Reviewer #1 (Public review):

Summary:

A central function of glial cells is the ensheathment of axons. Wrapping of larger-diameter axons involves myelin-forming glial classes (such as oligodendrocytes), whereas smaller axons are covered by non-myelin forming glial processes (such as olfactory ensheathing glia). While we have some insights into the underlying molecular mechanisms orchestrating myelination, our understanding of the signaling pathways at work in non-myelinating glia remains limited. As non-myelinating glial ensheathment of axons is highly conserved in both vertebrates and invertebrates, the nervous system of Drosophila melanogaster, and in particular the larval peripheral nerves, have emerged as powerful model to elucidate the regulation of axon ensheathment by a class of glia called wrapping glia. This study seeks to specifically address the question, as to which molecular mechanisms contribute to the regulation of the extent of glial ensheathment focusing on the interaction of wrapping glia with axons.

Strengths and Weaknesses:

For this purpose, the study combines state-of-the-art genetic approaches with high-resolution imaging, including classic electron microscopy. The genetic methods involve RNAi mediated knockdown, acute Crispr-Cas9 knock-outs and genetic epistasis approaches to manipulate gene function with the help of cell-type specific drivers. The successful use of acute Crispr-Cas9 mediated knockout tools (which required the generation of new genetic reagents for this study) will be of general interest to the Drosophila community.

The authors set out to identify new molecular determinants mediating the extent of axon wrapping in the peripheral nerves of third instar wandering Drosophila larvae. They could show that over-expressing a constitutive-active version of the Fibroblast growth factor receptor Heartless (Htl) causes an increase of wrapping glial branching, leading to the formation of swellings in nerves close to the cell body (named bulges). To identify new determinants involved in axon wrapping acting downstream of Htl, the authors next conducted an impressive large-scale genetic interaction screen (which has become rare, but remains a very powerful approach), and identified Uninflatable (Uif) in this way. Uif is a large single-pass transmembrane protein which contains a whole series of extracellular domains, including Epidermal growth factor-like domains. Linking this protein to glial branch formation is novel, as it has so far been mostly studied in the context of tracheal maturation and growth. Intriguingly, a knock-down or knock-out of uif reduces branch complexity and also suppresses htl over-expression defects. Importantly, uif over-expression causes the formation of excessive membrane stacks. Together these observations are in in line with the notion that htl may act upstream of uif.

Further epistasis experiments using this model implicated also the Notch signaling pathway as a crucial regulator of glial wrapping: reduction in Notch signaling reduces wrapping, whereas over-activation of the pathway increases axonal wrapping (but does not cause the formation of bulges). Importantly, defects caused by over-expression of uif can be suppressed by activated Notch signaling. Knock-down experiments in neurons suggest further that neither Delta nor Serrate act as neuronal ligands to activate Notch signaling in wrapping glia, whereas knock-down of Contactin, a GPI anchored Immunoglobulin domain containing protein led to reduced axon wrapping by glia, and thus could act as an activating ligand in this context.

Based on these results the authors put forward a model proposing that Uif normally suppresses Notch signaling, and that activation of Notch by Contactin leads to suppression of Htl, to trigger the ensheathment of axons. While these are intriguing propositions, future experiments will need to conclusively address whether and how Uif could "stabilize" a specific membrane domain capable to interact with specific axons.

Moreover, to obtain evidence for Uif suppression by Notch to inhibit "precocious" axon wrapping and for a "gradual increase" of Notch signaling that silences uif and htl, (1) reporters for N and Htl signaling in larvae, (2) monitoring of different stages at a time point when branch extension begins, and (3) a reagent enabling the visualization of Uif expression could be important next tools/approaches. Considering the qualitatively different phenotypes of reduced branching, compared to excessive membrane stacks close to cell bodies, it would perhaps be worthwhile to explore more deeply how membrane formation in wrapping glia is orchestrated at the subcellular level by Uif.

However, the points raised above remain at present technically difficult to address because of the lack of appropriate genetic reagents. Also more detailed electron microscopy analyses of early developmental stages and comparisons of effects on cell bodies compared to branches will be very labor-intensive, and indeed may represent a new study.

In summary, in light of the importance of correct ensheathment of axons by glia for neuronal function, the proposed model for the interactions between Htl, Uif and N to control the correct extent of neuron and glial contacts will be of general interest to the glial biology community.

Comments on revisions:

The authors have addressed all my comments. However, the sgRNAs in the Star method table are still all for cleavage just before the transmembrane domain, while the Supplemental figure suggests different locations.

Reviewer #2 (Public review):

Summary:

This paper aims to elucidate the gene regulatory network governing the development of cone photoreceptors, the light-sensing neurons responsible for high acuity and color vision in humans. The authors provide a comprehensive analysis through stage-matched comparisons of gene expression and chromatin accessibility using scRNA-seq and scATAC-seq from the cone-dominant 13-lined ground squirrel (13LGS) retina and the rod-dominant mouse retina. The abundance of cones in the 13LGS retina arises from a dominant trajectory from late retinal progenitor cells (RPCs) to photoreceptor precursors and then to cones, whereas only a small proportion of rods are generated from these precursors.

Strengths:

The paper presents intriguing insights into the gene regulatory network involved in 13LGS cone development. In particular, the authors highlight the expression of cone-promoting transcription factors such as Onecut2, Pou2f1, and Zic3 in late-stage neurogenic progenitors, which may be driven by 13LGS-specific cis-regulatory elements. The authors also characterize candidate cone-promoting genes Zic3 and Mef2C, which have been previously understudied. Overall, I found that the across-species analysis presented by this study is a useful resource for the field.

Comments on Revision:

The authors have addressed my questions, and the revised text now presents their findings more clearly.

Reviewer #2 (Public review):

Summary:

Sugimoto et al. explore the relationship between glucose dynamics-specifically value, variability, and autocorrelation-and coronary plaque vulnerability in patients with varying glucose tolerance levels. The study identifies three independent predictive factors for %NC and emphasizes the use of continuous glucose monitoring (CGM)-derived indices for coronary artery disease (CAD) risk assessment. By employing robust statistical methods and validating findings across datasets from Japan, America, and China, the authors highlight the limitations of conventional markers while proposing CGM as a novel approach for risk prediction.The study has the potential to reshape CAD risk assessment by emphasizing CGM-derived indices, aligning well with personalized medicine trends.

Further, the revised version includes expanded biological interpretation, improved statistical justification, and a new web-based calculator for clinical translation. Together, these updates make the study an important contribution to precision risk assessment in diabetes and cardiovascular research.

Strengths:

The introduction of autocorrelation as a predictive factor for plaque vulnerability adds a novel dimension to glucose dynamic analysis.

Inclusion of datasets from diverse regions enhances generalizability.

The use of a well-characterized cohort with controlled cholesterol and blood pressure levels strengthens the findings.

The focus on CGM-derived indices aligns with personalized medicine trends, showcasing potential for CAD risk stratification.

The benchmarking of CGM-derived measures against established CAD risk models (e.g., Framingham Risk Score) enhances interpretability and significance.

The addition of a web-based computational tool makes the proposed indices accessible for potential clinical and research use.

Weaknesses:

The biological mechanism linking glucose autocorrelation to plaque vulnerability, although plausibly associated with insulin clearance pathways, remains largely theoretical.

The primary cohort size is still modest, and while supported by power analysis and external datasets, broader prospective validation will be important.

Strict participant selection criteria as employed by the study may reduce applicability to broader populations.

CGM-derived indices like AC_Var and ADRR may be too complex for routine clinical use without simplified models or guidelines.

Comments on revised version:

The authors have thoroughly addressed previous concerns and produced a much stronger manuscript. The study now provides a coherent, validated, and well-reasoned argument for including autocorrelation as a third major dimension of glucose dynamics. It offers both conceptual novelty and translational potential and will likely stimulate further research on temporal glucose metrics in metabolic and cardiovascular risk assessment.

Reviewer #1 (Public review):

Summary:

The paper by ILBAY et al describes a screen in C. elegans for loss-of-function of factors that are presumed to constitutively downregulate heat shock or stress genes regulated by HSF-1. The hypothesis posits an active mechanism of downregulation of these genes under non-stressed conditions. The screen robustly identified ZNF-236, a multi zinc finger containing protein, whose loss upregulates heat-shock and stress-induced prion-like protein genes, but which does not appear to act in cis at the relevant promoters. The authors speculate that ZNF-236 acts indirectly on chromatin or chromatin domains to repress hs genes under non-stressed conditions.

Strengths:

The screen is clever, well-controlled and quite straightforward. I am convinced that ZNF-236 has something to do with keeping heat shock and other stress transcripts low. The mapping of potential binding sites of ZNF-236 is negative, despite the development of a new method to monitor binding sites. I am not sure whether this assay has a detection/sensitivity threshold limit, as it is not widely used. Up to this point, the data are solid, and the logic is easy to follow.

Weaknesses:

While the primary observations are well-documented, the mode of action of ZNF-236 is inadequately explored. Multi Zn finger proteins often bind RNA (TFIII3A is a classic example), and the following paper addresses multivalent functions of Zn finger proteins in RNA stability and processing: Mol Cell 2024 Oct 3;84(19):3826-3842.e8. doi: 10.1016/j.molcel.2024.08.010.). I see no evidence that would point to a role for ZNF-236 in nuclear organization, yet this is the authors' favorite hypothesis. In my opinion, this proposed mechanism is poorly justified, and certainly should not be posited without first testing whether ZNF-236 acts post-transcriptionally, directly down-regulating the relevant mRNAs in some way. It could regulate RNA stability, splicing, export or translation of the relevant RNAs rather than their transcription rates. This can be tested by monitoring whether ZNF-236 alters run-on transcription rates or not. If nascent RNA synthesis rates are not altered, but rather co- and/or post-transcriptional events, and if ZNF-236 is shown to bind RNA (which is likely), the paper could still postulate that the protein plays a role in downregulating stress and heat shock proteins. However, they could rule out that it acts on the promoter by altering RNA Pol II engagement. Another option that should be tested is that ZNF-236 acts by nucleating an H3K9me domain that might shift the affected genes to the nuclear envelope, sequestering them in a zone of low-level transcription. That is also easily tested by tracking the position of an affected gene in the presence and absence of SNF-236. This latter mechanism is also right in line with known modes of action for Zn finger proteins (in mammals, acting through KAP1 and SETDB1). A role for nucleating H3K9me could be easily tested in worms by screening MET-2 or SET-25 knockouts for heat shock or stress mRNA levels. These data sets are already published.

Without testing these two obvious pathways of action (through RNA or through H3K9me deposition), this paper is too preliminary.

Appraisal:

The authors achieved their initial aim with the screen, and the paper is of interest to the field. However, they do not adequately address the likely modes of action. Indeed, I think their results fail to support the conclusion or speculation that ZNF-236 acts on long-range chromatin organization. No solid evidence is presented to support this claim.

Impact:

If the paper were to address and/or rule out likely modes of action, the paper would be of major value to the field of heat shock and stress mRNA control.

Reviewer #1 (Public review):

Summary:

Authors explore how sex-peptide (SP) affects post-mating behaviours in adult females, such as receptivity and egg laying. This study identifies different neurons in the adult brain and the VNC that become activated by SP, largely by using an intersectional gene expression approach (split-GAL4) to narrow down the specific neurons involved. They confirm that SP binds to the well-known Sex Peptide Receptor (SPR), initiating a cascade of physiological and behavioural changes related to receptivity and egg laying.

Comments on revised version:

The authors have substantially strengthened the manuscript in response to our main concerns.

In particular, they now explicitly test multiple established PMR nodes (including SAG/SPSN as well as pC1, OviDN/OviEN/OviIN and vpoDN), which helps separate direct SP targets from downstream PMR circuitry and supports their interpretation that some of these known nodes can affect receptivity without necessarily inducing oviposition. They also addressed key technical/clarity points: the requested head/trunk expression controls are provided (Suppl Fig S1), and the VT003280 annotation is corrected (now FD6 rather than "SAG driver"). Overall, these additions make the central conclusion, that distinct CNS neuron subsets ("SPRINz") are sufficient to elicit PMR components, more convincing, and the added comparisons with genital tract expressing lines further argue against a simple "periphery only" explanation.

Reviewer #1 (Public review):

The manuscript by Shan et al seeks to define the role of the CHI3L1 protein in macrophages during the progression of MASH. The authors argue that the Chil1 gene is expressed highly in hepatic macrophages. Subsequently, they use Chil1 flx mice crossed to Clec4F-Cre or LysM-Cre to assess the role of this factor in the progression of MASH using a high fat high, fructose diet (HFFC). They found that loss of Chil1 in KCs (Clec4F Cre) leads to enhanced KC death and worsened hepatic steatosis. Using scRNA seq they also provide evidence that loss of this factor promotes gene programs related to cell death. From a mechanistic perspective they provide evidence that CHI3L serves as a glucose sink and thus loss of this molecule enhances macrophage glucose uptake and susceptibility to cell death. Using a bone marrow macrophage system and KCs they demonstrate that cell death induced by palmitic acid is attenuated by the addition of rCHI3L1. While the article is well written and potentially highlights a new mechanism of macrophage dysfunction in MASH and the authors have addressed some of my concerns there are some concerns about the current data that continue to limit my enthusiasm for the study. Please see my specific comments below.

Major:

(1) The authors' interpretation of the results from the KC ( Clec4F) and MdM KO (LysM-Cre) experiments is flawed. The authors have added new data that suggests LyM-Cre only leads to a 40% reduction of Chil1 in KCs and that this explains the difference in the phenotype compared to the Clec4F-Cre. However, this claim would be made stronger using flow sorted TIM4hi KCs as the plating method can lead to heterogenous populations and thus an underestimation of knockdown by qPCR. Moreover, in the supplemental data the authors show that Clec4f-Cre x Chil1flx leads to a significant knockdown of this gene in BMDMs. As BMDMs do not express Clec4f this data calls into question the rigor of the data. I am still concerned that the phenotype differences between Clec4f-cre and LyxM-cre is not related to the degree of knockdown in KCs but rather some other aspect of the model (microbiota etc). It woudl be more convincing if the authors could show the CHI3L reduction via IF in the tissue of these mice.

(2) Figure 4 suggests that KC death is increased with KO of Chil1. The authors have added new data with TIM4 that better characterizes this phenotype. The lack of TIM4 low, F4/80 hi cells further supports that their diet model is not producing any signs of the inflammatory changes that occur with MASLD and MASH. This is also supported by no meaningful changes in the CD11b hi, F4/80 int cells that are predominantly monocytes and early Mdms). It is also concerning that loss of KCs does not lead to an increase in Mo-KCs as has been demonstrated in several studies (PMID37639126, PMID:33997821). This would suggest that the degree of resident KC loss is trivial.

(3) The authors demonstrated that Clec4f-Cre itself was not responsible for the observed phenotype, which mitigates my concerns about this influencing their model.

(4) I remain somewhat concerned about the conclusion that Chil1 is highly expressed in liver macrophages. The author agrees that mRNA levels of this gene are hard to see in the datasets; however, they argue that IF demonstrates clear evidence of the protein, CHI3L. The IF in the paper only shows a high power view of one KC. I would like to see what percentage of KCs express CHI3L and how this changes with HFHC diet. In addition, showing the knockout IF would further validate the IF staining patterns.

Minor:

(1) The authors have answered my question about liver fibrosis. In line with their macrophage data their diet model does not appear to induce even mild MASH.

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate the role of deubiquitinases (DUBs) in modulating the efficacy of PROTAC-mediated degradation of the cell-cycle kinase AURKA. Using a focused siRNA screen of 97 human DUBs, they identify UCHL5 and OTUD6A as negative regulators of AURKA degradation by PROTACs. They further offer a mechanistic explanation of enhanced AURKA degradation in the nucleus via OTUD6A expression being restricted to the cytosol, thereby protecting the cytoplasmic pool of AURKA. These findings provide important insight into how subcellular localization and DUB activity influence the efficiency of targeted protein degradation strategies, which could have implications for therapy.

Strengths:

The manuscript is well-structured, with clearly defined objectives and well-supported conclusions.

The study employs a broad range of well-validated techniques-including live-cell imaging, proximity ligation assays, HiBiT reporter systems, and ubiquitin pulldowns - to dissect the regulation of PROTAC activity.

The authors use informative experimental controls, including assessment of cell-cycle progression effects, rescue experiments with siRNA-resistant constructs to confirm specificity, and the application of both AURKA-targeting PROTACs with different warheads and orthogonal degrader systems (e.g., dTAG-13 and dTAGv-1) to differentiate between target- and ligase-specific effects.

The identification of OTUD6A as a cytosol-restricted DUB that protects cytoplasmic but not nuclear AURKA is novel and may have therapeutic relevance for selectively targeting oncogenic nuclear AURKA pools.

Weaknesses:

Although UCHL5 and OTUD6A are shown to limit AURKA degradation, direct physical interaction was not assessed.

While the authors suggest that combining PROTACs with DUB inhibition could enhance degradation, this was not experimentally tested.

The authors acknowledge the apparent discrepancy between the enhanced degradation observed with CRBN-recruiting PROTACs and the lack of change in ubiquitination following UCHL5 knockdown, yet they do not propose any mechanistic explanation.

Reviewer #1 (Public review):

Summary:

Wang, Po-Kai et al., utilized the de novo polarization of MDCK cells cultured in Matrigel to assess the interdependence between polarity protein localization, centrosome positioning and apical membrane formation. They show that the inhibition of Plk4 with Centrinone does not prevent apical membrane formation, but does result in its delay, a phenotype the authors attribute to the loss of centrosomes due to the inhibition of centriole duplication. However, the targeted mutagenesis of specific centrosome proteins implicated in the positioning of centrosomes in other cell types (CEP164, ODF2, PCNT and CEP120), as well as the use of dominant negative constructs to inhibit centrosomal microtubule nucleation did not affect centrosome positioning in 3D cultured MDCK cells. A screen of proteins previously implicated in MDCK polarization revealed that the polarity protein Par-3 was upstream of centrosome positioning, similar to other cell types.

Strengths:

The investigation into the temporal requirement and interdependence of previously proposed regulators of cell polarization and lumen formation is valuable. The authors have provided a detailed analysis of many of these components at defined stages of polarity establishment, and well demonstrate that centrosomes are not necessary for apical polarity formation, but are involved in the efficient establishment of the apical membrane.

Weaknesses:

Key questions remain regarding the structure of the intracellular cytoskeleton following depletion of centrosomes, centrosome proteins,or abrogation of centrosome microtubule nucleation. The authors strengthen their model that centrosomes are positioned independently of microtubule nucleation using dominant negative Cdk5RAP2 and NEDD-1 constructs, however, the structure of the intracellular microtubule network remains unresolved and will be an important avenue for future investigation.

Reviewer #1 (Public review):

Summary:

In this manuscript, Chengjian Zhao et al. focused on the interactions between vascular, biliary, and neural networks in the liver microenvironment, addressing the critical bottleneck that the lack of high-resolution 3D visualization has hindered understanding of these interactions in liver disease.

Strengths:

This study developed a high-resolution multiplex 3D imaging method that integrates multicolor metallic compound nanoparticle (MCNP) perfusion with optimized CUBIC tissue clearing. This method enables the simultaneous 3D visualization of spatial networks of the portal vein, hepatic artery, bile ducts, and central vein in the mouse liver. The authors reported a perivascular structure termed the Periportal Lamellar Complex (PLC), which is identified along the portal vein axis. This study clarifies that the PLC comprises CD34⁺Sca-1⁺ dual-positive endothelial cells with a distinct gene expression profile, and reveals its colocalization with terminal bile duct branches and sympathetic nerve fibers under physiological conditions.

Comments on revisions:

The authors very nicely addressed all concerns from this reviewer. There are no further concerns or comments.

Reviewer #1 (Public review):

Domínguez-Rodrigo and colleagues make a largely convincing case for habitual elephant butchery by Early Pleistocene hominins at Olduvai Gorge (Tanzania), ca. 1.8-1.7 million years ago. They present this at a site scale (the EAK locality, which they excavated), as well as across the penecontemporaneous landscape, analyzing a series of findspots that contain stone tools and large-mammal bones. The latter are primarily elephants, but giraffids and bovids were also butchered in a few localities.

The authors claim that this is the earliest well-documented evidence for elephant butchery; doing so requires debunking other purported cases of elephant butchery in the literature, or in one case, reinterpreting elephant bone manipulation as being nutritional (fracturing to obtain marrow) rather than technological (to make bone tools). The authors' critical discussion of these cases may not be consensual, but it surely advances the scientific discourse. The authors conclude by suggesting that an evolutionary threshold was achieved at ca. 1.8 ma, whereby regular elephant consumption rich in fats and perhaps food surplus, more advanced extractive technology (the Acheulian toolkit), and larger human group size had coincided.

The fieldwork and spatial statistics methods are presented in detail and are solid and helpful, especially the excellent description (all too rare in zooarchaeology papers) of bone conservation and preservation procedures. The results are detailed and clearly presented.

The authors achieved their aims, showcasing recurring elephant butchery in 1.8-1.7 million-year-old archaeological contexts. The authors cautiously emphasize the temporal and spatial correlation of 1) elephant butchery, 2) Acheulian toolkits, and 3) larger sites, and discuss how these elements may be causally related.

Overall, this is an interesting manuscript of broad interest that presents original data and interpretations from the Early Pleistocene archaeology of Olduvai Gorge. These observations and the authors' critical review of previously published evidence are an important contribution that will form the basis for building models of Early Pleistocene hominin adaptation.

Reviewer #1 (Public review):

Summary:

This paper focuses on understanding how covalent inhibitors of peroxisome proliferator-activated receptor-gamma (PPARg) show improved inverse agonist activities. This work is important because PPARg plays essential roles in metabolic regulation, insulin sensitization, and adipogenesis. Like other nuclear receptors, PPARg, is a ligand-responsive transcriptional regulator. Its important role, coupled with its ligand-sensitive transcriptional activities, makes it an attractive therapeutic target for diabetes, inflammation, fibrosis, and cancer. Traditional non-covalent ligands like thiazolininediones (TZDs) show clinical benefit in metabolic diseases, but utility is limited by off-target effects and transient receptor engagement. In previous studies, the authors characterized and developed covalent PPARg inhibitors with improved inverse agonist activities. They also showed that these molecules engage unique PPARg ligand binding domain (LBD) conformations whereby the c-terminal helix 12 penetrates into the orthosteric binding pocket to stabilize a repressive state. In the nuclear receptor superclass of proteins, helix 12 is an allosteric switch that governs pharmacologic responses, and this new conformation was highly novel. In this study, the authors did a more thorough analysis of how two covalent inhibitors, SR33065 and SR36708 influence the structural dynamics of PPARg LBD.

Strengths:

(1) The authors employed a compelling integrated biochemical and biophysical approach.

(2) The cobinding studies are unique for the field of nuclear receptor structural biology, and I'm not aware of any similar structural mechanism described for this class of proteins.

(3) Overall, the results support their conclusions.

(4) The results open up exciting possibilities for the development of new ligands that exploit the potential bidirectional relationship between the covalent versus non-covalent ligands studied here.

Weaknesses:

All weaknesses were addressed by the Authors in revision.

Reviewer #1 (Public review):

Summary:

This study aims to explore how different forms of "fragile nucleosomes" facilitate RNA Polymerase II (Pol II) transcription along gene bodies in human cells. The authors propose that pan-acetylated, pan-phosphorylated, tailless, and combined acetylated/phosphorylated nucleosomes represent distinct fragile states that enable efficient transcription elongation. Using CUT&Tag-seq, RNA-seq, and DRB inhibition assays in HEK293T cells, they report a genome-wide correlation between histone pan-acetylation/phosphorylation and active Pol II occupancy, concluding that these modifications are essential for Pol II elongation.

Strengths:

(1) The manuscript tackles an important and long-standing question about how Pol II overcomes nucleosomal barriers during transcription.

(2) The use of genome-wide CUT&Tag-seq for multiple histone marks (H3K9ac, H4K12ac, H3S10ph, H4S1ph) alongside active Pol II mapping provides a valuable dataset for the community.

(3) The integration of inhibition (DRB) and recovery experiments offers insight into the coupling between Pol II activity and chromatin modifications.

(4) The concept of "fragile nucleosomes" as a unifying framework is potentially appealing and could stimulate further mechanistic studies.

Weaknesses:

(1) Misrepresentation of prior literature

The introduction incorrectly describes findings from Bintu et al., 2012. The cited work demonstrated that pan-acetylated or tailless nucleosomes reduce the nucleosomal barrier for Pol II passage, rather than showing no improvement. This misstatement undermines the rationale for the current study and should be corrected to accurately reflect prior evidence.

(2) Incorrect statement regarding hexasome fragility

The authors claim that hexasome nucleosomes "are not fragile," citing older in vitro work. However, recent studies clearly showed that hexasomes exist in cells (e.g., PMID 35597239) and that they markedly reduce the barrier to Pol II (e.g., PMID 40412388). These studies need to be acknowledged and discussed.

(3) Inaccurate mechanistic interpretation of DRB

The Results section states that DRB causes a "complete shutdown of transcription initiation (Ser5-CTD phosphorylation)." DRB is primarily a CDK9 inhibitor that blocks Pol II release from promoter-proximal pausing. While recent work (PMID: 40315851) suggests that CDK9 can contribute to CTD Ser5/Ser2 di-phosphorylation, the manuscript's claim of initiation shutdown by DRB should be revised to better align with the literature. The data in Figure 4A indicate that 1 µM DRB fully inhibits Pol II activity, yet much higher concentrations (10-100×) are needed to alter H3K9ac and H4K12ac levels. The authors should address this discrepancy by discussing the differential sensitivities of CTD phosphorylation versus histone modification turnover.

(4) Insufficient resolution of genome-wide correlations

Figure 1 presents only low-resolution maps, which are insufficient to determine whether pan-acetylation and pan-phosphorylation correlate with Pol II at promoters or gene bodies. The authors should provide normalized metagene plots (from TSS to TTS) across different subgroups to visualize modification patterns at higher resolution. In addition, the genome-wide distribution of another histone PTM with a different localization pattern should be included as a negative control.

(5) Conceptual framing

The manuscript frequently extrapolates correlative genome-wide data to mechanistic conclusions (e.g., that pan-acetylation/phosphorylation "generate" fragile nucleosomes). Without direct biochemical or structural evidence. Such causality statements should be toned down.

Reviewer #1 (Public review):

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

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

Several aspects of data interpretation, however, require clarification or reanalysis-most notably the single-channel analyses (event counts, Po metrics, and mixed parameters), the statistical treatment, and the interpretation of purported "OFF currents." Additional issues include PKD2L1-TRPP3 nomenclature consistency, kinetic comparison with ASICs, and the physiological relevance of the extreme acidification paradigm. Addressing these points will substantially improve reproducibility and mechanistic depth.

Overall, this is a scientifically important and technically sophisticated study that advances our understanding of CSF sensing, provided that the analytical and interpretative weaknesses are satisfactorily corrected.

(1) The authors should re-analyze electrophysiological data, focusing on macroscopic currents rather than statistically unreliable Po calculations. Remove or revise the Po analysis, which currently conflates current amplitude and open probability.

(2) PKD2L1-TRPP3 nomenclature should be clarified and all figure labels, legends, and text should use consistent terminology throughout.

(3) The authors should reinterpret the so-called OFF currents as pH-dependent recovery or relaxation phenomena, not as distinct current species. Remove the term "OFF response" from the manuscript.

(4) Evidence for physiological relevance should be provided, including data from milder acidification (pH 6.5-6.8) and, where appropriate, comparisons with ASIC-mediated currents to place PKD2L1 activity in context.

(5) Terminology and data presentation should be unified, adopting consistent use of "predominant" (instead of "exclusive") and "sustained" (instead of "tonic"), and all statistical formats and units should be standardized.

(6) The Discussion should be expanded to address potential Ca²⁺-dependent signaling mechanisms downstream of PKD2L1 activation and their possible roles in CSF flow regulation and central chemoreception.

Reviewer #1 (Public review):

Summary:

How the regenerative capacity of the heart varies among different species has been a long-standing question. Within teleosts, zebrafish can regenerate their hearts, while medaka and cavefish cannot. The authors examined heart regeneration in two livebearers, platyfish and swordtails. Interestingly, they found that these two fish species lack the compact myocardium layer that contains coronary vessels. Furthermore, these fish form a "pseudoaneurysm" after cryoinjury without initial deposition of fibrotic tissues. However, delayed leukocyte infiltration and prolonged inflammation lead to permanent scar tissue in the injured heart. Although their cardiomyocytes can also proliferate, platyfish and swordtails can only regenerate partially. The authors argue that the restorative mechanism of platyfish and swordtails likely reflects "evolutionary innovations in the ventricle type and the immune system".

Strengths:

The authors took advantage of the annotated genome of platyfish to perform transcriptomic analyses. The histological analyses and immunostaining are beautifully done.

Minor Weaknesses:

Transcriptomic analysis was only done for one time point. Different time points could be included to validate whether some processes occur at different time points. But this can be done in the future for more detailed studies."

Reviewer #1 (Public review):

Mitochondrial staining difference is convincing, but the status of the mitos, fused vs fragmented, elongated vs spherical, does not seem convincing. Given the density of mito staining in CySC, it is difficult to tell what is an elongated or fused mito vs the overlap of several smaller mitos.

I'm afraid the quantification and conclusions about the gstD1 staining in CySC vs. GSCs is just not convincing-I cannot see how they were able to distinguish the relevant signals to quantify once cell type vs the other.

The overall increase in gstD1 staining with the CySC SOD KD looks nice, but again I can't distinguish different cel types. This experiment would have been more convincing if the SOD KD was mosaic, so that individual samples would show changes in only some of the cells. Still, it seems that KD of SOD in the CySC does have an effect on the germline, which is interesting.

The effect of SOD KD on the number of less differentiated somatic cells seems clear. However, the effect on the germline is less clear and is somewhat confusing. Normally, a tumor of CySC or less differentiated Cyst cells, such as with activated JAK/STAT, also leads to a large increase in undifferentiated germ cells, not a decrease in germline as they conclude they observe here. The images do not appear to show reduced number of GSCs, but if they counted GSCs at the niche, then that is the correct way to do it, but its odd that they chose images that do not show the phenotype. In addition, lower number of GSCs could also be caused by "too many CySCs" which can kick out GSCs from the niche, rather than any affect on GSC redox state. Further, their conclusion of reduced germline overall, e.g. by vasa staining, does not appear to be true in the images they present and their indication that lower vasa equals fewer GSCs is invalid since all the early germline expresses Vasa.

The effect of somatic SOD KD is perhaps most striking in the observation of Eya+ cyst cells closer to the niche. The combination of increased Zfh1+ cells with many also being Eya+ demonstrates a strong effect on cyst cell differentiation, but one that is also confusing because they observe increases in both early cyst cells (Zfh1+) as well as late cyst cells (Eya+) or perhaps just an increase in the Zfh1/Eya double-positive state that is not normally common. The effects on the RTK and Hh pathways may also reflect this disturbed state of the Cyst cells.

However, the effect on germline differentiation is less clear-the images shown do not really demonstrate any change in BAM expression that I can tell, which is even more confusing given the clear effect on cyst cell differentiation.

For the last figure, any effect of SOD OE in the germline on the germline itself is apparently very subtle and is within the range observed between different "wt" genetic backgrounds.

Comments on revisions:

Upon re-re-review, the manuscript is improved but retains many of the flaws outlined in the first reviews.

Reviewer #1 (Public review):

Summary:

This study presents a comprehensive single-cell atlas of mouse anterior segment development, focusing on the trabecular meshwork and Schlemm's canal. The authors profiled ~130,000 cells across seven postnatal stages, providing detailed and solid characterization of cell types, developmental trajectories, and molecular programs.

Strengths:

The manuscript is well-written, with a clear structure and thorough introduction of previous literature, providing a strong context for the study. The characterization of cell types is detailed and robust, supported by both established and novel marker genes as well as experimental validation. The developmental model proposed is intriguing and well supported by the evidence. The study will serve as a valuable reference for researchers investigating anterior segment developmental mechanisms. Additionally, the discussion effectively situates the findings within the broader field, emphasizing their significance and potential impact for developmental biologists studying the visual system.

Weaknesses:

The weaknesses of the study are minor and addressable. As the study focuses on the mouse anterior segment, a brief discussion of potential human relevance would strengthen the work by relating the findings to human anterior segment cell types, developmental mechanisms, and possible implications for human eye disease. Data availability is currently limited, which restricts immediate use by the community. Similarly, the analysis code is not yet accessible, limiting the ability to reproduce and validate the computational analyses presented in the study.

Reviewer #1 (Public review):

Summary:

Matsen et al. describe an approach for training an antibody language model that explicitly tries to remove effects of "neutral mutation" from the language model training task, e.g. learning the codon table, which they claim results in biased functional predictions. They do so by modeling empirical sequence-derived likelihoods through a combination of a "mutation" model and a "selection" model; the mutation model is a non-neural Thrifty model previously developed by the authors, and the selection model is a small Transformer that is trained via gradient descent. The sequence likelihoods themselves are obtained from analyzing parent-child relationships in natural SHM datasets. The authors validate their method on several standard benchmark datasets and demonstrate its favorable computational cost. They discuss how deep learning models explicitly designed to capture selection and not mutation, trained on parent-child pairs, could potentially apply to other domains such as viral evolution or protein evolution at large.

Strengths:

Overall, we think the idea behind this manuscript is really clever and shows promising empirical results. Two aspects of the study are conceptually interesting: the first is factorizing the training likelihood objective to learn properties that are not explained by simple neutral mutation rules, and the second is training not on self-supervised sequence statistics but on the differences between sequences along an antibody evolutionary trajectory. If this approach generalizes to other domains of life, it could offer a new paradigm for training sequence-to-fitness models that is less biased by phylogeny or other aspects of the underlying mutation process.

Weaknesses:

Some claims made in the paper are weakly or indirectly supported by the data. In particular, the claim that learning the codon table contributes to biased functional effect predictions may be true, but requires more justification. Additionally, the paper could benefit from additional benchmarking and comparison to enhanced versions of existing methods, such as AbLang plus a multi-hit correction. Further descriptions of model components and validation metrics could help make the manuscript more readable.

Reviewer #1 (Public review):

This manuscript proposes that phosphorylation of a conserved Hsp70 residue (human T495 / yeast Ssa1 T492) is a BER-triggered, DDR-dependent phospho-switch that acts as a conserved brake on G1/S cell-cycle progression in response to DNA damage.

Although the topic is interesting and potentially useful, the strength of evidence of the mechanistic and "conserved checkpoint" claims that this site is directly activated by DNA damage is inadequate and fundamentally incorrect. The work requires extensive additional experimentation and substantial tempering of conclusions.

Specific comments:

(1) Activation of T495:

(a) The author's premise for the site being activated by DNA damage is Albuquerque et al, where PTMs on MMS treated yeast are analyzed. T492 (the yeast equivalent of human T495) is observed as phosphorylated. However, the authors fail to note that there is no untreated sample analysis in this study, and it is likely that T492 phosphorylation is also present in untreated cells. This is also backed up by later evidence from the same lab (Smolka et al), where they do not identify T492 as being dependent on Mec1/Tel/Rad53 kinases.

(b) The kinase(s) directly responsible for T495 phosphorylation are not identified. Instead, the authors show that knockdown or pharmacological inhibition of DNA-PKcs, ATM, Chk2, and CK1 attenuate pHsp70.

(c) ATM siRNA knockdown has no effect, while ATM inhibitors do, which the authors acknowledge but do not resolve. This discrepancy raises concerns about off-target drug effects.

(d) No in vitro kinase assays, motif analysis, or phosphosite mapping confirming these kinases as direct T495 kinases are presented. Thus, the proposed signaling cascade remains speculative.

(e) Smolka and many other labs characterized DDR sites as SQ/TQ motifs, and T492 doesn't fit that motif.

(f) No genetic tests in yeast (e.g., BER mutants) are used to connect Ssa1 T492 phosphorylation to BER in that system, despite the strong BER-centric model.

(g) Overexpression of MPG gives only a modest increase in pHsp70, while APE1 overexpression has no effect, and Polβ overexpression does not decrease pHsp70. These mixed results weaken the central claim that Hsp70 phosphorylation is a tuned sensor of BER burden.

(h) A major concern is that pHsp70 is only convincingly detected after very high, prolonged MMS (10 mM, 5 h) or 0.5 mM arsenite treatments. Other DNA-damaging agents (bleomycin, camptothecin, hydroxyurea) that robustly activate DDR kinases do not induce pHsp70. This suggests to me that the authors are observing a side effect of proteotoxic stress. This is likely (see Paull et al, PMID: 34116476).

(i) A recent study in Nature Communications (Omkar et al., 2025) demonstrates rapid phosphorylation of yeast T492 in a pkc1-dependent manner, diminishing the impact of these findings.

(2) Downstream Effects of T492/T495:

(a) The manuscript's central conceptual advance is that pHsp70 is a cell-cycle-regulated brake on G1/S. Yet in mammalian cells, the authors show only that pHsp70 appears late, after cells have traversed mitosis, and that blocking CDK1 (G2/M) prevents its accumulation.

(b) There is no functional test in human cells: no knockdown/rescue experiments with T495A or T495E, no cell-cycle profiling upon altering Hsp70 phosphorylation state, and no demonstration that pHsp70 actually causes any delay in S-phase entry, rather than simply correlating with late damage responses. The strong conclusion that pT495 "stalls cell cycle progression" (e.g., Figure 6 model) is therefore not supported in the human system.

(c) All functional conclusions rely on T492A/E point mutants at the endogenous SSA1 locus, usually in an ssa2Δ background, in a family of highly redundant Hsp70s. Without showing that this site is actually modified during their MMS treatments, the assignment of phenotypes to loss of a physiological phospho-switch is premature. The authors need to repeat their studies in an Ssa1-4 background, as in https://pubmed.ncbi.nlm.nih.gov/32205407/.

(d) The authors infer that T495E "locks" Hsc70 in a pseudo-open state based on reduced J-protein-stimulated ATPase activity, unchanged ATP binding, altered trypsin sensitivity, and retained tau binding. However, there is no direct comparison of phosphorylated vs T495E protein (e.g., via in vitro phosphorylation with LegK4 followed by side-by-side biochemical assays, or structural analysis). Thus, it remains unclear to what extent the glutamate substitution mimics a phosphate at this position.

(e) No client release kinetics, co-chaperone binding assays, or in vivo chaperone function tests are provided, yet the discussion builds a detailed model of a "pseudo-open" state that simultaneously resembles ATP-bound conformation and allows persistent substrate engagement.

Reviewer #1 (Public review):

Summary:

The authors aim to demonstrate that PGLYRP1 plays a dual role in host responses to B. pertussis infection. PGLYRP1 signaling is known to activate bactericidal responses due to recognition of peptidoglycan. Through NOD1 activation and TREM-1 engagement, it appears PGLYRP1 also has immunomodulator activities. The authors present mouse knockout studies and gene expression data to illustrate the role of PGLYRP1 in relation to B. pertussis peptidoglycan. Mice lacking PGLYRP1 had slightly lower pathology scores. When TCT peptidoglycan was removed from the bacteria, surprisingly IL23A, IL6, IL1B, and other pro-inflammatory genes encoding cytokines increased. The relationship to TCT and PGLYRP1 suggests the pathogen uses this strategy to decrease immune activation. The authors went on to show the relationship between PGLRP1 and TREM-1 as mediated by PGN using various versions of peptidoglycan. The study presents multiple angles of data to back up its findings and demonstrates an interesting strategy used by B. pertussis to downregulate innate responses to its presence during infection.

Strengths:

Use of knockout mice of the key factor being considered, paired with isogenic B. pertussis strains, to reveal the mechanism of immune modulation to benefit the bacteria. The authors used in vivo gene expression paired with in vivo assays to establish each aspect of the mechanism.

Weaknesses:

The main focus was on innate responses, and some analysis of antigen-specific antibody responses could improve the impact of the findings.

Reviewer #1 (Public review):

Summary:

The aim of this work is to directly image collagen in tissue using a new MRI method with positive contrast. The work presents a new MRI method that allows very short, powerful radio frequency (RF) pulses and very short switching times between transmission and reception of radio frequency signals.

Strengths:

The experiments with and without the removal of 1H hydrogen, which is not firmly bound to collagen, on tissue samples from tendons and bones, are very well suited to prove the detection of direct hydrogen signals from collagen. The new method has great potential value in medicine, as it allows for better investigation of ageing processes and many degenerative diseases in which functional tissue is replaced by connective tissue (collagen).

Weaknesses:

It is clear that, due to the relatively long time intervals between RF excitation and signal readout, standard hardware in whole-body MRI systems can only be used to examine surrounding water and not hydrogen bound to collagen molecules.

Reviewer #1 (Public review):

Summary:

This manuscript analyzes a large dataset of [NiFe]-CODHs with a focus on genomic context and operon organization. Beyond earlier phylogenetic and biochemical studies, it addresses CODH-HCP co-occurrence, clade-specific gene neighborhoods, and operon-level variation, offering new perspectives on functional diversification and adaptation.

Strengths:

The study has a valuable approach.

Weaknesses:

Several points should be addressed.

(1) The rationale for excluding clades G and H should be clarified. Inoue et al. (Extremophiles 26:9, 2022) defined [NiFe]-CODH phylogenetic clades A-H. In the present manuscript, clades A-H are depicted, yet the analyses and discussion focus only on clades A-F. If clades G and H were deliberately excluded (e.g., due to limited sequence data or lack of biochemical evidence), the rationale should be clearly stated. Providing even a brief explanation of their status or the reason for omission would help readers understand the scope and limitations of the study. In addition, although Figure 1 shows clades A-H and cites Inoue et al. (2022), the manuscript does not explicitly state how these clades are defined. An explicit acknowledgement of the clade framework would improve clarity and ensure that readers fully understand the basis for subsequent analyses.

(2) The co-occurrence data would benefit from clearer presentation in the supplementary material. At present, the supplementary data largely consist of raw values, making interpretation difficult. For example, in Figure 3b, the co-occurrence frequencies are hard to reconcile with the text: clade A shows no co-occurrence with clade B and even lower tendencies than clades E or F, while clade E appears relatively high. Similarly, the claim that clades C and D "more often co-occur, especially with A, E, and F" does not align with the numerical trends, where D and E show stronger co-occurrence but C does not. A concise, well-organized summary table would greatly improve clarity and prevent such misunderstandings.

(3) The rationale for analyzing gene neighborhoods at the single-operon level needs clarification. Many microorganisms encode more than one CODH operon, yet the analysis was carried out at the level of individual operons. The authors should clarify the biological rationale for this choice and discuss how focusing on single operons rather than considering the full complement per organism might affect the interpretation of genomic context.

Reviewer #1 (Public review):

Summary:

This study investigates how the brain processes facial expressions across development by analyzing intracranial EEG (iEEG) data from children (ages 5-10) and post-childhood individuals (ages 13-55). The researchers used a short film containing emotional facial expressions and applied AI-based models to decode brain responses to facial emotions. They found that in children, facial emotion information is represented primarily in the posterior superior temporal cortex (pSTC)-a sensory processing area-but not in the dorsolateral prefrontal cortex (DLPFC), which is involved in higher-level social cognition. In contrast, post-childhood individuals showed emotion encoding in both regions. Importantly, the complexity of emotions encoded in the pSTC increased with age, particularly for socially nuanced emotions like embarrassment, guilt, and pride.The authors claim that these findings suggest that emotion recognition matures through increasing involvement of the prefrontal cortex, supporting a developmental trajectory where top-down modulation enhances understanding of complex emotions as children grow older.

Strengths:

(1) The inclusion of pediatric iEEG makes this study uniquely positioned to offer high-resolution temporal and spatial insights into neural development compared to non-invasive approaches, e.g., fMRI, scalp EEG, etc.

(2) Using a naturalistic film paradigm enhances ecological validity compared to static image tasks often used in emotion studies.

(3) The idea of using state-of-the-art AI models to extract facial emotion features allows for high-dimensional and dynamic emotion labeling in real time.

Weaknesses:

(1) The study has notable limitations that constrain the generalizability and depth of its conclusions. The sample size was very small, with only nine children included and just two having sufficient electrode coverage in the posterior superior temporal cortex (pSTC), which weakens the reliability and statistical power of the findings, especially for analyses involving age. Authors pointed out that a similar sample size has been used in previous iEEG studies, but the cited works focus on adults and do not look at the developmental perspectives. Similar work looking at developmental changes in iEEG signals usually includes many more subjects (e.g., n = 101 children from Cross ZR et al., Nature Human Behavior, 2025) to account for inter-subject variabilities.

(2) Electrode coverage was also uneven across brain regions, with not all participants having electrodes in both the dorsolateral prefrontal cortex (DLPFC) and pSTC, making the conclusion regarding the different developmental changes between DLPFC and pSTC hard to interpret (related to point 3 below). It is understood that it is rare to have such iEEG data collected in this age group, and the electrode location is only determined by clinical needs. However, the scientific rigor should not be compromised by the limited data access. It's the authors' decision whether such an approach is valid and appropriate to address the scientific questions, here the developmental changes in the brain, given all the advantages and constraints of the data modality.

(3) The developmental differences observed were based on cross-sectional comparisons rather than longitudinal data, reducing the ability to draw causal conclusions about developmental trajectories. Also, see comments in point 2.

(4) Moreover, the analysis focused narrowly on DLPFC, neglecting other relevant prefrontal areas such as the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), which play key roles in emotion and social processing. Agree that this might be beyond the scope of this paper, but a discussion section might be insightful.

(5) Although the use of a naturalistic film stimulus enhances ecological validity, it comes at the cost of experimental control, with no behavioral confirmation of the emotions perceived by participants and uncertain model validity for complex emotional expressions in children. A non-facial music block that could have served as a control was available but not analyzed. The validation of AI model's emotional output needs to be tested. It is understood that we cannot collect these behavioral data retrospectively within the recorded subjects. Maybe potential post-hoc experiments and analyses could be done, e.g., collect behavioral, emotional perception data from age-matched healthy subjects.

(6) Generalizability is further limited by the fact that all participants were neurosurgical patients, potentially with neurological conditions such as epilepsy that may influence brain responses. At least some behavioral measures between the patient population and the healthy groups should be done to ensure the perception of emotions is similar.

(7) Additionally, the high temporal resolution of intracranial EEG was not fully utilized, as data were downsampled and averaged in 500-ms windows. It seems like the authors are trying to compromise the iEEG data analyses to match up with the AI's output resolution, which is 2Hz. It is not clear then why not directly use fMRI, which is non-invasive and seems to meet the needs here already. The advantages of using iEEG in this study are missing here.

(8) Finally, the absence of behavioral measures or eye-tracking data makes it difficult to directly link neural activity to emotional understanding or determine which facial features participants attended to. Related to point 5 as well.

Comments on revisions:

A behavioral measurement will help address a lot of these questions. If the data continues collecting, additional subjects with iEEG recording and also behavioral measurements would be valuable.

Reviewer #1 (Public review):

In this well-written and timely manuscript, Rieger et al. introduce Squidly, a new deep learning framework for catalytic residue prediction. The novelty of the work lies in the aspect of integrating per-residue embeddings from large protein language models (ESM2) with a biology-informed contrastive learning scheme that leverages enzyme class information to rationally mine hard positive/negative pairs. Importantly, the method avoids reliance on the use of predicted 3D structures, enabling scalability, speed, and broad applicability. The authors show that Squidly outperforms existing ML-based tools and even BLAST in certain settings, while an ensemble with BLAST achieves state-of-the-art performance across multiple benchmarks. Additionally, the introduction of the CataloDB benchmark, designed to test generalization at low sequence and structural identity, represents another important contribution of this work.

Reviewer #1 (Public review):

Summary:

This study used explicit-solvent simulations and coarse-grained models to identify the mechanistic features that allow for unidirectional motion of SMC on DNA. Shorter explicit-solvent models provides a description of relevant hydrogen bond energetics, which was then encoded in a coarse-grained structure-based model. In the structure-based model, the authors mimic chemical reactions as signaling changes in the energy landscape of the assembly. By cycling through the chemical cycle repeatedly, the authors show how these time-dependent energetic shifts naturally lead SMC to undergo translocation steps along DNA that are on a length scale that has been identified.

Strengths:

Simulating large-scale conformational changes in complex assemblies is extremely challenging. This study utilizes highly-detailed models to parameterize a coarse-grained model, thereby allowing the simulations to connect the dynamics of precise atomistic-level interactions with a large-scale conformational rearrangement. This study serves as an excellent example for this overall methodology, where future studies may further extend this approach to investigated any number of complex molecular assemblies.

Comments on revisions:

No additional recommendations. I removed the weakness description in the summary, since the authors have addressed that concern.

Reviewer #1 (Public review):

Summary:

Press et al test, in three experiments, whether responses in a speeded response task reflect people's expectations, and whether these expectations are best explained by the objective statistics of the experimental context (e.g., stimulus probabilities) or by participants' mental representation of these probabilities. The studies use a classical response time and accuracy task, in which people are (1) asked to make a response (with one hand), this response then (2) triggers the presentation of one of several stimuli (with different probabilities depending on the response), and participants (3) then make a speeded response to identify this stimulus (with the other hand). In Experiment 1, participants are asked to rate, after the experiment, the subjective probabilities of the different stimuli. In Experiments 2 and 3, they rated, after each trial, to what extent the stimulus was expected (Experiment 2), or whether they were surprised by the stimulus (Experiment 3). The authors test (using linear models) whether the subjective ratings in each experiment predict stimulus identification times and accuracies better than objective stimulus probabilities (Experiment 1), or than their objective probability derived from a Rescorla-Wagner model of prior stimulus history (Experiment 2 and 3). Across all three experiments, the results are identical. Response times are best described by contributions from both subjective and objective probabilities. Accuracy is best described by subjective probability.

Strengths:

This is an exciting series of studies that tests an assumption that is implicit in predictive theories of response preparation (i.e., that response speed/accuracy tracks subjective expectancies), but has not been properly tested so far, to my knowledge. I find the idea of measuring subjective expectancy and surprise in the same trials as the response very clever. The manuscript is extremely well written. The experiments are well thought-out, preregistered, and the results seem highly robust and replicable across studies.

Weaknesses:

In my assessment, this is a well-designed, implemented, and analysed series of studies. I have one substantial concern that I would like to see addressed, and two more minor ones.

(1) The key measure of the relationship between subjective ratings and response times/accuracy is inherently correlational. The causal relationship between both variables is therefore by definition ambiguous. I worry that the results don't reveal an influence of subjective expectancy of response times/accuracies, but the reverse: an influence of response times/accuracies on subjective expectancy ratings.

This potential issue is most prominent in Experiments 2 and 3, where people rate their expectations in a given trial directly after they made their response. We can assume that participants have at least some insight into whether their response in the current trial was correct/erroneous or fast/slow. I therefore wonder if the pattern of results can simply be explained by participants noticing they made an error (or that they responded very slowly) and subsequently being more inclined to rate that they did not expect this stimulus (in Experiment 2) or that they were surprised by it (in Experiment 3).

The specific pattern across the two response measures might support this interpretation. Typically, participants are more aware of the errors they make than of their response speed. From the above perspective, it would therefore be not surprising that all experiments show stronger associations between accuracy and subjective ratings than between response times and subjective ratings -- exactly as the three studies found.

I acknowledge that this problem is less strong in Experiment 1, where participants do not rate expectancy or surprise after each response, but make subjective estimates of stimulus probabilities after the experiment. Still, even here, the flow of information might be opposite to what the authors suggest. Participants might not have made more errors for stimuli that they thought as least likely, but instead might have used the number of their responses to identify a given stimulus as a proxy for rating their likelihood. For example, if they identify a square as a square 25% of the time, even though 5% of these responses were in error, it is perhaps no surprise if their rating of the stimulus likelihood better tracks the times they identified it as a square (25%) than the actual stimulus likelihoods (20%).

This potential reverse direction of effects would need to be ruled out to fully support the authors' claims.

(2) My second, more minor concern, is whether the Rescorla-Wagner model is truly the best approximation of objective stimulus statistics. It is traditionally a model of how people learn. Isn't it, therefore, already a model of subjective stimulus statistics, derived from the trial history, instead of objective ones? If this is correct, my interpretation of Experiments 2 and 3 would be (given my point 1 above is resolved) that subjective expectancy ratings predict responses better than this particular model of learning, meaning that it is not a good model of learning in this task. Comparing results against Rescorla-Wagner may even seem like a stronger test than comparing them against objective stimulus statistics - i.e., they show that subjective ratings capture expectancies better even than this model of learning. The authors already touch upon this point in the General Discussion, but I would like to see this expanded, and - ideally - comparisons against objective stimulus statistics (perhaps up to the current trial) to be included, so that the authors can truly support the claim that it is not the objective stimulus statistics that determine response speed and accuracy.

(3) There is a long history of research trying to link response times to subjective expectancies. For example, Simon and Craft (1989, Memory & Cognition) reported that stimuli of equal probability were identified more rapidly when participants had explicitly indicated they expect this stimulus to occur in the given trial, and there's similar more recent work trying to dissociate stimulus statistics and explicit expectations (e.g., Umbach et al., 2012, Frontiers; for a somewhat recent review, see Gaschler et al., 2014, Neuroscience & Biobehavioral Reviews). It has not become clear to me how the current results relate to this literature base. How do they impact this discussion, and how do they differ from what is already known?

Reviewer #1 (Public review):

This is an interesting manuscript aimed at improving the transcriptome characterization of 52 C. elegans neuron classes. Previous single-cell RNA seq studies already uncovered transcriptomes for these, but the data are incomplete, with a bias against genes with lower expression levels. Here, the authors use cell-specific reporter combinations to FACS purify neurons and use bulk RNA sequencing to obtain better sequencing depth. This reveals more rare transcripts, as well as non-coding RNAs, pseudo genes, etc. The authors develop computational approaches to combine the bulk and scRNA transcriptome results to obtain more definitive gene lists for the neurons examined.

To ultimately understand features of any cell, from morphology to function, an understanding of the full complement of the genes it expresses is a pre-requisite. This paper gets us a step closer to this goal, assembling a current "definitive list" of genes for a large proportion of C. elegans neurons. The computational approaches used to generate the list are based on reasonable assumptions, the data appear to have been treated appropriately statistically, and the conclusions are generally warranted. I have a few issues that the authors may chose to address:

(1) As part of getting rid of cross contamination in the bulk data, the authors model the scRNA data, extrapolate it to the bulk data and subtract out "contaminant" cell types. One wonders, however, given that low expressed genes are not represented in the scRNA data, whether the assignment of a gene to one or another cell type can really be made definitve. Indeed, it's possible that a gene is expressed at low levels in one cell, and in high levels in another, and would therefore be considered a contaminant. The result would be to throw out genes that actually are expressed in a given cell type. The definitive list would therefore be a conservative estimate, and not necessarily the correct estimate.

(2) It would be quite useful to have tested some genes with lower expression levels using in vivo gene-fusion reporters to assess whether the expression assignments hold up as predicted. i.e. provide another avenue of experimentation, non-computational, to confirm that the decontamination algorithm works.

(3) In many cases, each cell class would be composed of at least 2 if not more neurons. Is it possible that differences between members of a single class would be missed by applying the cleanup algorithms? Such transcripts would be represented only in a fraction of the cells isolated by scRNAseq, and might then be considered not real?

(4) I didn't quite catch whether the precise staging of animals was matched between the bulk and scRNAseq datasets. Importantly, there are many genes whose expression is highly stage specific or age specific so that even slight temporal difference might yield different sets of gene expression.

(5) To what extent does FACS sorting affect gene expression? Can the authors provide some controls?

Comments on revisions:

The authors have made reasonable arguments in response to my questions, and have done some additional experiments. I believe that although they did not do so, they could have generated additional reporters for the lower expressed genes, that would have validated their method of data integration. Nonetheless, I think the paper is rigorous and will be of use to the community.

Reviewer #1 (Public review):

Summary:

In this manuscript Lu & Cui et al. observe that adult male zebrafish are more resistant to infection and disease following exposure to Spring Viremia of Carp Virus (SVCV) than female fish. The authors then attempt to identify some of the molecular underpinnings of this apparent sexual dimorphism and focus their investigations on a gene called cytochrome P450, family 17, subfamily A, polypeptide 2 (cyp17a2) because it was among genes that they found to be more highly expressed in kidney tissue from males than in females. Their investigations lead them to propose a direct connection between cyp17a2 and modulation of interferon signaling as the key underlying driver of difference between male and female susceptibility to SVCV.

Strengths:

Strengths of this study include the interesting observation of a substantial difference between adult male and female zebrafish in their susceptibility to SVCV, and also the breadth of experiments that were performed linking cyp17a2 to infection phenotypes and molecularly to the stability of host and virus proteins in cell lines. The authors place the infection phenotype in an interesting and complex context of many other sexual dimorphisms in infection phenotypes in vertebrates. This study succeeds in highlighting an unexpected factor involved in antiviral immunity that will be an important subject for future investigations of infection, metabolism, and other contexts.

Weaknesses:

Weaknesses of this study include a proposed mechanism underlying the sexual dimorphism phenotype based on experimentation in only males, and widespread reliance on over-expression when investigating protein-protein interaction and localization.

Reviewer #1 (Public review):

Summary:

This study employed a saccade-shifting sequential working memory paradigm, manipulating whether a saccade occurred after each memory array to directly compare retinotopic and transsaccadic working memory for both spatial location and color. Across four participant groups (young and older healthy adults, and patients with Parkinson's disease and Alzheimer's disease), the authors found a consistent saccade-related cost specifically for spatial memory - but not for color - regardless of differences in memory precision. Using computational modeling, they demonstrate that data from healthy participants are best explained by a complex saccade-based updating model that incorporates distractor interference. Applying this model to the patient groups further elucidates the sources of spatial memory deficits in PD and AD. The authors then extend the model to explain copying deficits in these patient groups, providing evidence for the ecological validity of the proposed saccade-updating retinotopic mechanism.

Strengths:

Overall, the manuscript is well written, and the experimental design is both novel and appropriate for addressing the authors' key research questions. I found the study to be particularly comprehensive: it first characterizes saccade-related costs in healthy young adults, then replicates these findings in healthy older adults, demonstrating how this "remapping" cost in spatial working memory is age-independent. After establishing and validating the best-fitting model using data from both healthy groups, the authors apply this model to clinical populations to identify potential mechanisms underlying their spatial memory impairments. The computational modeling results offer a clearer framework for interpreting ambiguities between allocentric and retinotopic spatial representations, providing valuable insight into how the brain represents and updates visual information across saccades. Moreover, the findings from the older adult and patient groups highlight factors that may contribute to spatial working memory deficits in aging and neurological disease, underscoring the broader translational significance of this work.

Weaknesses:

Several concerns should be addressed to enhance the clarity of the manuscript:

(1) Relevance of the figure-copy results (pp. 13-15).

Is it necessary to include the figure-copy task results within the main text? The manuscript already presents a clear and coherent narrative without this section. The figure-copy task represents a substantial shift from the LOCUS paradigm to an entirely different task that does not measure the same construct. Moreover, the ROCF findings are not fully consistent with the LOCUS results, which introduces confusion and weakens the manuscript's coherence. While I understand the authors' intention to assess the ecological validity of their model, this section does not effectively strengthen the manuscript and may be better removed or placed in the Supplementary Materials.

(2) Model fitting across age groups (p. 9).

It is unclear whether it is appropriate to fit healthy young and healthy elderly participants' data to the same model simultaneously. If the goal of the model fitting is to account for behavioral performance across all conditions, combining these groups may be problematic, as the groups differ significantly in overall performance despite showing similar remapping costs. This suggests that model performance might differ meaningfully between age groups. For example, in Figure 4A, participants 22-42 (presumably the elderly group) show the best fit for the Dual (Saccade) model, implying that the Interference component may contribute less to explaining elderly performance.

Furthermore, although the most complex model emerges as the best-fitting model, the manuscript should explain how model complexity is penalized or balanced in the model comparison procedure. Additionally, are Fixation Decay and Saccade Update necessarily alternative mechanisms? Could both contribute simultaneously to spatial memory representation? A model that includes both mechanisms-e.g., Dual (Fixation) + Dual (Saccade) + Interference-could be tested to determine whether it outperforms Model 7 to rule out the sole contribution of complexity.

Minor point: On p. 9, line 336, Figure 4A does not appear to include the red dashed vertical line that is mentioned as separating the age groups.

(3) Clarification of conceptual terminology.

Some conceptual distinctions are unclear. For example, the relationship between "retinal memory" and "transsaccadic memory," as well as between "allocentric map" and "retinotopic representation," is not fully explained. Are these constructs related or distinct? Additionally, the manuscript uses terms such as "allocentric map," "retinotopic representation," and "reference frame" interchangeably, which creates ambiguity. It would be helpful for the authors to clarify the relationships among these terms and apply them consistently.

(4) Rationale for the selective disruption hypothesis (p. 4, lines 153-154).

The authors hypothesize that "saccades would selectively disrupt location memory while leaving colour memory intact." Providing theoretical or empirical justification for this prediction would strengthen the argument.

(5) Relationship between saccade cost and individual memory performance (p. 4, last paragraph).

The authors report that larger saccades were associated with greater spatial memory disruption. It would be informative to examine whether individual differences in the magnitude of saccade cost correlate with participants' overall/baseline memory performance (e.g. their memory precision in the no-saccade condition). Such analyses might offer insights into how memory capacity/ability relates to resilience against saccade-induced updating.

(6) Model fitting for the healthy elderly group to reveal memory-deficit factors (pp. 11-12).

The manuscript discusses model-based insights into components that contribute to spatial memory deficits in AD and PD, but does not discuss components that contribute to spatial memory deficits in the healthy elderly group. Given that the EC group also shows impairments in certain parameters, explaining and discussing these outcomes of the EC group could provide additional insights into age-related memory decline, which would strengthen the study's broader conclusions.

(7) Presentation of saccade conditions in Figure 5 (p. 11).

In Figure 5, it may be clearer to group the four saccade conditions together within each patient group. Since the main point is that saccadic interference on spatial memory remains robust across patient groups, grouping conditions by patient type rather than intermixing conditions would emphasize this interpretation.

Reviewer #1 (Public review):

Summary:

This manuscript describes the use of computational tools to design a mimetic of the interleukin-7 (IL-7) cytokine with superior stability and receptor binding activity compared to the naturally occurring molecule. The authors focused their engineering efforts on the loop regions to preserve receptor interfaces while remediating structural irregularities that destabilize the protein. They demonstrated the enhanced thermostability, production yield, and bioactivity of the resulting molecule through biophysical and functional studies. Overall, the manuscript is well written, novel, and of high interest to the fields of molecular engineering, immunology, biophysics, and protein therapeutic design. The experimental methodologies used are convincing; however, the article would benefit from more quantitative comparisons of bioactivity through titrations.

Comments on revisions:

All comments have been sufficiently addressed, with the exception of comment 24 from Reviewer 1. The authors need to modify the manuscript abstract, introduction, and/or discussion to clarify which limitations of IL-7 were addressed by their molecule and to note the limitations of their approach in terms of mitigating toxicity or enhancing half-life.

Reviewer #1 (Public review):

Summary:

This manuscript reports a prospective longitudinal study examining whether infants with high likelihood (HL) for autism differ from low-likelihood (LL) infants in two levels of word learning: brain-to-speech cortical entrainment and implicit word segmentation. The authors report reduced syllable tracking and post-learning word recognition in the HL group relative to the LL group. Importantly, both the syllable-tracking entrainment measure and the word recognition ERP measure are positively associated with verbal outcomes at 18-20 months, as indexed by the Mullen Verbal Developmental Quotient. Overall, I found this to be a thoughtfully designed and carefully executed study that tackles a difficult and important set of questions. With some clarifications and modest additional analyses or discussion on the points below, the manuscript has strong potential to make a substantial contribution to the literature on early language development and autism.

Strengths:

This is an important study that addresses a central question in developmental cognitive neuroscience: what mechanisms underlie variability in language learning, and what are the early neural correlates of these individual differences? While language development has a relatively well-defined sensitive period in typical development, the mechanisms of variability - particularly in the context of neurodevelopmental conditions - remain poorly understood, in part because longitudinal work in very young infants and toddlers is rare. The present study makes a valuable contribution by directly targeting this gap and by grounding the work in a strong theoretical tradition on statistical learning as a foundational mechanism for early language acquisition.

I especially appreciate the authors' meticulous approach to data quality and their clear, transparent description of the methods. The choice of partial least squares correlation (PLS-c) is well motivated, given the multidimensional nature of the data and collinearity among variables, and the manuscript does a commendable job explaining this technique to readers who may be less familiar with it.

The results reveal interesting developmental changes in syllable tracking and word segmentation from birth to 2 years in both HL and LL infants. Simply mapping these trajectories in both groups is highly valuable. Moreover, the associations between neural indices of brain-to-speech entrainment and word segmentation with later verbal outcomes in the LL group support a critical role for speech perception and statistical learning in early language development, with clear implications for understanding autism. Overall, this is a rich dataset with substantial potential to inform theory.

Weaknesses:

(1) Clarifying longitudinal vs. concurrent associations

Because the current analytical approach incorporates all time points, including the final visit, it is challenging to determine to what extent the brain-language associations are driven by longitudinal relationships vs. concurrent correlations at the last time point. This does not undermine the main findings, but clarifying this issue could significantly enhance the impact of the individual-differences results. If feasible, the authors might consider (a) showing that a model excluding the final visit still predicts verbal outcomes at the last visit in a similar way, or (b) more explicitly acknowledging in the discussion that the observed associations may be partly or largely driven by concurrent correlations. Either approach would help readers interpret the strength and nature of the longitudinal claims.

(2) Incorporating sleep status into longitudinal models

Sleep status changes systematically across developmental stages in this cohort. Given that some of the papers cited to justify the paradigm also note limitations in speech entrainment and word segmentation during sleep or in patients with impaired consciousness, it would be helpful to account for sleep more directly. Including sleep status as a factor or covariate in the longitudinal models, or at least elaborating more fully on its potential role and limitations, would further strengthen the conclusions and reassure readers that these effects are not primarily driven by differences in sleep-wake state.

(3) Use of PLS-c and potential group × condition interactions

I am relatively new to PLS-c. One question that arose is whether PLS-c could be extended to handle a two-way interaction between group and condition contrasts (STR vs. RND). If so, some of the more complex supplementary models testing developmental trajectories within each group (Page 8, Lines 258-265) might be more directly captured within a single, unified framework. Even a brief comment in the methods or discussion about the feasibility (or limitations) of modeling such interactions within PLS-c would be informative for readers and could streamline the analytic narrative.

(4) STR-only analyses and the role of RND

Page 8, Lines 241-245: This analysis is conducted only within the STR condition. The lack of group difference observed here appears consistent with the lack of group difference in word-level entrainment (Page 9, Lines 292-294), suggesting that HL and LL groups may not differ in statistical learning per se, but rather in syllabic-level entrainment. As a useful sanity check and potential extension, it might be informative to explore whether syllable-level entrainment in the RND condition differs between groups to a similar extent as in Figure 2C-D. In other work (e.g., adults vs. children; Moreau et al., 2022), group differences can be more pronounced for syllable-level than for word-level entrainment. Figure S6 seems to hint that a similar pattern may exist here. If feasible, including or briefly reporting such an analysis could help clarify the asymmetry between the two learning measures and further support the interpretation of syllabic-level differences.

(5) Multi-speaker input and voice perception (Page 15, Lines 475-483)

The multi-speaker nature of the speech input is an interesting and ecologically relevant feature of the design, but it does add interpretive complexity. The literature on voice perception in autism is still mixed: for example, Boucher et al. (2000) reported no differences in voice recognition and discrimination between children with autism and language-matched non-autistic peers, whereas behavioral work in autistic adults suggests atypical voice perception (e.g., Schelinski et al., 2016; Lin et al., 2015). I found the current interpretation in this paragraph somewhat difficult to follow, partly because the data do not directly test how HL and LL infants integrate or suppress voice information. I think the authors could strengthen this section by slightly softening and clarifying the claims.

(6) Asymmetry between EEG learning measures

Page 16, Lines 502-507 touches on the asymmetry between the two EEG learning measures but leaves some questions for the reader. The presence of word recognition ERPs in the LL group suggests that a failure to suppress voice information during learning did not prevent successful word learning. At the same time, there is an interesting complementary pattern in the HL group, who show LL-like word-level entrainment but does not exhibit robust word recognition. Explicitly discussing this asymmetry - why HL infants might show relatively preserved word-level entrainment yet reduced word recognition ERPs, whereas LL infants show both - would enrich the theoretical contribution of the manuscript.

References:

(1) Moreau, C. N., Joanisse, M. F., Mulgrew, J., & Batterink, L. J. (2022). No statistical learning advantage in children over adults: Evidence from behaviour and neural entrainment. Developmental Cognitive Neuroscience, 57, 101154. https://doi.org/10.1016/j.dcn.2022.101154

(2) Boucher, J., Lewis, V., & Collis, G. M. (2000). Voice processing abilities in children with autism, children with specific language impairments, and young typically developing children. Journal of Child Psychology and Psychiatry, 41(7), 847-857. https://doi.org/10.1111/1469-7610.00672

(3) Schelinski, S., Borowiak, K., & von Kriegstein, K. (2016). Temporal voice areas exist in autism spectrum disorder but are dysfunctional for voice identity recognition. Social Cognitive and Affective Neuroscience, 11(11), 1812-1822. https://doi.org/10.1093/scan/nsw089

(4) Lin, I.-F., Yamada, T., Komine, Y., Kato, N., Kato, M., & Kashino, M. (2015). Vocal identity recognition in autism spectrum disorder. PLOS ONE, 10(6), e0129451. https://doi.org/10.1371/journal.pone.0129451

Reviewer #1 (Public review):

Summary:

The authors present a novel investigation of the movement vigor of individuals completing a synchronous extension-flexion task. Participants were placed into groups of two (so-called "dyads") and asked to complete shared movements (connected via a virtual loaded spring) to targets placed at varying amplitudes. The authors attempted to quantify what, if any, adjustments in movement vigor individual participants made during the dyadic movements, given the combined or co-dependent nature of the task. This is a novel, timely question of interest within the broader field of human sensorimotor control.

Participants from each dyad were labeled as "slow" (low vigor) or "fast" (high vigor), and their respective contributions to the combined movement metrics were assessed. The authors presented four candidate models for dyad interactions: (a) independent motor plans (i.e., co-activity hypothesis), (b) individual-led motor plans (i.e., leader-follower hypothesis), (c) generalization to a weighted average motor plan (i.e., weighted adaptation hypothesis), and (d) an uncertainty-based model of dynamic partner-partner interaction (i.e., interactive adaptation hypothesis). The final model allowed for dynamic changes in individual motor plans (and therefore, movement vigor) based on partner-partner interactions and observations. After detailed observations of interaction torque and movement duration (or vigor), the authors concluded that the interactive adaptation model provided the best explanation of human-human interaction during self-paced dyadic movements.

Strengths:

The experimental setup (simultaneous wrist extension-flexion movements) has been thoroughly vetted. The task was designed particularly well, with adequate block pseudo-randomization to ensure general validity of the results. The analyses of torque interaction, movement kinematics, and vigor are sound, as are the statistical measures used to assess significance. The authors structured the work via a helpful comparison of several candidate models of human-human interaction dynamics, and how well said models explained variance in the vigor of solo and combined movements. The research question is timely and extends current neuroscientific understanding of sensorimotor control, particularly in social contexts.

Weaknesses:

(1) My chief concern about the study as it currently stands is the relatively low number of data points (n=10). The authors recruited 20 participants, but the primary conclusions are based on dyad-specific interactions (i.e., analyses of "fast" vs "slow" participants in each pair). Some of these analyses would benefit greatly, in terms of power, from the addition of more data points.

1a) The distribution of delta-vigor (Fast group vs Slow group) is highly skewed (see Figures 3D, S6D), with over half of the dyads exhibiting delta-vigor less than 0.2 (i.e., less than 20% of unit vigor). Given the relatively low number of dyads, it would be helpful for the authors to provide explicit listings of VigorFast, VigorSlow, and VigorCombined for each of the 10 separate dyads or pairings.

1b) The authors concluded that the interactive adaptation hypothesis provided the best summary of the combined movement dynamics in the study. If this is indeed the case, then the relative degree of difference in vigor between the fast and slow participants in a dyad should matter. How well did the interactive adaptation model explain variance in the dyads with relatively low delta-vigor (e.g., less than 0.2) vs relatively high delta-vigor?

(2) The authors shared the results of one analysis of reaction time, showing that the reaction times of the slow partners and the fast partners did not differ during the initial passive block. Did the authors observe any changes in RT of either the slow or fast partner during the combined (primary task) blocks (KL, KH, etc.)? If the pairs of participants did indeed employ a form of interactive adaptation, then it is certainly plausible that this interaction would manifest in the initial movement planning phase (i.e., RT) in addition to the vigor and smoothness of the movements themselves.

Reviewer #1 (Public review):

Summary

The strength of this manuscript lies in the behavior: mice use a continuous auditory background (pink vs brown noise) to set a rule for interpreting an identical single-whisker deflection (lick in W+ and withhold in W− contexts) while always licking to a brief 10 kHz tone. Behaviorally, animals acquire the rule and switch rapidly at block transitions and take a few trials to fully integrate the context cue. What's nice about this behavior is the separate auditory cue, which shows the animals remain engaged in the task, so it's not just that the mice check out (i.e., become disengaged in the W- context). The authors then use optical tools, combining cortex-wide optogenetic inactivation (using localized inhibition in a grid-like fashion) with widefield calcium imaging to map what regions are necessary for the task and what the local and global dynamics are. Classic whisker sensorimotor nodes (wS1/wS2/wM/ALM) behave as expected with silencing reducing whisker-evoked licking. Retrosplenial cortex (RSC) emerges as a somewhat unexpected, context-specific node: silencing RSC (and tjS1) increases licking selectively in W−, arguing that these regions contribute to applying the "don't lick" policy in that context. I say somewhat because work from the Delamater group points to this possibility, albeit in a Pavlovian conditioning task and without neural data. I would still recommend the authors of the current manuscript review that work to see whether there is a relevant framework or concept (Castiello, Zhang, Delamater, 'The retrosplenial cortex as a possible 'sensory integration' area: a neural network modeling approach of the differential outcomes effect of negative patterning', 2021, Neurobiology of Learning and Memory).

The widefield imaging shows that RSC is the earliest dorsal cortical area to show W+ vs W− divergence after the whisker stimulus, preceding whisker motor cortex, consistent with RSC injecting context into the sensorimotor flow. A "Context Off" control (continuous white noise; same block structure) impairs context discrimination, indicating the continuous background is actually used to set the rule (an important addition!) Pre-stimulus functional-connectivity analyses suggest that there is some activity correlation that maps to the context presumably due to the continuous background auditory context. Simultaneous opto+imaging projects perturbations into a low-dimensional subspace that separates lick vs no-lick trajectories in an interpretable way.

In my view, this is a clear, rigorous systems-level study that identifies an important role for RSC in context-dependent sensorimotor transformation, thereby expanding RSC's involvement beyond navigation/memory into active sensing and action selection. The behavioral paradigm is thoughtfully designed, the claims related to the imaging are well defended, and the causal mapping is strong. I have a few suggestions for clarity that may require a bit of data analysis. I also outline one key limitation that should be discussed, but is likely beyond the scope of this manuscript.

Major strengths

(1) The task is a major strength. It asks the animal to generate differential motor output to the same sensory stimulus, does so in a block-based manner, and the Context-Off condition convincingly shows that the continuous contextual cue is necessary. The auditory tone control ensures this is more than a 'motivational' context but is decision-related. In fact, the slightly higher bias to lick on the catch trials in the W+ context is further evidence for this.

(2) The dorsal-cortex optogenetic grid avoids a 'look-where-we-expect' approach and lets RSC fall out as a key node. The authors then follow this up with pharmacology and latency analyses to rule out simple motor confounds. Overall, this is rigorous and thoughtfully done.

(3) While the mesoscale imaging doesn't allow for cellular resolution, it allows for mapping of the flow of information. It places RSC early in the context-specific divergence after whisker onset, a valuable piece that complements prior work.

(4) The baseline (pre-stim) functional connectivity and the opto-perturbation projections into a task subspace increase the significance of the work by moving beyond local correlates.

Key limitation

The current optogenetic window begins ~10 ms before the sensory cue and extends 1s after, which is ideal for perturbing within-trial dynamics but cannot isolate whether RSC is required to maintain the context-specific rule during the baseline. Because context is continuously available, it makes me wonder whether RSC is the locus maintaining or, instead, gating the context signal. The paper's results are fully consistent with that possibility, but causality in the pre-stimulus window remains an open question. (As a pointer for future work, pre-stimulus-only inactivation, silencing around block switches, or context-omission probe trials (e.g., removing the background noise unexpectedly within a W+ or W- context block), could help separate 'holding' from 'gating' of the rule. But I'm not suggesting these are needed for this manuscript, but would be interesting for future studies.)

Reviewer #1 (Public review):

The authors address a set of important and challenging questions at the interface of (developmental) neuroscience, genetics, and computation. They ask how complex neural circuits could emerge from compact genomic information, and they outline a bold vision in which this process might eventually be harnessed to design synthetic biological intelligence through genetic control of synaptogenesis. These are significant and stimulating ideas that merit rigorous theoretical and experimental exploration.

However, the present work does not convincingly engage with these questions at a mechanistic level. Most of the circuit formation aspects appear to be adopted from prior models, and it is not clear how the main methodological modifications-introducing synaptic conductance and stochastic formalisms-provide new conceptual insight into genomic specification of neural circuitry. The manuscript does not include significant biological data or validation to support the proposed framework, and the results provided instead use artificial reinforcement learning benchmarks, which do not appear informative with respect to the biological claims.

Overall, while the manuscript raises intriguing themes and ambitions, the proposed model is conceptually disconnected from the biological problem it purports to address. The strength of evidence does not support the strong interpretative or translational claims, and substantial rethinking of the modeling framework, in particular its validation strategy, would be required for the work to match the claims of our improved understanding of the genomic basis of neural circuit formation and our ability to engineer it.

Reviewer #1 (Public review):

Summary:

The study examined the extent to which children's word recognition skill improves across early development, becoming faster, more accurate and less variable, and the extent to which word recognition skill is related to children's concurrent and later vocabulary knowledge.

Strengths:

The main strength of the study comes from the dataset, which recycles previously collected data from 24 studies to examine the development of word recognition skill using data from 1963 children. This maximizes the impact of previously collected data while also allowing the study to reliably ask big-picture questions on the development of word recognition skill and its relation to chronological age and vocabulary knowledge. Data analysis is rigorous, thought through and very clearly described. Data and code necessary to reproduce the manuscript are shared on the project's GitHub.

Weaknesses:

The limitations of the study are acknowledged to some extent, but need to be improved and ensured that they run throughout the manuscript. Thus, in the discussion, the authors note that the approach is observational and exploratory, and highlight for me a key alternative explanation of the findings, namely that faster children could be faster due to their larger vocabulary, rather than faster children learning more words. Indeed, the latter explanation for the relationship is called into question, given that growth in speed was not related to growth in vocabulary. Here, the authors note that the null result may be related to the fact that they do not sufficiently precise estimates of growth slopes, rather than taking the alternative explanation seriously that there may not be as causal a link between being a faster word learner and a better word learner (learn more words). This is especially since, but correct me if I'm wrong here, the current vocabulary size is not taken into consideration in the model examining vocabulary growth. Given the increasing number of studies showing that current vocabulary knowledge predicts vocabulary growth (Laing, Kalinowski et al, Siew & Vitevitch), one simple alternative explanation is that current vocabulary knowledge predicts both current word recognition skill and later vocabulary knowledge. Is there anything in the data speaking against this hypothesis?

Equally, while the SEM examines vocabulary growth controlling for age, I wonder about the other way around. What would happen to the effect of age on word recognition skill (in the LME model, S8) if one were to add concurrent vocabulary size? So does chronological age explain word recognition skill or vocabulary knowledge? Right now, the manuscript describes this effect purely related to chronological age, but is it age per se or other cognitive abilities, including a key change across development, namely, vocabulary size? Thus, the presentation of the skill learning hypothesis suggests that age is a proxy for experience, while you actually have here a very nice proxy for experience in terms of children's vocabulary size.

Critically, while the discussion is more nuanced, the way the abstract is concluded and the way the Introduction is phrased suggest that the study is able to answer a causal question, which, as the authors themselves note, is not possible. The abstract, for instance, states that word recognition becomes faster, more accurate and less variable...consistent with a process of skill learning. And also that this skill plays a role in supporting early language learning, which is very causal language. I don't think you can really claim that you are testing the two hypotheses you suggest here. The work is definitely embedded in the context of these hypotheses, but are you really able to test them? My worry is that while the discussion is more nuanced, the extent to which this study will then be cited down the line as showing that children learn more words down the line because they are faster at recognizing words, and anything that you can do to tamper with such interpretations would be good for the literature. For me, this should not just be relegated to the discussion but should be touched upon in the abstract and Introduction.

Finally, it would help to talk more about the mechanisms at work in any relationship between word recognition and language learning. It seems to me that this would rely on some predictive processing framework, given the description on page 4, and it would be good to make this clear (faster and more accurately you can recognize a ball, better use this evidence to infer the speaker's intended meaning). Equally, when referring to word recognition, it would be good to clarify what this refers to - how well a child knows what a word refers to (and in the context of LWL, what it does not refer to) or how quickly it directs attention to what is referred to.

With regards to the data, I wonder if there is a clustering of kids past 24 months that is happening here, looking at Figures 1 and 2, where it seems like there is less change past the 24-month point. Is there any way to look at whether the effect of age or vocabulary on word recognition is not linear but asymptotic?

Reviewer #1 (Public review):

Summary:

Wu and colleagues aimed to explain previous findings that adolescents, compared to adults, show reduced cooperation following cooperative behaviour from a partner in several social scenarios. The authors analysed behavioural data from adolescents and adults performing a zero-sum Prisoner's Dilemma task and compared a range of social and non-social reinforcement learning models to identify potential algorithmic differences. Their findings suggest that adolescents' lower cooperation is best explained by a reduced learning rate for cooperative outcomes, rather than differences in prior expectations about the cooperativeness of a partner. The authors situate their results within the broader literature, proposing that adolescents' behaviour reflects a stronger preference for self-interest rather than a deficit in mentalising.

Strengths:

The work as a whole suggests that, in line with past work, adolescents prioritise value accumulation, and this can be, in part, explained by algorithmic differences in wegithed value learning. The authors situate their work very clearly in past literature, and make it obvious the gap they are testing and trying to explain. The work also includes social contexts which move the field beyond non-social value accumulation in adolescents. The authors compare a series of formal approaches that might explain the results and establish generative and model-comparison procedures to demonstrate the validity of their winning model and individual parameters. The writing was clear, and the presentation of the results was logical and well-structured.

Weaknesses:

I had some concerns about the methods used to fit and approximate parameters of interest. Namely, the use of maximum likelihood versus hierarchical methods to fit models on an individual level, which may reduce some of the outliers noted in the supplement, and also may improve model identifiability.

There was also little discussion given the structure of the Prisoner's Dilemma, and the strategy of the game (that defection is always dominant), meaning that the preferences of the adolescents cannot necessarily be distinguished from the incentives of the game, i.e. they may seem less cooperative simply because they want to play the dominant strategy, rather than a lower preferences for cooperation if all else was the same.

The authors have now addressed my comments and concerns in their revised version.

Appraisal & Discussion:

Overall, I believe this work has the potential to make a meaningful contribution to the field. Its impact would be strengthened by more rigorous modelling checks and fitting procedures, as well as by framing the findings in terms of the specific game-theoretic context, rather than general cooperation.

Comments on revisions:

Thank you to the authors for addressing my comments and concerns.

Reviewer #1 (Public review):

Summary:

Zhang and colleagues examine neural representations underlying abstract navigation in entorhinal cortex (EC) and hippocampus (HC) using fMRI. This paper replicates a previously identified hexagonal modulation of abstract navigation vectors in abstract space in EC in a novel task involving navigating in a conceptual Greeble space. In HC, the authors identify a three-fold signal of the navigation angle. They also use a novel analysis technique (spectral analysis) to look at spatial patterns in these two areas and identify phase coupling between HC and EC. Interestingly, the three-fold pattern identified in the hippocampus explains quirks in participants' behavior where navigation performance follows a three-fold periodicity. Finally, the authors propose a EC-HPC PhaseSync Model to understand how the EC and HC construct cognitive maps. The wide array and creativity of the techniques used is impressive but because of their unique nature, the paper would benefit from more details on how some of these techniques were implemented.

Comments on revisions:

Most of my concerns were adequately addressed, and I believe the paper is greatly improved. I have two more points. I noticed that the legend for Figure 4 still refers to some components of the previous figure version, this should be updated to reflect the current version of the figure. I also think the paper would benefit from more details regarding some of the analyses. Specifically, the phase-amplitude coupling analysis should have a section in the methods which should be sure to clarify how the BOLD signals were reconstructed.

Reviewer #1 (Public review):

Summary

The authors propose a transformer-based model for prediction of condition- or tissue-specific alternative splicing and demonstrate its utility in design of RNAs with desired splicing outcomes, which is a novel application. The model is compared to relevant exising approaches (Pangolin and SpliceAI) and the authors clearly demonstrate its advantage. Overall, a compelling method that is well thought out and evaluated.

Strengths:

(1) The model is well thought out: rather than modeling a cassette exon using a single generic deep learning model as has been done e.g. in SpliceAI and related work, the authors propose a modular architecture that focuses on different regions around a potential exon skipping event, which enables the model to learn representations that are specific to those regions. Because each component in the model focuses on a fixed length short sequence segment, the model can learn position-specific features. Furthermore, the architecture of the model is designed to model alternative splicing events, whereas Pangolin and SpliceAI are focused on modeling individual splice junctions, which is an easier problem.

(2) The model is evaluated in a rigorous way - it is compared to the most relevant state-of-the-art models, uses machine learning best practices, and an ablation study demonstrates the contribution of each component of the architecture.

(3) Experimental work supports the computational predictions: Regulatory elements predicted by the model were experimentally verified; novel tissue-specific cassette exons were verified by LSV-seq.

(4) The authors use their model for sequence design to optimize splicing outcome, which is a novel application.

Weaknesses:

None noted.

Joint Public Review:

Summary:

This is an excellent, timely study investigating and characterizing the underlying neural activity that generates the neuroendocrine GnRH and LH surges that are responsible for triggering ovulation. Abundant evidence accumulated over the past 20 years implicated the population of kisspeptin neurons in the hypothalamic RP3V region (also referred to as the POA or AVPV/PeN kisspeptin neurons) as being involved in driving the GnRH surge in response to elevated estradiol (E2), also known as the "estrogen positive feedback". However, while former studies used Cfos coexpression as a marker of RP3V kisspeptin neuron activation at specific times and found this correlates with the timing of the LH surge, detailed examination of the live in vivo activity of these neurons before, during, and after the LH surge remained elusive due to technical challenges.

Here, Zhou and colleagues use fiber photometry to measure the long-term synchronous activity of RP3V kisspeptin neurons across different stages of the mouse estrous cycle, including on proestrus when the LH surge occurs, as well as in a well-established OVX+E2 mouse model of the LH surge.

The authors report that RP3V kisspeptin neuron activity is low on estrous and diestrus, but increases on proestrus several hours before the late afternoon LH surge, mirroring prior reports of rising GnRH neuron activity in proestrus female mice. The measured increase in RP3V kisspeptin activation is long, spanning ~13 hours in proestrus females and extending well beyond the end of the LH secretion, and is shown by the authors to be E2 dependent.

For this work, Kiss-Cre female mice received a Cre-dependent AAV injection, containing GCaMP6, to measure the neuronal activation of RP3V Kiss1 cells. Females exhibited periods of increased neuronal activation on the day of proestrus, beginning several hours prior to the LH surge and lasting for about 12 hours. Though oscillations in the pattern of GCaMP fluorescence were occasionally observed throughout the ovarian cycle, the frequency, duration, and amplitude of these oscillations were significantly higher on the day of proestrus. This increase in RP3V Kiss1 neuronal activation that precedes the increase in LH supports the hypothesis that these neurons are critical in regulating the LH surge. The authors compare this data to new data showing a similar increased activation pattern in GnRH neurons just prior to the LH surge, further supporting the hypothesis that RP3V Kiss1 cell activation causes the release of kisspeptin to stimulate GnRH neurons and produce the LH surge.

Strengths:

This study provides compelling data demonstrating that RP3V kisspeptin neuronal activity changes throughout the ovarian cycle, likely in response to changes in estradiol levels, and that neuronal activation increases on the day of the LH surge.

The observed increase in RP3V kisspeptin neuronal activation precedes the LH surge, which lends support to the hypothesis that these neurons play a role in regulating the estradiol-induced LH surge. Continuing to examine the complexities of the LH surge and the neuronal populations involved, as done in this study, is critical for developing therapeutic treatments for women's reproductive disorders.

This innovative study uses a within-subject design to examine neuronal activation in vivo across multiple hormone milieus, providing a thorough examination of the changes in activation of these neurons. The variability in neuronal activity surrounding the LH surge across ovarian cycles in the same animals is interesting and could not be achieved without this within-subjects design. The inclusion and comparison of ovary-intact females and OVX+E2 females is valuable to help test mechanisms under these two valuable LH surge conditions, and allows for further future studies to tease apart minor differences in the LH surge pattern between these 2 conditions.

This study provides an excellent experimental setup able to monitor the daily activity of preoptic kisspeptin neurons in freely moving female mice. It will be a valuable tool to assess the putative role of these kisspeptin neurons in various aspects of altered female fertility (aging, pathologies...). This approach also offers novel and useful insights into the impact of E2 and circadian cues on the electrical activity of RP3V kisspeptin neurons.

An intriguing cyclical oscillation in kisspeptin neural activity every 90 minutes exists, which may offer critical insight into how the RP3V kisspeptin system operates. Interestingly, there was also variability in the onset and duration of RP3V Kisspeptin neuron activity between and within mice in naturally cycling females. Preoptic kisspeptin neurons show an increased activity around the light/dark transition only on the day of proestrus, and this is associated with an increase in LH secretion. An original finding is the observation that the peak of kisspeptin neuron activation continues a few hours past the peak of LH, and the authors hypothesize that this prolonged activity could drive female sexual behaviors, which usually appear after the LH surge.

The authors demonstrated that ovariectomy resulted in very little neuronal activity in RP3V kisspeptin neurons. When these ovarietomized females were treated with estradiol benzoate (EB) and an LH surge was induced, there was an increase in RP3V kisspeptin neuronal activation, as was seen during proestrus. However, the magnitude of the change in activity was greater during proestrus than during the EB-induced LH surge. Interestingly, the authors noted a consistent peak in activity about 90 minutes prior to lights out on each day of the ovarian cycle and during EB treatment, but not in ovariectomized females. The functional purpose of this consistent neuronal activity at this time remains to be determined.

Though not part of this study, the comparison of neuronal activation of GnRH neurons during the LH surge to the current data was convincing, demonstrating a similar pattern of increased activation that precedes the LH surge.

In summary, the study is well-designed, uses proper controls and analyses, has robust data, and the paper is nicely organized and written. The data from these experiments is compelling, and the authors' claims and conclusions are nicely supported and justified by the data. The data support the hypothesis in the field that these RP3V neurons regulate the LH surge. Overall, these findings are important and novel, and lend valuable insight into the underlying neural mechanisms for neuroendocrine control of ovulation.

Weaknesses:

(1) LH levels were not measured in many mice or in robust temporal detail, such as every 30 or 60 min, to allow a more detailed comparison between the fine-scale timing of RP3V neuron activation with onset and timing of LH surge dynamics.

(2) The authors report that the peak LH value occurred 3.5 hours after the first RP3V kisspeptin neuron oscillation. However, it is likely, and indeed evident from the 2 example LH patterns shown in Figures 3A-B, that LH values start to increase several hours before the peak LH. This earlier rise in LH levels ("onset" of the surge) occurs much closer in time to the first RP3V kisspeptin neuron oscillatory activation, and as such, the ensuing LH secretion may not be as delayed as the authors suggest.

(3) The authors nicely show that there is some variation (~2 hours) in the peak of the first oscillation in proestrus females. Was this same variability present in OVX+E2 females, or was the variability smaller or absent in OVX+E2 versus proestrus? It is possible that the variability in proestrus mice is due to variability in the timing and magnitude of rising E2 levels, which would, in theory, be more tightly controlled and similar among mice in the OVX+E2 model. If so, the OVX+E2 mice may have less variability between mice for the onset of RP3V kisspeptin activity.

(4) One concern regarding this study is the lack of data showing the specificity of the AAV and the GCaMP6s signals. There are no data showing that GCaMP6s is limited to the RP3V and is not expressed in other Kiss1 populations in the brain. Given that 2ul of the AAV was injected, which seems like a lot considering it was close to the ventricle, it is important to show that the signal and measured activity are specific to the RP3V region. Though the authors discuss potential reasons for the low co-expression of GCaMP6 and kisspeptin immunoreactivity, it does raise some concern regarding the interpretation of these results. The low co-expression makes it difficult to confirm the Kiss1 cell-specificity of the Cre-dependent AAV injections. In addition, if GFP (GCaMP6s) and kisspeptin protein co-localization is low, it is possible that the activation of these neurons does not coincide with changes in kisspeptin or that these neurons are even expressing Kiss1 or kisspeptin at the time of activation. It is important to remember that the study measures activation of the kisspeptin neuron, and it does not reveal anything specific about the activity of the kisspeptin protein.

(5) One additional minor concern is that LH levels were not measured in the ovariectomized females during the expected time of the LH surge. The authors suggest that the lower magnitude of activation during the LH surge in these females, in comparison to proestrus females, may be the result of lower LH levels. It's hard to interpret the difference in magnitude of neuronal activation between EB-treated and proestrus females without knowing LH levels. In addition, it's possible that an LH surge did not occur in all EB-treated females, and thus, having LH levels would confirm the success of the EB treatment.

(6) This kisspeptin neuron peak activity is abolished in ovariectomized mice, and estradiol replacement restored this activity, but only partially. Circulating levels of estradiol were not measured in these different setups, but the authors hypothesize that the lack of full restoration may be due to the absence of other ovarian signals, possibly progesterone.

(7) Recordings in several mice show inter- and intra-variability in the time of peak onset. It is not shown whether this variability is associated with a similar variability in the timing of the LH surge onset in the recorded mice. The authors hypothesized that this variability indicates a poor involvement of the circadian input. However, no experiments were done to investigate the role of the (vasopressinergic-driven) circadian input on the kisspeptin neuron activation at the light/dark transition. Thus, we suggest that the authors be more tentative about this hypothesis.

Reviewer #1 (Public review):

This study aims to identify the proteins that compose the electrical synapse, which are much less understood than those of the chemical synapse. Identifying these proteins is important to understand how synaptogenesis and conductance are regulated in these synapses.

Using a proteomics approach, the authors identified more than 50 new proteins and used immunoprecipitation and immunostaining to validate their interaction of localization. One new protein, a scaffolding protein (Sipa1l3), shows particularly strong evidence of being an integral component of the electrical synapse. The function of Sipa1l3 remains to be determined.

Another strength is the use of two different model organisms (zebrafish and mice) to determine which components are conserved across species. This approach also expands the utility of this work to benefit researchers working with both species.

The methodology is robust and there is compelling evidence supporting the findings.

Comments on revisions:

I thank the authors for responding to the comments. No further recommendations.

Reviewer #1 (Public review):

Summary:

The authors note that there is a large corpus of research establishing the importance of LC-NE projections to medial prefrontal cortex (mPFC) of rats and mice in attentional set or 'rule' shifting behaviours. However, this is complex behavior and the authors were attempting to gain an understanding of how locus coeruleus modulation of the mPFC contributes to set shifting.

The authors replicated the ED-shift impairment following NE denervation of mPFC by chemogenetic inhibition of the LC. They further showed that LC inhibition changed the way neurons in mPFC responded to the cues, with a greater proportion of individual neurons responsive to 'switching', but the individual neurons also had broader tuning, responding to other aspects of the task (i.e., response choice and response history). The population dynamics was also changed by LC inhibition, with reduced separation of population vectors between early-post-switch trials, when responding was at chance, and later trials when responding was correct. This was what they set out to demonstrate and so one can conclude they achieved their aims.

The authors concluded that LC inhibition disrupted mPFC "encoding capacity for switching" and suggest that this "underlie[s] the behavioral deficits."

Strengths:

The principal strength is combining inactivation of LC with calcium imaging in mPFC. This enabled detailed consideration of the change in behavior (i.e., defining epochs of learning, with an 'early phase' when responding is at chance being compared to a 'later phase' when the behavioral switch has occurred) and how these are reflected in neuronal activity in the mPFC, with and without LC-NE input.

Comments on revised version:

In their response to reviewers, the authors say "We report p values using 2 decimal points and standard language as suggested by this reviewer". However, no changes were made in the manuscript: for example, "P = 4.2e-3" rather than "p = 0.004".

In their response to the reviewers, they wrote: "Upon closer examination of the behavioral data, we exclude several sessions where more trials were taken in IDS than in EDS." If those sessions in which EDSIDS. Most problematic is the fact that the manuscript now reads "Importantly, control mice (pooled from Fig. 1e, 1h, Supp. Fig. 1a, 1b) took more trials to complete EDS than IDS (Trials to criterion: IDS vs. EDS, 10 {plus minus} 1 trials vs. 16 {plus minus} 1 trials, P < 1e-3, Supp. Fig. 1c), further supporting the validity of attentional switching (as in Fig. 1c)" without mentioning that data has been excluded.

Reviewer #1 (Public review):

Summary:

The paper uses rigorous methods to determine phase dynamics from human cortical stereotactic EEGs. It finds that the power of the phase is higher at the lowest spatial phase. The application to data illustrates the solidity of the method and their potential for discovery.

Comments on revised submission:

The authors have provided responses to the previous recommendations.

Reviewer #1 (Public review):

Summary:

The authors scrutinized differences in C-terminal region variant profiles between Rett syndrome patients and healthy individuals and pinpointed that subtle genetic alternation can cause benign or pathogenic output, which harbors important implications in Rett syndrome diagnosis and proposes a therapeutic strategy. This work will be beneficial to clinicians and basic scientists who work on Rett syndrome, and carries the potential to be applied to other Mendelian rare diseases.

Strengths:

Well-designed genetic and molecular experiments translate genetic differences into functional and clinical changes. This is a unique study resolving subtle changes in sequences that give rise to dramatic phenotypic consequences.

Weaknesses:

There are many base-editing and protein-expression changes throughout the manuscript, and they cause confusion. It would be helpful to readers if authors could provide a simple summary diagram at the end of the paper.

Reviewer #1 (Public review):

Summary:

The authors integrate multiple large databases to test whether body sizes were positively associated with which species tolerate urban areas. In general, many plant families showed a positive association between body size and urban tolerance, whereas a smaller, though still non-trivial, percentage of animal families showed the same pattern. Notably, the authors are careful in the interpretation of their findings and provide helpful context for the ways that this analysis can be generative in shaping new hypotheses and theory around how urbanization influences biodiversity at large. They are careful to discuss how body size is an important trait, but the absence of a relationship between body size and urban tolerance in many families suggests a variety of other traits undergird urban success.

Strengths:

The authors aggregated a large dataset, but they also applied robust filters to ensure they had an adequate and representative number of detections for a given species, family, geography, etc. The authors also applied their analysis at multiple taxonomic scales (family and order), which allowed for a better interpretation of the patterns in the data and at what taxonomic scale body size might be important.

Weaknesses:

My main concern is that it is not fully clear how the measure of body size might influence the result. The authors were unable to obtain consistent measures of body size (mean, median, maximum, or sex variation). This, of course, could be very consequential as means and medians can differ quite a bit, and they certainly will differ substantially from a maximum. And of course, sex differences can be marked in multiple directions or absent altogether. The authors do note that they selected the measure that was most common in a family, but it was not clear whether species in that family that did not have that measure were removed or not. This could potentially shape the variability in the dataset and obscure true patterns. This may require additional clarity from the authors and is also a real constraint in compiling large data from disparate sources.

Reviewer #1 (Public review):

Summary:

In this paper, Qiu et al. developed a novel spatial navigation task to investigate the formation of multi-scale representations in the human brain. Over multiple sessions and diverse tasks, participants learned the location of 32 objects distributed across 4 different rooms. The key task was a "judgement of relative direction" task delivered in the scanner, which was designed to assess whether object representations reflect local (within-room) or global (across-room) similarity structures. In between the two scanning sessions, participants received extensive further training. The goal of this manipulation was to test how spatial representations change with learning.

Strengths:

The authors designed a very comprehensive set of tasks in virtual reality to teach participants a novel spatial map. The spatial layout is well-designed to address the question of interest in principle. Participants were trained in a multi-day procedure, and representations were assessed twice, allowing the authors to investigate changes in the representation over multiple days.

Weaknesses:

Unfortunately, I see multiple problems with the experimental design that make it difficult to draw conclusions from the results.

(1) In the JRD task (the key task in this paper), participants were instructed to imagine standing in front of the reference object and judge whether the second object was to their left or right. The authors assume that participants solve this task by retrieving the corresponding object locations from memory, rotating their imagined viewpoint and computing the target object's relative orientation. This is a challenging task, so it is not surprising that participants do not perform particularly well after the initial training (performance between 60-70% accuracy). Notably, the authors report that after extensive training, they reached more than 90% accuracy.

However, I wonder whether participants indeed perform the task as intended by the authors, especially after the second training session. A much simpler behavioural strategy is memorising the mapping between a reference object and an associated button press, irrespective of the specific target object. This basic strategy should lead to quite high success rates, since the same direction is always correct for four of the eight objects (the two objects located at the door and the two opposite the door). For the four remaining objects, the correct button press is still the same for four of the six target objects that are not located opposite to the reference object. Simply memorising the button press associated with each reference object should therefore lead to a high overall task accuracy without the necessity to mentally simulate the spatial geometry of the object relations at all.

I also wonder whether the random effect coefficients might reflect interindividual differences in such a strategy shift - someone who learnt this relationship between objects and buttons might show larger increases in RTs compared to someone who did not.

(2) On a related note, the neural effect that appears to reflect the emergence of a global representation might be more parsimoniously explained by the formation of pairwise associations between reference and target objects. Since both objects always came from the same room, an RDM reflecting how many times an object pair acted as a reference-target pair will correlate with the categorical RDM reflecting the rooms corresponding to each object. Since the categorical RDM is highly correlated with the global RDM, this means that what the authors measure here might not reflect the formation of a global spatial map, but simply the formation of pairwise associations between objects presented jointly.

(3) In general, the authors attribute changes in neural effects to new learning. But of course, many things can change between sessions (expectancy, fatigue, change in strategy, but also physiological factors...). Baseline phsiological effects are less likely to influence patterns of activity, so the RSA analyses should be less sensitive to this problem, but especially the basic differences in activation for the contrast of post-learning > pre-learning stages in the judgment of relative direction (JRD) task could in theory just reflect baseline differences in blood oxygenation, possibly due to differences in time of day, caffeine intake, sleep, etc. To really infer that any change in activity or representation is due to learning, an active control would have been great.

(4) RSA typically compares voxel patterns associated with specific stimuli. However, the authors always presented two objects on the screen simultaneously. From what I understand, this is not considered in the analysis ("The β-maps for each reference object were averaged across trials to create an overall β-map for that object."). Furthermore, participants were asked to perform a complex mental operation on each trial ("imagine standing at A, looking at B, then perform the corresponding motor response"). Assuming that participants did this (although see points 1 and 2 above), this means that the resulting neural representation likely reflects a mixture of the two object representations, the mental transformation and the corresponding motor command, and possibly additionally the semantic and perceptual similarity between the two presented words. This means that the βs taken to reflect the reference object representation must be very noisy.

This problem is aggravated by two additional points. Firstly, not all object pairs occurred equally often, because only a fraction of all potential pairs were sampled. If the selection of the object pairs is not carefully balanced, this could easily lead to sampling biases, which RSA is highly sensitive to.

Secondly, the events in the scanner are not jittered. Instead, they are phase-locked to the TR (1.2 sec TR, 1.2 sec fixation, 4.8 sec stimulus presentation). This means that every object onset starts at the same phase of the image acquisition, making HRF sampling inefficient and hurting trial-wise estimation of betas used for the RSA. This likely significantly weakens the strength of the neural inferences that are possible using this dataset.

(5) It is not clear why the authors focus their report of the results in the main manuscript on the preselected ROIs instead of showing whole-brain results. This can be misleading, as it provides the false impression that the neural effects are highly specific to those regions.

(6) I am missing behavioural support for the authors' claims.

Overall, I am not convinced that the main conclusion that global spatial representations emerge during learning is supported by the data. Unfortunately, I think there are some fundamental problems in the experimental design that might make it difficult to address the concerns.

However, if the authors can provide convincing evidence for their claims, I think the paper will have an impact on the field. The question of how multi-scale representations are represented in the human brain is a timely and important one.

Reviewer #1 (Public review):

Summary:

Englert et al. proposed a functional connectivity-based Attractor Neural Network (fcANN) to reveal attractor states and activity flows across various conditions, including resting state, task-evoked, and pathological conditions. The large-scale brain attractors reconstructed by fcANNs are orthogonal organization, which is in line with the free-energy theoretical framework. Additionally, the fcANN demonstrates differences in attractor states between individuals with autism and typically developing individuals.

The study used seven datasets, which ensures robust replication and validation of generalization across various conditions. The study is a representative example that combines experimental evidence based on fcANN and the theoretical framework. The fcANN projection offers an interesting way of visualization, allowing researchers to observe attractor states and activity flow patterns directly. Overall, the study may offer valuable insights into brain dynamics and computational neuroscience.

Comments on revision:

The authors have addressed my previous concerns and substantially improved the manuscript. Fig.4 and Fig.5 still keep fcHNN rather than the updated fcANN.

Reviewer #2 (Public review):

Summary:

Tissue-resident macrophages are more and more thought to exert key homeostatic functions and contribute to physiological responses. In the report of O'Brien and Colleagues, the idea that the macrophage-expressed scavenger receptor MARCO could regulate adrenal corticosteroid output at steady-state was explored. The authors found that male MARCO-deficient mice exhibited higher plasma aldosterone levels and higher lung ACE expression as compared to wild-type mice, while the availability of cholesterol and the machinery required to produce aldosterone in the adrenal gland were not affected by MARCO deficiency. The authors take these data to conclude that MARCO in alveolar macrophages can negatively regulate ACE expression and aldosterone production at steady-state and that MARCO-deficient mice suffer from a secondary hyperaldosteronism.

Strengths:

If properly demonstrated and validated, the fact that tissue-resident macrophages can exert physiological functions and influence endocrine systems would be highly significant and could be amenable to novel therapies.

Major weakness:

The comparison between C57BL/6J wild-type mice and knock-out mice for which a precise information about the genetic background and the history of breedings and crossings is lacking can lead to misinterpretations of the results obtained. Hence, MARCO-deficient mice should be compared with true littermate controls.

Reviewer #1 (Public review):

Summary:

Zhou and colleagues developed a computational model of replay that heavily builds on cognitive models of memory in context (e.g., the context-maintenance and retrieval model), which have been successfully used to explain memory phenomena in the past. Their model produces results that mirror previous empirical findings in rodents and offers a new computational framework for thinking about replay.

Strengths:

The model is compelling and seems to explain a number of findings from the rodent literature. It is commendable that the authors implement commonly used algorithms from wakefulness to model sleep/rest, thereby linking wake and sleep phenomena in a parsimonious way. Additionally, the manuscript's comprehensive perspective on replay, bridging humans and non-human animals, enhanced its theoretical contribution.

Weaknesses:

This reviewer is not a computational neuroscientist by training, so some comments may stem from misunderstandings. I hope the authors would see those instances as opportunities to clarify their findings for broader audiences.

(1) The model predicts that temporally close items will be co-reactivated, yet evidence from humans suggests that temporal context doesn't guide sleep benefits (instead, semantic connections seem to be of more importance; Liu and Ranganath 2021, Schechtman et al 2023). Could these findings be reconciled with the model or is this a limitation of the current framework?

(2) During replay, the model is set so that the next reactivated item is sampled without replacement (i.e., the model cannot get "stuck" on a single item). I'm not sure what the biological backing behind this is and why the brain can't reactivate the same item consistently. Furthermore, I'm afraid that such a rule may artificially generate sequential reactivation of items regardless of wake training. Could the authors explain this better or show that this isn't the case?

(3) If I understand correctly, there are two ways in which novelty (i.e., less exposure) is accounted for in the model. The first and more talked about is the suppression mechanism (lines 639-646). The second is a change in learning rates (lines 593-595). It's unclear to me why both procedures are needed, how they differ, and whether these are two different mechanisms that the model implements. Also, since the authors controlled the extent to which each item was experienced during wakefulness, it's not entirely clear to me which of the simulations manipulated novelty on an individual item level, as described in lines 593-595 (if any).

As to the first mechanism - experience-based suppression - I find it challenging to think of a biological mechanism that would achieve this and is selectively activated immediately before sleep (somehow anticipating its onset). In fact, the prominent synaptic homeostasis hypothesis suggests that such suppression, at least on a synaptic level, is exactly what sleep itself does (i.e., prune or weaken synapses that were enhanced due to learning during the day). This begs the question of whether certain sleep stages (or ultradian cycles) may be involved in pruning, whereas others leverage its results for reactivation (e.g., a sequential hypothesis; Rasch & Born, 2013). That could be a compelling synthesis of this literature. Regardless of whether the authors agree, I believe that this point is a major caveat to the current model. It is addressed in the discussion, but perhaps it would be beneficial to explicitly state to what extent the results rely on the assumption of a pre-sleep suppression mechanism.

(4) As the manuscript mentions, the only difference between sleep and wake in the model is the initial conditions (a0). This is an obvious simplification, especially given the last author's recent models discussing the very different roles of REM vs NREM. Could the authors suggest how different sleep stages may relate to the model or how it could be developed to interact with other successful models such as the ones the last author has developed (e.g., C-HORSE)? Finally, I wonder how the model would explain findings (including the authors') showing a preference for reactivation of weaker memories. The literature seems to suggest that it isn't just a matter of novelty or exposure, but encoding strength. Can the model explain this? Or would it require additional assumptions or some mechanism for selective endogenous reactivation during sleep and rest?

(5) Lines 186-200 - Perhaps I'm misunderstanding, but wouldn't it be trivial that an external cue at the end-item of Figure 7a would result in backward replay, simply because there is no potential for forward replay for sequences starting at the last item (there simply aren't any subsequent items)? The opposite is true, of course, for the first-item replay, which can't go backward. More generally, my understanding of the literature on forward vs backward replay is that neither is linked to the rodent's location. Both commonly happen at a resting station that is further away from the track. It seems as though the model's result may not hold if replay occurs away from the track (i.e. if a0 would be equal for both pre- and post-run).

(6) The manuscript describes a study by Bendor & Wilson (2012) and tightly mimics their results. However, notably, that study did not find triggered replay immediately following sound presentation, but rather a general bias toward reactivation of the cued sequence over longer stretches of time. In other words, it seems that the model's results don't fully mirror the empirical results. One idea that came to mind is that perhaps it is the R/L context - not the first R/L item - that is cued in this study. This is in line with other TMR studies showing what may be seen as contextual reactivation. If the authors think that such a simulation may better mirror the empirical results, I encourage them to try. If not, however, this limitation should be discussed.

(7) There is some discussion about replay's benefit to memory. One point of interest could be whether this benefit changes between wake and sleep. Relatedly, it would be interesting to see whether the proportion of forward replay, backward replay, or both correlated with memory benefits. I encourage the authors to extend the section on the function of replay and explore these questions.

(8) Replay has been mostly studied in rodents, with few exceptions, whereas CMR and similar models have mostly been used in humans. Although replay is considered a good model of episodic memory, it is still limited due to limited findings of sequential replay in humans and its reliance on very structured and inherently autocorrelated items (i.e., place fields). I'm wondering if the authors could speak to the implications of those limitations on the generalizability of their model. Relatedly, I wonder if the model could or does lead to generalization to some extent in a way that would align with the complementary learning systems framework.

Joint Public Review:

Meiotic recombination begins with DNA double-strand breaks (DSBs) generated by the conserved enzyme Spo11, which relies on several accessory factors that vary widely across eukaryotes. In C. elegans, multiple proteins have been implicated in promoting DSB formation, but their functional relationships and how they collectively recruit the DSB machinery to chromosome axes have remained unclear.

In this study, Raices et al. investigate the biochemical and genetic interactions among known DSB-promoting factors in C. elegans meiosis. Using yeast two-hybrid assays and co-immunoprecipitation, they map pairwise protein interactions and identify a connection between the chromatin-associated protein HIM-17 and the transcription factor XND-1. They also confirm the established interaction between DSB-1 and SPO-11 and show that DSB-1 associates with the nematode-specific factor HIM-5, which is required for X-chromosome DSB formation.

The authors extend these findings with genetic analyses, placing these factors into four epistasis groups based on single- and double-mutant phenotypes. Together, these biochemical and genetic data support a model describing how these proteins engage chromatin loops and localize to chromosome axes. The work provides a clearer view of how C. elegans assembles its DSB-forming machinery and how this process compares to mechanisms in other organisms.

Comment from the Reviewing Editor on the revised version:

The authors have adequately addressed the prior review comments. At this point, after going through multiple rounds of reviews and revisions, the community will be better served by having this paper out in public. This version was assessed by the editors without further input from the reviewers.

Reviewer #1 (Public review):

Summary:

In their article, Guo and coworkers investigate the Ca²⁺ signaling responses induced by Enteropathogenic Escherichia coli (EPEC) in epithelial cells and how these responses regulate NF-κB activation. The authors show that EPEC induces rapid, spatially coordinated Ca²⁺ transients mediated by extracellular ATP released through the type III secretion system (T3SS). Using high-speed Ca²⁺ imaging and stochastic modeling, they propose that low ATP levels trigger "Coordinated Ca²⁺ Responses from IP₃R Clusters" (CCRICs) via fast Ca²⁺ diffusion and Ca²⁺-induced Ca²⁺ release. These responses may dampen TNF-α-induced NF-κB activation through Ca²⁺-dependent modulation of O-GlcNAcylation of p65. The interdisciplinary work suggests a new perspective on calcium-mediated immune response by combining quantitative imaging, bacterial genetics, and computational modeling.

Strengths:

The study provides a new concept for host responses to bacterial infections and introduces the concept of Coordinated Ca²⁺ Responses from IP₃R Clusters (CCRICs) as synchronized, whole-cell-scale Ca²⁺ transients with the fast kinetics typical of local events. This is elegantly done by an interdisciplinary approach using quantitative measurements and mechanistic modelling.

Weaknesses:

(1) The effect of coordination by fast diffusion for small eATP concentrations is explained by the resulting low Ca2+ concentration that is not as strongly affected by calcium buffers compared to higher concentrations. While I agree with this statement on the relative level, CICR is based on the resulting absolute concentration at neighboring IP3Rs (to activate them). Thus, I do not fully agree with the explanation, or at least would expect to use the modelling approach to demonstrate this effect. Simulations for different activation and buffer concentrations could strengthen this point and exclude potential inhibition of channels at higher stimulation levels.

In this respect, I would also include the details of the modelling, such as implementation environment, parameters, and benchmarking. The description in the Supplementary Methods is very similar to the description in the main text. In terms of reproducibility, it would be important to at least provide simulation parameters, and providing the code would align with the emerging standards for reproducible science.

(2) Quantitative characterization of CCRICs:

The paper would benefit from a clearer definition of the term CCRICs and quantitative descriptors like duration, amplitude distribution, frequency, and spatial extent (also in relation to the comment on the EGTA measurements below). Furthermore, it remains unclear to me whether CCRICs represent a population of rapidly propagating micro-waves or truly simultaneous events. Maybe kymographs or wave-front propagation analyses (at least from simulations if experimental resolution is too bad) would strengthen this point.

(3) Specificity of pharmacological tools:

Suramin and U73122 are known to have off-target effects. Control experiments using alternative P2 receptor antagonists like PPADS or inactive U73343 analogs would strengthen the causal link.

Reviewer #1 (Public review):

Summary:

Drosophila larval type II neuroblasts generate diverse types of neurons by sequentially expressing different temporal identity genes during development. Previous studies have shown that transition from early temporal identity genes (such as Chinmo and Imp) to late temporal identity genes (such as Syp and Broad) depends on the activation of the expression of EcR by Seven-up (Svp) and progression through the G1/S transition of the cell cycle. In this study, Chaya and Syed examined if the expression of Syp and EcR is regulated by cell cycle and cytokinesis by knocking down CDK1 or Pav, respectively, throughout development or at specific developmental stages. They find that knocking down CDK1 or Pav either in all type II neuroblasts throughout the development or in single type neuroblast clones after larval hatching consistently leads to failure to activate late temporal identity genes Syp and EcR. To determine whether the failure of the activation of Syp and EcR is due to impaired Svp expression, they also examined Svp expression using a Svp-lacZ reporter line. They find that Svp is expressed normally in CDK1 RNAi neuroblasts. Further, knocking down CDK1 or Pav after Svp activation still leads to loss of Syp and EcR expression. Finally, they also extended their analysis to type I neuroblasts. They find that knocking down CDK1 or Pav, either at 0 hours or at 42 hours after larval hatching, also results in loss of Syp and EcR expression in type I neuroblasts. Based on these findings, the authors conclude that cycle and cytokinesis are required for the transition from early to late late temporal identity genes in both types of neuroblasts. These findings add mechanistic details to our understanding of the temporal patterning of Drosophila larval neuroblasts.

Strengths:

The data presented in the paper are solid and largely support their conclusion. Images are of high quality. The manuscript is well-written and clear.

Weaknesses:

The authors have addressed all the weaknesses in this revision.

Reviewer #1 (Public review):

Petrovic et al. investigate CCR5 endocytosis via arrestin2, with a particular focus on clathrin and AP2 contributions. The study is thorough and methodologically diverse. The NMR titration data clearly demonstrate chemical shift changes at the canonical clathrin-binding site (LIELD), present in both the 2S and 2L arrestin splice variants. To assess the effect of arrestin activation on clathrin binding, the authors compare: truncated arrestin (1-393), full-length arrestin, and 1-393 incubated with CCR5 phosphopeptides. All three bind clathrin comparably, whereas controls show no binding. These findings are consistent with prior crystal structures showing peptide-like binding of the LIELD motif, with disordered flanking regions. The manuscript also evaluates a non-canonical clathrin binding site specific to the 2L splice variant. Though this region has been shown to enhance beta2-adrenergic receptor binding, it appears not to affect CCR5 internalization.

Similar analyses applied to AP2 show a different result. AP2 binding is activation-dependent and influenced by the presence and level of phosphorylation of CCR5-derived phosphopeptides. These findings are reinforced by cellular internalization assays.

In sum, the results highlight splice-variant-dependent effects and phosphorylation-sensitive arrestin-partner interactions. The data argue against a (rapidly disappearing) one-size-fits-all model for GPCR-arrestin signaling and instead support a nuanced, receptor-specific view, with one example summarized effectively in the mechanistic figure.

Reviewer #1 (Public review):

Summary

The manuscript presents EIDT, a framework that extracts an "individuality index" from a source task to predict a participant's behaviour in a related target task under different conditions. However, the evidence that it truly enables cross-task individuality transfer is not convincing.

Strengths

The EIDT framework is clearly explained, and the experimental design and results are generally well-described. The performance of the proposed method is tested on two distinct paradigms: a Markov Decision Process (MDP) task (comparing 2-step and 3-step versions) and a handwritten digit recognition (MNIST) task under various conditions of difficulty and speed pressure. The results indicate that the EIDT framework generally achieved lower prediction error compared to baseline models and that it was better at predicting a specific individual's behaviour when using their own individuality index compared to using indices from others.

Furthermore, the individuality index appeared to form distinct clusters for different individuals, and the framework was better at predicting a specific individual's behaviour when using their own derived index compared to using indices from other individuals.

Comments on revisions:

I thank the author for the additional analyses. They have fully addressed all of my previous concerns, and I have no further recommendations.

Reviewer #1 (Public review):

Summary:

This study presents compelling new data that combine two FTD-tau mutations P301L/S320F (PL-SF), that reliably induce spontaneous full-length tau aggregation across multiple cellular systems. However, several conclusions would benefit from more validation. Key findings rely on quantification of overexposed immunoblot, and in some experiments, the tau bands shift in molecular weight that are not explained (and in some instances vary between experiments). The effect seems to be driven by the S320F mutation, with the P301L mutation enhancing the effect observed with S320F alone. Although the observation that Hsp70, but not the related Hsc70, enhances aggregation is intriguing, the mechanistic basis for these differences remains unclear despite both Hsp70 and Hsc70 binding to tau. Additional experiments clarifying which PL-SF tau species Hsp70 engages, how this interaction alters tau conformational landscapes, and whether other chaperones or cofactors contribute to this effect would help solidify the conclusions and build a mechanistic picture. Overexpression of Hsp70 in the context of PL tau did not increase tau aggregation, which raises questions about whether the observed effects are specific to the SF mutation. Hsp70 functions in the context of a larger network of chaperones and has been proposed to cooperate with other proteins/machinery to disassemble tau amyloids, perhaps to produce more seeds. This would be consistent with the presented observations. For example, co-IP experiments using Hsp70 as bait combined with proteomics could really help build a more complete picture of what tau species Hsp70 binds and what other factors cooperate to yield the observed increases in aggregation. As it stands, the Hsp70 component of the paper is not fully developed, and additional experiments to address these questions would strengthen this manuscript beyond simply presenting a new tool to study spontaneous tau aggregation.

Strengths:

(1) The PL-SF FL tau mutant aggregates spontaneously in different cellular systems and shows hallmarks of tau pathology linked to disease.

(2) PL-SF 4delta mutant reverses the spontaneous aggregation phenotype, consistent with these residues being critical for tau aggregation.

(3) PL-SF 4delta also loses the ability to recruit Hsp70/Hsc70, consistent with these residues also being critical for chaperone recruitment.

(4) The PL-SF tau mutant establishes a new system to study spontaneous tau assembly and to begin to compare it to seeded tau aggregation processes.

Weaknesses:

(1) Mechanistic insight into how Hsp70 but not Hsc70 increase PL-SF FL tau aggregation/pathology is missing. This is despite both chaperones binding to PL-SF FL tau. What species of tau does Hsp70 bind, and what cofactors are important in this process?

(2) The study relies heavily on densitometry of bands to draw conclusions; in several instances, the blots are overexposed to accurately quantify the signal.

Reviewer #1 (Public review):

Summary:

This study presents convincing findings that oligodendrocytes play a regulatory role in spontaneous neural activity synchronization during early postnatal development, with implications for adult brain function. Utilizing targeted genetic approaches, the authors demonstrate how oligodendrocyte depletion impacts Purkinje cell activity and behaviors dependent on cerebellar function. Delayed myelination during critical developmental windows is linked to persistent alterations in neural circuit function, underscoring the lasting impact of oligodendrocyte activity.

Strengths:

(1) The research leverages the anatomically distinct olivocerebellar circuit, a well-characterized system with known developmental timelines and inputs, strengthening the link between oligodendrocyte function and neural synchronization.

(2) Functional assessments, supported by behavioral tests, validate the findings of in vivo calcium imaging, enhancing the study's credibility.

(3) Extending the study to assess long-term effects of early life myelination disruptions adds depth to the implications for both circuit function and behavior.

Weaknesses:

(1) The study would benefit from a closer analysis of myelination during the periods when synchrony is recorded. Direct correlations between myelination and synchronized activity would substantiate the mechanistic link and clarify if observed behavioral deficits stem from altered myelination timing.

(2) Although the study focuses on Purkinje cells in the cerebellum, neural synchrony typically involves cross-regional interactions. Expanding the discussion on how localized Purkinje synchrony affects broader behaviors-such as anxiety, motor function, and sociality - would enhance the findings' functional significance.

(3) The authors discuss the possibility of oligodendrocyte-mediated synapse elimination as a possible mechanism behind their findings, drawing from relevant recent literature on oligodendrocyte precursor cells. However, there are no data presented supporting these assumptions. The authors should explain why they think the mechanism behind their observation extends beyond the contribution of myelination or remove this point from the discussion entirely.

Comment for resubmission: Although the argument on synaptic elimination has been removed, it has been replaced with similarly unclear speculation about roles for oligodendrocytes outside of conventional myelination or metabolic support, again without clear evidence. The authors measured MBP area but have not performed detailed analysis of oligodendrocyte biology to support the claims made in the discussion. Please consider removing this section or rephrasing it to align with the data presented.

(4) It would be valuable to investigate secondary effects of oligodendrocyte depletion on other glial cells, particularly astrocytes or microglia, which could influence long-term behavioral outcomes. Identifying whether the lasting effects stem from developmental oligodendrocyte function alone or also involve myelination could deepen the study's insights.

(5) The authors should explore the use of different methods to disturb myelin production for a longer time, in order to further determine if the observed effects are transient or if they could have longer-lasting effects.

(6) Throughout the paper, there are concerns about statistical analyses, particularly on the use of the Mann-Whitney test or using fields of view as biological replicates.

Reviewer #1 (Public review):

Summary:

Kashiwagi et al. undertook a population analysis of dendritic spine nanostructure applied to the objective grouping of 8 mouse models of neuropsychiatric disorders. They report that spine morphology in cultured hippocampal neurons shows a higher similarity among schizophrenia mouse models (compared with autism spectrum disorder (ASD) mouse models), and identify an effect of Ecrg4 (encoding small secretory peptides) on spine dynamics and shape in these models.

Strengths:

The study developed a method for objectively comparing spine properties in primary hippocampal neuron cultures from 8 mouse models of psychiatric disorders at the population level using high-resolution structured illumination microscopy (SIM) imaging. This novel technique identified two distinct groups of mouse models according to the population-level spine properties: those with ASD-related gene mutations and those with schizophrenia-related gene mutations. Functional studies, including gene knockdown and overexpression experiments, identified an effect of Ecrg4 on the spine phenotype of the schizophrenia model mice.

Weaknesses:

The main weakness is that the study is wholly in vitro, using cultured hippocampal neurons. The authors present this as an advantage, however, arguing that spine morphology as measured in a reduced culture system can demonstrate direct effects of gene mutations on neuronal phenotypes in the absence of indirect influences from nonneuronal cells or specific environments.

Another weakness is that CaMKIIαK42R/K42R mutant mice are presented as a schizophrenia model, the authors justifying this by saying that "CaMKII-related signaling pathway disruption has been implicated in the working memory deficits found in schizophrenia patients". Since mutations in CAMK2A cause autosomal dominant intellectual developmental disorder-53 (OMIM 617798) and autosomal recessive intellectual developmental disorder-63 (OMIM 618095), and mice carrying the CAMK2A E183V mutation exhibit ASD-related synaptic and behavioral phenotypes (PMID: 28130356), I think it's stretching credibility to refer to the CaMKIIαK42R/K42R mice as a schizophrenia model.

Although the manuscript is largely well written, there are some instances of ambiguous/unspecific language. This extends to the title (Decoding Spine Nanostructure in Mental Disorders Reveals a Schizophrenia-1 Linked Role for Ecrg4), which gives no indication that the work was in vitro on cultured neurons derived from mouse models.

Reviewer #1 (Public review):

Summary of goals:

The authors' stated goal (line 226) was to compare gene expression levels for gut hormones between males and females. As female flies contain more fat than males, they also sought to identify hormones that control this sex difference. Finally, they attempted to place their findings in the broader context of what is already known about established underlying mechanisms.

Strengths:

(1) The core research question of this work is interesting. The authors provide a reasonable hypothesis (neuro/entero-peptides may be involved) and well-designed experiments to address it.

(2) Some of the data are compelling, especially positive results that clearly implicate enteropeptides in sex-biased fat contents (Figures 1 and 3).

Weaknesses:

(1) The greatest weakness of this work is that it falls short of providing a clear mechanism for the regulation of sex-biased fat content by AstC and Tk. By and large, feminization of neurons or enteroendocrine cells with UAS-traF did not increase fat in males (Figure 2). The authors mention that ecdysone, juvenile hormone or Sex-lethal may instead play a role (lines 258-270), but this is speculative, making this study incomplete.

(2) Related to the above point, the cellular mechanisms by which AstC and Tk regulate fat content in males and females are only partially characterized. For example, knockdown of TkR99D in insulin-producing neurons (Figure 4E) but not pan-neuronally (Figure 4B) increases fat in males, but Tk itself only shows a tendency (Figure 3B). In females, the situation is even less clear: again, Tk only shows a tendency (Figure 3B), and pan-neuronal, but not IPC-specific knockdown of TkR99D decreases fat.

(3) The text sometimes misrepresents or contradicts the Results shown in the figures. UAS-traF expression in neurons or enteroendocrine cells did sometimes alter fat contents (Figure 2H, S), but the authors report that sex differences were unaffected (lines 164-166). On the other hand, although knockdown of Tk in enteroendocrine cells caused no significant effect (Figure 3B), the authors report this as a trend towards reduction (lines 182-183). This biased representation raises concerns about the interpretation of the data and the authors' conclusions.

(4) The authors find that in males, neuropeptide expression in the head is higher (Figure 1F-J). This may also play an important role in maintaining lower levels of fat in males, but this finding is not explored in the manuscript.

Appraisal of goal achievement & conclusions:

The authors were successful in identifying hormones that show sex bias in their expression and also control the male vs. female difference in fat content. However, elucidation of the relevant cellular pathways is incomplete. Additionally, some of their conclusions are not supported by the data (see Weaknesses, point 3).

Impact:

It is difficult to evaluate the impact of this study. This is in great part because the authors do not attempt to systematically place their findings about AstC/Tk in the broader context of their previous studies, which investigated the same phenomenon (Wat et al., 2021, eLife and Biswas et al., 2025, Cell Reports). As the underlying mechanisms are complex and likely redundant, it is necessary to generate a visual model to explain the pathways which regulate fat content in males and females.

Reviewer #1 (Public review):

Summary:

This is a strong paper that presents a clear advance in multi-animal tracking. The authors introduce an updated version of idtracker.ai that reframes identity assignment as a contrastive representation learning problem rather than a classification task requiring global fragments. This change leads to substantial gains in speed and accuracy and removes a known bottleneck in the original system. The benchmarking across species is comprehensive, the results are convincing, and the work significant.

Strengths:

The main strengths are the conceptual shift from classification to representation learning, the clear performance gains, and the improved robustness of the new version. Removing the need for global fragments makes the software much more flexible in practice, and the accuracy and speed improvements are well demonstrated across a diverse set of datasets. The authors' response also provides further support for the method's robustness.

The comparison to other methods is now better documented. The authors clarify which features are used, how failures are defined, how parameters are sampled, and how accuracy is assessed against human-validated data. This helps ensure that the evaluation is fair and that readers can understand the assumptions behind the benchmarks.

The software appears thoughtfully implemented, with GUI updates, integration with pose estimators, and tools such as idmatcher.ai for linking identities across videos. The overall presentation has been improved so that the limitations of the original idtracker.ai, the engineering optimizations, and the new contrastive formulation are more clearly separated. This makes the central ideas and contributions easier to follow.

Weaknesses:

I do not have major remaining criticisms. The authors have addressed my earlier concerns about the clarity and fairness of the comparison with prior methods, the benchmark design, and the memory usage analysis by adding methodological detail and clearly explaining their choices. At this point I view these aspects as transparent features of the experimental design that readers can take into account, rather than weaknesses of the work.

Overall, this is a high-quality paper. The improvements to idtracker.ai are well justified and practically significant, and the authors' response addresses the main concerns about clarity and evaluation. The conceptual contribution, thorough empirical validation, and thoughtful software implementation make this a valuable and impactful contribution to multi-animal tracking.

Reviewer #1 (Public review):

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

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

A crucial consideration for users of this dataset is the potential confounding effect between circadian phase and locomotor activity. This is particularly relevant because dentate gyrus activity is strongly modulated by locomotion. The authors acknowledge this issue in the Discussion and provide useful guidance for how to interpret their findings, considering this confound.

Taken together, this dataset represents a useful resource for the neuroscience community, particularly for investigators interested in how novel experience and circadian phase shape activity-related and immediate early gene expression in the hippocampus

Reviewer #1 (Public review):

Summary:

The manuscript presents a robust set of experiments that provide new insights into the role of STN neurons during active and passive avoidance tasks. These forms of avoidance have received comparatively less attention in the literature than the more extensively studied escape or freezing responses, despite being extremely relevant to human behaviour and more strongly influenced by cognitive control.

Strengths:

Understanding the neural infrastructure supporting avoidance behaviour would be a fundamental milestone in neuroscience. The authors employ sophisticated methods to delineate the role of STN neurons during avoidance behaviours. The work is thorough and the evidence presented is compelling. Experiments are carefully constructed, well-controlled, and the statistical analyses are appropriate.

Weaknesses:

One possible remaining conceptual concern that might require future work is determining whether STN primarily mediates higher-level cognitive avoidance or if its activation primarily modulates motor tone.

Reviewer #1 (Public review):

Summary:

Artiushin et al. establish a comprehensive 3D atlas of the brain of the orb-web building spider Uloborus diversus. First, they use immunohistochemistry detection of synapsin to mark and reconstruct the neuropils of the brain of six specimen and they generate a standard brain by averaging these brains. Onto this standard 3D brain, they plot immunohistochemical stainings of major transmitters to detect cholinergic, serotonergic, octopaminergic/taryminergic and GABAergic neurons, respectively. Further, they add information on the expression of a number of neuropeptides (Proctolin, AllatostatinA, CCAP and FMRFamide). Based on this data and 3D reconstructions, they extensively describe the morphology of the entire synganglion, the discernable neuropils and their neurotransmitter/neuromodulator content.

Strengths:

While 3D reconstruction of spider brains and the detection of some neuroactive substances have been published before, this seems to be the most comprehensive analysis so far both in terms of number of substances tested and the ambition to analyzing the entire synganglion. Interestingly, besides the previously described neuropils, they detect a novel brain structure, which they call the tonsillar neuropil.

Immunohistochemistry, imaging and 3D reconstruction are convincingly done and the data is extensively visualized in figures, schemes and very useful films, which allow the reader to work with the data. Due to its comprehensiveness, this dataset will be a valuable reference for researchers working on spider brains or on the evolution of arthropod brains.

Weaknesses:

As expected for such a descriptive groundwork, new insights or hypotheses are limited while the first description of the tonsillar neuropil is interesting. The reconstruction of the main tracts of the brain would be a very valuable complementary piece of data.

Reviewer #1 (Public review):

Summary

In their manuscript, Ho and colleagues investigate the importance of thymically-imprinted self-reactivity in determining CD8 T cell pathogenicity in non-obese diabetic (NOD) mice. The authors describe pre-existing functional biases associated with naive CD8 T cell self-reactivity based on CD5 levels, a well characterized proxy for T cell affinity to self-peptide. They find that naive CD5hi CD8 T cells are poised to respond to antigen challenge; these findings are largely consistent with previously published data on the C57Bl/6 background. The authors go on to suggest that naive CD5hi CD8 T cells are more diabetogenic as 1) the CD5hi naive CD8 T cell receptor repertoire has features associated with autoreactivity and contains a larger population of islet-specific T cells, and 2) the autoreactivity of "CD5hi" monoclonal islet-specific TCR transgenic T cells cannot be controlled by phosphatase over-expression. Thus, they implicate CD8 T cells with relatively higher levels of basal self-reactivity in autoimmunity. The data presented offers valuable insights and sets the foundation for future studies, but some conclusions are not yet fully supported.

Specific comments

There is value in presenting phenotypic differences between naive CD5lo and CD5hi CD8 T cells in the NOD background as most previous studies have used T cells harvested from C57Bl/6 mice or peripheral blood from healthy human donors.

The comparison of a marker of self-reactivity, CD5 in this case, on broad thymocyte populations (DN/DP/CD8SP) is cautioned. CD5 is upregulated with signals associated with b-selection and positive selection; CD5 levels will thus vary even among subsets within these broad developmental intermediates. This is a particularly important consideration when comparing CD5 across thymic intermediates in polyclonal versus TCR transgenic thymocytes due to the striking differences in thymic selection efficiency, resulting in different developmental population profiles. The higher levels of CD5 noted in the DN population of NOD8.3 mice, for example, is likely due to the shift towards more mature DN4 post-b-selection cells. Similarly, in the DP population, the larger population of post-positive selection cells in the NOD8.3 transgenic thymus may also skew CD5 levels significantly. Overall, the reported differences between NOD and NOD8.3 thymocyte subsets could be due largely to differences in differentiation/maturation stage rather than affinity for self-antigen during T cell development. The authors have added some additional text to the revised manuscript that acknowledges some of these limitations.

The lack of differences in CD5 levels of post-positive selection DP thymocytes, CD8 SP thymocytes, and CD8 T cells in the pancreas draining lymph nodes from NOD vs NOD8.3 mice also raises questions about the relevance of this model to address the question of basal self-reactivity and diabetogenicity and the authors' conclusion that "that intrinsic high CD5-associated self-reactivity in NOD8.3 T cells overrides the transgenic Pep-mediated protection observed in dLPC/NOD mice"; the phenotype of the polyclonal and NOD8.3 TCR transgenic CD8 T cells that were analyzed in the (spleen and) pancreas draining lymph nodes is not clear (i.e., are these gated on naive T cells?). Furthermore, the rationale for the comparison with NOD-BDC2.5 mice that carry an MHC II-restricted TCR is unclear.

In reference to the conclusion that transgenic Pep phosphatase does not inhibit the diabetogenic potential of "CD5hi" CD8 T cells, there is some concern that comparing diabetes development in mice receiving polyclonal versus TCR transgenic T cells specific for an islet antigen is not appropriate. The increased frequency and number of antigen specific T cells in the NOD8.3 mice may be responsible for some of the observed differences. Further justification for the comparison is suggested.

The manuscript presents an interesting observation that TCR sequences from CD5hi CD8 T cells may share certain characteristics with diabetogenic T cells found in patients (e.g., CDR3 length), and that autoantigen-specific T cells may be enriched within the CD5hi naive CD8 T cell population. However, the percentage of tetramer-positive cells among naive CD8 T cells appears unusually high in the data presented, and caution is warranted when comparing additional T cell receptor features of self-reactivity/auto-reactivity between CD4 and CD8 T cells.

The counts for the KEGG enrichment pathways presented are relatively low, and the robustness of the analysis should be carefully considered, particularly given that several significance values appear borderline. That said, the differentially expressed genes among CD5lo and CD5hi CD8 T cells are generally consistent with previously published datasets.

The manuscript includes some imprecise wording that may be misleading. For example (not exhaustive): The strength of TCR reactivity to foreign antigen is not "contributed by basal TCR signal" per se but rather correlates with sub-threshold TCR signals necessary for T cell development and survival, CD5 is not broadly expressed on all B cells as the text might suggest but is restricted to a specific subset of B cells, some of the proximal signaling molecules downstream of the preTCR are different than for the mature TCR, upregulation of CD127 at early timepoints post T cell activation is not directly suggestive of their "heightened capabilities in memory T cell homeostasis", etc. The statement "Our study exclusively examined female mice because the disease modeled is relevant in females" should be reconsidered. While the use of female NOD mice can be justified by their higher incidence of diabetes than their male counterparts, the current wording could be misleading.

For clarity and transparency, please consider while additional information is provided in the revised manuscript, gating strategies are not always clear (i.e., naive versus total CD8 T cells), and the age/status of the mice from which cells are harvested (i.e., prediabetic?) is not consistently provided as far as this reviewer noted.

Reviewer #1 (Public review):

Summary:

In this study the authors use a Drosophila model to demonstrate that Tachykinin (Tk) expression is regulated by the microbiota. In Drosophila conventionally reared (CR) flies are typically shorter lived than those raised without a microbiota (axenic). Here, knockdown of Tk expression is found to prevent lifespan shortening by the microbiota and the reduction of lipid stores typically seen in CR flies when compared to axenic counterparts. It does so without reducing food intake or fecundity which are often seen as necessary trade-offs for lifespan extension. Further, the strength of the interaction between Tk and the microbiota is found to be bacteria specific and is stronger in Acetobacter pomorum (Ap) mono-associated flies compared to Levilactobacillus brevis (Lb) mono-association. The impact on lipid storage was also only apparent in Ap-flies.

Building on these findings the authors show that gut specific knockdown is largely sufficient to explain these phenotypes. Knockdown of the Tk receptor, TkR99D, in neurons recapitulates the lifespan phenotype of intestinal Tk knockdown supporting a model whereby Tk from the gut signals to TkR99D expressing neurons to regulate lifespan. In addition, the authors show that FOXO may have a role in lifespan regulation by the Tk-microbiota interaction. However, they rule out a role for insulin producing cells or Akh-producing cells suggesting the microbiota-Tk interaction regulates lifespan through other, yet unidentified, mechanisms.

Major comments:

Overall, I find the key conclusions of the paper convincing. The authors present an extensive amount of experimental work, and their conclusions are well founded in the data. In particular, the impact of TkRNAi on lifespan and lipid levels, the central finding in this study, has been demonstrated multiple times in different experiments and using different genetic tools. As a result, I don't feel that additional experimental work is necessary to support the current conclusions.

However, I find it hard to assess the robustness of the lifespan data from the other manipulations used (TkR99DRNAi, TkRNAi in dFoxo mutants etc.) because information on the population size and whether these experiments have been replicated is lacking. Can the authors state in the figure legends the numbers of flies used for each lifespan and whether replicates have been done? For all other data it is clear how many replicates have been done, and the methods give enough detail for all experiments to be reproduced.

Significance:

Overall, I find the key conclusions of the paper convincing. The authors present an extensive amount of experimental work, and their conclusions are well founded in the data. We have known that the microbiota influence lifespan for some time but the mechanisms by which they do so have remained elusive. This study identifies one such mechanism and as a result opens several avenues for further research. The Tk-microbiota interaction is shown to be important for both lifespan and lipid homeostasis, although it's clear these are independent phenotypes. The fact that the outcome of the Tk-microbiota interaction depends on the bacterial species is of particular interest because it supports the idea that manipulation of the microbiota, or specific aspects of the host-microbiota interaction, may have therapeutic potential.

These findings will be of interest to a broad readership spanning host-microbiota interactions and their influence on host health. They move forward the study of microbial regulation of host longevity and have relevance to our understanding of microbial regulation of host lipid homeostasis. They will also be of significant interest to those studying the mechanisms of action and physiological roles of Tachykinins.

Field of expertise: Drosophila, gut, ageing, microbiota, innate immunity

Reviewer #1 (Public review):

Summary:

The manuscript by Hernandez-Nunez et al. provides a comprehensive characterization of how heart-brain circuits develop in a vertebrate brain, namely the zebrafish. The characterization is performed using a combination of modern and sophisticated imaging and neural manipulation techniques and achieves unprecedented clarity and detail in how the heart-brain communication develops early in life. The paper describes a three-stage program, where first an efferent-circuit from the motor vagus to the heart develops, followed by sympathetic innervation, and lastly sensory neurons innervate the heart.

Strengths:

The paper is very clearly and nicely written. The findings are novel and of high quality and relevance. The presentations are very clear and nicely interpreted. The analyses are well presented and applied.

Weaknesses:

From the heart rate traces, heart rate variability seems to be prominent and changes across days post-fertilization (dpf). That would be a useful dependent variable, considering that the variation captured by the models does not fully explain heart rate, both for sympathetic and parasympathetic efferents. Given the strong autorhythmicity of nodal tissue in neurogenic hearts, modulatory inputs could potentially predict heart rate variability with higher precision.

Reviewer #1 (Public review):

Summary:

This paper demonstrates the first application of voltage imaging using a genetically encoded voltage indicator, ArcLight, for recording the spontaneous activity of the developing spinal cord in zebrafish. This technology enabled better temporal resolution compared to what has been demonstrated with calcium imaging in past studies (Muto et al., 2011; Warp et al., 2012; Wan et al., 2019 ), which led to the discovery of the maturation process of "firing" shapes in spinal neurons. This maturation process occurs simultaneously with axonal elongation and network integration. Thus, voltage imaging revealed new biological details of the development of the spinal circuits.

Strengths:

The use of voltage imaging instead of calcium imaging revealed biological details of the functional maturation of spinal cord neurons in developing zebrafish.

Weaknesses:

This manuscript lacks many basic components and explanations necessary for understanding the methodologies used in this study.

Reviewer #1 (Public review):

Summary:

The manuscript reported a method for deep brain imaging with a GRIN lens that combines "low-NA telecentric scanning (LNTS) of laser excitation with high-NA fluorescence collection" to achieve a larger FOV than conventional approaches.

Strengths:

The manuscript presented in vivo structural images and calcium activity results in side-by-side comparison to wide-field epi fluorescence imaging through a GRIN lens and two-photon scanning imaging.

Weaknesses:

(1) Lack of sufficient technique information on the "high-NA (1.0) fluorescence collection". Is it custom-made or an off-the-shelf component? The only optical schematic, Figure 1, shows two lenses and a Si-PMT as the collection apparatus. There is no information about the lenses and the spacing between each component.

(2) There is no discussion about the speed limitation of the LNTS method, which, as a scanning-based method, is limited by the scanner speed. At a 10 Hz frame rate, the LNTS, although it has a better FOV, is much slower than widefield fluorescence imaging. The 10 Hz speed is not sufficient for some fast calcium activities.

(3) Supplementary Figure 5 is irrelevant to the main claim of the manuscript. This is a preliminary simulation related to the authors' proposed future work.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses / Areas for Clarification:

A few points would benefit from clarification, additional analysis, or revised presentation:

(1) It would help readers to see a concrete demonstration of the trade-off between NDT and collapsing thresholds, to give a sense of the scale of the identifiability problem motivating the work.

(2) Before moving to the empirical datasets, the manuscript really needs a simulation-based model-recovery comparison, since all major conclusions of the empirical applications rely on model comparison. One approach might be to simulate from (a) an FT model with across-trial drift variability and (b) one of the CT models, then fit both models to each of the simulated data sets. This would address a longstanding issue: sometimes CT models are preferred even when the estimated collapse in the thresholds is close to zero. A recovery study would confirm that model selection behaves sensibly in the new framework.

(3) An additional subtle point is that BIC is defined in terms of the maximised log-likelihood of the model for the data being modelled. In the joint model, the parameter estimates maximise the combined likelihood of behavioural and non-decision-time data. This means the behavioural log-likelihood evaluated at the joint MLEs is not the behavioural MLE. If BIC is being computed for the behavioural data only, this breaks the assumptions underlying BIC. The only valid BIC here would be one defined for the joint model using the joint likelihood.

(4) Table 1 sets up the Study 1 comparisons, but there's no row for the FT model. Similarly, Figures 10 and 13 would be more informative if they included FT predictions. This matters because, in Study 1, the FT model appears to fit aggregate accuracy better than the BIC-preferred collapsing model, currently shown only in Appendix 5. Some discussion of why would strengthen the argument.

(5) In Figure 7, the degree of decay underestimation is obscured by using a density plot rather than a scatterplot, consistent with the other panels of the same figure. Presenting it the same way would make the mis-recovery more transparent. The accompanying text may also need clarification: when data are generated from an FT model with across-trial drift variability, the NDT-informed model seems to infer FT boundaries essentially. If that's correct, the model must be misfitting the simulated data. This is actually a useful result as it suggests across-trial drift variability in FT models is discriminable from collapsing-threshold models. It would be good to make this explicit.

(6) Given the large recovery advantage of the exponential NDT-informed function over the hyperbolic one, the authors may want to consider whether the results favour adopting the former more generally. Given these findings, I would consider recommending the exponential NDT-informed model for future use.

(7) In Study 2 (Figure 13), all models qualitatively miss an interesting empirical pattern: under speed emphasis, errors are faster than corrects, while under accuracy emphasis, errors become slower. The error RT distribution in the speed condition is especially poorly captured. It would be helpful for the authors to comment, as it suggests that something theoretically relevant is missing from all models tested.

(8) The threshold visualisations extend to 3 seconds, yet both datasets show decisions mostly finishing by ~1.5 seconds. Shortening the x-axis would better reflect the empirical RT distributions and avoid unintentionally overstating the timescale of the empirical decision processes.

Reviewer #1 (Public review):

Summary:

Two major breakthroughs in the field of arbuscular mycorrhiza (AM) were the discoveries that first AM fungi obtain lipids (not only carbohydrates) from their plant hosts (Bravo et al 2017; Jiang et al 2017; Keymer et al 2017; Luginbuehl et al 2017) and second that presumably obligate biotrophic AM fungi can produce spores in the absence of host plants when exposed to myristate (Sugiura et al 2020; Tanaka et al 2022).

For this manuscript, Chen et al asked the question of whether myristate in the soil may also play a role in AM symbiosis when AM fungi live in symbiosis with their plant hosts. They show that myristate occurs in natural as well as agricultural soils, probably as a component of root exudates. Further, they treat AM fungi with myristate when grown in symbiosis in a Petri dish system with carrot hairy roots or in pots with alfalfa or rice to describe which effect the exogenous myristate has on symbiosis. Using 13C labelling, they show that myristate is taken up by AM fungi, although they can obtain sugars and lipids from the plant host. They also show that myristate leads to an increase in root colonization as well as expression of fungal genes involved in FA assimilation.

Interestingly, the effect of myristate on colonization depends on the plant species and the level of phosphate fertilization provided to the plant. The reason for this remains unknown.

Strengths:

The findings are interesting and provide an advance in our understanding of lipid use by the extraradical mycelium of AM fungi.

Weaknesses:

However, there are some misconceptions in the writing, and some experimental results remain poorly clear as they are presented in a highly descriptive manner without interpretation or explanation.

Reviewer #1 (Public review):

Summary:

In this work, Okell et al. describe the imaging protocol and analysis pipeline pertaining to the arterial spin labeling (ASL) MRI protocol acquired as part of the UK Biobank imaging study. In addition, they present preliminary analyses of the first 7000+ subjects in whom ASL data were acquired, and this represents the largest such study to date. Careful analyses revealed expected associations between ASL-based measures of cerebral hemodynamics and non-imaging-based markers, including heart and brain health, cognitive function, and lifestyle factors. As it measures physiology and not structure, ASL-based measures may be more sensitive to these factors compared with other imaging-based approaches.

Strengths:

This study represents the largest MRI study to date to include ASL data in a wide age range of adult participants. The ability to derive arterial transit time (ATT) information in addition to cerebral blood flow (CBF) is a considerable strength, as many studies focus only on the latter.

Some of the results (e.g., relationships with cardiac output and hypertension) are known and expected, while others (e.g., lower CBF and longer ATT correlating with hearing difficulty in auditory processing regions) are more novel and intriguing. Overall, the authors present very interesting physiological results, and the analyses are conducted and presented in a methodical manner.

The analyses regarding ATT distributions and the potential implications for selecting post-labeling delays (PLD) for single PLD ASL are highly relevant and well-presented.

Weaknesses:

At a total scan duration of 2 minutes, the ASL sequence utilized in this cohort is much shorter than that of a typical ASL sequence (closer to 5 minutes as mentioned by the authors). However, this implementation also included multiple (n=5) PLDs. As currently described, it is unclear how any repetitions were acquired at each PLD and whether these were acquired efficiently (i.e., with a Look-Locker readout) or whether individual repetitions within this acquisition were dedicated to a single PLD. If the latter, the number of repetitions per PLD (and consequently signal-to-noise-ratio, SNR) is likely to be very low. Have the authors performed any analyses to determine whether the signal in individual subjects generally lies above the noise threshold? This is particularly relevant for white matter, which is the focus of several findings discussed in the study.

Hematocrit is one of the variables regressed out in order to reduce the effect of potential confounding factors on the image-derived phenotypes. The effect of this, however, may be more complex than accounting for other factors (such as age and sex). The authors acknowledge that hematocrit influences ASL signal through its effect on longitudinal blood relaxation rates. However, it is unclear how the authors handled the fact that the longitudinal relaxation of blood (T1Blood) is explicitly needed in the kinetic model for deriving CBF from the ASL data. In addition, while it may reduce false positives related to the relationships between dietary factors and hematocrit, it could also mask the effects of anemia present in the cohort. The concern, therefore, is two-fold: (1) Were individual hematocrit values used to compute T1Blood values? (2) What effect would the deconfounding process have on this?

The authors leverage an observed inverse association between white matter hyperintensity volume and CBF as evidence that white matter perfusion can be sensitively measured using the imaging protocol utilized in this cohort. The relationship between white matter hyperintensities and perfusion, however, is not yet fully understood, and there is disagreement regarding whether this structural imaging marker necessarily represents impaired perfusion. Therefore, it may not be appropriate to use this finding as support for validation of the methodology.

Reviewer #2 (Public review):

Summary:

This is an interesting study exploring methods for reconstructing visual stimuli from neural activity in the mouse visual cortex. Specifically, it uses a competition dataset (published in the Dynamic Sensorium benchmark study) and a recent winning model architecture (DNEM, dynamic neural encoding model) to recover visual information stored in ensembles of mouse visual cortex.

Strengths:

This is a great start for a project addressing visual reconstruction. It is based on physiological data obtained at a single-cell resolution, the stimulus movies were reasonably naturalistic and representative of the real world, the study did not ignore important correlates such as eye position and pupil diameter, and of course, the reconstruction quality exceeded anything achieved by previous studies. There appear to be no major technical flaws in the study, and some potential confounds were addressed upon revision. The study is an enjoyable read.

Weaknesses:

The study is technically competent and benchmark-focused, but without significant conceptual or theoretical advances. The inclusion of neuronal data broadens the study's appeal, but the work does not explore potential principles of neural coding, which limits its relevance for neuroscience and may create some disappointment to some neuroscientists. The authors are transparent that their goal was methodological rather than explanatory, but this raises the question of why neuronal data were necessary at all, as more significant reconstruction improvements might be achievable using noise-less artificial video encoders alone (network-to-network decoding approaches have been done well by teams such as Han, Poggio, and Cheung, 2023, ICML). Yet, even within the methodological domain, the study does not articulate clear principles or heuristics that could guide future progress. The finding that more neurons improve reconstruction aligns with well-established results in the literature that show that higher neuronal numbers improve decoding in general (for example, Hung, Kreiman, Poggio, and DiCarlo, 2005) and thus may not constitute a novel insight.

Specific issues:

(1) The study showed that it could achieve high-quality video reconstructions from mouse visual cortex activity using a neural encoding model (DNEM), recovering 10-second video sequences and approaching a two-fold improvement in pixel-by-pixel correlation over attempts. As a reader, I was left with the question: okay, does this mean that we should all switch to DNEM for our investigations of mouse visual cortex? What makes this encoding model special? It is introduced as "a winning model of the Sensorium 2023 competition which achieved a score of 0.301...single trial correlation between predicted and ground truth neuronal activity," but as someone who does not follow this competition (most eLife readers are not likely to do so, either), I do not know how to gauge my response. Is this impressive? What is the best theoretical score, given noise and other limitations? Is the model inspired by the mouse brain in terms of mechanisms or architecture, or was it optimized to win the competition by overfitting it to the nuances of the data set? Of course, I know that as a reader, I am invited to read the references, but the study would stand better on its own, if it clarified how its findings depended on this model.

The revision helpfully added context to the Methods about the range of scores achieved by other models, but this information remains absent from the Abstract and other important sections. For instance, the Abstract states, "We achieve a pixel-level correlation of 0.57 between the ground truth movie and the reconstructions from single-trial neural responses," yet this point estimate (presented without confidence intervals or comparisons to controls) lacks meaning for readers who are not told how it compares to prior work or what level of performance would be considered strong. Without such context, the manuscript undercuts potentially meaningful achievements.

(2) Along those lines, the authors conclude that "the number of neurons in the dataset and the use of model ensembling are critical for high-quality reconstructions." If true, these principles should generalize across network architectures. I wondered whether the same dependencies would hold for other network types, as this could reveal more general insights. The authors replied that such extensions are expected (since prior work has shown similar effects for static images) but argued that testing this explicitly would require "substantial additional work," be "impractical," and likely not produce "surprising results." While practical difficulty alone is not a sufficient reason to leave an idea untested, I agree that the idea that "more neurons would help" would be unsurprising. The question then becomes: given that this is a conclusion already in the field, what new principle or understanding has been gained in this study?

(3) One major claim was that the quality of the reconstructions depended on the number of neurons in the dataset. There were approximately 8000 neurons recorded per mouse. The correlation difference between the reconstruction achieved by 1000 neurons and 8000 neurons was ~0.2. Is that a lot or a little? One might hypothesize that 7000 additional neurons could contribute more information, but perhaps, those neurons were redundant if their receptive fields are too close together or if they had the same orientation or spatiotemporal tuning. How correlated were these neurons in response to a given movie? Why did so many neurons offer such a limited increase in correlation? Originally, this question was meant to prompt deeper analysis of the neural data, but the authors did not engage with it, suggesting a limited understanding of the neuronal aspects of the dataset.

(4) We appreciated the experiments testing the capacity of the reconstruction process, by using synthetic stimuli created under a Gaussian process in a noise-free way. But this originally further raised questions: what is the theoretical capability for reconstruction of this processing pipeline, as a whole? Is 0.563 the best that one could achieve given the noisiness and/or neuron count of the Sensorium project? What if the team applied the pipeline to reconstruct the activity of a given artificial neural network's layer (e.g., some ResNet convolutional layer), using hidden units as proxies for neuronal calcium activity? In the revision, this concern was addressed nicely in the review in Supplementary Figure 3C. Also, one appreciates that as a follow up, the team produced error maps (New Figure 6) that highlight where in the frames the reconstruction are likely to fail. But the maps went unanalyzed further, and I am not sure if there was a systematic trend in the errors.

(5) I was encouraged by Figure 4, which shows how the reconstructions succeeded or failed across different spatial frequencies. The authors note that "the reconstruction process failed at high spatial frequencies," yet it also appears to struggle with low spatial frequencies, as the reconstructed images did not produce smooth surfaces (e.g., see the top rows of Figures 4A and 4B). In regions where one would expect a single continuous gradient, the reconstructions instead display specular, high-frequency noise. This issue is difficult to overlook and might deserve further discussion.

Reviewer #1 (Public review):

This manuscript introduces a biologically informed RNN (bioRNN) that predicts the effects of optogenetic perturbations in both synthetic and in vivo datasets. By comparing standard sigmoid RNNs (σRNNs) and bioRNNs, the authors make a compelling case that biologically grounded inductive biases improve generalization to perturbed conditions. This work is innovative, technically strong, and grounded in relevant neuroscience, particularly the pressing need for data-constrained models that generalize causally.

Comments on revisions:

The authors have addressed all my concerns.

Reviewer #1 (Public review):

Summary:

The authors analyzed the expression of ATAD2 protein in post-meiotic stages and characterized the localization of various testis-specific proteins in the testis of the Atad2 knockout (KO). By cytological analysis as well as the ATAC sequencing, the study showed that increased levels of HIRA histone chaperone, accumulation of histone H3.3 on post-meiotic nuclei, defective chromatin accessibility and also delayed deposition of protamines. Sperm from the Atad2 KO mice reduces the success of in vitro fertilization. The work was performed well, and most of the results are convincing. However, this manuscript does not suggest a molecular mechanism for how ATAD2 promotes the formation of testis-specific chromatin.

Strengths:

The paper describes the role of ATAD2 AAA+ ATPase in the proper localization of sperm-specific chromatin proteins such as protamine, suggesting the importance of the DNA replication-independent histone exchanges with the HIRA-histone H3.3 axis.

Weaknesses:

The work was performed well, and most of the results are convincing. However, this manuscript does not suggest a molecular mechanism for how ATAD2 promotes the formation of testis-specific chromatin.

Reviewer #3 (Public review):

Summary:

This paper presents a timely and significant contribution to the study of lysine acetoacetylation (Kacac). The authors successfully demonstrate a novel and practical chemo-immunological method using the reducing reagent NaBH4 to transform Kacac into lysine β-hydroxybutyrylation (Kbhb).

Strengths:

This innovative approach enables simultaneous investigation of Kacac and Kbhb, showcasing their potential in advancing our understanding of post-translational modifications and their roles in cellular metabolism and disease.

Weaknesses:

The study lacks supporting in vivo data, such as gene knockdown experiments, to validate the proposed conclusions at the cellular level.

Reviewer #1 (Public review):

Summary:

In this manuscript, Subhramanian et al. carefully examined how microglia adapt their surveillance strategies during chronic neurodegeneration, specifically in prion-infected mice. The authors used ex vivo time-lapse imaging and in vitro strategies and found that reactive microglia adopt a highly mobile, "kiss-and-ride" behavior, contrasting the more static surveillance typical of homeostatic microglia. The manuscript provides fundamental mechanistic insights into the dynamics of microglia-neuron interactions, implicates P2Y6 signaling in regulating mobility, and suggests that intrinsic reprogramming of microglia might underlie this behavior, the conclusions are therefore compelling.

Strengths:

(1) The novelty of the study is high, particularly the demonstration that microglia lose territorial confinement and dynamically migrate from neuron to neuron under chronic neurodegeneration.

(2) The possible implications of a stimulus-independent high mobility in reactive microglia are particularly striking. Although this is not fully explored.

(3) The use of time-lapse imaging in organotypic slices rather than overexpression models provided a more physiological approach.

(4) Microglia-neuron interactions in neurodegeneration have broad implications for understanding the progression of diseases, such as Alzheimer's and Parkinson's, that are associated with chronic inflammation.

Weaknesses:

Previous weaknesses were addressed.

Reviewer #1 (Public review):

Summary:

Siddiqui et al., investigate the question of how bacterial metabolism contributes to the attraction of C. elegans to specific bacteria. They show that C. elegans prefers three bacterial species when cultured in a leucine-enriched environment. These bacterial species release more isoamyl alcohol, a known C. elegans attractant, when cultured with leucine supplement than without leucine supplement. The study shows correlative evidence that isoamyl alcohol is produced from leucine by the Ehrlich pathway. In addition, they show that SNIF-1 is a receptor for isoamyl alcohol because a null mutant of this receptor exhibits lower chemotaxis to isoamyl alcohol and that chemotaxis to isoamyl alcohol is rescued by expression of snif-1 in AWC.

Strengths:

(1) This study takes a creative approach to examine the question of what specific volatile chemicals released by bacteria may signify to C. elegans by examining both bacterial metabolism and C. elegans preference behavior. Although C. elegans has long been known to be attracted to bacterial metabolites, this study may be one of the first to examine the possible role of a specific bacterial metabolic pathway in mediating attraction.

(2) A strength of the paper is the identification of SNIF-1 as a receptor for isoamyl alcohol. The ligands for very few olfactory receptors have been identified in C. elegans and so this is a significant addition to the field. The SNIF-1 null mutant strain will likely be a useful reagent for many labs examining olfactory and foraging behaviors.

Weaknesses:

(1) The authors write that the leucine metabolism via the Ehrlich pathway is required for production of isoamyl alcohol by three bacteria (CEent1, JUb66, BIGb0170), but their evidence for this is correlation and not causation. They show that the gene, ilvE (which is part of the Ehrlich pathway) is upregulated in CEent1 bacteria upon exposure to leucine. Although this indicates that the ilvE gene may be involved in leucine metabolism, it does not show causation. To show causation, they need to knockout ilvE from one of these strains, show that the bacteria does not have increased isoamyl alcohol production when cultured on leucine, and that the bacteria is no longer attractive to C. elegans.

(2) Although the authors do show that the three bacterial strains they focus on (CEent1, JUb66, and BIGb0170) are more attractive to C. elegans when supplemented with leucine. Some other strains such as BIGb0393 are also more attractive with leucine supplementation and produce isoamyl alcohol (Fig 1B and Supp Table 2). It is unclear why these other strains are not included with the selected three strains.

(3) The behavioral evidence that snif-1 gene encodes a receptor for isoamyl alcohol is compelling because of the mutant phenotype and rescue experiments. The evidence would be stronger with calcium imaging of AWC neurons in response to isoamyl alcohol in the receptor mutant with the expectation that the response would be reduced or abolished in the mutant compared to wildtype.

Reviewer #1 (Public review):

Summary:

This work by Al-Jezani et al. focused on characterizing clonally derived MSC populations from the synovium of normal and osteoarthritis (OA) patients. This included characterizing the cell surface marker expression in situ (at time of isolation), as well as after in vitro expansion. The group also tried to correlate marker expression with trilineage differential potential. They also tested the ability of the different sub-populations for their efficacy in repairing cartilage in a rat model of OA. The main finding of the study is that CD47hi MSCs may have a greater capacity to repair cartilage than CD47lo MSCs, suggesting that CD47 may be a novel marker of human MSCs that have enhanced chondrogenic potential.

Strengths:

Studies on cell characterization of the different clonal populations isolated indicate that the MSC are heterogenous and traditional cell surface markers for MSCs do not accurately predict the differentiation potential of MSCs. While this has been previously established in the field of MSC therapy, the authors did attempt to characterize clones derived from single cells, as well as evaluate the marker profile at the time of isolation. While the outcome of heterogeneity is not surprising, the methods used to isolate and characterze the cells were well developed. The interesting finding of the study is the identification of CD47 as a potential MSC marker that could be related to chondrogenic potential. The authors suggest that MSCs with high CD47 repaired cartilage more effectively than MSC with low CD47 in a rat OA model.

Comments on revisions:

Thank you for addressing the comments from the first review. No additional revisions.

Reviewer #1 (Public review):

The authors used fluorescence microscopy, image analysis, and mathematical modeling to study the effects of membrane affinity and diffusion rates of MinD monomer and dimer states on MinD gradient formation in B. subtilis. To test these effects, the authors experimentally examined MinD mutants that lock the protein in specific states, including Apo monomer (K16A), ATP-bound monomer (G12V) and ATP-bound dimer (D40A, hydrolysis defective), and compared to wild-type MinD. Overall, the experimental results support the conclusions that reversible membrane binding of MinD is critical for the formation of the MinD gradient, but the binding affinities between monomers and dimers are similar.

The modeling part is a new attempt to use the Monte Carlo method to test the conditions for the formation of the MinD gradient in B. subtilis. The modeling results provide good support for the observations and find that the MinD gradient is sensitive to different diffusion rates between monomers and dimers. This simulation is based on several assumptions and predictions, which raises new questions that need to be addressed experimentally in the future.

Reviewer #1 (Public review):

Summary:

This manuscript investigates the biological mechanism underlying the assembly and transport of the AcrAB-TolC efflux pump complex. By combining endogenous protein purification with cryo-EM analysis, the authors show that the AcrB trimer adopts three distinct conformations simultaneously and identify a previously uncharacterized lipoprotein, YbjP, as a potential additional component of the complex. The work aims to advance our understanding of the AcrAB-TolC efflux system in near-native conditions and may have broader implications for elucidating its physiological mechanism.

Strengths:

Overall, the manuscript is clearly presented, and several of the datasets are of high quality. The use of natively isolated complexes is a major strength, as it minimizes artifacts associated with reconstituted systems and enables the discovery of a novel subunit. The authors also distinguish two major assemblies-the TolC-YbjP sub-complex and the complete pump-which appear to correspond to the closed and open channel states, respectively. The conceptual advance is potentially meaningful, and the findings could be of broad interest to the field.

Weaknesses:

(1) As the identification of YbjP is a key contribution of this work, a deeper comparison with functional "anchor" proteins in other efflux pumps is needed. Including an additional supplementary figure illustrating these structural comparisons would be valuable.

(2) The observation of the LTO states in the presence of TolC represents an important extension of previous findings. A more detailed discussion comparing these LTO states to those reported in earlier structural and biochemical studies would improve the clarity and significance of this point.

Reviewer #1 (Public review):

Summary:

The manuscript by Lu and colleagues demonstrates convincingly that PRRT2 interacts with brain voltage-gated sodium channels to enhance slow inactivation in vitro and in vivo. The work is interesting and rigorously conducted. The relevance to normal physiology and disease pathophysiology (e.g., PRRT2-related genetic neurodevelopmental disorders) seems high. Some simple additional experiments could elevate the impact and make the study more complete.

Strengths:

Experiments are conducted rigorously, including experimenter blinding and appropriate controls. Data presentation is excellent and logical. The paper is well written for a general scientific audience.

Weaknesses:

There are a few missing experiments and one place where data are over-interpreted.

(1) An in vitro study of Nav1.6 is conspicuously absent. In addition to being a major brain Na channel, Nav1.6 is predominant in cerebellar Purkinje neurons, which the authors note lack PRRT2 expression. They speculate that the absence of PRRT2 in these neurons facilitates the high firing rate. This hypothesis would be strengthened if PRRT2 also enhanced slow inactivation of Nav1.6. If a stable Nav1.6 cell were not available, then simple transient co-transfection experiments would suffice.

(2) To further demonstrate the physiological impact of enhanced slow inactivation, the authors should consider a simple experiment in the stable cell line experiments (Figure 1) to test pulse frequency dependence of peak Na current. One would predict that PRRT2 expression will potentiate 'run down' of the channels, and this finding would be complementary to the biophysical data.

(3) The study of one K channel is limited, and the conclusion from these experiments represents an over-interpretation. I suggest removing these data unless many more K channels (ideally with measurable proxies for slow inactivation) were tested. These data do not contribute much to the story.

(4) In Figure 2, the authors should confirm that protein is indeed expressed in cells expressing each truncated PRRT2 construct. Absent expression should be ruled out as an explanation for absent enhancement of slow inactivation.

Reviewer #1 (Public review):

Summary:

This work provides valuable new insights into the Paleocene Asian mammal recovery and diversification dynamics during the first ten million years post-dinosaur extinction. Studies that have examined the mammalian recovery and diversification post-dinosaur extinction have primarily focused on the North American mammal fossil record, and it's unclear if patterns documented in North America are characteristic of global patterns. This study examines dietary metrics of Paleocene Asian mammals and found that there is a body size disparity increase before dietary niche expansion and that dietary metrics track climatic and paleobotanical trends of Asia during the first 10 million years after the dinosaur extinction.

Strengths:

The Asian Paleocene mammal fossil record is greatly understudied, and this work begins to fill important gaps. In particular, the use of interdisciplinary data (i.e., climatic and paleobotanical) is really interesting in conjunction with observed dietary metric trends.

Weaknesses:

While this work has the potential to be exciting and contribute greatly to our understanding of mammalian evolution during the first 10 million years post-dinosaur extinction, the major weakness is in the dental topographic analysis (DTA) dataset.

There are several specimens in Figure 1 that have broken cusps, deep wear facets, and general abrasion. Thus, any values generated from DTA are not accurate and cannot be used to support their claims. Furthermore, the authors analyze all tooth positions at once, which makes this study seem comprehensive (200 individual teeth), but it's unclear what sort of noise this introduces to the study. Typically, DTA studies will analyze a singular tooth position (e.g., Pampush et al. 2018 Biol. J. Linn. Soc.), allowing for more meaningful comparisons and an understanding of what value differences mean. Even so, the dataset consists of only 48 specimens. This means that even if all the specimens were pristinely preserved and generated DTA values could be trusted, it's still only 48 specimens (representing 4 different clades) to capture patterns across 10 million years. For example, the authors note that their results show an increase in OPCR and DNE values from the middle to the late Paleocene in pantodonts. However, if a singular tooth position is analyzed, such as the lower second molar, the middle and late Paleocene partitions are only represented by a singular specimen each. With a sample size this small, it's unlikely that the authors are capturing real trends, which makes the claims of this study highly questionable.

Reviewer #1 (Public review):

Summary:

This is an interesting and useful review highlighting the complex pathways through which pulmonary colonisation or infection with Mycobacterium tuberculosis (Mtb) may progress to develop symptomatic disease and transmit the pathogen. I found the section on immune correlates associated with individuals who have clearly been exposed to and reacted to Mtb but did not develop latent infections particularly valuable. However, several aspects would benefit from clarification.

Strengths:

The main strengths lie in the arguments presented for a multiplicity of immune pathways to TB disease.

Weaknesses:

The main weaknesses lie in clarity, particularly in the precise meanings of the three figures.

I accept that there is a 'goldilocks zone' that underpins the majority of TB cases we see and predominantly reflects different patterns of immune response, but the analogies used need to be more clearly thought through.

Reviewer #1 (Public review):

In this important study, the authors characterized the transformation of neural representations of olfactory stimuli from primary sensory cortex to multisensory regions in the medial temporal lobe and investigated how they were affected by non-associative learning. The authors used high-density silicon probe recordings from five different cortical regions while familiar vs. novel odors were presented to a head-restrained mouse. This is a timely study because unlike other sensory systems (e.g., vision), the progressive transformation of olfactory information is still poorly understood. The authors report that both odor identity and experience are encoded by all of these five cortical areas but nonetheless, some themes emerge. Single neuron tuning of odor identity is broad in the sensory cortices but becomes narrowly tuned in hippocampal regions. Furthermore, while experience affects neuronal response magnitudes in early sensory cortices, it changes the proportion of active neurons in hippocampal regions. Thus, this study is an important step forward in the ongoing quest to understand how olfactory information is progressively transformed along the olfactory pathway.

The study is well-executed. The direct comparison of neuronal representations from five different brain regions is impressive. Conclusions are based on single neuronal level as well as population level decoding analyses. Among all the reported results, one stands out for being remarkably robust. The authors show that the anterior olfactory nucleus (AON), which receives direct input from the olfactory bulb output neurons, was far superior at decoding odor identity as well as novelty compared to all the other brain regions. This is perhaps surprising because the other primary sensory region - the piriform cortex - has been thought to be the canonical site for representing odor identity. A vast majority of studies have focused on aPCx, but direct comparisons between odor coding in the AON and aPCx are rare. The experimental design of this current study allowed the authors to do so and the AON was found to convincingly outperform aPCx. Although this result goes against the canonical model, it is consistent with a few recent studies including one that predicted this outcome based on anatomical and functional comparisons between the AON-projecting tufted cells vs. the aPCx-projecting mitral cells in the olfactory bulb.

Future experiments are needed to probe the circuit mechanisms underlying the differential importance of the two primary olfactory cortices, as well as their potential causal roles in odor identification. Moreover, future work should test whether the decoding accuracy of odor identity and experience from neural data (as reported here) can predict the causal contributions of these regions, as revealed through perturbations during behavioral tasks that explicitly probe odor identification and/or experience.

Reviewer #1 (Public review):

Summary:

This manuscript describes critical intermediate reaction steps of a HA synthase at the molecular level; specifically, it examines the 2nd step, polymerization, adding GlcA to GlcNAc to form the initial disaccharide of the repeating HA structure. Unlike the vast majority of known glycosyltransferases, the viral HAS (a convenient proxy extrapolated to resemble the vertebrate forms) uses a single pocket to catalyze both monosaccharide transfer steps. The authors' work illustrates the interactions needed to bind & proof-read the UDP-GlcA using direct and '2nd layer' amino acid residues. This step also allows the HAS to distinguish the two UDP-sugars; this is very important as the enzymes are not known or observed to make homopolymers of only GlcA or GlcNAc, but only make the HA disaccharide repeats GlcNAc-GlcA.

Strengths:

Overall, the strengths of this paper lie in its techniques & analysis.

The authors make significant leaps forward towards understanding this process using a variety of tools and comparisons of wild-type & mutant enzymes. The work is well presented overall with respect to the text and illustrations (especially the 3D representations), and the robustness of the analyses & statistics is also noteworthy.

Furthermore, the authors make some strides towards creating novel sugar polymers using alternative primers & work with detergent binding to the HAS. The authors tested a wide variety of monosaccharides and several disaccharides for primer activity and observed that GlcA could be added to cellobiose and chitobiose, which are moderately close structural analogs to HA disaccharides. Did the authors also test the readily available HA tetramer (HA4, [GlcA-GlcNAc]2) as a primer in their system? This is a highly recommended experiment; if it works, then this molecule may also be useful for cryo-EM studies of CvHAS as well.

Weaknesses:

In the past, another report describing the failed attempt of elongating short primers (HA4 & chitin oligosaccharides larger than the cello- or chitobiose that have activity in this report) with a vertebrate HAS, XlHAS1, an enzyme that seems to behave like the CvHAS ( https://pubmed.ncbi.nlm.nih.gov/10473619/); this work should probably be cited and briefly discussed. It may be that the longer primers in the 1999 paper and/or the different construct or isolation specifics (detergent extract vs crude) were not conducive to the extension reaction, as the authors extracted recombinant enzyme.

There are a few areas that should be addressed for clarity and correctness, especially defining the class of HAS studied here (Class I-NR) as the results may (Class I-R) or may not (Class II) align (see comment (a) below), but overall, a very nicely done body of work that will significantly enhance understanding in the field.

Reviewer #2 (Public review):

Summary:

This study investigated whether the identity of a peripheral saccade target object is fed back to the foveal retinotopic cortex during saccade preparation, a critical prediction of the foveal prediction hypothesis proposed by Kroell & Rolfs (2022). To achieve this, the authors leveraged a gaze-contingent fMRI paradigm, where the peripheral saccade target was removed before the eyes landed near it, and used multivariate decoding analysis to quantify identity information in the foveal cortex. The results showed that the identity of the saccade target object can be decoded based on foveal cortex activity, despite the fovea never directly viewing the object, and that the foveal feedback representation was similar to passive viewing and not explained by spillover effects. Additionally, exploratory analysis suggested IPS as a candidate region mediating such foveal decodability. Overall, these findings provide neural evidence for the foveal cortex processing the features of the saccade target object, potentially supporting the maintenance of perceptual stability across saccadic eye movements.

Strengths:

This study is well-motivated by previous theoretical findings (Kroell & Rolfs, 2022), aiming to provide neural evidence for a potential neural mechanism of trans-saccadic perceptual stability. The question is important, and the gaze-contingent fMRI paradigm is a solid methodological choice for the research goal. The use of stimuli allowing orthogonal decoding of stimulus category vs stimulus shape is a nice strength, and the resulting distinctions in decoded information by brain region are clean. The results will be of interest to readers in the field, and they fill in some untested questions regarding pre-saccadic remapping and foveal feedback.

Weaknesses:

The authors have done a nice job addressing the previous weaknesses. The remaining weaknesses / limitations are appropriately discussed in the manuscript. E.g., the use of only 4 unique stimuli in the experiment. The findings are intriguing and relevant to saccadic remapping and foveal feedback, but somewhat limited in terms of the ability to draw theoretical distinctions between these related phenomena.

Specifics:

The revised manuscript is much improved in terms of framing and discussion of the prior literature, and the theoretical claims are now stated with appropriate nuance.

I have two remaining minor suggestions/comments, which the authors may optionally respond to:

(1) In the parametric modulation analysis, the authors' additional analyses nicely addresses my concern and strengthens the claim. However, the description in the revised manuscript (Pg 7 Ln 190-191) is minimal and may be difficult to grasp what the control analysis is about and how it rules out alternative explanations to the IPS findings. The authors may wish to elaborate on the description in the text.

(2) Out of curiosity (not badgering): The authors argued that the findings of Harrison et al. (2013) and Szinte et al. (2015) can be explained by feature integration between the currently attended location and its future, post-saccadic location. Couldn't the same argument apply in the current paradigm, where attention at the saccade target gets remapped to the pre-saccadic fovea (see also Rolfs et al., 2011 Fig 5), thus leading to the observed feature remapping?

Reviewer #2 (Public review):

Summary:

The author proposes a novel method for mapping single-cell data to specific locations with higher resolution than several existing tools.

Strengths:

The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus.

Comments on revised version:

I have no additional comments regarding the current version of the manuscript.

Reviewer #1 (Public review):

Summary:

This study examines whether changes in pupil size index prediction-error-related updating during associative learning, formalised as information gain via Kullback-Leibler (KL) divergence. Across two independent tasks, pupil responses scaled with KL divergence shortly after feedback, with the timing and direction of the response varying by task. Overall, the work supports the view that pupil size reflects information-theoretic processes in a context-dependent manner.

Strengths:

This study provides a novel and convincing contribution by linking pupil dilation to information-theoretic measures, such as KL divergence, supporting Zénon's hypothesis that pupil responses reflect information gain during learning. The robust methodology, including two independent datasets with distinct task structures, enhances the reliability and generalisability of the findings. By carefully analysing early and late time windows, the authors capture the timing and direction of prediction-error-related responses, offering new insights into the temporal dynamics of model updating. The use of an ideal-learner framework to quantify prediction errors, surprise, and uncertainty provides a principled account of the computational processes underlying pupil responses. The work also highlights the critical role of task context in shaping the direction and magnitude of these effects, revealing the adaptability of predictive processing mechanisms. Importantly, the conclusions are supported by rigorous control analyses and preprocessing sanity checks, as well as convergent results from frequentist and Bayesian linear mixed-effects modelling approaches.

Weaknesses:

Some aspects of directionality remain context-dependent, and on current evidence cannot be attributed specifically to whether average uncertainty increases or decreases across trials. Differences between the two tasks (e.g., sensory modality and learning regime) limit direct comparisons of effect direction and make mechanistic attribution cautious. In addition, subjective factors such as confidence were not measured and could influence both prediction-error signals and pupil responses. Importantly, the authors explicitly acknowledge these limitations, and the manuscript clearly frames them as areas for future work rather than settled conclusions.

Reviewer #2 (Public review):

Summary

This study investigates the role of GMCL1 in regulating the mitotic surveillance pathway (MSP), a protective mechanism that activates p53 following prolonged mitosis. The authors identify a physical interaction between 53BP1 and GMCL1, but not with GMCL2. They propose that the ubiquitin ligase complex CRL3-GMCL1 targets 53BP1 for degradation during mitosis, thereby preventing the formation of the "mitotic stopwatch" complex (53BP1-USP28-p53) and subsequent p53 activation. The authors show that high GMCL1 expression correlates with resistance to paclitaxel in cancer cell lines that express wild-type p53. Importantly, loss of GMCL1 restores paclitaxel sensitivity in these cells, but not in p53-deficient lines. They propose that GMCL1 overexpression enables cancer cells to bypass MSP-mediated p53 activation, promoting survival despite mitotic stress. Targeting GMCL1 may thus represent a therapeutic strategy to re-sensitize resistant tumors to taxane-based chemotherapy.

Strengths

This manuscript presents potentially interesting observations. The major strength of this article is the identification of GMCL1 as 53BP1 interaction partner. The authors identified relevant domains and show that GMCL1 controls 53BP1 stability. The authors further show a potentially interesting link between GMCL1 status and sensitivity to Taxol.

Weaknesses

A major limitation of the original manuscript was that the functional relevance of GMCL1 in regulating 53BP1 within an appropriate model system was not clearly demonstrated. In the revised version, the authors attempt to address this point. However, the new experiment is insufficiently controlled, making it difficult to interpret the results. State-of-the-art approaches would typically rely on single-cell tracking to monitor cell fate following release from a moderately prolonged mitosis.

In contrast, the authors use a population-based assay, but the reported rescue from arrest is minimal. If the assay were functioning robustly, one would expect that nearly all cells depleted of USP28 or 53BP1 should have entered S-phase at a defined time after release. Thus, the very small rescue effect of siTP53BP1 suggests that the current assay is not suitable. It is also likely that release from a 16-hour mitotic arrest induces defects independent of the 53BP1-dependent p53 response.

Furthermore, the cell-cycle duration of RPE1 cells is less than 20 hours. It is therefore unclear why cells are released for 30 hours before analysis. At this time point, many cells are likely to have progressed into the next cell cycle, making it impossible to draw conclusions regarding the immediate consequences of prolonged mitosis. As a result, the experiment cannot be evaluated due to inadequate controls.

To strengthen this part of the study, I recommend that the authors first establish an assay that reliably rescues the mitotic-arrest-induced G1 block upon depletion of p53, 53BP1, or USP28. Once this baseline is validated, GMCL1 knockout can then be introduced to quantify its contribution to the response.

A broader conceptual issue is that the evidence presented does not form a continuous line of reasoning. For example, it is not demonstrated that GMCL1 interacts with or regulates 53BP1 in RPE1 cells-the system in which the limited functional experiments are conducted.

There are also a number of inconsistencies and issues with data presentation that need to be addressed:

(1) Figure 2C: p21 levels appear identical between GMCL1 KO and WT rescue. If GMCL1 regulates p53 through 53BP1, p21 should be upregulated in the KO.

(2) Figure 2A vs. 2C: GMCL1 KO affects chromatin-bound 53BP1 in Figure 2A, yet in Figure 2C it affects 53BP1 levels specifically in G1-phase cells. This discrepancy requires clarification.

(3) Figure 2C quantification: The three biological repeats show an unusual pattern, with one repeat's data points lying exactly between the other two. It is unclear what the line represents; please clarify.

(4) Figure nomenclature: Some abbreviations (e.g., FLAG-KI in Fig. 1F, WKE in Fig. 1C-D, ΔMFF in Fig. 1E) are not defined in the figure legends. All abbreviations must be explained.

(5) Figure 2D: Please indicate how many times the experiment was reproduced. Quantification with statistical testing would strengthen the result. Pull-downs of 53BP1 with calculation of the ubiquitinated/total ratio could also support the conclusion.

(6) Figures 3A and 3C: The G1 bars share the same color as the error bars, making the graphs difficult to interpret. Please adjust the color scheme.

Reviewer #1 (Public review):

Summary:

This paper introduces a dual-pathway model for reconstructing naturalistic speech from intracranial ECoG data. It integrates an acoustic pathway (LSTM + HiFi-GAN for spectral detail) and a linguistic pathway (Transformer + Parler-TTS for linguistic content). Output from the two components is later merged via CosyVoice2.0 voice cloning. Using only 20 minutes of ECoG data per participant, the model achieves high acoustic fidelity and linguistic intelligibility.

Strengths:

(1) The proposed dual-pathway framework effectively integrates the strengths of neural-to-acoustic and neural-to-text decoding and aligns well with established neurobiological models of dual-stream processing in speech and language.

(2) The integrated approach achieves robust speech reconstruction using only 20 minutes of ECoG data per subject, demonstrating the efficiency of the proposed method.

(3) The use of multiple evaluation metrics (MOS, mel-spectrogram R², WER, PER) spanning acoustic, linguistic (phoneme and word), and perceptual dimensions, together with comparisons against noise-degraded baselines, adds strong quantitative rigor to the study.

Weaknesses:

(1) It is unclear how much the acoustic pathway contributes to the final reconstruction results, based on Figures 3B-E and 4E. Including results from Baseline 2 + CosyVoice and Baseline 3 + CosyVoice could help clarify this contribution.

(2) As noted in the limitations, the reconstruction results heavily rely on pre-trained generative models. However, no comparison is provided with state-of-the-art multimodal LLMs such as Qwen3-Omni, which can process auditory and textual information simultaneously. The rationale for using separate models (Wav2Vec for speech and TTS for text) instead of a single unified generative framework should be clearly justified. In addition, the adaptor employs an LSTM architecture for speech but a Transformer for text, which may introduce confounds in the performance comparison. Is there any theoretical or empirical motivation for adopting recurrent networks for auditory processing and Transformer-based models for textual processing?

(3) The model is trained on approximately 20 minutes of data per participant, which raises concerns about potential overfitting. It would be helpful if the authors could analyze whether test sentences with higher or lower reconstruction performance include words that were also present in the training set.

(4) The phoneme confusion matrix in Figure 4A does not appear to align with human phoneme confusion patterns. For instance, /s/ and /z/ differ only in voicing, yet the model does not seem to confuse these phonemes. Does this imply that the model and the human brain operate differently at the mechanistic level?

(5) In general, is the motivation for adopting the dual-pathway model to better align with the organization of the human brain, or to achieve improved engineering performance? If the goal is primarily engineering-oriented, the authors should compare their approach with a pretrained multimodal LLM rather than relying on the dual-pathway architecture. Conversely, if the design aims to mirror human brain function, additional analysis, such as detailed comparisons of phoneme confusion matrices, should be included to demonstrate that the model exhibits brain-like performance patterns.

Reviewer #1 (Public review):

Summary:

Here, the authors address the organization of reach-related activity in layer 2/3 across a broad swath of anterodorsal neocortex that included large subregions of M1, M2, and S1. In mice performing a novel variant water-reaching task, the authors measured activity using two-photon fluorescence imaging of a GECI expressed in excitatory projection neurons. The authors found a substantial diversity of response patterns using a number of metrics they developed for characterizing the PETHs of neurons across reach conditions (target locations). By mapping single-neuron properties across the cortex, the authors found substantial spatial variation, only some of which aligned with traditional boundaries between cortical regions. Using Gaussian mixture models, the authors found evidence of distinct response types in each region, with several types prominent in multiple cortical regions. Aggregating across regions, four primary subpopulations were apparent, each distinct in its average response properties. Strikingly, each subpopulation was observed in multiple regions, but subpopulation members from different regions exhibited largely similar response properties.

Strengths:

The work addresses a fundamental question in the field that has not previously been addressed at cellular resolution across such a broad cortical extent. I see this as truly foundational work that will support future investigation of how the rodent brain drives and controls reaching.

The quantification is thoughtful and rigorous. It is great that the authors provide an explanation for and intuition behind their response metrics, rather than burying everything in the Methods.

The Discussion and general contextualization of the results are thorough, thoughtful, and strong. It is great that the authors avoid the common over-interpretation of classical observations regarding cortical organization that are endemic in the field.

All things considered, this is the best paper regarding spatial structure in the motor system I have ever read. The breadth of cellular resolution activity measurement, the rigor of the quantification, and the clear and open-minded interrogation of the data collectively have produced a very special piece of work.

Weaknesses:

The behavioral task is very impressive and an important contribution to the field in its own right. However, given that it appears substantially different from the one used in the previous paper, the characterization of the behavior provided in the Results is too brief. More illustration of the behavior would be helpful. For example, it is rather deep into the paper when the authors reveal that the mice can whisk to help localize the target location. That should be expressed at the outset when the behavior is first described. Other suggestions for elaborating the behavior description are included below.

Statistical support for key claims is lacking. For example, "The five areas of interest varied in the fraction of neurons that were modulated: M2 had 14%, M1 had 23%, S1-fl had 30%, S1-hl had 25%, and S1-tr had 27%" - I cannot locate the statistical tests showing that these values are actually different. Another example is Figure 7, where a key observation is that distributions of PETH features are distinct across regions. It is clear that at least some distributions are not overlapping, but a clearer statistical basis for this key claim should be provided.

I understand that the authors are planning a follow-up study that addresses the relation between activity patterns and kinematics. One question about interpreting the results here though, is how much the activity variation across target locations may relate to the kinematic differences across these different conditions, as opposed to true higher-order movement features like reach direction.

Reviewer #1 (Public review):

WIPI1 is a PROPPIN family protein that has been implicated in Retromer-mediated membrane fission events. Although the cargos that it has been tested to be important for are diverse, one of the cargos that is unaffected is Beta1-Integrin. This leads the authors to assess another PROPPIN family protein - WIPI2, which is a homolog of WIPI1. KD using siRNA is effective and had no consequences on LAMP1, EGFR trafficking or GLUT1 trafficking. Integrin-B1, however, had a large and significant defect in its recycling from the endosome, with a clear endosomal colocalisation. Complementation experiments with WT WIPI2 recovered the phenotype, but various mutant WIPI2 complements resulted in elongated tubules, and there was also a dominant negative effect of the mutant. Integrin is a classic retreiver cargo, so the authors rationalise that WIPI2 may be playing a role with retreiver that WIPI1 plays with retromer. To assess this, they perform a set of immunoprecipitations. SNX17, the retreiver-associated sorting nexin, co-IPs with WIPI2 in a VPS26C-dependent manner. VPS26C but not VPS26 co-IPs with WIPI2, and the reciprocal with WIPI1. These interactions were not present for the FSSS mutation of WIPI2. WIPI2 localises to Rab11 endosomes mainly, as does retriever. Mutations of WIPI2 not only affected WIPI2 localisation, but also VPS35L mutations, indicating that there is a functional relationship between the two.

On the whole, I find the manuscript compelling. The manuscript is very clearly written, the results are convincing and well performed. The flow of experiments is logical, and although not comprehensive in the subsequent mechanistic understanding, the fundamental findings are important and convincing. My comments below are, on the whole, minor and are intended to support the communication of the findings to the field.

(1) The IP interaction data were convincing; however, for me and some others, an interaction is only convincing when performed in vitro, and understood at a structural level. I do not suggest the authors do that in this case; however, I think, at a minimum, some sensible moderation of claims would be useful here.

(2) I found the final localisation data and its interpretation confusing. My interpretation of that data would not be that the retreiver is relocalised, but rather that there is less of both recruited to the membrane and the remaining localisation distribution is shifted. In addition, I am not quite sure of the model here - is the idea that WIPI2 recruits retreiver, if that is the case, I find it hard to resolve with its role as a mediator of fission. Clarity would be appreciated here.

(3) I am concerned that the repeats being compared for statistical analysis are not biological repeats but technical repeats (cells in the same experiment). I should think the idea of the statistical comparison is to show experimental reproducibility and variability across biological repeats. Therefore, I would expect an appropriate number of biological repeats (3 or more minimum), to be the data compared in the statistical analysis and graphs. I think it is appropriate to average the technical repeats from each biological repeat. I find these to be useful resources https://doi.org/10.1083/jcb.202401074, https://doi.org/10.1083/jcb.200611141

Reviewer #1 (Public review):

A summary of what the authors were trying to achieve:

(1) Identify probiotic candidates based on the phylogenetic proximity and their presence in the lower respiratory tract based on phylogenetic analysis and on meta-analysis of 16S rRNA sequencing of mouse lung samples.

(2) Predefine probiotic candidates with overlapping and competing metabolic profiles based on a simple and easy-to-applicable score, taking carbon source use into consideration.

(3) Confirm the functionality of these candidate probiotics in vitro and define their mechanism of action (niche exclusion by either metabolic competition or active antibacterial strategies).

(4) Confirm the probiotic action in vivo.

Strengths:

The authors attempt to go the whole 9 yards from rational choice of phylogenetic close lower respiratory tract probiotics, over in silico modelling of niche index based on use of similar carbon sources with in vitro confirmation, to in vivo competition experiments in mice.

Weaknesses:

(1) The use of a carbon source is defined as growth to OD600 two SD above the blank level. While allowing a clear cutoff, this procedure does not take into account larger differences in the preferences of carbon sources between the pathogen and the probiotic candidate. If the pathogen is much better at taking up and processing a carbon source, the competition by the probiotic might be biologically irrelevant.

(2) The authors do not take into account the growth of candidate probiotics in the presence of Bt. In monoculture, three of the four most potent candidate probiotics grow to comparable levels as Bt in LSM.

(3) Niche exclusion in vivo is not shown. Mortality of hosts after infection with Bt is not a measure for competition of CP with the pathogen. Only Bt titers would prove a competitive effect. For CP17, less than half of the mice were actually colonized, but still, there is 100% protection. Activation of the host immune system would explain this and has to be excluded as an alternative reason for improved host survival.

Appraisal:

(1) Based on phylogenetic comparison and published resources on lower respiratory tract colonizing bacteria, the authors find a reasonably good number of candidate probiotics that grow in LSM and successfully compete with the pathogenic target bacterium Bt in vitro.

(2) In vivo, only host survival was tested, and a direct competition of CP with Bt by testing for Bt titers was not shown.

Impact:

Niche exclusion based on competition for environmentally provided metabolites is not a new concept and was experimentally tested, e.g. in the intestine. The authors show here that this concept could be translated into the resource-poor environment of the respiratory tract. It remains to be tested if the LSM growth-based competition data in vitro can be translated into niche exclusion in vivo.

Reviewer #1 (Public review):

Summary:

This research group has consistently performed cutting-edge research aiming to understand the role of hormones in the control of social behaviors, specifically by utilizing the genetically-tractable teleost fish, medaka, and the current work is no exception. The overall claim they make, that estrogens modulate social behaviors in males is supported, with important caveats. For one, there is no evidence these estrogens are generated by "neurons" as would be assumed by their main claim that it is NEUROestrogens that drive this effect. While indeed the aromatase they have investigated is expressed solely in the brain, in most teleosts, brain aromatase is only present in glial cells (astrocytes, radial glia). The authors should change this description so as not to mislead the reader. Below I detail more specific strengths and weaknesses of this manuscript.

Strengths:

Excellent use of the medaka model to disentangle the control of social behavior by sex steroid hormones

The findings are strong for the most part because deficits in the mutants are restored by the molecule (estrogens) that was no longer present due to the mutation

Presentation of the approach and findings are clear, allowing the reader to make their own inferences and compare them with the authors'

Includes multiple follow-up experiments, which leads to tests of internal replication and an impactful mechanistic proposal

Findings are provocative not just for teleost researchers, but for other species since, as the authors point out, the data suggest mechanisms of estrogenic control of social behaviors may be evolutionary ancient

Weaknesses:

The experimental design for studying aggression in males has flaws, but it appears a typical resident-intruder type assay is not appropriate for medaka. seems other species may be better for studying aggression in teleosts.

Reviewer #1 (Public review):

Summary:

This paper provides a novel method to improve the accuracy of predictions of the impact of ITN strategies, by using sub-national estimates of the duration of ITN access and use over time from cross-sectional survey data and annual country ITNs received.

Strengths:

The approach is novel, makes use of available data, and has considered all of the relevant components of ITN distributions.

Weaknesses:

(1) The main message of the paper was not very clear, and did not seem to fit the title. The title focuses on sub-national tailoring of ITN, but the abstract did not feature results directly about SNT. It was not very clear what the main result of the paper was - there are several ITN observations in the results and discussion. Most did not seem to be directly about SNT, but rather sub-national differences in use and access were accounted for in the analyses. It was not clear if the same conclusions would be reached without accounting for sub-national differences, but the estimates and predictions could be expected to be more accurate.

(2) Some of the results seemed to me to be apparent even without a modelling exercise (eg high coverage could not be maintained between campaigns, use would be higher with 2-yearly distributions rather than 3-yearly) or were not in themselves new insights (eg estimates of the duration of use). It would be helpful to clearly state what the novel results are in the abstract, the first paragraph of the discussion and the conclusions, and to make sure that the title is consistent.

(3) On L236, the link to SNT is stated: "the models indicate trends that can support sub-national tailoring of ITNs". They could indeed, but SNT itself is not done in this paper. It seems to be about improving sub-national predictions of the impact of single ITN strategies, by taking into account sub-national variation in access and use duration. This is useful, and the model developed has novel aspects.

(4) Individual countries may have records on when nets were distributed to the regions rather than needing to use the annual country number of nets together with the DHS data. It could be helpful to say what the analysis steps would be in that case.

(5) There were several assumptions that needed to be made in building the model. There is some validation of the timing of the distributions (L633 "verified where possible through discussion with interested parties nationally and internationally") and the fit of estimated access and use to survey data, and agreement between predictions of prevalence and MAP estimates. It would be helpful to say which assumptions are important for the results (and would be key knowledge gaps) and which would not make a difference. It might be possible to validate the net timing model using a country where net distributions are known reasonably well.

(6) What was assumed about what happens to old nets after a mass campaign was not clear. This assumption is likely to affect the predictions of access for the biennial distributions.

(7) L312 and elsewhere: That use given access declines with net age is plausible. However, I wondered if this could be partly a consequence of the assumptions in the model (eg the two exponential decays for access and use, the possible assumption that new nets displace the current ones when there is a mass campaign).

(8) The Methods section on Estimating historical use and access seemed to be aimed at readers familiar with formulae, but I think it could lose other interested readers. It could be useful to explain a little more about what is happening at each step and also why.

(9) The model was fitted to MAP estimates of PfPR2-10, which themselves come from a model. It may be that there is different uncertainty in the MAP estimates for different regions. I couldn't see this on the graph, but maybe the uncertainty is small. Was this taken into account in the fitting?

(10) Was uncertainty from each estimated component integrated into the other components?

(11) Eyeballing Figure 2 (Burkina Faso), there is a general pattern of decline in all the regions, some differences between the regions and some differences in how well the model fits between the regions. If possible, it could be helpful to say how much better the fit was when using region-specific compared to countrywide parameter values for access and use, and how different the results would be.

(12) The question of moving from a campaign every three to every two years may not be the most pertinent question in the current funding landscape. I realise that a paper is in development for a long time, but it would be helpful to comment on what else the model could be used for when fewer rather than more nets are likely to be available.

Reviewer #1 (Public review):

Summary:

Metabolic dysfunction-associated steatotic liver disease (MASLD) ranges from simple steatosis, steatohepatitis, fibrosis/cirrhosis, and hepatocellular carcinoma. In the current study, the authors aimed to determine the early molecular signatures differentiating patients with MASLD associated fibrosis from those patients with early MASLD but no symptoms. The authors recruited 109 obese individuals before bariatric surgery. They separated the cohorts as no MASLD (without histological abnormalities) and MASLD. The liver samples were then subjected to transcriptomic and metabolomic analysis. The serum samples were subjected to metabolomic analysis. The authors identified dysregulated lipid metabolism, including glyceride lipids, in the liver samples of MASLD patients compared to the no MASLD ones. Circulating metabolomic changes in lipid profiles slightly correlated with MASLD, possibly due to the no MASLD samples derived from obese patients. Several genes involved in lipid droplet formation were also found elevated in MASLD patients. Besides, elevated levels of amino acids, which are possibly related to collagen synthesis, were observed in MASLD patients. Several antioxidant metabolites were increased in MASLD patients. Furthermore, dysregulated genes involved in mitochondrial function and autophagy were identified in MASLD patients, likely linking oxidative stress to MASLD progression. The authors then determined the representative gene signatures in the development of fibrosis by comparing this cohort with the other two published cohorts. Top enriched pathways in fibrotic patients included GTPas signaling and innate immune responses, suggesting the involvement of GTPas in MASLD progression to fibrosis. The authors then challenged human patient derived 3D spheroid system with a dual PPARa/d agonist and found that this treatment restored the expression levels of GTPase-related genes in MASLD 3D spheroids. In conclusion, the authors suggested the involvement of upregulated GTPase-related genes during fibrosis initiation.

Concerns from first round of review:

(1) A recent study, via proteomic and transcriptomic analysis, revealed that four proteins (ADAMTSL2, AKR1B10, CFHR4 and TREM2) could be used to identify MASLD patients at risk of steatohepatitis (PMID: 37037945). It is not clear why the authors did not include this study in their comparison.

(2) The authors recruited 109 patients but only performed transcriptomic and metabolomic analysis in 94 liver samples. Why did the authors exclude other samples?

(3) The authors mentioned clinical data in Table 1 but did not present the table in this manuscript.

(4) The generated metabolomic data could be a very useful resource to the MASLD community. However, it is very confusing how the data was generated in those supplemental tables. There is no clear labeling of human clinical information in those tables. Also, what do those values mean in columns 47-154? This reviewer assumed that they are the raw data of metabolomic analysis in plasma samples. However, without clear clinical information in these patients, it is impossible that any scientist can use the data to reproduce the authors' findings.

(5) In Fig. 5B, the authors excluded the steatosis and fibrosis overlapped genes. Steatosis and fibrosis specific genes could simply reflect the outcomes rather than causes. In this case, the obtained results might not identify the gene signatures related to fibrosis initiation.

(6 In Fig. 6D, the authors used 3D liver spheroid to validate their findings. However, there is no images showing the 3D liver spheroid formation before and after PPARa/d agonist treatment. It is not clear whether the 3D liver spheroid was successfully established.

(7) The authors suggested that targeting LX-2 cells with Rac1 and Cdc42 inhibitors could reduce collagen production. Did the authors observe these two genes upregulated in mRNA and protein expression levels in their cohort when compared MASLD patients with and without fibrosis?

(8) Did the authors observe that the expression levels of Rac1 and Cdc42 are correlated with fibrosis progression in MASLD patients?

(9) Other studies have revealed several metabolite changes related to MASLD progression (PMID: 35434590, PMID: 22364559). However, the authors did not discuss the discrepancies between their findings with the previous studies.

Significance:

Overall, the current study might provide some new resources regarding transcriptomic and metabolomic data derived from obese patients with and without MASLD. The MASLD research community will be interested in the resource data.

Comments on revised version:

Thank you for the authors' responses to my concerns. I do not have any further comments.

for - Medium article - cogress - Part 1 - progress trap - James Gien Wong - definition - cogress - to - Medium article cogress - Part 2 - progress trap - James Gien Wong - https://hyp.is/t8FhpDGAEfC4J7f0NEFujg/medium.com/@gien_SRG/human-cogress-part-2-d6fd075a55c7 - to - Stop Reset Go hypothesis annotations - progress trap - Ronald Wright - https://jonudell.info/h/facet/?max=100&expanded=true&user=stopresetgo&exactTagSearch=true&any=ronald+wright - General - https://jonudell.info/h/facet/?max=100&expanded=true&user=stopresetgo&exactTagSearch=true&any=progress+trap - from - youtube - Planet Critical interview - Samuel Miller MacDonald - The Myth of Progress - https://hyp.is/r-hmFtjKEfCd8odATbINbA/www.youtube.com/watch?v=BEhmWEDkZUQ

Reviewer #1 (Public Review):

Summary:

Argunşah et al. describe and investigate the mechanisms underlying the differential response dynamics of barrel vs septa domains in the whisker-related primary somatosensory cortex (S1). Upon repeated stimulation, the authors report that the response ratio between multi- and single-whisker stimulation increases in layer (L) 4 neurons of the septal domain, while remaining constant in barrel L4 neurons. The authors attribute this divergence to differences in short-term synaptic plasticity, particularly within somatostatin-expressing (SST⁺) interneurons. This interpretation is supported by 1) the increased density of SST+ neurons in L4 of the septa compared to barrel domain, 2) the stronger response of (L2/3) SST+ neurons to repeated multi- vs single-whisker stimulation and 3) the reduced functional difference in single- versus multi-whisker response ratios across barrel and septal domains in Elfn1 KO mice, which lack a synaptic protein that confers characteristic short-term plasticity, notably in SST+ neurons. Consistently, a decoder trained on WT data fails to generalize to Elfn1 KO responses. Finally, the authors report a relative enrichment of S2- and M1-projecting cell densities in L4 of the septal domain compared to the barrel domain, suggesting that septal and barrel circuits may differentially route information about single vs multi-whisker stimulation downstream of S1.

Strengths:

This paper describes and aims to study a circuit underlying differential response between barrel columns and septal domains of the primary somatosensory cortex. This work supports the view these two domains contribute distinctly to the processing single versus multi-whisker inputs and highlight the role of SST+ neuron and their short-term plasticity. Together, this study suggests that the barrel cortex multiplexes whisker-derived sensory information across its domains, enabling parallel processing within S1.

Weaknesses:

Although the divergence in responses to repeated single- versus multi-whisker stimulation between barrel and septal domains is consistent with a role for SST⁺ neuron short-term plasticity, the evidence presented does not conclusively demonstrate that this mechanism is the critical driver of the difference. The lack of targeted recordings and manipulations limits the strength of this conclusion: SST⁺ neuron activity is not measured in L4, nor is it assessed in a domain-specific manner. The Elfn1 knockout manipulation does not appear to selectively affect either stimulus condition, domain or interneuron subtype. Finally, all experiments were performed under anesthesia, which raises concerns about how well the reported dynamics generalize to awake cortical processing.

Reviewer #2 (Public review):

Summary:

In this manuscript, Pereira de Castro and coworkers are studying potential competition between a more standard splicing factor SF1 and an alternative splicing factor called QK1. This is interesting because they bind to overlapping sequence motifs and could potentially have opposing effects on promoting the splicing reaction. To test this idea, the authors KD either SF1 or QK1 in mammalian cells and uncover several exons whose splicing regulation follows the predicted pattern of being promoted for splicing by SF1 and repressed by QK1. Importantly, these have introns enriched in SF1 and QK1 motifs. The authors then focus on one exon in particular with two tandem motifs to study the mechanism of this in greater detail and their results confirm the competition model. Mass spec analysis largely agrees with their proposal; however, it is complicated by apparently quick transition of SF1 bound complexes to later splicing intermediates. An inspired experiment in yeast shows how QK1 competition could potentially have a determinental impact on splicing in an orthogonal system. Overall these results show how splicing regulation can be achieved by competition between a "core" and alternative splicing factor and provide additional insight into the complex process of branch site recognition. The manuscript is exceptionally clear and the figures and data very logically presented. The work will be valuable to those in the splicing field who are interested in both mechanism and bioinformatics approaches to deconvolve any apparent "splicing code" being used by cells to regulate gene expression.

Strengths:

(1) The main discovery of the manuscript involving evidence for SF1/QK1 competition is quite interesting and important for this field. This evidence has been missing and may change how people think about branch site recognition.

(2) The experiments and the rationale behind them are clearly and logically presented.

(3) The experiments are carried out to a high standard and well-designed controls are included.

(4) The extrapolation of the result to yeast in order to show the potentially devastating consequences of QK1 competition was creative and informative.

Weaknesses:

Overall the weaknesses are relatively minor and involve cases where conclusions could potentially have been strengthened with additional experimentation. For example, pull-down of the U2 snRNP could be strengthened by detection of the snRNA whereas the proteins may themselves interact with these factors in the absence of the snRNA. In addition the discussion is a bit speculative given the data, but compelling nonetheless.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors investigate the nanoscopic distribution of glycine receptor subunits in the hippocampus, dorsal striatum, and ventral striatum of the mouse brain using single-molecule localization microscopy (SMLM). They demonstrate that only a small number of glycine receptors are localized at hippocampal inhibitory synapses. Using dual-color SMLM, they further show that clusters of glycine receptors are predominantly localized within gephyrin-positive synapses. A comparison between the dorsal and ventral striatum reveals that the ventral striatum contains approximately eight times more glycine receptors and this finding is consistent with electrophysiological data on postsynaptic inhibitory currents. Finally, using cultured hippocampal neurons, they examine the differential synaptic localization of glycine receptor subunits (α1, α2, and β). This study is significant as it provides insights into the nanoscopic localization patterns of glycine receptors in brain regions where this protein is expressed at low levels. Additionally, the study demonstrates the different localization patterns of GlyR in distinct striatal regions and its physiological relevance using SMLM and electrophysiological experiments. However, several concerns should be addressed.

Specific comments on the original version:

(1) Colocalization analysis in Figure 1A. The colocalization between Sylite and mEos-GlyRβ appears to be quite low. It is essential to assess whether the observed colocalization is not due to random overlap. The authors should consider quantifying colocalization using statistical methods, such as a pixel shift analysis, to determine whether colocalization frequencies remain similar after artificially displacing one of the channels.

(2) Inconsistency between Figure 3A and 3B. While Figure 3B indicates an ~8-fold difference in the number of mEos4b-GlyRβ detections per synapse between the dorsal and ventral striatum, Figure 3A does not appear to show a pronounced difference in the localization of mEos4b-GlyRβ on Sylite puncta between these two regions. If the images presented in Figure 3A are not representative, the authors should consider replacing them with more representative examples or providing an expanded images with multiple representative examples. Alternatively, if this inconsistency can be explained by differences in spot density within clusters, the authors should explain that.

(3) Quantification in Figure 5. It is recommended that the authors provide quantitative data on cluster formation and colocalization with Sylite puncta in Figure 5 to support their qualitative observations.

(4) Potential for pseudo replication. It's not clear whether they're performing stats tests across biological replica, images, or even synapses. They often quote mean +/- SEM with n = 1000s, and so does that mean they're doing tests on those 1000s? Need to clarify.

(5) Does mEoS effect expression levels or function of the protein? Can't see any experiments done to confirm this. Could suggest WB on homogenate, or mass spec?

(6) Quantification of protein numbers is challenging with SMLM. Issues include i) some of FP not correctly folded/mature, and ii) dependence of localisation rate on instrument, excitation/illumination intensities, and also the thresholds used in analysis. Can the authors compare with another protein that has known expression levels- e.g. PSD95? This is quite an ask, but if they could show copy number of something known to compare with, it would be useful.

(7) Rationale for doing nanobody dSTORM not clear at all. They don't explain the reason for doing the dSTORM experiments. Why not just rely on PALM for coincidence measurements, rather than tagging mEoS with a nanobody, and then doing dSTORM with that? Can they explain? Is it to get extra localisations- i.e. multiple per nanobody? If so, localising same FP multiple times wouldn't improve resolution. Also, no controls for nanobody dSTORM experiments- what about non-spec nb, or use on WT sections?

(8) What resolutions/precisions were obtained in SMLM experiments? Should perform Fourier Ring Correlation (FRC) on SR images to state resolutions obtained (particularly useful for when they're presenting distance histograms, as this will be dependent on resolution). Likewise for precision, what was mean precision? Can they show histograms of localisation precision.

(9) Why were DBSCAN parameters selected? How can they rule out multiple localisations per fluor? If low copy numbers (<10), then why bother with DBSCAN? Could just measure distance to each one.

(10) For microscopy experiment methods, state power densities, not % or "nominal power".

(11) In general, not much data presented. Any SI file with extra images etc.?

(12) Clarification of the discussion on GlyR expression and synaptic localization: The discussion on GlyR expression, complex formation, and synaptic localization is sometimes unclear, and needs terminological distinctions between "expression level", "complex formation" and "synaptic localization". For example, the authors state: "What then is the reason for the low protein expression of GlyRβ? One possibility is that the assembly of mature heteropentameric GlyR complexes depends critically on the expression of endogenous GlyR α subunits." Does this mean that GlyRβ proteins that fail to form complexes with GlyRα subunits are unstable and subject to rapid degradation? If so, the authors should clarify this point. The statement "This raises the interesting possibility that synaptic GlyRs may depend specifically on the concomitant expression of both α1 and β transcripts." suggests a dependency on α1 and β transcripts. However, is the authors' focus on synaptic localization or overall protein expression levels? If this means synaptic localization, it would be beneficial to state this explicitly to avoid confusion. To improve clarity, the authors should carefully distinguish between these different aspects of GlyR biology throughout the discussion. Additionally, a schematic diagram illustrating these processes would be highly beneficial for readers.

(13) Interpretation of GlyR localization in the context of nanodomains. The distribution of GlyR molecules on inhibitory synapses appears to be non-homogeneous, instead forming nanoclusters or nanodomains, similar to many other synaptic proteins. It is important to interpret GlyR localization in the context of nanodomain organization.

Significance:

The paper presents biological and technical advances. The biological insights revolve mostly on the documentation of Glycine receptors in particular synapses in forebrain, where they are typically expressed at very low levels. The authors provide compelling data indicating that the expression is of physiological significance. The authors have done a nice job of combining genetically tagged mice with advanced microscopy methods to tackle the question of distributions of synaptic proteins. Overall, these advances are more incremental than groundbreaking.

Comments on revised version:

The authors have addressed the majority of the significant issues raised in the review and revised the manuscript appropriately. One issue that can be further addressed relates to the issue of pseudo-replication. The authors state in their response that "All experiments were repeated at least twice to ensure reproducibility (N independent experiments). Statistical tests were performed on pooled data across the biological replicates; n denotes the number of data points used for testing (e.g., number of synaptic clusters, detections, cells, as specified in each case).". This suggests that they're not doing their stats on biological replicates, and instead are pseudo replicating. It's not clear how they have ensured reproducibility, when the stats seem to have been done on pooled data across repeats.

Reviewer #1 (Public review):

Summary:

This very thorough anatomical study addresses the innervation of the Drosophila male reproductive tract. Two distinct glutamatergic neuron types were classified: serotonergic (SGNs) and octopaminergic (OGNs). By expansion microscopy, it was established that glutamate and serotonin /octopamine are co-released. The expression of different receptors for 5-HT and OA in muscles and epithelial cells of the innervation target organs was characterized. The pattern of neurotransmitter receptor expression in the target organs suggests that seminal fluid and sperm transport and emission are subjected to complex regulation. While silencing of abdominal SGNs leads to male infertility and prevents sperm from entering the ejaculatory duct, silencing of OGNs does not render males infertile.

Strengths:

The studied neurons were analysed with different transgenes and methods, as well as antibodies against neurotransmitter synthesis enzymes, building a consistent picture of their neurotransmitter identity. The careful anatomical description of innervation patterns together with receptor expression patterns if the target organs provides a solid basis for advancing the understanding how seminal fluid and sperm transport and emission are subjected to complex regulation. The functional data showing that SGNs are required for male fertility and for the release of sperm from the seminal vesicle into the ejaculatory duct is convincing.

Weaknesses:

The functional analysis of the characterized neurons is not as comprehensive as the anatomical description and phenotypic characterization was limited to simple fertility assays. It is understandable that a full functional dissection is beyond the scope of the present work. The paper contains experiments showing neuron-independent peristaltic waves in the reproductive tract muscles, which are thematically not very well integrated into the paper. Although very interesting, one wonders if these experiments would not fit better into a future work that also explores these peristaltic waves and their interrelation with neuromodulation mechanistically.

Comments on revisions:

The manuscript has improved after fixing many small issues/errors. The new sections in the discussion are likewise adding to the quality of the manuscript.

Reviewer #1 (Public review):

Summary:

In this paper, the authors developed a chemical labeling reagent for P2X7 receptors, called X7-uP. This labeling reagent selectively labels endogenous P2X7 receptors with biotin based on ligand-directed NASA chemistry. After labeling the endogenous P2X7 receptor with biotin, the receptor can be fluorescently labeled with streptavidin-AlexaFluor647. The authors carefully examined the binding properties and labeling selectivity of X7-uP to P2X7, characterized the labeling site of P2X7 receptors, and demonstrated fluorescence imaging of P2X7 receptors. The data obtained by SDS-PAGE, Western blot, and fluorescence microscopy clearly shows that X7-uP labels the P2X7 receptor. Finally, the authors fluorescently labeled the endogenous P2X7 in BV2 cells, which are a murine microglia model, and used dSTORM to reveal a nanoscale P2X7 redistribution mechanism under inflammatory conditions at high resolution.

Strengths:

X7-uP selectively labels endogenous P2X7 receptors with biotin. Streptavidin-AlexaFluor647 binds to the biotin labeled to the P2X7 receptor, allowing visualization of endogenous P2X7 receptors.

Reviewer #1 (Public review):

Summary:

The co-localization of large conductance calcium- and voltage activated potassium (BK) channels with voltage-gated calcium channels (CaV) at the plasma membrane is important for the functional role of these channels in controlling cell excitability and physiology in a variety of systems. An important question in the field is where and how do BK and CaV channels assemble as 'ensembles' to allow this coordinated regulation - is this through preassembly early in the biosynthetic pathway, during trafficking to the cell surface or once channels are integrated into the plasma membrane. These questions also have broader implications for assembly of other ion channel complexes. Using an imaging based approach, this paper addresses the spatial distribution of BK-CaV ensembles using both overexpression strategies in tsa201 and INS-1 cells and analysis of endogenous channels in INS-1 cells using proximity ligation and superesolution approaches. In addition, the authors analyse the spatial distribution of mRNAs encoding BK and Cav1.3. The key conclusion of the paper that BK and CaV1.3 are co-localised as ensembles intracellularly in the ER and Golgi is well supported by the evidence. The experiments and analysis are carefully performed and the findings are very well presented.

Reviewer #1 (Public review):

Summary:

Adult (4mo) rats were tasked to either press one lever for an immediate reward or another for a delayed reward. The task had an adjusting amount structure in which (1) the number of pellets provided on the immediate reward lever changed as a function of the decisions made, (2) rats were prevented from pressing the same lever three times in a row.

While the authors have been very responsive to the reviews, and I appreciate that, unfortunately, the new analyses reported in this revision actually lead me to deeper concerns about the adequacy of the data to support the conclusions. In this revision, it has become clear that the conclusions are forced and not supported by the data. Alternative theories are not considered or presented. This revision has revealed deep problems with the task, the analyses, and the modeling.

Data Weaknesses

Most importantly, the inclusion of the task behavior data has revealed a deep problem with the entire structure of the data. As is obvious in Figure 1D, there is a slow learning effect that is changing over the sessions as the animals learn to stop taking the delayed outcome. Unfortunately, the 8s delays came *after* the 4s. The first 20 sessions contain 19 4s delays and 1 8s delay, while the last 20 sessions contain 14 8s delays and 6 4s delays. Given the changes across sessions, it is likely that a large part of the difference is due to across-session learning (which is never addressed or considered).

These data are not shown by subject and I suspect that individual subjects did all 4s then all 8s and some subjects switched tasks at different times. If my suspicion is true, then any comparisons between the 4s and 8s conditions (which are a major part of the author's claims) may have nothing to do with the delays, but rather with increased experience on the task.

Furthermore, the four "groups", which are still poorly defined, seem to have been assessed at a session-by-session level. So when did each animal fall into a given group? Why is Figure 1D not showing which session fell into which group and why are we not seeing each animal's progression? They also admit that animals used a mixture of strategies, which implies that the "group" assignment is an invalid analysis, as the groups do not accommodate strategy mixing.

Figure 2 shows that none of the differences of the group behavior against random choice with a basic p(delay) are significant. The use a KS test to measure these differences. KS tests are notoriously sensitive as KS tests simply measure whether there are any statistical differences between two distributions. They do not report the full statistics for Figure 2, but only say that the 4HI group was not significant (KS p-value = 0.72) and the 8LO showed a p-value of 0.1 (which they interpret as significant). p=0.1 is not significant. They don't report the value of the 4LO or 8HI groups (why not?), but say they are in-between these two extremes. That means *none* of the differences are significant.

They then test a model with additional parameters, and say that the model includes more than the minimal p_D parameter, but never report BIC or AIC model comparisons. In order to claim that the model is better than the bare p_D assumption, they should be reporting model-comparison statistics. But given that the p_D parameters are enough (q.v. Figure 2), this entire model seems unnecessary

It took me a while to determine what was being shown in Figure 3, but I was eventually able to determine that 0 was the time after the animal made the choice to wait out the delay side, so the 4s in Figure 3A1 with high power in the low-frequency (<5 Hz) range is the waiting time. They don't show the full 8s time. Nor do they show the spectrograms separated by group (assuming that group is the analytical tool they are using). In B they show only show theta power, but it is unclear how to interpret these changes over time.

In Figure 4, panel A is mostly useless because it is just five sample sessions showing firing rate plotted on the same panels as the immediate reward amount. If they want to claim correlation, they should show and test it. But moreover, this is not how neural data should be presented - we need to know what the cells are doing, population-wise. We need to have an understanding of the neural ensemble. These data are clearly being picked and chosen, which is not OK.

Figure 4, panels B and C show that the activity trivially reflects the reward that has been delivered to the animal, if I am understanding the graphs correctly. (The authors do not interpret it this way, but the data is, to my eyes, clear.) The "immediate" signal shows up immediately at choice and reflects the size of the immediate reward (which is varying). The "delay" signal shows up after the delay and does not, which makes sense as the animals get 6 pellets on the delayed side no matter what. In fact, the max delayed side activity = the max immediate side activity, which is 6 pellets. This is just reward-related firing.

Figure 5 is poorly laid out, switching the order in 5C to be 2 1 3 in E and F. (Why?!) The statistics for Figure 5 on page 17 should be asking whether there are differences between neuron types, not whether there is a choice x time interaction in a given neuron type. When I look at Figure 5F1-3, all three types look effectively similar with different levels of noise. It is unclear why they are doing this complicated PC analysis or what we should be drawing from it.

Figure 6 mis-states pie charts as "total number" rather than proportions.

Interpretation Weaknesses

The separation of cognitive effort into "resource-based" and "resistance-based" seems artificial to me. I still do not understand why the ability to resist a choice does not also depend on resource or why using resources are not a form of resistance. Doesn't every action in the end depend on the resources one has available? And doesn't every use of a resource resist one option by taking another? Even if one buys these two separate cognitive control processes (which at this point in reading the revision, I do not), the paper starts from the assumption that a baseline probability of waiting out the delays is a "resistance-based cognitive control" (why?) and a probability of choice that takes into account the size of the immediate value (confusingly abbreviated as ival) is a "resource-based cognitive control" (again, why?)

Reviewer #1 (Public review):

The paper from Hudait and Voth details a number of coarse-grained simulations as well as some experiments focused on the stability of HIV capsids in the presence of the drug lenacapavir. The authors find that LEN hyperstabilizes the capsid, making it fragile and prone to breaking inside the nuclear pore complex.

I found the paper interesting. I have a few suggestions for clarification and/or improvement.

(1) How directly comparable are the NPC-capsid and capsid-only simulations? A major result rests on the conclusion that the kinetics of rupture are faster inside the NPC, but are the numbers of LENs bound identical? Is the time really comparable, given that the simulations have different starting points? I'm not really doubting the result, but I think it could be made more rigorous/quantitative.

(2) Related to the above, it is stated on page 12 that, based on the estimated free-energy barrier, pentamer dissociation should occur in ~10 us of CG time. But certainly, the simulations cover at least this length of time?

(3) At first, I was surprised that even in a CG simulation, LEN would spontaneously bind to the correct site. But if I read the SI correctly, LEN was parameterized specifically to bind to hexamers and not pentamers. This is fine, but I think it's worth describing in the main text.

Reviewer #1 (Public review):

Summary:

Spinal projection neurons in the anterolateral tract transmit diverse somatosensory signals to the brain, including touch, temperature, itch, and pain. This group of spinal projection neurons is heterogeneous in their molecular identities, projection targets in the brain, and response properties. While most anterolateral tract projection neurons are multimodal (responding to more than one somatosensory modality), it has been shown that cold-selective projection neurons exist in lamina I of the spinal cord dorsal horn. Using a combination of anatomical and physiological approaches, the authors discovered that the cold-selective lamina I projection neurons are heavily innervated by Trpm8+ sensory neuron axons, with calb1+ spinal projection neurons primarily capturing these cold-selective lamina I projection neurons. These neurons project to specific brain targets, including the PBNrel and cPAG. This study adds to the ongoing effort in the field to identify and characterize spinal projection neuron subtypes, their physiology, and functions.

Strengths:

(1) The combination of anatomical and physiological analyses is powerful and offers a comprehensive understanding of the cold-selective lamina I projection neurons in the spinal cord dorsal horn. For example, the authors used detailed anatomical methods, including EM imaging of Trpm8+ axon terminals contacting the Phox2a+ lamina I projection neurons. Additionally, they recorded stimulus-evoked activity in Trpm8-recipient neurons, carefully selected by visual confirmation of tdTomato and GFP juxtaposition, which is technically challenging.

(2) This study identifies, for the first time, a molecular marker (calb1) that labels cold-selective lamina I projection neurons. Although calb1+ projection neurons are not entirely specific to cold-selective neurons, using an intersectional strategy combined with other genes enriched in this ALS group or cold-induced FosTRAP may further enhance specificity in the future.

(3) This study shows that cold-selective lamina I projection neurons specifically innervate certain brain targets of the anterolateral tract, including the NTS, PBNrel, and cPAG. This connectivity provides insights into the role of these neurons in cold sensation, which will be an exciting area for future research.

Weaknesses:

(1) The sample size for the ex vivo electrophysiology is small. Given the difficulty and complexity of the preparation, this is understandable. However, a larger sample size would have strengthened the authors' conclusions.

(2) The authors used tdTomato expression to identify brain targets innervated by these cold-selective lamina I projection neurons. Since tdTomato is a soluble fluorescent protein that fills the entire cell, using synaptophysin reporters (e.g., synaptophysin-GFP) would have been more convincing in revealing the synaptic targets of these projection neurons.

(3) The summary cartoon shown in Figure 7 can be misleading because this study did not determine whether these cold-selective lamina I projection neurons have collateral branches to multiple brain targets or if there are anatomical subtypes that may project exclusively to specific targets. For example, a recent study (Ding et al., Neuron, 2025) demonstrated that there are PBN-projecting spinal neurons that do not project to other rostral brain areas. Furthermore, based on the authors' bulk labeling experiments, the three main brain targets are NTS, PBNrel, and cPAG. The VPL projection is very sparse and almost negligible.