:

a. Perform Western Blot for KDM1A/B

b. Use a Selective Inhibitor: Use a more selective KDM1A inhibitor (e.g. GSK-LSD1)

c. Address the Discrepancy: Provide a clear biological explanation for why chemical inhibition leads to 4-cell arrest while a maternal KO of KDM1B survives to E10."

(5) "The authors' response that IF and CUT&RUN cannot be compared because of 'different analysis models' is not scientifically sound in this context.

Quantitative Contradiction: A 1,000-fold increase in global IF signal must be reflected in the genomic landscape. If only 251 genes show a gain in H3K4me2 in the CUT&RUN data, this represents a negligible fraction of the genome, directly contradicting the 'dramatic increase' claimed in the IF data.

Lack of Spike-in Normalization: Did the authors use a spike-in for their CUT&RUN? Without global normalization, CUT&RUN only reports relative changes. If H3K4me2 increased everywhere, a non-normalized CUT&RUN library would fail to show it, making the data misleading. Currently, the two datasets provide two different versions of biological reality. A third validation (e.g., Spike-in Normalized CUT&RUN) would be recommended to determine which dataset is accurate."

Reviewer #1 (Public review):

Summary:

This paper asks how the NK cell receptor KIR2DL4 binds HLA-G and undergoes endocytosis. The authors propose that an allosteric disulfide-bond switch controls whether the receptor is in a ligand-binding or non-binding state, and they support this model using mutagenesis, imaging, mass spectrometry, and structural prediction.

Strengths:

A major strength is the use of diverse, complementary approaches to validate the central claim. The authors combined unbiased random mutagenesis to identify key residues, confocal microscopy to track cellular localization , and mass spectrometry to quantify the redox states of specific disulfide bonds. These methods consistently support a single model: an allosteric disulfide switch. The transition between a Cys10-Cys28 bond and a Cys28-Cys74 bond serves as a functional switch that controls whether the receptor resides at the plasma membrane to bind ligand or remains inactive in endosomes.

Comments on revised version.

The revision substantively addresses the core weaknesses I raised, particularly on direct binding evidence, which was the most important gap. The remaining points (oligomerization confound in the SPR comparison, C10L not independently validated by imaging, and the 293T-only functional readout) are real but secondary so I'd suggest they be addressed with a sentence or two of acknowledgment in the Discussion rather than additional experiments.

Reviewer #1 (Public review):

Summary:

Despite setting global conservation goals for the conservation of genetic diversity, gathering enough genetic data to assess its current status or monitor change over time for all species would require substantial time and resources. Finding reliable proxies or predictors of genetic diversity change would be a valuable alternative. Selmoni and Schuman test for patterns and trends in coral reef animals' genetic diversity across space and time and evaluate how well environmental variables predict their genetic diversity.

Strengths:

The authors have compiled a large genomic dataset from 19 studies, 18 species, and 173 reefs, and use a standardized k-mer-based pipeline to process all datasets. The authors propose that using k-mers may be especially suitable for macrogenomic analyses because they are less computationally intense and do not require a reference genome. This is interesting because there are currently few examples of macrogenetics studies using genomic data, likely due in part to the difficulty associated with processing multiple disparate datasets in an efficient and standardized way.

Weaknesses:

I have concerns that the models don't fit the data structure, the data are not suitable to test for temporal change, and that the values being predicted do not reflect meaningful genetic diversity. The manuscript would also benefit from a more cohesive narrative structure, clearly stated research goals, and better engagement with previous literature on this topic.

Effects of time: The data are not suitable to test for temporal changes across all species. 11 of 19 datasets were only sampled over 1-2 years, and only 5 were sampled across 5 or more years. I don't know generation times for these species offhand, but this is generally too short to draw any conclusions about genetic diversity change over time. While it makes sense to include year in the models as a control variable, in my opinion these patterns should not be interpreted and removed from the discussion, or at least the conclusions should be tempered (for example the section "Are coral reef populations rapidly losing their genetic diversity?").

Environmental effects models: I'm not convinced that the geographic random intercept term (response variable) represents a local genetic distance that should be related to environments. How many species were sampled at each reef? If only 1 species is sampled, then the reef's average diversity reflects that species, not necessarily environmental effects. If multiple species are sampled at a reef, then I suspect this value is the average diversity of all the species sampled there. Knowing that species have different levels of diversity (e.g., Toczydlowski et al 2025 cited here), what does this metric mean and how is it affected by species composition vs environments? I do not see the value in predicting this variable without considering species identity.

Introduction: The macrogenetics literature is not well described in the introduction. Most of the cited studies indeed use raw data rather than summary statistics, and most do not aim to predict genetic diversity.

I wouldn't say that explaining more than 20% of variation in data is a major challenge in macrogenetics. The choice of how many, and which, predictors to use depends on the research question (e.g., on the spectrum of understanding to predicting, see Shmueli 2010 "To Explain or To Predict?"; Arif & MacNeil 2022 "Predictive models aren't for causal inference"). It's proposed here that explained variance can be increased by adding more, or more relevant, environmental predictors-but particularly for macrogenetics studies reusing data from different sources with different study and sampling designs and different species, much of the variation in the data will be due to these factors. That is why conditional R2s are typically quite high (>80%) in mixed models with species/study as random effects, see e.g. Clark & Pinsky 2024 or Karachaliou 2025. Having strong effects of particular variables would also require that all species respond to the predictor in the same way, which is not necessarily expected. Low explanatory capacity alone is not an issue if the goal is not prediction.

Reviewer #1 (Public review):

The authors use metabolic reconstruction to build genome-scale models from TARA metagenomes.

This is not the first paper that attempts a reconstruction of metabolism on this scale. And like those that came before, its weakness is in the analysis of the resulting model.

I was initially quite excited about this manuscript as the reconstruction itself seems well done. However, the analysis does not deliver major or minor results.

It could be said that the modelling cuts some corners, for example in the identification of the flux modes. I was not quite able to follow why this is necessary, as numerical linear algebra methods seem to be able to handle problems of this size. But then again, I am quite confident that the sampling approach that the authors use produces a good enough approximation.

The weak point of the manuscript is the analysis that follows. The conclusions contain very few hard results, and the results that are presented talk mostly about concepts that are somewhat arbitrarily introduced and hard to relate back to nature. The authors construct a mix of their own concepts and others borrowed from cancer research, and neither of the metrics that are used is very convincing.

For example, the authors say they contribute to the complexity-stability debate, but that debate focuses on a particular notion of stability. What the authors call stability here is an entirely different concept that is completely unrelated to the stability of the complexity-stability debate; I would rather describe it as a resilience metric rather than stability.

The problem is that the metrics that are used lack an underlying firm grounding. Ecology was for a long time struggling with the same problem (and to some extent still is), but eventually much progress has been made by rigorously deriving metrics that are easy to relate back. The same would be possible here. Using Steuer's structural kinetic method, the reconstructed metabolism could be described dynamically, which would allow genuine stability analysis as well as other crucial metrics such as the observability and controllability, impact and sensitivity, which would make it far easier to relate results back.

Reviewer #1 (Public review):

Summary:

This manuscript seeks to make use of information about Ct values from PCR testing of mosquito pools for West Nile virus infection to make inferences about mosquito prevalence and West Nile risk. It does so through analysis of empirical data and simulated data with a realistic agent-based model.

Strengths:

This work is conceptually innovative for mosquito-borne viruses, building on ideas developed primarily during work on SARS-CoV-2. Exploring this topic is worthwhile regardless of the outcome. The use of data, testing in multiple labs, and the complementarity of modeling and empirical data analysis are all strengths of the approach.

Weaknesses:

Some of the primary weaknesses include a dependence of the results on relatively narrow model assumptions, and a lack of compelling improvement over existing methods. None of these weaknesses are fatal flaws; they are modest weaknesses that limit the potential of or excitement about the method.

Reviewer #1 (Public review):

Summary:

The authors hypothesized that "RVM neurons operate across multiple temporal scales, integrating fast responses associated with reflex-linked control with slower fluctuations reflecting ongoing network or state-dependent modulation". The hypothesis was tested with the established ON/OFF-cell model and probabilistic modeling. The study is conceptually interesting and methodologically sophisticated. The findings build toward the conclusion that pain-control circuits operate across multiple timescales.

Strengths:

The use of Bayesian regression and Gaussian process modeling to quantify and characterize recovery dynamics and ongoing oscillatory activity.

The authors show that slow rhythmic activity appears preferentially in ON- and OFF-cells but not in NEUTRAL-cells, suggesting that the oscillations are related to pain-modulatory circuitry rather than being a generic feature of all recorded neurons.

The observation that some oscillatory activity is coherent with autonomic measures aligns with broader views of the RVM as a hub integrating nociceptive and homeostatic regulation.

Some pitfalls are appreciated and discussed by the authors, including the functional significance of slow fluctuations, the influence of anesthetics on global brain-state dynamics, the molecular profiles of the studied ON- and OFF-cells, and the heart rate as a covarying signal of RVM neuronal activity.

Weaknesses:

A general weakness is that the work is mostly descriptive and relies on anesthetized preparations. Whether the observed rhythms occur in awake animals and are linked to fluctuations in pain behavior needs to be confirmed in future studies.

The study measures limited autonomic variables. The causal relationship between "slow fluctuations" and "ongoing physiological state" is unclear and overstated, since the data presented appear correlational.

ON- and OFF-cells in the RVM are identified by their responses correlated with reflexive activity. The significance of the observed oscillations in spontaneous pain conditions is unclear.

It is uncertain whether the observed rhythms truly reflect intrinsic RVM organization rather than anesthesia-dependent phenomena; the authors appreciated this pitfall, though.

The Gaussian process analysis suggests predictability and quasi-periodicity, but predictability alone does not necessarily imply a true biological oscillator.

Conclusion:

The results support the authors' hypothesis. The findings provide a compelling conceptual message about the multiscale organization and dynamics of descending pain-control circuits and encourage further studies on the topic.

Reviewer #1 (Public review):

Summary:

Liao et al. present SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), a method for reconstructing spatial organization from diffusion-defined DNA barcode interactions without the use of optical imaging. In SCOPE, hydrogel beads bearing unique DNA barcodes contain both "sender" and "receiver" oligonucleotides. Upon enzymatic release, sender oligos diffuse locally and hybridize to receiver oligos on neighboring beads, forming chimeric molecules that encode spatial proximity. Sequencing these products yields an interaction matrix, which is then used to reconstruct a spatial coordinate map.

The authors demonstrate reconstruction of synthetic two-dimensional shapes, a large multicolor Snellen eye chart, and the interior surface of three-dimensional molds. The work expands the conceptual and experimental landscape of optics-free spatial sequencing.

Strengths:

SCOPE employs bidirectional sender and receiver oligonucleotides on every bead, rather than using asymmetric transmitter-receiver architectures found in other diffusion-based methods. The symmetric design may improve detection sensitivity and reconstruction strategies, and represents a meaningful variation on optics-free spatial encoding.

A notable strength of this study is the physical scale achieved. The authors reconstruct a Snellen chart spanning approximately 704 mm² and demonstrate molded 3D structures on the order of 75-100 mm³. Although some larger-scale warping is evident, and is discussed as potentially due to non-uniform diffusion, the relative local positioning across these large areas appears impressively accurate.

The authors extend reconstruction beyond two-dimensional arrays to three-dimensional molded surfaces. This demonstrates that the assay and the computational methods for interpreting proximity graphs can support non-planar spatial relationships, expanding the scope of optics-free spatial inference.

The revised manuscript also strengthens the interpretation of these three-dimensional experiments by providing additional evidence that the limited recovery of interior regions primarily reflects technical constraints associated with molecular recovery from the hydrogel beads on the interior rather than an inherent limitation of the SCOPE framework.

Weaknesses:

Although the method is discussed in the context of spatial genomics and potential tissue applications, it is currently demonstrated only on engineered two-dimensional bead arrays and three-dimensional shapes fabricated in molds. The authors appropriately acknowledge that additional work will be required to establish performance in heterogeneous biological tissues, where diffusion and molecular recovery are likely to be more complex.

The revised manuscript substantially clarifies the limitations of the current three-dimensional implementation by providing additional discussion and control experiments supporting the interpretation that reduced recovery of interior beads is primarily a technical limitation of the present protocol. Although limited volumetric sampling remains a current constraint of the method, the authors appropriately discuss applications in which surface-resolved reconstruction may still be informative.

The computational reconstruction workflow is now described in greater detail, including automated parameter selection, fixed versus optimized hyperparameters, and explicit discussion of the manual flattening step required for the largest Snellen reconstruction. The revised manuscript also explains that many anticipated tissue-section applications present a more constrained reconstruction problem than the intentionally challenging proof-of-concept demonstrations presented here, providing additional context for the expected performance of the method in future biological applications.

Reviewer #1 (Public review):

Summary:

This study by Akhtar et al. aims to investigate the link between systemic metabolism and respiratory demands, and how sleep and circadian clock regulate metabolic states and respiratory dynamics. The authors leverage genetic mutants that are defective in sleep and circadian behavior in combination with indirect respirometry and steady-state LC-MS-based metabolomics to address this question in the Drosophila model.

First, the authors performed respirometry (on groups of 25 flies) to measure oxygen consumption (VO2) and carbon dioxide production (VCO2) to calculate the respiratory quotient (RQ) across the 24-hour day (12h:12h light-dark cycle) and assess metabolic fuel utilization. They observed that among all the genotypes tested, wild type (WT) flies and per0 flies in LD and WT flies in DD exhibit RQ >1. They concluded the >1 RQ is consistent with active lipogenesis. In contrast, the short-sleep mutants fumin (fmn) and sleepless (sss) showed significantly different RQ; the fmn exhibits a slight reduction in RQ values, suggesting increased reliance on carbohydrate metabolism, while sss exhibit even lower RQ (0.94), consistent with a shift toward lipid and protein catabolism.

The authors then proceeded to bin these measurements in 12-hour partitions, ZT0-12 and ZT12-24, to assess diurnal differences in average values of VO2, VCO2, and RQ. They observed significant day-night differences in metabolic rates in WT-LD flies, with higher rates during the day. The diurnal differences remain in the short-sleep mutants, but the overall metabolic rates are higher. WT-DD flies exhibit the lowest respiratory activity although the day-night differences remain in free-running conditions. Finally, per01 mutants exhibit no significant change in day-night respiratory rates, suggesting that a functional circadian clock is necessary for diurnal differences in metabolic rates.

They then performed finer resolution 24-hours rhythmic analysis (RAIN and JTK) to determine if VO2, VCO2, and RQ exhibit 24-hour rhythmic and if there are genotype-specific differences. Based on their criteria, VCO2 is rhythmic in all conditions tested while VO2 is rhythmic in all conditions except in fmn-LD. Finally, RQ is rhythmic in all 3 mutants but not in WT-LD and WT-DD. Peak phases for the rhythms were deduced using JTK lag values.

The authors proceeded to leverage a previously published steady-state metabolite datasets to investigate potential association of RQ with metabolite profiles. Spearman correlation was performed to identify metabolites that exhibit coupling to respiratory output. Positive and negative lag analysis were subsequently performed to further characterize these associations based on the timing of the metabolite peak changes relative to RQ fluctuations. The authors suggest that a positive lag indicates that metabolite changes occur after shifts in RQ, and a negative lag signifies that metabolite changes precede RQ changes. To visualize metabolic pathways that exhibit these temporal relationships, clustered heatmap and enrichment analysis were performed. Through these analyses, they concluded that both sleep and circadian systems are essential for aligning metabolic substrate selection with energy demands, and different metabolic pathways are misregulated in the different mutants with sleep and circadian defects.

Strength:

The research questions this study explore are significant given metabolism and respiratory demand are central to animal biology. The experimental methods used, including the well characterized fly genetic mutants, the newly developed method for indirect calorimetry measurements, and LC-MS based metabolomics, are all appropriate. This study provides insights into the impact of sleep and circadian rhythm disruption on metabolism and respiratory demand and serves as a foundation for future mechanistic investigations.

Comments on revised version.

The authors have thoughtfully revised the manuscript. They have now provided clarifications regarding perceived conceptual flaws in the original version. They have also performed additional data analysis to support their conclusions and provide clarifications on statistical methods when appropriate. Overall, the revised manuscript is much improved and the conclusions are generally well supported by their results.

Reviewer #1 (Public review):

Strengths:

Thorough reanalysis of the experimental results obtained in previous studies, which led to the publication of the PNAS paper in 2016.

New experimental evidence to confirm that enzymes previously considered as participating in the ED, actually are not catalyzing the ED biochemical reactions, but are involved in other metabolic pathways. Also, the authors completely discarded the occurrence of the GDH/GK shunt in Synechocystis PCC 6803. Generally speaking, the manuscript is very clearly written, with a precise description of the previous findings, the mistakes which took place in the 2016 paper, and the strategies they have used to address those issues, in order to reach a thoroughly revised vision of the glucose metabolic pathways in Synechocystis PCC 6803. In this regard, the drawings shown in Figures 1 and 7 are very helpful for the reader to follow the story and understand the possible metabolic transformations depending on the working hypothesis.

Also, I commend the authors for openly describing previous mistakes. In this paper, they reassess past observations under the light of more recent findings, and to integrate the information in this manuscript. The scientific conclusions are solid and very interesting, and besides they use the opportunity to offer valuable advice to researchers. This is especially focused on the importance of careful biochemical characterization of enzymes, which should always be carried out when studying proteins which have been identified as a specific enzyme on the basis of sequence homology. In a similar way, they found that an insertional mutant was the cause for the absence of specific metabolites, which had been attributed to particularities of a metabolic pathway in that mutant, when it was actually due to a nucleotide insertion; given that currently, genome sequencing is an affordable technique, this kind of mistakes can now be easily prevented by confirming the correct generation of the mutant by DNA sequencing, as proposed by the authors in a recently published preprint (Theune et al, bioRxiv 10.64898/2026.04.08.717167).

Weaknesses:

The authors propose that EDA might be involved in the PEP-pyruvate-OAA node, or in the proline metabolism, but this requires further experimental work for clarification; what their results indicate clearly is that this enzyme is not actually catalyzing the transformation of KDPG to GAP, which is the second specific enzyme of the ED pathway. But the real physiological function in this cyanobacterium is still unconfirmed.

Another aspect which could be improved is that the recombinant expression of some genes was carried out in E. coli; even if this is a useful and valid research strategy, in studies like this (where there is a strong focus on the physiological function of enzymes in the original organism, Synechocystis PCC 6803), I think it would have been more appropriate to express the 6803 genes in another cyanobacterium easily amenable for genetic transformation and gene expression, which would produce the protein in a physiological environment more similar to another cyanobacterium (compared to E. coli, which is an heterotrophic bacterium). I am not sure this would change any of the obtained results, but certainly would confer additional robustness to the enzymatic results.

Comments on revised version.

The authors have provided satisfactory replies to all my suggestions and corrections, and I have no further changes to suggest.

Reviewer #1 (Public review):

Summary:

The authors test specific but related hypotheses regarding anti-predator responses of wild marmoset groups to predator and human playback sounds triggered to play via a motion sensor on camera-trap devices. The differential responses they observe to human noises and natural predator sounds are interesting, but greater inferences are limited due to a lack of clarity in the methods and analyses.

Strengths:

The authors create an excellent experimental design using a customised ABR system for testing the behavioural responses of wild, social-living, tiny, arboreal primates: pygmy marmosets. Much of the work is described with great transparency, and figures and tables are helpful in facilitating this.

Weaknesses:

The current study requires improvement in three areas, in my opinion, to permit readers to better evaluate the validity and importance of these results.

(1) Improve the framing of the paper:

The current title and justification for this study appear to point to a lack of previous studies testing specific hypotheses (line 51/52: "ABRs have not been applied to hypothesis testing". I find this a rather strange argument to make, given that a quick read through of other ABR papers, cited by the authors (e.g., Kasper et al., 2025, Epperly et al., 2021), are testing predictions set by ecological theory in the cascading effects of predator-prey dynamics. To me, even if these are not explicitly stating "X hypothesis" in their paper, they still appear to be studies guided by implicit hypotheses. To say that previous work with ABR did not test hypotheses is presumptuous, in my opinion. The entire paper would be much better appreciated if the authors could reframe the study for its importance to arboreal mammal/ tropical ecology, anthropogenic effects, and so on. Similarly, the authors should avoid use of phrasing such as "this study is the first direct test of .... " (lines 59/60) and should emphasize the true significance of their work, beyond it being the 'first' of something.

Similarly, on line 87, "demonstrating that the ABR system can be used to generate data for hypothesis testing" should be removed, as firstly sufficient sample size for any study depends on a number of study-specific parameters, and the authors do not actually demonstrate this in my opinion, given that many of their models end up suffering from singular fit. This is due to a lack of sample size, and also because they do not actually do any type of power analysis or something similar to demonstrate that they actually assessed sample size. So again, my suggestion is to reframe the paper to focus on the behavioural ecology and conservation-related impacts rather than this emphasis on methodology.

(2) Methods:

The authors generally do a great job providing sufficient detail on the ABR system and how each experiment was designed. Still, there is room for improvement, as I was confused a number of times. I also would recommend that the authors include a limitations section somewhere which considers the drawbacks of their study, in particular the lack of individual identity for behavioural responses of marmosets (especially given that they used focals, it seems), the groups being in close vicinity of one another/potentially related (?), the specific stimuli used, etc.

Points of confusion for me included what the control was for Experiment 1. Line 93 - 70 videos without playbacks are used (Table 1), but it is not clear how these videos were selected, and it is not a suitable control comparison for assessing the difference in behaviour associated with playbacks (playbacks with control sounds are). At most, these videos will give basal rates of behaviour (like vocalizations, etc.), but then it is not clear why these '70 videos' and how they were chosen to avoid bias. So, for example, in line 105 the authors write that focals were more likely to flee when hearing playback stimuli than in these "control" videos, but this is not convincing. If there was no fleeing after playback of control sounds (cicadas, macaws) - i.e., the true control in this experiment - then this should be the comparison that is emphasized.

Can the authors also clarify how they considered/assessed the sound playback level (normally done in Decibels) and if they did not normalize the sound level across the playback stimuli, why not, and what potential effect this could have on the results (i.e., something else to consider for the limitations section)?

Something else not discussed is the rate of exposure to predator and human noise for these wild monkeys. Are these rates within normal range/expectation for these monkeys? Thinking here of the number of videos captured for each group presumably means exposure to a playback unless 'control' videos were videos where no playback sound was emitted (see question above re: control videos). There were a lot more unsuccessful videos than successful ones that the authors could use, so trying to understand the potential impacts of this (see question re: trial/video # below as well).

(3) Analysis:

A few things are unclear and need more explanation in the way the authors conducted their analyses, although they do well to detail all steps of their statistical methods, which was great.

For assessing model fit, it is not clear what exactly was assessed with the 'performance package' line 467, as the authors do not go on to provide us with any results of the performance/fit. Instead, they tell us that the models did not fit well, with no parameter provided (e.g. lines 481-488). I'm familiar with overdispersion as a parameter that is reported for Poisson models (that does not seem to be provided here). Or by looking at changes in model estimates if one datapoint (and/or one group) is removed after another (with replacement, so keeps sample size static). On line 468/469, it says that model fit was assessed via conditional R2; could the authors provide a citation for this practice, and then provide the R2 parameter for the other models that were used/included in the end?

Also, please standardize how the GLMM results are presented. There should be the estimate, SE, Z or t, then p value. (line 99, 137).

Given the high rates of exposure to playbacks, I think the authors should test trial # (or video #) for a potential habituation effect, with earlier captures more likely to draw stronger responses than later video captures for each group.

Reviewer #1 (Public review):

Summary:

In this manuscript, Du et al. identify a putative URS (nucleotides −709 to −229) that contributes to CY/MMS-induced DDI2/3 transcription in the promoter of S. cerevisiae DDI2 through promoter mapping. They further showed that CY/MMS leads to histone loss, and that genetic depletion of histone triggers DDI2/3 expression in an Fzf1-dependent manner. Using MNase-seq, the authors demonstrate that CY treatment leads to nucleosome loss at the DDI2/3 promoter and coding sequences, and that this chromatin remodeling process requires Fzf1. Based on these results, the authors propose that Fzf1 promotes CY-induced DDI2/3 expression through two distinct mechanisms: as a transcription factor and as a regulator of nucleosome occupancy. This dual mode of regulation may contribute to the exceptionally high induction of DDI2/3 relative to other Fzf1 target genes in response to CY.

Strengths:

This manuscript identifies the URS region at the DDI2 promoter that regulates CY/MMS-induced DDI2 expression. In addition, the authors revealed an important role of nucleosome occupancy in regulating DDI2/3 transcription, proposing the intriguing dual regulation model of Fzf1. Overall, this work furthers our understanding of how Fzf1 mediates the increase of DDI2/3 expression in response to CY/MMS treatment.

Weaknesses:

Overall, the study is of interest, and the data are generally convincing; however, several conclusions would benefit from further experimental validation. Certain controls are necessary for several experiments to improve the strength of evidence. Several major points are listed below:

(1) For Figure 6A and Figure 7A, the authors concluded that there are 'joint effects' of CY treatment and histone depletion. However, it is unclear whether CY treatment acts dependently or independently of histone depletion. As shown in Figure 2, both CY and MMS can cause histone reduction. In addition, the depletion system in the RMY102 strain only depletes about half of the H3 (based on the western blots in Figure 5D, E). It would be necessary to test whether H3 abundance is further depleted in CY-treated RMY102 by Western blot.

(2) Proper controls are missing in Figure 6A and Figure 7A. The authors compare gene expression levels in RMY102 + histone depletion + CY/MMS treatment with RMY102 + non-histone depletion. There are two variables here: histone depletion and CY/MMS treatment. It would be more convincing to include RMY102 YPGal+ CY/MMS treatment (5-40 mM), so that the impact of histone depletion and CY/MMS treatment on Fzf1 target gene expression levels would be clearer.

(3) In lines 242-249 and 269-272, the authors compared RMY102 versus BY4741 to conclude that histone depletion affects dose dependency of CY/MMS treatment. However, RMY102 and BY4741 might have different responses to CY/MMS due to strain background differences. Thus, in line with point 2, showing the expression level curves for non-histone depleting RMY102 treated with different doses of CY/MMS would be necessary.

(4) Figure 4 shows that CY and MMS have differential impacts on cell growth, which is intriguing. However, the rest of the data did not provide further insights in regard to this observation. It might be helpful to speculate possible underlying mechanisms in the Discussion session.

Reviewer #1 (Public review):

Summary:

In this manuscript, Tuñí-Domínguez et al. present a large-scale, cross-study meta-analysis of 2,356 cell-free RNA sequencing (cfRNA-Seq) samples. The authors aimed to systematically evaluate the impact of pre-analytical variables and library preparation protocols on biological interpretation. By harmonizing publicly available and internally generated datasets through a uniform bioinformatics pipeline, the study seeks to establish standard quality control (QC) metrics and provide evidence-based guidelines for protocol selection in cfRNA-Seq biomarker discovery.

Strengths:

A major strength of the study is the scale and breadth of the harmonized dataset. The use of a common computational pipeline reduces variation arising from differences in bioinformatic processing and enables more direct comparisons among published datasets. The authors examine multiple complementary dimensions of data quality rather than relying on a single sequencing metric. The inclusion of variance-partition analyses, healthy-control-only analyses, and analyses restricted to samples with low gDNA contamination strengthens the evaluation of technical heterogeneity. The public availability of the analysis code and processing configurations further increases the reproducibility and potential utility of this work. The study convincingly demonstrates that technical and pre-analytical factors are major sources of variation across existing plasma cfRNA-seq datasets.

Weaknesses:

Several central conclusions are broader than the current cross-study design can fully support. Protocol category is strongly associated with dataset, laboratory, sample source, and collection procedure, making intrinsic protocol effects difficult to separate from study-specific effects, particularly for categories represented by only one or a few studies. The conclusion that technical variation generally overwhelms biological variation also requires qualification because diverse diseases and cancer types are combined into broad phenotype categories, and many phenotypes are concentrated within individual datasets. The interpretation and additional value of the proposed NG80 and NP80/NG80 metrics require further support, especially given their variable behavior across datasets. Most importantly, the thresholds used in the proposed universal QC framework are not independently validated and are strongly influenced by library-preparation strategy. The current findings therefore support protocol-dependent comparisons and identification of technical trade-offs more strongly than they support a universal definition of library quality.

Major concerns:

(1) Protocol effects remain difficult to distinguish from study-specific effects.

The manuscript interprets Broad Protocol Category as a major determinant of transcriptomic variation. However, protocol, dataset, laboratory, sample source, and collection procedure are strongly interconnected. Although both Dataset and BPC are included in the variance-partition model, several protocol categories are represented by only a limited number of studies.

This is particularly problematic for WRO, which is represented by a single study. Its apparent characteristics therefore cannot be separated from Reggiardo-specific laboratory, cohort, provider, or sample-processing effects. The authors should clarify the stability of the variance-partition results and, where feasible, provide sensitivity analyses. Alternatively, conclusions based on protocol categories represented by one or very few studies should be explicitly presented as study-specific observations rather than broadly validated protocol properties.

(2) The interpretation and robustness of the proposed library-diversity metrics require further support.

The manuscript attributes the high NG80 values in the Block and Sun datasets to gDNA contamination. However, other datasets with similarly low FSR values, including Wang and Giráldez, do not show comparably high NG80 values. This suggests that gDNA contamination alone is insufficient to explain library diversity and that sequencing depth, fragment length, mapping behavior, or library construction may also contribute.

The NP80/NG80 ratio is conceptually reasonable, but its additional value beyond the RNA-biotype composition shown in Figure 3C is unclear, particularly given the large variability observed in WRR datasets. The authors should further examine the relationships among FSR, NG80, sequencing depth, and protocol characteristics and assess the stability of NP80/NG80. If additional validation is not feasible, the interpretation and generality of these metrics should be moderated.

(3) The conclusion that technical variation generally overwhelms biological variation requires qualification.

The manuscript provides convincing evidence that technical heterogeneity is a major source of variation in the combined cross-study dataset. However, the conclusion that donor phenotype contributes only negligible variation may be broader than the analysis supports.

Phenotypes are reduced to healthy, cancer, and non-cancer disease categories despite substantial biological heterogeneity, and many disease groups are concentrated within particular studies. Consequently, disease-specific biological variation may partly be assigned to the Dataset term. A similar issue affects the cellular-origin analysis, where collection center and phenotype are substantially associated in the Chen dataset, making their individual contributions difficult to distinguish.

Where sample sizes permit, the authors should examine more specific disease categories or perform within-dataset analyses. Otherwise, the conclusions should be narrowed to state that technical variation dominates the present heterogeneous cross-study aggregation, rather than implying that phenotype-associated cfRNA signals are generally negligible.

(4) The proposed universal QC framework is insufficiently justified and protocol-dependent.

Figure 6 defines high-quality libraries using NG80 >1,000 together with FSR >20% or FER >75%. However, the manuscript does not explain how these thresholds were selected or validate them against an independent measure of reproducibility, analytical performance, or biomarker utility.

These criteria are also strongly affected by library-preparation strategy. FER and FSR favor libraries enriched for exonic or spliced RNA, whereas the NG80 cutoff disadvantages WRR libraries containing abundant noncoding transcripts. This is difficult to reconcile with the recommendation of WRR for exploratory transcriptomic and microbial analyses.

The authors should justify the threshold selection and assess its sensitivity and protocol dependence. Ideally, the criteria should be validated against an independent performance endpoint. Otherwise, Figure 6 should be reframed as a descriptive comparison, and protocol- or application-specific guidance should replace a universal binary definition of library quality.

Reviewer #1 (Public review):

Summary:

In this study, the authors use patient-specific induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from six patients carrying three distinct pathogenic LMNA variants to investigate disease mechanisms underlying LMNA-associated dilated cardiomyopathy (DCM). The authors report shared abnormalities in nuclear morphology, electrophysiology, calcium handling, and contractility across all patient-derived lines and demonstrate that correction of representative LMNA variants by CRISPR/Cas9 rescues several of these phenotypes. They further develop a high-throughput phenotypic drug screening platform using calcium transient measurements and identify cyproheptadine as the only compound among 1,280 FDA-approved drugs that consistently improves calcium transient abnormalities across all patient-derived lines.

The study addresses an important clinical problem and establishes a technically sophisticated patient-derived screening platform. However, while the experimental work is generally well executed, several of the major biological and translational conclusions are not sufficiently supported by the presented data.

Strengths:

The major strength of this study is the development of a patient-derived functional screening platform using multiple LMNA mutations rather than focusing on a single pathogenic variant. The inclusion of six patient-derived iPSC lines representing three distinct mutations increases the generalizability of the observations and allows identification of disease features that appear reproducible across different genetic backgrounds.

The phenotypic characterization is comprehensive and includes nuclear morphology, transcriptomics, manual and automated electrophysiology, calcium imaging, and impedance-based contractility measurements. Importantly, CRISPR-mediated correction of representative LMNA variants provides convincing evidence that the observed cellular abnormalities are directly attributable to the pathogenic variants.

Finally, the implementation of an unbiased high-throughput drug screen using patient-derived cardiomyocytes represents a valuable technical advance that could facilitate therapeutic discovery in inherited cardiomyopathies.

Weaknesses:

The principal weakness of the manuscript is that the central conclusions substantially exceed what is directly demonstrated by the data.

The manuscript repeatedly concludes that dysregulated calcium handling represents a shared pathogenic mechanism underlying LMNA-associated cardiomyopathy. However, the presented experiments establish only that abnormal calcium handling is a shared cellular phenotype across the studied variants. The data do not distinguish whether calcium dysregulation is a primary disease mechanism or whether it is secondary to the numerous upstream abnormalities already known to result from LMNA dysfunction, including altered nuclear architecture, defective mechanotransduction, chromatin remodeling, and transcriptional dysregulation. This distinction is critical because the manuscript repeatedly interprets correction of calcium handling as correction of the underlying disease process without directly demonstrating this relationship.

A related concern is the interpretation of the drug screening results. The primary screen is entirely based on normalization of calcium transient parameters (CTD75, FWHM, and T75-25). Consequently, the screen identifies compounds capable of correcting calcium cycling rather than compounds that necessarily modify disease biology. Although cyproheptadine subsequently improves impedance-derived contractile parameters, it remains unknown whether treatment rescues other defining features of LMNA cardiomyopathy, including abnormal electrophysiology, nuclear defects, transcriptional alterations, or broader cellular stress responses. Thus, the conclusion that cyproheptadine represents a "novel treatment" for LMNA-associated cardiomyopathy is considerably stronger than the evidence presented.

The mechanistic studies are also insufficient to support the proposed mode of action. The manuscript proposes CHRM2 as the likely mediator of cyproheptadine activity primarily because it is the only appreciably expressed known target in the transcriptomic dataset. However, no functional experiments test this hypothesis. Without genetic or pharmacological interrogation of CHRM2, the proposed mechanism remains speculative. Likewise, alternative mechanisms of cyproheptadine action, including serotonergic signaling, histamine receptor antagonism, direct calcium channel modulation, or antioxidant effects, are not investigated.

Another conceptual weakness is that the manuscript promises to distinguish both shared and variant-specific disease mechanisms but ultimately focuses almost exclusively on shared phenotypes. The transcriptomic analyses remain largely descriptive and are not leveraged to identify mutation-specific biological pathways or explain differences among the three LMNA variants. Given the unique cohort assembled in this study, this represents a missed opportunity to generate broader biological insight into LMNA-associated disease.

The transcriptomic analyses themselves would also benefit from more rigorous interpretation. RNA from multiple independent differentiations was pooled prior to sequencing, limiting assessment of biological variability and reducing confidence in statistical inference. Similarly, many functional analyses emphasize the number of wells, recording sweeps, or individual cells while the number of independent biological differentiations is less prominently presented. Greater emphasis on biological replication would strengthen confidence in the robustness of the findings.

Finally, the translational implications of the work should be interpreted more cautiously. All therapeutic studies are performed in relatively immature two-dimensional iPSC-derived cardiomyocytes. No validation is presented in engineered heart tissues, multicellular cardiac organoids, animal models, or human tissue. The current evidence supports the conclusion that cyproheptadine is a promising in vitro phenotypic modifier rather than an established therapeutic candidate for LMNA-associated cardiomyopathy.

Overall, this study establishes a valuable patient-derived platform for investigating LMNA-associated cardiomyopathy and demonstrates the utility of functional high-throughput screening in identifying compounds that improve disease-associated cellular phenotypes. However, the manuscript currently overstates both the mechanistic significance of calcium dysregulation and the therapeutic implications of cyproheptadine. A more restrained interpretation of the findings together with additional mechanistic validation would substantially strengthen the impact of the work.

Reviewer #2 (Public review):

Summary:

This study addresses an important and timely question in colorectal cancer biology by systematically examining the effects of the common driver mutations APC, KRAS G12D, and TP53 in murine colorectal organoids, with particular emphasis on how the order of APC and TP53 acquisition influences tumor phenotype. These mutations are well known to be frequent, truncal, and often co-occurring in colorectal cancer. While it is increasingly appreciated that mutational order can shape tumor behavior, studies directly comparing the phenotypic consequences of alternative APC-TP53 mutation orders remain rare. This work therefore addresses a relevant and timely question.

Strengths:

A major strength of the study is its focus on previously unexplored biology, combined with the generation of multiple isogenic murine organoid models with controlled mutational sequences. The authors employ careful and robust quality control of the CRISPR-mediated alterations, and the inclusion of both in vitro and in vivo experiments strengthens the relevance of the work.

Weaknesses:

There are, however, several limitations that should be considered when interpreting the findings. First, KRAS G12D activation is used as the initiating alteration, whereas APC loss is generally believed to be the initiating event in most human colorectal cancers. Second, the analysis is restricted to comparing only two mutation orders (KAT versus KTA), which limits the breadth of conclusions that can be drawn about mutation ordering more generally. Finally, key RNA-sequencing and in vivo experiments rely on a limited number of isogenic lines, which constrains interpretability.

The study aimed to systematically investigate how the accumulation and sequence of driver mutations influence colorectal cancer initiation. The data provide intriguing evidence that the relative timing of APC and TP53 loss may impact tumor initiation and survival in a hostile microenvironment. However, given the limited number of biological replicates, these observations should be interpreted with caution and would benefit from further validation.

Reviewer #1 (Public review):

Summary:

The manuscript by Montenegro and colleagues reports a uniquely significant set of compelling results from a carefully designed study. The findings are fundamental and should substantially advance our understanding of whether prenatal exposure to high levels of alcohol produces neural changes that are precursors to the development of Alzheimer-like pathology in an animal model of Fetal Alcohol Spectrum Disorder (FASD). The quest was to test whether prenatal exposure to high-dose alcohol in the mouse would result in selective damage that would result in Alzheimer disease-like cellular disorder and mnemonic impairment. Both outcomes emerged and were exacerbated in relevant transgenic mice. The untoward effect on memory endured and even worsened with age.

Strengths:

The authors noted the importance of using a validated animal model to test their hypotheses related to AD-like outcomes because human postmortem data are unavailable and even in vivo data are limited to younger FASD cohorts. The authors further noted limitations, which also anticipate experiments that could chart the temporal course of the effect and windows of prenatal alcohol exposure that result in damage or resilience. In short, as the authors state on lines 435-7, "These data provide the first experimental evidence that developmental alcohol exposure impacts core proteolytic pathways central to AD/ADRD pathogenesis."

Weaknesses:

Addressing the following would clarify several points in an already well-written paper:

(1) It would be useful to have a timeline of the study, much like the one provided for the water maze protocol. On that timeline, please include the sample sizes examined, ages at exposure, and other pertinent procedures, indicating which animals remained alive for testing, etc.

(2) What were the attrition rates for each study group?

(3) What are the human age equivalents of the maternal mice?

(4) It would be helpful to see a graph of the BECs of each animal relative to the doses given. That would clarify how the alcohol exposure amount and timing are the same and where they are different for all exposed mice, given that the alcohol levels were somewhat different by group, as noted in the Methods. Were these BEC differences at all related to group differences in outcome measures or memory performance?

Reviewer #1 (Public review):

Vasilevskaya and Keller test different models of cortical function through the lens of predictive processing, a powerful framework for the brain to learn and predict the statistics of the world via generative internal models. The authors use a clever combination of behavioral perturbations in closed-loop and open-loop visuomotor virtual reality assays, a paradigm the Keller lab pioneered and used effectively in the past decade, in conjunction with two photon imaging of neuronal calcium responses and targeted optogenetic perturbations of activity. They specifically put to test proposed hierarchical vs. non-hierarchical circuit implementations of predictive processing by analyzing the logic of inter-lamina interactions (superficial vs. deep; L2/3 vs. L5/6).

The authors conclude that both versions of predictive processing architectures they analyze are likely invalid and instead formulate an alternative novel model of cortical function based on a recently developed machine learning algorithm for self-supervised learning (joint embeddings of predictive architectures, JEPA) and its further refinements. JEPA borrows elements from predictive processing engaging two encoder networks and training the output of one network to predict the output of the other. In their new model of cortical computations, prediction errors neurons in L2/3 compare the deep layers (L5/6) activity, which is taken as a teaching signal, to a local, L2/3 prediction of this latent representation.

Specifically, the authors build on their previous work and reports from other groups that different sets of L2/3 neurons compute positive prediction errors (fire when sensory stimuli appear unexpectedly with respect to the movements of the animal; e.g., grating onsets in the absence of locomotion) and respectively negative prediction errors (fire when sensory stimuli are absent, while the brain expected them to be present; e.g. mice locomote but visual flow is suddenly halted - visuomotor mismatches). These L2/3 positive and negative prediction error neurons exchange messages with neurons in the deeper cortical layers that, the authors propose, build an internal representation (R) of the sensory stimuli given the animals' movements.

In the hierarchical model, internal representation neurons (R) are supposed to act as a teaching signal for both types of prediction error neurons; the output of the positive prediction error neurons is assumed to suppress activity of R such that the error between the teaching signal and the prediction is minimized; similarly, in the non-hierarchical version, R serves as a prediction for the prediction error neurons, and in turn it receives excitatory drive from the positive prediction error neurons and negative input from the negative prediction error neurons.

The authors find that the functional impact of L5 neurons to L2/3 neurons is not compatible with the non-hierarchical architecture they and other groups proposed, but rather in accordance with the hierarchical model. At the same time, the functional impact of L2/3 neurons (positive vs. negative prediction error neurons) on L5 neurons (internal representation) appears not compatible with the hierarchical model, but rather in accordance with the non-hierarchical implementation.

They further hypothesize that L2/3 prediction error neurons don't use sensory input, but rather the L5 activity as a teaching signal, and test it using perturbations (halts) of optogenetic stimulation of L5 neurons coupled with locomotion (Fig.7).

All in all, the question is topical, and the new model addresses a decades-long quest to develop a unifying model of cortical function. The findings reported here transform our understanding of cortical computations, opening new exciting avenues for future investigation. The experimental design and execution are rigorous; the arguments are clearly laid out (in spite of ample potential for confusion given the numerous loops and sign flips). These include a discussion of why the non-hierarchical model proposed by the same group does not hold, as well as potential caveats in interpreting the results and novel testable proposed experiments emerging from the JEPA-like model.

Comments on revised version

I commend the authors for nicely provided answers and addressing my concerns and clarifying their points on the relationship of their findings to JEPA and current state of understanding.

In particular for Q5 -I meant if there is also a positive correlation between optomotor mismatch response and visuomotor mismatch response when looking only at neurons that the authors identify as PE-?<br /> The authors provided the answer on point.

Overall, I think this is a fundamental study and the strength of evidence for the claims is exceptional as an exemplary use of existing approaches.

Reviewer #1 (Public review):

Summary:

This work asks the question of how different organelles and structures in the apicomplexan parasite Toxoplasma gondii are recycled and/or segregated to the daughter cells during cell replication. In particular, they consider an unusual cell structure called the residual body that links replicating cells during the intracellular infection stage of this parasite. The residual body has historically been considered a 'dumping ground' for unnecessary relics of the mother cell during division, but this notion is increasingly being revised. Indeed, cell replication in Toxoplasma is often misinterpreted as cell division (cytokinesis), but in fact, the cell replicates its organelles and structures to multiple 10s of copies in seemingly distinctly formed daughter cells, but cytokinesis is delayed for many such cycles and typically only occurs simultaneously with parasite egress from its host cell. The residual body is, in fact, the connection between these pre-cytokinetic replicated daughters, and effectively, this is still a single cell at this stage. The authors have previously shown that an actin network extends through the residual body between these daughter cells, and ER and mitochondria common to all cells are also linked through this structure. This study examining the fates of organelles during cell replication is timely for continuing our understanding of how this fascinating component of the cell participates in these processes. The authors use Halo-tags as their principal tool to track discrete populations of proteins, labelling their organelle locations, and this provides beautiful insight into these processes.

Strengths:

Using dyes conjugated to Halo tags this work elegantly tracks the fates of proteins synthesised by an original 'mother' cell over several replication cycles of pre-cytokinetic 'daughters'. Using this tool, they show that some organelles are made intact just once and that some of these can be subsequently sorted to the daughters (micronemes and rhoptries) while others are dismantled (IMC) and the daughters must make their own. A third set of organelles (largely synthesis, sorting and metabolic compartments) are divided and inherited, and new daughter-synthesised proteins are added to the preexisting maternal proteins in these structures. A role for actin and myosin is clearly demonstrated for micronemes and rhoptries, and this correlates with their relatively late inheritance into the developing daughters. Overall, this work gives clarity to the behaviours of several cell structures during replication and paves the way to better understanding the mechanisms that drive the differences between structures and the universality of these processes in other apicomplexan parasites. In particular, this study shows that the residue body is a region of the cell syncytium that organelles can be actively transported from. Therefore, it is a space that can actively contribute to the segregation of the late segregating micronemes and rhoptries.

Weaknesses:

In addressing the question of residual body participation in sorting of organelles, a clear definition of this structure is required including when and where it is delineated from the posterior of a mother cell during the formation of daughter structures. The authors' definition is as follows: 'The RB originates from the collapse of the maternal parasite during daughter cell budding and occupies the space previously occupied by the mother cell.' As such, a clear marker of the mother cell 'collapse' is required, but such a marker is not identified or used in the study to separate what might be considered an active part of the mother cell during early daughter formation, and the residual body. This might seem like moot a point, but it would help to give clarity to notions of recycling and 'reservoirs'. Mother cells retain their active invasion apparatus until very late in daughter formation and the need for micronemes and rhoptries to be released from this service late in the process might explain why they are only then trafficked to the cell posterior and then into the daughters. So, is this a distinct 'residual body' body function/reservoir or just a spatial constraint of this sequence of daughter formation? The authors elegantly show that MyoF is necessary for segregation of micronemes and rhoptries into daughters, and that MyoF depletion leads to accumulation of these organelles within the residual body. Moreover, restored expression of MyoF can then recover these organelles. This clearly demonstrates the activity of the residual body as part of the syncytium space that participates in the maintenance of the vacuole. But does it imply that this space necessarily handles all inherited micronemes and rhoptries as a 'trafficking hub'? My concern with the lack of a clear definition could provide some misinterpretation or overinterpretation of the contribution residual body.

A further, remarkable conclusion is that maternal micronemes are evenly segregated into daughters through an active process for 'balanced microneme inheritance'. The proportion of maternal micronemes is quantified up to the 8-cell stage and shown to be not significantly different between cells. But would this result be expected with random assortment at this stage? The authors model the probability of a 32-cell stage vacuole occurring with each daughter having within 0-3 maternal micronemes and this is considered unlikely. However, the authors neither present the modelling for the 8-cell stage or show quantification of 32-cell vacuoles. They do show some images of large vacuoles, but it is not possible to determine the distribution of maternal micronemes in these images. A regulated process of segregation would require a complex mechanism where some form of microneme counting would be required to create the proposed balance. It is, therefore, important to have strong data supporting such a hypothesis, but this is not currently presented.

Reviewer #2 (Public review):

The authors use a library of influenza A viruses from different strains, classified in lab-adapted, human, avian, and swine according to the animal from which they were isolated. They propose that the cow mammary gland serves as a mixing vessel for influenza A viruses. As a first approach, the authors assess susceptibility to infection across different cell types, including continuous and primary cell lines, bovine mammary cells, and mammary explants. All these cells support polymerase activity. Then, they analyzed changes in the bovine virus's viral fitness relative to an avian precursor. The authors use single-gene replacement to study whether and which RNP segments improve viral transcription. As part of this section, they also test IFN-specific antagonism by NS1 to assess the input of segment 8. Quantitative glycomic analysis was performed on the continuous bovine mammary cell line to demonstrate the presence of both a2,3 and a2,6, which is consistent with their observation that these cells can be co-infected with human and avian IAVs simultaneously. The main question, however, is: what is the glycome in the explants, or directly from tissues?

Overall, the manuscript is clearly written and provides new insights into the behaviour of the cattle isolate, now compared with a representative group of model or precursor HAs of different origins.

It would be great if a consistent nomenclature for the IAV strains could be used in the study. There is a mix of origin (Texas), animal from which the virus was isolated (mallard), or abbreviations that do not follow guidelines (IAV07). Are the USSR and Udorn not lab-adapted?

The experimental setup includes bovine mammary primary and continuous cells, as well as mammary explants. Some of the most significant differences, for example, in viral fitness studies and co-infection experiments, are observed in these explants. Perhaps there could be some additional focus on this observation. The implications in comparison to the results obtained in cultured cells could be described. How will the human and other HA subtype viruses fare in the explants?

Comments on revised version.

The authors have satisfactorily addressed the reviewers' comments.

Reviewer #1 (Public review):

In this article, the authors set out to understand how evolutionary selection could introduce structural priors into neural networks that act as an inductive bias to accelerate learning. To do so, the authors propose an evolutionary conditioning (ED) algorithm and analyse its properties. I find the conceptual framing of the paper very interesting, and it addresses an important question. Since the paper adopts a mostly theoretical approach with no comparison to empirical biological data, I do have a couple of concerns regarding the setup of the computational framework/method, which I think is incomplete and limits how much we can conclude from the current results.

Major Concerns:

(1) The evolutionary conditioning (EC) algorithm proposed works as fine-tuning training, plus propagation of the best parent network's weights to the next generation with added Gaussian noise. Conceptually, I find this to be a fairly implausible mechanism for evolution, since it requires carrying the entire set of network weights at some precision. The authors themselves point out that direct weight transfer could be problematic in the introduction.

(2) More importantly, I would like the authors to conduct a baseline / null model comparison, in which the "evolution" process consists simply of training a neural network for a small number of iterations, adding Gaussian noise, and repeating. The resulting network at each step serves as the "generations", which is then trained further. The same learning speed and dynamics analyses should be applied to this null model. What I am getting at is that I am not sure to what extent the EC algorithm can be thought of as "running a few iterations of SGD" and chaining them together; how much work is the selection process in the GA actually doing?

(3) I am also unclear on why the EC algorithm does not improve throughout learning. Is this behavior the result of applying only a small number of fine-tuning steps? Presumably, with longer fine-tuning, the individual networks in the middle generations would also improve in performance?

(4) The EC algorithm applied to a single problem seems somewhat artificial in its setup. I would conceptualize evolution as learning a prior that conditions the network for a range of survival-related tasks. A more realistic setup would apply EC to an ensemble of tasks and then examine its impact on learning a specific task afterwards.

Reviewer #1 (Public review):

Summary:

This paper proposes a non-decision time (NDT)-informed approach to estimating time-varying decision thresholds in diffusion models of decision making. The manuscript motivates the method well, outlines the identifiability issues it is intended to address, and evaluates it using simulations and two empirical datasets. The aim is clear, the scope is deliberately focused, and the manuscript is well written. The core idea is interesting, technically grounded, and a meaningful contribution to ongoing work on collapsing thresholds.

Strengths:

The manuscript is logically structured and easy to follow. The emphasis on parameter recovery is appropriate and appreciated. The finding that the exponential NDT-informed function produces substantially better recovery than the hyperbolic form is useful, given the importance placed on identifiability earlier in the paper. The threshold visualisations are also helpful for interpreting what the models are doing. Overall, the work offers a well-defined, methodologically oriented contribution that will interest researchers working on time-varying thresholds.

Weaknesses / Areas for Clarification:

A few points would benefit from additional clarification following the previous revision:

Returning to one point from my original review. The applications to empirical data describe 6 models, including the FT-DDM with across-trial variability described as a benchmark. Yet the modelling results for both studies (Tables 2 and 3) show only 5 models, omitting the benchmark model. I acknowledge the comment about this in the response letter (no other FT-DDM models in the main text have across-trial variability). Nevertheless, for this to serve as a benchmark, the modelling results really should be reported in Tables 2 and 3 with the other 5 models, and the model goodness of fit figures currently shown in Appendix 7 incorporated into Figures 10 and 13 of the main text. Unfortunately, as it currently reads, it looks as if something is being obscured, which I don't believe is the intention. This won't change the primary conclusions, but it will increase transparency and ease of interpretation.

Many thanks for introducing Appendix 1 to the revised manuscript. Additional motivation for the central issue is always helpful. However, I'm not (yet) convinced the simulation study in Appendix 1 achieves the intended aim. The simulation study shows that NDT misspecification leads to poorer recovery of other CT parameters (i.e., if a generating value is perturbed and then held fixed at that perturbed value during estimation, recovery of the remaining parameters deteriorates). This demonstrates the consequences of fixing NDT incorrectly, but it does not seem to address the central claim of the manuscript: that NDT and the CT threshold parameters trade off when they are estimated simultaneously. I had expected the simulation study to estimate NDT alongside the remaining model parameters, mirroring the estimation procedure used in the empirical analyses. Such a simulation would directly test whether the two sets of parameters compensate for one another during estimation, and whether this results in poor recovery of the NDT parameters.

Reviewer #1 (Public review):

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

Summary:

Combining in vitro refolding, SEC-based assembly assays, peptide-library screening, MALDI-TOF, LC-MS/MS, structural analysis and immunopeptidomics, this manuscript investigates the peptide-binding principles of the promiscuous chicken MHC-I molecule BF2*21:01.

Strengths:

Although the peptide motif of BF2*21:01 is highly complex, this manuscript identified several principles, including a preference for 10-mer peptides, co-variation between P2 and Pc-2, effects of P3 and Pc-3, and a strong cellular preference for Leu at Pc. The results are important for avian MHC biology and poultry vaccine epitope prediction.

Reviewer #1 (Public review):

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

Summary:

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

Strengths:

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

Comments on previous version.

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

Reviewer #1 (Public review):

This study by Vitar et al. probes the molecular identity and functional specialization of pH-sensing channels in cerebrospinal fluid-contacting neurons (CSFcNs). Combining patch-clamp electrophysiology, laser-based local acidification, immunohistochemistry, and confocal imaging, the authors propose that PKD2L1 channels localized to the apical protrusion (ApPr) function as the predominant dual-mode pH sensor in these cells.

The work establishes a compelling spatial-physiological link between channel localization and chemosensory behavior. The integration of optical and electrical approaches is technically strong, and the separation of phasic and sustained response modes offers a useful conceptual advance for understanding how CSF composition is monitored.

Comments on revised version:

I thank the authors for their extensive revisions and detailed responses to the reviewers' comments. The manuscript has been substantially improved, and most of the major concerns raised in the initial review have been adequately addressed. In particular, the additional analyses of PKD2L1 channel activity, the incorporation of physiologically relevant pH conditions, the clarification of ASIC involvement, and the expanded Discussion have significantly strengthened the study.

Major scientific concerns largely addressed:

Quantification of PKD2L1 channel activity<br /> The authors appropriately addressed my previous concerns regarding the use of Po as the sole measure of channel activity. The inclusion of additional parameters such as apparent Po, open time, nmax, holding current, and membrane charge provides a more robust assessment of PKD2L1 activity and substantially strengthens the conclusions.

Physiological relevance of pH modulation<br /> The inclusion of experiments at pH 6.5 and the additional analyses of holding current and resting membrane potential are valuable additions. These experiments considerably improve the physiological relevance of the study.

ASIC contribution<br /> The additional pharmacological experiments using ASIC blockers are helpful and support the conclusion that the photolysis-evoked response in the apical process is predominantly mediated by PKD2L1 channels.

Functional implications<br /> The expanded Discussion regarding Ca2+-dependent signaling, neurosecretion, and the potential physiological roles of CSFcNs considerably improves the manuscript.

Remaining concerns:<br /> Continued overstatement regarding "exclusive" localization and function:

Although the authors softened some statements in the revised manuscript, the term "exclusive" remains in several key locations, including the title.

For example:<br /> "PKD2L1 channels segregated to the apical compartment are the exclusive dual-mode pH sensor..."

The data clearly demonstrate strong enrichment of functional PKD2L1 channels in the apical process. However, the available evidence does not fully justify the term "exclusive," particularly because:

- PKD2L1 immunoreactivity is still detectable outside the apical process.<br /> - ASIC-mediated responses are present in CSFcNs.<br /> - The authors themselves use more appropriate terminology such as "predominantly located" in the Discussion.

Therefore, I recommend replacing "exclusive" with more conservative terminology such as:

- predominant<br /> - predominantly localized<br /> - enriched<br /> - functionally segregated

throughout the manuscript, including the title, Abstract, Introduction, Results, and Discussion.

Use of the term "tonic current"

The manuscript continues to use the term "PKD2L1 tonic current."

While the dibucaine-sensitive holding current is clearly present, the precise mechanism generating this current remains uncertain. Indeed, the authors themselves acknowledge in the Discussion that:

- an alternative conducting state may exist, or<br /> - unresolved brief channel openings may account for the current.

Therefore, the data support the existence of a sustained PKD2L1-associated current, but do not yet definitively establish a distinct tonic gating mode of the channel.

I therefore recommend replacing:

"tonic current"

with a more neutral expression such as:

- sustained current<br /> - PKD2L1-associated holding current<br /> - sustained PKD2L1-mediated current throughout the manuscript.

Continued use of "off-current" and "off-response":<br /> The revised manuscript has improved considerably in this regard. However, the terms "off-current" and "off-response" still remain in portions of the text and figure legends.

Because the manuscript itself demonstrates that the response reflects recovery from transient acidification rather than a separate OFF signaling mechanism, these terms remain potentially misleading.

I recommend replacing them with terminology such as:<br /> - photolysis-evoked PKD2L1 current<br /> - recovery current<br /> - proton-removal-induced current

throughout the manuscript, including figure legends.

Minor editorial corrections<br /> Figure 1Bd Please change: "po" to "Po" for consistency with standard channel physiology nomenclature.<br /> Figure 1Ca Please add units (mV) to the voltage labels shown on the left side of the traces.<br /> Figure 3E Please change: "Norm po" to "Norm Po".<br /> Figure 4Fb Please replace: "sec" with "s" to conform with SI unit conventions.

The authors have addressed the majority of my previous concerns and the manuscript has been substantially improved. The remaining issues are primarily related to terminology and overinterpretation rather than experimental deficiencies.

Reviewer #1 (Public review):

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

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

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

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

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

Reviewer #1 (Public review):

Summary:

This article describes a new software package, HSSM, for simulating and fitting sequential sampling models. The package consists of three modules - one for simulating the models, one to train neural networks on mappings from behavior to parameter values, and one for combining these tools to fit particular models to users' data.

Strengths:

This is a very detailed description of a new package that is building on an already highly successful package. It promises to become a go-to software package for cognitive modelers, experimentalists, and practitioners.

Weaknesses:

I have only a few critiques of the article, and some are a matter of taste:

(1) I think it would be helpful for the authors to take the reader through one complete example at the end of the article, including loading in a dataset, fitting it with a regression model, checking model convergence, reading out parameter values, and doing posterior predictive checks (etc). It would be helpful to see it all in one place to get a sense of how much code is required to go through the whole process. One or two actual examples would help readers who are not already familiar with HDDM.

(2) The article assumes a certain level of computing and modeling expertise, which somewhat limits its reach. There are many abbreviations and references to other software tools that a reader might not be familiar with. Such readers might feel like HSSM is beyond their reach. Below is a non-exhaustive list of such undefined or unexplained terms:<br /> DDM, API, LBA, fMRI, EEG, JAX, PyTorch, ONNX, MCMC, VI, MAP, PyMC, PyTensor, NUTS, ArviZ, WAIC, LOO, KDE, CLI, YAML, GUI, LBA, RDM, QP.

(3) I found some of the figures/listings to be unpolished, unhelpful, and/or unnecessary. For instance, Figure 3 and Listing 3 seem to just be zoomed-in pieces of Figure 2. In Figure 4, what is the meaning of the little globe traveling between the within-trial and across-trial rows? Why is Figure 5 a figure and not a listing? Does HSSM not produce these plots directly? I have the same question for Figure 7. Figures 8 and 9 seem unnecessary to me, but perhaps they serve a function that I'm missing. Perhaps they could be turned into supplements for Figure 2.

Reviewer #1 (Public review):

Summary:

The manuscript further explores the single-cell atlas of Clytia hemisphaerica by incorporating the planula larva. It compares the cell clusters with the previously established atlas of the medusa. It identifies similarities and differences between the two life stages.

Strengths:

The manuscript provides an important set of single-cell data that have not been assessed previously: the Clytia planula. The data is further supplemented with high-quality EM-based histology and an extensive in situ hybridisation of selected genes.

Weaknesses:

The detailed analysis does not go deep into the comparison between stages, nor does it provide an analysis of genes within the clusters; it could be described as remaining overall rather superficial.

Reviewer #1 (Public review):

Summary:

Using cryofixation and serial block-face electron microscopy (SBEM), P. Vijayakumar and K. Cauwenberghs characterize extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs) within native Drosophila olfactory sensilla. The study provides a unique and valuable dataset comprising approximately 7,800 extracellular particles, systematically describing their morphology, size, density, and distribution. The ultrastructural analysis across different sensillum classes offers insights into the potential biogenesis and functions of these extracellular particles.

Strengths:

Cryofixation preserves EVs and NVEPs within native tissue conditions. The detailed quantification of a very large dataset provides a unique source of information on extracellular particle number, categories, and distribution. The expertise of the group in the method and the tissue explored, as well as their detailed quantification, provide confidence in the dataset and observations.

Weaknesses:

Major comments

(1) As the authors state, the rare observation of EV budding or MVB release events suggests that these are transient processes, whereas EVs and NVEPs are retained for relatively long periods within the sensillum lumen. The current analyses may overinterpret steady-state vesicle abundance as differences in vesicle production.

(2) Given the above conclusion, differences between sensillum classes may be somewhat overstated.<br /> a) The absolute number of EVs and NVEPs per sensillum is highly variable, even within the same sensillum class (Figure 3D). For example, a substantial proportion of coeloconic sensilla have an empty lumen (Figure 3C). Consequently, expressing the data as ratios or proportions (Figures 3B, 4C, and 4E) may exaggerate differences between sensillum classes and should therefore be interpreted with caution.<br /> b) The rate of EV/NVEP production is unknown. For a similar rate of production across sensillum classes, Figure 3E suggests that the differences in lumen morphology and size may largely explain variation in EV density and distribution.

That said, I agree that ab1 sensilla display a striking enrichment of large cargo-filled EVs compared with the other sensillum classes, while coeloconic sensilla display enrichment in small dense filled EVs (Figure 4C). Together, large and cargo-filled EV observation provides strong support for differences in EV biogenesis between ab1 sensilla and the other sensillum classes. In that context, I also think the EV size distribution shown in Figure 4 - Figure Supplement 1 should be moved into the main figure, as it demonstrates that the majority of EVs in the ab1 lumen are relatively large and are therefore likely to represent microvesicles. Could you clarify why ab1 sensilla are only included in Figure 4 and not analysed in Figure 3?

(3) Approximately 10% of ORNs appear to be degenerating in 6-8-day-old flies, which seems unexpectedly high. This contrasts with the relatively infrequent occurrence of auxiliary cell apoptosis or complete sensillum degeneration. In these "degenerating ORNs", the authors state that the hallmarks of ORN apoptosis are restricted to the dendrites. As hallmarks, they state dendrite truncation, fragmentation and blebbing. Rather than apoptosis, I wonder whether these observations might instead represent ciliary truncation and ectosome shedding, followed by degradation of the shed ciliary membrane into EVs. Ciliary truncation and ectosome shedding, followed by ciliary regrowth, are dynamic processes that have been described across multiple species. This interpretation could explain large EVs that remain in the lumen long after the cilium has regenerated. It would reconcile this article with the general agreement that cilia are a prime site for the budding of EVs across species. Additional evidence supporting apoptosis of the ORNs would help distinguish between these possibilities. Otherwise, I believe the author should reconsider their interpretation.

Reviewer #1 (Public review):

Summary

The authors present a valuable study of the gasdermins and caspases encoded by Callorhinchus milii, a shark that is one of the most basal members of the cartilaginous fishes. C. milii encodes GSDME and PJVK as well as another gene here called GSDMA/B (which has also been termed GSDMEc in other work). This latter gene is the ancestral gene for bird/reptile/amphibians GSDMA that, in turn, is the ancestral gene to mammal GSDMA, B, C, and D. Prior work had shown that more ancient animals have only GSDME and PJVK, and in these animals caspase-3 and caspase-1 can both independently cleave GSDME. Prior work had also shown that in birds/reptiles/amphibians, GSDMA is cleaved by caspase-1, and GSDME is only cleaved by caspase-3. Here, the authors demonstrate that the more ancient C. milii gene is similar to the bird/reptile/amphibian GSDMA in that it is cleaved by caspase-1. They further demonstrate that C. milii does not encode inflammasomes that would activate CmiCASP1, and instead this caspase is an LPS sensor through its CARD domain analogous to mammal caspase-4/5/11. The data supporting these conclusions are convincing, and could be strengthened by primary cell studies from C. milii in future studies. They further provide evidence that this gasdermin can kill bacteria directly, but the data supporting this conclusion are incomplete.

Strengths:

The data demonstrating that CmiCASP1 is an LPS sensor via its CARD domain is thorough and convincing.

The data demonstrating that CmiCASP1 cleaves and activates GSDMA/B and that this causes pyroptosis is also thorough and convincing.

Weaknesses:

I think that the gene/protein referred to in this paper as GSDMA/B was in prior publications called GSDMEc (doi 10.3389/fcell.2022.952015). Is this correct? If not, the relationship or lack thereof to GSDMEc needs to be described. If the authors wish to rename the gene, this needs to be justified and discussed clearly. Also, a gene name with a slash is not typical and was initially confusing to me as it made me think the authors were referring to two different genes.

The authors do not have data from primary cells from C. milii to demonstrate that the LPS sensing by CmiCASP1 and the pyroptosis induction by GSDMA/B is relevant in the native cell types. This is a common limitation in publications that seek to study diverse animals where tools may not be available. This issue can be studied in future publications.

The ability of gasdermins to kill bacteria is controversial.

This bactericidal effect was first shown by the cited article Liu et al. 2016 from Judy Lieberman's lab. I reviewed that manuscript at Nature, and I implored the authors to remove that data from the paper because the experimental design had a high risk of not being physiologically relevant. Indeed, my lab had previously published that bacteria survive the process of pyroptosis and they must be killed by secondary efferocytic phagocytes attracted to the pyroptotic corpse (doi 10.1084/jem.20151613). I have continued to consider whether gasdermins could kill bacteria over the decade since that 2016 paper, and wrote a detailed argument describing how this is unlikely to be physiologically relevant in a recent review article (see Box 3 in doi 10.1038/s41564-026-02272-z).

In the author's current manuscript, the experiments performed show a very mild effect in the linear range of a reduction of perhaps 20% of the control bacteria in Figure 5A. This is a minimal effect compared to antimicrobial peptides, which will reduce colony-forming units by 99.9%. Take a look at the magnitude of effect in Figure 1 of an example paper looking at polymyxin or colistin killing of Acinetobacter (doi: 10.1128/AAC.00756-12), where CFUs are reduced by about 3 logs (1000-fold) in 30 minutes. The magnitude of effect in Figure 5A is not even 2-fold. Further, it would be very challenging to determine whether the concentration of gasdermin protein used in the assay is equivalent to the concentration that exists in cells. The methods section needs to be clearer to explain how many effective cell lysates of 293T cells were exposed to how many bacteria, because these concentrated lysates are of unspecified concentration.

Regarding cardiolipin binding, this is a lipid that has a small head group attached to 4 lipid chains, resulting in a cone-like shape with the polar groups at the cone tip and the lipids forming the wide cone base. As such, it creates a larger lipid area than polar area, thus naturally creating a curved membrane shape such that cardiolipin is in the leaflet of the interior of a curvature. Thus, in the mitochondria, it exists in the inner membrane in the mitochondria and allows for the bends that form the cristae, where it faces the surface that is concave (nicely diagrammed in Figure 1 of doi 10.3390/biom16010071). Similarly, cardiolipin enriches in the inner leaflets of membranes at the poles of rod-shaped bacteria to allow for the curvature of the membrane at the poles. Therefore, cardiolipin is not exposed in the outer leaflet of the outer membrane of Gram-negative bacteria; instead, the primary lipid in the outer leaflet is LPS.

A competing mechanism that could explain the results is that the opening of gasdermin pores in eukaryotic cell plasma membranes occurs concomitant with the generation of ROS from mitochondria. This could occur by gasdermins inserting into mitochondria, as supported by DOI: 10.1038/s41419-025-07760-4. The resulting ROS production due to mitochondrial dysfunction could cause the bactericidal toxicity seen in the cell extracts.

Reviewer #1 (Public review):

Summary:

In this well-written and well-presented manuscript, Arafat and colleagues describe the proper use and advantages of multi-task batteries to understand the organization of the human brain. The authors present both simulation and empirical results suggesting a substantial advantage in using many short tasks vs a single localizer in identifying specific task-engaged regions. The natural question arises as to which tasks should be used within the battery, and how they should be organized. The authors address this question by demonstrating a data-driven strategy for task selection that outperforms random selection, and they further demonstrate the advantages of highly interspersed tasks over a more typical one-task-per-run strategy.

Strengths:

In general, I find this work highly compelling. The topic itself should be of high interest to the majority of researchers conducting human functional neuroimaging studies. The manuscript itself is comprehensive and sound. The analyses and data are truly excellent, with only a few, relatively minor issues that can be improved. The authors do an exceptional job of laying out the motivation and logic for almost every analysis and conclusion in the manuscript.

I want to point specifically to the potential impact of this work. While the claims made here are very appropriately constrained to the conclusions that can be drawn from the actual analyses, their impact is potentially far-reaching. By the end of this manuscript, we are left with a set of ideas that in effect overturns 2-3 decades of received knowledge about how functional neuroimaging tasks should be designed to optimally understand the organization of the human brain.

Thanks to this paper, I personally will be rethinking how I design all of my fMRI studies in the future after reading this work. The authors are to be commended for this excellent contribution to the literature.

Weaknesses:

I struggled to understand the motivation and logic of the "connectivity modeling" section of the analyses.

Reviewer #1 (Public review):

Summary:

This manuscript's major strength is the identification of Col20a1 as a novel marker for terminal Schwann cells (tSCs) and the generation of the Col20a1-CreERT2 knock-in mouse line, which represents a valuable new genetic tool for studying tSC biology at the neuromuscular junction. However, the central conclusion that terminal Schwann cells regulate presynaptic vesicle homeostasis is not sufficiently supported because the electrophysiological and ultrastructural analyses are based on limited sample sizes. Increasing the number of animals and NMJs analyzed would substantially strengthen the conclusions. In addition, further validation of Col20a1 expression using RNAscope or immunostaining, together with comparisons to established tSC markers such as Kir4.1 and NG2, would help establish its specificity. Finally, the developmental appearance of Col20a1-positive axonal Schwann cells is intriguing and warrants further investigation to determine whether these cells migrate and differentiate into terminal Schwann cells during postnatal development.

Strengths:

The authors identified Col20a1 as a specific marker of terminal Schwann cells (tSCs) at the neuromuscular junction (NMJ) in mice and generated a Col20a1-CreERT2 knock-in mouse line for in vivo labeling of tSCs. This represents a novel and significant technical advance for the NMJ field, providing a valuable genetic tool for studying the development, maintenance, and function of terminal Schwann cells in vivo.

Weaknesses:

The major weakness of this manuscript is that the sample sizes are too small to support the authors' conclusion that "Terminal Schwann Cells Regulate Presynaptic Vesicle Homeostasis but Not Neuromuscular Junction Integrity in Mice."

Although the authors state that "Quantification of the tdTomato-positive NMJ ratio showed an ablation efficiency of approximately 80%, with only ~20% of NMJs retaining escaper tSCs" (page 9), they do not provide the sample size or sufficient quantitative information for the Col20a1-tdTomato/DTA ablation experiment shown in Figure 4. This information is essential for evaluating the robustness and reproducibility of the ablation strategy.

The electrophysiological analyses are also based on very limited sample sizes. According to Figure 6, mEPP recordings were obtained from 10 NMJs from 3 control mice and 14 NMJs from 5 tSC-ablated mice. The EPP recordings were based on similarly small numbers (control, n = 10 NMJs from 3 mice; tSC-ablated, n = 14 NMJs from 5 mice). Thus, only approximately 2-3 NMJs were analyzed per tSC-ablated mouse on average. Given the inherent variability among individual NMJs and animals, these sample sizes are insufficient to support broad conclusions regarding the effects of terminal Schwann cell ablation on synaptic transmission.

In addition, the authors describe the phenotype as "leaky" presynaptic spontaneous release, but this terminology is not defined and lacks mechanistic explanation. It is therefore unclear what specific physiological alteration the authors intend to describe.

The sample sizes for the electron microscopy analyses (Figure 7) also appear to be limited, making it difficult to determine whether the reported changes in synaptic vesicle distribution are representative or statistically robust. Because the central conclusion relies heavily on these electrophysiological and ultrastructural data, the evidence presented is not sufficient to support the claim that terminal Schwann cells regulate presynaptic vesicle homeostasis. At present, this conclusion is overly broad and not adequately supported by the available data.

Minor comments:

(1) While several figures contain high-quality NMJ images (e.g., Figures 1B and 4), the image quality in other figures should be improved. For example, the S100B immunostaining appears overexposed in some panels, making it difficult to distinguish individual Schwann cells or visualize the boundaries between adjacent cells.

(2) Some neuromuscular junctions shown in Figure 2C appear to be partially denervated. The authors should clarify whether these represent normal variability, effects of the experimental manipulation, or imaging artifacts.

(3) The authors should specify the muscle preparation used in Figure 3, as this information is necessary for interpreting the results and comparing them with previous studies.

Reviewer #1 (Public review):

Summary:

The authors build a reusable, disease-agnostic pipeline that retrieves GWAS risk genes from the GWAS Catalog by ontology terms, filters them, and maps them onto Human Protein Atlas (HPA v24) co-expression modules at three biological scales (tissue/organ, brain region, cell type). Enrichment is assessed by a consensus of Fisher's exact test with Benjamini-Hochberg correction and 10⁶-iteration Monte Carlo simulation. Applied to AD, DLB/PD, and FTD/ALS, the analysis reports convergent neuronal-module enrichment across all three diseases, AD-specific enrichment in liver- and immune-associated modules, and DLB-specific enrichment in ciliary modules, followed by a DrugBank-based survey of compounds targeting module gene products.

Strengths:

(1) The core premise is sound and well-motivated: risk-gene lists are hard to interpret because most variants are low-penetrance and broadly expressed, and projecting them onto a multiscale expression atlas is a reasonable route from statistical association toward tissue/cell context.

(2) The pipeline is delivered as reusable, open code (GitHub) built entirely on public inputs (HPA, GWAS Catalog, DrugBank), which is a significant contribution to the field and provides opportunities for replication and expansion.

(3) The dual-enrichment design (fold enrichment with FDR correction plus a 10⁶-iteration Monte Carlo empirical null) is more defensible than any single test, and the "consensus" logic is sound.

(4) The multiscale framing (organ → brain region → cell type), with UMAP module projections, is genuinely helpful and makes the mapping legible to broad scientific backgrounds.

(5) The authors are commendably restrained on one key point: they explicitly report that most risk genes are broadly expressed and not brain-selective, rather than overstating neuronal specificity.

(6) The AD liver/immune convergence is nicely discussed and integrated, and the NAFLD-AD discussion (Kupffer-cell/hepatocyte Aβ clearance, locus coeruleus noradrenergic parallels, "type 3 diabetes") is thorough and well-referenced, even where it remains speculative.

Weaknesses:

(1) Gene-to-variant mapping via the author-reported gene field is unclear. The Methods assign genes using the GWAS Catalog author-reported gene field, which predominantly reflects the nearest gene to the lead SNP and may not be the effector gene; it is also inconsistent across studies and different time periods of publication (i.e., changing methodologies in genomics and GWAS procedures). Because every downstream result depends on the gene set, this choice likely introduces noise and bias into all enrichment, specificity, and drug claims. More modern practices link variants to genes via fine-mapping plus eQTL/pQTL colocalization, or integrative scores. At minimum, the sensitivity of the main signatures to nearest-gene versus colocalization-based assignment should be demonstrated.

(2) The suggestive threshold (p < 1×10⁻⁵) trades specificity for coverage in the analysis that requires specificity. Relaxing from 5×10⁻⁸ substantially raises the false-positive fraction of the gene set. This is defensible for exploratory coverage in under-powered DLB/FTD, but the headline claims concern disease specificity (limited gene-set overlap; distinct signatures). Non-overlap among partially false-positive lists can lead to biological specificity conclusions that may not actually exist. A genome-wide-threshold sensitivity analysis is needed to show the signatures persist. Also, see concerns below about the DLB designation.

(3) "Disease specificity" is confounded by GWAS power. AD GWAS (e.g., Bellenguez; Kunkle; Sherva ~205,500 cases) vastly outpower DLB and FTD discovery. The gene counts (453/278/219) and the minimal three-way overlap (only two genes) track sample size and locus density as much as biology. The claim of "disease-specific genetic architectures" should be tempered and ideally power-matched (e.g., subsampling AD, or restricting to comparable effective N) before specificity is asserted. Also, see concerns below about the DLB designation.

(4) Linkage disequilibrium structure at gene-dense loci is not addressed and may inflate the lipid/liver signal. The overlap genes named as driving the liver/lipid theme include APOC1, APOC2, and APOE, all of which reside within the same chromosome-19 linkage disequilibrium (along with TOMM40). Counting co-regulated, physically clustered genes from one association signal as independent risk genes risks inflation of enrichment for lipid/lipoprotein modules. The five "liver-enhanced" genes flagged in the text again lead with APOE. Evaluation of one gene per independent signal is essential before the liver/lipid signature can be interpreted as multi-gene convergence rather than a single strong gene driving the effect.

(5) The enrichment background is not clear. Risk genes are filtered to HPA brain-detected transcripts, but the Methods do not state whether the Fisher/Monte Carlo background (N_Total) is likewise restricted to brain-expressed/HPA-detected genes or reflects all protein-coding genes, which may introduce bias. Specific information on the Fisher/Monte Carlo should be provided to ensure that the enrichment background is the same. If they are not, additional analyses should be performed to ensure robustness of the findings when restricted to brain-expressed only or the broader background.

(6) Cross-scale "consensus" is not independent confirmation. The same genes reappear across tissue, brain, and cell modules, so agreement across scales is partly built-in rather than corroborating. The neuronal-signature counts (82 AD / 62 DLB / 53 FTD; 186 "unique" genes) should be accompanied by a clear statement of how much cross-scale evidence is non-redundant. Also, see concerns below about DLB designation.

(7) Temporal claims are overstated. Using control HPA tissue avoids end-stage confounds but, by construction, cannot capture disease-state programs central to AD. More importantly, framing these modules as "baseline vulnerability hotspots that precede clinical neurodegeneration" is not tested, as nothing here is longitudinal. This assumption is presented as a finding and should be reworded as a hypothesis.

(8) DLB and PD should not be merged, and the cilia-associated risk genes cannot be termed causal based on the study design. The supporting literature is almost entirely PD (Schmidt iPSC-NPCs from sporadic PD; LRRK2 PD striatum), yet DLB and PD are merged, and the signature is branded "DLB." These should not be merged, particularly as it relates to sporadic PD. Although there are similar genetic risk factors (i.e., GBA, SCNA), of which GBA is unfortunately not mentioned in the manuscript, there are major genetic differences in sporadic PD and even PD with dementia (PDD) and DLB. It is not clear why PDD was not incorporated.

Cross-sectional expression overlap with GWAS genes cannot establish that cilia-associated risk genes are "causative vulnerabilities rather than secondary effects." Please soften to association and rename to reflect the synucleinopathy grouping. Further, additional discussion of important differential genes in this category (i.e., APOE and GBA) is needed, and the pathological description of DLB is incomplete in the introduction (i.e., focuses only on synuclein).

(9) The drug/repurposing analysis rests on a weak targeting rationale. Most of the 1,777 drugs target co-expression-module neighbors of risk genes, not risk genes themselves, so "substantial repurposing reservoir" likely overstates the impact. The anesthetics example (sevoflurane/halothane/desflurane hitting GABA_A subunits and ATP2B2) is a near circular argument, as anesthetics necessarily engage neuronal ion channels. This finding does not independently confirm the neuronal signature.

Statin-dementia and hydroxychloroquine/amodiaquine links are pre-existing, and the Tirzepatide→cilia→DLB inference is highly speculative. This section should be labeled hypothesis-generating, with direct-risk-gene targets separated from module-neighbor targets.

(10) Interpretation leans heavily on nominal (p < 0.05) modules. Several of the most novel claims (parts of the liver and immune signatures) rest on nominally significant modules that do not survive FDR (itself set leniently at p_adj < 0.1). The text should make consistently explicit which claims are FDR/Monte-Carlo-supported versus nominal-only, and de-emphasize conclusions resting solely on the latter.

Reviewer #1 (Public review):

This study by Thapliyal and Glauser investigates the neural mechanisms that contribute to the progressive suppression of thermonociceptive behavior that is induced under conditions of starvation. Several previous studies have demonstrated that when starved, C. elegans alters its preferences for a variety of sensory cues, including CO2, temperature, and odors, in order to prioritize food seeking over other behavioral drives. The varied mechanisms that underlie the ability of internal states to alter behavioral responses are not fully understood, however there is growing evidence for a role by neuropeptidergic signaling as well as capacity for functionally distinct microcircuits, formed by distinct internal states, to trigger similar behavior outcomes.

Within the physiological range of C. elegans (~15-25C), starvation triggers a profound reduction in temperature-driven thermotaxis behaviors. This reduction involves the recruitment of the amphid sensory neuron pair AWC. The AWC neurons primarily act to sense appetitive chemosensory cues, however under starvation conditions begin to display temperature responses that previous studies have linked to the reduction in thermotaxis navigation. Here, Thapliyal and Glauser investigate the impact of starvation on thermonociceptive responses, innate escape behaviors that are triggered by exposure to noxious temperatures above 26C or rapid thermal stimuli below 26C. They compare the strength of thermonociceptive behaviors, specifically heat-triggered reversals, in worms experiencing either early food deprivation (1 hour off food) or prolonged starvation (6 hours off food). Their experiments demonstrate a progressive loss of heat-triggered reversals that is mediated by AWC and ASI neurons, as well as both glutamateric and neuropeptidergic signaling.

At the level of neural activity, this study reports that the transition from early food deprivation to prolonged starvation reconfigures the temperature-driven activity of AWC neurons from mostly excitatory to a heterogenous mix combining excitatory and inhibitory responses. This finding is interesting in light of previous work that reported the opposite transition in temperature-driven AWC responses when comparing well-fed worms to those kept from food for 3 hours. Specifically, these differences highlight the differences between temperature responses within the C. elegans physiological temperature range (previous studies) and their noxious temperature response (this study). This study also identifies neural and genetic mechanisms that contribute to differences in thermonociceptive responses at +1 versus +6 hours starvation; interestingly, these mechanisms are also partially distinct from those that contribute to differences in negative thermotaxis behaviors in well-fed and +3 hours starvation worms. A limitation of this manuscript is that these differences are not particularly acknowledged or addressed, other than the hypothesis that independent mechanisms underlie negative thermotaxis versus thermonociceptive stimuli.

In this revised article, the authors commanding knowledge of the distinction between thermotaxis navigation (especially negative thermotaxis) and thermonociceptive behaviors is communicated with an admirable depth and clarity to the readers; this study's new findings are helpfully contextualized within the previous literature.

This study represents an important addition to the growing evidence that C. elegans sensory behaviors are strongly impacted by internal states, and that neuropeptigergic signaling plays a key role in mediating behavioral plasticity. To that end, the authors have provided compelling evidence of their claims.

Reviewer #1 (Public review):

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

The authors investigated the response of worms to the odorant 1-octanol (1-oct) using a combination of microfluidics-based behavioral analysis and whole-network calcium imaging. They hypothesized that 1-oct may be encoded through two simultaneous, opposing afferent pathways: a repulsive pathway driven by ASH, and an attractive pathway driven by AWC. And the ultimate chemotactic outcome is likely determined by the balance between these two pathways.

It is not surprising that 1-octanol is encoded as attractive at low concentrations and repulsive at higher concentrations. However, the novel aspect of this study is the discovery of the combinatorial coding of 1-oct in the periphery, where it serves as both an attractant and a repellent. Furthermore, the study uses this dual encoding as a model to explore the neural basis of sensory-driven behaviors at a whole-network scale in this organism. The basic conclusions of this study are well supported by the behavioral and imaging experiments, though there are certain aspects of the manuscript that would benefit from further clarification.

A key issue is that several previous studies have demonstrated a combinatorial and concentration-dependent coding of odorant sensing in the nematode peripheral nervous system. Specifically, ASH and AWC are the primary receptors for repellent and attractive responses, respectively. However, other neurons such as AWB, AWA, and ADL are also involved in the coding process. These neurons likely communicate with different interneurons to contribute to 1-oct-induced outputs. The authors' conclusion that loss of tax-4 reduces attractive responses and that osm-9 mutants reduce repulsive responses is not entirely convincing. TAX-4 is required for both AWC (an attractive neuron) and AWB (a repulsive neuron), and osm-9 is essential for ASH, ADL, and AWA (attraction-associated). Therefore, the observed effects on the attractive and repulsive responses could be more complex. Additionally, the interpretation of results involving the use of IAA to reduce the contribution of AWC at lower concentrations lacks clarity.

The authors did not observe any increased correlation between motor command interneurons and sensory neurons, which is consistent with the absence of a consistent relationship between state transitions and 1-oct application. Furthermore, they did not observe significant entrainment of AIB activity with the 2.2 mM 1-oct application. This might be due to the animals being anesthetized with 1 mM tetramisole hydrochloride, which could affect neural activity and/or feedback from locomotion.

Reviewer #2 (Public review):

Summary

The authors aimed to investigate how microbial metabolites, including hydrogen and short-chain fatty acids, influence feeding behavior and clock-gene expression in mice. Specifically, they examined these effects across different microbial environments, including a reduced-community model, germ-free mice, and specific-pathogen-free mice. The study addresses an important and poorly understood question concerning how microbial metabolism may influence host daily rhythms and feeding patterns.

Strengths

The manuscript presents a thoughtful and innovative investigation into the relationship between microbial metabolism, feeding behavior, and host clock-gene expression. A major strength is the use of a reduced microbial community together with real-time measurements of hydrogen production, which provides a useful experimental framework for separating microbial metabolic activity from direct host nutrient intake. The inclusion of germ-free and specific-pathogen-free mice also allows the authors to examine how these effects depend on microbial complexity.

The revised manuscript has addressed several concerns raised in the original review. The authors have clarified aspects of stool collection, provided additional methodological information, refined their use of the terms "circadian" and "diurnal," and added experiments examining the osmotic effects of lactulose across different microbial environments. These revisions improve the clarity and interpretation of the work. The finding that lactulose-induced microbial activity is associated with altered feeding behavior and clock-gene expression in the reduced-community model, but not consistently in germ-free or specific-pathogen-free mice, remains intriguing and highlights the complexity of microbial-host interactions.

Weaknesses

Despite these improvements, several important concerns remain unresolved. Most notably, the response regarding excluded food-intake measurements is insufficient. The authors report that the probability of excluding measurements differed significantly between treatment and control groups in the key feeding experiment shown in Figure 4A/S5A. However, they do not provide the underlying number or percentage of excluded observations, the direction of the imbalance, how many animals were affected, whether the exclusions occurred before or after treatment, or which exclusion criteria accounted for the removed values. Because food-intake rate was calculated from repeated measurements of cumulative intake, differential exclusion of observations could influence both the estimated slope and the reported treatment effect. The authors should provide these details and demonstrate that the principal feeding result is robust to the exclusion procedure.

There is also ambiguity surrounding the analysis and presentation of the quantitative polymerase chain reaction data. Figure 3B-C and Supplementary Figure 4A-C use conflicting mathematical labels, the raw cycle-threshold and replicate-level delta cycle-threshold values are not provided, and the rebuttal appears to conflate logarithmic transformation with exponentiation. I suspect that this may primarily reflect terminology or figure-labeling errors rather than an incorrect underlying analysis. The negative values shown in Figure 3 suggest that the authors may have plotted negative delta-delta cycle-threshold values, corresponding to log2 fold change. Nevertheless, the current description makes the workflow difficult to verify.

Several mechanistic and interpretive issues were acknowledged but only partially addressed. The authors appropriately softened their interpretation of the clock-gene findings and clarified that hormone concentrations were measured at only one time point. However, they did not provide baseline evidence that the positive and negative limbs of the clock-gene network are normally in counterphase, did not discuss the possible involvement of AMP-activated protein kinase, and did not address the limitation of measuring total rather than active glucagon-like peptide 1. These issues should be explicitly discussed as limitations of the current study and as priorities for future work.

Overall assessment

The authors have mostly achieved their aims by providing novel evidence that acute changes in microbial metabolism may influence feeding behavior and clock-gene expression in a simplified microbial environment. The experimental approach is creative, and the study has the potential to contribute meaningfully to the fields of microbiome research and circadian biology. However, unresolved concerns regarding differential data exclusion and the transparency of the gene-expression analysis currently limit confidence in the strength of the evidence supporting the principal conclusions.

Addressing these reporting and analytical issues would substantially strengthen the manuscript and improve its value to researchers studying microbial metabolism, feeding behavior, and host biological rhythms.

Reviewer #1 (Public review):

In this methods paper, the authors introduce a novel and innovative imaging approach for simultaneous in vivo multiphoton imaging of the mouse brain combined with DMD-based one-photon patterned photo-stimulation in different axial planes. This is a highly exciting technique that enables the axial decoupling of optical imaging of deep neural circuits from surface photo-stimulation of spatially precise (tens of micrometres) brain spots. This method builds on previous developments from the same laboratory, combining DMD-based patterned photo-stimulation with in vivo electrophysiological recordings. To my knowledge, this is the first instance in which patterned photo-stimulation has been combined and axially decoupled from two-photon (2P) imaging.

Beginning with a thorough characterisation of the optical resolution of the photo-stimulation system, the authors applied this method to the olfactory bulb (OB) network, in which sensory inputs are topographically organised at the surface of the OB and thus ideally suited to demonstrate the relevance of this approach. They first showed that this technique can be used to rapidly reveal connectivity patterns of OB output neurons and to identify sister mitral cells. In addition, they manipulated a specific glomerular inhibitory population and demonstrated that these neurons provide spatially heterogeneous long-range inhibition of OB output neurons, with differential effects on mitral and tufted cells (a result previously observed in a paper from the same lab: Banerjee et al., 2015, Neuron). Altogether, the data demonstrate that this technique is well-suited for high-throughput functional mapping of neural circuit properties. The results are compelling and illustrate both the significant advance represented by this method and its feasibility.

Despite my initial enthusiasm, there are several concerns in the present study that must be addressed in order to rule out confounding observations and to resolve remaining uncertainties regarding photo-stimulation resolution. These include the following:

(1) Spatial resolution: Although the authors provide convincing data on spatial resolution in vitro, several observations throughout the paper suggest that the effective photo-stimulation precision may be lower than initially reported. For instance, in Figure 2, the authors observe repeated responses in neighbouring glomeruli (e.g., glomeruli #3 & #5, #4 & #6). To what extent could light scattering along the X/Y/Z-axis above the targeted glomerulus recruit en passage axons, resulting in the inadvertent activation of multiple glomeruli?

A further observation concerns the presence of "inhibited" sister mitral cells (Figure 3). The authors claim this is reminiscent of the differential spike-timing reported between sister cells (Dwawale et al., 2010, Nat Neuro). However, observing both excitatory and inhibitory responses following stimulation of glutamatergic inputs is an altogether different matter, particularly given that sister mitral cells are reciprocally connected via gap junctions. This observation requires further clarification and raises serious questions about the effective resolution of the stimulation. Could the inhibited cell simply correspond to a non-sister mitral cell receiving disynaptic feed-forward inhibition? To verify sister cell identity, the authors could confirm that the predicted sister cells share a similar odour receptive field compared to randomly selected mitral cell pairs. In their previous study employing analogous DMD-based photo-stimulation (Dhawale et al., 2010, Nat. Neurosci.), sister mitral cells did not exhibit such opposite response profiles (firing rate correlation of ∼0.7 between sister cells). Could the authors verify that a comparable activity correlation is also observed among the sister cells identified using ADePT in the present study? In Figure S6, the authors show recordings and stimulation of the same neurons co-expressing GCaMP and ChR2. Applying this experimental design to the mitral/tufted cell population (using a Tbet-Cre mouse transduced in the OB with both GCaMP and Chrimson virus) would constitute a valuable control to clarify the nature of these "inhibited" sister cells.

An additional concern relates to the 21 out of 162 mitral cells that were activated by two distinct glomeruli - a finding that is incompatible with the established OB wiring diagram and that further challenges the claimed stimulation resolution.

A critical control experiment is also absent: in a Thy1-GCaMP6 mouse lacking any light-sensitive opsin, do the authors observe any unintended side effects of photo-stimulation?

Regarding sister cells (Figure 3), tufted cells are not analysed alongside mitral cells in this dataset, whereas this is elegantly performed in Figure 5 using the DAT+ model. Could the authors also demonstrate how the technique can reveal the complete family portrait of sister mitral and tufted cells?

(2) The authors have explored only a limited set of photo-stimulation parameters, primarily varying light intensity. They should present additional tests, such as varying the spot size (which appears to be arbitrarily fixed at 30-50 µm) and the z plane of stimulation. The level of activation can vary considerably: for example, in Figure 3a(iii), identical stimulations elicit responses of markedly different amplitudes (see glom#3 and #4). In Figure 2, 5 out of 15 glomeruli failed to respond - could the choice of z-plane account for this variability? The stimulation duration (50-150 ms) also appears somewhat arbitrary: can the authors demonstrate that the technique is compatible with finer temporal patterns (e.g., 10 Hz stimulation for 500 ms using 20 ms light pulses)? What are the spatiotemporal and axial scanning limits of this approach, and can two or three glomeruli be targeted simultaneously with temporally patterned stimulation?

(3) One particularly relevant application of this method would be to guide photo-stimulation based on prior functional measurements - for instance, by generating a photo-stimulation mask specifically targeting odour-responsive glomeruli. In the DAT-Cre × Thy1-GCaMP6 experiment shown in Figure 5e, which glomeruli are activated by a given odour, and how does this odor responsiveness influence the efficiency of DAT+ cell-mediated inhibition?

Reviewer #1 (Public review):

Summary:

This paper reports an important MEG study that is interesting from many different angles. By manipulating presentation rate (Fast vs. Slow) and "Structure" (word list vs. sentence list vs. story), the authors revealed the spatiotemporal dynamics of processing constituents at different speeds and semantic-conceptual scales. The methods are solid, and the results would be interesting to both researchers interested in the neural basis of structure building and researchers interested in the consequences of presentation rate, which, as the authors note, is understudied for visual sentence presentation. Overall, I enjoyed reading this paper and agree that it reports valuable findings for language processing, but there are a few points where clarity can be improved for readers to better evaluate and appreciate this work.

Strengths:

This paper studies the effects of presentation rate and linguistic structure building at different scales with MEG, which is perhaps one of the first MEG studies approaching these questions, and MEG is an appropriate technology to investigate spatiotemporal dynamics in the brain. The authors conducted a breadth of analysis, enabling us to fully understand what is shown by their data.

Weaknesses:

Here I would like to point out a few points where clarity can be improved (e.g., analytical details) for readers to better appreciate this paper.

(1) The "mean proficiency" was reported on page 5, but I did not see the details of the proficiency task, which also need to be reported.

(2) I was confused about the baseline correction part of the MEG analysis on page 10, and I would appreciate it if the authors laid out the rationale more clearly. From what I understand, the authors did not perform baseline correction, which is completely understandable, as word-lists, sentence-lists, and stories would have different baselines, and the baselines also keep building up as the trial evolves (which is a part of the study, instead of something to be parsed out). In this context, it becomes confusing why the authors singled out the Fast presentation condition to be unsuitable for baseline correction ("Since the prestimulus period cannot be analyzed during the Fast presentation..."). If the rationale is that the Fast condition did not have a long-enough blank screen between segments, this would not be a problem, as baselining is done in ERP RSVP studies anyway all the time. I am also curious what is meant by "The Fast and Slow presentation conditions were separated and demeaned independently". Would this make comparing the Fast and Slow conditions trickier? Overall, I think the analytical choices are potentially defensible, but more explanations of the rationale are needed.

(3) For the Results section, the authors should check the text again to increase clarity for statistical reporting for readers. For example, on page 14, the p-values were not always reported, and the dfs for t-values were also not always reported. For "pairwise comparisons", one would expect a reporting like "ps < ..." instead of a singular "p < 0.001".

(4) For behavioral results, was the analysis of RT directed at all trials or correct trials only? It would be worth checking whether the RT results hold true when analyzing only correct trials, or whether they were primarily driven by incorrect trials. Note that I don't find it a problem to include all trials for the MEG analyses (which the authors did), as we would believe that the participants were doing the task anyway despite it being challenging, and task difficulty was an inherent component of the research goal instead of something to be parsed out.

Reviewer #1 (Public review):

Summary:

Chen and colleagues utilize in vivo electrophysiology to characterize distance-encoding neurons in the retrosplenial cortex of rats during both random foraging and goal-oriented navigation. They observe a subset of RSC neurons that encode distance to a hidden goal location and that HD coding is enhanced during goal-directed navigation when compared to foraging. They also demonstrate that goal distance coding is preserved in the dark. Distance encoding has been shown in RSC in prior publications, but examining it with respect to a behaviorally relevant location will be of interest to the field. That said, the manuscript needs substantially more methodological detail before I am convinced that goal-distance-to-goal (DTG) coding is not an artifact of self-motion or of other spatial tuning already known to exist in the area. The authors also introduce several new machine-learning approaches that are hard to interpret without clearer justification or a demonstrated need. Addressing the points below would provide more convincing evidence for DTG coding and enhance readability.

Strengths:

The task is useful for determining whether the goal distance is encoded in neural populations.

Retrosplenial cortex is an excellent candidate region for examining representations related to goal distance.

The analytical framework is state-of-the-art and is useful for determining the contribution of goal distance to complex activation in retrosplenial cortex that possesses mixed selectivity.

Weaknesses:

The analyses/simulations intended to ascertain the relationship between other known spatial/self-motion codes in RSC and DTG coding are insufficient. I struggle with how DTG and speed can be convincingly disentangled given task structure. I suspect many DTG cells are in fact speed-modulated, and that if the task was flipped such that the animal had to run through the goal location rather than stop, DTG tuning curves would be mirrored. There are a couple of simple things that could help:

(1) Show significantly more examples of DTG neurons (perhaps all of them) alongside their corresponding spatial (e.g., EBC and HD) and self-motion tuning curves (e.g., speed and angular speed) for multiple goal locations.

(2) The relationship between the DTG tuning curve and the speed tuning curve should be presented.

(3) Report what fraction of DTG cells are tuned to a random, non-goal location, what fraction qualify as DTG by chance, and how much better goal decoding is than decoding to a random location (Figure 2a).

(4) We need visualizations of DTG reliability both within and across goal locations (see below).

The GLM analyses are meant to address the unique contribution of distance to goal, but there are issues with this approach.

(1) The five behavioral variables included in the analysis are not independent and will covary strongly. Forward selection is greedy, so once one member of a correlated set is admitted, the remaining members' unique contribution to held-out log-likelihood may fall below the 0.01 bits/spike threshold even if they are genuinely encoded. The pairwise correlation (or mutual information) structure among the five variables should be reported for all sessions, as well as the Δllh values of the variables rejected at each step, so readers can judge how close the near-misses were.

(2) What is the L1 penalty and how was it selected? L1 shrinkage lowers each candidate's Δllh, so a stronger penalty yields smaller selected models. This is also important when considering that the basis sets for each variable have different dimensionality. Is a single L1 penalty shared across candidate models?

Reliability of DTG responses.

(1) The 1D DTG tuning curves lack error bars, which should be presented to convey reliability. These should be shown for individual DTG neurons for multiple goal locations.

(2) The 2D DTG ratemaps in Figure 1 are not especially compelling; it would be helpful to see all examples in the supplement.

Several observations suggest that some DTG neurons may actually be encoding boundaries.

(1) The distribution of DTG peaks is strongly bimodal, with peaks either at the goal or at the boundary. Together with the mixed-selectivity results, this suggests that some DTG cells may show a boundary response or activity at the goal related to speed or acceleration. The manuscript at present does not effectively rule out this possibility. This could be addressed by showing that DTG coding is preserved across different goal locations.

(2) An arena-expansion condition would clearly distinguish DTG responses from boundary responses.

(3) It would be useful to see the peak sorted plot (1E) cross-validated within and across goal locations. I suspect that DTGs with intermediate-distance peaks shift with goal location while the others do not. If they remain fixed, it would solidify the presence of the phenomenon.

More characterization of the neurons that are 'important' for goal distance decoding that are not DTG cells is needed (i.e., the IMP population). As I understand it, IMP and DTG cells were grouped for decoding analyses, but the two populations do not overlap. The IMP population is much larger than the DTG cells, and it is unclear what these neurons are doing. Moreover, it seems that the IMP sub-class could alone be used to decode distance to goal. This is difficult to reconcile with the framing of DTG cells as the substrate of goal-distance coding and deserves comment. Decoding should be conducted with the IMP cells alone to show what the DTG cells actually add.

Reviewer #1 (Public review):

Summary:

The authors have achieved an excellent, thorough anatomical characterization of all spinal projecting neurons in the larval zebrafish. The comprehensive nature of their labeling approach and their quantification will make this work an instant reference benchmark for a wide number of zebrafish researchers. In addition, the scholarly approach in comparisons with other work in and outside of fish makes the manuscript valuable to researchers outside the field who would like to know how to translate between zebrafish and mouse terminologies.

Strengths:

The figures are clear and easy to follow. The literature review is impressive. The authors are careful to note the few limitations of their approach (eg the absence of Mauthner cell labeling and associated large neuron weak label). The manuscript does the whole field a major service.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #1 (Public review):

Summary:

This manuscript presents a new foundation model, EvoDiff, for designing primary protein sequences. By leveraging an evolutionary-scale dataset, the model can be applied to evolution-guided sequence generation, sequence inpainting, and functional scaffolding.

Strengths:

The model provides an efficient approach for designing protein sequences and could be useful for developing protein therapeutics, engineering enzymes, designing biomaterials, and many other applications. The manuscript presents solid results showing that proteins designed by EvoDiff can achieve the same biological functions as their wild-type counterparts.

Weaknesses:

Compared with other sequence-generation models, EvoDiff does not substantially improve the success rate, suggesting that significant experimental effort is still required to screen and identify successful hits.

Reviewer #1 (Public review):

Summary:

In the manuscript by Francis-Oliveira et al., the authors investigated whether mild adolescent social isolation in female mice produces a latent vulnerability that emerges during the postpartum period as impaired maternal caregiving. They further tested the hypothesis that maternal deficits alter offspring social development. Based on their findings, the authors propose that adolescent psychosocial adversity disrupts maternal behavior and that resulting alterations in offspring social function are mediated through dysfunction of the midcingulate cortex (mCg) to prelimbic cortex (PrL) pathway. They further suggest that exposure to experienced parous females during the postpartum period can rescue maternal behavior and normalize offspring outcomes through restoration of activity in this circuit.

To test these hypotheses, the authors exposed female mice to mild social isolation during late adolescence and subsequently bred those females. Maternal behaviors were assessed postpartum, and offspring were evaluated in adulthood using assays of sociability, social novelty recognition, social odor recognition, anxiety-like behavior, locomotion, and non-social memory. The authors also measured corticosterone levels in control and stress-reared offspring. To test circuit-specific effects, the authors employed chemogenetic activation and inhibition of the mCg→PrL pathway using DREADDs and performed electrophysiological recordings from identified projection neurons. Finally, stressed dams were co-housed with experienced parous females during the postpartum period to determine whether maternal and offspring phenotypes could be rescued, as well as the social deficits previously observed in offspring.

The authors found that adolescent isolation selectively impaired pup-directed maternal behaviors, including nursing, licking, nest building, and pup retrieval, while leaving self-directed behaviors intact. Adult offspring of stressed dams exhibited deficits in sociability, social novelty recognition, and social odor discrimination, but showed no impairments in locomotor activity, anxiety-like behavior, or novel object recognition. Chemogenetic activation of the mCg→PrL pathway restored social behavior in stressed offspring, whereas inhibition of the pathway induced social impairments in controls. Co-housing stressed dams with experienced parous females restored maternal caregiving, normalized offspring social behavior, and rescued reduced firing of mCg→PrL neurons observed in offspring of stressed dams. Collectively, these findings support the authors' model that adolescent psychosocial adversity disrupts maternal caregiving and contributes to offspring social deficits through dysfunction of the mCg→PrL circuit.

Strengths:

The study includes multiple levels of assessment, including behavioral measures, behavioral intervention, the use of DREADDs for circuit manipulation to both test effects of activation versus inhibition on behavioral outcomes as well as physiology experiments. The multilevel approach is a strength.

Weaknesses:

(1) Interpretation of the parous co-housing experiment:

The principal limitation of the study is that the communal housing paradigm does not distinguish rescue of maternal behavior in the stressed dam from direct caregiving provided by the experienced parous female. The authors interpret the rescue experiment as evidence that social support and/or social learning from experienced mothers restores maternal behavior in stressed dams, which in turn normalizes social behavior in offspring. However, pups were continuously housed with both the stressed dam and the parous female from P0-P7, and the parous female had unrestricted access to the pups throughout the intervention period. The parous female was removed only briefly during maternal behavior testing. This design raises an important alternative interpretation. The experienced parous female may have directly provided substantial maternal care to the pups, supplementing or compensating for deficits in the stressed dam. The rescue of offspring phenotypes may reflect care received from the parous female rather than improved caregiving by the stressed dam.

Were caregiving behaviors of the stressed dam and parous female quantified separately during the co-housing period? What proportion of licking, nursing, retrieval, and nest maintenance was performed by each animal? Can the authors exclude the possibility that direct maternal care from the parous female, rather than social learning or social support, accounted for the rescue of offspring outcomes? Without such controls, the central claim that restoration of maternal behavior in the stressed dam mediates normalization of offspring phenotypes is not fully supported.

(2) Specificity of the behavioral phenotype and rescue:

The manuscript repeatedly frames the findings as restoration of offspring outcomes and intergenerational vulnerability. However, the behavioral phenotype appears highly selective and restricted primarily to social behaviors. Offspring exhibited impairments in sociability, social novelty recognition, and social odor discrimination, but showed normal locomotion, anxiety-related behavior, and non-social memory. Thus, the authors should more explicitly acknowledge that maternal adversity produced a domain-specific social phenotype rather than broad behavioral dysfunction. Interestingly, the rescue studies only evaluated a subset of the affected behaviors, making it unclear whether co-housing with parous females restored broader aspects of offspring neural or behavioral function or selectively improved specific social behaviors.

Why was social olfactory recognition not included in the rescue experiments? Why were additional behavioral measures not reported following circuit activation or parous co-housing (e.g., anxiety-like behavior and novel object test) - were these improved in controls by enriched early parenting? Did manipulation of the mCg→PrL pathway or co-housing with parous females influence anxiety-like or depressive-like behaviors despite the absence of baseline group differences?

(3) Strength of the DREADD-mediated causal claims:

The DREADD experiments implicate the mCg→PrL pathway in regulating social behavior; however, several aspects limit the strength of the causal conclusions. Sample sizes were relatively small (n = 6/group). In addition, the variance observed in the DREADD cohorts appears substantially reduced relative to that observed in the non-surgical cohorts. For example, vehicle-treated groups appear more clearly separated than would be expected based on the original behavioral data presented in Figure 2. Can the authors comment on potential reasons for this discrepancy?

Second, although activation and inhibition experiments support involvement of the mCg→PrL pathway in social behavior, the manipulations do not fully recapitulate the broader phenotype observed in stressed offspring. Thus, the data support a role for this pathway but may not justify the stronger conclusion that dysfunction of this circuit alone accounts for the entirety of the offspring phenotype.

The authors argue that altered maternal behavior is causal for social deficits in offspring. However, in the absence of a cross-fostering experiment, the study cannot fully exclude alternative explanations, including direct influences of caregiving by the co-housed parous female, gestational effects, altered maternal physiology during pregnancy, or germline-mediated influences. A cross-fostering design would substantially strengthen the causal interpretation of the findings.

Additionally, clarification of litter effects is important: For example, how many litters contributed to each experimental group? Was litter treated as a random effect in statistical analyses? Were multiple offspring from the same litter analyzed as independent observations? Because maternal behavior is manipulated at the litter level, litter rather than individual offspring may represent the appropriate experimental unit for many analyses.

(4) Integration of corticosterone findings into the mechanistic model:

The corticosterone findings appear somewhat disconnected from the central mechanistic narrative. The authors report elevated corticosterone levels in stressed offspring and suggest that HPA-axis dysregulation may contribute to the observed behavioral phenotype. However, the manuscript does not establish whether corticosterone plays a causal role in the social deficits or instead represents a parallel physiological consequence of altered maternal care.

Were corticosterone levels normalized by co-housing with parous females? Did DREADD-mediated activation of the mCg→PrL pathway normalize corticosterone levels? Could corticosterone manipulation alone drive aspects of the behavioral phenotype independent of circuit manipulation? Do corticosterone levels correlate with the severity of social behavioral impairments? It is difficult to determine whether corticosterone is mechanistically relevant or simply serves as an associated physiological marker. The authors should either more directly integrate the endocrine findings into their mechanistic framework or temper discussion suggesting a causal role for HPA-axis dysfunction.

In summary, this manuscript addresses an important and understudied question concerning how adolescent adversity influences maternal caregiving and offspring social development. The behavioral, circuit, and electrophysiological findings are generally coherent and support a role for the mCg→PrL pathway in mediating offspring social outcomes. However, the strongest mechanistic claim, that social support rescues offspring phenotypes by restoring maternal behavior in stressed dams, is weakened by the communal rearing design, which allows direct caregiving by parous females. Additional clarification regarding caregiver-specific behaviors, litter effects, the role of corticosterone, and the specificity of the DREADD-mediated phenocopy would strengthen the causal interpretation of the findings. Overall, the study is potentially impactful, but several conclusions currently extend beyond what is directly supported by the data.

Reviewer #1 (Public review):

Summary:

The authors addressed how viral-mediated expression of amyloid in medial septum (MS) cholinergic neurons, or broadband amyloid expression, affects the integrity of MS cholinergic neurons in aging mice, as well as cognition, sleep, and hyperexcitability. Using fiber photometry and viral tracing, they show that MS cholinergic neurons are active during wakefulness and REM sleep and that they also project to many different areas. Next, they show that when they express a viral vector carrying APP to encode amyloid beta in MS cholinergic neurons, these neurons express amyloid as they do in a globally expressing APP model (APP-NLGF). They find that amyloid may spread largely following MS projections and that MS die over time presumably due to amyloid expression. They also describe the emergence of memory deficits and reduced REM sleep attributable to loss of MS cholinergic neurons. Lastly, they report a higher burden of epileptiform activity in mice with broadband amyloid expression and the emergence of neuroinflammation in MS, which may be contributing to cell loss and network dysfunction.

Strengths:

(1) New insights on a potential role of MS cholinergic neurons in spreading amyloid.

(2) Use of several different methods to address effects of MS dysfunction in aging mice (AAV, global, lesioning).

(3) Combination of activity-related readouts including fiber photometry, EEG coupled to histological, behavioral, tracing, and neuropathology measures.

(4) Consideration of potential confounds to behavioral measures using proxies of anxiety-related behavior.

Weaknesses:

(1) The authors aim to model the prodromal phase of Alzheimer's disease (AD) neuropathology, which is a very promising area to target therapeutic intervention. While reduction in basal forebrain volume has been reported early in AD, presumably functional changes may be happening much earlier, i.e., even before MS start to degenerate or before REM sleep is reduced. This view has been proposed by human studies showing increased ChAT reactivity in MCI (PMID: 11835370) and evidence in mouse models showing that MS cholinergic neurons may be hyperactive early and degenerate late with distinct implications for memory (PMID: 41717904). Thus, functional changes could be considered before structural changes could be discussed, as earlier ages in this model could reveal such early changes.

(2) One limitation of the tracing methodology (Figure 1) that could be improved is sample size, as only 2 mice have been used. Moreover, it would be interesting to conduct the same tracing experiments in APP mice to see how these projections are affected by amyloid pathology.

(3) Figure 3 measurements included the whole hippocampal formation, but a region-specific analysis would be warranted as the authors discuss specific accumulation areas.

(4) Figure 5 novel object recognition comparisons use a group of 10 sec exploration, which is unclear why. Novel vs familiar comparisons and reporting of discrimination indexes are considered more robust measurements to report.

(5) Interictal spike detection would benefit from more methodological detail and examples of spikes detected. Reference 72 does not seem to detail interictal spike detection. Moreover, when during sleep do these spikes happen? It has been shown that they occur primarily during REM sleep when mice show cholinergic hyperactivity (PMID: 37714307). From panel 7B, it seems they occur during NREM, which may be explained by a diminished drive of cholinergic circuits to drive spikes in these mice (vs REM in younger mice). Thus, a NREM vs REM vs Wake analysis will be insightful.

Reviewer #1 (Public review):

Summary:

This paper from Bardossy et al. explores whether viral macrodomains in dual-host viruses contribute to infection in the mosquito vector. Using the CHIKV Caribbean strain, the authors generated nsP3 macrodomain catalytic site mutants (N24A or N24D) and identified a compensatory mutation site at position 31 during virus propagation in Vero cells. They then assessed the impact of these mutations on viral growth kinetics in A549 (human) and U4.4 (Ae albopictus cells), as well as on infectivity and dissemination in vivo in Ae. aegypti and Ae. albopictus. Biochemical and structural analyses of recombinant macrodomain proteins (alone or in combination) revealed effects on stability, catalytic activity, and ADP-ribose binding. Overall, the study demonstrates that CHIKV macrodomain catalytic activity plays an important role in virus infectivity and dissemination within the mosquito vector.

Strengths:

A complete set of experimental approaches spanning generation of recombinant viruses, in vitro characterization, in vivo studies in mosquitoes, and detailed biochemical and structural characterization.

Weaknesses:

(1) The sequence analysis of the generated stocks revealed the emergence of a second-site mutation at position 31 of the nsP3 macrodomain when (N24A or N24D) CHIKV mutants were generated on Vero cells. However, it is not clear from the text or the experimental design how many independent replicates were performed. Based on the current description, it appears this was done only once, which raises the question of whether mutations at position 31 represent a reproducible outcome of infection. This is particularly important because experiments in A549 cells did not reveal emergence of mutations at position 31. To strengthen this finding, the experiment should be performed at least three independent times.

(2) Based on the primer information used to generate amplicons for sequencing, the amplicons evaluated do not span the full nsP3 gene as stated in the text (Line 105). Instead, they cover only the first 119 amino acids of the macrodomain (160 aa long). Thus, the current data do not rule out the emergence of other compensatory mutations elsewhere in the nsP3 macrodomain or in the full-length protein. Additional sequencing is recommended, or the text should clearly state that only a portion of the macrodomain was sequenced.

(3) Another key question is whether this is a specific feature of the Caribbean strain or a feature conserved across different CHIKV lineages.

(4) The use of A549 cells (interferon-competent) to study CHIKV infection is somewhat surprising, as the current literature indicates that this cell line is not efficiently infected by Asian or ECSA lineages of CHIKV (PMID: 17604450) unless the Mxra8 receptor is overexpressed (PMID: 29769725) or IFN signaling is inhibited (PMID: 31682641). The data presented here are compelling and suggest specific features of the Caribbean strain that enable efficient infection of this cell line (Do the authors observe detectable cytopathic effect (CPE) in CHIKV-infected A549 cells?).

However, to further support the authors' claim related to human immunocompetent cells, it would be important to demonstrate the phenotype in an additional interferon-competent cell line that is well-established as highly permissive to CHIKV, such as human fibroblasts.

(5) To fully support the conclusion stated in lines 234- 237, the authors should fully sequence the virus stock used to demonstrate that no additional mutations (beyond N24D-D31H/N) are present that could contribute to the enhanced dissemination phenotype. This is especially important if the experiment was performed with only one stock of virus, given justified gain-of-function concerns.

(6) The authors did not assess transmission but transmission potential (only viral dissemination to heads was measured). The sentence at line 360 should be modified to accurately reflect the data-supported conclusion.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

The paper lacks rigor in talking about evolutionary-related issues of the state of the fitness landscape. As an example, the paper mentions that these super-compensators flatten the landscape. While I understand where this is coming from, I think that the fitness landscape in this context is a static entity and cannot be flattened or otherwise altered. A much more accurate description is that a sequence with a super-compensator is located in a flatter-than-expected segment of the fitness landscape, or on a flat fitness ridge. These issues are more semantic in nature, and while the manuscript would benefit from it being shown to an expert in molecular evolution or fitness landscapes, this issue does not take away from the importance of the results.

Reviewer #1 (Public review):

A previous study from the same team (McDougle & Taylor, 2019) demonstrated that explicit strategies during visuomotor adaptation can be dissociated into retrieval-based and algorithmic strategies. However, whether these distinct forms of explicit processing differentially influence implicit recalibration has remained unresolved, with previous studies providing evidence both for relatively independent explicit and implicit processes and for interactions between them. This study addresses this question through a series of experiments that used Critical and Non-Critical targets to induce distinct strategic modes while maintaining comparable adaptation at the Critical target.

Experiment 1 replicated previous findings showing broader implicit generalization under algorithmic strategies. However, this broader generalization could be explained by spillover effects arising from adaptation at the Non-Critical targets. Experiment 2 was designed to reduce such spillover effects by increasing the spatial separation between the Critical and Non-Critical targets. Although broader generalization was still observed in the algorithmic condition, this effect was interpreted as reflecting greater variability in reaching behavior at the Critical target. Finally, Experiment 3 introduced additional controls using an error-clamp paradigm, and the difference in generalization width between the two strategies largely disappeared.

Together, these findings led the authors to conclude that implicit recalibration is relatively insensitive to the type of explicit strategy employed and is primarily shaped by the statistics of the movement plans on which learning occurs.

The experimental design using Critical and Non-Critical targets is particularly interesting and represents a creative approach to manipulating strategy use. Reaction times were generally longer in the algorithmic group, even at the Critical target, suggesting that the manipulation was at least partially successful in biasing participants toward algorithmic versus retrieval-based strategies. The results that the implicit recalibration is independent of the explicit strategy (how you aim) but depends on the aiming point by the explicit strategies (where you aim) are basically reasonable.

I would like the authors to clarify two points.

First, how reasonable is it to infer the use of distinct explicit strategies primarily from reaction time differences? While longer reaction times in the algorithmic group are consistent with greater computational demands, it remains unclear whether the longer reaction times observed at the Critical target necessarily reflect different strategy implementations at that location. In particular, could the increased cognitive demands associated with the Non-Critical targets in the algorithmic condition have carried over to the Critical target, thereby prolonging reaction times without implying qualitatively different strategies at the Critical target itself?

Second, the interpretation of Experiment 3 is not entirely clear to me. The manuscript argues that the algorithmic group continued to exhibit greater reaching variability than the retrieval group. If this variability indeed reflects greater variability in movement plans, one might expect a broader implicit generalization function in the algorithmic group. However, the generalization widths were comparable between groups. Could this result instead suggest that the implicit recalibration process itself generalized more narrowly in the algorithmic group, thereby offsetting the broader distribution of movement plans? More generally, I would appreciate further clarification regarding the relationship between reaching variability, movement-plan variability, and the resulting width of the implicit generalization function.