Reviewer #3 (Public review):

Summary:

The work describes an optofluidic automation setup to optically inhibit and enrich selected bacterial populations in confined microchannels through negative selection using light stimulation. The work is well described and the manuscript is well constructed.

Major comment:

The authors reported that methylene blue with 2uM incubation has superior performance than UV light. But it's also noted on line 152 there is an inhibition effect from the chemical affecting ~40% of the growth rate.

It will be noteworthy what is the growth curve or at least the MIC of methylene blue used on the MG1655 E. coli by the authors.

Significance:

The optics part of the work is well described, however the materials and methods details of the biological and microfluidic part can be extended.

Overall, the system demonstrated the practical use of combining microfluidics for enrichment of microbial population as a novel alternative method, despite that the efficiency is currently subpar to conventional methods.

But combining further with deep learning phenotype or growth rate monitoring, the technology represents a new path for phenotypic selection which is also novel that conventional methods cannot offer. The work will benefit readers in applied science seeking for new target enrichment based on optofluidics.

Reviewer #3 (Public review):

Summary:

This manuscript investigates the function of Su(Hw) binding sites found in two boundaries/insulators, homie and nhomie, in TAD formation that encompasses the eve gene. They tested the hypothesis that Su(Hw) binds homie and nhomie, thereby forming a stem-loop TAD. The authors used transgene reporters with various mutations, and the results support the hypothesis strongly.

Strengths:

They combine reporter assays (GFP and LacZ expression) with MicroC contact profiling to robustly support their conclusion. Overall, they propose how Su(Hw) mediates physical interaction between boundary elements (homie and nhomie).

Weaknesses:

The writing is quite dense and not easily accessible to outside readers.

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

Reviewer #3 (Public review):

The manuscript by Fuller et al describes a crosstalk between ARTG2A with components of the early secretory pathway, namely RAB1A and ARFGAP1. They show that ATG2A is recruited to membranes positive for RAB1A, which they also show to interact with ATG2A. In agreement with earlier findings by other groups, silencing RAB1A negatively affects autophagy. While ARFGAP1 was also found on ATG2A positive membranes, silencing ARFGAP1 had no impact autophagy. Notably, these ARFGAP1 positive membranes are not Golgi membranes.

The findings are interesting and the data are in general of good quality.

Comments on the previous version:

The revisions carried out by the authors are fine. The new data on ArfGAP1 and about the indirectness of the ATG2A and Rab1A interaction improve both clarity and strength of the manuscript. I have no further comments.

Reviewer #3 (Public review):

The authors have addressed most concerns from the initial review, significantly enhancing the manuscript. The expanded characterisation of the engineered THP1-CD1c system provides strong evidence that the observed T-cell responses are unlikely to result from residual conventional MHC recognition. The specificity of the CD1c-autoreactive T-cell lines is further confirmed by multimer staining, their inactivity against THP1-KO cells, and TCR-transfer experiments.

The revised data support the main conclusion that human CD1c-autoreactive T-cells recognise Mtb-infected cells that express Cd1c and exhibit cytotoxic effector functions. The difference between TCR-dependent recognition, shown by EM1 and EM2, and cytotoxicity, demonstrated by the original T-cell lines, is now more clearly presented.

Several limitations remain. The specific CD1c-associated lipid signalling molecule that enhances recognition of Mtb-infected cells has yet to be identified. Additionally, the bacterial luminescence assay lacks validation against CFU counts and cannot differentiate between intracellular and extracellular bacteria. The single-cell RNA sequencing was conducted with only two donors, and the lung immunohistochemistry remains qualitative without comparison to healthy or non-TB inflammatory tissues. These constraints limit detailed mechanistic insights and broader applicability, but they are appropriately reflected in the manuscript.

Overall, this is an important study that advances understanding of human CD1c-autoreactive T-cells in the context of mycobacterial infection. The evidence supporting the principal conclusions is solid, provided that altered CD1c-associated lipid presentation remains as a mechanistic hypothesis and the reduction in Mtb luminescence is not taken as direct evidence of selective intracellular bacterial killing.

Reviewer #3 (Public review):

Summary:

In this manuscript, Max Schwarze and colleagues examined the coupling distance between presynaptic Ca²⁺ channels and the vesicular release sensor at neocortical synapses in mouse. They propose that Ca²⁺ channel-release sensor coupling differs across cortical areas, with relatively loose (microdomain) coupling in prefrontal cortex (PFC) and tighter (nanodomain) coupling in primary somatosensory cortex (S1) for comparable pyramidal-neuron synapse types. To test this, they combine paired recordings and minimal stimulation with chelator manipulations (EGTA/BAPTA), mean-variance/MPFA-style analyses, presynaptic Ca²⁺ imaging, and computational modeling. They conclude that presynaptic coupling organization is area-specific in the mature cortex and contributes to regional differences in synaptic timing, reliability, and short-term plasticity.

Strengths:

This study tackles an important question and is strengthened by a cohesive body of evidence assembled from multiple complementary approaches. A major asset is the inclusion of high-value datasets, particularly the paired recordings between L5 pyramidal neurons and the systematic assessment of EGTA sensitivity, which provide a solid functional foundation for the authors' central claims. The work is further distinguished by its genuinely multimodal design: combining electrophysiology with presynaptic calcium imaging (and integrating these observations with quantitative analyses and modeling) offers a more mechanistic view of neurotransmitter release than any single method could provide. Overall, the direct, within-framework comparison of presynaptic release-control mechanisms across cortical areas for comparable synapse types is compelling and gives the conclusions a level of robustness and interpretability that is often difficult to achieve in studies of cortical synaptic diversity.

Weaknesses:

The principal limitation is incomplete cellular and synaptic specificity in parts of the study. The L2/3-L5PN experiments rely on minimal extracellular stimulation and therefore do not unambiguously identify the presynaptic neuron, its subtype or the number of recruited axons. Similarly, calcium imaging was performed at boutons on L5PN axon collaterals without identifying their postsynaptic targets. The imaging measurements could therefore combine boutons contacting pyramidal neurons and interneurons, potentially obscuring target-dependent differences in presynaptic calcium regulation. Recent connectomic studies demonstrate that local L5 pyramidal-cell axons can distribute substantial fractions of their output to inhibitory neurons, although the exact proportions depend strongly on pyramidal-cell subtype and distance along the axon.

The quantitative coupling-distance estimate is also model-dependent. The approximately 50-nm estimate for PFC synapses follows from a particular ring-like VGCC geometry and release-sensor model. The simulations demonstrate that this configuration is compatible with the data, but they do not uniquely identify the underlying molecular architecture.

Overall, the experiments support the narrower conclusion that the examined PFC and S1 synapses differ in functional Ca²⁺-channel-release-sensor coupling. The associated differences in synaptic timing, efficacy and plasticity are compelling, although coupling distance is not isolated causally from other regional differences in release-site number and quantal properties. The proposal that loose coupling is a general correlate of higher-order cortical function remains an interesting but currently speculative interpretation. Further comparisons across additional cortical regions and genetically or projection-defined synapse types will be particularly helpful in establishing the broader generality of this concept

Comments on revised version.

The authors have addressed most of my comments in the revised manuscript. I have only one remaining, relatively minor suggestion concerning point 5. I appreciate that the authors now acknowledge the possibility that the imaged boutons may contact different postsynaptic targets. However, the argument that interneuron-targeting boutons are likely to make only a minor contribution, based on the overall proportions of excitatory neurons or inhibitory synapses in the cortex, may not fully resolve this concern. Excitatory pyramidal neurons can distribute their outputs non-randomly across excitatory and inhibitory targets, and this distribution may depend on pyramidal-cell subtype and axonal distance. For example, a recent MICrONS/Allen Institute connectomic analysis of L5 extratelencephalic neurons in mouse visual cortex found that approximately two-thirds of their proximal synaptic outputs contacted inhibitory neurons. The proportion was close to 80% near the soma and decreased progressively with distance along the axon.

These findings concern a specific L5 pyramidal-cell subtype in visual cortex and therefore cannot be transferred directly to the PFC and S1 preparations examined here. Nevertheless, they illustrate that the postsynaptic target distribution of L5 pyramidal-neuron boutons cannot necessarily be inferred from the overall abundance of excitatory and inhibitory neurons or synapses.

Reviewer #3 (Public review):

Summary:

The authors explore the impact of a modest (<2-fold) reduction in the expression of Bcl11b on the differentiation of CD8+ T cells, with a special focus on the generation of memory-like cells (sometimes called "virtual" memory cells - or TVM). The manuscript covers a lot of ground, but highlights are using diverse models to show that reduced Bcl11b expression during thymic development (but not in naïve CD8+ T cells that have accessed the periphery) leads to enhanced generation of cells with phenotypic, transcriptional, epigenetic and functional characteristics of TVM; that this is a cell-intrinsic effect, but not apparently driven by enhanced responsiveness to cytokines (which promote TVM in some models); decreased Bcl11b improves T cell sensitivity at the mature (and likely in immature thymocytes). Myriad approaches and controls are used, providing a very thoroughly explored model.

This is a tour-de-force in applying the geneticists tool-box for investigating how a tantalizingly modest decrease in Bcl11b expression impacts the generation of TVM-like cells during thymic development. It is unreasonable to request additional data, but clarification of some key conclusions is needed.

Reviewer #3 (Public review):

Summary:

Tran et. al. investigate how inflammation in the skin influences the early stages of melanomagenesis. They use an autochthonous, tamoxifen-inducible mouse melanoma model (LSL-BrafV600E;Ptenfl/fl;Tyr::CreERT2, "BPT") to examine three inflammatory perturbations: transient depletion of regulatory T cells, acute ultraviolet-B irradiation, and contact hypersensitivity induced by 2,4-dinitrofluorobenzene (DNFB). They report that each perturbation promotes the recruitment of immune cells, especially inflammatory monocytes and macrophages, increased expression of inflammatory and tissue-remodeling factors, and enhanced vascular permeability, which ultimately increases the outgrowth of premalignant melanocytes measured by local pigmentation and expression of the melanocyte-associated gene Tyrp1. In the DNFB model, the authors showed that treatment with dexamethasone reduces the effects of contact hypersensitivity on pigmentation, inflammatory gene expression, and vascular leakage, potentially providing a translational angle.

Strengths:

An interesting observation is that transient Treg depletion promotes premalignant melanocyte outgrowth in the autochthonous BPT model while inhibiting the growth of transplantable B16 F10 tumors. This contrast is consistent with a role for Tregs in limiting inflammatory disruption of the skin during early tumorigenesis and highlights the value of autochthonous models. These findings may also have broader implications for understanding the stage- and context-dependent functions of Tregs in cancer.

Another strength of this manuscript is the comparison of three mechanistically distinct inflammatory perturbations. Treg depletion, UVB irradiation, and DNFB-induced contact hypersensitivity engage different inflammatory pathways but converge on myeloid-cell recruitment, inflammatory gene expression, and increased vascular permeability. This convergence strengthens the conclusion that an acute inflammatory microenvironment is associated with enhanced melanocyte outgrowth during the early premalignant phase.

Weaknesses:

The paper convincingly establishes a correlation between the inflammatory signature and melanocyte outgrowth across three distinct perturbations. However, the mechanistic claim that myeloid cells and/or vascular remodeling drive melanocyte expansion rests primarily on the dexamethasone experiments in the DNFB model. Because dexamethasone broadly affects immune, stromal, endothelial, and melanocytic compartments, these experiments do not establish that inflammatory monocytes/macrophages or vascular destabilization are specifically required for the melanocyte response.

A related limitation is that the proposed monocytic origin of the inflammatory macrophage population following perturbation remains inferred. Although the scRNA-seq data and pseudotime analysis in Figure 4 - Supplement 2 are consistent with a trajectory from monocytes to macrophages, they do not exclude local reprogramming of resident macrophages into an inflammatory state. This alternative is particularly relevant because resident macrophage populations have been implicated in vascular remodeling and tumor outgrowth (PMIDs: 36493773 and 40216154).

Finally, the assessment of melanocyte outgrowth is largely through increased pigmentation and whole-ear Tyrp1 expression. Although these measurements may reflect increased melanocyte abundance, they may also be influenced by melanogenic activity or increased Tyrp1 expression per cell. More direct evidence of melanocyte proliferation, such as Ki67 or EdU/BrdU staining specifically within TdTomato-positive melanocytes, would support the use of "expansion" and "proliferation" throughout the manuscript. Histopathological characterization of lesion architecture, atypia, proliferation, and invasion would also help establish the premalignant nature of the lesions.

Reviewer #3 (Public review):

Summary:

The authors tested the hypothesis that interactions among size- and age-matched rivals will lead to the emergence of social roles, accompanied by divergence in four aspects of individual phenotypes: growth, feeding behavior, fighting behaviors, and gene expression in clownfish.

Strengths:

The data of growth, feeding rate, and fighting behaviors support the authors claim.

Weaknesses:

The results obtained solely from the whole-body transcriptome are limited in supporting the authors' research question. However, the revised manuscript explicitly states this as a limitation.

Reviewer #3 (Public review):

Summary:

Zhu et al. present a proof-of-concept for targeting the lysosomal calcium channel MCOLN1/TRPML1endolysosomal ion channels to restore type 2 diabetes mellitus (T2DM). Using synthetic TRPML1 agonists (ML-SA8) and genetic manipulation, the authors demonstrate that TRPML1 stimulation triggers localized lysosomal calcium release. This calcium efflux sequentially activates CaMKKβ and phosphorylates AMPK at Thr172 in various cell models, including palmitic acid-induced insulin-resistant HepG2 cells. This signaling pathway promotes GLUT4 translocation to the plasma membrane and increases intracellular glucose uptake. When administered daily to diabetic db/db mice over six weeks, ML-SA8 lowers fasting and random blood glucose, improves oral glucose and insulin tolerance tests, reduces hepatic steatosis, and lowers serum ALT and AST levels.

Strengths:

Based on the TFEB-independent pathway activated by TRPML1 and the experimental approaches described by Medina's group (PMID: 31822666), the authors use a combination of pharmacological and genetic tools to dissect such an intracellular signaling pathway. Additionally, the animal experiments show consistent phenotypic improvements across independent metabolic parameters. The ability of ML-SA8 to restore glycogen deposition and clear hepatic lipid accumulation in db/db mice without causing weight loss or overt toxicity provides a strong rationale for exploring lysosomal targets in metabolic disease.

Weaknesses:

(1) The authors focus almost exclusively on hepatic GLUT4 to explain the observed glucose disposal. However, other glucose transporter isoforms such as GLUT2 dominate basal glucose transport. While the authors show increased AMPK phosphorylation in skeletal muscle and adipose tissue, they do not measure GLUT4 translocation or glucose uptake in these primary disposal organs. As a result, attributing systemic glycemic recovery primarily to hepatic GLUT4 translocation overlooks the major physiological roles of peripheral tissues.

(2) In both HepG2 cells and mouse liver tissues, ML-SA8 treatment increases total GLUT4 protein expression in addition to plasma membrane localization. Because total protein pools expand, the enrichment of GLUT4 in plasma membrane fractions cannot be cleanly attributed to acute vesicular translocation alone. The manuscript does not explain the timescale or mechanism behind this rapid total protein upregulation, leaving a mechanistic gap between acute ion channel gating and protein expression.

(3) While the in vitro specificity of ML-SA8 is well-controlled, the systemic animal experiments lack a specific rescue or knockout control. Small-molecule agonists administered intraperitoneally over six weeks can exert off-target effects. Without demonstrating that co-administering the TRPML1 inhibitor ML-SI5 blunts the therapeutic effect in vivo, or showing that ML-SA8 lacks efficacy in TRPML1-null mice, the definitive link between in vivo glycemic recovery and TRPML1 activation remains incomplete.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors describe two additions to an existing toolbox (SpikeInterface, Buccino et al., 2020, eLife). The first addition is an empirical simulator for extracellular recordings, in which spikes from predefined templates are added up with Gaussian noise. The second addition involves granting user-level access to intermediate processing steps along spike sorting algorithms. The authors demonstrate the toolbox by evaluating functions (e.g., event detection) or sets of functions (e.g., feature extraction + clustering) on their simulated data and suggest that a specific combination of function implementations provides performance improvement relative to kilosort4 (Pachitariu et al., 2024, Nature Methods).

The validity of the work is poor. In particular, the simulator is unrealistic and the ground truth dataset is too short. Several spike sorters are used as straw men, and most sorters compared have never been described in peer-reviewed literature or in sufficient detail. The purely feedforward architecture of the modules is very limiting and irrelevant for modern sorters. Finally, the reporting of results is sporadic and does not follow scientific reporting standards.

General comments:

(1) Abstract, lines 14-16: "We then leverage these results to create a modular component-based spike sorter that can outperform Kilosort 4 on dense and large simulated recordings and produce similar quantitative results on real data." However, the artificial data are not "large" - they are very short. And "similar quantitative results" cannot be assessed on real data because those data do not have any ground truth. Because this is a revision and the authors have already received similar feedback from this Reviewer, I am not sure what to recommend.

(2) In a previous comment, I indicated that the simulator itself is overly simplistic, and indicated that as far as I am concerned, the authors must improve it in one of two ways: (1) use a set of biophysical equations, with multi-compartmental modeling of currents and return currents; (2) use noised data from extracellular recordings; or (3) some combination thereof. The authors explained in their answer - but not in the MS - some of the shortcomings of biophysical simulators, but chose to do neither. My comment therefore remains unaddressed in the MS.

(3) In a previous comment, I indicated that the duration of 10 minutes is too short. The authors chose to extend the duration to 30 minutes, which is insufficient for units that have low firing rates. Units that fire e.g., 0.1 spikes/s would have fewer than 200 spikes. If this MS is to be taken seriously, the simulation should be done on durations that (1) are similar to the potential applications - which may be many hours or even days; (2) allow identification of low-firing neurons. Therefore, about 3 hours is the bare minimum. Therefore, at present, my comment remains unaddressed.

(4) In a previous comment, I indicated that some sorters have never been described in peer reviewed papers and therefore, they must either be removed from the present comparisons or be described in full. The authors chose to persist in relying on un-reviewed online documentation. Thus, my comment remains unaddressed, and the comparisons that involve those sorters (e.g., TDC, TDC2, SpyKING Circus 2) are simply invalid.

(5) In a previous comment, I indicated that some sorters (SpyKING Circus 2 and TDC2) are effectively straw men and suggested to reorganize the MS to demonstrate its main goal. Specifically, I suggested to reorganize the manuscript so that after every module is evaluated separately based on a limited ground truth dataset, a single "best" sorter would be constructed, and then tested extensively (and compared to the de facto state of the art). Such reorganization would both demonstrate the utility of a modular approach and clarify the general usefulness of the outcome. The authors agreed that these are straw men and gave a historical account of why these were included. However, they did not explain these considerations in the MS itself, and chose not to reorganize the MS. Therefore, my comment remains unaddressed.

(6) In a previous comment, I commented about the presentation, description, and interpretation of the results, and indicated that the choice to report point estimates makes any conclusions based on those results invalid. The authors did not make any changes to the reporting. Therefore, none of the results reported in the MS can be taken as an outcome of scientific inquiry. In other words, the MS does not deliver any solid findings - neither scientific nor methodological.

Reviewer #3 (Public review):

Summary:

The authors systematically compare six culture-media formulations using induced pluripotent stem cell-derived retinal pigment epithelium and fetal retinal pigment epithelium. They examine cell morphology, marker expression, barrier function, polarized secretion, lipid accumulation, ultrastructure, mitochondrial respiration, glycolytic function, and intracellular and extracellular metabolites. The results demonstrate that culture-medium composition and the choice of serum or B27 supplementation substantially influence retinal pigment epithelium phenotype and metabolism. Rather than identifying a single optimal medium, the study provides a comparative framework to guide medium selection according to the biological question being investigated.

Strengths:

The head-to-head comparison of six media under otherwise similar culture conditions addresses an important source of variability in retinal pigment epithelium research. The study uses a broad range of complementary approaches, including imaging, transepithelial resistance, electron microscopy, extracellular flux analysis, and targeted metabolomics. The inclusion of both induced pluripotent stem cell-derived and fetal retinal pigment epithelium increases the potential relevance of the findings across different cell sources. The matched comparisons of serum and B27 supplementation within MEMα and human plasma-like medium are particularly informative because they help distinguish supplement-associated effects from those caused by the basal medium. Overall, the dataset has the potential to serve as a valuable resource for selecting culture conditions and interpreting findings across retinal pigment epithelium studies.

Weaknesses:

The most important limitation is that the experimental unit and degree of biological replication are not clearly defined. It is unclear whether individual observations represent independent donors, clones, differentiated lines, culture preparations, wells, images, or sections. This makes it difficult to determine the independence, robustness, and generalizability of several comparisons.

The metabolic analyses also require additional methodological clarification. For intracellular metabolomics, the culture format, cellular biomass, extraction volume, pooling strategy, and normalization method are not reported sufficiently. Normalization of extracellular measurements to unspent medium accounts for differences in starting metabolite abundance but not for differences in cell number or biomass. Similarly, normalization of intracellular signals to medium 1 does not correct for differences in the amount of cellular material extracted.

For the Seahorse experiments, the main figures present unnormalized values even though the media produce differences in cell number, size, and protein content. These raw measurements represent total metabolic activity per well and may not reflect activity per cell. It is also unclear how normalization was performed because the Methods describe cell-count and protein measurements from two wells, whereas the stress tests included five to six wells per condition. In addition, measurements obtained after transfer into a common assay medium reflect metabolic adaptations retained from the preceding culture conditions rather than real-time metabolism within the original media.

Other limitations include insufficient information about the biological replication underlying the sub-RPE deposit analysis and the inability to fully interpret the effects of X-VIVO 10 because its composition is proprietary. Finally, public availability of the underlying metabolomics data would be important for a study intended to serve as a community resource.

Reviewer #3 (Public review):

Summary:

The primary objective of this study is to develop high-throughput screening assays utilizing homogeneous 3D cell cultures that more accurately replicate the intricate architecture and cellular communication found in tissues. The authors have chosen pancreatic islet β-cells as a model system to evaluate agents that modulate insulin release, which is particularly relevant given the increasing prevalence of diabetes mellitus-a significant global health concern. Moreover, the incorporation of human-based 3D spheroids, organoids, or organ-on-chip technologies into drug discovery protocols is essential for enhancing clinical translation, as candidate compounds identified using animal models have often demonstrated limited success in clinical settings.

Strengths:

This study was thoughtfully planned and skillfully carried out. The use of micropatterned hydrogels to observe 19 spheroids at once is an ingenious aspect, which has been effectively validated with Ca microfluorography. Overall, I found this investigation to be exceptionally well-executed and free from notable flaws, as the results clearly back up the conclusions. Additionally, the developed method achieved the proposed aims, providing a high-throughput format with 3D cultures. I believe this study deserves publication.

Reviewer #3 (Public review):

Short overview:

This study asks whether the perception of a volatile and attractive cue, diacetyl, leads to changes in metabolic nutrient-responsive programs in C. elegans. Using multi-omic and genetic evidence, they connect the transcriptional response to diacetyl to early activation of the DHAP-glycerol shunt, suggesting worms may activate this metabolic pathway in preparation for food intake. This work also identifies transcription factors involved, and while it does not test whether this response is diacetyl-specific, could identify a conserved pathway of food intake preparation.

Summary:

This study asks whether C. elegans can use a volatile food cue alone, in the absence of ingestion, to anticipate nutrient availability. Using the attractive odorant diacetyl, the authors show that fasting worms rapidly induce the DHAP-Glycerol shunt, a metabolic pathway normally associated with glucotoxicity and hyperosmotic stress, and that this response depends on the transcription factor MDT-15. This drives measurable metabolic rewiring (glycerol and phosphatidylglycerol accumulation) and confers protection against subsequent hyperosmotic stress. Prolonged diacetyl exposure further triggers a second, HLH-30-dependent wave of fmo-2 expression linked to depleted NTP levels, resulting in enhanced heat tolerance and food-seeking behavior upon refeeding. Together, the authors build a testable model for a pathway linking sensory cue detection to gene expression, metabolism, and physiological changes.

Strengths:

The central finding that smell alone without ingestion activates a metabolic-stress adaptation pathway is highly interesting and supported by a convergence of methods including RNA-seq, transcriptional reporters, metabolomics, and functional behavior assays. The transcriptomic time course distinguishes two temporally separable gene expression waves (the shunt at 30 min and fmo-2 at 90 min), and epistasis experiments comparing food status and osmotic stress (Fig. 1i-j) convincingly argue diacetyl acts as a food-predictive cue rather than mimicking osmotic stress. A particular strength is the test of the relationship between the two pathways identified in the study. RNAi knockdown of DHAP-Glycerol shunt enzymes block fmo-2 induction, and pretreatment with salt then switching to food deprivation shows that it is the shunt's activation state that drives fmo-2 expression. This is further reinforced by depletion of energetic mechanism (NTP/ATP). Figure 5 extends this upstream to MDT-15 validated through gene expression, metabolomics, and functional readouts. Throughout the study, transcriptional and metabolic findings are paired with functional outcomes such as hyperosmotic protection, heat stress resistance, or survival, strengthening the paper's model. Together, the authors largely achieve their aim of establishing that a food-related olfactory cue is sufficient to trigger an anticipatory metabolic and transcriptional program. The core claim that diacetyl sensing activates the shunt and subsequent fmo-2 induction and physiological protection is well supported by convergent genetic and biochemical experiments.

Weaknesses:

The authors show that diacetyl-induced fmo-2 induction does not require canonical olfactory sensing since neither diacetyl receptor mutants (odr-10, sri-14) nor a cilia-deficient strain (daf-19; daf-12) blocked the response. However, the pathway characterized here is defined almost entirely through diacetyl, and it remains unclear whether the anticipatory response reflects general food-predictive olfaction or a diacetyl-specific effect. This distinction is important because the authors' central hypothesis is whether volatile food cues in general can be used to anticipate nutrient availability. Testing a limited number of additional attractive odorants, ideally sensed through distinct chemosensory receptors, for a limited number of phenotypes, would establish whether this pathway is generalized to food-predictive smells or only diacetyl. This would increase the impact of the work. Identifying the mechanism(s) for diacetyl perception would as well, but is much more challenging and less likely to be feasible in this work.

The RNA-seq and reporter data disagree on when fmo-2 induction begins, and thus do not fully support that there are two temporally distinct waves. RNA-seq (sampled at 5, 15, 30, and 90 min) shows fmo-2 is not a significantly differentially expressed gene at 30 min and only reaches significance at 90 min, while the fmo-2 transcriptional reporter (Fig.S3a) shows detectable induction as early as 30 minutes. Other readouts in the paper use the reporter for fmo-2 but qPCR for shunt genes, further muddling the timing since mRNA should precede reporter visualization. Since reporter signal generally lags transcript level detection, this discrepancy could be further clarified through a time-course qPCR (like what was tested for the shunt genes) between 30 and 90 minutes. This would help establish whether the two waves are separated by time or whether this reflects a difference in detection method.

Minor weaknesses:

In Figure 1i, gpdh-1 induction is compared between control and 200 mM NaCl (3 hr exposure), with food present or absent. While this directly tests food status, a 3-hour exposure approaches the ~5-6-hour window previously shown to be sufficient for salt-food associative learning to form (worms move toward the high-salt side of a gradient plate if pretreated with high salt and food). This raises the possibility that, in the food-present condition, part of the measured gpdh-1 response could reflect an emerging learned association between salt and food forming during the assay itself, rather than purely reflecting an interaction between nutrient status and osmotic stress signaling. Testing a shorter exposure window (e.g., under 1 hour, as used for the diacetyl exposure in panel j) would help clarify this.

Thrashing is used throughout the paper as the primary readout for hyperosmotic stress resistance, including the central result that diacetyl protects against subsequent hyperosmotic stress (Fig. 2g). However, the authors don't explain why this was chosen as the main stress-resistance readout.

nhr-49 knockdown reduces gpdh-1 induction in diacetyl-mediated hyperosmotic protection but has no effect on development or survival on sustained hyperosmotic stress, raising the question of how the role of nhr-49 may be distinct from mdt-15 in this context.

Reviewer #3 (Public review):

Summary:

This is an interesting study investigating the relationship between transdiagnostic symptom profiles and computational parameters of abstraction and metacognition. The authors found that a transdiagnostic dimension they term compulsive hypersensitivity was negatively related to abstraction ability and metacognitive sensitivity, and a dimension termed social withdrawal was positively related to metacognitive sensitivity. Overall, the question of whether and how higher-level cognitive processes relate to transdiagnostic psychiatric factors is interesting and addresses a relevant gap in the literature.

Strengths:

This is an overall well-designed study, and the authors have clearly given considerable thought to data quality and validation during the study design. This is evident, for instance, in the use of multiple approaches to assess inattentive responding and acquiescence tendencies. In addition, the use of advanced modelling approaches for the task data represents a strength and allows the authors to capture individual differences in task behaviour in a more nuanced and mechanistic manner in comparison to what would have been possible with model-agnostic analyses.

Weaknesses:

Nevertheless, I would like to raise several concerns, detailed below.

(1) My most pressing concern regards the exclusion rate of roughly half the sample. Of the 512 participants who were tested, only 249 (48.6%) were included in the final analyses, meaning that more than half of the recruited participants were excluded. Although the authors state that these exclusions were based on preregistered criteria, this high exclusion rate still warrants further investigation. Pre-registering exclusion criteria does not eliminate the potential for selection bias or establish that the resulting sample is representative of the recruited sample. In fact, the authors even report that the excluded participants differed significantly on several psychiatric scores. I believe that a detailed account of the exclusion, including how included and excluded participants differed on relevant demographic or study variables, would be beneficial.

Additionally, the authors state that a sensitivity analysis including participants excluded from the primary analysis was preregistered but was not conducted because the excluded and included participants differed significantly. This does not seem a sufficient justification for omitting a preregistered sensitivity analysis; in fact, systematic differences between included and excluded participants warrant this kind of sensitivity analysis. I agree with the authors that this may lead to changes in task parameter estimates. However, I believe this change would be an informative result of the sensitivity analyses rather than a methodological problem to avoid.

Finally, I would like the authors to clarify some inconsistencies in the preregistered exclusion criteria. In the pre-registration, they first state that all participants who fail any infrequency question will be excluded. Later in the pre-registration, they state that participants with two or more failed questions will be excluded and that a sensitivity analysis will be done, in which participants with only one failed question are retained. Neither of these criteria matches what is reported in the manuscript ("Attention check + infrequency item mistake more than 2").

(2) The current sample size of n = 249 participants is not sufficient for a factor analysis with 176 questionnaire items, and I believe that the solution of using factor weights from a previous larger sample is generally sensible. I also commend the authors for wanting to assess the validity of this approach. However, both the methods and results reported for the comparison between the factor analysis based on the current, smaller sample and the large, previous sample are insufficient and warrant substantially more detail. Firstly, it is unclear to me whether the three-factor solution in the factor analysis with the current, smaller sample was empirically grounded or whether three factors were extracted to match the three-factor solution from the larger sample. For both factor analyses, I would recommend reporting the factor extraction methods, the criterion used to determine the number of factors, and whether alternative factor solutions were considered. Secondly, it is unclear what exactly was correlated across the two factor analyses (e.g., factor scores, item loadings, factor weights?). Finally, I do not believe that the result of significant correlation is sufficient to conclude that the dimensions replicate between samples, particularly given they indicate only moderate correspondence (r = 0.46, for instance, corresponds to only 21% shared variance), thereby providing very limited evidence for replication of the factor structure.

These concerns are particularly pressing given that the authors state the consistency of transdiagnostic symptom dimensions across samples and countries as a primary result in the discussion.

(3) It is also unclear to me whether any exclusion was applied based on the first part of the Deary-Liewald reaction time task. The supplement implies that it was ("This task result was used to [...] exclude participants who exhibit problematic behaviours"), but I could not find a corresponding report in the manuscript.

(4) The manuscript refers to an attentional check questionnaire used to identify and exclude inattentive participants, but does not provide a reference for this measure or describe the questions included. Given the substantial overall exclusion rate, it is particularly important that all exclusion procedures and criteria are described in sufficient detail to allow readers to assess their appropriateness and reproducibility. The authors should therefore provide the relevant reference and/or report the specific questions and criteria used to determine inattention.

Relatedly, when describing the control-neutral items in the Supplementary Materials, the authors state that further details are provided in the Supplementary Materials. However, I cannot find another, separate Supplementary Material that may contain this information

Reviewer #3 (Public review):

Summary:

The cell wall and plasma membrane are tightly associated in plant cells, but even after plasmolysis, sites of strong contact remain between the plasma membrane and cell wall. Several hypotheses have been introduced about the molecular makeup of these sites of PM-CW adhesion (e.g., Rui et al 2026 Cell; Qin et al 2026 Current Biol; Pérez-Sancho et al 2025 Cell). Here, the authors implicate two transmembrane lectin proteins in PM-CW adhesion via overexpression in Nicotiana benthamiana and via Arabidopsis knockout phenotypes for one of these candidates, the lectin receptor kinase LecRK-1.9. They further show that the PM-CW adhesion function of LecRK-1.9 requires the extracellular domain, suggesting that this lectin-like domain may interact with the cell wall. Interestingly, LecRK-1.9 has also been implicated in extracellular ATP binding in the context of biotic and abiotic stress responses (e.g., Choi et al 2014 Science).

Strengths:

Overall, the work is carefully conducted with high-quality imaging and quantitative image analysis. The results present an interesting candidate for future studies of plasma membrane-to-cell-wall attachment.

Weaknesses:

There are two major caveats to this work. First, all work was conducted with the kinase-dead version of LecRK-1.9, which eliminates a significant biological function of this protein, as evidenced by the major differences in expression pattern of wild-type vs kinase-dead LecRK-1.9. Second, controls are essential to document the expression levels of different protein variants and controls, since LecRK-1.9 expression is correlated with Hechtian strand density.

Reviewer #3 (Public review):

The manuscript frames its work in fairness and disparities but does not show or directly test that its approach decreases differences between White and African Americans. While it is stated that the objective is not to equalize performance across groups, large parts of the paper repeatedly claim that the methods mitigate cross-ethnicity disparities and improve fairness. Improving prediction in African American participants relative to a non-adapted model is not necessarily the same as reducing the disparity between African American and White American participants. The adapted model should be evaluated in both groups, and the post-adaptation performance gap should be reported directly.

Additional prediction performance measures are needed. For example, in Li et al, different results and conclusions are made with MSE and the correlation between observed and predicted variables. In that paper particularly, aggression measures showed better correlation in African Americans but better MSE in White Americans. Such differences are important to note as they likely suggest different mechanisms.

Similarly, characteristics of the cognitive outcome need to be understood. For example, differences in MSE or MAE may reflect a difference in variance between the groups. The group with a larger variance will have a larger MSE. Correlation or other performance measures that are invariant to different mean or variance shifts can be helpful here.

While the authors note that for the paper they treat racial and ethnic backgrounds interchangeably, I do not think that is the best given the differences between them and the impact and history they have in American culture. Overall, the authors likely need to do a better job conceptualizing their results in the history of minoritized populations in the United States. It is immensely important not to treat them as biological domains without considerable qualification and to avoid language implying that observed domain differences are intrinsic properties of racial groups.

Changes in feature weight are not a proper way to identify the mechanisms of improved performance. At most, these analyses characterize how model coefficients change when target-group data are incorporated or upweighted.

The cross-validation strategy is suboptimal. First, the use of the matched splits of the ABCD data introduces data leakage. To match a validation set to the training set in such a manner requires that each split knows about the other split's characteristics. That is data leakage. Though the impact could be small. Second, African American breakdowns are not balanced across sites and scanners. Domain adaption methods may be learning a shortcut or proxy for African American like site, scanner, or something else. A likely better approach would be some sort of leave X sites out approach, where a model is trained on White Americans from a set of sites, adapted with African Americans from those sites, and applied (with and without adaptation) to the White and African Americans from the left-out sites.

Baseline models for comparisons to the domain adaptation are missing. Some simpler ones include a target-only model trained on the same 10-100 African American participants and a pooled model with a group indicator and group-by-feature interaction. Without these comparisons, it is difficult to know whether balanced weighting is learning target-specific neurobiological information or merely recalibrating the prediction distribution.

There are a few statistical issues:

(1) The repeated MAE estimates are therefore not independent observations. Paired t-tests cannot be applied across repetitions. Subject-level bootstrap or permutation procedures that repeat the complete training and testing process are needed

(2) The caption describes approximate 95% confidence intervals as {plus minus}1.96 × SD/n. Conventionally, the standard error would involve SD/sqrt(n). However, even if corrected, there would still be issues about the dependence among the overlapping resamples.

(3) Ten repetitions are likely insufficient, especially in the case of ten target participants. Results at n = 10 may be extremely sensitive to which children are selected. The authors should use substantially more repetitions and report the full distribution of results.

(4) The Friedman and Wilcoxon comparisons treat the 80 imaging phenotypes as the observational units. These phenotypes are highly dependent because they are derived from the same participants, many use overlapping images, and numerous task contrasts and structural measures are strongly correlated. This non-independence can make the comparison among adaptation methods look much more precise than it is. A hierarchical analysis by modality or a resampling strategy that preserves dependence among phenotypes would be more appropriate.

(5) The gap metric and AUIC are difficult to interpret. Gap is the absolute relative difference between target-group and source-group MAE, normalized by source-group MAE. It is sensitive to the denominator and may produce large values whenever source-group. MAE is relatively small. Reporting signed raw MAE differences and MAE ratios alongside this derived score would help. Similarly, the AUIC combines errors with an arbitrary sequence of target-sample sizes. IStatistically significant differences in AUIC do not necessarily indicate practically meaningful differences among methods.

(6) The correlation between baseline gap and adaptation gain is partly tautological. Those with the widest gaps have the most room for improvement and likely thus show the greatest improvement. While still of value, the authors may want to tone down their interpretation of the correlation and describe its limitation.

(7) Given that the sample sizes vary from approximately 4,000 to more than 11,000 depending on modality, the authors may want to consider a reduced sample matched in size across modalities. It is hard to fully know if the conclusion that connectivity is more robust given the wide-scale differences in sample size and feature dimensionality.

(8) PLS are sensitive to many factors like scaling and collinearity. Many recent papers have been written about their limitations when used for subtyping. Some of these hold for prediction too. I think showing the results are consistent with different prediction algorithms is needed. SVR and ridge regression are two common methods for regression prediction with neuroimaging data.

(9) The feature interpretation is partly circular. The method with the largest performance gain is selected, and its coefficient changes are then used to explain that gain. A method designed to give target observations greater influence will unsurprisingly change its coefficients more than naïve inclusion.

(10) The practical and ethical deployment scenario is underspecified. Supervised adaptation requires labelled cognitive outcomes from the target population and, as currently framed, may require choosing a model based on an individual's racial category. What are the implications of deploying race-specific models that need to be considered? It is not self-evident that this approach is preferable to developing a broadly representative model or directly modeling the social and technical sources of distribution shift.

(11) The paper is worded and interpreted much too strongly. The current study supports the conclusion that, within ABCD, giving a small labelled target-group sample greater influence can sometimes improve held-out target-group MAE relative to naïvely adding the same participants. It does not yet establish that the method improves fairness or identifies mechanisms of racial bias. Likewise, the abstract and conclusion overstate the results. The abstract states that all adaptation methods reduced target-group prediction error, while the Results show near-zero or negative benefits for several functional-connectivity phenotypes and instability of PRED and interpolation below 30 target participants. Similarly, "substantially reduce disparities," "improve equity," "consistently," and "practical path forward" are stronger than the analyses support. Finally, the limitations section is incomplete and omits the more consequential limitations.

(11) That only ten labelled participants are needed to change the results is troubling. This is a shockingly low number. Giving ten target observations disproportionate influence can move the fitted model, particularly when the balanced-weighting ratio is high. A measurable MAE change is therefore possible, but it may reflect a shift in intercept or slope rather than learning a stable target-group brain-cognition relationship. Further, the manuscript does not report the numerical improvement for the n = 10 condition in the text or a table. Visual inspection of Figure 4 suggests reductions of roughly 0.10-0.25 standardized MAE units for some high-gap structural phenotypes, approximately 10-20%, while low-gap connectivity phenotypes show little or no gain.

(12) The study lacks genuine external validation, which may be needed to fully convince readers that such a low number of subjects is needed to reduce biases.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

This study represents a single-centre cross-sectional investigation, and most findings remain correlative in nature. Additional mechanistic and/or longitudinal evidence would be required to unravel causality and the underlying mechanism.

Reviewer #3 (Public review):

Summary:

Using mice that overexpress S1PR1 in myeloid cells or specifically in neutrophils, the authors show that increased S1PR1 promotes neutrophil release from the bone marrow and accumulation in blood and peripheral tissues without causing baseline tissue injury. These cells acquire a CXCR4-high, CXCR2-low, CD101-low phenotype, survive longer, and display enhanced mitochondrial metabolism and mTOR signaling, together with reduced apoptotic, inflammatory, and ROS-related programs. Although phagocytosis is preserved, ROS production is markedly reduced. This is associated with impaired bacterial clearance in the lung but improved outcomes during influenza infection, including better survival, less weight loss, improved oxygenation, lower viral burden, and reduced lung inflammation. In contrast, myeloid S1PR1 deletion produces little detectable phenotype. The authors therefore propose that S1PR1 separates neutrophil persistence from inflammatory function, improving tolerance to viral lung injury at the expense of antibacterial defense.

Strengths:

This is a technically solid paper using novel mouse models to overexpress S1PR1 specifically in myeloid cells as well as neutrophils. The data are striking with respect to neutrophil expansion. The diverse roles of neutrophils and their population heterogeneity are an important scientific area that has led to many recent breakthroughs - PMC11785525; PMC12823425, thus this is a timely study.

Weaknesses:

The study mainly demonstrates what S1PR1 overexpression is sufficient to do, rather than establishing the physiological role of endogenous S1PR1. The conclusions should therefore be narrowed unless the authors provide stronger loss-of-function and physiological validation. As written, the abstract ("S1PR1 promotes mitochondrial fitness, enhances survival, and reduces inflammatory output") and the conclusion ("S1PR1 serves as a key regulatory axis") are sufficiency claims but should not be promoted as necessity claims. The honest sentence is: "Thus, a better conclusion would be that enforced S1PR1 expression is sufficient to reprogram neutrophils".

The authors do not confirm efficient S1pr1 deletion in neutrophils. Furthermore, the knockout is examined only under steady-state conditions and limited in vitro stimulation, but not in the bacterial or influenza models where the transgenic phenotype is observed. Without these experiments, the study cannot establish whether endogenous S1PR1 is necessary for the reported functions.

The degree of S1PR1 overexpression is not quantified relative to normal physiological levels. The authors should determine whether naturally occurring S1PR1-high neutrophils display the same survival, metabolic, trafficking, and inflammatory features observed in the transgenic cells.

Analysis of relevant human or mouse datasets, including sepsis, ARDS, viral infection, cancer, or aging, would also help establish whether this neutrophil state exists physiologically.

Surface S1PR1 expression appears similar between control and transgenic neutrophils, whereas total intracellular receptor is increased. This suggests that the phenotype may depend on receptor internalization or endosomal signaling. An internalization-deficient S1PR1 model, such as S1P1-S5A, would help distinguish sustained surface signaling from internalization-dependent signaling. The authors should also determine whether the phenotype requires ligand binding, Gi signaling, and mTOR activity.

The reduction in CXCR2 and decreased neutrophil accumulation in the airways could alone explain the protection from influenza-induced lung injury. The current experiments do not clearly distinguish neutrophil reprogramming from defective migration into the alveolar space.

Although this may be outside the scope of the current study, the authors should directly test whether CXCR2 inhibition reproduces the phenotype.

The reported reduction in viral load should also be confirmed using plaque assay or TCID50, and the possible contribution of NET formation should be examined.

Reviewer #3 (Public review):

Summary:

Most rod-shaped bacteria grow by one of two mechanisms: growth from the pole or growth from the midcell. It is rare for a single species to utilize both modes of growth, although a few examples do exist. Here, the authors have artificially induced E. coli cells to grow from the poles, either by expressing mreB from Myxoccocus xanthus in E. coli, leading to the mislocalization of MreB to the poles in large aggregates, or by forcing the localization of major cell wall synthesis proteins to the cell pole. The fact that cells switched modes of growth suggests an evolutionary pathway from midcell to polar growing cells as well as suggests that there might be unknown conditions in nature when cells may switch growth modes.

Strengths:

(1) The authors use a strain that has replaced mreB with a functional fluorescent version at the native site. This eliminates any effects of having two copies of mreB. Because MreB is fluorescently tagged, they can monitor its localization when mreB from M. xanthus is expressed in E. coli. They notice that MreBec now forms bright polar foci and that there appear to be changes to the cell wall at the pole.

(2) D-amino acids are specific to the cell wall, and fluorescent versions (FDAA) have been used to mark sites of new cell wall insertion. The authors use these FDAAs to determine how cell wall synthesis correlates to MreB and if that changes when MreBmx is expressed. Again, there is pretty clear evidence that cell wall synthesis follows MreB localization to the pole.

(3) MreB itself does not synthesize the cell wall, but localizes the proteins, such as PBP2, that do. Using a published method to force proteins to the pole, the authors show that when they target PBP2 to the pole, they can phenocopy the polar growth seen when MreB is polar. Interestingly, these cells become resistant to A22, a drug that targets MreB, suggesting that localized growth at the pole does not require MreB and is sufficient to maintain rod shape.

Weaknesses:

(1) The authors do not show what the poles of control cells look like, making it difficult to determine if there is a change when MreBmx is expressed. However, the localization of both MreBec and MreBmx clearly forms bright foci at the pole.

(2) While more quantification is needed, the authors show some evidence that RodZ, an MreB interaction partner, is needed for this polar growth, as cells lacking rodZ still form foci at pole-like regions when MreBmx is in the cell; however, these cells remain spherical and do not elongate from these foci.

When MreB is deleted, and cells become spherical, the authors were unable to cause the polar growth mode. They suggest that this is due to the lack of a preexisting pole; however, experimental evidence to test this is missing.

Conclusion:

Overall, the authors do a good job of showing that E. coli can grow with a polar method rather than a midcell method of cell wall insertion. It is unclear why MreBec forms at poles when MreBmx is present and even if this MreB is functional. The foci look similar to inclusion bodies, which are normally aggregates of misfolded proteins that migrate to the poles. Past work has shown that when MreB is more polarly localized, branches form, which is not seen here. Importantly, the authors also show that there is feedback between the localization of MreB and PBP2 as both appear to regulate the localization of the other.

Reviewer #3 (Public review):

Summary:

This study modeled vocal learning in zebra finches with a network of three components: a pathway with delayed/slow Hebbian learning that mimics the HVC-RA projection, a pathway with reinforcement learning that mimics the HVC-BG-RA projection, and a motor unit that mimics the syrinx and produces song output. The model convincingly reproduces key features of song learning and is a valuable step towards understanding how vocal learning is substantiated in the song system. Further examination of model assumptions and presentation of testable predictions would increase the impact of the study.

Strengths:

(1) The model reproduces several key features of song learning, including learning in a non-convex performance landscape, decreasing motor variability during learning, the relative importance of the HVC-RA and HVC-BG-RA pathways at different learning stages, and sleep-related deterioration.

(2) The model incorporates several key physiological properties of the song system, including performance-dependent dopamine signals to the BG, the neural variability in the system, and multiple global/local optima of the motor production landscape.

(3) The study convincingly demonstrates the advantages of a dual-pathway network over a single-pathway one.

(4) The model demonstrates the counterintuitive benefit of sleep-related performance deterioration for facilitating the escape from local optima to reach the global optimum.

(5) The model seems robust to some variations in model parameters and task structure.

Weaknesses:

(1) The study could be more impactful if the model can generate new, testable predictions. The predictions provided in the Discussion are not well justified. For example, with the HVC spine turnover, it seems unlikely that BG lesions would abolish overnight performance deterioration. Because the volatility term is inversely related to learning during the day, the changes in LMAN and RA during sleep are not necessarily larger than those during the day.

(2) The section "Neural activity patterns in the model parallels song system neurophysiology" seems fully anticipated because the model construction is based on the known neural activity patterns. Are there new testable predictions from the model?

(3) Certain model assumptions lack explanation or justification.<br /> a) The authors treat "the delayed maturation of the cortical pathway" as an important component. However, it is unclear if/how this component was implemented in the model. If it was not included in the model, the authors should remove statements related to the delayed maturation idea.<br /> b) How critical is the inverse relationship between learning and sleep-deterioration? If it is known that sleep-related deterioration is inversely related to learning during the previous day, a citation should be added. Similarly, it should be clarified if the spine turnover observed in HVC depends on previous plastic changes, like the assumed inverse relationship in the model.<br /> c) Spine turnover has been demonstrated in HVC and not yet in Area X, but the model implements volatility only in the HVC-BG pathway. It would be important to compare the effects of sleep-related volatility in the HVC-RA and HVC-BG-RA projections.<br /> d) In Table 1/Figure 8, the learning rate for HVC-BG is 10^4 times bigger than the learning rate for HVC-RA. What is the biological justification for this difference?

(4) Related to #3, it is unclear how changing those assumptions would affect model performance.

(5) It is not explained/shown why cross-day exploration (with sleep, sporadic) is better than continuous, non-sleep-related ones. Figure 7B presents results with different noise levels, which may approximate non-sleep-related, continuous volatility, but only for the single-pathway model. Comparable simulations by adding continuous volatility in the dual-pathway model would be helpful. For example, would just a bigger intrinsic noise in either pathway confer the same benefit in escaping local optima, e.g., epsilon-greedy exploration? If the authors can establish the advantages of sleep-specific volatility in its particular form (based on day learning) and relate it to other sensorimotor learning behaviors, it could increase the general impact of the study.

(6) The model description can be improved.<br /> a) What are mBG and mRA in Eqs 6/7?<br /> b) Where is the learning rate specified in Equations 1-8?<br /> c) It is unexplained why Equations 1-3 are not in the same form: Equations 1 and 2 normalize by activity, but Equation 3 normalizes by weight (Equation 8 also normalizes by weight). The authors should confirm that these equations are correct.<br /> d) How J_HVC is generated should be defined.<br /> e) How is R calculated in Equation 10? Does this model maintain a PPE for each syllable or for the overall performance? Would it make any difference in learning?<br /> f) Are the weight updates in Equations 9 and 13 added at different time points (e.g., immediately after each spike versus after a syllable). This should be clarified (perhaps a diagram would help).<br /> g) How are the optimal threshold and slope determined for Equation 14?<br /> h) Parameters in Equation 20 are not defined.

Reviewer #3 (Public review):

Summary:

This study examines how dominance hierarchy influences innate defensive behaviors in pair-housed male mice exposed to two types of naturalistic threats: a transient looming stimulus and a sustained live rat. The authors show that social presence reduces fear-related behaviors and promotes active defense, with dominant mice benefiting more prominently. They also demonstrate that threat exposure reinforces social roles and increases group cohesion. The work highlights the bidirectional interaction between social structure and defensive behavior.

Strengths:

This study makes a valuable contribution to behavioral neuroscience through its well-designed examination of socially modulated fear. A key strength is the use of two ethologically relevant threat paradigms - a transient looming stimulus and a sustained live predator, enabling a nuanced comparison of defensive behaviors. The experimental design is robust, systematically comparing animals tested alone versus with their cage mate to cleanly isolate social effects. The behavioral analysis is sophisticated, employing detailed transition maps that reveal how social context reshapes behavioral sequences, going beyond simple duration measurements. The finding that social modulation is rank-dependent adds significant depth, linking social hierarchy to adaptive defense strategies. Furthermore, the demonstration that threat exposure reciprocally enhances social cohesion provides a compelling systems-level perspective. Together, these elements establish a strong behavioral framework for future investigations into the neural circuits underlying socially modulated innate fear.

Comments on revised version.

The authors have addressed the initial major criticism regarding the lack of causal evidence for neural mechanisms, which has alleviated our concerns. This provides a more solid behavioral foundation for future investigations into neural circuit mechanisms.

Reviewer #3 (Public review):

Summary:

The manuscript builds on the observation that, at some synapses, low-frequency stimulation causes synaptic depression which can be reversed by subsequent high-frequency stimulation. Such low-frequency depression (LFD) cannot be easily explained by the depletion of a single vesicle pool. Here, Silva and colleagues propose a model of activity-dependent vesicle trafficking to explain LFD at synapses between cerebellar granule cells and molecular layer interneurons.

Strengths:

Overall, LFD is interesting and worthy of examination, and the authors provide new experimental results that are of the high quality expected from this group.

Weaknesses:

The study proposes a novel model of vesicle trafficking that is not explained by known biological mechanisms, and the manuscript does not adequately compare or discuss alternative models.

I have several concerns about how the authors interpret the data. First, the manuscript's primary conceptual advance is the idea that LFD involves vesicle undocking, rather than depletion. However, most experiments were performed under conditions that promote vesicle depletion (3 mM extracellular Ca2+). When experiments were repeated in physiological Ca2+, there appeared to be little or no LFD (stats are not provided). Second, the RS/DS/DU/undocking model, though not outside the realm of possibility, is not readily explained by known mechanisms and is only loosely supported by experimental findings. Third, when simulating LFD, the authors do not compare alternative models and use inappropriate language to imply that a model fit represents the truth (e.g. "the finding of identical experimental and simulated values confirms that the undocking mechanism accounts for LFD"). Finally, the model is presented in an overly complicated manner. The sheer amount of terms and nomenclature makes the manuscript confusing and difficult to read. Overall, the manuscript would benefit from added experiments and more statistics, a better justification and evaluation of the model, and more nuanced language.

Comments on revised version.

I appreciate the authors' detailed responses to my initial review of the manuscript. My suggestions reflect a sincere attempt to improve the clarity and accuracy of the paper. I disagree with some of the authors conclusions and their responses to my comments, but do not wish to make any further suggestions. The authors are entitled to their own views.

A final note: If the authors want to refrain from making definitive statements on underlying cellular mechanisms, they should consider amending the title of the manuscript.

Reviewer #3 (Public review):

Summary:

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

Strength:

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

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

Weakness:

The conclusion of the paper, especially the concept of distinct cortical encoding for each modality, is unfortunately only partially supported by the results. Although acknowledged by the authors, fundamental limitations related to CI stimulation might have weaken the results.

The authors stimulated in a Monopolar mode which, albeit being clinically relevant, notoriously generates a high current spread in rodent models. Thus, it seems possible that current spread ended stimulating indistinctly higher turns of the cochlea or even the modiolus in a non-specific manner, greatly reducing (or smearing) the place-coding/frequency resolution of each electrode, which in turn could explain the coarse topographic (or non-random) organization of the cortical responses.

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

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

Comments on revised version.

The reviewer would like to thank the Authors for their work on this new version. The reviewer is satisfied with the current state of manuscript and its associated public review and has no further comment.

Reviewer #3 (Public review):

I thank the authors for their extensive revision of this paper, and I found some elements greatly improved.

In particular, the authors do embrace a somewhat more speculative tone in the current version, which I think is fitting for this work, as the data seem (to me) to be not fully conclusive. The data set collected here is clearly valuable and unique (and I would encourage the authors to make it publicly available!), however, my overall impression is that the specific analyses reported here might not fully.

Despite the revised description of methods, results and figures, I still have trouble understanding many of the results and the authors conclusive interpretation of them. These are my main reservations:

(1) Regarding "individual prediction tendency" - thank you for adding clarifying methodological details and showing the data in a new Figure (#2). Honestly, however, I still can't say that I fully understand the result. For example, why is there also a significant response in the random condition as well? And how do you interpret the interesting time-course (with a peak ~200ms prior to the stimulus, and a reduction overtime from there?<br /> Also (I may have missed this, but...) what neural data was used to train the classifier and derive the "prediction tendency" index? Was it just the broadband neural response? Is there a way to know which sensors contributed to this metric (e.g., are they predominantly auditory? Frontal?)? And is there a way to establish the statistical significance of this metric (e.g., how good the decoder actually was in predicting behavioral sensitivity?). I don't see any statistics in the results section describing the individual prediction tendency.

(2) Regarding the TRF analysis - Thanks for clarifying the approach used to obtain 2-second long "segments" of speech tracking. This is an interesting approach, however I think quite new(?) , and for me it raises a whole new set of questions, as well as additional controls and data that I would have liked to see, to be convinced that results are significant. I will elaborate:

- Do I understand correctly that you segment the real and predicted neural response into 2-second-long segments and then calculate the Pearsons' correlation between them to assess the goodness of the model? This is very unclear, since in the methods section you state only that "the same" analysis was performed as for the full data - but what exactly? Clearly, values will be very different when using such short segments. I feel that additional details are still required (and perhaps data shown) to fully understand the "semantic violation" analysis of TRFs.

- I would like to reiterate my previous comment regarding the use of permutation tests to verify the validity of TRF-based measures derived. This would be especially important when using new approaches (such as the segmentation used here). The authors argue that this is not needed since this was not done in their previously published study. However, this sounds a bit like "two wrongs make a right" argument... why not just do it, and let us know that this 2-second segmentation approach allows estimating reliable speech tracking?

- Following up on my previous comment that defining "clusters" as at least two neighboring channels (Figure 3) - the fact that this is a default in Fieldtrip is by no means sufficient justification! This seems quite liberal to me, especially given the many comparisons performed. Here too, permutations can help to determine the necessary data-driven threshold for corrections. This is of course critical for interpreting the result shown in Figures 3E&G that are critical "take home messages" of the paper - i.e., that the prediction-index from the first part of the experiment is related to speech tracking in the second part of the experiment. To my eyes, this does not look extremely convincing, but perhaps the authors can show more conclusive data to support this (e.g., scatter plots of the betas across participant?).<br /> - A similar point can be made for the effect of semantic violations (though here the scalp-level result is somewhat more clustered). The authors point out that the semantic effect is a "replication" of their result reported in Schubert et al. 2023, but if I am not mistaken the results there were somewhat different (as was the manipulation). It would be nice to explicitly discuss the similarity/difference between these effects.

(3) Regarding the ocular-TRFs -

- Maybe this is just me, but I believe that effects that are robust should be clearly visible in the data, without the need for fancy "black-box" statistical models. In the case of the ocular TRFs, it is hard for me to see how these time-courses are not just noise (and, again, a permutation test would have helped to convince me...). The inconsistent results for horizontal and vertical eye-movements vis a vis the experimental conditions (single vs. multi-speaker conditions) don't help either, despite the authors argument that these are "independent" - but why should this be the case, especially if there is nothing really to look at in this task?<br /> - I remain with this scepticism for the mediation-portion of the analysis as well... But perhaps replications from other groups or making the data public will help shed further light on this in the future.

Minor<br /> - Thanks for adding information about the creation of semantic-violation stimuli. Since the violations and lexical-controls were taken from different audio recordings, it would have been nice to verify that differences between neural responses cannot be attributed to differences in articulations (e.g., by comparing their spectro-temporal properties).

Reviewer #3 (Public review):

Summary:

Dzhala and colleagues present findings from organotypic slice cultures suggesting that simultaneous modulation of the complementary cation-chloride cotransporters NKCC1 and KCC2 through inhibition of the WNK-SPAK/OSR1 pathway reduces seizure-like activity. The manuscript is generally well written, and the data support the conclusion that WNK463 exerts robust anti-ictal effects in this model. However, several issues should be addressed to strengthen the mechanistic interpretation and statistical rigor of the study, and improve confidence in the conclusions.

Strengths:

(1) The study addresses an important mechanistic question by investigating how inhibition of the WNK-SPAK/OSR1 pathway with WNK463 influences seizure activity and neuronal chloride homeostasis.

(2) The experimental design is logical and comprehensive, progressing from characterization of chloride dynamics to pharmacological and genetic interrogation of the underlying mechanism using multiple complementary approaches, including pharmacological inhibition, siRNA-mediated knockdown, electrophysiology, and chloride imaging.

(3) The combination of simultaneous extracellular electrophysiology and two-photon chloride imaging provides complementary functional and mechanistic information and represents a major technical strength of the study.

(4) The TTX experiments elegantly distinguish activity-dependent chloride accumulation from resting intracellular chloride concentration, substantially strengthening the central mechanistic conclusions.

Weaknesses:

(1) The mechanistic conclusions regarding KCC2 activation and NKCC1 inhibition are stronger than the data directly support. Throughout the manuscript, the authors conclude that WNK463 activates KCC2 and inhibits NKCC1. Although this interpretation is consistent with the established biology of the WNK-SPAK/OSR1 pathway, the evidence presented here is indirect. Specifically, the authors infer KCC2 activation and NKCC1 inhibition from the observation that pharmacological inhibition or siRNA-mediated knockdown of these transporters alters the effects of WNK463, together with measurements of chloride dynamics. While these findings are compatible with a KCC2- and NKCC1-dependent mechanism, they do not directly establish that WNK463 regulates either transporter. Direct evidence would require measurements of transporter activity, phosphorylation state, membrane expression, or other biochemical indices of transporter regulation. I therefore recommend that the authors both temper the mechanistic language throughout the manuscript and explicitly acknowledge in the Discussion that the proposed regulation of KCC2 and NKCC1 is inferred from indirect evidence rather than directly demonstrated in the present study.

(2) The conclusions drawn from the siRNA-mediated knockdown experiments should be interpreted more cautiously. First, it is unclear whether silencing NKCC1 or KCC2 induced compensatory changes in the expression or function of the complementary cotransporter. Given the well-established interplay between NKCC1 and KCC2 in regulating intracellular chloride homeostasis, compensatory adaptations could influence the interpretation of these experiments and should be addressed or acknowledged as a limitation. Second, the sample size for the siRNA experiments appears relatively small. It is unclear how many independent animals contributed slices to each experimental group, making it difficult to assess the degree of biological replication. In addition, effect sizes are not reported. Clarifying the number of biological replicates and reporting effect sizes would improve the rigor of the statistical analysis and increase confidence in these findings.

(3) The final pharmacological experiments require clarification, as the conclusions appear internally inconsistent. Earlier experiments suggest that the anticonvulsant effects of WNK463 depend on coordinated regulation of both NKCC1 and KCC2. However, in the final experiment, the authors state that simultaneous pharmacological inhibition of NKCC1 and KCC2 does not prevent the anticonvulsant effects of WNK463. In contrast, the accompanying statistical analysis indicates that combined transporter inhibition significantly reduces the effect of WNK463 relative to control conditions. These interpretations appear inconsistent and make it difficult to determine the extent to which the anticonvulsant action of WNK463 depends on NKCC1 and KCC2. The authors should clarify whether simultaneous inhibition of both transporters completely abolishes, partially attenuates, or merely reduces the magnitude of the WNK463 response, and revise the text accordingly. If the effect is only partially attenuated, alternative mechanisms contributing to the anticonvulsant actions of WNK463 should also be considered and discussed.

(4) The statistical analysis and reporting require further attention. First, median values should not be reported with standard deviations, as standard deviation describes variability around the mean rather than the median. For non-normally distributed data, the authors should report median values together with an appropriate measure of variability, such as the interquartile range (25th-75th percentile) or another suitable summary. Second, in several instances, ANOVA results are reported using only a single degree of freedom value (e.g., page 6, DF = 53). This is incomplete, as an F statistic is defined by two degrees of freedom: the numerator degrees of freedom (between-group variability) and the denominator degrees of freedom (within-group variability). Reporting statistical results using standard notation (F(df_between, df_within) = F statistic, p = value) would improve clarity and allow proper interpretation of the analyses.

Reviewer #3 (Public review):

Summary:

Ludlow et al. investigate the control strategy that flies use to stabilize flight during small roll perturbations. Using 3D kinematic analysis of freely flying Drosophila, Ludlow et al. ask how manipulating steering motor neurons alters the fast stabilization reflex, which spans only a few wingbeats. Bilaterally activating i1 or i2 wing steering motor neurons during free flight pitches the fly down via decreases in the wing stroke amplitude, whereas inhibiting the b3 wing steering motor neurons pitches the fly up via increases in the wing stroke amplitude. These results suggest that asymmetric recruitment of these steering muscles may be used to rotate the fly around the roll axis. Then, using quasi-steady aerodynamic modeling, they linearly interpolate changes in six kinematic features of wing strokes to describe which wing parameters produce the greatest corrective roll torque. They repeated this analysis with data from optogenetic activation of wing steering motor neurons. They argue that modeling of these optogenetic perturbations supports the hypothesis that i1, i2, and b3 muscles contribute to rotation around the roll axis by calculating roll torque changes from changing kinematics of a single wing, despite bilateral optogenetic activation. Finally, they support their claims about the redundancy of steering motor neurons by presenting connectomic analyses of the direct pathways from haltere sensory neurons to wing steering motor neurons. This analysis reveals two independent pathways from halteres to two wing muscle groups (b1 and b2 vs. i1, i2, and b3). Taken together, these data provide some evidence for redundant roll stabilization control strategies in Drosophila.

Strengths:

The central strength of this work is the high-quality free-flight kinematic dataset, which provides a detailed picture of how wing-stroke parameters change during natural roll perturbations and correction. The integration of quasi-steady aerodynamic modeling with these kinematic data offers a principled framework for linking muscle activity to torque generation. The use of split-Gal4 lines to target individual wing steering motor neurons with cell-type specificity provides a potentially precise approach to probe the contribution of specific muscles to roll torque. Together, these tools position this study to make a meaningful contribution to understanding the sensorimotor control strategies underlying flight stabilization in Drosophila.

Weaknesses:

The GtACR1 silencing experiments lack validation that the optogenetic manipulation actually suppresses motor neuron activity. Without a positive control to calibrate light intensity and duration, the absence of kinematic effects cannot be interpreted with confidence. The confocal images of the driver lines provided are insufficient to uniquely identify the targeted motor neurons and do not clearly show expression in the brain and nerve cord. The kinematic modeling relies on flight profiles derived from a single fly and a single trial, raising questions about whether they capture the full range of natural variation. Finally, the connectomics analysis largely recapitulates prior work and provides little new insight.

There is no evidence that optogenetic silencing of wing motor neurons with GtACR1 is actually suppressing their activity. There are many factors that could impact the efficacy of this manipulation: transgene expression, light intensity and duration, etc. While electrophysiology experiments would be ideal, this would be challenging. Another option would be to use a positive control with an obvious phenotype to calibrate the light intensity and duration. For example, silencing all motor neurons (e.g., using OK371-Gal4 or another driver labeling glutamatergic neurons) should essentially paralyze the fly. Without additional evidence, the lack of a kinematic effect in the GtACR experiments is not convincing.

The confocal images in Figure 1C are of poor quality and do not help identify the motor neurons. They only point to cell body locations, and the morphologies of the neurons are not clear. The glial sheath also appears to be labeled. The images as they currently exist are not sufficient to uniquely identify the wing motor neurons and should be updated, including brains, since the experiments manipulate activity across the nervous system. It should also be clarified that these split Gal4 lines were not generated in this work.

The connectomics analysis does not add much on top of what was already known. It doesn't necessarily need to be removed, and the authors acknowledge that it is basically repeating prior analyses in prior publications from another connectome dataset. But it could be reduced to a schematic summarizing prior work. The one thing that should be clarified is what the "haltere afferents" actually are. Campaniform sensilla only or other sensory neurons (e.g., haltere chordotonal neurons)?

In Figures 3 and 4, 50 kinematic profiles are created using data from a single fly and a single trial. Are these kinematic profiles representative of the range that flies use? Would the results generalize across different bouts? Figure 1i-m shows a much narrower distribution of kinematic features compared to Figure 3, which might reduce the resulting torque calculations. Why not sample from the distribution of data in 1i-m in Figure 3? Could there be some sort of bootstrapping for the modeling in Figures 3-4?

Reviewer #3 (Public review):

Summary:

This work studied the prion-like α-synuclein spreading hypothesis from the view of different host genotypes (M83 transgenic vs wild-type), fibril species (mouse vs human PFFs), and disease epicenter (striatum vs hippocampus). Major results include tracking neurodegeneration longitudinally with in vivo MRI, behavior, and survival in the same mice. Furthermore, this work sought to link atrophy patterns to structural connectivity and regional SNCA expression. Finally, the authors tested whether a connectome-based SIR spreading model could predict the atrophy in silico and generalize across seed sites.

Strengths:

(1) Same mice imaged repeatedly across four timepoints (−7, 30, 90, 120 dpi), giving true within-subject volumetric trajectories rather than cross-sectional snapshots.

(2) Investigate the atrophy pattern for striatal-vs-hippocampal seeding in PD.

Weaknesses:

(1) The hypothesis (regional vulnerability) is not novel, although the manuscript presents compelling and interesting results supporting it in Figures 2 and 3.

(2) The findings primarily establish statistical associations rather than causal mechanisms. This limitation appears inherent to the cross-cohort dataset utilized, which the authors should explicitly address in the discussion.

(3) The descriptions of the statistical analyses in Sections 2.5 and 2.6 lack sufficient detail. The authors should provide additional technical specifics to ensure reproducibility.

(4) Given that VBM was used to determine atrophy patterns, it is necessary to address how the multiple comparisons problem was handled in the statistical analysis to control for false positives.

Reviewer #3 (Public review):

Summary:

Statham and colleagues test an assumption underpinning a very large literature: that the stop-signal reaction time (SSRT) indexes the speed or efficacy of top-down inhibitory control. They argue instead, and support their claims with a total of eight datasets, that SSRT is substantially occupied by visuomotor deadtime (i.e., incompressible sensory and motor delays common to all visually guided responses), which varies across individuals, conditions and populations in ways that mimic effects usually attributed to inhibitory control. They propose two remedies: subtracting an independent estimate of visuomotor deadtime (T₀) from SSRT, and a new index, the selective stopping delay (ΔT), from the stimulus-selective stopping task.

Strengths:

The paper's principal strength is the combination of these components. That SSRT must contain peripheral delays is not itself new, as the authors point out (Boucher et al., 2007; Salinas and Stanford, 2013; Bompas et al., 2020). What is new is the quantification of the problem at scale, across seven archival datasets and a preregistered replication, together with the demonstration that T₀ can be recovered from existing stop-task data. That is important, as it provides a diagnostic that can be applied to data already collected. The authors' offer to assist others in doing so is exemplary. The supplementary analyses of trial numbers and participant pooling are very useful, and the paper provides important sanity checks, notably confirming that stop and ignore signals produce indistinguishable initial interference before pooling them.

Weaknesses:

The evidence for the central claim is strong but presented in a way that overstates it. Figure 2 reports 85% and 80% shared variance between SSRT and T₀, but these pool across datasets and, more critically, across response modality: manual and saccadic estimates from the same participants are plotted together with a single regression line through both. Because manual and saccadic deadtimes differ by roughly 130 ms, the resulting correlation largely reflects a between-condition difference rather than covariation among individuals. The numbers that speak to individual differences are more modest (40% for manual responses; 7% for saccades). The manual result is convincing and consequential; the saccadic result is not, and the explanation in terms of restricted range, while plausible, is offered after the fact and is directly testable by reporting the reliability of saccadic T₀ or correcting the correlation for attenuation. This limitation is arguably good news for the paper's practical message, since it implies saccadic measures are relatively protected, but the manuscript should make clear (including in the abstract) that the strong individual-differences case rests on the manual data, where motor execution delay is the main driver.

A related point concerns interpretation rather than analysis. Since SSRT is, on the authors' own account, approximately the sum of T₀ and a decision-related component, covariation between the two is expected on structural grounds; the preregistered correlation with reaction times from separate speeded blocks mitigates this, but the finding is less surprising than its current framing implies. What would determine whether past conclusions must be revised is not whether SSRT correlates with T₀ across individuals, but whether the decision-related component tracks the independent variable in any given study. The alcohol reanalysis could be a test case for this: the authors show that alcohol raises T₀ commensurately with SSRT and conclude the effects are "consistent with these effects being fully driven by visuomotor delays," yet (unless I missed something) they do not report the corrected measure for these data, while they do so for signal contrast and response modality. Running that analysis, and stating plainly what Campbell et al. (2017) would have concluded under the proposed treatment, would be an important demonstration.

The case for ΔT is the least developed part of the paper. ΔT is a difference between two independently estimated, individually noisy quantities, extracted by a non-trivial procedure (see also below), and no reliability estimates are reported for T₀, TS or ΔT. This would be possible based on the two-session design (and the group has prior work on the reliability of cognitive control measures). This matters because the argument that ΔT is superior rests, to some extent, on null findings: ΔT does not correlate with SSRT, with stopping accuracy, or with the differential response to stop and ignore trials. These null correlations are interpreted as freedom from confounds, but an unreliable measure would produce the same pattern, and the seven participants with implausible negative ΔT values indicate that noise is not negligible.

In addition, the subjective correction of dip onsets ("Departure points were visually inspected and adjusted if it was deemed that the algorithm had placed them in inappropriate places"), which is critical to the paper's central measurement, should be blinded to condition or show inter-rater agreement. Since T₀ and TS are compared across conditions and ΔT is their difference, this introduces researcher degrees of freedom.

This is particularly critical when a dip is not easy to extract. Figure 3 depicts an idealized ignore-trial distribution with a clean, deep dip. Real distributions are unlikely to look like this, and dip depth should depend on the behavioral relevance and salience of the ignored event; published work on rapid manual inhibition indicates that dips to behaviorally irrelevant events can be very shallow. Since ΔT is extractable only where the dip is resolvable, the generality of the method can be questioned. Ideally, the empirical distributions underlying every dataset analyzed should be shown to alleviate this concern.

One uncontrolled procedural difference also deserves comment. Corrective feedback about stopping too often or stopping too rarely was given after manual blocks only; saccadic blocks received none, and fixed rather than staircased delays were used throughout. Since the manual-saccadic contrast carries much of the argument, and saccadic blocks yielded both lower stopping accuracy (36% vs 52%) and far more exclusions (8 vs 1 of 37), this asymmetry offers an alternative to the interpretation that saccades are simply harder to inhibit.

A final point concerns the comparison between response modalities. Raw SSRT suggests that saccadic inhibition is faster than manual (174 vs 266 ms), while both proposed corrections reverse this, with SSRT−T₀ and ΔT each indicating that saccadic inhibition is slower (the latter consistently across nearly every participant). This is one of the clearest illustrations of the paper's thesis, but it is not taken up in the discussion, which returns to modality only to note that saccadic T₀ varies little (the one reference to variation across action modalities appears in the modeling section, without stating its direction). It would also benefit from a caveat. Both corrected measures subtract the same T₀, and manual and saccadic T₀ differ by roughly 130 ms, so the two do not corroborate one another independently (TS is itself longer for manual responses, and yields a shorter ΔT only once the larger manual T₀ is removed). The accuracy of the subtraction therefore matters here: if the manual regression slope of 0.75 reflects sub-additivity rather than attenuation, subtracting the full T₀ would overcorrect manual responses more than saccadic ones, and could produce the reversal on its own.

These concerns qualify rather than undermine the contribution. The core observation is robust, the diagnostic is practical and immediately applicable, and the case that a large body of work requires re-examination is well made. If the corrected analyses are carried through on the datasets already in hand, this will be an important paper for anyone who uses the stop-signal task.

Reviewer #4 (Public review):

Summary:

Short photoperiod is an important experimental manipulation in neurobiology, endocrinology, and metabolism studies. However, the molecular mechanisms by which short photoperiod gives rise to behavioral phenotypes that are seen in seasonal affective disorders remain unknown. Using the classic circadian model organism Drosophila, this study examines short photoperiod-induced hypersomnolence and identifies the circadian photoreceptor cryptochrome as a regulator of GABAergic tone within the clock neural circuit to promote wakefulness under short photoperiod conditions. The discovery has broad implications for understanding how short photoperiod modulates neural inhibition in circadian circuits in regulating sleep.

Strengths:

The Drosophila model provided a powerful platform to dissect the molecular mechanisms underlying short photoperiod-induced hypersomnolence. A battery of behavioral, imaging, circuit-manipulation approaches were employed to test the novel hypothesis that the circadian photoreceptor cryptochrome modulates GABAergic tone within the clock neural circuit to promote wakefulness under short photoperiod conditions.

Weaknesses:

The current model proposed by the authors suggests that the small ventral lateral neurons of the Drosophila clock circuit are GABAergic; however, this remains unclear. At present, the field lacks sufficient data and validated reagents to definitively establish the GABAergic identity of these neuropeptidergic neurons.

比 AVO 的满分更有说服力：只动“保留推理”和“上下文压缩”两个开关，基线同源、变量可数，这才是消融实验该长的样子。但同样只在公开集上，且起点 13.3% 很低——低分区的三倍不能线性外推到高分区，压缩带来的收益通常随基线上升而衰减。

把 30% 和 100% 相减当成 harness 的贡献，是这轮报道里最常见的读法错误。两个数字来自不同评测设置：一边是 ARC Prize 跑裸模型，一边是 NVIDIA 自建观测接口、记忆与监督器的完整系统。NVIDIA 原文明说过这不是受控消融，本文没有转述这句限定。

满分只在公开集上成立。ARC 设半私有/私有集，正是为了拦住针对公开题目的过拟合与反复调参，因此公开集 100 分不能推出私有集能力。任何把它转述成“达到人类水平”的说法，都已经丢掉了这句限定。

全文最该被引用的一句：NVIDIA 自己先声明这不是受控消融。也就是说 12% 的动作数优势里，后端模型、观测表示、记忆、上下文管理四个变量同时在变，无法归因给任何单一设计。读跑分新闻时，这类作者自认的免责声明信息量往往大于标题数字。

Reviewer #3 (Public review):

Summary:

In this manuscript by Ashworth and colleagues, the authors investigate the temporal dynamics of the rostral ventromedial medulla (RVM), a key output node in a major descending pain-modulation circuit. Using data from extrasellar single-unit recordings of RVM ON, OFF, and NEUTRAL cells in lightly anesthetized rats, the authors' computational modeling yielded two major findings: (1) heat-evoked ON burst and OFF pause, followed by exponential recovery components in10s of seconds; and (2) ON and OFF cells exhibit periodic fluctuations in ~5-minute cycles that are statistically predictable.

Strengths:

The manuscript's concept is innovative, offering the first quantitative analysis of multi-timescale dynamics in physiologically characterized RVM pain-modulating neurons. This advances a field that has mostly depended on qualitative or single-timescale descriptions. The authors use contemporary Gaussian process and probabilistic models to capture statistically predictable slow dynamics. The study is further strengthened by identifying ON-, OFF-, and NEUTRAL-type cells using well-established criteria grounded in decades of RVM research. The combination of rapid reflex-related responses and slower ongoing rhythms supports a dual-timescale framework, providing a more integrated understanding of how these neurons may regulate reflex activity and state-dependent processes.

Weaknesses:

Several limitations are noted. Incomplete characterization of light anesthesia during recording sessions, such as methohexital stability and clear criteria for identifying "lightly anesthetized" states. While the NEUTRAL cell control is helpful, it does not fully address concerns about circuit specificity or systemic confounds. The findings are male-dominant, which may limit their generalizability. The synchrony between ON and OFF cells was suggested but not directly tested. The heart rate coherence with ON, OFF, and NEUTRAL cell activity results is intriguing but does not fully clarify how these neurons influence heart rate, particularly within the "lightly anesthetized" model.

Reviewer #3 (Public review):

Summary:

The authors investigate how sleep loss and circadian disruption affect whole-organism metabolism in Drosophila melanogaster. They used chamber-based flow-through respirometry to measure oxygen consumption, carbon dioxide production, in wild-type flies and in mutants with impaired sleep or circadian function. These measurements were then integrated with a previously published metabolomics dataset to explore how respiratory dynamics align with metabolic pathways. The central claim is that wild-type flies display anticipatory coordination of metabolic processes with circadian time, while mutants exhibit reactive shifts in substrate use, redox imbalance, and signs of mitochondrial stress.

Strengths:

The study has several strengths. Continuous high-resolution respirometry in flies is challenging, and its application across multiple genotypes provides good comparative insight. The conceptual framework distinguishing anticipatory from reactive metabolic regulation is interesting. The translational framing helps place the work in a broader context of sleep, circadian biology, and metabolic health.

Weaknesses:

At the same time, the evidence supporting the conclusions is somewhat limited. The metabolomics data were not newly generated but repurposed from prior work, reducing novelty. The biological replication in the respirometry assays is low, with only a small number of chambers per genotype. Importantly, respiratory parameters in flies are strongly influenced by locomotor activity, yet no direct measurements of activity were included, making it difficult to separate intrinsic metabolic changes from behavioral differences in mutants. In addition, repeated claims of "mitochondrial stress" are not directly substantiated by assays of mitochondrial function. The study also excluded female flies entirely, despite well-documented sex differences in metabolism, which narrows the generality of the findings.

Reviewer #3 (Public review):

Summary:

In the manuscript titled "Dual regulation of chemical stress-induced DDI2/1 3 expression by a transcription factor Fzf1 and nucleosome in Saccharomyces cerevisiae" Du et al have discovered a dual role of Fzf1 in transcriptional control of DDI2/3 during cyanamide (CY) or MMS treatment. While previous literature established that Fzf1 regulates multiple targets (DDI2/3, SSU1, YHB1, and YNR064C) by binding the CS2 consensus sequence, it remained unclear why DDI2/3 uniquely undergoes a massive 1,000-fold induction under cyanamide (CY) stress, whereas the others show only a 20- to 40-fold induction. In this work, the authors showed that Fzf1 functions beyond standard transcriptional activation. Using MNase-seq and a series of promoter truncation mutants, the authors mapped Upstream Repressing Sequences (URS) in the DDI2/3 promoter that are heavily occupied by nucleosomes. The authors showed that Fzf1 is essential for chromatin remodelling and nucleosome eviction (specifically at the -2 nucleosome position) to de-repress the DDI2/DDI3 expression.

Strengths:

The two-tier mechanism of action of Fzf1 in controlling the DDI2/3 expression during CY/MMS stress is compelling and novel.

Weaknesses:

While the authors presented the in vivo MNase-seq data that show Fzf1 is necessary for nucleosome displacement, the current study lacks any in vitro mechanistic proof. As the authors acknowledge, it remains unknown whether Fzf1 directly displaces nucleosomes on its own (perhaps through unmapped post-translational modifications induced by chemical stress) or whether its ZF5 activation domain merely acts as a scaffold to recruit separate chromatin remodelling complexes.

To test nucleosome repression, the authors utilised extreme global interventions, such as deleting the SPT10 gene or halting de novo histone synthesis using a galactose-to-glucose medium shift in the RMY102 strain. While these methods effectively deplete histones and induce DDI2/3 up to 30- to 350-fold, completely depleting cellular histones causes massive, pleiotropic secondary effects across the entire genome, which can obscure specific regulatory relationships.

Reviewer #3 (Public review):

Summary:

The goal of this study was to test the hypothesis that prenatal alcohol exposure (PAE) can affect Alzheimer's disease (AD) pathogenesis using biochemical proxies, histology, and a behavioral paradigm sensitive to AD-related memory decline. Major strengths include the breadth of techniques used, consideration of different AD-related molecular markers, and the use of different ages as well as appropriate controls. The authors largely achieved their aims to show that PAE does affect amyloid precursor fragments (CTFs) and notch signaling very early on as well as long-term effects in adulthood, which may uncover a previously underappreciated mechanism that may contribute to AD-related neuropathology and behavioral outcomes during lifespan.

Strengths:

(1) Several techniques are used to address molecular, behavioral, and histological PAE-related changes.

(2) There is use of appropriate controls and an AD-relevant mouse model.

(3) Different ages are used to address age-related and long-term effects in AD and control mice.

(4) The novelty of results shows early changes in amyloid-related processes, affected by PAE.

Weaknesses:

(1) It is unclear as to whether there are sex differences, particularly in the adult cohort.

(2) More clarity is needed on sample size per cohort and whether mice that were used for anatomy and biochemical analyses were previously used for behavior. Including a table and noting any overlap would be useful.

(3) In many instances, two-way ANOVAs with treatment (PAE vs vehicle) and genotype as factors will be useful to report (e.g., Figure 1).

(4) In Figure 5 and line 253, it is stated that older mice have more severe deficits, but there are no direct statistical comparisons with younger AD mice.

(5 Lines 270-271 refer to mice as "presymptomatic", but these mice do have behavioral symptoms. Do the authors mean no neuropathology yet? Any data showing lack of robust neuropathology would be useful.

Reviewer #3 (Public review):

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

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

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

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

While the work is overall convincing and provides important insights into the circuit-level implementation of predictive processing, I think the connection to JEPA networks would benefit from a more in-depth discussion of its relationship to recently proposed and implemented models. Below, I address the specific points raised by the authors (flanked by ' ... ' to make the author's statements stand out), in particular in relation to the model proposed by Nejad et al. (2025):

- 'The two proposals indeed share similarities in assuming that bottom-up input for both L2/3 and L5 arrives from thalamus, and that representations formed in L2/3 are used for predicting the activity of L5. However, there are a few important differences between the JEPA implementation proposal formulated here and the Nejad et al. model.

(1) There is no proposed mapping of computations in the Nejad et al. model onto different JEPA networks. We assume that the suggested mapping would be L4 and L5 as encoder networks, and L2/3 as a predictor network? In that case, it is different to our proposal, in which L2/3 is part of the encoder network.'

I think there is some confusion here. In Nejad et al., both L2/3 and L5 function as encoder networks. Each receives sensory input (with L2/3 receiving this input delayed via L4) and computes a latent representation of that input, denoted z_{L2/3} and z_{L5}, respectively. The prediction is obtained by comparing the output of L2/3 with L5 latent representations (via W_{L2/3->L5} * z_{L2/3}). In other words, L5 is the target in the learning objective.

Although, they did not explicitly state the mapping with JEPA, their model has the same fundamental property - predictive learning happens in the latent space. Also, this appears very similar to the roles assigned to L2/3 and L5 in your Figure 9B. The fact that you made this more explicit and the new data included, is in my view, a very interesting contribution. However, from an architectural perspective, it appears that your proposal and the model of Nejad et al. are conceptually very similar, and I do not see a substantial difference between the two. This should be made more clear in the Discussion.

- '2. Our proposal contains explicit prediction error neuron cell types within L2/3, while prediction errors in Nejad et al. are encoded in the gradients, and the layer origin of these signals is hypothesized to be L5 ('the learning-driving error signal originates in L5'). Hence, also the role of L5-L2/3 connection is distinct between the two proposals. In Nejad et al. this connection serves the role of error propagation and update for predictions in L2/3, while in our proposal this connection contains teaching signal (target representations) that are compared to predictions within L2/3. Similarly, the functional role of L2/3-L5 connection is also different, since in Nejad et al, it is supposed to carry predictions of L5 activity, whereas in our proposal we expect it to drive plasticity in L5 encoder.'

Indeed, in Nejad et al., the layer-dependent mismatch responses are modeled as gradients with respect to neuronal activity, and the model does not explicitly include prediction error neurons. However, this appears to be a modeling choice rather than a fundamental aspect of the proposal, and it does not preclude an implementation with explicit prediction error neurons. In fact, the authors explicitly acknowledge this possibility in the Discussion:

"The second approach would be to recast our model within a predictive coding framework... Predictive coding jointly optimizes both model parameters and neuronal activities, which could naturally lead to prediction errors observable in the activity of both L2/3 and L5 neurons. Note that these two views are not mutually exclusive."

While Nejad et al. hypothesize that the learning-driving error signals (that are distinct from their mismatch responses) originate in L5, the abstract loss function itself does not uniquely specify where the underlying comparison between the predicted representation (W_{L2/3 -> L5} z_{L2/3}) and the target representation (z_{L5}) must be implemented. The proposed biological implementation places this computation in L5, but from my understanding, the computational objective itself does not require this specific localization.

That said, I agree that your proposed model introduces a genuine difference. The functional roles assigned to the vertical projections are effectively reversed: in Nejad et al., the L2/3->L5 projection carries the prediction, whereas the L5->L2/3 projection conveys the error/gradient. In your architecture, by contrast, the L5->L2/3 projection carries the teaching signal (target). This is, in my view, a real and testable interpretational divergence that is worth stating clearly.

Therefore, I think the novelty lies less in the computational architecture itself and more in committing to a particular biological implementation-one that adds cell-type-specific detail to an implementation that Nejad et al. hypothesised as being compatible with their framework.

- '3. The difference outlined above also makes it evident that the two proposals should differ in how deep and superficial layers are expected to influence the activity of one another. Indeed, the proposal in Nejad et al. is based on the cortical column idea, and according to eq. 2 and 3 in the Methods, activity in L5 is a function of activity in L2/3, while activity in L2/3 is not a function of activity in L5. Our proposal is based on idea of layers forming parallel networks, where horizontal communication is the dominant mode of cortico-cortical interactions, and activity in deep layers serve as a teaching signal for L2/3. In our case, we expect the opposite - that activity in L2/3 depends on activity of L5, while activity of L5 is not immediately dependent on activity of L2/3 (only via plasticity route). This led us to propose one of direct tests for our framework - silencing L2/3 in a familiar setting should result in no immediate changes to L5 activity and behavior of the animal.'

My reading of Nejad et al. is consistent with your interpretation of the equations. Specifically, Eq. 3 makes L5 activity depend on L2/3 activity (albeit weakly, with a = 0.3), whereas Eq. 2 contains no L5 term, so L2/3 activity does not depend directly on L5 activity. In that model, the L5->L2/3 pathway carries the learning gradient rather than contributing to the activity dynamics. By contrast, in your proposed model, L2/3 activity depends on L5 activity, whereas L5 activity is not immediately dependent on L2/3 activity (except indirectly through learning/plasticity). So, you state that "silencing L2/3 in a familiar setting should result in no immediate changes in L5 activity.<br /> [...].

However, I am unsure how to reconcile this prediction with the results shown in Fig. 6. If I understand the figure correctly, optogenetic activation of Rrad-positive (positive prediction error) L2/3 neurons produces a small increase in L5 activity, whereas activation of Adamts2-positive (negative prediction error) L2/3 neurons produces a decrease in L5 activity. Although these experiments involve activation rather than silencing, they nevertheless suggest that perturbing L2/3 activity can have an immediate effect on L5 activity. Could you clarify how this is consistent with the proposed model? In other words, what aspect of the proposed circuitry makes activation effective while silencing is predicted to have no immediate consequence?

For comparison, Nejad et al. performed a related perturbation analysis in Fig. S15 by scaling the output of L2/3 neurons exhibiting positive mismatch signals (defined through the activity gradients), which increased L5 activity, whereas scaling neurons with negative mismatch signals produced the opposite effect. I am not entirely sure how directly these simulations map onto the experiments shown in your Fig. 6, since the Nejad simulations were performed during mismatch conditions, if I have understood them correctly.

- '4. The proposal in Nejad et al. relies on input reconstruction or variance maximization within the L5 autoencoder network to avoid collapse. Instead, our proposal has no reconstruction objective.'

Nejad et al. only require two encoders (like in JEPA), how these two are learnt can be done in several ways. While Nejad et al. focus on using a reconstruction loss to learn the L5 target, they also show that it works equally well with non-reconstruction objectives. Therefore, I do not think the presence or absence of a reconstruction objective constitutes a fundamental distinction between the two proposals.

As your current work presents a conceptual architecture rather than a fully implemented learning algorithm (in a model), the mechanism that would prevent representational collapse has not yet been defined. From my understanding, every joint-embedding approach must address this issue, whether through reconstruction, variance/covariance regularization, stop-gradient or EMA mechanisms, or other approaches. Thus, the absence of a reconstruction objective (or another anti-collapse mechanism) is not, in itself, a distinguishing feature of the proposed architecture, but rather an as-yet unspecified design choice within the learning objective.

- '5. Lastly, there is time-delay between inputs to L5 and L2/3 that is proposed in Nejad et al., while this is not something inherent to our proposal.'

I agree that the temporal delay introduced by L4 is a key component of the Nejad et al. model and is currently absent from your proposal. However, I would expect temporal delays to emerge naturally in your framework as well, given the multisynaptic and highly parallel organization of cortical circuits. More generally, implementing predictive learning over time (as in JEPA) requires comparing representations at times t and t+1, which seems to require some form of temporal delay. How else would you suggest this is done?

In general, I think the manuscript would benefit from a clearer discussion of its relationship to the model proposed by Nejad et al. (perhaps following the discussion above), including both the shared conceptual claims and the aspects that genuinely differ between the two frameworks. At present, some of the claims are presented as novel, although at least some of these core ideas have already been proposed in Nejad et al.

For example, the authors state: "Thus, we propose that layer 2/3 functions to predict layer 5 activity, not sensory input per se, hence making predictions in the internal representation space, not input space." This appears to be exactly what Nejad et al. proposed as discussed above - in their model L2/3 predicts L5 activity (purely in the latent space), as they state in the abstract.

That said, there are some interesting differences, and I think the community would greatly benefit from making these clear, including the roles assigned to interlaminar connections and the interpretation of the signals carried by these pathways. These differences are interesting and potentially testable, and I think the manuscript would be strengthened by explicitly distinguishing which aspects are in line with the ideas already present in Nejad et al. and which aspects represent new contributions.

Reviewer #3 (Public review):

Summary:

Knoerzer-Suckow et al. explore the mechanisms of organelle inheritance during endodyogeny in Toxoplasma gondii using an innovative dual-labeling approach to track the distribution of maternal organelles into daughter parasites. They can clearly distinguish between maternal and daughter-derived organelles using their dual-labeling Halo Tag approach. They reveal that different organelles are trafficked to daughter parasites in three broad patterns they have binned into groups. Their findings reveal a role for MyoF in the inheritance of micronemes and rhoptries, and notably, they observe that the inner membrane complex (IMC) is not recycled. Instead, the IMC undergoes a pronounced relocalization to the posterior of the maternal cell, where it is likely targeted for degradation.

Strength:

The data surrounding their MyoF knockdown experiments, IMC degradation, and trafficking of MIC2 after auxin washout are convincing. These data add to the knowledge of how organelle inheritance occurs in T. gondii, increasing the field's understanding of endodyogeny.

Weakness:

The inability to achieve higher temporal resolution due to phototoxicity precluded tracking of single micronemes, thus it remains possible that some micronemes follow a path similar to rhoptries and enter daughter cells before development of the residual body while others are recycled via the residual body.

Reviewer #3 (Public review):

The current paper addresses an important issue in evidence accumulation models: many modelers implement flat decision boundaries because the collapsing alternatives are hard to reliably estimate. Here, using simulations the authors demonstrate that parameter recovery can be drastically improved by providing the model with additional data (specifically, an EEG-informed estimate of non-decision time). Moreover, in two empirical datasets it is shown that those EEG-informed models provide a better fit to the data. The method seems sound and promising and might inform future work on the debate regarding flat vs collapsing choice boundaries. As an evidence-accumulation enthusiast, I am quite excited about this work, although for the more broader audience the immediate applicability of this approach seems limited because it does require EEG data (i.e. limiting widespread use of the method or e.g. answering questions about individual differences that require very large N).

Comments on revised version.

I thought the authors carefully addressed my concerns.

Reviewer #3 (Public review):

Using cryofixation-based serial block face electron microscopy of several subregions of the Drosophila antenna, the authors segment and assemble a high-resolution atlas of the anatomical structure, density, and spatial distribution of extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs) in different Drosophila olfactory sensilla types. This systematic and thorough description is an important prerequisite to understanding the function of extracellular particles in intercellular signaling in the nervous system. Additionally, they describe examples of putative biogenesis events (budding/fusion), as well as neuronal and axonal cell degeneration events and measure the changes in particle accumulation in these altered microenvironments.

Overall, this is a significant and comprehensive analysis and represents an invaluable resource to this burgeoning field. The authors assemble an important dataset and their claims match the level of evidence provided.

Strengths:

(1) The authors use segmentations from four different patches of the antenna to provide a systematic ultrastructural survey of extracellular particles in native insect sensilla. The dataset captures the diversity of sensilla types and reconstructs ~7800 particles.

(2) We commend the authors for making the EM volumes available in the public Cell Image Library with accession numbers. It would be helpful to the community to also make the segmentations for this great resource easily accessible.

(3) The sample preparation technique appears to minimize typical artifacts associated with chemical fixation, as evidenced by the high reported sphericity of EVs.

Specific points:

(1) In Figure 3C, the authors should include a continuous measure of particle distribution in the sensilla. Currently, the authors define three categories of particle localization. In the five examples shown in Figure 3A, the spatial distribution of these particles appears quite distinct across classes. For example, EVs/NVEPs in large and small basoconic sensilla are largely restricted to the area proximal to the base, with a limited number located more distally. In contrast, intermediate sensilla show a marked concentration of particles more distally.

(2) The conclusion of different EV ratios across sensillum classes stems from a Kruskal-Wallis of p = 0.0476, with none surviving pairwise comparisons. This is not a strongly supported conclusion and is probably better characterized as a trend.

(3) The statement "selective enrichment of large, cargo-filled vesicles within the ab1 lumen suggests specialized EV-mediated communication adapted to the coordination demands of this neuronal population" seems speculative for a Results section without supporting functional evidence. It would seem better suited for the Discussion.

(4) Figure 4: Criteria for defining the classes of EVs.<br /> a) The authors should explain the rationale for classifying EVs using relative density rather than absolute density? We would expect EVs with similar contents to have similar electron density (similar darkness in the images). Would classifying them relative to the background, which itself might vary across sensilla or regions, create a possible confound, especially when comparing across sensillum classes?<br /> b) The two example images (in Figure 4A) of the cargo-filled EVs appear to have different densities themselves. Do the cargo-filled ones also display systematic differences in density and, if so, why is this another class instead of being a subcategory within the dense and lucent classes (i.e. dense with/without cargo, lucent with/without cargo)? The dense and lucent classes are defined by their density, whereas this is a more structural property.<br /> c) Regarding "Double" and "Ball-and-Socket" EVs, does the density vary between the two particles involved (e.g., does the inner structure consistently differ in density from the outer)?

(5) What was the rationale for the 200μm and 1000μm size cutoffs? A continuous distribution of maximum particle sizes would provide a clearer understanding of the data.

Reviewer #3 (Public review):

In this manuscript, the authors focused on Callorhinchus milii GSDMA/B (CmiGSDMA/B) and its upstream inflammatory caspase, CmiCASP1, and revealed that LPS directly engages the CARD domain of CmiCASP1, triggering its activation, which subsequently promotes the proteolytic cleavage of CmiGSDMA/B, yielding two N-terminal fragments with opposite functions. Moreover, consistent with GSDMD, the functional N241 of CmiGSDMA/B can mediate pyroptosis and exhibit bactericidal activity against Gram-negative bacteria in vitro. Based on these observations, the authors clarified that they uncovered an ancestral LPS-sensing CASP1-GSDMA/B axis in cartilaginous fish; however, several issues should be addressed.

(1) The evidence for direct and functional LPS sensing by CmiCASP1 remains insufficient. Although the authors propose that LPS directly binds the CARD domain of CmiCASP1 to trigger a non-canonical inflammasome-like pathway, the current support mainly comes from pull-down, competition, and in vitro cleavage/activity assays. These results are suggestive but do not yet establish a direct, specific, and physiologically relevant interaction. Additional quantitative binding and specificity analyses are needed to exclude indirect association, aggregation, or other assay artifacts. Therefore, the claim that CmiCASP1 functions as a bona fide direct LPS sensor appears overstated at this stage.

(2) The proposed antagonistic role of N288 is not yet convincingly supported. While the "dual-fragment antagonistic regulation" model is interesting, it currently relies mainly on overexpression/co-expression, co-IP, and localization analyses, which do not clearly distinguish a physiological inhibitory mechanism from a non-specific dosage or sequestration effect. Stronger support would require evidence for the relative generation and timing of N241 and N288, as well as quantitative data showing that N288 interferes with N241 membrane targeting, oligomerization, or pore formation. Testing the effect of selectively blocking D288 cleavage in the full-length protein would also strengthen this conclusion. At present, the antagonistic model remains premature.

(3) The physiological and evolutionary claims are stronger than the available evidence. Although the study shows that CmiCASP1 can cleave CmiGSDMA/B and that this module can be reconstituted in heterologous mammalian systems, these data do not demonstrate that such a pathway operates in elephant shark cells or tissues under physiological conditions. A similar concern applies to the antibacterial assays, which use HEK293T lysates rather than purified N241, making it difficult to exclude contributions from host-derived factors. The authors should either provide more direct evidence in a relevant chondrichthyan context or substantially tone down the evolutionary and physiological interpretations.

(4) The inhibitor data do not convincingly demonstrate suppression of CmiCASP1 activation. Although the authors state that Z-VAD-FMK blocks CmiGSDMA/B cleavage and pyroptotic phenotypes, Figure 1L and Figure 2H do not clearly show that CmiCASP1 activation or processing itself is inhibited. If CmiCASP1 remains processed in the presence of the inhibitor, it becomes unclear whether Z-VAD-FMK blocks CmiCASP1 activation, catalytic activity, or only downstream substrate cleavage. This point should be clarified with more direct biochemical evidence.

(5) The dosage control for GSDM-derived proteins in the antibacterial assays is unclear. In Figure 5, antibacterial activity is tested using HEK293T lysates or concentrated supernatants containing full-length CmiGSDMA/B, N241, or N288, but it is not clear how protein amounts were normalized across conditions. Differences in expression, stability, or recovery could substantially affect the apparent antibacterial activity. The authors should clarify how input was controlled and ideally provide quantitative normalization or matched-concentration assays to support the comparison.

Reviewer #3 (Public review):

Summary:

This study introduces a principled framework for optimizing multi-task batteries for individualized functional brain mapping. Through simulations and empirical validation, the authors show that selecting tasks to maximize differences in regional response profiles can substantially improve the identification of functional brain regions. The work represents a valuable methodological advance for precision functional mapping, although some assumptions underlying the broader applicability of the framework would benefit from further discussion.

Strengths:

The manuscript addresses an important methodological challenge in precision functional mapping using a rigorous combination of theoretical analyses, simulations, and empirical validation. The framework is practical and well supported by open-source software and a publicly available task library, making it readily accessible for adoption and further development by the research community. The manuscript is well written, logically structured, and clearly presents both the methodological framework and its practical implementation.

Weaknesses:

(1) The abstract and introduction emphasize the application of the framework to individualized brain parcellation. While the presented analyses convincingly demonstrate improved prediction of held-out task responses using atlas-guided parcel assignments, they do not directly validate whether the optimized task batteries improve the estimation of an individual's true functional boundaries. The empirical validation relies on atlas-defined parcel identities as the reference standard, yet substantial inter-individual variability in the location and extent of functional regions - particularly within association cortex - has been well documented. Consequently, improved recovery of atlas-defined parcel labels does not necessarily imply more accurate recovery of an individual's functional organization. It would therefore be valuable to clarify this distinction in the abstract and discussion and to discuss how inter-individual variability may influence the interpretation and generalizability of the parcellation analyses.

(2) The framework assumes that informative task batteries can be designed to distinguish neighboring functional regions. While this is compelling for well-characterized systems with distinct functional response profiles, it is less clear how the approach generalizes to finer-scale subdivisions within association cortex (e.g., subnetworks), where neighboring regions may exhibit highly similar task-response profiles and their functional roles remain incompletely understood. In these settings, the relevant functional dimensions may not yet be known, making it difficult to design optimized task batteries a priori. It would therefore be valuable for the authors to discuss how the framework could be extended to such cases.

(3) More generally, the framework assumes that the sampled task space adequately captures the functional dimensions that differentiate cortical regions. However, particularly within the association cortex, neighboring regions may exhibit similar task-response profiles while differing in the information they represent, their interactions with other regions, the computations they perform, or their cortical layer-specific response patterns. In such cases, the dimensions that best distinguish cortical organization may not be fully reflected in task-response profiles alone, but instead become apparent through complementary approaches such as representational analyses, task-evoked or resting-state connectivity, computational modelling, or laminar response profiles. It would therefore be valuable to discuss how the proposed framework relates to these complementary perspectives.

(4) Many task batteries inherently contain tasks that vary substantially in cognitive demand. Given that task difficulty is itself a major organizational axis in association cortex, it would be helpful for the authors to discuss how the optimization framework accounts for this. Specifically, could differences in task difficulty drive regional differentiation, even when tasks probe similar underlying cognitive processes? If so, how does the framework distinguish between organizational differences arising from a common demand axis and those reflecting more specific functional specializations?

(5) The Discussion places the proposed framework in the broader context of precision functional mapping and refers readers to a companion paper demonstrating advantages over resting-state ("inside-out") approaches. Given that these comparisons motivate several of the broader recommendations made in the Discussion, it would be helpful to provide a brief summary of the main findings of the companion paper here. This would allow readers to better understand the basis for these conclusions without relying on a separate manuscript.

Reviewer #3 (Public review):

Summary:

This manuscript reports a novel genetic model, Col20a1-CreERT2 knock-in mouse, to target terminal Schwann cells (tSCs) at mouse neuromuscular junctions in a cell-type-specific and temporal manner. The authors analyzed multiple publicly available single-cell transcriptome databases to identify Col20a1 as the tSC marker. The authors crossed Col20a1-CreERT2 and Rosa26-LSL-tdTomato to label tSCs successfully. In addition, the authors generated Col20a1-CreERT2; Rosa-tdT/ diphtheria toxin subunit A (DTA) to specifically ablate tSCs and analyze the role of tSCs in motor behavior, neuromuscular junction electrophysiological function, and the histology and ultrastructure of neuromuscular junctions.

Strengths:

The Col20a1-CreERT2 x Rosa26-LSL-tdTomato mice successfully labeled the tSCs at NMJs and reported the developmental distribution of the Col20a1-positive cell population. The Col20a1-CreERT2; Rosa-tdT/DTA mice successfully ablated tSCs, which did not cause changes in gross neuromuscular junction architecture, neuromuscular synapse physiology, or motor behavior.

Weaknesses:

The conclusion of this manuscript will be strengthened by additional analysis showing time-course data of tSC ablation and replacement by non-recombined Schwann Cells. Currently, it is not clear when and how long the tSCs are ablated, which makes it difficult to interpret the data and phenotype. Detailed review comments are provided to the authors in the "recommendations for the authors" section.

Reviewer #3 (Public review):

Summary:

The manuscript presents the development of a software tool and a computational workflow for the comparison and biological interpretation of GWAS results among three neurodegenerative diseases - AD, PD and FTD - using the data from the Human Protein Atlas. The multi-scale content analysis produces a representation of GWAS results highlighting enrichment at the level of brain regions, organ systems/tissues and cell types as well as in terms of molecular pathways. The manuscript argues that DLB, AD and FTD have differential 'modules' revealed by the procedure.

Strengths:

The system is leveraging vast knowledge sources including both the GWAS catalog and the HPA, and it is integrating these data. This synthesis of information is useful. It is making use of large investment in data generation and data warehouses in order to yield interpretation of epidemiologic results from GWAS in biological terms that may yield insight into disease processes and differences between diseases. The multiscale analysis recognizes the various biological lenses at which the implications of GWAS can be evaluated.

Weaknesses:

There is a lack of controls and/or disease comparators in the study. It is hard to assess that the workflow is performing 'as expected' without a set of positive or negative controls - or at least comparators - to gauge the performance of the tool. The statistical methods are simplistic and rely on Fisher's exact tests, UMAP analyses and clustering with little justification. There is a lack of power analysis and specification of the number of genes required for the procedure to 'work'. The mapping of GWAS hits to genes is simplistic and may in many cases be erroneous, and the implications of errors in these mappings are not considered. Many of the hits are not brain-specific - highlighting the complexity of gene function and the pleiotropic nature of gene activity. Moreover, the mapping between 'tissue enrichment' and 'tissue that is the functional driver of the GWAS signal' may be a logical flaw in the reasoning of the authors. Just because a tissue - such as liver enrichment in AD - is enriched in the GWAS gene-mapping analysis does not mean that that tissue found to be enriched is in fact the functional tissue that gave rise to the GWAS signal. Genes have different isoforms, functions, and regulatory mechanisms in different parts of the organism in different parts of development. In a phrase, the enrichment observed could be correlative and not causative, and in fact the enrichment could be driven by some hidden variable not considered. The etiologic tissue for neurodegenerative disease is the brain. The findings are not necessarily surprising or novel in the distinction between PD, AD, and FTD. Finally, the figures are perhaps not the best way to present results. There are many small pie charts that lack interesting results; the figures are in general hard to read and could use refinement in terms of fonts.

Reviewer #3 (Public review):

Summary:

This work describes how two chemosensory neurons in C. elegans drive opposite behaviors in response to a volatile cue. Because they have different concentration dependencies, this leads to different behavioral responses (attraction at low concentration and repulsion at high concentration). It has been known that many odorants that are attractive at low concentrations are aversive at high concentrations, and the implicated neurons (at least AWC for attraction and ASH for repulsion) have been well established. Nonetheless, by studying behavior and neural responses in a common context (odor pulses, as opposed to gradients) this provides a clear picture of how these sensory neurons may guide the dose dependent response by separately modulating odor entry and odor exit behaviors.

Strengths:

(1) This work provides good evidence that worms are attracted to low concentrations and repelled by high concentrations of 1-oct. Calcium imaging also makes it clear that dose-dependence of this response is stronger for ASH than AWC.

(2) This work presents calcium imaging and behavior with the same stimulus (sudden pulses in volatile odor concentration), while previous studies often focus on using neuronal responses to pulses to understand navigation of gentle gradients.

Weaknesses:

(1) As a whole it is not clear precisely how important AWC is (compared to other cells) for the attractive response (as the authors correctly acknowledge).

(2) The evidence that AIB minus AVA contains relevant information is weak. It appears the entrainment index in Fig. 6H for AIB-AVA could easily be explained by the negative entrainment between AVA and the stimulus (along with no effect or role for AIB). This is suggested by the similar p-values and similar distribution of random EIs (stretched and mirrored) between the first and last rows of this figure.

Reviewer #3 (Public review):

In this study, the authors aim to provide the most comprehensive and detailed topographic map to date of spinal projection neurons in the larval zebrafish brain. They achieve this by retrogradely photoactivating, in the rostral spinal cord, a photoconvertible GFP expressed pan-neuronally, and by constructing a template larval zebrafish brain atlas to regionalize the location of all labeled somata across the brain. The labeling strategy, together with the chosen animal model, provides strong support for the completeness of the dataset. The generation of a standardized anatomical atlas establishes a rigorous framework for analysis. Molecular and anatomical evidence suggesting evolutionary conservation of selected regions of interest appears solid.

Overall, the authors successfully achieve their aim. By generating this atlas of spinal projection neurons, they provide not only an anatomical framework of the regions involved, but also an important reference for improving the orientation and regionalization of the zebrafish brain, which has historically been difficult to define. This work may serve as a valuable resource for future evolutionary, developmental, and comparative studies of spinally projecting neuronal populations implicated in diverse functions.

Reviewer #3 (Public review):

Summary:

The central idea of the study is strong and potentially important: that the vulnerability of the cholinergic medial-septal population can account for a substantial fraction of prodromal-like AD phenotypes, thereby shifting part of the mechanistic focus from cortex-centered pathology to subcortical neuromodulatory circuit failure. The work has several notable strengths. The authors combine circuit mapping, calcium photometry, longitudinal EEG/EMG sleep phenotyping, histology, behavior, and a caspase-based lesion comparison to build a multi-level case for medial septal cholinergic involvement in REM Sleep and memory phenotypes. The inclusion of both a focal amyloid model and a partial cholinergic ablation model is especially valuable because it attempts to separate effects of Ch-neuronal loss from effects of amyloid itself.

However, the manuscript has several issues, from manuscript formatting to experimental design, overarching statements, insufficient exclusion of alternative explanations, incomplete quantification details for key histological results, a discussion that often moves beyond the actual data into speculative translational framing, and a discussion that completely ignores the early presence of p-tau in human AD patients and even lacks supplementary materials.

Strengths:

(1) The conceptual premise is compelling: cholinergic basal forebrain vulnerability is a real and important feature of AD, and testing whether selective medial septal cholinergic pathology can drive REM sleep and cognitive phenotypes is mechanistically interesting and clinically relevant.

(2) The experimental framework is broad and generally thoughtful, spanning anatomy, function, sleep architecture, EEG spectral parameterization, behavior, and histopathology.

(3) The projection mapping and photometry provide a useful systems-level introduction, establishing that MSChAT neurons are Wake/REM sleep-active and project strongly to hippocampal and cortical targets before the disease manipulations are introduced.

(4) The MSΔChAT comparison group is valuable because it allows the authors to argue that some phenotypes track with cholinergic loss rather than amyloid per se.

(5) The longitudinal sleep analysis is one of the strongest parts of the study, especially the emphasis on REM sleep quantity and bout architecture over time rather than relying only on an endpoint comparison.

Weaknesses:

(1) The title overreaches in its use of "prodromal phase." In the clinic, "prodromal AD" denotes a biomarker‑positive, pre‑dementia phase with subtle, progressive cognitive decline before widespread neurodegeneration, whereas here the authors demonstrate substantial cholinergic degeneration alongside cognitive impairment, which corresponds to advanced pathology within these models rather than a clinically prodromal stage. Moreover, APP knock‑in mice are amyloid‑centric, lack tau pathology, and don't recapitulate human disease staging; therefore, it would be better to avoid terms used for AD staging in the clinic. A more accurate framing of the title would be "Modeling the prodromal-like phase in an Alzheimer's disease mouse model".

(2) The opening statement in the abstract (line no 22) is overstated. Current evidence supports that changes in REM sleep, slow‑wave sleep disruption, and excessive daytime sleepiness are associated with a higher risk of AD and reflect early involvement of brain regions vulnerable to AD proteinopathy. No study indicates that REM sleep changes per se are a strong predictor on their own. For example, Jin et 2025 studied REM latency in AD and concluded that prolonged REM latency may be a marker of early neurodegeneration (PMID: 39868572). Thus, the opening statements need to be modified.

(3) Line 63: The current phrasing of neuromodulators being also essential for orchestrating sleep/wake states is very simplistic. Sleep/wake regulation is a highly complex process involving several interacting neurotransmitters and neuromodulatory systems. I recommend revising this sentence to reflect the broader, multi‑system nature of sleep/wake control.

(4) Line 64: "ACh is required for the generation of REMS" is incomplete. The sentence implies REM sleep generation depends exclusively on ACh. Instead, the sentence must emphasize that ACh is a crucial component of a broader REM sleep circuitry and explain why it is critical for REM sleep.

(5) Line 65: The sentence "Importantly, reductions and alterations in REMS have emerged as strong predictors of clinical AD onset" (Reference 37) is an overstatement of the evidence; Peas et al. 2017 analyzed a dementia cohort that included AD cases and concluded: "Despite contemporary interest in slow-wave sleep and dementia pathology, our findings implicate REM sleep mechanisms as predictors of clinical dementia." The authors should rephrase this to reflect that the study examined REM sleep changes in a mixed dementia population with AD, rather than to establish REM alterations as strong, standalone predictors of AD onset.

(6) Lines 73-75 address human Alzheimer's studies and state that basal BF-Ch neurons are vulnerable to Aβ but largely omit the well-established contribution of early tau pathology. In human AD patients, p-tau accumulation in BF is an early event (Braak I-II) and is closely associated with BF-Ch neuronal loss and BF atrophy and has been documented extensively. By relying almost exclusively on Aβ-centric framing, the current text risks implying that BF-Ch degeneration is solely amyloid-driven, which is not accurate. Even though the mouse model used here is "amyloid-heavy" and lacks tau pathology, the introduction should acknowledge the role of p-tau (especially when the paragraph contextualizes human studies) and clarify that in humans, BF-Ch vulnerability reflects converging amyloid and tau insults, so that readers do not infer a purely amyloid-dependent mechanism from the way the background is presented.

(7) Line 92: and elsewhere in the manuscript, I recommend avoiding the term "prodromal phase" and instead using the phrase "prodromal-like phase in an AD mouse model". The authors should be more precise in describing the disease stage in animal models that don't recapitulate human disease staging and ensure that clinical staging terminology is specific to human studies.

(8) Age and duration of pathology are major concerns. The different models are not adequately matched for amyloid exposure duration and age at testing. Age is the strongest risk factor for AD, and varying both chronological age and time under pathology across groups is a major design flaw. In MSChAT-AppNL-G-F/GFP mice, AAV injection was delivered at 11-13 weeks of age, and animals were sacrificed at 13-14 months post-injection (roughly 15-16 months old), whereas AppNL-G-F/NL-G-F knock-in mice and APPWT were 13-14 months old at the time of termination. Thereby, there is a difference in the duration of Aβ exposure across models. This mismatch directly weakens comparisons such as the lower epileptiform spike counts in MSChAT-AppNL-G-F versus AppNL-G-F/NL-G-F mice, because differences could simply reflect shorter cumulative pathology exposure rather than a genuinely weaker circuit-specific effect.

The same issue affects the internal control logic of the MSΔChAT model, which is intended to isolate cholinergic neuron loss from amyloid aggregation. For this comparison to be clean, ages and exposure durations should be aligned as closely as possible. Instead, MSΔChAT mice are tested earlier than the AppNL-G-F/NL-G-F and MSChAT-AppNL-G-F/MSChAT-GFP cohorts, introducing a 4 to 7-month age gap that complicates attribution of phenotypic differences solely to cholinergic loss versus amyloid pathology.

Finally, the absence of sham-operated controls is a concern, as it prevents separating the effects of the surgical procedure and AAV delivery from those of amyloid expression or cholinergic ablation.

(9) Line 115 through 117: The text cites Figure 2D, but does not refer to Figure 2C for the statement "their phenotypes were then compared in detail with MSChAT-AppNL-G-F and AppNL-G-F/NL-G-F global knock-in mice that were aged at the same time". Figure 2C depicts D54D2 amyloid staining in MSChAT-GFP vs MSChAT-AppNL-G-F mice. For clarity and consistency, I suggest adding a Figure 2C notation to this sentence (e.g., "Figures 2A, 2C").

(10) In Figure 1C-D, the authors map MSChAT projection targets across a wide range of brain areas, including hippocampal subfields, mPFC, primary cortices, entorhinal cortex, olfactory bulb, thalamus, anterior hypothalamus, amygdala, and medial habenula, and identify several of these as substrates through which MSChAT activity could influence REM sleep and cognition. However, the lateral hypothalamic area (LHA) is conspicuously absent from both the listed projection targets and the tracing panels shown in Figure 1D, despite the anterior hypothalamus being reported as an innervated region.

This omission is notable given that LHA-MCH neurons are among the best-established REM-sleep-promoting neurons, and the authors themselves cite prior work implicating LHA-MCH neurons in the AppNL-G-F REM sleep phenotype (ref. 49, 107; line 403) as an alternative cell-circuit candidate, a claim they explicitly try to weigh against their own MSChAT-centered model in the discussion.

a) The MSChAT neurons are reported to be REM sleep- and wake-active (Figure 1A-B), the same vigilance-state profile as LHA-MCH neurons,<br /> b) The Discussion directly engages with LHA-MCH neurons as a competing/complementary REM sleep-generating mechanism, and<br /> c) The reported anterior hypothalamus innervation (Figure 3C) raises the question of whether MSChAT axons specifically innervate LHA, and whether any projections specifically to LHA or LHA-specific amyloid deposition were examined. Clarifying this would help position the proposed MSChAT-hippocampal circuit mechanism relative to the well-established LHA-MCH REM sleep node.

(11) Line 125: "13- to 14-month-old MSChAT-AppNL-G-F mice immunohistochemical analyses employing the amyloid-specific antibodies....", in the methods section (Line 652) the authors mention MSChAT-AppNL-G-F and MSChAT-GFP mice were perfused 13-14 months after AAV injection (age at the time of injection was 11-13 weeks of age). This leaves the question of how they have 13- to 14-month-old MSChAT-AppNL-G-F mice available to study Amyloid-β load.

(12) Line 174-175: As currently written, the sentence could be read as both wild-type and homozygous AppNL-G-F/NL-G-F mice received AAV injections and were then aged 13-14 months post‑injection. In fact, the Methods clearly state that knock‑in mice are simply aged from birth without any AAV manipulation. The sentence should be rephrased to avoid suggesting that global APP knock‑in animals are part of the AAV‑injected cohorts.

(13) Lines 182-183, 196-197, and 209 refer to "Supplementary information" and imply that detailed behavioral data and analyses are provided in that section. However, in the current submission, the supplementary material consists only of Figures S1-S7 (Amyloid marker and cerebral vasculature, Aβ in hippocampus, GABA and glutamatergic neurotransmission, and sleep/wake parameters) and does not include supplementary figures or tables for the behavioral assays described in the main text. This discrepancy makes it impossible to verify the full behavioral dataset and the analyses referred to in the results section. The authors should carefully check the submission package and ensure that all referenced supplementary figures, tables, and detailed behavioral results are included and appropriately labeled.

(14) The lack of details for histological quantification is a major concern for a manuscript in which major conclusions hinge on Aβ load and MS-Ch neuronal counts. The histological quantification section is severely under-specified. The authors describe a 23% MSChAT loss, differences in regional Aβ burden, and a vascular association; however, the methods section is strangely silent about the quantification pipeline. For Aβ quantification, it is not clear whether "load" reflects percent positive area, plaque counts, or another metric; which Fiji thresholding algorithm(s) were used; how ROIs were defined; how staining batch effects were controlled; and how autofluorescence was normalized. For neuronal counts, the strategy for identifying and counting ChAT-positive neurons, normalization, and blinding are not described. There are no details on section spacing, axis of counting, the number of sections counted per animal, or whether both hemispheres were analyzed. Given that the reported differences are modest and central to the main claims, a more detailed and rigorous description of the image-analysis pipeline is essential.

(15) Statistical annotations in figures: There is inconsistency in how statistical significance is indicated across the figures. For example, in Figure 5C, the significance between MSΔChAT and AAV‑Aβ⁻ is indicated by a connecting bracket (**), whereas the comparison between AAV‑Aβ⁻ and AAV‑Aβ⁺ is marked by asterisks (***) placed above AAV‑Aβ⁺. In addition, the single asterisk above KI-Aβ⁺ does not clearly specify which pairwise comparison it refers to (e.g., AAV‑Aβ⁻ vs WT‑Aβ⁻ or another contrast). This heterogeneity makes it difficult to decipher exactly which group comparisons have been tested and found significant. The notation should be standardized and explicitly linked to the corresponding pairwise comparisons (for example, by using consistent brackets/lines and specifying all contrasts in the figure legend). Figures must be self-explanatory.

(16) Figure 5D statistical notation and group comparisons: The statistical markings in Figure 5D do not seem to match the results text and are difficult to interpret. The authors state that both MSΔChAT and AAV‑Aβ⁺ mice lack a preference for the novel object compared with AAV‑Aβ⁻ controls, yet the figure does not clearly indicate significance for MSΔChAT versus AAV‑Aβ⁻, and the notation over AAV‑Aβ⁺ is ambiguous. As a result, it is unclear which group differences are being tested and reported. It would be preferable to use the standard convention of placing significance annotations directly over the experimental groups (e.g., AAV‑Aβ⁺, KI-Aβ⁺⁺, MSΔChAT) or use notation above brackets to ensure that the figure labels are fully consistent with the statistical statements in the results.

(17) The discussion contains many compelling ideas, but it needs pruning and recalibration. The best discussion points are those linking the lesion comparison to REM sleep/cognitive outcomes and those situating MS cholinergic neurons within broader REM sleep circuitry. The least convincing sections are those implying disease-stage equivalence, prion-like spread, and direct therapeutic implications without sufficient evidentiary support.

(18) Line 448: The authors discussing reduced anxiety-like behavior in their model corroborates with the 3xTg mouse model (Ref: 116). Interestingly, they don't consider or include reports of anxiety-like disorders from human cohort studies that indicate the prevalence of higher anxiety and its association with preclinical and prodromal AD stages (SCD, MCI) and progression of AD. This apparent contradiction with the human literature is not discussed in the discussion section. The authors should explicitly address how their anxiolytic-like phenotype fits with clinical data (e.g., species differences, task specificity, disease stage, or model limitations) and clarify whether they view this as a limitation of the model or as evidence for a more complex relationship between amyloid, cholinergic dysfunction, and emotional behavior.

(19) Line 654 states, "Comparable durations of amyloid pathology," but this is not fully substantiated, as the onset and progression of amyloid in the AAV-driven MSChAT-AppNL-G-F model versus the global AppNL-G-F knock-in model are not described. The data support comparison at a similar late-stage amyloid burden, but not necessarily equal duration of pathology.

Reviewer #3 (Public review):

Summary:

The authors investigated the role of the nsP3 macrodomain catalytic activity in the replication and transmission of CHIKV in mosquito vectors. The conserved dual-host alphavirus catalytic site N24 has previously been shown to be essential for ADP-ribosylhydrolase activity. Despite this, mosquito-specific alphaviruses do not share this catalytic site. To assess whether the macrodomain catalytic activity of a dual-host virus was essential in insect hosts, the authors targeted the N24 site to abolish catalysis while maintaining binding capacity. The loss of ADP-ribosylation led to the emergence of compensatory mutations at site D31 that impact viral infectivity, dissemination, and transmission in Aedes sp. mosquitoes in vivo. The conclusions are well supported by the results and provide insight into the importance of nsP3 macrodomain activity in the mosquito vector, which hasn't been explored before.

Strengths:

The main strength of this study is the use of Aedes sp. mosquito models to investigate the selective pressure of macrodomain mutations in vivo. The functional characterization as well as the structural analysis of the mutants provide supporting evidence of a potential role of the compensatory mutations at site D31 in substrate recognition.

Weaknesses:

A considerable part of this study relies on the use of N24 mutant viral stocks generated in Vero cells, which yields an additional mutation at site 31 and consequently doesn't allow the authors to properly dissect the effect of mutation of N24 and D31 independently. It would be recommended to generate stocks with individual mutations in both A549 and U4.4 cells, pooling and concentrating them if needed. Replication of the N24A mutant in A549 cells does not lead to mutation at residue 31. Yet surprisingly, there is no reversion from N back to D at site 31 when the double mutant Vero stocks are passaged in A549. Since they are double mutants, it isn't possible to assess whether the defects in the growth of mutants N24A/T-D31N and N24D-D31H/N compared to WT are due to site 24 or 31, or both (Figure 2, panel c). Even though the authors emphasize that the compensatory mutation could have additional roles that impact viral infectivity and transmission in mosquito cells, it would strengthen the work to show that these mutations would spontaneously appear in stocks generated directly in mosquito cells. As a corollary, is it known whether insect-specific alphaviruses that lack macrodomain catalytic activity have corresponding mutations at site 31?

Additionally, there is a lack of consistency in the prevalence of WT virus at days 5 and 7 in in vivo experiments with Ae. albopictus and Ae. aegypti (Figure 3 and Supplementary Figure 2). This raises concern about the reproducibility of these experiments.

The inability to tease apart the roles of N24 and D31 in mosquito hosts partially prevented the authors from fully achieving their aims, but the work is nonetheless of interest to the field and suggests that more work is necessary to fully understand the role of the nsP3 macrodomain and its catalytic activity in the two disparate but obligate hosts for CHIKV and other dual-host alphaviruses.

Reviewer #3 (Public review):

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

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

However, I have a number of major concerns regarding the presentation and positioning of the work.

(1) It would improve the manuscript to clarify the present contribution with respect to a previous study by the same authors, namely Pokusaeva et al. 2019. Did the authors apply the same protocol to generate a new library of mutants, or did they re-analyse an already published library? If the library is not new, ambiguous sentences like "Nevertheless, to our knowledge, the His3p library remains one of the largest and most comprehensive resources that contains multi-site mutants" should be reformulated.

(2) Pokusaeva et al. 2019 is cited for the library and also for the deep neural network. It would be beneficial to briefly describe the architecture, the inputs and outputs, and the training procedure. Was the network trained on the current library? What is the purpose of this network? It looks more like an additive linear model (except for the global sigmoid) than a deep neural network. How does it relate to global epistasis models? The sigmoid function is designed to capture plateauing effects; doesn't that introduce some circularity issue in the reasoning?

(3) Are the super-compensatory mutations observed (conserved) across evolution? Beyond the fact that they are accompanied by mildly deleterious mutations in natural sequences. Can we predict them with variant effect predictors?

(4) The AAindex mention should be accompanied by a citation.

(5) Equations should be numbered. WCN formula seems to contain misformatting issues.

(6) A more explicit description of the structural data analysed (which PDB entry?) should be provided.

(7) I believe the citation Van Cleve and Weissman 2015 for the ProteinGym benchmark is incorrect. Additionally, is the Rosetta citation adequate?

(8) How is the definition of rescueability sensitive to the threshold choice?

Reviewer #3 (Public review):

Summary:

This manuscript asks whether two forms of explicit strategy use in visuomotor adaptation, i.e., algorithmic mental rotation and retrieval of a cached aiming solution, differentially influence implicit recalibration. The question is relevant because much prior work treats explicit strategy as a unitary process, whereas the algorithmic/retrieval distinction is theoretically meaningful and grounded in cognitive theory. Across three experiments, the authors report that algorithmic strategy conditions initially produced broader fitted implicit generalization functions than retrieval conditions, but that this difference was reduced or eliminated when reach variability and sensory prediction errors were more tightly controlled.

Strengths:

The paper is clearly written, theoretically well-motivated, and employs a commendably transparent and progressive experimental logic. The three-experiment structure, in which confounds are systematically identified and addressed, represents a strong model of cumulative experimental design (I will certainly use it in teaching courses on experimental methods):

Experiment 1 establishes an apparent difference in implicit generalization breadth. Experiment 2 attempts to reduce error spillover from Non-Critical targets by increasing angular separation and using delayed endpoint feedback. Experiment 3 uses an error-clamp design to decouple variable reaching from error feedback. This sequence is appropriate for testing whether the initial difference reflects a strategy-dependent change in implicit recalibration or instead follows from the distribution of movement plans and error exposure. The authors also provide reaction-time and performance data that are broadly consistent with the intended distinction between algorithmic and retrieval-like task performance.

Weaknesses:

The evidence does not support the strongest claims made in the manuscript, namely that algorithmic and retrieval strategies generally do not reshape implicit recalibration.

In general, I am skeptical of the authors' interpretation of null results. Several central conclusions depend on non-significant group differences, especially in Experiment 3. Non-significant tests are repeatedly treated as evidence that groups are equivalent or that confounds are absent (e.g., implicit recalibration magnitude (Algorithmic: 11.43 {plus minus} 6.43{degree sign}; Retrieval: 15.49 {plus minus} 8.99{degree sign}; t(38) = −1.65, p = .11), adaptation level before Exclusion probes (F(1,256) = 3.04, p = .08) and Exclusion RT differences (F(1,266) = 3.15, p = .08), whereas a modest model-dependent breadth effect (bootstrap p = .02) is treated as meaningful (for more on the model-dependent breadth effect, see below).

Without confidence intervals, equivalence tests, or Bayesian analyses, I think that the authors' interpretations comprise an inferential gap. A failure to find a significant difference is not equivalent to evidence of equivalence, particularly given that the implicit recalibration signal gets progressively attenuated across experiments (Experiment 1: ~16-17{degree sign}; Experiment 2: ~11-15{degree sign}; Experiment 3: ~7-8{degree sign}). With a substantially diminished signal in Experiment 3, the null result could partly reflect reduced statistical sensitivity rather than true equivalence.

My main technical concern is the analysis of generalization breadth already alluded to. The central claims rely on group-level Gaussian fits to only seven Exclusion probe locations spanning −45{degree sign} to +45{degree sign} around the Critical target. In several cases, the fitted centers and widths are poorly constrained by the sampled range. For example, in Experiment 2 the algorithmic group's fitted center is shifted to approximately 29{degree sign}, meaning that the probe range samples the function asymmetrically relative to its own peak. In Experiment 3, fitted centers are near or outside the sampled range, while estimated widths are very broad. Under these conditions, the width parameter may partly reflect extrapolation or parameter trade-offs between center, amplitude, and width rather than a genuine difference in generalization breadth.

Lastly, I think that the authors' use of an error-clamp paradigm is, from an experimental point of view, quite elegant. By controlling the sensory prediction error independently of reach direction, they can isolate implicit recalibration from the confounds identified in Experiments 1 and 2. However, I see a fundamental problem or question concerning construct validity here: In Experiments 1 and 2, the algorithmic strategy was operationalized as participants computing a counterrotated aiming direction in response to a visible cursor rotation. This is a naturalistic context where mental rotation is both required and meaningfully connected to task success. In Experiment 3, however, there is no visuomotor rotation to compensate for. The error-clamp renders the cursor feedback task-irrelevant. Instead, participants are instructed via text commands (e.g., "move towards 45{degree sign}") to reach invisible locations, rendering the "algorithmic strategy" in this context essentially an instructed spatial navigation toward arbitrary angular locations, not genuine visuomotor mental rotation driven by an error signal.

To put it differently, are we sure that the cognitive process engaged by the algorithmic group in Experiment 3 is the same as the algorithmic mental rotation strategy in Experiments 1 and 2? If not, then the null result in Experiment 3 may not speak to the original question about how algorithmic strategies interact with implicit recalibration after all. Instead, it may reflect the absence of a genuine strategy manipulation.

To their credit, the authors report a compelling RT dissociation that mirrors Experiments 1 and 2: The algorithmic group shows slower RT, which is decreasing over training (0.98s → 0.76s), whereas the retrieval group exhibits faster, stable RT (0.52s → 0.45s). While this pattern is consistent with genuine strategy differences persisting in Experiment 3, it could also reflect the greater spatial precision demands of reaching to invisible targets from text instructions, rather than genuine mental rotation per se. Reaching to an invisible location defined by a verbal angular label is inherently more demanding than reaching to a visible target, regardless of strategy type, and this demand is asymmetrically present in the two groups, since Non-Critical targets are invisible for the algorithmic group but visible for the retrieval group.

Thus, from my point of view, experiment 3 should not be used as definitive evidence that algorithmic and retrieval strategies during standard visuomotor adaptation cannot differentially influence implicit recalibration.

Overall, the manuscript addresses a meaningful question and the multi-experiment structure is useful. The evidence is incomplete for the broad claim that implicit recalibration is insensitive to strategy type. The study would make a clearer contribution if the authors narrowed the claims, strengthened the generalization analyses, and treated null effects with appropriate inferential tools.

Reviewer #3 (Public review):

Summary:

The manuscript investigates the function of the Drosophila Xkr protein, a homolog of mammalian Xkr8 that lacks the canonical caspase-cleavage motif. The authors show that apoptotic stimuli increase Xkr protein abundance through a post-transcriptional mechanism and that Xkr promotes phosphatidylserine (PS) exposure during apoptosis. Using immunoprecipitation coupled with mass spectrometry, they identify TM9SF4 as an Xkr-interacting protein and further implicate TM9SF4, Sac1, dORP2, dORP9, and Vap33 in regulating apoptotic PS exposure and efferocytosis. Based on these findings, the authors propose that Xkr regulates PS transport at ER-PM contact sites. Similar observations are also presented in human cells.

Strengths:

Overall, this is an interesting study. The authors provide convincing evidence that Drosophila Xkr participates in apoptotic PS exposure and employ multiple complementary approaches to support the involvement of several proteins in this pathway. The identification of TM9SF4 as a potential regulator of Xkr-mediated PS exposure is likely to be of broad interest.

Weaknesses:

I am less convinced by the evidence supporting the proposed role of ER-PM contact sites, and several mechanistic conclusions appear to extend beyond the data presented. Addressing the following points would substantially strengthen the manuscript.

Major concerns:

(1) In Figure 2A and related text, it is unclear whether the mass spectrometry analysis was performed using untreated cells or AcD-treated cells. If the objective was to identify apoptosis-associated Xkr interactors, it would be helpful to clarify the experimental condition and explain whether apoptosis-specific interactors were analyzed separately.

(2) In Figure 2B, 2E, and several other co-IP results, a negative control of Flag tag only is required to exclude experimental errors like insufficient washing, etc.

(3) In Figure S3B, S3F, and several other BiFC results, an mVC-only negative control would be important to exclude nonspecific fluorescence complementation.

(4) In Figure 2G, the quantitative values appear inconsistent with the flow cytometry histograms. The peak shift following Sac1 knockdown appears smaller than that of TM9SF4 knockdown, whereas the quantified values suggest the opposite. Please clarify this apparent discrepancy.

(5) I find the interpretation in Lines 223-227 difficult to reconcile with the data. Knockdown of both tm9sf4 and sac1 impaired apoptotic PS exposure to a similar extent as xkr knockout. However, while xkr deficiency significantly reduced efferocytosis, sac1 knockdown produced only a modest, statistically insignificant effect. These observations suggest that impaired PS exposure alone may not fully account for the efferocytosis phenotype observed in xkr-deficient cells. These results appear difficult to reconcile with the proposed model, which needs careful discussion.

(6) In Lines 274-275, the authors state that 'increased Xkr may accelerate non-vesicular PS transport for efficient apoptotic PS exposure'. However, Xkr protein levels increase only ~8 h after AcD treatment, whereas PS exposure occurs much earlier. Thus, alternative explanations like Xkr relocalization (Figure S5C), rather than increased abundance, may also explain how Xkr mediates PS transport. An Xkr overexpression experiment could be helpful to support this statement.

(7) The interpretation of the MAPPER experiments requires further clarification. In Line 283, the authors refer to "the intracellular proportion of the signal for each protein overlapping with MAPPER." Since MAPPER is designed to label ER-PM contact sites, which are located on the plasma membrane, intracellular MAPPER fluorescence likely represents the ER network rather than bona fide ER-PM contacts. Throughout the manuscript (including Figure S6, etc.), intracellular MAPPER puncta appear to be interpreted as ER-PM contacts, which may not be appropriate. In contrast, the peripheral MAPPER puncta observed along the cell cortex (e.g., Figure S5C after AcD treatment) are more consistent with authentic ER-PM contact sites. It is also not obvious that these cortical MAPPER signals colocalize with Xkr(Figure S5C). Thus, while the data support a role for the ER, they do not yet convincingly demonstrate Xkr clustering at ER-PM contact sites.

(8) In the Xkr knockout cells, all fluorescence signals appear substantially low in intensity. Differences in protein distribution are difficult to interpret when overall probe expression also appears altered. It would be helpful to demonstrate that probe expression levels are comparable between conditions. Furthermore, as noted above, intracellular MAPPER signal may primarily represent ER rather than ER-PM contacts. Finally, despite the reduced signal intensity, the remaining MAPPER and PS signals still appear well colocalized in the knockout cells, similar to the observations in Figure 2J. The interpretation in Lines 285-288 should therefore be reconsidered.

Reviewer #3 (Public review):

Summary:

Shin et al. examine hippocampal-prefrontal interactions during sleep using simultaneous CA1 and prefrontal cortex recordings in rats performing a spatial memory task. They identify high-frequency oscillation (HFO) events in PFC during REM sleep that occur in theta-modulated chains and are associated with increased CA1-PFC coherence and sequential, sparse reactivation of cortical ensembles. This pattern contrasts with the synchronous reactivation observed during NREM cortical ripples. Together with a simple cholinergic network model, the authors propose that REM HFO chains represent a distinct mechanism for hippocampal-cortical coordination that complements NREM ripple-mediated processing during sleep.

Strengths:

A major strength of the work is the extensive electrophysiological dataset, which includes simultaneous recordings of large neuronal populations in both hippocampus and prefrontal cortex across behaviour and subsequent sleep. The analyses linking high-frequency events to population dynamics, interregional coherence, and ensemble reactivation are technically sophisticated and provide an incredibly detailed description of REM-associated cortical activity patterns. In particular, the demonstration that REM HFOs occur in chains aligned to theta phase and organise sequential activation of cortical assemblies represents a potentially important advance in understanding the neural structure of REM sleep activity. The integration of experimental data with a computational model further provides a useful framework for interpreting the observed differences between REM and NREM network states in terms of neuromodulatory influences.

Weaknesses:

While overall this study provides a highly valuable body of work, there are two primary limitations, which if overcome, would provide substantially more significance to the overall characterisation of REM HFOs. Specifically:

Distinction from wake HFOs<br /> The results largely support the authors' claim that REM HFO chains represent a distinct pattern of neural coordination compared to NREM cortical ripples. The analyses consistently show differences between REM and NREM events in terms of neuronal modulation, ensemble structure, and interregional coupling. However, similar high-frequency events during wake are not examined. Since REM sleep shares several network features with wakefulness, including strong theta oscillations, evaluating whether comparable PFC HFOs occur during wake would provide clarity on whether these events are specific to REM sleep (and its associated functions) or represent more general theta-associated phenomenon.

Link to memory consolidation<br /> The manuscript proposes throughout that REM HFO chains may contribute to memory consolidation by coordinating hippocampal-cortical reactivation, but the evidence for this functional role remains indirect. The authors do highlight this as a limitation of the study - the inability to link their findings to learning - but it is not clear why. Further details of the behaviour results should be included. If no learning occurred across the eight behavioural sessions, this should be reported. If learning did occur, but could not be linked to HFO events, this should also be reported.

Comments on revised version.

The authors have since addressed these weaknesses. In supplementary figure S11 the authors now show that while HFOs were detectable during wake, they were not associated with gamma/theta oscillations or theta modulation of unit activity. This suggests that HFOs during REM are a distinct feature of REM sleep and not comparable to HFOs during NREM or wake. It would be interesting for future work to identify the significance of wake PFC HFOs, whether there are differences between HFOs during running compared to stationary behaviour, and their relationship to hippocampal sharp-wave ripples and memory consolidation.

Regarding the link between REM HFOs and memory consolidation, the authors have further acknowledged this as a limitation of the study and requirement for a more specific experimental design to test related hypotheses. Nevertheless, they do show a clear trajectory of learning in the rats and corresponding increase in reactivation of task-related activity which could be associated with REM sleep HFOs. This study paves the way for future experiments to more directly test this link.

Reviewer #3 (Public review):

Summary:

In this manuscript, Price et al. report the physiological conditions and proteins involved in the formation of mitochondria-derived compartments (MDCs), specialized domains exclusively containing outer mitochondrial membrane (OMM) proteins, in budding yeast. Hughes and his colleagues have previously established MDCs as unique multilamellar membrane structures derived from the OMM that arise with both mitochondrial metabolic perturbation and hydrophobic OMM protein load. Whether cells undergo MDC formation in response to physiological changes in mitochondrial biogenesis remains to be explored. In this study, the authors sought to test if glucose restriction, carbon-source switching, and salt stress can induce MDC formation, and found that these situations, which naturally promote acute mitochondrial biogenesis concomitantly with metabolic transitions, trigger MDC induction. Under these conditions, loss of Snf1, an AMP-activated protein kinase that facilitates mitochondrial biogenesis under metabolic stress, almost completely abolished MDC formation. Snf1 induces MDC induction under metabolic stress via phosphorylating (suppressing) Mig1, a transcriptional repressor of mitochondrial biogenesis. Consistent with this idea, loss of Mig1 mostly rescues MDC formation under glucose restriction or salt stress in cells lacking Snf1. The authors further found that acute induction of Hap4, a core activator of mitochondrial biogenesis, is sufficient to trigger MDC formation even without metabolic stress. Finally, cells lacking Tom70 and Tom71, protein receptors of the TOM (translocase of the outer membrane) complex that mediate targeting of hydrophobic mitochondrial proteins, almost failed to form MDCs under glucose restriction. Correctively, the authors propose that MDCs act in the reduction of OMM protein load upon metabolic stress-induced acute mitochondrial biogenesis.

Strengths:

The experiments for this study are well-designed, and the resulting data are mostly convincing, with proper controls and significant statistics to support their conclusions. The paper potentially provides new insights into the physiology of MDC formation.

Weaknesses:

There are only a few new mechanistic advancements in this paper.

Reviewer #3 (Public review):

Summary:

The authors present an innovative approach to tackle the lateral organization of mucins and trans-sialidases (TS) on the cell membrane of the organism Trypanosoma cruzi. By applying dual-color super-resolution microscopy (STORM), the authors report on a differential nanoscale distribution between mucins and TS on the cell membrane. Moreover, they find that 60% of mucins and TS are organized in nanoclusters with an inter-nanocluster distance following a random distribution. The remaining 40% of both proteins are organized in a non-random manner, and, using simulations, the authors claim that they are organized in rectilinear fibers.

Strengths:

The authors use dual-color STORM microscopy to unravel the protein nanoscale organization of mucins and TS on the cell membrane of Trypanosoma cruzi for the first time. They perform a dedicated analysis of the localizations and clustering of both proteins. Moreover, they perform, for every type of analysis on real data, simulations to compare their results for random organization. They also use an analysis approach together with simulations to propose that the lateral organization of both non-clustered proteins are within rectilinear fibers. They also complement their microscopy findings with BN-PAGE. Overall, the use of advanced microscopy techniques, corresponding data analysis and simulations is very solid and remarkable.

Weaknesses:

As the authors point out, they do not provide a molecular/biophysical mechanism explaining the non-random lateral organization of mucins and TS (both clustered and individual proteins).

Reviewer #3 (Public review):

Summary:

Akter et al. identify caspase 5 activation and Gasdermin E cleavage as a novel downstream executioner of ferroptotic cell lysis induced by erastin and ML162. These data are novel and very interesting to the wider cell death community.

Strengths:

Strengths of the study include the use and validation of findings in several mesenchymal ovarian cancer cell lines, the rigorous validation using small molecule approaches, siRNA-mediated silencing and CRISPR/Cas9-mediated knockouts with re-expression.

Weaknesses:

A weakness of the study is the fact that ferroptosis was not induced genetically (GPX4 ko) and, hence, off-targets of the mode of induction cannot be ruled out at this point (e.g. ML162 also targets TrxR1). Moreover, it would be vital to understand at which point in ferroptosis execution caspase 5 is activated in a time-resolved kinetic together with lipid ROS tracing to also obtain hints as to its possible activation.

Conclusion:

Despite the weaknesses described, this is a very interesting, timely, and well-executed study with the described limitations. The work provides important mechanistic insights into the interplay between ferroptosis and pyroptosis with possible consequences for inflammatory responses.

Reviewer #3 (Public review):

Summary:

The authors performed single-cell RNA sequencing of adult zebrafish hearts and identified markers for distinct cardiomyocyte subpopulations. One marker, phlda2, marks primordial cardiomyocytes. They generated transgenic reporter lines to characterize phlda2 expression patterns and a phlda2-NTR ablation line to determine the functional requirement of primordial cardiomyocytes during heart regeneration. They found that phlda2+ primordial cardiomyocytes are essential for myocardial morphogenesis and coronary vessel development. Interestingly, when phlda2+ primordial cardiomyocytes are ablated during heart regeneration, gata4+ cortical cardiomyocytes, coronary vessel revascularization, and scar tissue formation are not affected.

Strengths:

The authors identified a new primordial cardiomyocyte marker, phlda2. They further demonstrated that primordial cardiomyocytes are important for heart morphogenesis but dispensable for heart regeneration. Their findings reveal a potential difference between heart development and regeneration programs.

Weakness:

Despite the interesting findings, the authors did not provide supplemental data for their scRNAseq to demonstrate the data quality and support their conclusions, and some results are not well described.

Reviewer #3 (Public review):

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

Comments:

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

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

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

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

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

Comments on revised version.

These comments have been addressed in the revised version.

Reviewer #3 (Public review):

Summary:

This study tries to identify the genetic signatures of adaptation to starvation conditions. For this, outbred populations of Drosophila melanogaster were selected for starvation resistance by using the 20% surviving adults after starvation to start the next generation. This was done for 60 generations while parallel populations were kept under control conditions. At the end of the experiment, starvation-selected flies showed increased survival, longevity, and TGA storage. DNA poolseq data from control and starvation populations were compared to identify genomic regions with low heterozygosity and signatures of selective sweeps, and SNPs with differences in allele frequency. The candidate regions point to mitochondrial and metabolic pathways as the targets of selection for starvation resistance.

The authors replicate the experimental design, selection approach, data collection, and analyses from Hardy et al 2018 (https://doi.org/10.1093/molbev/msx254), which also investigated adaptation to starvation conditions but used a different Drosophila melanogaster population. In this sense, the current study recapitulates most of the findings from Hardy et al. (2018). The analyses of the mitochondrial results, including the overlap with human data, are the novelty of this paper. However, those analyses are not very well justified. The fact that this study is almost identical to Hardy et al is not clearly stated nor discussed in the manuscript.

Strengths:

The authors made use of an experimental evolution approach to identify the genetic basis underlying adaptation. This is a powerful approach that has proven very successful in the past. They used a good number of replicates (four per condition), an appropriate depth of sequencing, and quantified higher-order phenotypes to validate the claim that the populations had evolved increased starvation resistance.

Weaknesses :

Although the findings of this study seem credible based on the known biology of starvation resistance, there are several aspects of the experimental design that weaken my confidence in the results. The points below should be clarified, and the limitations of the experimental design and analyses need to be included in the discussion.

(1) Pooled genomic data were collected for the four replicates at the end of 60 generations of selection, and four replicates were kept under control conditions. No data were collected at the beginning of the experiment, which is the current standard in Evolve and Resequence experiments. To infer the genomic regions underlying adaptation to starvation, evolved control and starved cages are compared. Although this will identify regions that are possibly truly caused by adaptation to starvation stress, the available data doesn't allow to determine, for example: a) whether the differences between control and starvation regimes are due to changes in control cages relative to the starting population, combined with no changes in starvation cages relative to the starting population; b) whether the differences across replicates are due to different genomic composition at the start of the experiment that could have been amplified by drift.

(2) Selection was applied by starving flies until ~80% of the population died. The 20% surviving flies were used to seed the next generation. The control populations, on the other hand, were propagated using the whole population. Given that only the starvation populations were subject to such a strong bottleneck, it is not possible to disentangle whether the genomic signatures at the end of the experiment are due to this, and not necessarily to starvation resistance. For example, the low heterozygosity blocks and the very great changes in allele frequency could be a natural result of such a bottleneck. A proper comparison would have been to select a random 20% of the control individuals to seed every generation.

(3) The analyses that involve human populations are poorly justified, and the enrichment tests are not clearly explained. There is no evidence of signatures of selection for starvation resistance in human datasets (as mentioned in the text, line 112), and yet the authors claim that their analyses that identify branch-specific alleles for a set of four human populations serve as a dataset for it. I don't think the results of this analysis and further overlap with candidate genes identified in the Drosophila experiment support the conclusion that polygenic adaptation of metabolic pathways is conserved across species (line 303).

(4) The conclusion that adaptation to starvation conditions is repeatable is not justified by the data. The overlap across replicates is very low in every metric.

(5) The methods are poorly described. In most of the sections, there is not enough information to be able to replicate the experiments or the analyses. Several of the analyses presented in the results are not described in the methods. Without this information, it is very difficult to assess whether the analyses were correctly done or whether the results are robust.

Reviewer #3 (Public review):

Summary:

PLCβ3 is activated by both Gαq and Gβγ subunits. This paper follows previous solution and cryoEM studies of the PLCβ3 / Gβγ complex to delineate the molecular details of activation using cellular BRET assays and cryoEM.

Strengths:

The authors find evidence for multiple binding sites on PLCβ3 for Gβγ and suggest that Gβγ is not bone fide activator per se but enhances Gαq activation by positioning the catalytic site towards substrate. The authors also find that this activation is not through recruitment of the enzyme to the membrane by Gβγ released upon G protein activation in accord with other PLCβ enzymes.

Weaknesses:

(1) The main issue is that the author's mechanism does not fully explain how Gβγ activation occurs for PLCβ2 in reconstituted systems in the absence of Gαq subunits but will be investigating this in future studies.

Reviewer #3 (Public review):

Summary:

Drosophila, like other animals, use sophisticated taste systems with specialized chemoreceptors to identify gustatory cues in their environment. Multiple gustatory cues associated with a food source are often encountered simultaneously, but our understanding of how this sensory information is detected and integrated remains incompletely understood. This valuable study investigates how salt, amino acids, or their combination are detected by specific combinations of peripheral Ionotropic Receptors, leading to behavioral attraction. The authors show that distinct combinations of IR76b, IR25a, and IR20a confer sensitivity to salt, arginine, or both. They also show striking evidence that cells co-expressing IR25a/IR20a display a synergistic response to a mixture of sub-activating concentrations of these tastants. Together, these experiments lead to the conclusion that combinatorial expression of different subunits and synergistic responses to taste mixtures facilitates integration of taste cues beginning in the periphery. However, in its current form, key methodological details are missing or inadequately described, which complicates interpretation. Additionally, characterization is heavily focused on the population of IR20a+ neurons in the tarsi, while the response properties of the newly-identified, functionally distinct population in the labellum are investigated only through behavioral analysis, limiting the description of potentially additional IR20a complexes. Ultimately, more in-depth biochemical characterization of the IR complexes described will be required to fully support the conclusion that combinatorial assembly of distinct IR20a receptors enables peripheral integration of taste mixtures.

Strengths:

The authors characterize the expression pattern of IR20a in the tarsi as well as in the labellum, a tissue for which IR20a expression has been a point of debate. Multiple levels of analysis, including behavioral assays, physiological recordings, as well as ectopic and heterologous expression systems, are used to characterize the response properties of different combinations of IR subunits, demonstrating remarkably consistent behavior of the IR-complexes across cell types. Well-controlled genetic analysis and the use of multiple behavioral assays provide additional support for their results, including the surprising demonstration of synergistic responses to mixtures of tastants that supports the idea of peripheral integration of gustatory inputs. This report also identifies a distinct IR, IR56b, required for starvation-enhanced responses to salt.

Weaknesses:

(1) The title states that IR20a integrates L-arginine and salt signals via distinct subunit assemblies, though the paper lacks direct evidence that IR20a serves as a multimodal tuning receptor in distinct functional assemblies. Heterologous expression shows that co-expression of IR20a/IR25a confers sensitivity to Arg, IR76b confers sensitivity to NaCl, and IR20a/IR25a/IR76b co-expression confers sensitivity to both Arg and NaCl. This seems to be interpreted to mean that all three subunits are assembling into a single complex. However, current results do not show any difference in salt response when IR76b is expressed alone compared to alongside IR25a+IR20a. Without more direct evidence for co-assembly of all three subunits, it is equally plausible that the responses observed represent activity of distinct IR25a/IR20a and IR76b receptors for Arg and salt, respectively. In this model, genetic disruption resulting in expression of either IR25a or IR20a alone with IR76b could disrupt its activity or membrane trafficking (as seen here and in previous studies) while co-expression of both IR20a and IR25a relieves this inhibition by sequestering IR20a/IR25a into a distinct complex from IR76b. Direct biochemical characterization, for instance in the form of co-immunoprecipitation or FRET, will be required to differentiate between these possibilities.

(2) Key methodological details are missing throughout the manuscript. For instance, incomplete genotype and staining information is provided for images in Figure 1, making it difficult to interpret what is being shown. Additionally, for the calcium imaging methods, what is the imaging speed? How are max values calculated (is this the average of several images or just a single maximum)? How are ligands diluted and delivered to cells, and were they applied in a manner that allowed for subsequent washout?

(3) The composition of the S2 imaging bath buffer requires clarification. As described, the bath buffer appears to lack any Ca2+ or other IR-permeable cations. If this is indeed the case, more detail should be provided about why this bath buffer was selected and what this means for the source and mechanism of calcium responses observed, since it would not reflect direct IR-mediated transduction. It is also notable that addition of water gives such a detectable change in the tarsal preps.

(4) Visualization of IR20a driver activity in the labellum is interesting. Previous descriptions of labellar expression of IR20a range from no expression to expression in bitter neurons, so the current data linking IR20a to a different population of IR76b+ neurons warrants careful analysis in light of this discrepancy. However, some of the strongest presented evidence for expression is found in Figure 1, where the images are quite small, making it difficult to distinguish the morphology and sensillar innervation pattern of the cells labeled by the IR20a driver. In Figure 1A, several of the arrows do not appear to be associated with any visible fluorescence. It is similarly difficult to assess overlap. Including higher-resolution images and/or validating labellar expression, using antibodies, in situ hybridization, RT-PCR, or transcriptomics would strengthen these claims.

(5) Similarly, Figure 2 shows that IR20a is not required for Ca2+ responses to AAs or KCl in the legs, but is required for behavioral preferences and PER responses in the labellum. This suggests that IR20a receptors may function differently in different tissues, though direct evidence is lacking. Calcium imaging from a weakly expressed driver may be difficult, but electrophysiological recordings from relevant labellar sensilla or ectopic/heterologous reconstitution of the molecular receptors found there would give important insights into the response properties of these other IR20a receptor type(s) and could provide evidence for additional IR20a-containing complexes. The current paper focuses exclusively on IR25a/IR20a/IR76b, which do seem to reliably reproduce the Arg/NaCl responses observed in the tarsi, but even for the tarsal neurons it is unclear that this represents an exhaustive list of all the relevant IR20a-interacting subunits coexpressed in these cells. For instance, Koh et al., 2014 (PMID: 25123314) found several additional IR driver lines, including IR56b, were active in the 5v/s tarsal sensilla.

Reviewer #3 (Public review):

Summary:

Here the authors investigate the role of the Trypanosoma brucei polo-like kinase TbPLK in the function of flagellum-associated cellular structures in trypanosomes. They set out to test the hypothesis that a key substrate of TbPLK is the kinesin protein KIN-G, and that TbPLK phosphorylation of KIN-G regulates its functions in cells.

Strengths:

Using in vitro biochemistry with purified proteins, the authors convincingly demonstrate that TbPLK phosphorylates KIN-G at 29 sites. Moreover, they convincingly show that phosphorylation at one site, T301, impairs the binding of purified KIN-G to purified microtubules. They further confirm that inhibition of TbPLK in cells reduces KIN-G phosphorylation at T301 (and S569). Using immunofluorescence-based imaging approaches, they also show that TbPLK colocalizes with KIN-G at centrin arms during early S-phase of the cell cycle. Centin arms are structures that are located near the basal body and flagellum and are important for new flagellum biogenesis, Golgi positioning, and cell division. To evaluate the function of KIN-G phosphorylation in cells, they depleted KIN-G by RNAi, simultaneously expressed phospho-mimetic (T301D) and phospho-ablative mutant proteins, and used immunofluorescene to examine the impact on flagellum-associated cellular structures. They show that expression of the phospho-mimetic mutant KIN-G-T301D causes the following defects: reduced cell proliferation, disruption of centrin arm and Golgi biogenesis, impairment of FAZ elongation and flagellum positioning, and misplacement of the cell division plane. The data convincingly support the conclusion that KIN-G phosphorylation on T301 plays an important role in regulating the cellular functions of this kinesin motor protein.

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether spontaneous gamma-band oscillations over the parieto-occipital region can be volitionally upregulated using EEG neurofeedback, and whether this upregulation reduces subsequent pain perception and nociceptive-evoked brain responses. Gamma-band activity has been repeatedly associated with pain processing, but most available evidence remains correlational, and previous attempts to modulate pain-related gamma activity using non-invasive stimulation have not produced robust analgesic effects. The present study therefore addresses an important question: whether real-time neurofeedback may provide a more effective way to train endogenous gamma activity and thereby influence pain.

Strengths:

A major strength of the study is the use of an active/sham neurofeedback design. The authors also combine subjective pain ratings with laser-evoked potentials, which provides converging behavioural and neurophysiological outcome measures. The manuscript is clearly written overall, and the study addresses a question of broad interest for pain neuroscience and neurofeedback research.

Weaknesses:

A number of aspects limit the strength of the conclusions. The first and most important issue concerns the interpretation of scalp gamma-band activity. Gamma-band oscillations recorded with scalp EEG are difficult to measure reliably, are not observable in all participants, and can be strongly affected by muscle activity. The authors acknowledge this issue and include posterior neck EMG, but the control remains limited. A lack of correlation between one posterior neck EMG channel and Pz gamma power is not sufficient to exclude muscle contamination, especially because gamma-band artifacts can arise from multiple muscle groups and may not be well captured by a single EMG channel. This is particularly important because changes in posture, facial tension, breathing, and arousal could all influence high-frequency scalp activity.

Second, the evidence for a causal relationship between parieto-occipital gamma activity and pain perception should be interpreted cautiously. The authors show that gamma power increased in approximately half of the active neurofeedback participants and that these responders showed reduced pain ratings and laser-evoked potentials. However, because the main analgesic effect is tied to responder classification, it remains difficult to separate the specific effect of gamma upregulation from broader individual differences in task engagement, suggestibility, relaxation ability, attentional state, or neurofeedback learning capacity.

A third limitation concerns the control condition and blinding. Participants were reportedly blinded to group allocation, and the credibility ratings appear similar between groups, which is reassuring. However, it is not clear whether the experimenters were also blinded during data collection and interaction with participants. This matters because neurofeedback studies are particularly vulnerable to expectancy.

The choice of the two neurofeedback scenarios requires clearer justification. The manuscript describes a deep ocean scene followed by a seaside scene with relaxation instructions, but it is not clear why these two scenarios were selected, and whether they were matched for attentional engagement and affective content. This is not a minor point, because both groups showed reductions in pain ratings after the entire neurofeedback procedure.

The comparison with tACS is interesting but currently underdeveloped. The authors suggest that neurofeedback may succeed where gamma-frequency tACS failed because it allows real-time, personalized, self-regulatory modulation of ongoing activity. This is plausible, but the manuscript should discuss this distinction more deeply. Neurofeedback may not simply be a different way of modulating gamma; it may recruit volitional control, attentional engagement, immersion, expectation, etc. These mechanisms could be central to the observed pain reduction and may partly explain why neurofeedback effects differ from those of externally applied stimulation.

Overall, this is an interesting study that introduces a promising neurofeedback approach for experimental pain modulation. The findings are encouraging, especially the convergence between subjective ratings and laser-evoked potentials in responders. However, the conclusions should be tempered. The current evidence supports the feasibility of training gamma-band activity in a subset of participants and suggests that successful training is associated with reduced experimental pain.

Reviewer #3 (Public review):

Summary:

This manuscript reports that RNAi depletion of the inner-ring nucleoporin NPP-3/NUP205 in Caenorhabditis elegans embryos causes nuclear envelope rupture, premature chromatin condensation, and relocalization of condensed prophase chromosomes to the nuclear periphery. Through a candidate epistasis screen, the authors argue that this relocalization requires spindle assembly checkpoint (SAC) components (MDF-1, MDF-2, SAN-1), inner kinetochore proteins (HCP-3, HCP-4, and partially KNL-1), and NE rupture-repair factors (BAF-1, LEM-2), but not the CEC-4 heterochromatin- or SUN-1/POT-1 telomere-anchoring pathways. They further show that NPP-3 loss extends prophase and the NEBD-to-anaphase interval in a SAC-dependent manner, redistributes MDF-1/MDF-2, and reduces import of KNL-1/BUB-1/HCP-1. Co-depletion of NPP-3 with MDF-1 abolishes both the arrest and the peripheral localization while increasing lagging chromosomes, HUS-1 foci, micronuclei, and lethality, which the authors interpret as evidence that peripheral positioning is protective.

Weaknesses:

(1) The "protective" conclusion is largely correlative. The protective claim rests on the observation that co-depleting MDF-1 (or MDF-2) with NPP-3 removes the peripheral localization and simultaneously increases DNA damage, micronuclei, and lethality. However, depleting a SAC component removes at least three things at once: the peripheral localization, the prophase extension, and the NEBD-to-anaphase arrest. Because loss of the SAC independently causes premature anaphase and genomic instability through well-established mechanisms unrelated to chromosome positioning, the current design cannot separate damage caused by loss of a protective peripheral location from damage caused by checkpoint bypass. As presented, the increased damage is at least as consistent with simple SAC bypass. To support the protective model, the authors should provide a manipulation that disrupts peripheral positioning without abrogating the SAC-dependent arrest (for example, via the BAF-1/LEM-2 or kinetochore depletion) and show that damage still increases. The LEM-2 co-depletion, which partially suppresses positioning, is a natural place to test whether micronuclei and HUS-1 foci also rise.

(2) Knockdown efficiency of the partner gene in double RNAi is not verified. The double depletions are performed by cloning both gene fragments into a single vector. This risks reducing the effective dose of each dsRNA, so an apparent suppression in an npp-3; gene X (RNAi) condition could reflect weaker NPP-3 knockdown rather than a true epistatic relationship. The authors partially address this by showing that NPP-3::mCherry is still reduced in npp-3;mdf-1 (Figure S4A/B), which is helpful, but they do not demonstrate efficient knockdown of the partner genes in any double condition. For the key epistasis conclusions (MDF-1, MDF-2, HCP-3, HCP-4 suppressions), the knockdown of the second gene should be independently validated with a reporter strain for the second protein.

(3) Alternative explanations for the transcriptomic and H3K9me3 data are not excluded. NPP-3 depletion blocks nuclear import of molecules smaller than ~70 kDa and arrests development at early gastrulation. Both the RNA-seq changes (30% of genes downregulated) and the increased H3K9me3 signal could therefore be secondary consequences of nucleocytoplasmic transport failure and developmental arrest rather than evidence of position-dependent transcriptional repression. Notably, the authors' own finding that up- and down-regulated genes show no chromosomal positional bias (Figure S2C/D) argues against a model in which peripheral repositioning drives silencing of specific chromatin domains. This section should be reframed more cautiously, with the transport/arrest confound explicitly discussed, and RNA-seq replicate number and differential-expression thresholds reported.

(4) Evidence for SAC "activation in prophase" is indirect, and the effect is small. The claim of a novel prophase role for the SAC rests on MDF-1/MDF-2 intensity changes that are repeatedly described as "modest," "slight," or "mild," measured with small n and Student's t-tests, together with phenotypic suppression of prophase extension. There is no direct readout of SAC catalytic activity (for example, MCC assembly). The prophase-extension suppression by MDF-1 is the strongest evidence; the intensity data are weak support. I recommend tempering "the SAC is activated in prophase" to a hypothesis, and strengthening it with a more direct assay if feasible.

(5) The BAF-1 arm of the model is inferred rather than demonstrated. The authors state that baf-1(RNAi) and npp-3;baf-1 produced clustering too severe for epistasis, so BAF-1's requirement for peripheral localization is not actually established genetically; it rests on increased BAF-1 accumulation (correlative) plus the LEM-2 partial suppression. The proposed BAF-1/CENP-C bridge is extrapolated from Drosophila (ref. 71). This is reasonable as a discussion hypothesis but should not be presented in the abstract or summary model as an established dependency.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Comments on revised version.

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

i got doberman, and i feel like that is true to my personality, cause im very efficient i try to do as good as a job as needed to do or complete, i do disagree about the impatient part cause my patient level is pretty good and i will prove that, or i will at least try to meet that expectation as much as i can, and i do take risks cause without risk there is no reward or close to it in my mind.

Reviewer #3 (Public review):

Summary:

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

Strengths:

This study presents several key strengths of the optimised E. coli cell-free protein synthesis system in terms of its design, performance and accessibility.<br /> - The reaction mixture has been dramatically simplified, with the number of essential core components successfully reduced from up to thirty-five in conventional systems to just seven.<br /> - The "fast lysate" protocol is a significant advance in terms of procedure.<br /> - The system's ability to synthesise challenging, functional proteins is evidence of its robustness.

Comments on previous version.

The authors have adequately addressed my previous concerns.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors investigated TBRS etiology by using new human pluripotent stem cell models, modeling varying levels of TBRS-associated loss of DNMT3A function. They identified increased lineage-specific proliferation of precursors in TBRS ventral MGE-like progenitors, which they propose was related to increased signaling through the PIK3/AKT/mTOR pathway. Furthermore, they show that reduced DNA methylation during MGE-like progenitor differentiation into GABAergic interneurons can cause a premature expression of neuronal and synaptic genes, triggering precocious neuronal maturation. In conclusion, they propose that TBRS-derived GABAergic neurons exhibit hyperactivity that can alters the development and structure of neuronal networks.

Strengths:

Overall, the data presented is convincing, from an early developmental point of view, given that the iPSC-derived 2D cultures or organoids used do not get to reach a mature state. Nonetheless, the data clearly show the effects that deleterious mutations in TBRS can cause during the period of neurogenesis, which was missing in the field.

Comments on revised version.

The authors have responded to the reviewer's comments satisfactorily, and the manuscript has been much improved.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors aimed to assess the mechanisms of social influence on charitable giving, particularly by separating the role of donation magnitude and variability in others' donations, and by examining the role of incremental social information in a learning framework. They additionally investigated individual differences in the magnitude effects in relation to self-reported psychopathy and empathy. The main findings suggest that magnitude and variability of others' donation impacted the magnitude and variability of the participants' donations, respectively, and that the weight of social information on individual decisions correlates positively with psychopathy, but not with empathy.

Strengths:

(1) The findings extend previous evidence for social influence on charitable giving to contexts where social information is provided incrementally, and to effects on the variability in social information (in addition to the mean).

(2) Individual differences suggest a role for psychopathy, but not empathy.

(3) Findings are replicated across all 4 (or for some findings 3 out of the 4) experiments, which helps strengthen the claims.

(4) Multiple experiments are a strength, especially Experiment 4, which helped address concerns/potential confounds in the previous experiments, increase representativeness of the sample, add incentive compatibility, and generalize to another task domain (perceptual).

(5) For modelling, strong model and parameter recovery was obtained, thus validating the modelling pipelines.

(6) The experiments were pre-registered, though it's unclear whether only planned analyses were pre-registered, or specific directional hypotheses. It would help if the manuscript took the reader through the pre-registration (and any deviation from it), instead of expecting the reader to do the comparison between the pre-registrations and actual manuscripts.

(7) The studies are appropriately powered, and power analyses are provided.

Weaknesses

(1) Lack of rationale and justification for the between-subjects design.

While this design may be appropriate in some cases (for example, for the generalization of donation to new charities or as a potential "intervention"), it would have been great to know if the findings related to social influence extend to a within-subjects design, especially given the weak results related to the effects of standard deviation in others' donations. It is possible that variability in others' responses would have a stronger effect if manipulated within individuals, since the same individual exposed to both high-SD and low-SD social information may weight low-SD information more, but this effect may lack when individuals are only exposed to the same variability across trials.

(2) Motivation for the RL framework.

The use of reinforcement learning (RL) isn't very well motivated, both in the introduction and methods/results (given the task). In particular, why is RL relevant to studying the problem of social influence, which isn't inherently a learning problem? This should be better motivated in the introduction. Second, when taking the task into account, it's unclear why RL is an appropriate model, given that from the perspective of the participant, the 5 others are different individuals, so the model shouldn't assume that predicting an individual's donation should be related to the previous individual's donation. Unless participants are informed that there is some dependency between the 5 donors they observe on each trial? If so, this should be made clear.

(3) Specifics of modelling analyses, and separability between prediction and second donation data.

Does the RL-based model (either prediction-only or hybrid) explain more variance in second donations than a simple linear regression model predicting second donation from initial donation and the mean of others' donations (or each individual other's donation)? It could be helpful to add some models that include social influence (i.e., integration of social and individual information) but no learning mechanisms per se. If this is not done, I do not believe that current results show that participants combine "their initial self-donation tendencies with their predictions of observed others' giving to guide their second individual donations". While participants may update their predictions, the authors should test multiple models of prediction update (fit only on the prediction data to understand the specific mechanisms of prediction update independently of second donation - for example, is it RL, or could it just be a running average, or some other heuristic? In parallel, it would be helpful to test whether it's the learned predictions (or whatever other prediction update mechanism was found to best explain the prediction data) or the actual others' donation information that best explains second donation - when combined with initial donation. These latter models would be fit on second donation data only in order to be comparable. If it's not possible to separate people's predictions from the actual social information (others' donations) then this should be acknowledged as a limitation. Ultimately, separating the modelling by data type (prediction only vs second donation data only) would help provide more insights into the learning mechanisms (if any) and whether it's learned prediction, or just social information, which influences second donation.

(4) Missing statistics in generalization to novel donation results.

On page 13, in the generalization effect, the authors mention that "Compared with participants exposed to High-SD social information, those exposed to Low-SD social information exhibited less variability in their novel donations, with this effect being especially pronounced in the Low-Mean condition." Was this supported by a significant interaction between SD and Mean condition? If so, please report the statistics of the interaction; if not, it's probably better to refrain from making this claim.

(5) Behavioral index of social influence individual differences.

For the first analysis reported on the association with psychopathy (Figure S9), as well as empathy (Figure S10), the absolute change between first and second donation does not seem like the appropriate marker of social influence. While I understand from Figure 2 that most participants changed their donation in a direction consistent with the social information, it would appear more appropriate to calculate an index of donation change consistent with influence, so calculated as D2 - D1 for the high mean groups and D1 - D2 for the low mean groups. This would be a better measure to interpret high values as an index of social influence.

(6) Interpretation of psychopathy effects.

a) The general idea that high psychopathy would be associated with increased social influence seems counterintuitive. While I appreciate that the authors controlled for additional variables such as age, gender, condition, and other model parameters, is it possible that this effect could be instead explained by the availability heuristic (the social information is more readily available to participants than their individual choice from the baseline trials), lower memory for their own choice, or lower IQ/cognitive abilities? These appear to be important confounds to address to be able to interpret the findings.

b) Related to this, and given that psychopathy/empathy were negatively/positively related to baseline donation amounts, it would be good to account for baseline mean donation amount in the individual difference analyses.

c) Finally, the authors interpret this association in line with other studies that have shown strategic social blending in psychopathy - while this seems possible in contexts where others are present, it doesn't really seem to be the case in this task. Did participants believe the other donors were watching them somehow? It also appears contradictory for the incentivized experiment, whereby if high psychopathy participants would no longer be able to "maintain a favorable social image while still pursuing their own self-interests" (p.23), since as soon as incentivization is added, participants' own self-interests are directly in conflict with the social image. Was participants' understanding of the incentive compatibility tested in Experiment 4?

(7) Asymmetry between generous vs stingy social influence and link with psychopathy.

a) Was such an asymmetry present - in other words, were people more strongly influenced by generous others or stingy others, or were the two comparable? I believe some analyses could be added to test this, and this is also where a within-subject design could help (e.g., different parameters for the two directions of social influence at the individual levels).

b) Related to that, does the correlation with psychopathy vary between conditions? It appears important to test if the increased social susceptibility is general or specific to increases (~high mean group, generous social influence) or decreases (~low mean group, stingy social influence) in donation. I understand that the main effect of psychopathy survived controlling for conditions, but it would still be interesting to test for an interaction between psychopathy and condition in predicting donation changes (calculated as suggested in point 5 above) or social influence weight.

(8) Perceptual task in Experiment 4.

a) While it is good to show that there was no correlation between psychopathy and initial estimate in the perceptual task, were there differences in initial estimate accuracy (i.e., difference between initial estimate and correct answer) along psychopathology? If so, this should be controlled for in the analyses. Given that social influence is always in the direction of the true value, the proportional deviations between initial estimate and social information could yield larger numerical differences and induce larger changes in estimate.

b) Even if previous studies have excluded rounds in which participants update their estimate in the opposite direction of the social information or move beyond it, I believe analyses that include those rounds should be included, especially in the context of individual difference analyses. Could it be that individuals who are high in psychopathy or low in empathy have a higher proportion of rounds where they go against the social influence? The same question applies to the main 4 experiments (in case this criterion was applied to) as well as the perceptual task.

c) Because the perceptual task was completed by the same participants as Experiment 4, were the two social influence measures correlated across tasks? Was psychopathy better predicted by a combination of predictors across the two tasks?

(9) Were individual difference measures examined in relation to the variability effect?

(10) Discussion.

The authors argue against a role for opportunistic conformity. While I tend to agree with their interpretation, I believe that it could be strengthened as follows:

a) First, it relies on a null result (the absence of a difference in decreases between low-mean low-SD and low-mean high-SD groups), which I do not believe was explicitly tested; and even if it was, it should ideally be corroborated by Bayesian statistics to provide strength of evidence for the null effect.

b) Second, this could be a great opportunity to dive into the mechanisms of social influence in the model, by testing the theory that only the lowest (or highest) donation from the group (rather than the mean, or the learned prediction) influences donation. Could a subset of participants be better fitted by such a model?

(11) Methods. Maybe I missed it, but it's unclear what participants were told about the other donors they are observing. It is mentioned that they were fully debriefed after the experiment, but what they were told in the instructions appears important. Was believability tested (this also relates to my comment #1 about the rationale for a between-subjects design, which creates fairly biased sets of social information from the perspective of a single participant)? And related to my comment #2, what participants were told about the donors could help justify the rationale for the RL framework.

Reviewer #3 (Public review):

Summary:

Chovanec and Yin used their newly developed sci-L3-Strand-seq powerful method to characterize SCE after Cas9 cleavage in a human cell line, using either a single target site or an element repeated 237 times in the genome. SCE are often neglected in DNA repair analyses since they are “genetically silent”. Interestingly, the authors found enrichment of SCE at unique Cas9 sites, but only a modest enrichment of SCE when Cas9 targets 237 sites in the genome. The genetic control of SCE formation at Cas9 sites is not deliberately addressed in this paper. However, the authors found that targeted SCE seem to be enriched in a subpopulation of cells, particularly “permissive” for SCE, but the determinants of such a population are unknown. Finally, the power of the sci-L3-Strand-seq allowed the authors to characterize a specific type of SCE based on the analysis of reciprocal daughter-cell pairs' genomes that is associated with a specific type of chromosomal rearrangement compatible with the ones observed in HR defective BRCA1/2 deficient cells.

Strengths:

This is an interesting paper that molecularly explores sister chromatid exchanges, which represent an important challenge in molecular biology since they are genetically silent.

Weaknesses:

A complexity of the current paper is that it heavily relies on a recently published paper (Chovanec et al 2026, NAR) describing the powerful but complex technique sci-L3-Strand-seq. Knowledge of this paper is a prerequisite to understanding the current manuscript because no reminder is provided. In addition, the current manuscript presents the use of the sci-L3-Strand-seq technique in the study of SCE after Cas9-induced DSBs, while a companion study is referred to several times for containing results about SCE in XRCC1 KO. At some point, one questions the relevance of splitting the use of sci-L3-Strand-seq in different papers instead of making a single integrated one.

Reviewer #3 (Public review):

Summary:

The authors construct a computational chimera by attaching a C. elegans connectome to a Drosophila body biomechanical model and use deep reinforcement learning to link neural activity to motor output. The model is able to produce walking, but is considered a priori to be scientifically meaningless, and the work is treated as a cautionary tale in complex interpretation layers unconstrained by experiment or data.

Strengths:

In a period of increasing excitement about linking AI and neuroscience, I respect very much that the authors work through a nontrivial example of nonsense results, rather than just making a theoretical case. It offers a clear and memorable existence proof that matching outputs of complex trained networks does not mean the internal dynamics are themselves emulated.

Weaknesses:

While I understand that the work was a rapidly produced comment on science-by-press-release, the message seems too important to be treated in quite as pithy a manner as it is. In particular, because the computational experiment is so memorable, it is worth getting the message right to avoid a set of readers who take from it that they should dismiss this category of neuroAI wholesale (which the authors absolutely do not imply!).

One part of me reads this work and thinks that by intentionally wiring up the sensory feedback in a particularly nonsense way, the authors have just made a bad model, and sometimes bad models can still generate sensible outputs, especially when expressive models are optimized to fit those sensible outputs. But I think this work is trying to say something more specific than this, and I would like it to be a bit clearer about that. The authors do a fairly good job of sharing a view about what should have been done instead, but this message would benefit from having some more concrete suggestions to avoid a simplistic interpretation. A few thoughts:

(1) It's not entirely obvious to me that the model is "scientifically meaningless." As the authors know extremely well, Drosophila walking is thought to be driven by simple central pattern generators coupled to leg-specific implementations. The C. elegans neural circuit is clearly capable of producing rhythmic activity as well. A version of the model they ran could have identified biologically valid rhythmic activity in the C elegans circuit and mapped it via the DRL to the right locomotor behavior in the fly. While this would not be a good emulation of the fly, it's not a concept devoid of scientific meaning. Similarly, if the ANN is converting a rhythmic signal to coordinated walking, it's not obvious to me that there aren't useful principles to identify in how it achieves this - it's basically the equivalent of that post-CPG circuitry, no?

(2) Similarly, is this outcome going to be relatively specific to rhythmic behaviors? I suspect that it would be harder to push the C. elegans connectome to produce some behaviors than others - for example, adding in visual navigation and other motor patterns, or a ring attractor. Rhythmic circuits arise in many places, and both biology and dynamical systems tell us they can come from numerous configurations of elements and interactions.

(3) Aside from the nonsense formulation of the problem, I would have liked to know more about what the authors should have done to know their model was useless. Put another way, if the authors hadn't known that their model was bad from the beginning (e.g., if they had stuck a fly brain in the middle of it, gotten the sensory feedback right), would there have been some way to figure out if it was meaningful or meaningless based on the results of the trained model itself?

Reviewer #3 (Public review):

Summary:

The manuscript evaluated behavioral phenotypes in the Cntnap2 knockout mouse using two behavioral paradigms: trace fear conditioning and a radial maze task. The trace fear conditioning training is normal, but memory generalization is impaired. The inflexibility is suggested to be related to low activity in dCA1 neurons, which can be rescued by ChR2. The radial maze task data suggested a similar conclusion. Brain-wide cFos mapping indicated impairments in the Cntnap2 knockout mouse. The brain-wide cFos mapping does not show direct correlations with Cntnap2, limiting the interpretation of these data in the context of this paper.

Strengths:

The behavior data are solid.

Weaknesses:

The underlying mechanism is not fully investigated.

Major points：

(1) The authors should thoroughly check their manuscript as there are many typos in the current version that affect the readability.

(2) In trace fear conditioning, the tone test impairment can be rescued by ChR2. Have the authors tried rescue experiments with Cntnap2? Rescue experiments in the radial maze task are also essential, either with ChR2 or Cntnap2.

(3) The quality of the cFos example image in Figure 3 is too low. The authors should also provide example images for the other brain regions in the supplementary data, if possible.

(4) The causal link between the brain-wide cFos mapping and the Cntnap2 knockout is weak. How to explain the increase of cFos cell densities in some brain regions, but the decrease in others?

Reviewer #3 (Public review):

Summary:

This interesting manuscript provides evidence that the well-established consequences of (reduced) mTOR activity on longevity are, at least in part, mediated by regulation of dafachronic acid (DA) availability and its signalling via its nuclear receptor DAF-12 in C.elegans, with some supporting evidence derived from mouse studies that similar processes may be functional in mammalian systems, i.e., be evolutionarily conserved. Earlier studies by the group have established that DA/DAF-12 signaling promotes adult longevity in several contexts. DA is a bile acid look-alike, and DAF-12 is a homolog of mammalian bile acid-activated nuclear receptors FXR and VDR: recent experimental studies and human cohort studies have indicated a role of (specific) bile acids in mammalian longevity.

The hypothesis that mTOR and DA/DAF-12 signaling interact to modulate longevity in C.elegans is novel and of great potential interest. The hypothesis has rigorously been tested in a series of well-performed experiments employing mutant strains, functional genomic screens, and DA exposures, etc.. It is convincingly demonstrated that DA/DAF-12 does not directly impact mTOR (assayed on AMPK phosphorylation) and acts downstream of the pathway. The short-chain hydrogenase DHS-26 (mammalian homologue DHRS1) was identified as a downstream target and modulator of this mTOR-DA-DAF12 axis by modulating the lifespan of the mTOR regulator raga-1. As the components of this axis are expressed in different cell types of the worms, this finding indicates a neuroendocrine mode of action. Mode of action of DHS-26 appears to be based on modulation of cholesterol and lathosterol, i.e., substrate availability for DA production.

Strengths:

Overall, the manuscript is well-written and builds up the story in a clear fashion. The conclusions are based on solid data and of relevance for ageing research, also because the mechanism identified appears to be evolutionary conserved.

Weaknesses:

No overt weaknesses were identified by this reviewer.

Reviewer #3 (Public review):

Summary:

This paper attempts to and succeeds in demonstrating that Orf9b is able to bind small molecules using X-ray fragment screening, SPR and FP assays. Exploration of sites from the fragment screening is performed along with fragment linking with inter-dimer lipid moieties.

Strengths:

The experimental work looks strong and well performed. The interpretation of the data is appropriate and was often validated through orthogonal methods and follow-up compounds. The use of Tom70 to find binders that might disrupt interactions between Orf9b and Tom70 is elegant.

Weaknesses:

The use of Chai-1 to predict co-folded structures with binding molecules was not properly described - no mention of this in the methods. It was not commented on whether the compounds which were found were attempted to be co-crystallised. If they were but negative data was collected (didn't crystallise, didn't diffract or no additional density was found), then this needs to be stated.

Reviewer #3 (Public review):

Summary:

Here the authors investigate the role of the Trypanosoma brucei polo-like kinase TbPLK in the function of flagellum-associated cellular structures in trypanosomes. They set out to test the hypothesis that a key substrate of TbPLK is the kinesin protein KIN-G, and that TbPLK phosphorylation of KIN-G regulates its functions in cells.

Strengths:

Using in vitro biochemistry with purified proteins, the authors convincingly demonstrate that TbPLK phosphorylates KIN-G at 29 sites. Moreover, they convincingly show that phosphorylation at one site, T301, impairs the binding of purified KIN-G to purified microtubules. They further confirm that inhibition of TbPLK in cells reduces KIN-G phosphorylation at T301 (and S569). Using immunofluorescence-based imaging approaches, they also show that TbPLK colocalizes with KIN-G at centrin arms during early S-phase of the cell cycle. Centin arms are structures that are located near the basal body and flagellum and are important for new flagellum biogenesis, Golgi positioning, and cell division. To evaluate the function of KIN-G phosphorylation in cells, they depleted KIN-G by RNAi, simultaneously expressed phospho-mimetic (T301D) and phospho-ablative mutant proteins, and used immunofluorescene to examine the impact on flagellum-associated cellular structures. They show that expression of the phospho-mimetic mutant KIN-G-T301D causes the following defects: reduced cell proliferation, disruption of centrin arm and Golgi biogenesis, impairment of FAZ elongation and flagellum positioning, and misplacement of the cell division plane. The data convincingly support the conclusion that KIN-G phosphorylation on T301 plays an important role in regulating the cellular functions of this kinesin motor protein.

Weaknesses:

The authors have addressed prior weaknesses in the manuscript through additional experimentation and rewording of the conclusions.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigate the contribution of the type VI secretion system of Bacteroidales to gut microbiome assembly and the targeting of closely related species. They demonstrate that B. acidifaciens relies on T6SS-mediated antagonism to prevent displacement by co-resident Bacteroidales and other members of the microbiome, allowing it to persist in the gut. They also developed new tools for analyzing the distribution of mobile genetic elements. This study advances our understanding of how molecular systems contribute to shaping complex microbial communities.

Strengths:

The use of a gnotobiotic model colonized with a wild-mouse microbiome is a significant strength of this study. This approach allows tracking of microbiome changes over time and evaluating the targeting by Bacteroidales carrying T6SS in a more natural setting. The development of ICE-seq for mapping the distribution of the T6SS in the microbiome is remarkable, enabling the study of how this bacterial weapon is transferred between microbiome members without requiring long-read metagenomics methods.

Weaknesses:

Some conclusions are based on a limited number of mice per condition. This could be due to the complexity of using a gnotobiotic mouse model, but this should be considered when interpreting the data.

Overall, the authors successfully achieved their objectives, and their experimental design and results support their findings. As mentioned in the discussion, it would be important to investigate the role of the T6SS in resilience to microbiome disturbances, such as antibiotics, diet, or pathogen invasion. This work represents a step forward in understanding how contact-dependent competition influences the gut microbiome in relevant ecological contexts.

Reviewer #3 (Public review):

The transition from planktonic to benthic depends upon several physical and chemical cues. Nitric oxide (NO) is known as a critical player in the induction of larval metamorphosis in several invertebrates. Although NO is a widespread signalling molecule in a broad range of organisms regulating key physiological processes, internal regulatory mechanisms studies are scarce. While the UV sensing in larvae of the annelid Platynereis dumerilii using ciliary photoreceptors has been studied, the neuronal signalling mechanism remains unknown. In this study, Kei Jokura et al. investigated how annelid Platynereis dumerilii larvae detect UV sensing and modulate swimming behaviour through nitric oxide feedback. Using existing resources of Platynereis larval connectome/volume EM data, they identified NOS-expressing interneurons within the ciliary photoreceptors circuit (cPRCs). They demonstrated that NO is produced in cPRCs during UV/violet stimulation by using a fluorescent NO-reporter line. Further, they demonstrated that Nitric oxide signalling mediates UV-avoidance behaviour by using NOS-mutant larvae. Finally, they mapped out the signalled mechanisms of the cPRC circuit using published spatially mapped single-cell transcriptome data of Platynereis larvae, the Ca sensor lines, in situ HCR, and immunostaining. Additionally, by using their findings from Ca imagining data of cPRC, INNOS and INRGWa cells collected in wild-type, NOS knockout and NIT-GC2 morphant larvae, Kei Jokura et al. developed a mixed cellular-circuit-level mathematical model. However, my expertise in mathematical modelling is limited, so I cannot comment on this section.

Comments on revised version.

Thank you for the opportunity to re-evaluate this manuscript. I have reviewed the authors' responses and the revised manuscript. The authors have carefully and satisfactorily addressed all of my previous comments and concerns. The revisions have strengthened the paper, and I have no further suggestions.

Reviewer #3 (Public review):

Summary:

The authors present analyses of different fitness measures derived from empirical data from yeast knock-out mutants and the long-term evolution experiment (LTEE) with Escherichia coli to explore discrepancies and identify preferred methods to estimate relative fitness in high-throughput experiments. Their work has three components. They first discuss the different "encodings" of relative abundance data and conclude that logit-transformations are preferred, because they transform nonlinear abundance trajectories into linear trajectories with greater predictive power. Next, they compare per-generation with per-growth cycle relative fitness estimates inferred from simulations of pairwise competitions based on published growth traits for the yeast strains and on published pairwise competition measurements for the LTEE data. Both data sets show quantitative and qualitative (i.e. rank order) discrepancies of estimates across different time scales, which are highlighted by considering possible underlying causes (i.e. trade-offs between growth traits) and consequences (i.e. epistasis among mutations affecting different growth traits). Finally, the authors compare simulated pairwise and bulk (i.e. where many mutants compete during a growth cycle in a single environment) competition assays based on the yeast knock-out mutants and demonstrate an optimal ratio of collective mutants to wild-type strains that minimizes both sampling error and overestimation of fitness estimates when compared with pairwise competitions.

Strengths:

The study deals with a highly relevant topic. Fitness is central to general evolutionary theory, but also poorly defined and implies different traits for different organisms and conditions. For microbes, which are often used in evolution experiments, high-throughput experiments may yield different measures to quantify abundance over time, from individual growth traits to bulk competition experiments. Hence, it is relevant to consider discrepancies among those measures and identify preferred measures with respect to predicting population dynamic and evolutionary processes. The present study contributes to this aim by (i) making readers aware of differences among commonly used fitness estimates, (ii) showing that simulated (yeast) and calculated (E. coli) competitive fitness may differ across time scales, and (iii) showing that bulk competitions may yield relative fitness estimates that are systematically higher than pairwise competitions. The study is rather thorough on the theory side, with extensive derivations and analyses of various fitness measures using their resource competition model in the Supplementary Information. The study ends with a few practical recommendations for preferred methods to infer relative fitness estimates, that may be useful for experimentalists and stimulate further investigations.

Comments on revisions:

I appreciate the thorough and effective response to all recommendations and have no further comments.

Reviewer #3 (Public review):

Summary:

This study investigates how frontostriatal circuits encode elapsed time and exhibit decision-related dynamics during an auditory change-detection task. Using population-level temporal decoding and analyses of low-dimensional neural dynamics, the authors compare activity in the frontal orienting field (FOF) and anterior dorsal striatum (ADS). The manuscript addresses an important question in systems neuroscience: how cortical and striatal circuits represent elapsed time and signal action initiation during decision-making.

The results suggest that FOF and ADS differ in how they represent decision-related information near decision commitment or behavioral report. In particular, FOF shows greater movement-aligned changes in temporal decoding and population geometry than ADS. These findings are potentially important because they may help clarify how cortical and striatal circuits contribute to timing, decision formation, and action initiation.

Strengths:

A major strength of the study is its use of population-level analyses to identify temporal structure and movement-aligned changes in neural dynamics. The analyses provide evidence that neural dynamics and low-dimensional population geometry change around the time of behavioral report, especially in FOF. This provides a useful population-level description of decision-related dynamics beyond what could be inferred from average firing rates alone.

Another strength is that FOF and ADS activity were recorded simultaneously during the same auditory change-detection task. This design strengthens the regional comparison by minimizing confounds related to session-to-session variability, including differences in task engagement, decision accuracy, or other behavioral variables across recordings. The simultaneous recordings therefore provide a strong basis for comparing temporal decoding and population dynamics between cortical and striatal circuits.

Weaknesses:

One limitation is that the physiological interpretation of the population-geometry analyses remains somewhat abstract. Concepts such as low-dimensional subspaces, subspace alignment, and subspace rotation are potentially powerful, but it is not always clear what specific changes in neural activity give rise to these effects. For example, it is difficult to tell whether changes in population geometry primarily reflect recruitment of different neurons, or changes in the dominant temporal profiles of the same neurons. This limits the physiological interpretability of the population-level findings.

A second limitation is that the mechanistic interpretation of the FOF-ADS difference remains underdeveloped. The observed differences could reflect an internally generated transition in frontostriatal dynamics, similar to the dynamical-regime and neural-mode transition described by Luo et al. (2025). Alternatively, they could reflect a circuit-readout process, analogous to the framework proposed by Stine et al. (2023), in which cortical activity drives threshold crossing in a downstream circuit, triggering orienting or motor signals that terminate the decision process. The current manuscript describes the regional differences clearly, but it does not fully discuss these mechanistic interpretations.

Finally, the strength of the evidence would be easier to evaluate if the manuscript more clearly reported the number of animals contributing to each major analysis and the consistency of the main effects across animals. Because many analyses are performed across sessions, the absence of this information makes it difficult to assess whether the key findings are robust across animals or could be influenced by one or a small number of animals.

Reviewer #3 (Public review):

Shimogawa et al. describe the generation of acetylated aSyn variants by genetic code expansion to elucidate effects on vesicle binding, aggregation, and seeding effects. The authors compared a semi-synthetic approach to obtain acetylated aSyn variants with genetic code expansion and concluded that the latter was more efficient in generating all 12 variants studied here, despite the low yields for some of them. Selected acetylated variants were used in advanced NMR, FCS, and cryo-EM experiments to elucidate structural and functional changes caused by acetylation of aSyn. Finally, site-specific differences in deacetylation by HDAC 8 were identified.

The study is of high scientific quality, and the results are convincingly supported by the experimental data provided. The challenges the authors report regarding semi-synthetic access to aSyn are somewhat surprising, as this protein has been made by a variety of different semi-synthesis strategies in satisfactory yields and without similar problems being reported.

The role of PTMs such as acetylation in neurodegenerative diseases is of high relevance for the field, and a particular strength of this study is the use of authentic acetylated aSyn instead of acetylation-mimicking mutations. The finding that certain lysine acetylations can slow down aggregation even when present only at 10-25% of total aSyn is exciting and bears some potential for diagnostics and therapeutic intervention.

Reviewer #3 (Public review):

Major strengths include the use of realistic retinal motion recorded during virtual walking, an elegant manipulation of curl, converging behavioral and modeling evidence, and grounding in control theory. This provides a novel and important contribution to our understanding of how the brain processes motion information and intuition about how that information might be used to guide steering. In addition, they provide a computational mechanism by which retinal flow curl can be used as a control signal.

The revised ms has been strengthened by more explicit discussion of the literature where there has been mixed evidence for the use of the Focus of Expansion. Since the ms is a strong test of the use of curl as a heading signal, this allows a deeper understanding of the importance of the finding and historical context. The ms has also been strengthened by a more explicit discussion of integration of the time-varying signal over periods of several seconds, which is an important demonstration. The implications of the ms are still a little unclear, as the results involve visual judgements in seated subjects. The use of different sources of information when humans walk from one place to another in real life may be complex and involve a variety of different sources of information.

Reviewer #3 (Public review):

Summary:

This study by Gangadharan and colleagues seeks to establish a quantitative biochemical model for the microtubule polymerase activity of Stu2. Stu2 is the budding yeast member of the XMAP215 protein family, which is broadly conserved across eukaryotes. XMAP215 proteins play a wide variety of important roles in cells, and these are attributes to effects on microtubule dynamics. Many studies over the last ~20 years have shown that XMA215 proteins selectively associate with microtubule ends where they increase rates of microtubule assembly and disassembly. More recently, structural biology and biochemical studies by the authors and other groups have shown that the multiple TOG domains on XMAP215 proteins are tubulin-binding domains that selectively bind to curved tubulin, which is present in solution and at microtubule ends, but not to straight tubulin which is present in the walls of the microtubule lattice. This has led to the general model that XMAP215 proteins promote polymerization by delivering soluble tubulin to the growing plus end, and two distinct models have been proposed to explain the mechanism. The 'concentrating reactants' model proposed previously by the authors suggests that TOG domains grab hold of tubulin in solution and concentrate at the microtubule end. The 'polarized unfurling' model proposed by the Al Bassam lab suggests that XMAP215 delivers multiple tubulins to the end, using a stepwise mechanism involving different roles for each TOG domain. The current study seeks to improve our understanding of the mechanism by developing a quantitative model to explain the binding and release of tubulins, the number of Stu2 molecules at the end, and the overall rate of tubulin addition. The authors accomplish this goal using new experimental data. The final model fills in new details of the mechanism. The authors draw a comparison between Stu2 and the actin polymerase which bears similarity to the Ena/VASP and suggest a convergent strategy for cytoskeletal polymerases.

Strengths:

This is a focused and clearly written study that incorporates prior knowledge of XMAP215 and draws inspiration from the actin field. The data are clear and convincing, and the study accomplishes its goal of generating a new, quantitative model for Stu2. The model will be important for microtubule researchers to predict and test key points for altering XMAP215 activity across different organisms and potentially for different tubulin substrates. The comparison to Ena/VASP may also inspire similar comparisons across other microtubule and actin regulators, which could lead to new insights across cytoskeletal fields.

Weaknesses:

The study is without major weaknesses.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

Comments on revised version:

I have read the authors revisions and find them compelling and they address fully the minor points raised in my review.

Reviewer #3 (Public review):

Summary:

The study describes an increase in body growth and body composition in both mice and women. In mice, the impact on growth is mainly seen during the first pregnancy, and the changes postpartum on body composition are also different during the first and second pregnancies. The study has used various knock-out models in the growth hormone axis to understand these changes as well as some gene expression analysis related to GH, IGF-1 and estrogen signalling pathways.

Strengths:

(1) The inclusion of various knock-out mouse models that allow for exploration of mechanisms related to the above-mentioned changes.

(2) The investigation of gene expression of GHR, IGF-1R and ER pathways.

Weaknesses:

The human findings are dependent on the patient's recollection of bodily changes after their pregnancies.

Conclusion:

The authors have partly achieved their aim of describing changes in growth and body composition that remain after pregnancy and the mechanisms behind these changes. This study may have importance for a wide variety of research areas as well as in the clinical setting. The study is also unique in its attempt to bridge findings in mice to a unique human model of congenital GH deficiency.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigate whether gender information is encoded in the brain in a way that is invariant to the object being perceived. They design an fMRI experiment in which 22 participants perform a one-back repetition detection task in a block design. Images shown are of three types (faces, objects, and bodies) and of two perceived genders, male and female. They perform MVPA, RSA, and functional connectivity analyses to determine whether gender information is invariant to the type of image being perceived. They report an area in the posterior right middle temporal gyrus (rMTG) that is found in their gender decoding analysis across categories. To confirm that this area encodes gender information, they perform a regression-based RSA with category and gender model RDMs, and report that the gender model RDM is significantly correlated with brain representations in that area. Finally, to further investigate the representations in this area, they perform a model-based RSA in which they first fine-tune a deep neural network for gender classification, and then study the correlation between model RDMs and brain RDMs. Consistent with a previous report in face processing (Jiahui et al., 2023), they find that gender information is more consistent with representations in middle-to-late layers of the networks. Additional functional connectivity and PPI analyses are reported to reveal differences in co-fluctuation of brain activity within occipital and parietal nodes when perceiving different types of male/female images. Based on these results, the authors conclude that rMTG represents gender information invariant of the category perceived, although rMTG also afforded decoding of category information.

Strengths:

Whether perceived gender is represented in a manner invariant to the category of the stimulus is a legitimate and interesting question, and one of relevance particularly to the face and person perception literature.

The model-based RSA, in which RDMs from networks fine-tuned for gender classification are compared against brain RDMs, is an interesting approach, and the layer-wise profile the authors obtain converges with a previous report in the face processing literature (Jiahui et al., 2023).

Weaknesses:

A substantial number of inferences are drawn on the basis of weak statistical methods and a suboptimal design. My concerns are set out below, ordered by severity.

(1) The statistical tests are not appropriate for classification and RSA, and are prone to false positives. Classification accuracies and RSA correlations may be positively biased, and the true null distribution may therefore be centered above the nominal chance level, or above zero in the case of RSA. Testing against a theoretical value with a one-sample t-test under these conditions inflates the false positive rate, especially with few test samples per classification, and does not afford valid population inference for information-like measures (Combrisson & Jerbi, 2015; Allefeld et al., 2016). The concern applies to every inferential claim in the manuscript, including the identification of the rMTG cluster on which the paper's central conclusion rests. The established remedy is permutation testing, in which the labels are randomly permuted and the full analysis, including cross-validation, is re-computed so that any bias is captured in the empirical null distribution (Stelzer et al., 2013; Etzel & Braver, 2013). This approach has been applied in comparable face-decoding studies using both classification and RSA (Guntupalli et al., 2017). I raise this methodological concern here because it is the clearest way to convey why the reported statistics cannot be safely interpreted at face value.

(2) The decoding analyses do not appear to test generalization to left-out stimuli. From my reading of the design, each run contained all six conditions presented three times in random order, with each block containing 12 images (10 unique plus two repetitions serving as catch trials). If all images were presented in every run, the same images would be present in both the training and test sets of the cross-validation. Under these conditions, the interpretation of a general "gender" code is difficult to justify: the classifier may be exploiting low-level image features specific to the particular exemplars rather than gender per se. This bears directly on the paper's central claim, which concerns an abstract, category-invariant representation of gender, a claim that requires decoding to generalize to stimuli the classifier has not encountered.

(3) There is no evidence that participants perceived the stimuli's gender as the authors assumed. Perceived gender may be subject-specific, yet no norming is reported establishing that participants actually rated or processed the stimuli according to the gender the authors assigned to each image. Some images are likely to be more ambiguous than others. This is a construct validity issue rather than an analysis issue: the class labels used throughout the decoding analyses, and the gender model RDM used in the RSA, both rest on an assumption about the participants' percepts that is never tested against the participants themselves.

(4) The rMTG ROI reported in Figure 2c appears to overlap almost perfectly with the motion-sensitive area hMT+. The reported effects may therefore be driven, at least in part, by low-level motion signals arising from the rapid on/off changes of the stimuli and the associated optic flow. I am not claiming that the results are fully driven by this, but no control reported in the manuscript rules it out, and this region is the centerpiece of the paper's conclusion.

(5) Stimulus size is confounded with category in the functional connectivity analyses. The authors report that functional connectivity differed between faces and objects, and between bodies and objects. However, faces and bodies were shown with the same visual extent, while objects were larger. Given that the nodes being investigated are in visual areas, it is unclear how these differences can be attributed to category rather than to the low-level difference in stimulus size. The same confound bears on the behavioral task performed within the scanner: participants can perform the one-back task more easily, simply by detecting size differences, since two images of different sizes are clearly not the same image, rather than by processing the image content. This affects what can be assumed about participants' attention to the stimulus category or gender.

(6) No motion quality control is reported for the functional connectivity analyses. Functional connectivity is well known to be highly susceptible to head motion, yet the manuscript reports no summary of how much subject motion there was, no indication of whether volumes with excessive motion were removed or censored, and no account of quality control on the measured data more generally.

(7) The use of famous faces introduces an avoidable confound. The face stimuli were famous faces. Famous and familiar faces are known to recruit substantially more widespread activity than unfamiliar faces, extending well beyond the core visual system (Gobbini & Haxby, 2007; Natu & O'Toole, 2011; Visconti di Oleggio Castello et al., 2017; Kovacs, 2020). For a study focused specifically on gender, this introduces a source of variance that unfamiliar faces would have avoided, and it complicates the comparison of the face conditions against the body and object conditions.

(8) The rationale and benefit of fine-tuning the deep neural networks are not established. The manuscript does not report the original, non-fine-tuned accuracy of the models that required fine-tuning, so the benefit of the procedure cannot be assessed; given that the final validation accuracy is low, it is unclear that fine-tuning actually helped. AlexNet and VGG are trained for object classification on large datasets, and fine-tuning with 2,000 training images may not be sufficient to genuinely shift the objective. Whether the activation patterns and RDMs changed in any significant manner after fine-tuning is not reported, and the rationale for selecting the specific layers used is not stated.

(9) Taken together, the analyses as presented do not establish the paper's central claim. My concern is not that the reported effects are necessarily absent, but that the combination of statistical tests that do not account for possible positive bias, a cross-validation scheme that may not guarantee generalization across stimuli, a key region that coincides with a motion-sensitive area, and gender labels that were never validated against participants' own perception leaves too many open questions for the results to be evaluated as they stand.

(10) I would add one broader consideration. Perceived gender is likely to depend on culture and to vary across individuals. A binary male/female contrast in 22 participants, without evidence that those participants perceived the stimuli as the authors intended, is a narrow operationalization of a construct that is unlikely to be so simple. Even if the analyses were fully sound, caution would be warranted in generalizing from this design to claims about how the brain universally represents gender.

Reviewer #3 (Public review):

Summary

The work of Pal and colleagues considers a hierarchical and multi-population version of the "oscillatory recurrent gated neural integrator circuits" (ORGaNICs) model, showing through analytics that the model captures multiple relevant experimental results: first of all, its oscillatory dynamics produce a profile with high resemblance to experimental results, both in terms of decay of power at high frequency and in terms of shifting peak as a function of stimulus contrast. Second, inter-areal communication subspace dimensionality is lower than within-area dimensionality. The authors then proceed to further characterize the model's response properties as a function of input and feedback gain. In particular, they find that frequencies transmitted with higher strength also carry more information, that changing gain modifies the dimensionality of communication subspaces, and that these properties can be used in a three-layer model, where an upstream area can select which downstream area to communicate to, based on the strength of feedback gain.

Strengths

This work demonstrates that a single-circuit model with normalization properties can capture both the oscillatory dynamics and the inter-areal communication properties measured in cortical circuits, matching multiple experimental results. The full analytical tractability of the model is highly advantageous, allowing for easier exploration of parameters, replicability, and effective interpretations of results compared to purely numerical approaches.

The work also makes a useful conceptual link between normalization, coherence-based communication, and subspace-based communication. In particular, it shows how both phenomena can emerge from the same circuit dynamics, where normalization is a key factor.

Interestingly, the model is also extended to multiple areas, showing how attention (in the form of changes in feedback gain) can synchronize the activity of a downstream area with one of two upstream areas, thus effectively selecting which area to communicate with.

In general, this is an interesting computational framework and a useful starting point for future modeling work. A particular strength is that it connects normalization, oscillatory dynamics, coherence, and communication subspaces within one analytically tractable model, making it possible to generate mechanistic hypotheses about when inter-areal communication should be stronger, lower-dimensional, or preferentially routed through feedback.

Weaknesses

Although I see the analytic approach as a strength, at the same time I regard the lack of any numerical comparison as a big weakness. Circuit simulations would not only confirm the correctness of the analytics, but also offer further insights on the error margins and on the regimes where the analytics are valid. This is because, to my understanding, the analytics are based on a linear approximation around the operating regime, which means deviations might be expected, especially for high gain levels in the input, or in the feedforward and feedback pathways.

Another problem is that the analytically tractable model seems to rely on effective connectivity weights that break Dale's law. Numerical simulations with explicitly modeled excitatory and inhibitory units might give insights into effects due, e.g., to the additional transmission delays mentioned in the Discussion.

Another weakness is the use of the term "predictions" to indicate features of the model dynamics that are purely described in the context of the model parameters. Although the model's response properties may certainly lead to predictions, I think the term requires a better contextualization in terms of neurophysiology and experimental neuroscience. The Discussion draws very interesting and valuable bridges between neuron morphology, interneuron types, and model parameters. But it seems it's left to the reader to backtrack and figure out which biological mechanisms or experimental manipulations should correspond to changes in input or feedback gain, and how these should be distinguished from possible changes in feedforward gain.

Relatedly, the manuscript places substantial emphasis on modulation of feedback gain, but does not comparably explore modulation of the feedforward gain, β2, which regulates the V1-to-V2 drive. This seems important because changes in feedforward gain could also influence communication subspace dimensionality and oscillatory dynamics. Therefore, predictions related to top-down feedback modulations should be taken with a grain of salt.

Last but not least, the model dynamics are split among multiple elements and nonlinear interactions, reaching a level of complexity far higher than the other ORGaNICs formulations present in the literature. The authors derive these dynamics in the supplementary material, as a dynamical system that converges to a fixed-point solution that includes "exact divisive normalization". I wonder, however, if there could be simpler solutions that also produce normalization, either approximate or in a different form than the one proposed by the authors. Note also that the designation of "excitatory neurons" is misleading: despite the presence of two explicitly inhibitory populations, the "excitatory" units also interact with negative effective weights both recurrently and in the inter-areal interactions, thus breaking Dale's law.

Reviewer #3 (Public review):

Summary:

Esfahany et al present a novel, UI-based tool to detect dentate spikes from hippocampal local field potential recordings, called Toothy. Toothy is easily accessible, compatible with many popular recording formats, and guides users entirely via UI through the dentate spike curation and analysis process. The functional and interactive visualizations enable users to gain a detailed understanding of their data and rigorously analyze dentate spike phenomena. This tool will be broadly useful for anyone who studies hippocampal electrophysiology. Furthermore, by expanding access to dentate spike analysis, it may encourage more scientists to explore this understudied but critical phenomenon.

Strengths:

(1) Toothy provides several ways for users to interact directly with parameters, revealing the ramifications of these choices. Most parameters are adjustable and made obvious via a UI panel. Their effects are then visualized across channels and individual events. This will help users think critically when selecting parameters.

(2) Toothy is fully UI-based and pip-installable, lowering the barrier to entry far below what most electrophysiology analysis tools offer.

(3) The channel selection tool is broadly useful for identifying DG hilus and CA1 pyramidal locations. Since subregional and laminar localization of electrode sites is critical to correctly interpret hippocampal recordings, this tool could be more generally used to identify site locations across the hippocampus.

Weaknesses:

(1) The rationale behind parameter choices is not explained. In order to function "not as a black-box detector", as the authors state, all initial parameter choices should be explained with citations. If possible, these citations would also be available from Toothy directly, alongside citations describing alternative parameter choices. This will help users make informed choices. For instance, a user analyzing data from rats would need to adjust the default ripple frequency band upwards (150-250Hz), and would benefit from guidance to adjust this properly.

(2) The Results describe the functions of Toothy from the perspective of the user, but there is no Methods section describing what Toothy does between UI displays. This would allow readers to compare the tool directly to analysis pipelines as described in the Methods sections from other papers. Particular attention should be paid to justifying the analysis decisions that cannot be changed by the user, such as detecting events off of a single representative channel instead of across a consensus of multiple channels.

(3) It's unclear whether or how Toothy evaluates data quality to confirm that its analyses return interpretable results. At a minimum, the tool should confirm adequate sampling rate (e.g. <=1kHz) and inter-site spacing for CSD (e.g. <=50um).

(4) The paper does not put Toothy into context among the other common open-source electrophysiology analysis toolboxes. Consider Rippl-AI (Navas-Olive & Rubio et al, 2024) or pynapple (Viejo et al, 2023), to give a few examples. The paper would be strengthened by addressing how Toothy extends beyond the capacities of these other tools and how Toothy can be integrated into a workflow that also uses these other tools.

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence how dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strength:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuit interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of the role of dopamine in cognitive functions. Additional experiments investigating the contribution of MBON-α1 to the circuit, connectomic analysis together with a dissection of dopamine receptor function further strengthen the proposed circuit motif and its biological relevance.

Weaknesses:

While the study is well designed, presents compelling findings and has been further strengthened by additional experiments, some aspects remain unclear. The role of DPM neurons in memory consolidation seems not yet fully resolved, as different genetic approaches yield variable results. Furthermore, some manipulations impair memory without affecting sleep fragmentation - or vice versa, suggesting that the observed memory deficits cannot be explained solely by impaired sleep-dependent consolidation. It would also have been interesting to discuss potential mechanisms by which dopamine receptor-mediated cAMP signaling could lead to a reduction in Ca²⁺ signals. I am confident that these questions can be addressed in future studies.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopaminergic circuits interact to regulate memory consolidation in Drosophila and may reveal general principles underlying the neural regulation of memory.

Reviewer #3 (Public review):

Summary:

This article by Scheib et al. investigates how layer 5 extratelencephalic (ET) neurons in the frontal cortex encode sensorimotor information during motor learning, focusing on differences between their apical tuft dendrites and somas. The authors alternated recordings among these ET neuronal compartments in the mouse anterior lateral motor cortex (ALM) during a cued directional licking task with a target port shift. They found that while tuft dendrites predominantly encode sensory cues, with a subset selectively active during corrective actions, somatic activity was more strongly associated with action timing. Additionally, learning induced divergent plasticity: tuft dendrites increased their selectivity but decreased response gain, maintaining stable net selectivity, whereas somas showed increased net selectivity early in learning. Together, these findings reveal distinct sensorimotor representations and learning-related plasticity in dendritic and somatic compartments, providing insight into how compartment-specific activity in the frontal cortex may contribute to motor skill acquisition.

Strengths:

The authors developed an innovative imaging approach and a comprehensive data analysis pipeline to address a knowledge gap in the literature. By alternating imaging of dendritic tufts and somas in the same animals, they compare compartment-specific activity during motor learning and identify distinct encoding of task variables and learning-related plasticity across these compartments. Interestingly, a subset of dendritic tufts shows activity associated with corrective actions. The findings are discussed in the context of current theories of dendritic computation, credit assignment, and motor learning, providing a useful foundation for future mechanistic studies.

Weaknesses:

No major weaknesses were identified.

Reviewer #3 (Public review):

Summary:

This study introduces a new analytical framework to analyze how viral variant frequencies change over time and in different locations. Two examples are given that demonstrate where this approach can be useful and where other approaches can be ambiguous in characterizing novel variants. The authors then demonstrate that the spatiotemporal dynamics of variant frequencies can be used to predict future epidemic growth rates and to investigate how variants differ in immune escape.

Strengths:

(1) Examples are provided that make the study accessible for a general audience.

(2) The authors demonstrate that their approach is predictive both of overall epidemic growth rates and immunological distance between variants.

(3) The approach introduced in this study can be readily applied to current and future epidemiological challenges that are similar to SARS-CoV-2 with respect to the relative evolutionary timescales wherever there is spatiotemporal heterogeneity in the susceptible population.

Weaknesses:

(1) The authors conclude their abstract claiming that their method provides an early signal of epidemic growth. Can this be quantified? Could the authors perform retrospective analyses for sequences available through various cutoff times, identify how early significant new variants are detected, and compare this to other detection methods?

(2) Analysis depicted in Figure 4 and Figure S9 could be explored further than speculatively attributing weak correlation to declining reporting rates for US states. Exploring how correlation between data and prediction varies over time during the test period might identify periods/events that explain weak correlation overall. The authors could explore predicting growth rates for estimated state prevalences rather than reported cases.

Reviewer #3 (Public review):

Summary:

The dbGist dataset/tool would provide substantial value to the cancer research community.

Strengths:

The manuscript presents dbGIST, a dedicated GIST-focused multiomics resource integrating data from 37 centers and ~2k samples across genomics, transcriptomics, proteomics, phosphoproteomics, and single-cell transcriptomics. Given that GIST is virtually absent from major cancer genomics consortia (TCGA, ICGC), this resource fills a genuine gap and represents a valuable contribution to the GIST research community.

(1) The MCM7 case study effectively demonstrates the platform's utility, linking a resource-derived candidate to survival outcomes.

(2) The LLM-assisted interface (dbGIST Assistant) is a reasonable addition for accessibility, lowering the barrier for clinicians and wet-lab researchers, who may not always have the skill set required for proper data analysis, especially for a rich and wide dataset like the dataset in question.

Weaknesses:

(1) Data deposition (major):

While the manuscript references public accessions for raw source datasets and provides a GitHub repository for code, it remains unclear where the **curated, harmonized data matrices** - which represent the core value-add of this work - are independently deposited. Access to these processed data appears to depend entirely on the dbGIST web interface and API. The authors should deposit the harmonized matrices in a persistent, general-purpose repository to ensure long-term availability independent of the web platform.

(2) LLM agent capabilities underspecified:

The manuscript would benefit from a clearer description of the assistant's capabilities and boundaries. Specifically, what tools or actions are available to the LLM agent? Can it execute code against the underlying data, trigger analytical modules programmatically, or is it limited to natural-language explanation of pre-computed results? Clarifying this would help readers assess the scope of the AI layer and distinguish it from agentic platforms that perform computation on behalf of the user.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors describe the results of a high throughput screen for small molecule activators of GCN2. Ultimately, they find 3 promising compounds. One of these three, compound 20 (C20) is of the most interest both for its potency and specificity. The major new finding is that this molecule appears to activate GCN2 independent of GCN1, which suggests that it works by a potentially novel mechanism. Biochemical analysis suggests that each bind in the ATP binding pocket of GCN2, and that at least in vitro C20 is a potent agonist. Structural modeling provides insight into how the three compounds might dock in the pocket and generates testable hypotheses as to why C20 perhaps acts through a different mechanism than other molecules.

Strengths:

Of the 3 compounds identified by the authors, C20 is of the most interest, not just for its intriguing mechanistic distinction as being GCN1-independent (shown genetically in two distinct cell lines, CHO and 293T, and in contrast to other GCN2 activators) but also for its potency. Ultimately, C20 might be a tool for providing mechanistic insight into the details of GCN2 activation and regulation and could be exploited therapeutically.

Weaknesses:

The chief limitation of this work is that the experiments exploring the effects of C20 on ISR output in cells are limited, so how useful these compounds are both experimentally and therapeutically remains to be determined.

Comments on revised version.

The authors have satisfactorily addressed my comments. A more extensive analysis of UPR signaling in cells (transcription and cell death in particular) would have further strengthened the paper, but that can be left to future work.

Reviewer #3 (Public review):

Summary:

The present paper by Shinoda et al. from the Miura group builds upon findings reported in an earlier study by the same team (Shinoda et al., PNAS, 2019), which identified a non-apoptotic role for the Drosophila executioner caspase Dcp-1 in promoting wing tissue growth. That earlier work attributed this function primarily to Dcp-1 and to Decay, a caspase structurally related to executioner caspases, but not to DrICE, the principal apoptotic executioner caspase. The authors further proposed that this non-apoptotic caspase activity operates independently of the initiator caspase Dronc.

In the current study, the authors both corroborate aspects of their previous findings and extend the investigation to mechanisms regulating Dcp-1 in this context. They identify roles for the giant IAP Bruce, two BCL-2 family members, and autophagy-related components in modulating non-apoptotic Dcp-1 activity. Moreover, they show that Bruce binds to a BIR-like peptide exposed upon Dcp-1 cleavage, but not to DrICE. The study further suggests that low levels of Dcp-1 activity promote wing tissue growth, whereas excessive activity induces cell death, as evidenced by impaired wing development following Dcp-1 overexpression. Overall, the manuscript provides several intriguing insights into the non-apoptotic regulation of the comparatively weak apoptotic executioner caspase Dcp-1 and complements the group's earlier work. However, several concerns remain regarding certain interpretations of the data and the experimental rigour of some of the results.

Strengths:

A major strength of the work is its systematic genetic and biochemical approaches, which combine tissue-specific manipulation with protein interaction mapping to explore how Dcp-1 is regulated. The identification of several regulatory factors, including an inhibitor of cell death protein and components linked to autophagy, provides a coherent framework for understanding how Dcp-1 activity might be tuned.

Weaknesses:

The evidence supporting some key claims remains incomplete. In particular, the type of cell death form induced when Dcp-1 is overexpressed is not clearly established, and additional tests would be needed to distinguish between the different cell death types.

Likely impact:

The study contributes to a growing body of work showing that proteins traditionally associated with cell death can have broader roles in tissue development. This conceptual advance is likely to be of interest to researchers studying growth control and tissue maintenance.

Specific points:

(1) Nature of the wing ablation phenotype<br /> A central concern is whether the wing ablation phenotype observed upon Dcp-1 overexpression truly reflects apoptotic cell death. The authors show in Fig. 1c that nuclei in cells overexpressing Dcp-1, but not DrICE, zymogens are highly condensed, which is suggestive of apoptosis. However, it is equally plausible that this phenotype reflects a form of non-apoptotic, Dcp-1-dependent cell death (e.g. autophagy-dependent cell death). This distinction could be readily addressed using TUNEL labelling and direct caspase activity assays. The latter would be particularly informative, as it remains unclear whether zymogen Dcp-1 is capable of cleaving standard effector caspase reporters in vivo. Does the anti-cleaved Dcp-1 antibody detect Dcp-1 activation following overexpression of the Dcp-1 zymogen?

(2) Role of Decay<br /> In their earlier study, the authors identified Decay as another caspase influencing wing growth, albeit more modestly than Dcp-1. It is therefore unclear why this line of investigation was not pursued further in the current work. This omission is notable, as Decay is not implicated in apoptosis and, to date, no substantial physiological function has been assigned to this caspase in any system. At minimum, this point should be discussed explicitly.

(3) Fig. 2: Proximity labelling analysis<br /> The authors use TurboID-mediated proximity labelling to reveal distinct Dcp-1- and DrICE-associated proteomes across tissues, with a particular focus on the wing disc. They further demonstrate that RNAi-mediated knockdown of the Dcp-1-associated proteins Sirt1 and Fkbp59 suppresses the wing ablation phenotype induced by Dcp-1 overexpression, suggesting that these factors are required for Dcp-1 activity. However, it should be clarified whether Bruce was identified as a Dcp-1 interactor in the proximity labelling dataset, given its proposed central regulatory role. In addition, further discussion of Fkbp59, its known functions and how it might mechanistically influence Dcp-1 activity, would be valuable.

(4) Fig. 3: Autophagy-related factors<br /> Given that Sirt1 is known to promote autophagy, the authors next examine autophagy-related proteins and identify roles for Atg2, Atg8a, Debcl, and Buffy in Dcp-1 activation. Notably, these proteins do not promote cell death in the Hid-induced canonical apoptotic pathway. However, it is important to determine whether knockdown of Debcl, Buffy, Atg2, or Atg8a alone affects wing development in the absence of Dcp-1 overexpression, to exclude the possibility that these perturbations independently impair wing formation.

(5) Evidence for canonical autophagy<br /> The involvement of autophagy would be more convincingly demonstrated by testing additional core autophagy genes, such as Atg7, Atg5, and Atg12, as well as performing a combined knockdown of Atg8a and Atg8b. Moreover, direct assessment of autophagy at the cellular level using established genetic reporters would substantially strengthen the conclusions.

(6) Figs. 4-5: Functional consequences<br /> It would be informative to determine whether Synr, Debcl, or Buffy influence wing size on their own and whether their overexpression enhances wing growth.

(7) Terminology and interpretation of cell death<br /> Taken together, the results suggest that Dcp-1 zymogen overexpression induces a form of non-apoptotic cell death, potentially autophagy-dependent or related. The reviewer does not understand the authors' insistence on referring to this process as apoptosis. The authors should be more cautious in their terminology: there is no canonical versus non-canonical apoptosis, there is simply apoptosis. Without stronger evidence, these effects should not be described as apoptotic cell death.

Comments on revised version.

In the revised manuscript, the authors addressed each of my concerns in good faith and, in my opinion, responded to them thoroughly and satisfactorily. I have no further concerns.

Reviewer #3 (Public review):

Sutlief and colleagues report behavioral and neural results from mice performing a patch foraging task. Behaviorally, they argue that time since last reward is a major determinant of when mice decide to leave a patch. In the brain, they find neurons in the dorsomedial striatum that show step-like changes in their firing rate at a range of times following reward. Population analyses show that the cumulative fraction of neurons that have undergone such a step-like change in firing rate can be used to predict patch-leaving times with impressive accuracy.

Overall, this is an interesting set of results that has been analyzed in a principled way. The manuscript is well written, the results are explained clearly, and the evidence supporting the authors' conclusions is strong. The manuscript is therefore a potentially valuable contribution to the growing literature assessing how the brain solves stopping problems like the patch foraging scenario. I have suggestions for the authors to consider that might further increase the rigor of their results, and a few suggestions for improving the clarity of the work for readers.

(1) I don't quite understand how the behavioral task works. Are mice rewarded for making discrete nose poke responses in the investment and context ports? Or are they required to nose poke and hold? Is reward given with some probability per response (which decreases with time in the patch), or is the reward probability a function of elapsed time in the patch, time since last response, or dwell time in the port? Also, exactly what equation defines how reward probability changes over time for the high- and low-value contexts? I couldn't find these details anywhere in the manuscript, and they would be helpful for better understanding the behavior and the later neural results.

(2) How was the optimal strategy determined? Several features of the author's task violate the assumption of the marginal value theorem, so computing the optimal residence time is not a straightforward application of the classic model. There's a diagram in Figure 1h that depicts an MVT-like graphical solution, but the conventions of the plot are not familiar to me, and there's no description of how it works in the results or methods. More detail here would be much appreciated. In a similar vein, the authors report that mice generally exceeded optimal residence times in patches, but no statistical comparison is provided to back up that statement. There should be some formal test of this if it is to be included in the results.

(3) The authors argue that time since last reward is the predominant determinant of patch leaving time. However, as the authors note, time since last reward is correlated with other task variables (patch reward rate, time in patch, etc.). I don't trust that SVM coefficients can be interpreted as straightforward measures of a variable's importance for classification performance in the case of correlated predictors. A better approach would be to assess how well the model performs as subsets of variables are added or removed from the model.

(4) For the SVM analysis, I'm not quite understanding how or why the authors are using 5 s after mice left the patch as additional "Leave" examples. For instance, is time since entry computed for the investment patch, or the context patch that mice enter after they leave the investment patch? Similarly, is the time since the last reward relative to the investment patch, or the reward the mouse is likely to receive at the context patch? Moreover, I'm not sure it's safe to assume that because the mouse left at time t, time t+1 necessarily reflects conditions on which the mouse would definitely leave again. If we're thinking about the stay/leave decision as something that is being repeated sequentially on a fast time scale to determine how long mice stay in the patch, it doesn't follow that observing a mouse leave means that any patch conditions after that would necessarily result in the same decision. If that were the case, it would mean that seeing a mouse leave a patch after 2 s would preclude ever observing a residence time longer than 2 s, which is clearly not compatible with the authors' data. Ultimately, it's only possible to observe one decision to leave per trial; including data points beyond that as additional leave examples seems overly speculative to me.

(5) The authors validate their approach for quantifying step-like changes in firing rate using simulations of constant-rate Poisson spiking and observe a low false positive rate. This is encouraging, but it doesn't seem like the only way in which their method could go awry, or even the most concerning way. I would be much more interested in seeing the false positive rate for continuous, ramp-like changes in firing rate, which would be much more likely to trip up the authors' approach and are also the major relevant alternative hypothesis to step-like changes in firing rate. Random walks in firing rate might also be worth testing.

(6) The finding that cumulative "transitioned" neurons is predictive of patch leaving is interesting. However, I can't help but wonder how truly informative this variable is for predicting patch leaving. It seems as though neurons can only transition firing rates one time. That means that as time in the patch increases, the fraction of transitioned neurons naturally increases. Similarly, all visits must eventually end with the mouse leaving the patch, so the hazard rate of leaving increases with time in the patch. Given that, can the authors be certain that the cumulative transitioned neurons are really what's predicting patch leaving time, or would any generically increasing function perform roughly the same? An interesting test would be to mismatch the neural predictor and behavior at the level of trials. If this mechanism is really specific, rather than something that captures the general structure of an increasing hazard rate of leaving, then prediction of leaving time should work substantially better when the neural predictor is correctly matched to behavior on the trial for which it was recorded.

Reviewer #3 (Public review):

Zappit is an open-source implementation of arbitrary-access laser-scanning optogenetics for manipulation of neuronal activity in mice. As the method requires expertise ranging from optics, hardware control and programming, the authors make the point that this powerful strategy is underutilized in the field, and put forward a well-documented modular hardware and software platform aligned to the Allen Mouse Brain Atlas aimed at enabling the larger scientific community to use this approach (democratizing) for controlling cortical activity during behavior in mice.

The authors favor a galvanometric approach to laser targeting. The system is inexpensive, easy to build, well-documented and user friendly (Matlab based GUI and GitHub repository). The photo-stimulation laser is directed into an X-Y galvo scanner targeted to the specimen using a dichroic mirror and focused on the sample using a Plössl lens as scan lens which is also used as an objective. The scan lens/objective images the specimen onto a camera via tube lens (also a Plössl lens) in a 0.5X magnification ensuring to fit the extent of the mouse brain onto the camera sensor (USB-3 Basler acA120-40um).

The authors report short and reproducible onsite time (~ 0.5 ms) and block (mask) the stimulation source using the laser analog control (~0.5 ms). The system is reliable, aiming at up to 20 stimulation sites per sequence considered as quasi-simultaneous (10 ms). They minimize rebound by gentle ramping down of stimulation over 250 ms.

The system is fast to calibrate by mapping scanner positions to pixel space in the camera space and mapping stereotaxic coordinate onto the image of the exposed skull. The theoretical x-y PSF is 70 µm (measured ~90µm) while the authors make the point that due to scattering the photo-stimulation spot size (lateral extent) is about 1 mm in diameter. This is what they also observe in electrophysiological recordings using silicon probes. The effective radius of inactivation depends on laser power, but was about 1 mm for laser powers (1-2-4 mW) on which the authors observed significant behavioral perturbations - in several tasks: 1) a delayed response somatosensory discrimination, 2) a visual detection task assessing changes in temporal frequency of a drifting visual stimulus; and 3) a visual discrimination (International Brain Laboratory task) in which mice were tasked to report the location of visual stimuli by turning a wheel. As proof of principle, the authors used a photo-stimulation set composed of 52 bilateral sites positioned at 0.5 mm interval covering a large network of frontal, motor and somatosensory cortical areas. Indeed, photo-inhibition of frontal motor cortex sites produced robust increases in reaction time. In contrast, stimulation at other motor and somatosensory sites produced modest, but significant decreases in reaction times.

While the approach is not novel, it does serve the need of better disseminating this technique in the research community. Overall, the Zappit is well-documented and easy to build and use, and will have impact in increasing robust use of site directed photo-stimulation (exciting/inhibiting ensembles of neurons at particular ~1 mm size regions of interests across the dorsal surface of the brain). The authors also note that the axial resolution is ~1.5 mm.

Concerns & comments:

(1) While the authors argue that it offers the best utility to affordability trade-off - faster than motorized drivers and require much less power than DMDs (100X) and less expensive/easier to use compared to SLMs, in the current form, the manuscript does not clearly list the limitations of the approach. At such, in my opinion, the authors should include side by side comparisons (perhaps as a table). For example, clear statements should be included with respect to comparisons in lateral (x-y), axial (z) spatial resolution, as well as temporal sequential aspect of Zappit and other photo-stimulation techniques involving DMDs or SLMs.

(2) Is power really a limitation in terms of the laser sources? Or is this a disadvantage mainly because using less power has beneficial effects on the tissue health? It may be useful to provide metrics of comparisons along these lines between Zappit and DMD-based approaches.

(3) Arbitrary-scanning vs random scanning may be more appropriate to describe to strategy.

Reviewer #3 (Public review):

Summary:

In this manuscript, Duan et al perform a combinatorial TF overexpression screen combined with single-cell RNA-seq (Reprogram-Seq) to extract general principles of how combinatorial TF interactions drive distinct gene regulatory networks in reprogrammed cells. Using a library of 105 TFs, they induce different cell fates, many of which resemble in vivo cell identities. They observe that combinations of TFs have better reprogramming results than inductions driven by a single TF. By looking at gene expression enrichment/depletion in different reprogrammed cell clusters, they infer functional GRNs induced by specific TF combinations and identify GRNs specific for certain cell types. They also identify TFs that could improve known TF cocktails for the induction of certain cell fates. They observe that TFs with cooperative interactions regarding the regulation of gene expression may lead to better reprogramming results, and finally, they build a bottom-up approach that utilizes the single-cell transcriptomes to predict TFs driving certain reprogrammed fates.

Strengths:

Reprogram-Seq is not new, but the strength of the study lies in the fact that the authors assess the induction outcome from a large number of different TF combinations. The authors are thus able to make broad observations, such as the modularity of GRNs and TF cooperativity, as well as propose new TFs and TF interactions to be tested for the induction of certain cell fates. The manuscript is well written, and the conclusions are, in general, supported well by the authors' analyses and data.

Weaknesses:

The study would benefit from some further analysis and discussion to better tighten the conclusions:

While both expression enrichment and depletion were used to define perturbation clusters, the authors then focused on analyzing functional gene groups only for the enriched genes. Are there any functional relations between the repressed genes within a perturbation cluster? Do the authors observe the same modularity (in terms of regulation by TFs) for repressed genes as they do for induced genes?

Can the authors give a description of how neomorphic TF interactions work? How would gene expression be affected in single vs double perturbation in those cases?

What does it mean functionally when a TF pair shows more than one type of interaction (as shown in Supplementary Table 6), and how do such interactions correlate with successful transcriptional reprogramming?

In the last Results section, the authors are able to use the transcriptome to predict the TF that was used for the induction. Could the authors discuss some plausible applications of this TF prediction method? For example, could they use it on in vivo single-cell RNA-seq of a certain cell type to predict candidate TFs for the induction of that cell type?

It would help the reader if, at the end of each Results section, the authors add a concluding paragraph, highlighting the most important conclusions and findings (same as they have done in the section titled "Combinatorial TF over-expression reprograms MEFs to diverse states").

Reviewer #3 (Public review):

This manuscript studies the connection between neural activity collected through electrocorticography and hidden vector representations from autoregressive language models, with the specific aim of studying the influence of language model size on this connection. Neural activity was measured from subjects that listened to a segment from a podcast, and the representations from language models were calculated using the written transcription as the input text. The ability of vector representations to predict neural activity was evaluated using 10-fold cross-validation with ridge regression models.

The main results are that (as well summarized in section headings):<br /> (1) Larger models predict neural activity better.

(2) The ability of language model representations to predict neural activity differs across electrodes and brain regions.

(3) The layer that best predicts neural activity differs according to model size, with the "SMALL" model showing a correspondence between layer number and the language processing hierarchy.

(4) There seems to be a similar relationship between the time lag and the ability of language model representations to predict neural activity across models.

Strengths:

(1) The experimental and modeling protocols generally seem solid, which yielded results that answer the authors' primary research question.

(2) Electrocorticography data is especially hard to collect, so these results make a nice addition to recent functional magnetic resonance imaging studies.

Weaknesses:

(1) The interpretation of some results seems unjustified, although this may just be a presentational issue.

a) Figure 2B: The authors interpret the results as "a plateau in the maximal encoding performance," when some readers might interpret this rather as a decline after 13 billion parameters. Can this be further supported by a significance test like that shown in Figure 4B?

b) Figure S1A: It looks like the drop in PCA max correlation is larger for larger models, which may suggest to some readers that the same trend observed for ridge max correlation may not hold, contra the authors' claim that all results replicate. Why not include a similar figure as Figure 2B as part of Figure S1?

(2) Discussion of what might be driving the main result about the influence of model size appears to be missing (cf. the authors aim to provide an explanation of what seems to drive the influence of the layer location in Paragraph 3 of the Discussion section). What explanations have been proposed in the previous functional magnetic resonance imaging studies? Do those explanations also hold in the context of this study?

(3) The GloVe-based selection of language-sensitive electrodes (at least to me) isn't explained/motivated clearly enough (I think a more detailed explanation should be included in the Materials and Methods section). If the electrodes are selected based on GloVe embeddings, then isn't the main experiment just showing that representations from larger language models track more closely with GloVe embeddings? What justifies this methodology?

(4) (Minor weakness) The main experiments are largely replications of previous functional magnetic resonance imaging studies, with the exception of the one lag-based analysis. Is there anything else that the electrocorticography data can reveal that functional magnetic resonance imaging data can't?

Comments on revised version.

I reread the manuscript, my previous review, and the authors' response to it. I thank the authors for clarifying any misunderstanding from my end (e.g. the different LLM tokenizers) and feel that the authors addressed my concerns very carefully.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

(1) While the inducible Cre lines used by the authors target both peripheral and CNS tissues, this potential confound is mitigated by the lack of impact on peripheral disease burden.

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

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

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

Reviewer #3 (Public review):

Summary:

The study provides an in-depth phenotyping of a novel zebrafish larval model of ADHD. This topic is interesting, and the model and the approach are relevant and well-justified. While the paper has a massive amount of high-quality data, the general structure and presentation of this material lack focus and a clearly articulated rationale.

Strengths:

The paper is methodologically sound, well-presented, and well- illustrated. It has a clear logical rationale and reasonable experimental design.

Weaknesses:

The amount of high-quality data is impressive, yet the general structure and presentation of this material lack focus and a clearly articulated rationale.

(1) First, it is unclear why VR is necessary here. It needs a better explanation in both the abstract and the intro section of the manuscript.

(2) Second, data need to be better presented (most important things first, least important - shorter or move to the Supplementary materials). Currently, it is too much to be clear and easy to follow.

(3) Discussion needs to better state the novelty and the significance of these findings. What does the study offer that is new? Why was it important to perform? What big questions does it address?

(4) The authors should better discuss the study limitations and future directions of research.

(5) There should be a stronger conclusion with a take-home message to emphasize what new information the study brings and why it is important.

(6) The overall style of the paper should be improved. Currently, it reads like a dry bulleted CRO report, not a usual scholarly paper.

(7) Optimize the text flow. Currently, the overall flow of the discussion needs to be smoother - it now reads as a selection of bulleted paragraphs, with few connections between them.

Reviewer #3 (Public review):

This study makes clever use of generative AI to create stimuli that are pixel-for-pixel identical but which have radically different meanings depending on their orientation, to investigate the perception of animacy while retaining control over low-level image features (so-called 'anagram' stimuli).

The authors present seven elegantly designed experiments in a commendably compact format.

Experiments 1 and 2 involved a working memory paradigm in which participants had to spot which of five objects in an array changed after a pause. Importantly, the changed object was an anagram stimulus that in one orientation matched the animacy/inanimacy of the changed object, and in the other orientation was the opposite (e.g., a rabbit is replaced by either a dog or a boot, where the dog and boot stimuli are actually identical, just rotated by 90 degrees). They found a difference in accuracy depending on whether the animacy of the objects matched.

Experiments 3 and 4 used a visual search task in which the participants had to localize the target, and the distractors were anagrams that either matched the target in terms of animacy or did not. There was a significant cost in terms of response time when the animacy of the target was the same as that of the distractors. Experiments 5 and 6 also used a similar visual search design, except that the task was to determine if the target was present or absent from the display, and the distractors again either matched or differed from the target in terms of animacy. Again, the authors found slower responses when the distractor arrays matched the animacy of the target than when they differed.

An obvious potential concern about the studies is addressed by Experiment 7. It is unclear if the observed effects are related to the specific orientations of the target and distractor stimuli selected in each condition. For example, it could be that all the animate versions of the anagrams involved tall and skinny shapes, while all the inanimate versions involved wide and short objects, due to the 90-degree rotational difference between the two versions of the stimuli. To control for this, the authors repeated the visual search experiment but with convex-hull silhouettes of each of the stimuli. In other words, all targets and distractors from each trial were replaced by a black splotch with approximately the same overall outline (envelope) as the corresponding stimulus. Importantly, in contrast to the anagram stimuli, the silhouettes had had no meaningful semantic interpretation, and their animacy did not change depending on their orientation.

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence how dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strength:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuit interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of the role of dopamine in cognitive functions. Additional experiments investigating the contribution of MBON-α1 to the circuit, connectomic analysis together with a dissection of dopamine receptor function further strengthen the proposed circuit motif and its biological relevance.

Weaknesses:

While the study is well designed, presents compelling findings and has been further strengthened by additional experiments, some aspects remain unclear. The role of DPM neurons in memory consolidation seems not yet fully resolved, as different genetic approaches yield variable results. Furthermore, some manipulations impair memory without affecting sleep fragmentation - or vice versa, suggesting that the observed memory deficits cannot be explained solely by impaired sleep-dependent consolidation. It would also have been interesting to discuss potential mechanisms by which dopamine receptor-mediated cAMP signaling could lead to a reduction in Ca²⁺ signals. I am confident that these questions can be addressed in future studies.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopaminergic circuits interact to regulate memory consolidation in Drosophila and may reveal general principles underlying the neural regulation of memory.

Reviewer #3 (Public review):

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

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

Reviewer #3 (Public review):

Summary:

The authors investigated the role of the zona incerta in motivation and cue-reward associations. Using chemogenetic and optogenetic manipulations of the ZI, they altered motivation in cued and uncued variants of the progressive ratio task and rescued deficits in motivation induced by chronic stress. They further use fiber photometry to demonstrate that the ZI tracks the formation of cue-reward associations.

Strengths:

(1) The authors fill an important gap in the literature linking sensory input to motivation via the zona incerta.

(2) The authors demonstrate that ZI tracks cue value rather than just tracking sensory input.

(3) The authors demonstrate that the ZI excitation rescues stress-induced suppression of motivation.

(4) The authors perform several important control tasks, demonstrating that their findings are not a result of alterations in locomotor activity, food consumption, or memory.

Weaknesses:

In Figure 1D and E (inhibitory vs excitatory DREADDS), the control groups in the Gi group appear to have more elevated breakpoints than the control groups in the Gq group, although a statistical comparison between the two is not reported. It is not clear if this is because the two groups were given a different reinforcement schedule, this should be made clearer.

In Figure 1E, it is important to note that although the authors found a significant planned comparison between Gq VEH and Gq CNO, the interaction was not significant, nor were comparisons to mice injected with control virus. Thus, activation of ZI GABA neurons appears to be a relatively weak effect.

In Figure 5, the authors see what is likely a significant difference in lever presses during acclimation between the Gi and GFP groups, which they state is an expected difference. However, it is difficult to see why this would be expected. While Gi:CNO manipulation yielded lower breakpoints in Figure 1D, it did not yield lower FR1 responding for food in Fig S3 (although this was FR1 for food dispenser visits rather than lever press). One reason I ask is that the authors highlight the differences in CS+/CS- between groups, but the biggest difference between groups appears to be in acclimation, which may be driving the group x block interaction.

In Figure 6, the authors demonstrate that optogenetic stimulation during cue light increases the breakpoint in females, but not in males. They suggest that this may be because the males did not sufficiently discriminate the cue light before optogenetic manipulation began. If this were the case, then the authors would need to use "cue discrimination" as a factor to determine if it is a better predictor than sex.

The authors' work demonstrates that chemogenetic inhibition of GABAergic ZI cells reduces uncued motivation for reward but enhances cued responses under extinction. The authors state that this is a paradoxical finding that suggests that the ZI operates within a redundant motivation network. However, a critical difference between the two tasks is that one measures motivation for food while the other measures persistent responding under food extinction, which are not the same process. Thus, a simpler explanation is that ZI inhibition reduces motivation and impairs extinction.